diff --git a/.gitignore b/.gitignore index 2715aa6..f5d6161 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,10 @@ *.Rout *.csv +*.class +*.o + +results +cover_tree hlearn-allknn hlearn-linear @@ -7,5 +12,5 @@ hlearn-linear *.swo dist/ gitignore/ -examples/ +examples/old/ scripts/ diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..80c5c7d --- /dev/null +++ b/.travis.yml @@ -0,0 +1,32 @@ +# NB: don't set `language: haskell` here + +# The following enables several GHC versions to be tested; often it's enough to test only against the last release in a major GHC version. Feel free to omit lines listings versions you don't need/want testing for. +env: + - CABALVER=1.22 GHCVER=7.10.1 LLVMVER=3.5 + - CABALVER=1.22 GHCVER=7.10.2 LLVMVER=3.5 + # - CABALVER=head GHCVER=head # see section about GHC HEAD snapshots + +before_install: + - export PATH="~/.cabal/bin:/opt/ghc/$GHCVER/bin:/opt/cabal/$CABALVER/bin:/usr/bin:$PATH" + +install: + - travis_retry sudo curl https://raw.githubusercontent.com/mikeizbicki/HLearn/travis/install/ubuntu-precise.sh | sh + #- mkdir /home/travis/.cabal + #- echo 'optimization: False' >> /home/travis/.cabal/config + # Sometimes GHC uses too much RAM for the travis server, causing the install script to fail. + # In this case, we can fix the problem by reinstalling without optimizations. + +# Here starts the actual work to be performed for the package under test; any command which exits with a non-zero exit code causes the build to fail. +script: + - if [ -f configure.ac ]; then autoreconf -i; fi + - cabal configure -fO0 --enable-tests --enable-benchmarks -v2 # -v2 provides useful information for debugging + - cabal build # this builds all libraries and executables (including tests/benchmarks) + - cabal test + - cabal check + - cabal sdist # tests that a source-distribution can be generated + +# Check that the resulting source distribution can be built & installed. +# If there are no other `.tar.gz` files in `dist`, this can be even simpler: +# `cabal install --force-reinstalls dist/*-*.tar.gz` + - SRC_TGZ=$(cabal info . | awk '{print $2;exit}').tar.gz && + (cd dist && cabal install --force-reinstalls "$SRC_TGZ") diff --git a/HLearn.cabal b/HLearn.cabal new file mode 100644 index 0000000..2072917 --- /dev/null +++ b/HLearn.cabal @@ -0,0 +1,218 @@ +Name: HLearn +Version: 2.0.0.0 +Synopsis: Homomorphic machine learning +Category: Data Mining, Machine Learning, Algorithms, Data structures +License: BSD3 +License-file: LICENSE +Author: Mike izbicki +Maintainer: mike@izbicki.me +Build-Type: Simple +Cabal-Version: >=1.8 +homepage: http://github.com/mikeizbicki/HLearn/ +bug-reports: http://github.com/mikeizbicki/HLearn/issues + +Description: + HLearn is an experimental, but high performance machine learning library. + For example, it currently has the world's fastest nearest neighbor implementation. + It uses for fast numeric computations. + The on github contains much more information. + +source-repository head + type: git + location: http://github.com/mikeizbicki/hlearn + +-------------------------------------------------------------------------------- + +Library + Build-Depends: + -- common dependencies + base >= 4.8 && <4.9, + subhask >= 0.1, + + -- control + mtl >= 2.1.2, + + -- i/o + ansi-terminal >= 0.6.1.1, + directory >= 1.2, + time >= 1.4.2 + + -- visualization +-- diagrams-svg >= 0.6, +-- diagrams-lib >= 1.3, +-- process >= 1.1 +-- graphviz >= 2999.16 + + hs-source-dirs: + src + + Exposed-modules: + + HLearn.History + HLearn.History.Timing + +-- HLearn.Data.Graph +-- HLearn.Data.Image + HLearn.Data.LoadData + HLearn.Data.SpaceTree + HLearn.Data.SpaceTree.CoverTree + HLearn.Data.SpaceTree.CoverTree_Specialized + HLearn.Data.SpaceTree.CoverTree.Unsafe + HLearn.Data.SpaceTree.Algorithms + HLearn.Data.SpaceTree.Algorithms_Specialized + +-- HLearn.Evaluation.CrossValidation + + HLearn.Classifiers.Linear + HLearn.Models.Distributions + + HLearn.Optimization.Multivariate + HLearn.Optimization.Univariate + +-- HLearn.Optimization.Amoeba +-- HLearn.Optimization.Conic +-- HLearn.Optimization.StepSize +-- HLearn.Optimization.StochasticGradientDescent +-- HLearn.Optimization.StepSize.Linear +-- HLearn.Optimization.StepSize.Const +-- HLearn.Optimization.StepSize.AlmeidaLanglois + + Other-modules: + + Extensions: + FlexibleInstances + FlexibleContexts + MultiParamTypeClasses + FunctionalDependencies + UndecidableInstances + ScopedTypeVariables + BangPatterns + TypeOperators + GeneralizedNewtypeDeriving + TypeFamilies + StandaloneDeriving + GADTs + KindSignatures + ConstraintKinds + DeriveDataTypeable + RankNTypes + ImpredicativeTypes + + DataKinds + PolyKinds + AutoDeriveTypeable + TemplateHaskell + QuasiQuotes + RebindableSyntax + NoImplicitPrelude + UnboxedTuples + MagicHash + PolymorphicComponents + ExplicitNamespaces + EmptyDataDecls + + PartialTypeSignatures + MultiWayIf + +-------------------------------------------------------------------------------- + +Test-Suite BashTests + type: exitcode-stdio-1.0 + hs-source-dirs: test + main-is: BashTests.hs + build-depends: base, process + +--------------------------------------- + +Test-Suite QuickCheck-Unoptimized + type: exitcode-stdio-1.0 + hs-source-dirs: test + main-is: QuickCheck.hs + + ghc-options: + -O0 + + build-depends: + subhask, + HLearn, + test-framework-quickcheck2 >= 0.3.0, + test-framework >= 0.8.0 + +Test-Suite QuickCheck-Optimized + type: exitcode-stdio-1.0 + hs-source-dirs: test + main-is: QuickCheck.hs + + ghc-options: + -O1 + + build-depends: + subhask, + HLearn, + test-framework-quickcheck2 >= 0.3.0, + test-framework >= 0.8.0 + +--------------------------------------- + +Test-Suite Example0001 + type: exitcode-stdio-1.0 + hs-source-dirs: examples + main-is: example0001-optimization-univariate.hs + build-depends: HLearn, subhask, base + +Test-Suite Example0002 + type: exitcode-stdio-1.0 + hs-source-dirs: examples + main-is: example0002-optimization-multivariate.hs + build-depends: HLearn, subhask, base + +Test-Suite Example0003 + type: exitcode-stdio-1.0 + hs-source-dirs: examples + main-is: example0003-classification.hs + build-depends: HLearn, subhask, base + +-------------------------------------------------------------------------------- + +executable hlearn-allknn + main-is: Main.hs + hs-source-dirs: executables/hlearn-allknn + + ghc-options: + -threaded + -rtsopts + + -funbox-strict-fields + -fexcess-precision + +-- -prof -osuf p_o -hisuf p_hi + + -fllvm + -optlo-O3 + -optlo-enable-fp-mad + -optlo-enable-no-infs-fp-math + -optlo-enable-no-nans-fp-math + -optlo-enable-unsafe-fp-math + +-- -ddump-to-file +-- -ddump-rule-firings +-- -ddump-rule-rewrites +-- -ddump-rules +-- -ddump-simpl +-- -ddump-simpl-stats +-- -dppr-debug +-- -dsuppress-module-prefixes +-- -dsuppress-uniques +-- -dsuppress-idinfo +-- -dsuppress-coercions +-- -dsuppress-type-applications + +-- -ddump-cmm + + build-depends: + base , + HLearn , + subhask , + + cmdargs, + MonadRandom diff --git a/README.md b/README.md index 57ed04f..5d5c4f1 100644 --- a/README.md +++ b/README.md @@ -1,105 +1,86 @@ -#Homomorphic Learning - -HLearn is a suite of libraries for interpreting machine learning models according to their algebraic structure. -Every structure has associated algorithms useful for learning. -When we show that a model is an instance of a particular structure, we get the associated algorithms "for free." - -| Structure | What we get | -|:--------------|:--------------------------------------| -| Monoid | parallel batch training | -| Monoid | online training | -| Monoid | fast cross-validation | -| Abelian group | "untraining" of data points | -| Abelian group | more fast cross-validation | -| R-Module | weighted data points | -| Vector space | fractionally weighted data points | -| Functor | fast simple preprocessing of data | -| Monad | fast complex preprocessing of data | - -This interpretation of machine learning is somewhat limiting in that not all models have obvious algebraic structure. But many important models do. Currently implemented models include: - -* **Univariate distributions**: exponential, log-normal, normal, kernel density estimator, binomial, categorical, geometric, poisson - -* **Multivariate distributions**: normal, categorical, subset of markov networks - -* **Classifiers**: naive bayes, full bayes, perceptron, bagging, boosting (sort of) - -* **Univariate regression**: exponential, logistic, power law, polynomial - -* **NP-hard approximations**: k-centers, bin packing, multiprocessor scheduling - -* **Other**: markov chains, many nearest neighbor based algorithms with cover trees - -Note: These models not included in the latest hackage releases: decision stumps/trees, and k-nearest neighbor (kd-tree based) - -## Example: normal distribution - -Every model in HLearn is trained from a data set using the function `train`. The type signature specifies which model we're training. - - let dataset = [1,2,3,4,5,6] - let dist = train dataset :: Normal Double - -We can train in parallel using the higher order function `parallel`. The GHC run time automatically takes advantage of multiple cores on your compuer. If you have 4 cores, then run time is 4x faster. - - let dist' = parallel train dataset :: Normal Double - -We can also train in online mode. This is where you add data points to an already existing model using either the function `add1dp` or `addBatch`. - - let dist_online1 = add1dp dist 7 - let dist_online2 = addBatch dist [7,8,9,10] - -Finally, once we've trained a data point, we can do all the normal operations on it we would expect. One common operation on distributions is evaluating the probability density function. We do this with the `pdf` function. - - pdf dist 10 - -For more details on why the Normal distribution has algebraic structure and what we can do with it, see the blog post [Normal distributions form a monoid and why machine learning experts should care](http://izbicki.me/blog/gausian-distributions-are-monoids). - -## More documentation - -There are three main sources of documentation. First, there are a number of tutorials on my personal blog. These provide the most detail and are geared towards the beginner. They're probably the easiest way to get started. Next, there are two papers about the internals of the HLearn library. They are a good resource for understanding the theory behind why the library works. Finally, there's the hackage documentation. - -Tutorial links: - -* Current version: - * [The categorical distribution's monoid/group/module Structure](http://izbicki.me/blog/the-categorical-distributions-algebraic-structure) - * [The categorical distribution's functor/monad structure](http://izbicki.me/blog/functors-and-monads-for-analyzing-data) - * [Markov Networks, monoids, and futurama](http://izbicki.me/blog/markov-networks-monoids-and-futurama) - - * [Solving NP-complete problems with HLearn, and how to write your own HomTrainer instances](http://izbicki.me/public/papers/monoids-for-approximating-np-complete-problems.pdf) - -* Previous versions: - * [Nuclear weapon statistics using monoids, groups, and modules](http://izbicki.me/blog/nuclear-weapon-statistics-using-monoids-groups-and-modules-in-haskell) - * [Gaussian distributions form a monoid](http://izbicki.me/blog/gausian-distributions-are-monoids) +# HLearn + + + +HLearn is a high performance machine learning library written in [Haskell](http://haskell.org). +For example, it currently has the fastest nearest neighbor implementation for arbitrary metric spaces (see [this blog post](http://izbicki.me)). + +HLearn is also a research project. +The research goal is to discover the "best possible" interface for machine learning. +This involves two competing demands: +The library should be as fast as low-level libraries written in C/C++/Fortran/Assembly; +but it should be as flexible as libraries written in high level languages like Python/R/Matlab. +[Julia](http://julialang.org/) is making amazing progress in this direction, +but HLearn is more ambitious. +In particular, HLearn's goal is to be *faster* than the low level languages and *more flexible* than the high level languages. + +To achieve this goal, HLearn uses a very different interface than standard learning libraries. +The H in HLearn stands for three separate concepts that are fundamental to HLearn's design: + +1. The H stands for [Haskell](http://haskell.org). +Machine learning is about estimating *functions* from data, +so it makes sense that a functional programming langauge would be well suited for machine learning. +But Functional programming languages are not widely used in machine learning because they traditionally lack strong support for the fast numerical computations required for learning algorithms. +HLearn uses the [SubHask](http://github.com/mikeizbicki/subhask) library to get this fast numeric support in Haskell. +The two libraries are being developed in tandem with each other. + + + + +1. The H stands for [Homomorphisms](https://duckduckgo.com/l/?kh=-1&uddg=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FHomomorphism). +Homomorphisms are a fundamental concept in [abstract algebra](https://duckduckgo.com/l/?kh=-1&uddg=https%3A%2F%2Fen.wikipedia.org%2Fwiki%2FAbstract_algebra), +and HLearn exploits the algebraic structures inherrent in learning systems. +The following table gives a brief overview of what these structures give us: + + | Structure | What we get | + |:--------------|:--------------------------------------| + | Monoid | parallel batch training | + | Monoid | online training | + | Monoid | fast cross-validation | + | Abelian group | "untraining" of data points | + | Abelian group | more fast cross-validation | + | R-Module | weighted data points | + | Vector space | fractionally weighted data points | + | Functor | fast simple preprocessing of data | + | Monad | fast complex preprocessing of data | + +1. The H stands for the [History monad](https://github.com/mikeizbicki/HLearn/blob/master/src/HLearn/History.hs). +One of the most difficult tasks of developing a new learning algorithm is debugging the optimization procedure. +There has previously been essentially no work on making this debugging process easier, +and the `History` monad tries to solve this problem. +It lets you thread debugging information throughout the optimization code *without modifying the original code*. +Furthermore, there is no runtime overhead associated with this technique. + +The downside of HLearn's ambition is that it currently does not implement many of the popular machine learning techniques. + +## More Documentation + +Due to the rapid pace of development, HLearn's documentation is sparse. +That said, the [examples](https://github.com/mikeizbicki/HLearn/tree/master/examples) folder is a good place to start. +The haddock documentation embedded within the code is decent; +but unfortunately, hackage is unable to compile the haddocks because it uses an older version of GHC. + +HLearn has several academic papers: + +* ICML15 - [Faster Cover Trees](http://izbicki.me/public/papers/icml2015-faster-cover-trees.pdf) +* ICML13 - [Algebraic Classifiers: a generic approach to fast cross-validation, online training, and parallel training](http://izbicki.me/public/papers/icml2013-algebraic-classifiers.pdf) +* TFP13 - [HLearn: A Machine Learning Library for Haskell](http://izbicki.me/public/papers/tfp2013-hlearn-a-machine-learning-library-for-haskell.pdf) -Comparison to other libraries: +There are also a number of blog posts on [my personal website](http://izbicki.me). +Unfortunately, they are mostly out of date with the latest version of HLearn. +They might help you understand some of the main concepts in HLearn, but the code they use won't work at all. +* [The categorical distribution's monoid/group/module Structure](http://izbicki.me/blog/the-categorical-distributions-algebraic-structure) +* [The categorical distribution's functor/monad structure](http://izbicki.me/blog/functors-and-monads-for-analyzing-data) +* [Markov Networks, monoids, and futurama](http://izbicki.me/blog/markov-networks-monoids-and-futurama) +* [Solving NP-complete problems with HLearn, and how to write your own HomTrainer instances](http://izbicki.me/public/papers/monoids-for-approximating-np-complete-problems.pdf) +* [Nuclear weapon statistics using monoids, groups, and modules](http://izbicki.me/blog/nuclear-weapon-statistics-using-monoids-groups-and-modules-in-haskell) +* [Gaussian distributions form a monoid](http://izbicki.me/blog/gausian-distributions-are-monoids) * [HLearn cross-validates >400x faster than Weka](http://izbicki.me/blog/hlearn-cross-validates-400x-faster-than-weka) * [HLearn's code is shorter and clearer than Weka's](http://izbicki.me/blog/hlearns-code-is-shorter-and-clearer-than-wekas) -Paper links: - -* TFP13 - [HLearn: A Machine Learning Library for Haskell](http://izbicki.me/public/papers/tfp2013-hlearn-a-machine-learning-library-for-haskell.pdf) -* ICML13 - [Algebraic Classifiers: a generic approach to fast cross-validation, online training, and parallel training](http://izbicki.me/public/papers/icml2013-algebraic-classifiers.pdf) - -Hackage links: - -* [HLearn-algebra](http://hackage.haskell.org/package/HLearn-algebra) -* [HLearn-distributions](http://hackage.haskell.org/package/HLearn-distributions) -* [HLearn-classification](http://hackage.haskell.org/package/HLearn-classification) -* [HLearn-datastructures](http://hackage.haskell.org/package/HLearn-datastructures) -* [HLearn-approximation](http://hackage.haskell.org/package/HLearn-approximation) - -## Future directions - -HLearn is under active development. At present, it is primarily a research tool. This means that the interfaces may change significantly in the future (but will definitely follow the [PVP](http://www.haskell.org/haskellwiki/Package_versioning_policy)). I'm hoping HLearn will eventually become a stable package that will make it easy to incorporate machine learning techniques into Haskell programs. - -Current development is focused in two areas. First, implementing new models and their algebraic structures. Many unimplemented models have "trivial" algebraic structure. But for many successful models it is unknown whether they can have interesting structure. The second area is investigating new structures. Many models have Functor/Applicative/Monoid structure (or in some strict sense *almost* have these structures) and I'm working on how to exploit these structures. - -**Any comments / questions / pull requests are greatly appreciated!** - -## covertree +## Contributing -The code for the "Faster Cover Trees" paper at ICML2015 is contained within this repo. -It shouldn't be considered in a released state yet though. -There is still some work left to clean it up and make it easy to use. -The work should be finished before the actual conference. + +I'd love to have you contribute, and I'd be happy to help you get started! +Just [create an issue](https://github.com/mikeizbicki/hlearn/issues) to let me know you're interested and we can work something out. diff --git a/Setup.hs b/Setup.hs new file mode 100644 index 0000000..9a994af --- /dev/null +++ b/Setup.hs @@ -0,0 +1,2 @@ +import Distribution.Simple +main = defaultMain diff --git a/bench/allknn/AllKnn.java b/bench/allknn/AllKnn.java new file mode 100644 index 0000000..028a27f --- /dev/null +++ b/bench/allknn/AllKnn.java @@ -0,0 +1,101 @@ +/** + * This file is modified from https://github.com/zoq/benchmarks/blob/master/methods/weka/allknn.py + */ + +import java.io.*; +import weka.core.*; +import weka.core.neighboursearch.KDTree; +import weka.core.converters.ConverterUtils.DataSource; + +/** + * This class use the weka libary to implement All K-Nearest-Neighbors. + */ +public class AllKnn { + private static final String USAGE = String + .format(" This program will calculate the all k-nearest-neighbors of a set\n" + + "of points using kd-trees. You may specify a separate set of\n" + + "reference points and query points, or just a reference set which\n" + + "will be used as both the reference and query set.\n\n" + + "Required options:\n" + + "-r [string] File containing the reference dataset.\n" + + "-k [int] Number of furthest neighbors to find.\n\n" + + "Options:\n" + + "-l [int] Leaf size for tree building. Default value 20.\n" + + "-q [string] File containing query points (optional).\n" + + " Default value ''.\n"); + + public static void main(String args[]) { + //Timers timer = new Timers(); + try { + // Get the data set path. + String referenceFile = Utils.getOption('r', args); + String queryFile = Utils.getOption('q', args); + if (referenceFile.length() == 0) + throw new IllegalArgumentException("Required option: File containing" + + "the reference dataset."); + + // Load input dataset. + DataSource source = new DataSource(referenceFile); + Instances referenceData = source.getDataSet(); + + Instances queryData = null; + if (queryFile.length() != 0) + { + source = new DataSource(queryFile); + queryData = source.getDataSet(); + } + + //timer.StartTimer("total_time"); + + // Get all the parameters. + String leafSize = Utils.getOption('l', args); + String neighbors = Utils.getOption('k', args); + + // Validate options. + int k = 0; + if (neighbors.length() == 0) + { + throw new IllegalArgumentException("Required option: Number of " + + "furthest neighbors to find."); + } + else + { + k = Integer.parseInt(neighbors); + if (k < 1 || k > referenceData.numInstances()) + throw new IllegalArgumentException("[Fatal] Invalid k"); + } + + int l = 20; + if (leafSize.length() != 0) + l = Integer.parseInt(leafSize); + + // Create KDTree. + KDTree tree = new KDTree(); + tree.setMaxInstInLeaf(l); + tree.setInstances(referenceData); + + // Perform All K-Nearest-Neighbors. + if (queryFile.length() != 0) + { + for (int i = 0; i < queryData.numInstances(); i++) + { + Instances out = tree.kNearestNeighbours(queryData.instance(i), k); + } + } + else + { + for (int i = 0; i < referenceData.numInstances(); i++) + { + Instances out = tree.kNearestNeighbours(referenceData.instance(i), k); + } + } + + //timer.StopTimer("total_time"); + //timer.PrintTimer("total_time"); + } catch (IOException e) { + System.err.println(USAGE); + } catch (Exception e) { + e.printStackTrace(); + } + } +} diff --git a/bench/allknn/README.md b/bench/allknn/README.md new file mode 100644 index 0000000..15b8abf --- /dev/null +++ b/bench/allknn/README.md @@ -0,0 +1,22 @@ +# bench/allknn + +This folder contains scripts that run all nearest neighbor searches in a number of libraries. +For the most part, the scripts are very bare-bones. +For example, they don't even output the results. + +To run the scripts, you'll obviously first need to install the libraries. +The `/install` folder in this repo contains scripts for installing all of these libraries. +With all the libraries installed, just call the `runtest.sh` script with a single parameter that is the dataset to test on. + +The table below provides a brief description of the libraries compared against. + +| Library | Description | +|-------------------|---------------------------| +| [FLANN](http://www.cs.ubc.ca/research/flann/) | The Fast Library for Approximate Nearest Neighbor queries. This C++ library is the standard method for nearest neighbor in Matlab/Octave and the [OpenCV](http://opencv.org) computer vision toolkit. | +| [Julia](http://julia.org) | A popular new language designed from the ground up for fast data processing. Julia supports faster nearest neighbor queries using the [KDTrees.jl](https://github.com/JuliaGeometry/KDTrees.jl) package. | +| [Langford's cover tree](http://hunch.net/~jl/projects/cover_tree/cover_tree.html) | A reference implementation for the cover tree data structure created by John Langford. The implementation is in C, and the data structure is widely included in C/C++ machine learning libraries. | +| [MLPack](http://mlpack.org) | A C++ library for machine learning. MLPack was the first library to demonstrate the utility of generic programming in machine learning. The interface for nearest neighbor queries lets you use either a cover tree or kdtree. +| [R](http://r-project.org) | A popular language for statisticians. Nearest neighbor queries are implemented in the [FNN](https://cran.r-project.org/web/packages/FNN/index.html) package, which provides bindings to the C-based [ANN](http://www.cs.umd.edu/~mount/ANN/) library for kdtrees. | +| [scikit-learn](scikitlearn.org) | The Python machine learning toolkit. The documentation is very beginner friendly and easy to learn. The interface for nearest neighbor queries lets you use either a [ball tree](https://en.wikipedia.org/wiki/Ball_tree) or [kdtree](https://en.wikipedia.org/wiki/K-d_tree) to speed up the calculations. Both data structures were written in [Cython](http://cython.org). | +| [Weka](http://weka.org) | A Java data mining tool with a popular GUI frontend. Nearest neighbor queries in Weka are very, very slow for me and not remotely competitive with any of the libraries above. | + diff --git a/scripts/allknn-comparelibs/allknn.R b/bench/allknn/allknn.R similarity index 100% rename from scripts/allknn-comparelibs/allknn.R rename to bench/allknn/allknn.R diff --git a/bench/allknn/allknn.flann b/bench/allknn/allknn.flann new file mode 100755 index 0000000..f8dd907 --- /dev/null +++ b/bench/allknn/allknn.flann @@ -0,0 +1,19 @@ +#!/usr/bin/env python + +import sys +import numpy as np +from pyflann import * + +print "loading csv" +f = open(sys.argv[1]) +data = np.loadtxt(fname=f, delimiter=',') + +print "building index" +precision=1 +if len(sys.argv) > 3: + precision=float(sys.argv[3]) +flann = FLANN() +params = flann.build_index(data, algorithm="autotuned", target_precision=precision) + +print "performing queries" +result,dists = flann.nn_index(data, int(sys.argv[2]), checks=params["checks"]); diff --git a/bench/allknn/allknn.julia b/bench/allknn/allknn.julia new file mode 100755 index 0000000..63cf482 --- /dev/null +++ b/bench/allknn/allknn.julia @@ -0,0 +1,16 @@ +#!/usr/bin/env julia + +println("compiling KDTrees") +using KDTrees + +println("reading csv") +data = transpose(readcsv(ARGS[1])) + +println("constructing kdtree") +tree = KDTree(data) + +println("performing queries") +for k in 1:size(data,2) + p = data[:,k] + knn(tree, vec(p), int(ARGS[2])) +end diff --git a/bench/allknn/allknn.scikit b/bench/allknn/allknn.scikit new file mode 100755 index 0000000..bc91722 --- /dev/null +++ b/bench/allknn/allknn.scikit @@ -0,0 +1,12 @@ +#!/usr/bin/env python + +import sys +from sklearn.neighbors import NearestNeighbors +import numpy as np + +f = open(sys.argv[1]) +data = np.loadtxt(fname=f, delimiter=',') + +nbrs = NearestNeighbors(n_neighbors=int(sys.argv[2]), algorithm=sys.argv[3]).fit(data) +distances, indices = nbrs.kneighbors(data) + diff --git a/bench/allknn/cover_tree/COPYING b/bench/allknn/cover_tree/COPYING new file mode 100644 index 0000000..e77696a --- /dev/null +++ b/bench/allknn/cover_tree/COPYING @@ -0,0 +1,339 @@ + GNU GENERAL PUBLIC LICENSE + Version 2, June 1991 + + Copyright (C) 1989, 1991 Free Software Foundation, Inc. + 675 Mass Ave, Cambridge, MA 02139, USA + Everyone is permitted to copy and distribute verbatim copies + of this license document, but changing it is not allowed. + + Preamble + + The licenses for most software are designed to take away your +freedom to share and change it. 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If this is what you want to do, use the GNU Library General +Public License instead of this License. diff --git a/bench/allknn/cover_tree/Makefile b/bench/allknn/cover_tree/Makefile new file mode 100644 index 0000000..1f566cd --- /dev/null +++ b/bench/allknn/cover_tree/Makefile @@ -0,0 +1,18 @@ +CFLAGS=-O3 -ffast-math -funroll-loops -static # -msse -march=athlon + +all: test_nn knn + +cover_tree.o: cover_tree.cc cover_tree.h + g++ -g -c -Wall $(CFLAGS) cover_tree.cc + +point.o: point.cc point.h + g++ -g -c -Wall $(CFLAGS) point.cc + +test_nn: point.o cover_tree.o stack.h point.h test_nn.cc + g++ -Wall $(CFLAGS) -o test_nn test_nn.cc cover_tree.o point.o + +knn: point.o cover_tree.o stack.h point.h knn.cc + g++ -g -Wall $(CFLAGS) -o knn knn.cc cover_tree.o point.o + +clean: + rm *.o diff --git a/bench/allknn/cover_tree/README b/bench/allknn/cover_tree/README new file mode 100644 index 0000000..4cab88f --- /dev/null +++ b/bench/allknn/cover_tree/README @@ -0,0 +1,17 @@ +This is a first pass implementation of a cover tree in c/c++. It is +somewhat optimized and not terribly robust. + + +'test_nn ' checks the running times of tree creation and + querying for nearest neighbors for every point in . + +'knn ' computes the nearest neighbors of every point in + amongst the points in + +Copyright is owned by John Langford. + +The code is licensed under the GPL. + +The file 'cover_tree.{cc,h}' contains the core cover tree computation code. +The file 'point.{cc,h}' contains the definition of a point as well as +parsing and printing functions. diff --git a/bench/allknn/cover_tree/cover_tree.cc b/bench/allknn/cover_tree/cover_tree.cc new file mode 100644 index 0000000..ecd9927 --- /dev/null +++ b/bench/allknn/cover_tree/cover_tree.cc @@ -0,0 +1,765 @@ +#include "cover_tree.h" +#include +#include +#include +#include +using namespace std; + +struct ds_node { + v_array dist; + point p; +}; + +float base = 1.3; +float p=0; + +float il2 = 1. / log(base); +inline float dist_of_scale (int s) +{ + return pow(base, s); +} + +inline int get_scale(float d) +{ + return (int) ceilf(il2 * log(d+1)); +} +#include + +int min(int f1, int f2) +{ + if ( f1 <= f2 ) + return f1; + else + return f2; +} + +float max(float f1, float f2) +{ + if ( f1 <= f2 ) + return f2; + else + return f1; +} + +node new_node(const point &p) +{ + node new_node; + new_node.p = p; + return new_node; +} + +node new_leaf(const point &p) +{ + node new_leaf = {p,0.,0.,NULL,0,100}; + return new_leaf; +} + +float max_set(v_array &v) +{ + float max = 0.; + for (int i = 0; i < v.index; i++) + if ( max < v[i].dist.last()) + max = v[i].dist.last(); + return max; +} + +void print_space(int s) +{ + for (int i = 0; i < s; i++) + printf(" "); +} + +void print(int depth, node &top_node) +{ + print_space(depth); + print(top_node.p); + if ( top_node.num_children > 0 ) { + print_space(depth); printf("scale = %i\n",top_node.scale); + print_space(depth); printf("max_dist = %f\n",top_node.max_dist); + print_space(depth); printf("num children = %i\n",top_node.num_children); + for (int i = 0; i < top_node.num_children;i++) + print(depth+1, top_node.children[i]); + } +} + +void split(v_array& point_set, v_array& far_set, int max_scale) +{ + unsigned int new_index = 0; + float fmax = dist_of_scale(max_scale); + for (int i = 0; i < point_set.index; i++){ + if (point_set[i].dist.last() <= fmax) { + point_set[new_index++] = point_set[i]; + } + else + push(far_set,point_set[i]); + } + point_set.index=new_index; +} + +void dist_split(v_array& point_set, + v_array& new_point_set, + point new_point, + int max_scale) +{ + unsigned int new_index = 0; + float fmax = dist_of_scale(max_scale); + for(int i = 0; i < point_set.index; i++) + { + float new_d; + new_d = distance(new_point, point_set[i].p, fmax); + if (new_d <= fmax ) { + push(point_set[i].dist, new_d); + push(new_point_set,point_set[i]); + } + else + point_set[new_index++] = point_set[i]; + } + point_set.index = new_index; +} + +/* + max_scale is the maximum scale of the node we might create here. + point_set contains points which are 2*max_scale or less away. +*/ + +node batch_insert(const point& p, + int max_scale, + int top_scale, + v_array& point_set, + v_array& consumed_set, + v_array >& stack) +{ + if (point_set.index == 0) + return new_leaf(p); + else { + float max_dist = max_set(point_set); //O(|point_set|) + int next_scale = min (max_scale - 1, get_scale(max_dist)); + if (top_scale - next_scale == 100) // We have points with distance 0. + { + v_array children; + push(children,new_leaf(p)); + while (point_set.index > 0) + { + push(children,new_leaf(point_set.last().p)); + push(consumed_set,point_set.last()); + point_set.decr(); + } + node n = new_node(p); + n.scale = 100; // A magic number meant to be larger than all scales. + n.max_dist = 0; + alloc(children,children.index); + n.num_children = children.index; + n.children = children.elements; + return n; + } + else + { + v_array far = pop(stack); + split(point_set,far,max_scale); //O(|point_set|) + + node child = batch_insert(p, next_scale, top_scale, point_set, consumed_set, stack); + + if (point_set.index == 0) + { + push(stack,point_set); + point_set=far; + return child; + } + else { + node n = new_node(p); + v_array children; + push(children, child); + v_array new_point_set = pop(stack); + v_array new_consumed_set = pop(stack); + while (point_set.index != 0) { //O(|point_set| * num_children) + point new_point = point_set.last().p; + float new_dist = point_set.last().dist.last(); + push(consumed_set, point_set.last()); + point_set.decr(); + + dist_split(point_set, new_point_set, new_point, max_scale); //O(|point_saet|) + dist_split(far,new_point_set,new_point,max_scale); //O(|far|) + + node new_child = + batch_insert(new_point, next_scale, top_scale, new_point_set, new_consumed_set, stack); + new_child.parent_dist = new_dist; + + push(children, new_child); + + float fmax = dist_of_scale(max_scale); + for(int i = 0; i< new_point_set.index; i++) //O(|new_point_set|) + { + new_point_set[i].dist.decr(); + if (new_point_set[i].dist.last() <= fmax) + push(point_set, new_point_set[i]); + else + push(far, new_point_set[i]); + } + for(int i = 0; i< new_consumed_set.index; i++) //O(|new_point_set|) + { + new_consumed_set[i].dist.decr(); + push(consumed_set, new_consumed_set[i]); + } + new_point_set.index = 0; + new_consumed_set.index = 0; + } + push(stack,new_point_set); + push(stack,new_consumed_set); + push(stack,point_set); + point_set=far; + n.scale = top_scale - max_scale; + n.max_dist = max_set(consumed_set); + alloc(children,children.index); + n.num_children = children.index; + n.children = children.elements; + return n; + } + } + } +} + +node batch_create(v_array points) +{ + assert(points.index > 0); + v_array point_set; + v_array > stack; + + for (int i = 1; i < points.index; i++) { + ds_node temp; + push(temp.dist, distance(points[0], points[i], MAXFLOAT)); + temp.p = points[i]; + push(point_set,temp); + } + v_array consumed_set; + + float max_dist = max_set(point_set); + + node top = batch_insert (points[0], + get_scale(max_dist), + get_scale(max_dist), + point_set, + consumed_set, + stack); + for (int i = 0; i &heights) +{ + if (heights.index <= d) + for(;heights.index <= d;) + push(heights,0); + heights[d] = heights[d] + 1; +} + +int height_dist(const node top_node,v_array &heights) +{ + if (top_node.num_children == 0) + { + add_height(0,heights); + return 0; + } + else + { + int max_v=0; + for (int i = 0; i max_v) + max_v = d; + } + add_height(1 + max_v, heights); + return (1 + max_v); + } +} + +void depth_dist(int top_scale, const node top_node,v_array &depths) +{ + if (top_node.num_children > 0) + for (int i = 0; i &breadths) +{ + if (top_node.num_children == 0) + add_height(0,breadths); + else + { + for (int i = 0; i dist - p2 -> dist; +} + +#define SWAP(a, b) \ + do \ + { \ + d_node tmp = * a; \ + * a = * b; \ + * b = tmp; \ + } while (0) + +void halfsort (v_array cover_set) +{ + if (cover_set.index <= 1) + return; + register d_node *base_ptr = cover_set.elements; + + d_node *hi = &base_ptr[cover_set.index - 1]; + d_node *right_ptr = hi; + d_node *left_ptr; + + while (right_ptr > base_ptr) + { + d_node *mid = base_ptr + ((hi - base_ptr) >> 1); + + if (compare ( mid, base_ptr) < 0.) + SWAP (mid, base_ptr); + if (compare ( hi, mid) < 0.) + SWAP (mid, hi); + else + goto jump_over; + if (compare ( mid, base_ptr) < 0.) + SWAP (mid, base_ptr); + jump_over:; + + left_ptr = base_ptr + 1; + right_ptr = hi - 1; + + do + { + while (compare (left_ptr, mid) < 0.) + left_ptr ++; + + while (compare (mid, right_ptr) < 0.) + right_ptr --; + + if (left_ptr < right_ptr) + { + SWAP (left_ptr, right_ptr); + if (mid == left_ptr) + mid = right_ptr; + else if (mid == right_ptr) + mid = left_ptr; + left_ptr ++; + right_ptr --; + } + else if (left_ptr == right_ptr) + { + left_ptr ++; + right_ptr --; + break; + } + } + while (left_ptr <= right_ptr); + + hi = right_ptr; + } +} + +v_array > get_cover_sets(v_array > > &spare_cover_sets) +{ + v_array > ret = pop(spare_cover_sets); + while (ret.index < 101) + { + v_array temp; + push(ret, temp); + } + return ret; +} + +inline bool shell(float parent_query_dist, float child_parent_dist, float upper_bound) +{ + return parent_query_dist - child_parent_dist <= upper_bound; + // && child_parent_dist - parent_query_dist <= upper_bound; +} + +int internal_k =1; +void update_k(float *k_upper_bound, float upper_bound) +{ + float *end = k_upper_bound + internal_k-1; + float *begin = k_upper_bound; + for (;end != begin; begin++) + { + if (upper_bound < *(begin+1)) + *begin = *(begin+1); + else { + *begin = upper_bound; + break; + } + } + if (end == begin) + *begin = upper_bound; +} +float *alloc_k() +{ + return (float *)malloc(sizeof(float) * internal_k); +} +void set_k(float* begin, float max) +{ + for(float *end = begin+internal_k;end != begin; begin++) + *begin = max; +} + +float internal_epsilon =0.; +void update_epsilon(float *upper_bound, float new_dist) {} +float *alloc_epsilon() +{ + return (float *)malloc(sizeof(float)); +} +void set_epsilon(float* begin, float max) +{ + *begin = internal_epsilon; +} + +void update_unequal(float *upper_bound, float new_dist) +{ + if (new_dist != 0.) + *upper_bound = new_dist; +} +float* (*alloc_unequal)() = alloc_epsilon; +void set_unequal(float* begin, float max) +{ + *begin = max; +} + +void (*update)(float *foo, float bar) = update_k; +void (*setter)(float *foo, float bar) = set_k; +float* (*alloc_upper)() = alloc_k; + +inline void copy_zero_set(node* query_chi, float* new_upper_bound, + v_array &zero_set, v_array &new_zero_set) +{ + new_zero_set.index = 0; + d_node *end = zero_set.elements + zero_set.index; + for (d_node *ele = zero_set.elements; ele != end ; ele++) + { + float upper_dist = *new_upper_bound + query_chi->max_dist; + if (shell(ele->dist, query_chi->parent_dist, upper_dist)) + { + float d = distance(query_chi->p, ele->n->p, upper_dist); + + if (d <= upper_dist) + { + if (d < *new_upper_bound) + update(new_upper_bound, d); + d_node temp = {d, ele->n}; + push(new_zero_set,temp); + } + } + } +} + +inline void copy_cover_sets(node* query_chi, float* new_upper_bound, + v_array > &cover_sets, + v_array > &new_cover_sets, + int current_scale, int max_scale) +{ + for (; current_scale <= max_scale; current_scale++) + { + d_node* ele = cover_sets[current_scale].elements; + d_node* end = cover_sets[current_scale].elements + cover_sets[current_scale].index; + for (; ele != end; ele++) + { + float upper_dist = *new_upper_bound + query_chi->max_dist + ele->n->max_dist; + if (shell(ele->dist, query_chi->parent_dist, upper_dist)) + { + float d = distance(query_chi->p, ele->n->p, upper_dist); + + if (d <= upper_dist) + { + if (d < *new_upper_bound) + update(new_upper_bound,d); + d_node temp = {d, ele->n}; + push(new_cover_sets[current_scale],temp); + } + } + } + } +} + +void print_query(const node *top_node) +{ + printf ("query = \n"); + float *p = (float *)top_node->p; + print(p); + if ( top_node->num_children > 0 ) { + printf("scale = %i\n",top_node->scale); + printf("max_dist = %f\n",top_node->max_dist); + printf("num children = %i\n",top_node->num_children); + } +} + +void print_cover_sets(v_array > &cover_sets, + v_array &zero_set, + int current_scale, int max_scale) +{ + printf("cover set = \n"); + for (; current_scale <= max_scale; current_scale++) + { + d_node* ele = cover_sets[current_scale].elements; + d_node* end = cover_sets[current_scale].elements + cover_sets[current_scale].index; + printf ("%i\n", current_scale); + for (; ele != end; ele++) + { + node *n = (node *)ele->n; + print(n->p); + } + } + d_node *end = zero_set.elements + zero_set.index; + printf ("infinity\n"); + for (d_node *ele = zero_set.elements; ele != end ; ele++) + { + node *n = (node *)ele->n; + print(n->p); + } +} + + +/* + An optimization to consider: + Make all distance evaluations occur in descend. + + Instead of passing a cover_set, pass a stack of cover sets. The + last element holds d_nodes with your distance. The next lower + element holds a d_node with the distance to your query parent, + next = query grand parent, etc.. + + Compute distances in the presence of the tighter upper bound. + */ + +inline void descend(const node* query, float* upper_bound, + int current_scale, + int &max_scale, v_array > &cover_sets, + v_array &zero_set) +{ + d_node *end = cover_sets[current_scale].elements + cover_sets[current_scale].index; + for (d_node *parent = cover_sets[current_scale].elements; parent != end; parent++) + { + const node *par = parent->n; + float upper_dist = *upper_bound + query->max_dist + query->max_dist; + if (parent->dist <= upper_dist + par->max_dist && par->children) + { + node *chi = par->children; + if (parent->dist <= upper_dist + chi->max_dist) + { + if (chi->num_children > 0) + { + if (max_scale < chi->scale) + max_scale = chi->scale; + d_node temp = {parent->dist, chi}; + push(cover_sets[chi->scale], temp); + } + else if (parent->dist <= upper_dist) + { + d_node temp = {parent->dist, chi}; + push(zero_set, temp); + } + } + node *child_end = par->children + par->num_children; + for (chi++; chi != child_end; chi++) + { + float upper_chi = *upper_bound + chi->max_dist + query->max_dist + query->max_dist; + if (shell(parent->dist, chi->parent_dist, upper_chi)) + { + float d = distance(query->p, chi->p, upper_chi); + if (d <= upper_chi) + { + if (d < *upper_bound) + update(upper_bound, d); + if (chi->num_children > 0) + { + if (max_scale < chi->scale) + max_scale = chi->scale; + d_node temp = {d, chi}; + push(cover_sets[chi->scale],temp); + } + else + if (d <= upper_chi - chi->max_dist) + { + d_node temp = {d, chi}; + push(zero_set, temp); + } + } + } + } + } + } +} + +void brute_nearest(const node* query,v_array zero_set, + float* upper_bound, + v_array > &results, + v_array > &spare_zero_sets) +{ + if (query->num_children > 0) + { + v_array new_zero_set = pop(spare_zero_sets); + node* query_chi = query->children; + brute_nearest(query_chi, zero_set, upper_bound, results, spare_zero_sets); + float* new_upper_bound = alloc_upper(); + + node *child_end = query->children + query->num_children; + for (query_chi++;query_chi != child_end; query_chi++) + { + setter(new_upper_bound,*upper_bound + query_chi->parent_dist); + copy_zero_set(query_chi, new_upper_bound, zero_set, new_zero_set); + brute_nearest(query_chi, new_zero_set, new_upper_bound, results, spare_zero_sets); + } + free (new_upper_bound); + new_zero_set.index = 0; + push(spare_zero_sets, new_zero_set); + } + else + { + v_array temp; + push(temp, query->p); + d_node *end = zero_set.elements + zero_set.index; + for (d_node *ele = zero_set.elements; ele != end ; ele++) + if (ele->dist <= *upper_bound) + push(temp, ele->n->p); + push(results,temp); + } +} + +void internal_batch_nearest_neighbor(const node *query, + v_array > &cover_sets, + v_array &zero_set, + int current_scale, + int max_scale, + float* upper_bound, + v_array > &results, + v_array > > &spare_cover_sets, + v_array > &spare_zero_sets) +{ + if (current_scale > max_scale) // All remaining points are in the zero set. + brute_nearest(query, zero_set, upper_bound, results, spare_zero_sets); + else + if (query->scale <= current_scale && query->scale != 100) + // Our query has too much scale. Reduce. + { + node *query_chi = query->children; + v_array new_zero_set = pop(spare_zero_sets); + v_array > new_cover_sets = get_cover_sets(spare_cover_sets); + float* new_upper_bound = alloc_upper(); + + node *child_end = query->children + query->num_children; + for (query_chi++; query_chi != child_end; query_chi++) + { + setter(new_upper_bound,*upper_bound + query_chi->parent_dist); + copy_zero_set(query_chi, new_upper_bound, zero_set, new_zero_set); + copy_cover_sets(query_chi, new_upper_bound, cover_sets, new_cover_sets, + current_scale, max_scale); + internal_batch_nearest_neighbor(query_chi, new_cover_sets, new_zero_set, + current_scale, max_scale, new_upper_bound, + results, spare_cover_sets, spare_zero_sets); + } + free (new_upper_bound); + new_zero_set.index = 0; + push(spare_zero_sets, new_zero_set); + push(spare_cover_sets, new_cover_sets); + internal_batch_nearest_neighbor(query->children, cover_sets, zero_set, + current_scale, max_scale, upper_bound, results, + spare_cover_sets, spare_zero_sets); + } + else // reduce cover set scale + { + halfsort(cover_sets[current_scale]); + descend(query, upper_bound, current_scale, max_scale,cover_sets, zero_set); + cover_sets[current_scale++].index = 0; + internal_batch_nearest_neighbor(query, cover_sets, zero_set, + current_scale, max_scale, upper_bound, results, + spare_cover_sets, spare_zero_sets); + } +} + +void batch_nearest_neighbor(const node &top_node, const node &query, + v_array > &results) +{ + v_array > > spare_cover_sets; + v_array > spare_zero_sets; + + v_array > cover_sets = get_cover_sets(spare_cover_sets); + v_array zero_set = pop(spare_zero_sets); + + float* upper_bound = alloc_upper(); + setter(upper_bound,MAXFLOAT); + + float top_dist = distance(query.p, top_node.p, MAXFLOAT); + update(upper_bound, top_dist); + d_node temp = {top_dist, &top_node}; + push(cover_sets[0], temp); + + internal_batch_nearest_neighbor(&query,cover_sets,zero_set,0,0,upper_bound,results, + spare_cover_sets,spare_zero_sets); + + free(upper_bound); + push(spare_cover_sets, cover_sets); + + for (int i = 0; i < spare_cover_sets.index; i++) + { + v_array > cover_sets = spare_cover_sets[i]; + for (int j = 0; j < cover_sets.index; j++) + free (cover_sets[j].elements); + free(cover_sets.elements); + } + free(spare_cover_sets.elements); + + push(spare_zero_sets, zero_set); + + for (int i = 0; i < spare_zero_sets.index; i++) + free(spare_zero_sets[i].elements); + free(spare_zero_sets.elements); +} + +void k_nearest_neighbor(const node &top_node, const node &query, + v_array > &results, int k) +{ + internal_k = k; + update = update_k; + setter = set_k; + alloc_upper = alloc_k; + + batch_nearest_neighbor(top_node, query,results); +} + +void epsilon_nearest_neighbor(const node &top_node, const node &query, + v_array > &results, float epsilon) +{ + internal_epsilon = epsilon; + update = update_epsilon; + setter = set_epsilon; + alloc_upper = alloc_epsilon; + + batch_nearest_neighbor(top_node, query,results); +} + +void unequal_nearest_neighbor(const node &top_node, const node &query, + v_array > &results) +{ + update = update_unequal; + setter = set_unequal; + alloc_upper = alloc_unequal; + + batch_nearest_neighbor(top_node, query, results); +} diff --git a/bench/allknn/cover_tree/cover_tree.h b/bench/allknn/cover_tree/cover_tree.h new file mode 100644 index 0000000..4d366c6 --- /dev/null +++ b/bench/allknn/cover_tree/cover_tree.h @@ -0,0 +1,35 @@ +#include +#include +#include +#include +#define NDEBUG +#include +#include "point.h" + +struct node { + point p; + float max_dist; // The maximum distance to any grandchild. + float parent_dist; // The distance to the parent. + node* children; + unsigned short int num_children; // The number of children. + short int scale; // Essentially, an upper bound on the distance to any child. +}; + +void print(int depth, node &top_node); + +//construction +node new_leaf(const point &p); +node batch_create(v_array points); +//node insert(point, node *top_node); // not yet implemented +//void remove(point, node *top_node); // not yet implemented +//query +void k_nearest_neighbor(const node &tope_node, const node &query, + v_array > &results, int k); +void epsilon_nearest_neighbor(const node &tope_node, const node &query, + v_array > &results, float epsilon); +void unequal_nearest_neighbor(const node &tope_node, const node &query, + v_array > &results); +//information gathering +int height_dist(const node top_node,v_array &heights); +void breadth_dist(const node top_node,v_array &breadths); +void depth_dist(int top_scale, const node top_node,v_array &depths); diff --git a/bench/allknn/cover_tree/knn.cc b/bench/allknn/cover_tree/knn.cc new file mode 100644 index 0000000..e2a7475 --- /dev/null +++ b/bench/allknn/cover_tree/knn.cc @@ -0,0 +1,43 @@ +#include "cover_tree.h" + +// Compute the k nearest neighbors + +int main(int argc, char *argv[]) +{ + printf("log %f\n",log(-1)); + + int k = atoi(argv[1]); + + v_array set_of_points = parse_points(fopen(argv[2],"r")); + v_array set_of_queries = parse_points(fopen(argv[3],"r")); + + numdist=0; + node top = batch_create(set_of_points); + printf("numdist in batch_create: %lu\n", numdist); + + v_array > res; + + numdist=0; + if (!strcmp(argv[4],"dual")) { + printf("dual tree search\n"); + node top_query = batch_create(set_of_queries); + k_nearest_neighbor(top,top_query,res,k); + } + else { + printf("single tree search\n"); + for (int i = 0; i < set_of_queries.index; i++) + k_nearest_neighbor(top,new_leaf(set_of_queries[i]),res,k); + } + printf("numdist in k_nearest_neighbor: %lu\n", numdist); + + /* + printf("Printing results\n"); + for (int i = 0; i < res.index; i++) + { + for (int j = 0; j +#include +#include +#include +using namespace std; + +//typedef float v4sf __attribute__ ((mode(V4SF))); + +const int batch = 120;//must be a multiple of 8 + +int point_len = 0; +unsigned long numdist=0; + +//Assumption: points are a multiples of 8 long +float distance(point p1, point p2, float upper_bound) +{ + numdist++; + float sum = 0.; + float *end = p1 + point_len; + upper_bound *= upper_bound; + for (float *batch_end = p1 + batch; batch_end <= end; batch_end += batch) + { + for (; p1 != batch_end; p1+=2, p2+=2) + { + float d1 = *p1 - *p2; + float d2 = *(p1+1) - *(p2+1); + d1 *= d1; + d2 *= d2; + sum = sum + d1 + d2; + } + if (sum > upper_bound) + return sqrt(sum); + } + for (; p1 != end; p1+=1, p2+=1) + { + float d1 = *p1 - *p2; + float d2 = *(p1+1) - *(p2+1); + d1 *= d1; + d2 *= d2; + sum = sum + d1 + d2; + } + return sqrt(sum); +} +/* +//Assumption: points are a multiples of 8 long +float sse_distance(point p1, point p2, float upper_bound) +{ + v4sf sum = {0.,0.,0.,0.}; + float *end = p1 + point_len; + upper_bound *= upper_bound; + for (float *batch_end = p1 + batch; batch_end <= end; batch_end += batch) + { + for (; p1 != batch_end; p1+=8, p2+=8) + { + v4sf v1 = __builtin_ia32_loadaps(p1); + v4sf v2 = __builtin_ia32_loadaps(p2); + v4sf v3 = __builtin_ia32_loadaps(p1+4); + v4sf v4 = __builtin_ia32_loadaps(p2+4); + v1 = __builtin_ia32_subps(v1, v2); + v3 = __builtin_ia32_subps(v3, v4); + v1 = __builtin_ia32_mulps(v1, v1); + v3 = __builtin_ia32_mulps(v3, v3); + v1 = __builtin_ia32_addps(v1,v3); + sum = __builtin_ia32_addps(sum,v1); + } + v4sf temp = __builtin_ia32_addps(sum,__builtin_ia32_shufps(sum,sum,14)); + temp = __builtin_ia32_addss(temp,__builtin_ia32_shufps(temp,temp,1)); + if (((float *)&temp)[0] > upper_bound) + { + temp = __builtin_ia32_sqrtss(temp); + return ((float *)&temp)[0]; + } + } + for (; p1 != end; p1+=8, p2+=8) + { + v4sf v1 = __builtin_ia32_loadaps(p1); + v4sf v2 = __builtin_ia32_loadaps(p2); + v4sf v3 = __builtin_ia32_loadaps(p1+4); + v4sf v4 = __builtin_ia32_loadaps(p2+4); + v1 = __builtin_ia32_subps(v1, v2); + v3 = __builtin_ia32_subps(v3, v4); + v1 = __builtin_ia32_mulps(v1, v1); + v3 = __builtin_ia32_mulps(v3, v3); + v1 = __builtin_ia32_addps(v1,v3); + sum = __builtin_ia32_addps(sum,v1); + } + sum = __builtin_ia32_addps(sum,__builtin_ia32_shufps(sum,sum,14)); + sum = __builtin_ia32_addss(sum,__builtin_ia32_shufps(sum,sum,1)); + sum = __builtin_ia32_sqrtss(sum); + return ((float *) & sum)[0]; +}*/ + +/* +float distance(point p1, point p2, float upper_bound) +{ + return fabsf(p1 - p2); +} + +v_array parse_points(FILE *input) +{ + v_array ret; + for (int i = 0; i< 1000; i++) + push(ret,(float) i); + return ret; +} + +void print(point &p) +{ + printf("%f ",p); + printf("\n"); +} + +*/ + +v_array parse_points(FILE *input) +{ + v_array parsed; + char c; + v_array p; + while ( (c = getc(input)) != EOF ) + { + ungetc(c,input); + + while ((c = getc(input)) != '\n' ) + { + while (c != '0' && c != '1' && c != '2' && c != '3' + && c != '4' && c != '5' && c != '6' && c != '7' + && c != '8' && c != '9' && c != '\n' && c != EOF && c != '-') + c = getc(input); + if (c != '\n' && c != EOF) { + ungetc(c,input); + float f; + fscanf(input, "%f",&f); + push(p,f); + } + else + if (c == '\n') + ungetc(c,input); + } + + if (p.index %8 > 0) + for (int i = 8 - p.index %8; i> 0; i--) + push(p,(float) 0.); + float *new_p; + posix_memalign((void **)&new_p, 16, p.index*sizeof(float)); + memcpy(new_p,p.elements,sizeof(float)*p.index); + + if (point_len > 0 && point_len != p.index) + { + printf("Can't handle vectors of differing length, bailing\n"); + exit(0); + } + + point_len = p.index; + p.index = 0; + push(parsed,new_p); + } + return parsed; +} + +void print(point &p) +{ + for (int i = 0; i +#include + +typedef float* point; + +extern unsigned long numdist; + +float complete_distance(point v1, point v2); +float distance(point v1, point v2, float upper_bound); +v_array parse_points(FILE *input); +void print(point &p); diff --git a/bench/allknn/cover_tree/stack.h b/bench/allknn/cover_tree/stack.h new file mode 100644 index 0000000..bc9d5fc --- /dev/null +++ b/bench/allknn/cover_tree/stack.h @@ -0,0 +1,37 @@ +#include + +template class v_array{ + public: + int index; + int length; + T* elements; + + T last() { return elements[index-1];} + void decr() { index--;} + v_array() { index = 0; length=0; elements = NULL;} + T& operator[](unsigned int i) { return elements[i]; } +}; + +template void push(v_array& v, const T &new_ele) +{ + while(v.index >= v.length) + { + v.length = 2*v.length + 3; + v.elements = (T *)realloc(v.elements,sizeof(T) * v.length); + } + v[v.index++] = new_ele; +} + +template void alloc(v_array& v, int length) +{ + v.elements = (T *)realloc(v.elements, sizeof(T) * length); + v.length = length; +} + +template v_array pop(v_array > &stack) +{ + if (stack.index > 0) + return stack[--stack.index]; + else + return v_array(); +} diff --git a/bench/allknn/cover_tree/test_nn.cc b/bench/allknn/cover_tree/test_nn.cc new file mode 100644 index 0000000..fe2e0e7 --- /dev/null +++ b/bench/allknn/cover_tree/test_nn.cc @@ -0,0 +1,226 @@ +#include "cover_tree.h" +#include +#include +#include +#include +#include +#include +using namespace std; + +float diff_timeval(timeval t1, timeval t2) +{ + return (float) (t1.tv_sec - t2.tv_sec) + (t1.tv_usec - t2.tv_usec) * 1e-6; +} + +float diff_clock(clock_t t1, clock_t t2) +{ + return (float) (t1 - t2) / (float) CLOCKS_PER_SEC; +} + +int compare(const void* p1, const void* p2) +{ + if (p1 v = make_same(10000); + if (argc <2 ) + { + cout << "usage: test_nn " << endl; + exit(1); + } + int k = atoi(argv[1]); + FILE* fd = fopen(argv[2],"r"); + v_array v = parse_points(fd); + printf("point length = %f\n",v[0]); + //printf("point length = %i\n",v[0].index); + //printf("first point = \n"); + //print(v.elements[0]); + + cout << "fart" << endl; + timeval parsed; + clock_t parsed_clock = clock(); + gettimeofday(&parsed,NULL); + //printf("parse in %f seconds\n",parsed-start); + + cout << "batch_create" << endl; + node top = batch_create(v); + cout << "done." << endl; + timeval created; + // clock_t created_clock = clock(); + gettimeofday(&created, NULL); + //printf("created in %f seconds\n",diff(created,parsed)); + + //print(0, top); + /* v_array depths; + depth_dist(top.scale, top, depths); + + printf("depth distribution = \n"); + for (int i = 0; i < depths.index; i++) + if (depths[i] > 0) + printf("%i\t",i); + printf("\n"); + for (int i = 0; i < depths.index; i++) + if (depths[i] > 0) + printf("%i\t",depths[i]); + printf("\n"); + + v_array heights; + printf("max height = %i\n",height_dist(top, heights)); + + printf("height distribution = \n"); + for (int i = 0; i < heights.index; i++) + printf("%i\t",i); + printf("\n"); + for (int i = 0; i < heights.index; i++) + printf("%i\t",heights[i]); + printf("\n"); + + v_array breadths; + breadth_dist(top,breadths); + + printf("breadth distribution = \n"); + for (int i = 0; i < breadths.index; i++) + if (breadths[i] > 0) + printf("%i\t",i); + printf("\n"); + for (int i = 0; i < breadths.index; i++) + if (breadths[i] > 0) + printf("%i\t",breadths[i]); + printf("\n");*/ + + cout << "v_array" << endl; + v_array > res; + /* for (int i = 0; i < v.index; i++) + k_nearest_neighbor(top,new_leaf(v[i]),res,k); */ + cout << "starting knn" << endl; + k_nearest_neighbor(top,top,res,k); + + /* printf("Printing results\n"); + for (int i = 0; i< res.index; i++) + { + for (int j = 0; j > brute_neighbors; + for (int i=0; i < res.index && i < thresh; i++) { + point this_point = res[i][0]; + float upper_dist[k]; + point min_points[k]; + for (int j=0; j us; + push(us,this_point); + for (int j = 0; j&1 >> "stdout.$sanitizedcmd") + echo "$runtime" >> "stderr.$sanitizedcmd" + runtime=$(tail -1 <<< "$runtime") + runseconds=$(../../time2sec.hs $runtime) + + echo "$runtime $runseconds" + + #cat "stdout.$sanitizedcmd" + #cat "stderr.$sanitizedcmd" + + echo "$sanitizedcmd $runtime $runseconds" >> results +done diff --git a/scripts/allknn-comparelibs/time2sec.hs b/bench/allknn/time2sec.hs similarity index 100% rename from scripts/allknn-comparelibs/time2sec.hs rename to bench/allknn/time2sec.hs diff --git a/examples/comparison/allknn/test.sh b/examples/comparison/allknn/test.sh deleted file mode 100755 index 68659c6..0000000 --- a/examples/comparison/allknn/test.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/bin/bash - -K=1 - -time ./hlearn-allknn -k $K -r $1 --pca --verbose -n "tmp/neighbors_hlearn.csv" --distances-file="tmp/distances_hlearn.csv" +RTS -K1000M -N4 -#time ./hlearn-allknn -k $K -r $1 --varshift --verbose -n "tmp/neighbors_hlearn.csv" --distances-file="tmp/distances_hlearn.csv" +RTS -K1000M # -N4 - -#time range_search --max 100 -r $1 -n neighbors_mlpack.csv -d distances_mlpack.csv -v - -mlpack_neighbors="tmp/neighbors_mlpack.csv" -mlpack_distances="tmp/distances_mlpack.csv" - -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -S -time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -c -S -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -c -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -N - -echo "-------------------------------------" - -#head neighbors_hlearn.csv -#echo "---" -#head neighbors_mlpack.csv - -echo "num differences: " `diff tmp/neighbors_hlearn.csv tmp/neighbors_mlpack.csv | wc -l` " / " `cat $1 | wc -l` diff --git a/examples/comparison/logreg/logreg.py b/examples/comparison/logreg/logreg.py deleted file mode 100755 index e7cde97..0000000 --- a/examples/comparison/logreg/logreg.py +++ /dev/null @@ -1,25 +0,0 @@ -#!/usr/bin/python - -import sys - -import numpy as np - -from sklearn import linear_model - -dataset = sys.argv[1] -col = int(sys.argv[2]) - -csv = np.loadtxt(dataset, dtype=np.object, delimiter=",") - -if col==0: - x = csv[:,1:].astype(np.float) - y = csv[:,0] -else: - x = csv[:,0:col].astype(np.float) - y = csv[:,col] - -logreg = linear_model.LogisticRegression() #tol=0.00000001) - -m = logreg.fit(x,y) - -print m.get_params diff --git a/examples/comparison/logreg/test.sh b/examples/comparison/logreg/test.sh deleted file mode 100755 index 9c09a47..0000000 --- a/examples/comparison/logreg/test.sh +++ /dev/null @@ -1,32 +0,0 @@ -time="/usr/bin/time --format=%U" - -tests=" -../../datasets/uci/wine.csv,0 -../../datasets/uci/wdbc-mod2.csv,0 -../../datasets/uci/wpbc-mod2.csv,0 -../../datasets/uci/sonar.all-data,60 -../../datasets/uci/ionosphere.csv,0 -../../datasets/uci/pima-indians-diabetes.csv,8 -../../datasets/uci/magic04.data,10 -../../datasets/uci/haberman.data,3 -../../datasets/uci/optdigits.train.data,64 -../../datasets/uci/pendigits.train.data,16 -../../datasets/uci/winequality-red.csv,11 -../../datasets/uci/winequality-white.csv,11 -" - -for i in $tests; do - IFS=',' - set $i - - longfile=$1 - shortfile=`basename $1` - col=$2 - - sklearn=`/usr/bin/time --format="%U" ./logreg.py $longfile $col 2>&1 >/dev/null` - echo "sklearn $shortfile $sklearn" - - hlearn=`/usr/bin/time --format="%U" hlearn-linear -d $longfile -l $col --nocv 2>&1 >/dev/null | tail -n 1` - echo "hlearn $shortfile $hlearn" - -done diff --git a/examples/comparison/sgd/scikit.py b/examples/comparison/sgd/scikit.py deleted file mode 100755 index 4bc354d..0000000 --- a/examples/comparison/sgd/scikit.py +++ /dev/null @@ -1,37 +0,0 @@ -#!/usr/bin/python - -import sys -import numpy as np -from sklearn import cross_validation -from sklearn.linear_model import SGDClassifier - -# load data - -dataset = sys.argv[1] -col = int(sys.argv[2]) -csv = np.loadtxt(dataset, dtype=np.object, delimiter=",") - -if col==0: - x = csv[:,1:].astype(np.float) - y = csv[:,0] -else: - x = csv[:,0:col].astype(np.float) - y = csv[:,col] - - -# set model hyperparsms - -clf = SGDClassifier(loss="log", penalty="none",n_iter=10000,alpha=0.0001,shuffle=False) -print clf -#m = clf.fit(x, y) -#print m.get_params - -# cross-validate - -scores = cross_validation.cross_val_score(clf, x, y, cv=5, scoring='accuracy') -print "scores =", scores -print " mean =", np.mean(scores) -print " stddev =", np.std(scores) - -# print results - diff --git a/examples/comparison/sgd/test.sh b/examples/comparison/sgd/test.sh deleted file mode 100755 index 45cfcc9..0000000 --- a/examples/comparison/sgd/test.sh +++ /dev/null @@ -1,32 +0,0 @@ -time="/usr/bin/time --format=%U" - -tests=" -../../../datasets/uci/wine.csv,0 -../../../datasets/uci/wdbc-mod2.csv,0 -../../../datasets/uci/wpbc-mod2.csv,0 -../../../datasets/uci/sonar.all-data,60 -../../../datasets/uci/ionosphere.csv,0 -../../../datasets/uci/pima-indians-diabetes.csv,8 -../../../datasets/uci/magic04.data,10 -../../../datasets/uci/haberman.data,3 -../../../datasets/uci/optdigits.train.data,64 -../../../datasets/uci/pendigits.train.data,16 -../../../datasets/uci/winequality-red.csv,11 -../../../datasets/uci/winequality-white.csv,11 -" - -for i in $tests; do - IFS=',' - set $i - - longfile=$1 - shortfile=`basename $1` - col=$2 - - sklearn=`/usr/bin/time --format="%U" ./scikit.py $longfile $col 2>&1 >/dev/null` - echo "sklearn $shortfile $sklearn" - - hlearn=`/usr/bin/time --format="%U" hlearn-linear -d $longfile -l $col --nocv 2>&1 >/dev/null | tail -n 1` - echo "hlearn $shortfile $hlearn" - -done diff --git a/examples/criterion/ghc.sh b/examples/criterion/ghc.sh deleted file mode 100755 index cf6e156..0000000 --- a/examples/criterion/ghc.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -#prof="-prof" -prof="" -opt="-O2" -sandbox="-no-user-package-db -package-db ../../.cabal-sandbox/x86_64-linux-ghc-7.8.2-packages.conf.d" -core="-ddump-simpl -dsuppress-module-prefixes -dsuppress-uniques -dsuppress-uniques -dsuppress-type-applications -dsuppress-idinfo -keep-s-file -keep-llvm-files" - -ghc -fforce-recomp $prof $opt $sandbox $core "$1" > "$1.core" diff --git a/examples/criterion/historyOverhead.hs b/examples/criterion/historyOverhead.hs deleted file mode 100644 index f10067f..0000000 --- a/examples/criterion/historyOverhead.hs +++ /dev/null @@ -1,90 +0,0 @@ -{-# OPTIONS_GHC -fllvm -O2 #-} -{-# LANGUAGE NoImplicitPrelude #-} -{-# LANGUAGE ScopedTypeVariables #-} - -import Criterion -import Criterion.Main -import Prelude (IO, toRational) - -import qualified Data.Vector.Storable as VS - -import SubHask -import SubHask.Algebra.HMatrix -import HLearn.History -import HLearn.History.DisplayMethods -import HLearn.Optimization.Common - --- {-# NOINLINE emptyReturn #-} --- emptyReturn :: (HistoryMonad m, Reportable m Double) => Int -> m Double -emptyReturn n = optimize return (10::Double) (maxIterations n) - -emptyReport n = optimize report (10::Double) (maxIterations n) - -emptyCollectReports (n::Int) = optimize - (\itr -> optimize report itr ( maxIterations 10) ) - (10::Double) - (maxIterations $ round $ toRational n / 10) - -main = do - defaultMain - [ bgroup "simpleHistory" - [ bgroup "emptyReturn" - [ bench "10" $ nf (runSimpleHistory . emptyReturn) 10 - , bench "100" $ nf (runSimpleHistory . emptyReturn) 100 - , bench "1000" $ nf (runSimpleHistory . emptyReturn) 1000 - , bench "10000" $ nf (runSimpleHistory . emptyReturn) 10000 - , bench "100000" $ nf (runSimpleHistory . emptyReturn) 100000 - , bench "1000000" $ nf (runSimpleHistory . emptyReturn) 1000000 - ] - , bgroup "emptyReport" - [ bench "10" $ nf (runSimpleHistory . emptyReport) 10 - , bench "100" $ nf (runSimpleHistory . emptyReport) 100 - , bench "1000" $ nf (runSimpleHistory . emptyReport) 1000 - , bench "10000" $ nf (runSimpleHistory . emptyReport) 10000 - , bench "100000" $ nf (runSimpleHistory . emptyReport) 100000 - , bench "1000000" $ nf (runSimpleHistory . emptyReport) 1000000 - ] - , bgroup "collectReports . emptyReport" - [ bench "10" $ nf (runSimpleHistory . collectReports . emptyReport) 10 - , bench "100" $ nf (runSimpleHistory . collectReports . emptyReport) 100 - , bench "1000" $ nf (runSimpleHistory . collectReports . emptyReport) 1000 - , bench "10000" $ nf (runSimpleHistory . collectReports . emptyReport) 10000 - , bench "100000" $ nf (runSimpleHistory . collectReports . emptyReport) 100000 - , bench "1000000" $ nf (runSimpleHistory . collectReports . emptyReport) 1000000 - ] - , bgroup "emptyCollectReports" - [ bench "10" $ nf (runSimpleHistory . emptyCollectReports) 10 - , bench "100" $ nf (runSimpleHistory . emptyCollectReports) 100 - , bench "1000" $ nf (runSimpleHistory . emptyCollectReports) 1000 - , bench "10000" $ nf (runSimpleHistory . emptyCollectReports) 10000 - , bench "100000" $ nf (runSimpleHistory . emptyCollectReports) 100000 - , bench "1000000" $ nf (runSimpleHistory . emptyCollectReports) 1000000 - ] - ] - , bgroup "dynamicHistory" - [ bgroup "emptyReturn" - [ bench "10" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReturn 10 - , bench "100" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReturn 100 - , bench "1000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReturn 1000 - , bench "10000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReturn 10000 - , bench "100000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReturn 100000 - , bench "1000000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReturn 1000000 - ] - , bgroup "emptyReport" - [ bench "10" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReport 10 - , bench "100" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReport 100 - , bench "1000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReport 1000 - , bench "10000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReport 10000 - , bench "100000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReport 100000 - , bench "1000000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyReport 1000000 - ] - , bgroup "emptyCollectReports" - [ bench "10" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyCollectReports 10 - , bench "100" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyCollectReports 100 - , bench "1000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyCollectReports 1000 - , bench "10000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyCollectReports 10000 - , bench "100000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyCollectReports 100000 - , bench "1000000" $ nfIO $ runDynamicHistory idDisplayMethod $ emptyCollectReports 1000000 - ] - ] - ] diff --git a/examples/criterion/linsolv.hs b/examples/criterion/linsolv.hs deleted file mode 100644 index b5ce558..0000000 --- a/examples/criterion/linsolv.hs +++ /dev/null @@ -1,454 +0,0 @@ -{-# OPTIONS_GHC -O2 -fllvm #-} -{-# LANGUAGE StandaloneDeriving #-} -{-# LANGUAGE BangPatterns #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE DeriveDataTypeable #-} -{-# LANGUAGE ConstraintKinds #-} -{-# LANGUAGE NoImplicitPrelude, RebindableSyntax #-} - -{-# LANGUAGE RankNTypes #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE FlexibleContexts #-} -{-# LANGUAGE MultiParamTypeClasses #-} - -import Control.DeepSeq -import Control.Monad -import Control.Monad.Random -import Criterion -import Criterion.Main -import System.IO -import qualified Prelude as P - -import System.IO.Unsafe -import Debug.Trace - -import qualified Data.Vector.Storable as VS -import qualified Numeric.LinearAlgebra as HM - -import SubHask -import SubHask.Algebra.HMatrix -import HLearn.History -import HLearn.History.DisplayMethods -import HLearn.Optimization.Common -import HLearn.Optimization.GradientDescent -import HLearn.Optimization.LineMinimization.Univariate -import HLearn.Optimization.LineMinimization.Multivariate - -deriving instance Typeable HM.Matrix -type instance Scalar (HM.Matrix r) = r - -{-# NOINLINE emptyOptimize #-} -emptyOptimize :: (HistoryMonad m, Reportable m Double) => Int -> m Double -emptyOptimize n = optimize return (10::Double) (maxIterations n) - -emptyDynamicHistory :: Int -> Double --- emptyDynamicHistory n = unsafePerformIO $ runDynamicHistory (linearTrace) $ emptyOptimize n -emptyDynamicHistory n = unsafePerformIO $ runDynamicHistory idDisplayMethod $ emptyOptimize n - -emptySimpleHistory :: Int -> Double -emptySimpleHistory n = runSimpleHistory $ emptyOptimize n - --- | ax = b --- linsolveH :: --- ( v ~ Matrix r --- , InnerProductSpace v --- , Field (Scalar v) --- , Ord (Scalar v) --- , Typeable (Scalar v) --- , Typeable v --- ) => v --- -> v --- -> History v --- linsolveH a b = undefined -linsolveP !a !b = fmap (^.x1) $ conjugateGradientDescent_ - ( cgdQuadraticStep a b ) --- ( backtracking (wolfe 1e-4 1e-1) ) --- ( lineSearchBrent ( brentTollerance 1e-6 || maxIterations 20) ) - steepestDescent --- fletcherReeves -- polakRibiere - f - f' - b - ( maxIterations 50 --- , multiplicativeTollerance 1e-10 - ) - where - f :: Matrix Double -> Double - -- f x = toSingleton $ 0.5 *. trans x * a * x - trans b * x - f !x = abs $ f' x - - f' !x = a*x - b --- f' !x = a*x - b - -data CGD v = CGD - { r0 :: !v - , d0 :: !v - , x0 :: !v - , alpha0 :: !(Scalar v) - , fx0 :: !(Scalar v) - , a :: !(Outer v) - } - deriving (Typeable) - -instance Has_fx1 CGD v where - fx1 = lens Main.fx0 undefined -- Main.fx0 undefined - --- linsolveH :: --- ( HistoryMonad m --- , Reportable m (CGD (Matrix Double)) --- ) => Matrix Double -> Matrix Double -> m (Matrix Double) -linsolveH a b = fmap x0 $ optimize - ( \cgd -> do - let x1 = x0 cgd + alpha0 cgd *. r0 cgd - r1 = r0 cgd - alpha0 cgd *. a * r0 cgd --- r1 = b - a*x1 --- r1 = -(f' x1) -- (b - a*x1) - alpha1 = r1 <> r1 / (toSingleton $ trans r1 * a * r1) - alpha1' <- lineSearchBrent (maxIterations 20) f f' x1 (f' x1) 1 - trace ( "" - ++ "; r0<>r1 = "++show (r0 cgd <> r1) - ++ "; alpha1 = "++show (alpha1) --- ++ "; alpha1' = "++show (alpha1') - ++ "; f x = "++show (f x1) --- ++ "; |f' x|2 = "++show (abs $ innerProductNorm $ f' x1) --- ++ "\n; x1 = "++show (x1) --- ++ "\n; r1="++show (r1) --- ++ "\n; r0="++show (r0 cgd) --- ++ "\n" --- ++ "; |f (x1+0.5*alpha1*r1)| = "++show (abs $ f $ x1+0.5*alpha1*.r1)++"\n" --- ++ "; |f (x1+0.6*alpha1*r1)| = "++show (abs $ f $ x1+0.6*alpha1*.r1)++"\n" --- ++ "; |f (x1+0.7*alpha1*r1)| = "++show (abs $ f $ x1+0.7*alpha1*.r1)++"\n" --- ++ "; |f (x1+0.8*alpha1*r1)| = "++show (abs $ f $ x1+0.8*alpha1*.r1)++"\n" --- ++ "; |f (x1+0.9*alpha1*r1)| = "++show (abs $ f $ x1+0.9*alpha1*.r1)++"\n" --- ++ "; |f (x1+1.0*alpha1*r1)| = "++show (abs $ f $ x1+1.0*alpha1*.r1)++"\n" --- ++ "; |f (x1+1.1*alpha1*r1)| = "++show (abs $ f $ x1+1.1*alpha1*.r1)++"\n" --- ++ "; |f (x1+1.2*alpha1*r1)| = "++show (abs $ f $ x1+1.2*alpha1*.r1)++"\n" --- ++ "; |f (x1+1.3*alpha1*r1)| = "++show (abs $ f $ x1+1.3*alpha1*.r1)++"\n" --- ++ "; |f (x1+1.4*alpha1*r1)| = "++show (abs $ f $ x1+1.4*alpha1*.r1)++"\n" --- ++ "; |f (x1+1.5*alpha1*r1)| = "++show (abs $ f $ x1+1.5*alpha1*.r1)++"\n" --- ++ "\n\n------" - ) $ - return $ cgd - { r0 = r1 - , d0 = r1 - , x0 = x1 - , alpha0 = alpha1 - } - ) - ( let x0 = zero -- mkMatrix 2 1 [0.01,-0.5] - r0 = b -a*x0 - in CGD - { r0 = r0 - , d0 = r0 - , x0 = x0 - , alpha0 = r0 <> r0 / (toSingleton $ trans r0 * a * r0) - , Main.fx0 = f x0 - , a = a --- , alpha0 = negate $ runSimpleHistory $ lineSearchBrent (maxIterations 20) f f' x0 (f' x0) 1 - } :: CGD (Matrix Double) - ) - ( maxIterations 10 ) - where - f x = toSingleton $ 0.5 *. trans x * a * x - trans b * x --- f x = abs $ a*x - b - f' x = (a*x - b) - --- backtrack :: (Ord r, Field r) => (r -> r) -> r -> r -> r -> r -backtrack f fx gamma x = go x - where - go x = --- trace ("backtrack: x="++show x) $ - if fx > f x' - then x' - else go x' - where - x' = gamma * x - -type StepSize m v = - ( InnerProductSpace v - , HistoryMonad m - ) => (v -> Scalar v) - -> (v -> v) - -> (v -> Outer v) - -> CGD v - -> m (Scalar v) - -lsGradientDescentOptimal :: StepSize m (Matrix Double) -lsGradientDescentOptimal f f' _ cgd = do - let alpha1 = r0 cgd <> r0 cgd / (toSingleton $ trans (d0 cgd) * a cgd * d0 cgd) - return alpha1 - -lsBrent :: - ( Reportable m (Scalar v) - , Reportable m (LineBracket (Scalar v)) - , Reportable m (Brent (Scalar v)) - ) => StopCondition m (Brent (Scalar v)) -> StepSize m v -lsBrent !stops !f _ _ !cgd = do - let g y = f $ x0 cgd + y *. d0 cgd - let stepGuess = alpha0 cgd - bracket <- lineBracket g (stepGuess/2) (stepGuess*2) - brent <- brent g bracket stops - return $ _x brent - -type ConjMethod = forall v. InnerProductSpace v => v -> CGD v -> Scalar v - -type Direction = forall v. InnerProductSpace v => CGD v -> v - -cgd :: - ( InnerProductSpace v - , HistoryMonad m - , Reportable m (Scalar v) - , Reportable m (LineBracket (Scalar v)) - , Reportable m (Brent (Scalar v)) - , Reportable m (CGD v) - , v ~ Matrix Double - ) => (v -> Scalar v) - -> (v -> v) - -> (v -> Outer v) - -> m (v) -cgd = cgd' lsGradientDescentOptimal undefined - -cgd' :: - ( InnerProductSpace v - , HistoryMonad m - , Reportable m (Scalar v) - , Reportable m (LineBracket (Scalar v)) - , Reportable m (Brent (Scalar v)) - , Reportable m (CGD v) - , v ~ Matrix Double - ) => StepSize m v - -> Direction - -> (v -> Scalar v) - -> (v -> v) - -> (v -> Outer v) - -> m (v) -cgd' !step !dir !f !f' !f'' = fmap x0 $ optimize - ( \cgd -> do - alpha1 <- step f f' f'' cgd --- let alpha1 = r0 cgd <> r0 cgd / (toSingleton $ trans (d0 cgd) * a cgd * d0 cgd) - - let x1 = x0 cgd + alpha1 *. d0 cgd - r1 = -f' x1 - beta1 = r1 <> r1 / r0 cgd <> r0 cgd --- beta1 = conj r1 cgd - d1 = r1 + beta1 *. d0 cgd - --- trace ( "" --- ++ "; r0<>r1 = "++show (r0 cgd <> r1) --- ++ "; alpha1 = "++show (alpha1) --- -- ++ "; alpha1' = "++show (alpha1') --- ++ "; f x = "++show (f x1) --- ) $ - return $ cgd - { x0 = x1 - , r0 = r1 - , d0 = d1 - , alpha0 = alpha1 - , Main.fx0 = f $ x1 - } - ) - ( let x0 = zero - f'x0 = f' zero - in CGD - { r0 = -f'x0 - , d0 = -f'x0 - , x0 = x0 - , alpha0 = 1 - , Main.fx0 = f x0 - , a = f'' x0 - } - ) --- ( maxIterations 15 || multiplicativeTollerance 1e-6 ) - ( multiplicativeTollerance 1e-1 ) - -linsolveJ a b = fmap x0 $ optimize - ( \cgd -> do - let x1 = x0 cgd + alpha0 cgd *. d0 cgd - r1 = r0 cgd - alpha0 cgd *. a * d0 cgd - beta1 = r1 <> r1 / r0 cgd <> r0 cgd - d1 = r1 + beta1 *. d0 cgd - - alpha1 = r1 <> r1 / (toSingleton $ trans d1 * a * d1) - return $ cgd - { r0 = r1 - , d0 = d1 - , x0 = x1 - , alpha0 = alpha1 - } - ) - ( let x0 = zero -- mkMatrix 2 1 [0.01,-0.5] - r0 = b -a*x0 - d0 = r0 - in CGD - { r0 = r0 - , d0 = d0 - , x0 = x0 - , alpha0 = r0 <> r0 / (toSingleton $ trans d0 * a * d0) - , Main.fx0 = f x0 - , a = a - } :: CGD (Matrix Double) - ) - ( maxIterations 11 ) - where --- f x = abs $ a*x - b - f x = toSingleton $ 0.5 *. trans x * a * x - trans b * x - f' x = (a*x - b) --- g :: HM.Matrix Double -> Double --- g x = HM.sumElements $ HM.scale 0.5 (HM.trans x HM.<> a' HM.<> x) P.- (HM.trans x HM.<> b') --- --- g' :: HM.Matrix Double -> HM.Matrix Double --- g' x = a' HM.<> x P.- b' - --- linsolve :: --- ( Ring v --- , Field (Scalar v) --- , Ord (Scalar v) --- ) => v --- -> v --- -> v --- linsolveDyn a b = unsafePerformIO $ runDynamicHistory (removeLineMin ||| linearTrace) (linsolveP a b) --- linsolveDyn a b = unsafePerformIO $ runDynamicHistory (linearTrace) (linsolveP a b) --- linsolveDyn !a !b = unsafePerformIO $ runDynamicHistory idDisplayMethod (linsolveH a b) - -linsolveDyn :: Matrix Double -> Matrix Double -> Matrix Double -linsolveDyn !a !b = unsafePerformIO $ runDynamicHistory idDisplayMethod (cgd f f' f'') --- linsolveDyn !a !b = unsafePerformIO $ runDynamicHistory linearTrace (cgd f f') - where - f :: Matrix Double -> Double - f x = toSingleton $ 0.5 *. trans x * a * x - trans b * x - - f' :: Matrix Double -> Matrix Double - f' x = (a*x - b) - - f'' _ = a - -linsolveSimp :: Matrix Double -> Matrix Double -> Matrix Double -linsolveSimp !a !b = runSimpleHistory $ cgd f f' f'' - where - f :: Matrix Double -> Double - f x = toSingleton $ 0.5 *. trans x * a * x - trans b * x - - f' :: Matrix Double -> Matrix Double - f' x = (a*x - b) - - f'' _ = a - --- xs = [1,2,2,2] :: [Double] --- ys = [1,0.5,2,3] :: [Double] --- zs = [0,1] :: [Double] - --- xs = [3,2,2,6] :: [Double] --- ys = xs --- zs = [2,-8] :: [Double] --- --- a = mkMatrix 2 2 xs --- x = mkMatrix 2 1 ys --- z = mkMatrix 2 1 [1,-0.5] :: Matrix Double --- q = mkMatrix 2 1 [0,1] :: Matrix Double --- -- r = mkMatrix 2 1 [-2,-1] :: Matrix Double --- b = mkMatrix 2 1 zs --- --- a' = 2 HM.>< 2 $ xs --- x' = 2 HM.>< 1 $ ys --- z' = 2 HM.>< 1 $ [1,-0.5] :: HM.Matrix Double --- q' = 2 HM.>< 1 $ [0,1] :: HM.Matrix Double --- -- r' = 2 HM.>< 1 $ [-2,-1] :: HM.Matrix Double --- b' = 2 HM.>< 1 $ zs - --- cgdQuadraticStep :: MultivariateLineSearch -cgdQuadraticStep a b _ _ !x0 !f'x0 _ = {-# SCC cgdQuadraticStep #-} do - return $ r <> r / (toSingleton $ trans r * a * r) --- return $ 0.5 *. (r <> r / (toSingleton $ trans f'x0 * a * f'x0)) --- return 1 - where - r = b - a*x0 -- f'x0 - -randomVec :: MonadRandom m => Int -> m (VS.Vector Double) -randomVec n = liftM (VS.fromList . take n) getRandoms - -randomPSD :: MonadRandom m => Int -> m (HM.Matrix Double) --- randomPSD n = liftM (foldl1' (P.+)) $ replicateM n (do v <- randomVec n; return $ v `HM.outer` v ) -randomPSD n = do - xs <- replicateM n (randomVec n) - return $ gramMatrix xs - -gramMatrix :: [VS.Vector Double] -> HM.Matrix Double -gramMatrix xs = HM.trans m HM.<> m - where - m = HM.fromLists $ map VS.toList xs - -main = do - - putStrLn "------------------------" - putStrLn "initializing matrices..." - - let dim=200::Int - --- let hm1 = dim HM.>< dim $ xs --- hm2 = dim HM.>< dim $ ys - let hm1 = evalRand (randomPSD dim) $ mkStdGen 1 - hm2 = evalRand (randomPSD dim) $ mkStdGen 2 - let zs = randoms (mkStdGen 2) :: [Double] - hmv = dim HM.>< 1 $ zs - deepseq hm1 $ deepseq hm2 $ deepseq hmv $ return () - - let chol1 = HM.chol hm1 - chol2 = HM.chol hm2 - deepseq hm1 $ deepseq hm2 $ return () - - let sm1 = fromHMatrix hm1 --mkMatrix dim dim xs - sm2 = fromHMatrix hm2 --mkMatrix dim dim ys - smv = mkMatrix dim 1 zs - deepseq sm1 $ deepseq sm2 $ return () - - let v1 = VS.fromList $ take dim zs - deepseq v1 $ return () - --- putStrLn $ "hm1="++show hm1++"\n\n\n" - - let x = HM.linearSolve hm1 hmv - putStrLn $ "hm1 * x = hmv ==> x = " ++ show x - putStrLn $ "hm1 * x - hmv = " ++ show ((hm1 HM.<> x) `HM.sub` hmv) - putStrLn "" - - let x=HM.inv hm1 HM.<> hmv - putStrLn $ "x = hm1^-1 * hmv = " ++ show x - putStrLn $ "hm1 * x - hmv = " ++ show ((hm1 HM.<> x) `HM.sub` hmv) - putStrLn "" - - let x = linsolveDyn sm1 smv - putStrLn $ "sm1 * x = smv ==> x = " ++ show x - putStrLn $ "sm1 * x - smv = " ++ show ((sm1 * x) - smv) - putStrLn "" - - putStrLn "------------------------" - putStrLn "begining tests..." - --- print $ emptySimpleHistory 1000 - - defaultMain - [ bgroup "history" - [ --- [ bench "emptyRunHistory - 10" $ nf emptyRunHistory 10 --- , bench "emptyRunHistory - 100" $ nf emptyRunHistory 100 --- , bench "emptyRunHistory - 1000" $ nf emptyRunHistory 1000 - ] - , bgroup "multiply" - [ bench "hmatrix" $ nf (hm1 HM.<>) hm2 - , bench "hmatrix - chol" $ nf (chol1 HM.<>) chol2 - , bench "subhask" $ nf (sm1 *) sm2 - ] - , bgroup "linsolv" - [ bench "hmatrix - linearSolve" $ nf (HM.linearSolve hm1) hmv - , bench "hmatrix - cholSolve" $ nf (HM.cholSolve hm1) hmv --- -- , bench "hmatrix - linearSolveLS" $ nf (HM.linearSolveLS hm1) hmv --- -- , bench "hmatrix - linearSolveSVD" $ nf (HM.linearSolveSVD hm1) hmv --- , bench "hmatrix - naive" $ nf (HM.inv hm1 HM.<>) hmv - , bench "subhask - linsolveSimp" $ nf (linsolveSimp sm1) smv - , bench "subhask - linsolveDyn" $ nf (linsolveDyn sm1) smv --- , bench "subhask - naive" $ nf (reciprocal sm1 *) smv --- [ - ] - ] - - putStrLn "done." diff --git a/examples/example0001-optimization-univariate.hs b/examples/example0001-optimization-univariate.hs new file mode 100644 index 0000000..7b9255a --- /dev/null +++ b/examples/example0001-optimization-univariate.hs @@ -0,0 +1,42 @@ +{-# LANGUAGE NoImplicitPrelude #-} +{-# LANGUAGE ScopedTypeVariables #-} +{-# LANGUAGE RebindableSyntax #-} + +import SubHask +import SubHask.Algebra.Vector +import SubHask.Category.Trans.Derivative + +import HLearn.History +import HLearn.Optimization.Univariate + +import System.IO + +-------------------------------------------------------------------------------- + +x0 :: OrdField a => a +x0 = (-5) + +optmethod :: OrdField a => (a -> a) -> a +optmethod f = x1 $ evalHistory $ fminuncM_brent (maxIterations 20 || noProgress) x0 (return . f) + +-------------------------------------------------------------------------------- + +main = do + + let f_slopes :: Float = optmethod slopes + d_slopes :: Double = optmethod slopes + r_slopes :: Rational = optmethod slopes + + putStrLn $ "f_slopes = " ++ show f_slopes + putStrLn $ "d_slopes = " ++ show d_slopes + putStrLn $ "r_slopes = " ++ show r_slopes + + let f_slopesWithDiscontinuity :: Float = optmethod slopesWithDiscontinuity + d_slopesWithDiscontinuity :: Double = optmethod slopesWithDiscontinuity + r_slopesWithDiscontinuity :: Rational = optmethod slopesWithDiscontinuity + + putStrLn $ "f_slopesWithDiscontinuity = " ++ show f_slopesWithDiscontinuity + putStrLn $ "d_slopesWithDiscontinuity = " ++ show d_slopesWithDiscontinuity + putStrLn $ "r_slopesWithDiscontinuity = " ++ show r_slopesWithDiscontinuity + + putStrLn "done." diff --git a/examples/example0002-optimization-multivariate.hs b/examples/example0002-optimization-multivariate.hs new file mode 100644 index 0000000..02a89e4 --- /dev/null +++ b/examples/example0002-optimization-multivariate.hs @@ -0,0 +1,35 @@ +{-# LANGUAGE NoImplicitPrelude #-} +{-# LANGUAGE ScopedTypeVariables #-} +{-# LANGUAGE RebindableSyntax #-} +{-# LANGUAGE DataKinds #-} +{-# LANGUAGE ConstraintKinds #-} +{-# LANGUAGE RankNTypes #-} + +import SubHask +import SubHask.Algebra.Vector +import SubHask.Category.Trans.Derivative + +import HLearn.History +import HLearn.Optimization.Univariate +import HLearn.Optimization.Multivariate + +import System.IO + +-------------------------------------------------------------------------------- + +main = do + let x0 = unsafeToModule [1,2,1,2] :: SVector 4 Double + f = rosenbrock + lineSearch = lineSearch_brent ( stop_brent 1e-12 || maxIterations 20 ) + + stop :: StopCondition a + stop = maxIterations 20 + + let cgd conj = evalHistory $ fminunc_cgd_ conj lineSearch stop x0 f + + putStrLn $ "steepestDescent = " ++ show (fx1 $ cgd steepestDescent) + putStrLn $ "fletcherReeves = " ++ show (fx1 $ cgd fletcherReeves) + putStrLn $ "polakRibiere = " ++ show (fx1 $ cgd polakRibiere) + putStrLn $ "hestenesStiefel = " ++ show (fx1 $ cgd hestenesStiefel) + + putStrLn $ "bfgs = " ++ show (fx1 $ evalHistory $ fminunc_bfgs_ lineSearch stop x0 f ) diff --git a/examples/example0003-classification.hs b/examples/example0003-classification.hs new file mode 100644 index 0000000..a544877 --- /dev/null +++ b/examples/example0003-classification.hs @@ -0,0 +1,42 @@ +{-# LANGUAGE NoImplicitPrelude #-} +{-# LANGUAGE ScopedTypeVariables #-} +{-# LANGUAGE RebindableSyntax #-} +{-# LANGUAGE DataKinds #-} +{-# LANGUAGE ConstraintKinds #-} +{-# LANGUAGE RankNTypes #-} + +import SubHask +import SubHask.Algebra.Array +import SubHask.Algebra.Vector +import SubHask.Algebra.Container + +import HLearn.Data.LoadData +import HLearn.Classifiers.Linear +import HLearn.History + +import qualified Prelude as P +import System.IO + +-------------------------------------------------------------------------------- + +main = do + xs :: BArray (Labeled' (SVector "dyn" Double) (Lexical String)) + <- loadCSVLabeled' 0 "/home/user/proj/hlearn/datasets/csv/uci/wine.csv" +-- <- loadCSVLabeled' 8 "/home/user/proj/hlearn/datasets/csv/uci/pima-indians-diabetes.csv" + + glm <- runHistory + ( (displayFilter (maxReportLevel 2) dispIteration) + + summaryTable + ) + $ trainLogisticRegression 1e-3 xs + + putStrLn $ "loss_01 = "++show (validate loss_01 (toList xs) glm) + putStrLn $ "loss_logistic = "++show (validate loss_logistic (toList xs) glm) + putStrLn $ "loss_hinge = "++show (validate loss_hinge (toList xs) glm) + +-- putStrLn "" +-- print $ show $ weights glm!Lexical "1" +-- print $ show $ weights glm!Lexical "2" +-- print $ show $ weights glm!Lexical "3" + + putStrLn "done." diff --git a/examples/old/Perceptron.hs b/examples/old/Perceptron.hs deleted file mode 100644 index eeda5f6..0000000 --- a/examples/old/Perceptron.hs +++ /dev/null @@ -1,49 +0,0 @@ -{-# LANGUAGE TypeSynonymInstances #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE MultiParamTypeClasses #-} - -import HLearn.Algebra -import HLearn.Models.Classifiers - ---------------------------------------- - -newtype Vector2 = Vector2 [Double] - deriving (Read,Show,Eq,Ord) - -instance Abelian Vector2 -instance Monoid Vector2 where - mempty = Vector2 [0,0] - (Vector2 a) `mappend` (Vector2 b) = Vector2 $ zipWith (+) a b - -instance Group Vector2 where - inverse (Vector2 xs) = Vector2 $ map negate xs - -type Ring Vector2 = Double - -instance Module Vector2 where - r .* (Vector2 xs) = Vector2 $ fmap (r*) xs - -instance MetricSpace Vector2 where - distance (Vector2 a) (Vector2 b) = sqrt . sum . map (^2) $ zipWith (-) a b - ---------------------------------------- - -ds = [ ('o', Vector2 [-1,1.5]) - , ('x', Vector2 [2,2]) - , ('t', Vector2 [1,-1]) - ] - -m = train ds :: Perceptron Char Vector2 - --- | main creates an ascii plot of where the decision boundary for the perceptron is -main = sequence_ $ map putStrLn boundary - -boundary = map (map go) [[(x/10,y/10) | x<-[-20..20]] | y<-(reverse [-20..20])] - where - go (x,y) - | x == 0 && y == 0 = '+' - | x == 0 = '|' - | y == 0 = '-' - | otherwise = classify m $ Vector2 [x,y] - diff --git a/examples/old/blogs/blog-categorical-algebra.lhs b/examples/old/blogs/blog-categorical-algebra.lhs deleted file mode 100644 index ed5e2f4..0000000 --- a/examples/old/blogs/blog-categorical-algebra.lhs +++ /dev/null @@ -1,243 +0,0 @@ -histogram of simonDist The categorical distribution is the main distribution for handling discrete data. I like to think of it as a histogram. For example, let's say Simon has a bag full of marbles. There are four "categories" of marbles---red, green, blue, and white. Now, if Simon reaches into the bag and randomly selects a marble, what's the probability it will be green? We would use the categorical distribution to find out. - -In this article, we'll go over the math behind the categorical distribution, the algebraic structure of the distribution, and how to manipulate it within Haskell's HLearn library. We'll also see some examples of how this focus on algebra makes HLearn's interface more powerful than other common statistical packages. Everything that we're going to see is in a certain sense very "obvious" to a statistician, but this algebraic framework also makes it convenient. And since programmers are inherently lazy, this is a Very Good Thing. - -Before delving into the "cool stuff," we have to look at some of the mechanics of the HLearn library. - -Preliminaries - -The HLearn-distributions package contains all the functions we need to manipulate categorical distributions. Let's install it: - -$ cabal install HLearn-distributions - -We import our libraries: - ->import Control.DeepSeq ->import HLearn.Algebra ->import HLearn.Models.Distributions - -We create a data type for Simon's marbles: - ->data Marble = Red | Green | Blue | White -> deriving (Read,Show,Eq,Ord) - -marbles - -The easiest way to represent Simon's bag of marbles is with a list: - ->simonBag :: [Marble] ->simonBag = [Red, Red, Red, Green, Blue, Green, Red, Blue, Green, Green, Red, Red, Blue, Red, Red, Red, White] - -And now we're ready to train a categorical distribution of the marbles in Simon's bag: - ->simonDist = train simonBag :: Categorical Double Marble - -We can load up ghci and plot the distribution with the conveniently named function plotDistribution: - -ghci> plotDistribution (plotFile "simonDist" $ PDF 400 300) simonDist - -This gives us a histogram of probabilities: - -marbles trained into categorical - -In the HLearn library, every statistical model is generated from data using either train or train'. Because these functions are overloaded, we must specify the type of simonDist so that the compiler knows which model to generate. Categorical takes two parameters. The first is the type of the discrete data (Marble). The second is the type of the probability (Double). We could easily create Categorical distributions with different types depending on the requirements for our application. For example: - ->stringDist = train (map show simonBag) :: Categorical Float String - -This is the first "cool thing" about Categorical: We can make distributions over any user-defined type. This makes programming with probabilities easier, more intuitive, and more convenient. Most other statistical libraries would require you to assign numbers corresponding to each color of marble, and then create a distribution over those numbers. - -Now that we have a distribution, we can find some probabilities. If Simon pulls a marble from the bag, what's the probability that it would Red? - -$latex P(\text{red}) = \frac{\text{\# red marbes}}{\text{\# total marbles}}$ - -We can use the pdf function to do this calculation for us: - -ghci> pdf simonDist Red -0.5626 -ghci> pdf simonDist Blue -0.1876 -ghci> pdf simonDist Green -0.1876 -ghci> pdf simonDist White -6.26e-2 - -If we sum all the probabilities, as expected we would get 1: - -ghci> sum $ map (pdf simonDist) [Red,Green,Blue,White] -1.0 - -Due to rounding errors, you may not always get 1. If you absolutely, positively, have to avoid rounding errors, you should use Rational probabilities: - ->simonDistRational = train simonBag :: Categorical Rational Marble - -Rationals are slower, but won't be subject to floating point errors. - -This is just about all the functionality you would get in a "normal" stats package like R or NumPy. But using Haskell's nice support for algebra, we can get some extra cool features. -Semigroup - -First, let's talk about semigroups. A semigroup is any data structure that has a binary operation (<>) that joins two of those data structures together. The categorical distribution is a semigroup. - -Don wants to play marbles with Simon, and he has his own bag. Don's bag contains only red and blue marbles: - ->donBag = [Red,Blue,Red,Blue,Red,Blue,Blue,Red,Blue,Blue] - -We can train a categorical distribution on Don's bag in the same way we did earlier: - ->donDist = train donBag :: Categorical Double Marble - -In order to play marbles together, Don and Simon will have to add their bags together. - ->bothBag = simonBag ++ donBag - -Now, we have two options for training our distribution. First is the naive way, we can train the distribution directly on the combined bag: - ->bothDist = train bothBag :: Categorical Double Marble - -This is the way we would have to approach this problem in most statistical libraries. But with HLearn, we have a more efficient alternative. We can combine the trained distributions using the semigroup operation: - ->bothDist' = simonDist <> donDist - -Under the hood, the categorical distribution stores the number of times each possibility occurred in the training data. The <> operator just adds the corresponding counts from each distribution together: - -semigroup and bothDist - -This method is more efficient because it avoids repeating work we've already done. Categorical's semigroup operation runs in time O(1), so no matter how big the bags are, we can calculate the distribution very quickly. The naive method, in contrast, requires time O(n). If our bags had millions or billions of marbles inside them, this would be a considerable savings! - -We get another cool performance trick "for free" based on the fact that Categorical is a semigroup: The function train can be automatically parallelized using the higher order function parallel. I won't go into the details about how this works, but here's how you do it in practice. - -First, we must show the compiler how to resolve the Marble data type down to "normal form." This basically means we must show the compiler how to fully compute the data type. (We only have to do this because Marble is a type we created. If we were using a built in type, like a String, we could skip this step.) This is fairly easy for a type as simple as Marble: - ->instance NFData Marble where -> rnf Red = () -> rnf Blue = () -> rnf Green = () -> rnf White = () - -Then, we can perform the parallel computation by: - ->simonDist_par = parallel train simonBag :: Categorical Double Marble - -Other languages require a programmer to manually create parallel versions of their functions. But in Haskell with the HLearn library, we get these parallel versions for free! All we have to do is ask for it! -Monoid - -A monoid is a semigroup with an empty element, which is called mempty in Haskell. It obeys the law that: - -M <> mempty == mempty <> M == M - -And it is easy to show that Categorical is also a monoid. We get this empty element by training on an empty data set: - -mempty = train ([] :: [Marble]) :: Categorical Double Marble - -The HomTrainer type class requires that all its instances also be instances of Monoid. This lets the compiler automatically derive "online trainers" for us. An online trainer can add new data points to our statistical model without retraining it from scratch. - -For example, we could use the function add1dp (stands for: add one data point) to add another white marble into Simon's bag: - ->simonDistWhite = add1dp simonDist White - -This also gives us another approach for our earlier problem of combining Simon and Don's bags. We could use the function addBatch: - ->bothDist'' = addBatch simonDist donBag - -Because Categorical is a monoid, we maintain the property that: - -bothDist == bothDist' == bothDist'' - -Again, statisticians have always known that you could add new points into a categorical distribution without training from scratch. The cool thing here is that the compiler is deriving all of these functions for us, and it's giving us a consistent interface for use with different data structures. All we had to do to get these benefits was tell the compiler that Categorical is a monoid. This makes designing and programming libraries much easier, quicker, and less error prone. -Group - -A group is a monoid with the additional property that all elements have an inverse. This lets us perform subtraction on groups. And Categorical is a group. - -Ed wants to play marbles too, but he doesn't have any of his own. So Simon offers to give Ed some of from his own bag. He gives Ed one of each color: - ->edBag = [Red,Green,Blue,White] - -Now, if Simon draws a marble from his bag, what's the probability it will be blue? - -To answer this question without algebra, we'd have to go back to the original data set, remove the marbles Simon gave Ed, then retrain the distribution. This is awkward and computationally expensive. But if we take advantage of Categorical's group structure, we can just subtract directly from the distribution itself. This makes more sense intuitively and is easier computationally. - ->simonDist2 = subBatch simonDist edBag - -This is a shorthand notation for using the group operations directly: - ->edDist = train edBag :: Categorical Double Marble ->simonDist2' = simonDist <> (inverse edDist) - -The way the inverse operation works is it multiplies the counts for each category by -1. In picture form, this flips the distribution upside down: - -edDist inversification - -Then, adding an upside down distribution to a normal one is just subtracting the histogram columns and renormalizing: - -Simon substraction edDist - -Notice that the green bar in edDist looks really big---much bigger than the green bar in simonDist. But when we subtract it away from simonDist, we still have some green marbles left over in simonDist2. This is because the histogram is only showing the probability of a green marble, and not the actual number of marbles. - -Finally, there's one more crazy trick we can perform with the Categorical group. It's perfectly okay to have both positive and negative marbles in the same distribution. For example: - -ghci> plotDistribution (plotFile "mixedDist" $ PDF 400 300) (edDist <> (inverse donDist)) - -results in: - -mixedDist-300 - -Most statisticians would probably say that these upside down Categoricals are not "real distributions." But at the very least, they are a convenient mathematical trick that makes working with distributions much more pleasant. -Module - -Finally, an R-Module is a group with two additional properties. First, it is abelian. That means <> is commutative. So, for all a, b: - -a <> b == b <> a - -Second, the data type supports multiplication by any element in the ring R. In Haskell, you can think of a ring as any member of the Num type class. - -How is this useful? It let's "retrain" our distribution on the data points it has already seen. Back to the example... - -Well, Ed---being the clever guy that he is---recently developed a marble copying machine. That's right! You just stick some marbles in on one end, and on the other end out pop 10 exact duplicates. Ed's not just clever, but pretty nice too. He duplicates his new marbles and gives all of them back to Simon. What's Simon's new distribution look like? - -Again, the naive way to answer this question would be to retrain from scratch: - ->duplicateBag = simonBag ++ (concat $ replicate 10 edBag) ->duplicateDist = train duplicateBag :: Categorical Double Marble - -Slightly better is to take advantage of the Semigroup property, and just apply that over and over again: - ->duplicateDist' = simonDist2 <> (foldl1 (<>) $ replicate 10 edDist) - -But even better is to take advantage of the fact that Categorical is a module and the (.*) operator: - ->duplicateDist'' = simonDist2 <> 10 .* edDist - -In picture form: - -module example - -Also notice that without the scalar multiplication, we would get back our original distribution: - -module example-mod - -Another way to think about the module's scalar multiplication is that it allows us to weight our distributions. - -Ed just realized that he still needs a marble, and has decided to take one. Someone has left their Marble bag sitting nearby, but he's not sure whose it is. He thinks that Simon is more forgetful than Don is, so he assigns a 60% probability that the bag is Simon's and a 40% probability that it is Don's. When he takes a marble, what's the probability that it is red? - -We create a weighted distribution using module multiplication: - ->weightedDist = 0.6 .* simonDist <> 0.4 .* donDist - -Then in ghci: - -ghci> pdf weightedDist Red -0.4929577464788732 - -We can also train directly on weighted data using the trainW function: - ->weightedDataDist = trainW [(0.4,Red),(0.5,Green),(0.2,Green),(3.7,White)] :: Categorical Double Marble - -which gives us: - -weightedDataDist-300 -The Takeaway and next posts - -Talking about the categorical distribution in algebraic terms let's us do some cool new stuff with our distributions that we can't easily do in other libraries. None of this is statistically ground breaking. The cool thing is that algebra just makes everything so convenient to work with. - -I think I'll do another post on some cool tricks with the kernel density estimator that are not possible at all in other libraries, then do a post about the category (formal category-theoretic sense) of statistical training methods. At that point, we'll be ready to jump into machine learning tasks. Depending on my mood we might take a pit stop to discuss the computational aspects of free groups and modules and how these relate to machine learning applications. - -Sign up for the RSS feed to stay tuned! diff --git a/examples/old/blogs/blog-categorical-functor.lhs b/examples/old/blogs/blog-categorical-functor.lhs deleted file mode 100644 index 622a8bf..0000000 --- a/examples/old/blogs/blog-categorical-functor.lhs +++ /dev/null @@ -1,57 +0,0 @@ -If our learning model is a functor, then we get asymptotically faster preprocessing of data. - -{-# LANGUAGE RebindableSyntax #-} - -> import Control.ConstraintKinds -> import Prelude hiding (fmap,return,(>>=),(>>)) - -> import GHC.Float - -> import HLearn.Algebra -> import HLearn.Models.Distributions - - ifThenElse = undefined - -> data Marble = Red | Green | Blue | White -> deriving (Read,Show,Eq,Ord) - -> xs = [ Red,Green,Red,Blue,Green,Red,Blue,White ] - -> m1 = train xs :: Categorical Double Marble - -> m2 = fmap show m1 - -> forgetBlue :: Marble -> Maybe Marble -> forgetBlue Blue = Nothing -> forgetBlue marble = Just marble - -> doublered :: (Num prob) => Marble -> Categorical prob Marble -> doublered Red = 2 .* train1dp Red -> doublered dp = train1dp dp - -> forgetBlueM :: (Num prob) => Marble -> Categorical prob Marble -> forgetBlueM Blue = mempty -> forgetBlueM dp = train1dp dp - -> addnoise :: (Fractional prob) => Marble -> Categorical prob Marble -> addnoise dp = 0.6 .* train1dp dp <> 0.1 .* train [ Red,Green,Blue,White ] - -> redgreennoise :: (Fractional prob) => Marble -> Categorical prob Marble -> redgreennoise Red = trainW [(0.7,Red),(0.3,Green)] -> redgreennoise Green = trainW [(0.1,Red),(0.9,Green)] -> redgreennoise dp = train1dp dp - - m3 :: Categorical Double Marble - m3 = do - dp <- train xs - dp' <- addnoise dp - return dp' - - -> ys = [1..1000] :: [Double] - -> kde1 = train ys :: KDE Gaussian 10 Double - -> kde1_float = fmap double2Float kde1 - -> kde_squared = fmap (^2) kde1 diff --git a/examples/old/blogs/blog-categorical-functor/blog-categorical-functor.lhs b/examples/old/blogs/blog-categorical-functor/blog-categorical-functor.lhs deleted file mode 100644 index a083fdd..0000000 --- a/examples/old/blogs/blog-categorical-functor/blog-categorical-functor.lhs +++ /dev/null @@ -1,327 +0,0 @@ -> {-# LANGUAGE RebindableSyntax #-} - -Functors and monads for analyzing data - -July 29, 2013 in Computer Science, Haskell, HLearn | No comments (edit) - -marblesDist-modFunctors and monads are powerful design patterns used in Haskell. They give us two cool tricks for analyzing data. First, we can “preprocess” data after we’ve already trained a model. The model will be automatically updated to reflect the changes. Second, this whole process happens asymptotically faster than the standard method of preprocessing. In some cases, you can do it in constant time no matter how many data points you have! - -This post focuses on how to use functors and monads in practice with the HLearn library. We won’t talk about their category theoretic foundations; instead, we’ll go through ten concrete examples involving the categorical distribution. This distribution is somewhat awkwardly named for our purposes because it has nothing to do with category theory—it is the most general distribution over non-numeric (i.e. categorical) data. It’s simplicity should make the examples a little easier to follow. Some more complicated models (e.g. the kernel density estimator and Bayesian classifier) also have functor and monad instances, but we’ll save those for another post. -Setting up the problem - -Before we dive into using functors and monads, we need to set up our code and create some data. Let’s install the packages: - -$ cabal install HLearn-distributions-1.1 - -Import our modules: - -> import Control.ConstraintKinds.Functor -> import Control.ConstraintKinds.Monad -> import Prelude hiding (Functor(..), Monad (..)) -> -> import HLearn.Algebra -> import HLearn.Models.Distributions - -For efficiency reasons we’ll be using the Functor and Monad instances provided by the ConstraintKinds package and language extension. From the user’s perspective, everything works the same as normal monads. - -Now let’s create a simple marble data type, and a small bag of marbles for our data set. - -> data Marble = Red | Pink | Green | Blue | White -> deriving (Read,Show,Eq,Ord) -> -> bagOfMarbles = [ Pink,Green,Red,Blue,Green,Red,Green,Pink,Blue,White ] - -This is a very small data set just to make things easy to visualize. Everything we’ll talk about works just as well on arbitrarily large data sets. - -We train a categorical distribution on this data set using the train function: - -> marblesDist = train bagOfMarbles :: Categorical Double Marble - -The Categorical type takes two parameters. The first is the type of our probabilities, and the second is the type of our data points. If you stick your hand into the bag and draw a random marble, this distribution tells you the probability of drawing each color. - -Let’s plot our distribution: - -ghci> plotDistribution (plotFile "marblesDist" $ PNG 400 300) marblesDist - -marblesDist-mod -Functors - -Okay. Now we’re ready for the juicy bits. We’ll start by talking about the list functor. This will motivate the advantages of the categorical distribution functor. - -A functor is a container that lets us “map” a function onto every element of the container. Lists are a functor, and so we can apply a function to our data set using the map function. - -map :: (a -> b) -> [a] -> [b] - -Example 1: - -Let’s say instead of a distribution over the marbles’ colors, I want a distribution over the marbles’ weights. I might have a function that associates a weight with each type of marble: - -> marbleWeight :: Marble -> Int -- weight in grams -> marbleWeight Red = 3 -> marbleWeight Pink = 2 -> marbleWeight Green = 3 -> marbleWeight Blue = 6 -> marbleWeight White = 2 - -I can generate my new distribution by first transforming my data set, and then training on the result. Notice that the type of our distribution has changed. It is no longer a categorical distribution over marbles; it’s a distribution over ints. - -> weightsDist = train $ map marbleWeight bagOfMarbles :: Categorical Double Int - -ghci> plotDistribution (plotFile "weightsDist" $ PNG 400 300) weightsDist - -weightsDist-mod - -This is the standard way of preprocessing data. But we can do better because the categorical distribution is also a functor. Functors have a function called fmap that is analogous to calling map on a list. This is its type signature specialized for the Categorical type: - -fmap :: (Ord dp0, Ord dp1) => (dp0 -> dp1) -> Categorical prob dp0 -> Categorical prob dp1 - -We can use fmap to apply the marbleWeights function directly to the distribution: - -> weightDist' = fmap marbleWeight marblesDist - -This is guaranteed to generate the same exact answer, but it is much faster. It takes only constant time to call Categorical’s fmap, no matter how much data we have! - -Let me put that another way. Below is a diagram showing the two possible ways to generate a model on a preprocessed data set. Every arrow represents a function application. - -blog-categorical-functor - -The normal way to preprocess data is to take the bottom left path. But because our model is a functor, the top right path becomes available. This path is better because it has the shorter run time. - -Furthermore, let’s say we want to experiment with k different preprocessing functions. The standard method will take \Theta(nk) time, whereas using the categorical functor takes time \Theta(n k). - -Example 2: - -For another example, what if we don’t want to differentiate between red and pink marbles? The following function converts all the pink marbles to red. - -> pink2red :: Marble -> Marble -> pink2red Pink = Red -> pink2red dp = dp - -Let’s apply it to our distribution, and plot the results: - -> nopinkDist = fmap pink2red marblesDist - -ghci> plotDistribution (plotFile "nopinkDist" $ PNG 400 300) nopinkDist - -nopinkDist-mod - -That’s about all that a Functor can do by itself. When we call fmap, we can only process individual data points. We can’t change the number of points in the resulting distribution or do other complex processing. Monads give us this power. -Monads - -Monads are functors with two more functions. The first is called return. Its type signature is - -return :: (Ord dp) => dp -> Categorical prob dp - -We’ve actually seen this function already in previous posts. It’s equivalent to the train1dp function found in the HomTrainer type class. All it does is train a categorical distribution on a single data point. - -The next function is called join. It’s a little bit trickier, and it’s where all the magic lies. Its type signature is: - -join :: (Ord dp) => Categorical prob (Categorical prob dp) -> Categorical prob dp - -As input, join takes a categorical distribution whose data points are other categorical distributions. It then “flattens” the distribution into one that does not take other distributions as input. - -Example 3 - -Let’s write a function that removes all the pink marbles from out data set. Whenever we encounter a pink marble, we’ll want to replace it with an empty categorical distribution. If the marble is not pink, we’ll create a singleton distribution from it. - -> forgetPink :: (Num prob) => Marble -> Categorical prob Marble -> forgetPink Pink = mempty -> forgetPink dp = train1dp dp -> -> nopinkDist2 = join $ fmap forgetPink marblesDist - -ghci> plotDistribution (plotFile "nopinkDist2" $ PNG 400 300) nopinkDist2 - -nopinkDist2-mod - -This idiom of join ( fmap … ) is used a lot. For convenience, the >>= operator (called bind) combines these steps for us. It is defined as: - -(>>=) :: Categorical prob dp0 -> (dp0 -> Categorical prob dp1) -> Categorical prob dp1 -dist >>= f = join $ fmap f dist - -Under this notation, our new distribution can be defined as: - -> nopinkDist2' = marblesDist >>= forgetPink - -Example 4 - -Besides removing data points, we can also add new ones. Let’s double the number of pink marbles in our training data: - -> doublePink :: (Num prob) => Marble -> Categorical prob Marble -> doublePink Pink = 2 .* train1dp Pink -> doublePink dp = train1dp dp -> -> doublepinkDist = marblesDist >>= doublePink - -ghci> plotDistribution (plotFile "doublepinkDist" $ PNG 400 300) doublepinkDist - -doublepinkDist-mod - -Example 5 - -One common machine learning task is to factor noise into our sampling process. Mistakes are often made when collecting data. Adding noise lets us consider the likelihood of those mistakes on our final distribution. In this example, we’ll add a uniform noise to every sample. - -Notice that we are using fractional weights for our noise, and that the weights are carefully adjusted so that the total number of marbles in the distribution still sums to one. We don’t want to add or remove marbles while creating noise. - -> addNoise :: (Fractional prob) => Marble -> Categorical prob Marble -> addNoise dp = 0.5 .* train1dp dp <> 0.1 .* train [ Red,Pink,Green,Blue,White ] -> -> noiseDist = marblesDist >>= addNoise - -ghci> plotDistribution (plotFile "noiseDist" $ PNG 400 300) noiseDist - -noiseDist-mod - -Adding uniform noise just made all our probabilities closer together. - -Example 6 - -Of course, the amount of noise we add to each sample doesn’t have to be the same everywhere. If I suffer from red-green color blindness, then I might use this as my noise function: - -> rgNoise :: (Fractional prob) => Marble -> Categorical prob Marble -> rgNoise Red = trainW [(0.7,Red),(0.3,Green)] -> rgNoise Green = trainW [(0.1,Red),(0.9,Green)] -> rgNoise dp = train1dp dp -> -> rgNoiseDist = marblesDist >>= rgNoise - -ghci> plotDistribution (plotFile "rgNoiseDist" $ PNG 400 300) rgNoiseDist - -rgNoiseDist-mod - -Because of my color blindness, the probability of drawing a red marble from the bag is higher than drawing a green marble. This is despite the fact that we observed more green marbles in our training data. - -Example 7 - -In the real world, we can never know exactly how much error we have in the samples. Luckily, we can try to learn it by conducting a second experiment. We’ll first experimentally determine how red-green color blind I am, then we’ll use that to update our already trained distribution. - -To determine the true error rate, we need some unbiased source of truth. In this case, we can just use someone with good vision. They will select ten red marbles and ten green marbles, and I will guess what color they are. - -Let’s train a distribution on what I think green marbles look like: - -> greenMarbles = [Green,Red,Green,Red,Green,Red,Red,Green,Green,Green] -> greenDist = train greenMarbles :: Categorical Double Marble - -and what I think red marbles look like: - -> redMarbles = [Red,Green,Red,Green,Red,Red,Green,Green,Red,Red] -> redDist = train redMarbles :: Categorical Double Marble - -Now we’ll create the noise function based off of our empirical data. The (/.) function is scalar division, and we can use it because the categorical distribution is a vector space. We’re dividing by the number of data points in the distribution so that the distribution we output has an effective training size of one. This ensures that we’re not accidentally creating new data points when applying our function to another distribution. - -> rgNoise2 :: Marble -> Categorical Double Marble -> rgNoise2 Green = greenDist /. numdp greenDist -> rgNoise2 Red = redDist /. numdp redDist -> rgNoise2 dp = train1dp dp -> -> rgNoiseDist2 = marblesDist >>= rgNoise2 - -ghci> plotDistribution (plotFile "rgNoiseDist2" $ PNG 400 300) rgNoiseDist2 - -rgNoiseDist2-mod - -Example 8 - -Finally, we can chain our preprocessing functions together in arbitrary ways. - -> allDist = marblesDist >>= forgetPink >>= addNoise >>= rgNoise - -ghci> plotDistribution (plotFile "allDist" $ PNG 400 300) allDist - -allDist-mod - -But wait! Where’d that pink come from? Wasn’t the call to forgetPink supposed to remove it? The answer is that we did remove it, but then we added it back in with our noise functions. When using monadic functions, we must be careful about the order we apply them in. This is just as true when using regular functions. - -Here’s another distribution created from those same functions in a different order: - -> allDist2 = marblesDist >>= addNoise >>= rgNoise >>= forgetPink - -ghci> plotDistribution (plotFile "allDist" $ PNG 400 300) allDist2 - -allDist2-mod - -We can also use Haskell’s do notation to accomplish the same exact thing: - -> allDist2' :: Categorical Double Marble -> allDist2' = do -> dp <- train bagOfMarbles -> dp <- addNoise dp -> dp <- rgNoise dp -> dp <- forgetPink dp -> return dp - -(Since we’re using a custom Monad definition, do notation requires the RebindableSyntax extension.) - -Example 9 - -Do notation gives us a convenient way to preprocess multiple data sets into a single data set. Let’s create two new data sets and their corresponding distributions for us to work with: - -> bag1 = [Red,Pink,Green,Blue,White] -> bag2 = [Red,Blue,White] -> -> bag1dist = train bag1 :: Categorical Double Marble -> bag2dist = train bag2 :: Categorical Double Marble - -Now, we’ll create a third data set that is a weighted combination of bag1 and bag2. We will do this by repeated sampling. On every iteration, with a 20% probability we’ll sample from bag1, and with an 80% probability we’ll sample from bag2. Imperative pseudo-code for this algorithm is: - -let comboDist be an empty distribution -loop until desired accuracy achieved: - let r be a random number from 0 to 1 - if r > 0.2: - sample dp1 from bag1 - add dp1 to comboDist - else: - sample dp2 from bag2 - add dp2 to comboDist - -This sampling procedure will obviously not give us an exact answer. But since the categorical distribution supports weighted data points, we can use this simpler pseudo-code to generate an exact answer: - -let comboDist be an empty distribution -foreach datapoint dp1 in bag1: - foreach datapoint dp2 in bag2: - add dp1 with weight 0.2 to comboDist - add dp2 with weight 0.8 to comboDist - -Using do notation, we can express this as: - -> comboDist :: Categorical Double Marble -> comboDist = do -> dp1 <- bag1dist -> dp2 <- bag2dist -> trainW [(0.2,dp1),(0.8,dp2)] - -plotDistribution (plotFile "comboDist" $ PNG 400 300) comboDist - -comboDist-mod - -And because the Categorical functor takes constant time, constructing comboDist also takes constant time. The naive imperative algorithm would have taken time \Theta (|\text{bag1}|*|\text{bag2}|). - -When combining multiple distributions this way, the number of data points in our final distribution will be the product of the number of data points in the initial distributions: - -ghci> numdp combination -15 - -Example 10 - -Finally, arbitrarily complex preprocessing functions can be written using Haskell’s do notation. And remember, no matter how complicated these functions are, their run time never depends on the number of elements in the initial data set. - -This function adds uniform sampling noise to our bagOfMarbles, but only on those marbles that are also contained in bag2 above. - -> comboDist2 :: Categorical Double Marble -> comboDist2 = do -> dp1 <- marblesDist -> dp2 <- bag2dist -> if dp1==dp2 -> then addNoise dp1 -> else return dp1 - -plotDistribution (plotFile "comboDist2" $ PNG 400 300) comboDist2 - -comboDist2-mod -Conclusion - -This application of monads to machine learning generalizes the monad used in probabilistic functional programming. The main difference is that PFP focused on manipulating already known distributions, not training them from data. Also, if you enjoy this kind of thing, you might be interested in the n-category cafe discussion on category theory in machine learning from a few years back. - -In future posts, we’ll look at functors and monads for continuous distributions, multivariate distributions, and classifiers. - -Subscribe to the RSS feed to stay tuned! diff --git a/examples/old/blogs/blog-gaussian.lhs b/examples/old/blogs/blog-gaussian.lhs deleted file mode 100644 index 5c104fd..0000000 --- a/examples/old/blogs/blog-gaussian.lhs +++ /dev/null @@ -1,181 +0,0 @@ -My Experiments in Truth -Home -Subscribe to feed -Gausian distributions are monoids -November 24, 2012 in Uncategorized | No comments (edit) -This is the first in a series of posts about the HLearn library for haskell that I’ve been working on for the past few months. The idea of the library is to show that abstract algebra—specifically monoids, groups, and homomorphisms—are useful not just in esoteric functional programming, but also in real world machine learning problems. In particular, by framing a learning algorithm according to these algebraic properties, we get three things for free: (1) an online version of the algorithm; (2) a parallel version of the algorithm; and (3) a procedure for cross-validation that runs asymptotically faster than the standard version. - -We’ll start with the example of a Gaussian distribution. Gaussians are ubiquitous in learning algorithms because they accurately describe most data. But more importantly, they are easy to work with. They are fully determined by their mean and variance, and these parameters are easy to calculate. - -In this post we’ll start with examples of why the monoid and group properties of Gaussians are useful in practice, then we’ll look at the math underlying these examples, and finally we’ll see that this technique is extremely fast in practice and results in near perfect parallelization. - -HLEARN BY EXAMPLE - -Install the libraries from a shell: - -$ cabal install hlearn -Then import the libraries into a literate haskell file: - -> import HLearn.Algebra -> import HLearn.Models.Distributions.Gaussian - -> import Criterion.Main -> import Statistics.Distribution.Normal -> import qualified Data.Vector.Unboxed as VU - -Now let’s create some data to work with. For simplicity’s sake, we’ll use a made up data set of how much money people make. Every entry represents one person making that salary. (We use a small data set here for ease of explanation. When we stress test this library at the end of the post we use much larger data sets.) - -> gradstudents = [15e3,25e3,18e3,17e3,9e3] :: [Double] -> teachers = [40e3,35e3,89e3,50e3,52e3,97e3] :: [Double] -> doctors = [130e3,105e3,250e3] :: [Double] - -In order to train a Gaussian distribution from the data, we simply use the train function, like so: - -> gradstudents_gaussian = train gradstudents :: Gaussian Double -> teachers_gaussian = train teachers :: Gaussian Double -> doctors_gaussian = train doctors :: Gaussian Double - -The train function is a member of the HomTrainer type class, which we’ll talk more about later. Also, now that we’ve trained some Gaussian distributions, we can perform all the normal calculations we might want to do on a distribution. For example, taking the mean, standard deviation, pdf, and cdf. - -Now for the interesting bits. We start by showing that the Gaussian is a semigroup. A semigroup is any data structure that has an associative binary operation called (<>). Basically, we can think of (<>) as “adding” or “merging” the two structures together. (Semigroups are monoids with only a mappend function.) - -So how do we use this? Well, what if we decide we want a Gaussian over everyone’s salaries? Using the traditional approach, we’d have to recompute this from scratch. - -> all_salaries = concat [gradstudents,teachers,doctors] -> traditional_all_gaussian = train all_salaries :: Gaussian Double - -But this repeats work we’ve already done. On a real world data set with millions or billions of samples, this would be very slow. Better would be to merge the Gaussians we’ve already trained into one final Gaussian. We can do that with the semigroup operation (<>): - -> semigroup_all_gaussian = gradstudents_gaussian <> teachers_gaussian <> doctors_gaussian - -Now, - -traditional_all_gaussian == semigroup_all_gaussian -The coolest part about this is that the semigroup operation takes time O(1), no matter how much data we’ve trained the Gaussians on. The naive approach takes time O(n), so we’ve got a pretty big speed up! - -Next, a monoid is a semigroup with an identity. The identity for a Gaussian is easy to define—simply train on the empty data set! - -> gaussian_identity = train ([]::[Double]) :: Gaussian Double - -Now, - -gaussian_identity == mempty -But we’ve still got one more trick up our sleeves. The Gaussian distribution is not just a monoid, but also a group. Groups appear all the time in abstract algebra, but they haven’t seen much attention in functional programming for some reason. Well groups are simple: they’re just monoids with an inverse. This inverse lets us do “subtraction” on our data structures. - -So back to our salary example. Lets say we’ve calculated all our salaries, but we’ve realized that including grad students in the salary calculations was a mistake. (They’re not real people after all.) In a normal library, we would have to recalculate everything from scratch again, excluding the grad students: - -> nograds = concat [teachers,doctors] -> traditional_nograds_gaussian = train nograds :: Gaussian Double - -But as we’ve already discussed, this takes a lot of time. We can use the inverse function to do this same operation in constant time: - -> group_nograds_gaussian = semigroup_all_gaussian <> (inverse gradstudents_gaussian) - -And now, - -traditional_nograds_gaussian == group_nograds_gaussian -Again, we’ve converted an operation that would have taken time O(n) into one that takes time O(1). Can’t get much better than that! - -THE HOMTRAINER TYPE CLASS - -As I’ve already mentioned, the HomTrainer type class is the basis of the HLearn library. Basically, any learning algorithm that is also a semigroup homomorphism can be made an instance of HomTrainer. This means that if xs and ys are lists of data points, the class obeys the following law: - -train (xs ++ ys) == (train xs) <> (train ys) -Based on this property alone, we get the three “free” properties I mentioned in the introduction. (1) We get an online algorithm for free. The function add1dp can be used to add a single new point to an existing Gaussian distribution. Let’s say I forgot about one of the graduate students—I’m sure this would never happen in real life—I can add their salary like this: - -> gradstudents_updated_gaussian = add1dp gradstudents_gaussian (10e3 :: Double) - -This updated Gaussian is exactly what we would get if we had included the new data point in the original data set. - -(2) We get a parallel algorithm. We can use the higher order function parallel to parallelize any application of train. For example, - -> gradstudents_parallel_gaussian = (parallel train) gradstudents :: Gaussian Double - -The function parallel automatically detects the number of processors your computer has and evenly distributes the work load over them. As we’ll see in the performance section, this results in perfect parallelization of the training function. Parallelization literally could not be any simpler! - -(3) We get asymptotically faster cross-validation; but that’s not really applicable to a Gaussian distribution so we’ll ignore it here. - -One last note about the HomTrainer class: we never actually have to define the train function for our learning algorithm. All we have to do is define the semigroup operation, and the compiler will derive our training function for us! We’ll save a discussion of why this homomorphism property gives us these results for another post. Instead, we’ll just take a look at what the Gaussian distribution’s semigroup operation looks like. - -THE SEMIGROUP OPERATION - -Our Gaussian data type is defined as: - -data Gaussian datapoint = Gaussian - { n :: {-# UNPACK #-} !Int -- ^ The number of samples trained on - , m1 :: !datapoint -- ^ The mean (first moment) of the trained distribution - , m2 :: !datapoint -- ^ The variance (second moment) times (n-1) - , dc :: {-# UNPACK #-} !Int -- ^ The number of \"dummy points\" that have been added - } -In order to estimate a Gaussian from a sample, we must find the total number of samples (n), the mean (m1), and the variance (calculated from m2). (We’ll explain what dc means a little later.) Therefore, we must figure out an appropriate definition for our semigroup operation below: - -(Gaussian na m1a m2a dca) <> (Gaussian nb m1b m2b dcb) = Gaussian n' m1' m2' dc' -First, we calculate the number of samples n’. The number of samples in the resulting distribution is simply the sum of the number of samples in both the input distributions: - - - -Second, we calculate the new average m1′. We start with the definition that the final mean is: - - - -Then we split the summation according to whether the input element was from the left Gaussian a or right Gaussian b, and substitute with the definition of the mean above: - - - -Notice that this is simply the weighted average of the two means. This makes intuitive sense. But there is a slight problem with this definition: When implemented on a computer with floating point arithmetic, we will get infinity whenever n’ is 0. We solve this problem by adding a “dummy” element into the Gaussian whenever n’ would be zero. This increases n’ from 0 to 1, preventing the division by 0. The variable dc counts how many dummy variables have been added, so that we can remove them before performing calculations (e.g. finding the pdf) that would be affected by an incorrect number of samples. - -Finally, we must calculate the new m2′. We start with the definition that the variance times (n-1) is: - - - -Then, we do some algebra, split the summations according to which input Gaussian the data point came from, and resubstitute the definition of m2 to get: - - - - - - -Notice that this equation has no divisions in it. This is why we are storing m2 as the variance times (n-1) rather than simply the variance. Adding in the extra divisions causes training our Gaussian distribution to run about 4x slower. I’d say haskell is getting pretty fast if the number of floating point divisions we perform is impacting our code’s performance that much! - -PERFORMANCE - -This algebraic interpretation of the Gaussian distribution has excellent time and space performance. To show this, we’ll compare performance to the excellent Haskell package called “statistics” that also has support for Gaussian distributions. We use the criterion package to create three tests: - -> size = 10^8 -> main = defaultMain -> [ bench "statistics-Gaussian" $ whnf (normalFromSample . VU.enumFromN 0) (size) -> , bench "HLearn-Gaussian" $ whnf -> (train :: VU.Vector Double -> Gaussian Double) -> (VU.enumFromN (0::Double) size) -> , bench "HLearn-Gaussian-Parallel" $ whnf -> (parallel $ (train :: VU.Vector Double -> Gaussian Double)) -> (VU.enumFromN (0::Double) size) -> ] - -In these test, we time three different methods of constructing Gaussian distributions given 100,000,000 data points. On my laptop with 2 cores, I get these results: - -statistics-Gaussian 2.85 sec -HLearn-Gaussian 1.91 sec -HLearn-Gaussian-Parallel 0.96 sec - - -Pretty nice! The algebraic method managed to outperform the traditional method for training a Gaussian by a handy margin. Plus, our parallel algorithm runs exactly twice as fast on two processors. Theoretically, this should scale to an arbitrary number of processors, but I don’t have a bigger machine to try it out on. - -Another interesting advantage of the HLearn library is that we can trade off time and space performance by changing which data structures store our data set. Specifically, we can use the same functions to train on a list or an unboxed vector. We do this by using the ConstraintKinds package on hackage that extends the base type classes like Functor and Foldable to work on classes that require constraints. Thus, we have a Functor instance of Vector.Unboxed. This is not possible without ConstraintKinds. - -Using this benchmark code: - -main = do - print $ (train [0..fromIntegral size::Double] :: Gaussian Double) - print $ (train (VU.enumFromN (0::Double) size) :: Gaussian Double) -We generate the following heap profile: - - - -Processing the data as a vector requires that we allocate all the memory in advance. This lets the program run faster, but prevents us from loading data sets larger than the amount of memory we have. Processing the data as a list, however, allows us to allocate the memory only as we use it. But because lists are boxed and lazy data structures, we must accept that our program will run about 10x slower. Lucky for us, GHC takes care of all the boring details of making this happen seamlessly. We only have to write our train function once. - -FUTURE POSTS - -There’s still at least four more major topics to cover in the HLearn library: (1) We can extend this discussion to show how the Naive Bayes learning algorithm has a similar monoid and group structure. (2) There are many more learning algorithms with group structures we can look into. (3) We can look at exactly how all these higher order functions, like batch and parallel work under the hood. And (4) we can see how the fast cross-validation I briefly mentioned works and why it’s important. - -Subscribe to the RSS feed and stay tuned! diff --git a/examples/old/blogs/blog-multivariate.lhs b/examples/old/blogs/blog-multivariate.lhs deleted file mode 100644 index 2a4997a..0000000 --- a/examples/old/blogs/blog-multivariate.lhs +++ /dev/null @@ -1,326 +0,0 @@ -n this post, we’re going to look at how to manipulate multivariate distributions in Haskell’s HLearn library. There are many ways to represent multivariate distributions, but we’ll use a technique called Markov networks. These networks have the algebraic structure called a monoid (and group and vector space), and training them is a homomorphism. Despite the scary names, these mathematical structures make working with our distributions really easy and convenient—they give us online and parallel training algorithms “for free.” If you want to go into the details of how, you can check out my TFP13 submission, but in this post we’ll ignore those mathy details to focus on how to use the library in practice. We’ll use a running example of creating a distribution over characters in the show Futurama. - -Prelimiaries: Creating the data Types - -As usual, this post is a literate haskell file. To run this code, you’ll need to install the hlearn-distributions package. This package requires GHC version at least 7.6. - -bash> cabal install hlearn-distributions-1.0.0.1 - -Now for some code. We start with our language extensions and imports: - ->{-# LANGUAGE DataKinds #-} ->{-# LANGUAGE TypeFamilies #-} ->{-# LANGUAGE TemplateHaskell #-} -> ->import HLearn.Algebra ->import HLearn.Models.Distributions - -Next, we’ll create data type to represent Futurama characters. There are a lot of characters, so we’ll need to keep things pretty organized. The data type will have a record for everything we might want to know about a character. Each of these records will be one of the variables in our multivariate distribution, and all of our data points will have this type. - -FuturamaCast - ->data Character = Character -> { _name :: String -> , _species :: String -> , _job :: Job -> , _isGood :: Maybe Bool -> , _age :: Double -- in years -> , _height :: Double -- in feet -> , _weight :: Double -- in pounds -> } -> deriving (Read,Show,Eq,Ord) -> ->data Job = Manager | Crew | Henchman | Other -> deriving (Read,Show,Eq,Ord) - -Now, in order for our library to be able to interpret the Character type, we call the template haskell function: - ->makeTypeLenses ''Character - -This function creates a bunch of data types and type classes for us. These “type lenses” give us a type-safe way to reference the different variables in our multivariate distribution. We’ll see how to use these type level lenses a bit later. There’s no need to understand what’s going on under the hood, but if you’re curious then checkout the hackage documentation or source code. -Training a distribution - -Now, we’re ready to create a data set and start training. Here’s a list of the employees of Planet Express provided by the resident bureaucrat Hermes Conrad. This list will be our first data set. - -hermes-zoom - ->planetExpress = -> [ Character "Philip J. Fry" "human" Crew (Just True) 1026 5.8 195 -> , Character "Turanga Leela" "alien" Crew (Just True) 43 5.9 170 -> , Character "Professor Farnsworth" "human" Manager (Just True) 85 5.5 160 -> , Character "Hermes Conrad" "human" Manager (Just True) 36 5.3 210 -> , Character "Amy Wong" "human" Other (Just True) 21 5.4 140 -> , Character "Zoidberg" "alien" Other (Just True) 212 5.8 225 -> , Character "Cubert Farnsworth" "human" Other (Just True) 8 4.3 135 -> ] - -Let’s train a distribution from this data. Here’s how we would train a distribution where every variable is independent of every other variable: - ->dist1 = train planetExpress :: Multivariate Character -> '[ Independent Categorical '[String,String,Job,Maybe Bool] -> , Independent Normal '[Double,Double,Double] -> ] -> Double - -In the HLearn library, we always use the function train to train a model from data points. We specify which model to train in the type signature. - -As you can see, the Multivariate distribution takes three type parameters. The first parameter is the type of our data point, in this case Character. The second parameter describes the dependency structure of our distribution. We’ll go over the syntax for the dependency structure in a bit. For now, just notice that it’s a type-level list of distributions. Finally, the third parameter is the type we will use to store our probabilities. - -What can we do with this distribution? One simple task we can do is to find marginal distributions. The marginal distribution is the distribution of a certain variable ignoring all the other variables. For example, let’s say I want a distribution of the species that work at planet express. I can get this by: - ->dist1a = getMargin TH_species dist1 - -Notice that we specified which variable we’re taking the marginal of by using the type level lens TH_species. This data constructor was automatically created for us by out template haskell function makeTypeLenses. Every one of our records in the data type has its own unique type lens. It’s name is the name of the record, prefixed by TH. These lenses let us infer the types of our marginal distributions at compile time, rather than at run time. For example, the type of the marginal distribution of species is: - -ghci> :t dist1a -dist1a :: Categorical String Double - -That is, a categorical distributions whose data points are Strings and which stores probabilities as a Double. Now, if I wanted a distribution of the weights of the employees, I can get that by: - ->dist1b = getMargin TH_weight dist1 - -And the type of this distribution is: - -ghci> :t dist1b -dist1b :: Normal Double - -Now, I can easily plot these marginal distributions with the plotDistribution function: - -ghci> plotDistribution (plotFile "dist1a" $ PNG 250 250) dist1a -ghci> plotDistribution (plotFile "dist1b" $ PNG 250 250) dist1b - - -dist1adist1b - -futurama-bender-smoking-cigar-wallpaperBut wait! I accidentally forgot to include Bender in the planetExpress data set! What can I do? - -In a traditional statistics library, we would have to retrain our data from scratch. If we had billions of elements in our data set, this would be an expensive mistake. But in our HLearn library, we can take advantage of the model’s monoid structure. In particular, the compiler used this structure to automatically derive a function called add1dp for us. Let’s look at its type: - -ghci> :t add1dp -add1dp :: HomTrainer model => model -> Datapoint model -> model - -It’s pretty simple. The function takes a model and adds the data point associated with that model. It returns the model we would have gotten if the data point had been in our original data set. This is called online training. - -Again, because our distributions form monoids, the compiler derived an efficient and exact online training algorithm for us automatically. - -So let’s create a new distribution that considers bender: - ->bender = Character "Bender Rodriguez" "robot" Crew (Just True) 44 6.1 612 ->dist1' = add1dp dist1 bender - -And plot our new marginals: - -ghci> plotDistribution (plotFile "dist1-withbender-species" $ PNG 250 250) $ - getMargin TH_species dist1' -ghci> plotDistribution (plotFile "dist1-withbender-weight" $ PNG 250 250) $ - getMargin TH_weight dist1' - - -dist1-withbender-speciesdist1-withbender-weight - -Notice that our categorical marginal has clearly changed, but that our normal marginal doesn’t seemed to have changed at all. This is because the plotting routines automatically scale the distribution, and the normal distribution, when scaled, always looks the same. We can double check that we actually did change the weight distribution by comparing the mean: - -ghci> mean dist1b -176.42857142857142 -ghci> mean $ getMargin TH_weight dist1' -230.875 - -Bender’s weight really changed the distribution after all! -Complicated DependencE structureS - -That’s cool, but our original distribution isn’t very interesting. What makes multivariate distributions interesting is when the variables affect each other. This is true in our case, so we’d like to be able to model it. For example, we’ve already seen that robots are much heavier than organic lifeforms, and are throwing off our statistics. The HLearn library supports a small subset of Markov Networks for expressing these dependencies. - -We represent Markov Networks as graphs with undirected edges. Every attribute in our distribution is a node, and every dependence between attributes is an edge. We can draw this graph with the plotNetwork command: - -ghci> plotNetwork "dist1-network" dist1 - -dist1-networkAs expected, there are no edges in our graph because everything is independent. Let’s create a more interesting distribution and plot its Markov network. - ->dist2 = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , MultiCategorical '[String] -> , Independent Categorical '[Job,Maybe Bool] -> , Independent Normal '[Double,Double,Double] -> ] -> Double - -ghci> plotNetwork "dist2-network" dist2 - - dist2-network - -Okay, so what just happened? - -The syntax for representing the dependence structure is a little confusing, so let’s go step by step. We represent the dependence information in the graph as a list of types. Each element in the list describes both the marginal distribution and the dependence structure for one or more records in our data type. We must list these elements in the same order as the original data type. - -Notice that we’ve made two changes to the list. First, our list now starts with the type Ignore ‘[String]. This means that the first string in our data type—the name—will be ignored. Notice that TH_name is no longer in the Markov Network. This makes sense because we expect that a character’s name should not tell us too much about any of their other attributes. - -Second, we’ve added a dependence. The MultiCategorical distribution makes everything afterward in the list dependent on that item, but not the things before it. This means that the exact types of dependencies it can specify are dependent on the order of the records in our data type. Let’s see what happens if we change the location of the MultiCategorical: - ->dist3 = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , Independent Categorical '[String] -> , MultiCategorical '[Job] -> , Independent Categorical '[Maybe Bool] -> , Independent Normal '[Double,Double,Double] -> ] -> Double - -ghci> plotNetwork "dist3-network" dist3 - -dist3-network - -As you can see, our species no longer have any relation to anything else. Unfortunately, using this syntax, the order of list elements is important, and so the order we specify our data records is important. - -Finally, we can substitute any valid univariate distribution for our Normal and Categorical distributions. The HLearn library currently supports Binomial, Exponential, Geometric, LogNormal, and Poisson distributions. These just don’t make much sense for modelling Futurama characters, so we’re not using them. - -Now, we might be tempted to specify that every variable is fully dependent on every other variable. In order to do this, we have to introduce the “Dependent” type. Any valid multivariate distribution can follow Dependent, but only those records specified in the type-list will actually be dependent on each other. For example: - ->dist4 = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , MultiCategorical '[String,Job,Maybe Bool] -> , Dependent MultiNormal '[Double,Double,Double] -> ] -> Double - -ghci> plotNetwork "dist4-network" dist4 - -distb-network - -Undoubtably, this is in always going to be the case—everything always has a slight influence on everything else. Unfortunately, it is not easy in practice to model these fully dependent distributions. We need roughly \Theta(2^{n e}) data points to accurately train a distribution, where n is the number of nodes in our graph and e is the number of edges in our network. Thus, by selecting that two attributes are independent of each other, we can greatly reduce the amount of data we need to train an accurate distribution. - -I realize that this syntax is a little awkward. I chose it because it was relatively easy to implement. Future versions of the library should support a more intuitive syntax. I also plan to use copulas to greatly expand the expressiveness of these distributions. In the mean time, the best way to figure out the dependencies in a Markov Network are just to plot it and see visually. - -Okay. So what distribution makes the most sense for Futurama characters? We’ll say that everything depends on both the characters’ species and job, and that their weight depends on their height. - ->planetExpressDist = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , MultiCategorical '[String,Job] -> , Independent Categorical '[Maybe Bool] -> , Independent Normal '[Double] -> , Dependent MultiNormal '[Double,Double] -> ] -> Double - -ghci> plotNetwork "planetExpress-network" planetExpressDist - -dist4-network - -We still don’t have enough data to to train this network, so let’s create some more. We start by creating a type for our Markov network called FuturamaDist. This is just for convenience so we don’t have to retype the dependence structure many times. - ->type FuturamaDist = Multivariate Character -> '[ Ignore '[String] -> , MultiCategorical '[String,Job] -> , Independent Categorical '[Maybe Bool] -> , Independent Normal '[Double] -> , Dependent MultiNormal '[Double,Double] -> ] -> Double - -Next, we train some more distribubtions of this type on some of the characters. We’ll start with Mom Corporation and the brave Space Forces. - - 200-futurama_mom_and_sons 200-kif and zapp - ->momCorporation = -> [ Character "Mom" "human" Manager (Just False) 100 5.5 130 -> , Character "Walt" "human" Henchman (Just False) 22 6.1 170 -> , Character "Larry" "human" Henchman (Just False) 18 5.9 180 -> , Character "Igner" "human" Henchman (Just False) 15 5.8 175 -> ] ->momDist = train momCorporation :: FuturamaDist - ->spaceForce = -> [ Character "Zapp Brannigan" "human" Manager (Nothing) 45 6.0 230 -> , Character "Kif Kroker" "alien" Crew (Just True) 113 4.5 120 -> ] ->spaceDist = train spaceForce :: FuturamaDist - -And now some more robots: - -200-robotmafia 200-hedonismbot - ->robots = -> [ bender -> , Character "Calculon" "robot" Other (Nothing) 123 6.8 650 -> , Character "The Crushinator" "robot" Other (Nothing) 45 8.0 4500 -> , Character "Clamps" "robot" Henchman (Just False) 134 5.8 330 -> , Character "DonBot" "robot" Manager (Just False) 178 5.8 520 -> , Character "Hedonismbot" "robot" Other (Just False) 69 4.3 1200 -> , Character "Preacherbot" "robot" Manager (Nothing) 45 5.8 350 -> , Character "Roberto" "robot" Other (Just False) 77 5.9 250 -> , Character "Robot Devil" "robot" Other (Just False) 895 6.0 280 -> , Character "Robot Santa" "robot" Other (Just False) 488 6.3 950 -> ] ->robotDist = train robots :: FuturamaDist - -Now we’re going to take advantage of the monoid structure of our multivariate distributions to combine all of these distributions into one. - ->futuramaDist = planetExpressDist <> momDist <> spaceDist <> robotDist - -The resulting distribution is equivalent to having trained a distribution from scratch on all of the data points: - -train (planetExpress++momCorporation++spaceForces++robots) :: FuturamaDist - -We can take advantage of this property any time we use the train function to automatically parallelize our code. The higher order function parallel will split the training task evenly over each of your available processors, then merge them together with the monoid operation. This results in “theoretically perfect” parallel training of these models. - -parallel train (planetExpress++momCorporation++spaceForces++robots) :: FuturamaDist - -Again, this is only possible because the distributions have a monoid structure. - -Now, let’s ask some questions of our distribution. If I pick a character at random, what’s the probability that they’re a good guy? Let’s plot the marginal. - -ghci> plotDistribution (plotFile "goodguy" $ PNG 250 250) $ getMargin TH_isGood futuramaDist - -goodguy - -But what if I only want to pick from those characters that are humans, or those characters that are robots? Statisticians call this conditioning. We can do that with the condition function: - -ghci> plotDistribution (plotFile "goodguy-human" $ PNG 250 250) $ - getMargin TH_isGood $ condition TH_species "human" futuramaDist -ghci> plotDistribution (plotFile "goodguy-robot" $ PNG 250 250) $ - getMargin TH_isGood $ condition TH_species "robot" futuramaDist - - - -Preacherbotgoodguy-human goodguy-robot -On the left is the plot for humans, and on the right the plot for robots. Apparently, original robot sin is much worse than that in humans! If only they would listen to Preacherbot and repent of their wicked ways… - -Now let’s ask: What’s the average age of an evil robot? - -ghci> mean $ getMargin TH_age $ - condition TH_isGood (Just False) $ condition TH_species "robot" futuramaDist -273.0769230769231 - -Notice that conditioning a distribution is a commutative operation. That means we can condition in any order and still get the exact same results. Let’s try it: - -ghci> mean $ getMargin TH_age $ - condition TH_species "robot" $ condition TH_isGood (Just False) futuramaDist -273.0769230769231 - -There’s one last thing for us to consider. What does our Markov network look like after conditioning? Let’s find out! - -plotNetwork "condition-species-isGood" $ - condition TH_species "robot" $ condition TH_isGood (Just False) futuramaDist - -condition-species-isGood - -Notice that conditioning against these variables caused them to go away from our Markov Network. - -Finally, there’s another similar process to conditioning called “marginalizing out.” This lets us ignore the effects of a single attribute without specifically saying what that attribute must be. When we marginalize out on our Markov network, we get the same dependence structure as if we conditioned. - -plotNetwork "marginalizeOut-species-isGood" $ - marginalizeOut TH_species $ marginalizeOut TH_isGood futuramaDist - -condition-species-isGood - -Effectively, what the marginalizeOut function does is “forget” the extra dependencies, whereas the condition function “applies” those dependencies. In the end, the resulting Markov network has the same structure, but different values. - - - -Finally, at the start of the post, I mentioned that our multivariate distributions have group and vector space structure. This gives us two more operations we can use: the inverse and scalar multiplication. You can find more posts on how to take advantage of these structures here and here. -Next time… - -futurama-spacesuits - -The best part of all of this is still coming. Next, we’ll take a look at full on Bayesian classification and why it forms a monoid. Besides online and parallel trainers, this also gives us a fast cross-validation method. - -There’ll also be a posts about the monoid structure of Markov chains, the Free HomTrainer, and how this whole algebraic framework applies to NP-approximation algorithms as well. diff --git a/examples/old/blogs/blog-multivariate.lhs.orig b/examples/old/blogs/blog-multivariate.lhs.orig deleted file mode 100644 index d2f85c8..0000000 --- a/examples/old/blogs/blog-multivariate.lhs.orig +++ /dev/null @@ -1,336 +0,0 @@ -In this post, we’re going to look at how to manipulate multivariate distributions in Haskell’s HLearn library. There are many ways to represent multivariate distributions, but we’ll use a technique called Markov networks. These networks have the algebraic structure called a monoid (and group and vector space), and training them is a homomorphism. Despite the scary names, these mathematical structures make working with our distributions really easy and convenient—they give us online and parallel training algorithms “for free.” If you want to go into the details of how, you can check out my TFP13 submission, but in this post we’ll ignore those mathy details to focus on how to use the library in practice. We’ll use a running example of creating a distribution over characters in the show Futurama. - -Prelimiaries: Creating the data Types - -As usual, this post is a literate haskell file. To run this code, you’ll need to install the hlearn-distributions package. This package requires GHC version at least 7.6. - -bash> cabal install hlearn-distributions-1.0.0.1 - -Now for some code. We start with our language extensions and imports: - ->{-# LANGUAGE DataKinds #-} ->{-# LANGUAGE TypeFamilies #-} ->{-# LANGUAGE TemplateHaskell #-} -> ->import HLearn.Algebra ->import HLearn.Models.Distributions - -Next, we’ll create data type to represent Futurama characters. There are a lot of characters, so we’ll need to keep things pretty organized. The data type will have a record for everything we might want to know about a character. Each of these records will be one of the variables in our multivariate distribution, and all of our data points will have this type. - -FuturamaCast - ->data Character = Character -> { _name :: String -> , _species :: String -> , _job :: Job -> , _isGood :: Maybe Bool -> , _age :: Double -- in years -> , _height :: Double -- in feet -> , _weight :: Double -- in pounds -> } -> deriving (Read,Show,Eq,Ord) -> ->data Job = Manager | Crew | Henchman | Other -> deriving (Read,Show,Eq,Ord) - -Now, in order for our library to be able to interpret the Character type, we call the template haskell function: - ->makeIndex ''Character - ->instance HasDepIndex Character where -> type DepIndexList Character = '[TH_name,TH_species,TH_job,TH_isGood,TH_age,TH_height,TH_weight ] -> depIndexList _ = TH_name:::TH_species:::TH_job:::TH_isGood:::TH_age:::TH_height:::TH_weight:::HNil - -This function creates a bunch of data types and type classes for us. These “type lenses” give us a type-safe way to reference the different variables in our multivariate distribution. We’ll see how to use these type level lenses a bit later. There’s no need to understand what’s going on under the hood, but if you’re curious then checkout the hackage documentation or source code. -Training a distribution - -Now, we’re ready to create a data set and start training. Here’s a list of the employees of Planet Express provided by the resident bureaucrat Hermes Conrad. This list will be our first data set. - -hermes-zoom - ->planetExpress = -> [ Character "Philip J. Fry" "human" Crew (Just True) 1026 5.8 195 -> , Character "Turanga Leela" "alien" Crew (Just True) 43 5.9 170 -> , Character "Professor Farnsworth" "human" Manager (Just True) 85 5.5 160 -> , Character "Hermes Conrad" "human" Manager (Just True) 36 5.3 210 -> , Character "Amy Wong" "human" Other (Just True) 21 5.4 140 -> , Character "Zoidberg" "alien" Other (Just True) 212 5.8 225 -> , Character "Cubert Farnsworth" "human" Other (Just True) 8 4.3 135 -> ] - -Let’s train a distribution from this data. Here’s how we would train a distribution where every variable is independent of every other variable: - ->dist1 = train planetExpress :: Multivariate -> '[ Independent Categorical '[String,String,Job,Maybe Bool] -> , Independent Normal '[Double,Double,Double] -> ] -> Double -> Character - -In the HLearn library, we always use the function train to train a model from data points. We specify which model to train in the type signature. - -As you can see, the Multivariate distribution takes three type parameters. The first parameter is the type of our data point, in this case Character. The second parameter describes the dependency structure of our distribution. We’ll go over the syntax for the dependency structure in a bit. For now, just notice that it’s a type-level list of distributions. Finally, the third parameter is the type we will use to store our probabilities. - -What can we do with this distribution? One simple task we can do is to find marginal distributions. The marginal distribution is the distribution of a certain variable ignoring all the other variables. For example, let’s say I want a distribution of the species that work at planet express. I can get this by: - --- >dist1a = getMargin TH_species dist1 - -Notice that we specified which variable we’re taking the marginal of by using the type level lens TH_species. This data constructor was automatically created for us by out template haskell function makeTypeLenses. Every one of our records in the data type has its own unique type lens. It’s name is the name of the record, prefixed by TH. These lenses let us infer the types of our marginal distributions at compile time, rather than at run time. For example, the type of the marginal distribution of species is: - -ghci> :t dist1a -dist1a :: Categorical String Double - -That is, a categorical distributions whose data points are Strings and which stores probabilities as a Double. Now, if I wanted a distribution of the weights of the employees, I can get that by: - --- >dist1b = getMargin TH_weight dist1 - -And the type of this distribution is: - -ghci> :t dist1b -dist1b :: Normal Double - -Now, I can easily plot these marginal distributions with the plotDistribution function: - -ghci> plotDistribution (plotFile "dist1a" $ PNG 250 250) dist1a -ghci> plotDistribution (plotFile "dist1b" $ PNG 250 250) dist1b - - -dist1adist1b - -futurama-bender-smoking-cigar-wallpaperBut wait! I accidentally forgot to include Bender in the planetExpress data set! What can I do? - -In a traditional statistics library, we would have to retrain our data from scratch. If we had billions of elements in our data set, this would be an expensive mistake. But in our HLearn library, we can take advantage of the model’s monoid structure. In particular, the compiler used this structure to automatically derive a function called add1dp for us. Let’s look at its type: - -ghci> :t add1dp -add1dp :: HomTrainer model => model -> Datapoint model -> model - -It’s pretty simple. The function takes a model and adds the data point associated with that model. It returns the model we would have gotten if the data point had been in our original data set. This is called online training. - -Again, because our distributions form monoids, the compiler derived an efficient and exact online training algorithm for us automatically. - -So let’s create a new distribution that considers bender: - ->bender = Character "Bender Rodriguez" "robot" Crew (Just True) 44 6.1 612 ->dist1' = add1dp dist1 bender - -And plot our new marginals: - -ghci> plotDistribution (plotFile "dist1-withbender-species" $ PNG 250 250) $ - getMargin TH_species dist1' -ghci> plotDistribution (plotFile "dist1-withbender-weight" $ PNG 250 250) $ - getMargin TH_weight dist1' - - -dist1-withbender-speciesdist1-withbender-weight - -Notice that our categorical marginal has clearly changed, but that our normal marginal doesn’t seemed to have changed at all. This is because the plotting routines automatically scale the distribution, and the normal distribution, when scaled, always looks the same. We can double check that we actually did change the weight distribution by comparing the mean: - -ghci> mean dist1b -176.42857142857142 -ghci> mean $ getMargin TH_weight dist1' -230.875 - -Bender’s weight really changed the distribution after all! -Complicated DependencE structureS - -That’s cool, but our original distribution isn’t very interesting. What makes multivariate distributions interesting is when the variables affect each other. This is true in our case, so we’d like to be able to model it. For example, we’ve already seen that robots are much heavier than organic lifeforms, and are throwing off our statistics. The HLearn library supports a small subset of Markov Networks for expressing these dependencies. - -We represent Markov Networks as graphs with undirected edges. Every attribute in our distribution is a node, and every dependence between attributes is an edge. We can draw this graph with the plotNetwork command: - -ghci> plotNetwork "dist1-network" dist1 - -dist1-networkAs expected, there are no edges in our graph because everything is independent. Let’s create a more interesting distribution and plot its Markov network. - ->dist2 = train planetExpress :: Multivariate -> '[ Ignore '[String] -> , MultiCategorical '[String] -> , Independent Categorical '[Job,Maybe Bool] -> , Independent Normal '[Double,Double,Double] -> ] -> Double -> Character - -ghci> plotNetwork "dist2-network" dist2 - - dist2-network - -Okay, so what just happened? - -The syntax for representing the dependence structure is a little confusing, so let’s go step by step. We represent the dependence information in the graph as a list of types. Each element in the list describes both the marginal distribution and the dependence structure for one or more records in our data type. We must list these elements in the same order as the original data type. - -Notice that we’ve made two changes to the list. First, our list now starts with the type Ignore ‘[String]. This means that the first string in our data type—the name—will be ignored. Notice that TH_name is no longer in the Markov Network. This makes sense because we expect that a character’s name should not tell us too much about any of their other attributes. - -Second, we’ve added a dependence. The MultiCategorical distribution makes everything afterward in the list dependent on that item, but not the things before it. This means that the exact types of dependencies it can specify are dependent on the order of the records in our data type. Let’s see what happens if we change the location of the MultiCategorical: - ->dist3 = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , Independent Categorical '[String] -> , MultiCategorical '[Job] -> , Independent Categorical '[Maybe Bool] -> , Independent Normal '[Double,Double,Double] -> ] -> Double - -ghci> plotNetwork "dist3-network" dist3 - -dist3-network - -As you can see, our species no longer have any relation to anything else. Unfortunately, using this syntax, the order of list elements is important, and so the order we specify our data records is important. - -Finally, we can substitute any valid univariate distribution for our Normal and Categorical distributions. The HLearn library currently supports Binomial, Exponential, Geometric, LogNormal, and Poisson distributions. These just don’t make much sense for modelling Futurama characters, so we’re not using them. - -Now, we might be tempted to specify that every variable is fully dependent on every other variable. In order to do this, we have to introduce the “Dependent” type. Any valid multivariate distribution can follow Dependent, but only those records specified in the type-list will actually be dependent on each other. For example: - ->dist4 = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , MultiCategorical '[String,Job,Maybe Bool] -> , Dependent MultiNormal '[Double,Double,Double] -> ] -> Double - -ghci> plotNetwork "dist4-network" dist4 - -distb-network - -Undoubtably, this is in always going to be the case—everything always has a slight influence on everything else. Unfortunately, it is not easy in practice to model these fully dependent distributions. We need roughly \Theta(2^{n e}) data points to accurately train a distribution, where n is the number of nodes in our graph and e is the number of edges in our network. Thus, by selecting that two attributes are independent of each other, we can greatly reduce the amount of data we need to train an accurate distribution. - -I realize that this syntax is a little awkward. I chose it because it was relatively easy to implement. Future versions of the library should support a more intuitive syntax. I also plan to use copulas to greatly expand the expressiveness of these distributions. In the mean time, the best way to figure out the dependencies in a Markov Network are just to plot it and see visually. - -Okay. So what distribution makes the most sense for Futurama characters? We’ll say that everything depends on both the characters’ species and job, and that their weight depends on their height. - ->planetExpressDist = train planetExpress :: Multivariate Character -> '[ Ignore '[String] -> , MultiCategorical '[String,Job] -> , Independent Categorical '[Maybe Bool] -> , Independent Normal '[Double] -> , Dependent MultiNormal '[Double,Double] -> ] -> Double - -ghci> plotNetwork "planetExpress-network" planetExpressDist - -dist4-network - -We still don’t have enough data to to train this network, so let’s create some more. We start by creating a type for our Markov network called FuturamaDist. This is just for convenience so we don’t have to retype the dependence structure many times. - ->type FuturamaDist = Multivariate -> -- '[Ignore '[String] -> '[ MultiCategorical '[String] -> , MultiCategorical '[String,Job] -> , Independent Categorical '[Maybe Bool] -> , Independent Normal '[Double] -> , Dependent MultiNormal '[Double,Double] -> ] -> Double -> Character - -Next, we train some more distribubtions of this type on some of the characters. We’ll start with Mom Corporation and the brave Space Forces. - - 200-futurama_mom_and_sons 200-kif and zapp - ->momCorporation = -> [ Character "Mom" "human" Manager (Just False) 100 5.5 130 -> , Character "Walt" "human" Henchman (Just False) 22 6.1 170 -> , Character "Larry" "human" Henchman (Just False) 18 5.9 180 -> , Character "Igner" "human" Henchman (Just False) 15 5.8 175 -> ] ->momDist = train momCorporation :: FuturamaDist - ->spaceForce = -> [ Character "Zapp Brannigan" "human" Manager (Nothing) 45 6.0 230 -> , Character "Kif Kroker" "alien" Crew (Just True) 113 4.5 120 -> ] ->spaceDist = train spaceForce :: FuturamaDist - -And now some more robots: - -200-robotmafia 200-hedonismbot - ->robots = -> [ bender -> , Character "Calculon" "robot" Other (Nothing) 123 6.8 650 -> , Character "The Crushinator" "robot" Other (Nothing) 45 8.0 4500 -> , Character "Clamps" "robot" Henchman (Just False) 134 5.8 330 -> , Character "DonBot" "robot" Manager (Just False) 178 5.8 520 -> , Character "Hedonismbot" "robot" Other (Just False) 69 4.3 1200 -> , Character "Preacherbot" "robot" Manager (Nothing) 45 5.8 350 -> , Character "Roberto" "robot" Other (Just False) 77 5.9 250 -> , Character "Robot Devil" "robot" Other (Just False) 895 6.0 280 -> , Character "Robot Santa" "robot" Other (Just False) 488 6.3 950 -> ] ->robotDist = train robots :: FuturamaDist - -Now we’re going to take advantage of the monoid structure of our multivariate distributions to combine all of these distributions into one. - ->futuramaDist = planetExpressDist <> momDist <> spaceDist <> robotDist - -The resulting distribution is equivalent to having trained a distribution from scratch on all of the data points: - -train (planetExpress++momCorporation++spaceForces++robots) :: FuturamaDist - -We can take advantage of this property any time we use the train function to automatically parallelize our code. The higher order function parallel will split the training task evenly over each of your available processors, then merge them together with the monoid operation. This results in “theoretically perfect” parallel training of these models. - -parallel train (planetExpress++momCorporation++spaceForces++robots) :: FuturamaDist - -Again, this is only possible because the distributions have a monoid structure. - -Now, let’s ask some questions of our distribution. If I pick a character at random, what’s the probability that they’re a good guy? Let’s plot the marginal. - -ghci> plotDistribution (plotFile "goodguy" $ PNG 250 250) $ getMargin TH_isGood futuramaDist - -goodguy - -But what if I only want to pick from those characters that are humans, or those characters that are robots? Statisticians call this conditioning. We can do that with the condition function: - -ghci> plotDistribution (plotFile "goodguy-human" $ PNG 250 250) $ - getMargin TH_isGood $ condition TH_species "human" futuramaDist -ghci> plotDistribution (plotFile "goodguy-robot" $ PNG 250 250) $ - getMargin TH_isGood $ condition TH_species "robot" futuramaDist - - - -Preacherbotgoodguy-human goodguy-robot -On the left is the plot for humans, and on the right the plot for robots. Apparently, original robot sin is much worse than that in humans! If only they would listen to Preacherbot and repent of their wicked ways… - -Now let’s ask: What’s the average age of an evil robot? - -ghci> mean $ getMargin TH_age $ - condition TH_isGood (Just False) $ condition TH_species "robot" futuramaDist -273.0769230769231 - -Notice that conditioning a distribution is a commutative operation. That means we can condition in any order and still get the exact same results. Let’s try it: - -ghci> mean $ getMargin TH_age $ - condition TH_species "robot" $ condition TH_isGood (Just False) futuramaDist -273.0769230769231 - -There’s one last thing for us to consider. What does our Markov network look like after conditioning? Let’s find out! - -plotNetwork "condition-species-isGood" $ - condition TH_species "robot" $ condition TH_isGood (Just False) futuramaDist - -condition-species-isGood - -Notice that conditioning against these variables caused them to go away from our Markov Network. - -Finally, there’s another similar process to conditioning called “marginalizing out.” This lets us ignore the effects of a single attribute without specifically saying what that attribute must be. When we marginalize out on our Markov network, we get the same dependence structure as if we conditioned. - -plotNetwork "marginalizeOut-species-isGood" $ - marginalizeOut TH_species $ marginalizeOut TH_isGood futuramaDist - -condition-species-isGood - -Effectively, what the marginalizeOut function does is “forget” the extra dependencies, whereas the condition function “applies” those dependencies. In the end, the resulting Markov network has the same structure, but different values. - - - -Finally, at the start of the post, I mentioned that our multivariate distributions have group and vector space structure. This gives us two more operations we can use: the inverse and scalar multiplication. You can find more posts on how to take advantage of these structures here and here. -Next time… - -futurama-spacesuits - -The best part of all of this is still coming. Next, we’ll take a look at full on Bayesian classification and why it forms a monoid. Besides online and parallel trainers, this also gives us a fast cross-validation method. - -There’ll also be a posts about the monoid structure of Markov chains, the Free HomTrainer, and how this whole algebraic framework applies to NP-approximation algorithms as well. - -Subscribe to the RSS feed to stay tuned. diff --git a/examples/old/blogs/blog-nukes-csvconverter.lhs b/examples/old/blogs/blog-nukes-csvconverter.lhs deleted file mode 100644 index 3b92e57..0000000 --- a/examples/old/blogs/blog-nukes-csvconverter.lhs +++ /dev/null @@ -1,17 +0,0 @@ -import Control.Lens -import Data.Csv -import qualified Data.Vector as V -import qualified Data.ByteString.Lazy.Char8 as BS -import System.IO - -main = do - Right rawdata <- fmap (decode True) $ BS.readFile "nukes.csv" - :: IO (Either String (V.Vector (String, String, String, Int, Int))) - - let bigList = concat $ V.toList $ fmap (\(country,nuketype,warhead,yeild,num) -> replicate num (country,nuketype,warhead,yeild)) rawdata - - hout <- openFile "nukes-list.csv" WriteMode - sequence_ $ map (\(country,nuketype,warhead,yeild) -> hPutStrLn hout $ show country++","++show nuketype++","++show warhead++","++show yeild) bigList - hClose hout - - print "done" \ No newline at end of file diff --git a/examples/old/blogs/blog-nukes.lhs b/examples/old/blogs/blog-nukes.lhs deleted file mode 100644 index 1661239..0000000 --- a/examples/old/blogs/blog-nukes.lhs +++ /dev/null @@ -1,247 +0,0 @@ -The Bulletin of the Atomic Scientists tracks the nuclear capabilities of every country. We're going to use their data to demonstrate Haskell's HLearn library and the usefulness of abstract algebra to statistics. Specifically, we'll see that categorical distributions and kernel density estimates have monoid, group, and module algebraic structures. We'll then take advantage of these structures to efficiently answer real-world statistical questions about nuclear war. It'll be a WOPR! - -Before we get into the math, we'll need to review the basics of nuclear politics. - -The nuclear Non-Proliferation Treaty (NPT) is the main treaty governing nuclear weapons. Basically, it says that there are five countries that are "allowed" to have nuclear weapons: the USA, UK, France, Russia, and China. "Allowed" is in quotes because the treaty specifies that these countries must eventually get rid of their nuclear weapons at some future, unspecified date. When another country, for example Iran, signs the NPT, they are agreeing to not develop nuclear weapons. What they get in exchange is help from the 5 nuclear weapons states in developing their own civilian nuclear power programs. (Iran has the legitimate complaint that Western countries are actively trying to stop its civilian nuclear program when they're supposed to be helping it, but that's a whole 'nother can of worms.) - -Currently, every country is a member of the NPT except for Israel, Pakistan, India, and North Korea. These four countries have their own nuclear weapons programs, and are thus not allowed to join the NPT. Because their nuclear stockpiles are relatively small and undeployed, we're going to ignore them in this analysis for the sake of clarity. - -** Programming preliminaries - -Now that we have the political background, we're ready to do some statistics. First, let's import our libraries: - ->import Control.Lens ->import Data.Csv ->import qualified Data.Vector as V ->import qualified Data.ByteString.Lazy.Char8 as BS -> ->import HLearn.Algebra ->import HLearn.Models.Distributions ->import HLearn.Gnuplot.Distributions - -Next, we load our data from nukes.csv using the Cassava package (http://hackage.haskell.org/package/cassava): - ->main = do -> Right rawdata <- fmap (fmap V.toList . decode True) $ BS.readFile "nukes-list.csv" -> :: IO (Either String [(String, String, String, Int)]) - -And we'll use the Lens package to parse the CSV file into a series of variables containing just the values we want: - -> let list_usa = fmap (\row -> row^._4) $ filter (\row -> (row^._1)=="USA" ) rawdata -> let list_uk = fmap (\row -> row^._4) $ filter (\row -> (row^._1)=="UK" ) rawdata -> let list_france = fmap (\row -> row^._4) $ filter (\row -> (row^._1)=="France") rawdata -> let list_russia = fmap (\row -> row^._4) $ filter (\row -> (row^._1)=="Russia") rawdata -> let list_china = fmap (\row -> row^._4) $ filter (\row -> (row^._1)=="China" ) rawdata - -NOTE: To understand the above code requires knowledge of the Cassava and Lens packages. For the rest of this article, it's not important that you understand how it works. All you need to understand is what each of these list_country variables looks like. So let's print one: - -> putStrLn $ "List of American nuclear weapons = " ++ show list_usa - -List of American nuclear weapons = fromList [335,335,335,335,335,335,335,335,335,335 ... 1200,1200,1200,1200,1200] - -If we want to know how many weapons are in the American arsenal, we can take the length of the list: - -> putStrLn $ "Number of American weapons = " ++ show (length list_usa) -> putStrLn $ "Explosive power of American weapons = " ++ show (sum list_usa) - -> putStrLn $ "USA = " ++ show (length list_usa) ++ ","++ show (sum list_usa) -> putStrLn $ "UK = " ++ show (length list_uk) ++ ","++ show (sum list_uk) -> putStrLn $ "France = " ++ show (length list_france) ++ ","++ show (sum list_france) -> putStrLn $ "Russia = " ++ show (length list_russia) ++ ","++ show (sum list_russia) -> putStrLn $ "China = " ++ show (length list_china) ++ ","++ show (sum list_china) - -We get that there are 1951 American nuclear weapons. - -To get the total number of weapons in the world, we concatenate every country's list of weapons and find the length of that: - -> let list_all = list_usa ++ list_uk ++ list_france ++ list_russia ++ list_china -> putStrLn $ "Number of nukes in the whole world = " ++ show (length list_all) - -And we get that there are 5079 nuclear weapons. - -Now let's do some algebra! - -** Monoids and groups - -In a previous post, we saw that the Gaussian distribution forms a group. This means that is has all the properties of a monoid---an empty element (mempty) that represents the distribution trained on no data, and a binary operation (mappend) that merges two distributions together---plus an inverse. This inverse lets us "subtract" two Gaussians from each other. - -It turns out that many other distributions also have this group property. For example, the categorical distribution is used for measuring discrete data. We train our categorical distribution like: - -> let cat_usa = train list_usa :: Categorical Int Double -> let cat_uk = train list_uk :: Categorical Int Double -> let cat_france = train list_france :: Categorical Int Double -> let cat_russia = train list_russia :: Categorical Int Double -> let cat_china = train list_china :: Categorical Int Double - -Now, because training the categorical distribution is a group homomorphism, we can train a distribution over all nukes by either: - -> let cat_allA = train list_all :: Categorical Int Double - -or - -> let cat_allB = cat_usa <> cat_uk <> cat_france <> cat_russia <> cat_china - -and we will get the same result no matter what. Since we've already done the calculations for each of the the countries already, method B will be more efficient---it won't have to repeat work we've already done. - -But nuclear war planners don't particularly care about this complete list of nuclear weapons. It contains nuclear weapons that have to be dropped from bombers. These free fall nukes are rarely useful in a nuclear war because they can't survive a surprise "first strike" attack. That is, if the US launched a surprise attack on the Russians, we would be able to destroy all their bombers and so wouldn't be vulnerable to them. - -What we really care about is the "survivable" nuclear weapons. - -> let list_bomber = fmap (\row -> row^._4) $ filter (\row -> (row^._2)=="Bomber") rawdata - -Then, we use our group inverse to calculate the global survivable arsenal: - -> let cat_bomber = train list_bomber :: Categorical Int Double -> let cat_survivable = cat_allB <> (inverse cat_bomber) - -Notice that we calculated this value indirectly---there was no possible way to combine our variables above to generate this value without using the inverse! This is the power of groups in statistics. - -** More distributions - -The categorical distribution is not sufficient to accurately describe the distribution of nuclear weapons. This is because we don't actually know the yield of a given warhead. Like all things, it has some manufacturing tolerances that we must consider. For example, if we detonate a 300 kt warhead, the actual explosion might be 275 kt, 350 kt, or the bomb might even "fizzle out" and have almost a 0kt explosion. - -We'll model this by using a kernel density estimator (KDE). - -> let kdeparams = KDEParams -> { bandwidth = Constant 20 -> , samplePoints = genSamplePoints -> 0 -- minimum -> 4000 -- maximum -> 4000 -- number of samples -> , kernel = KernelBox Gaussian -> } :: KDEParams Double - -Now, we'll train kernel density estimates on our data: - - -> let kde_usa = train' kdeparams list_usa :: KDE Double -> let kde_uk = train' kdeparams list_uk :: KDE Double -> let kde_france = train' kdeparams list_france :: KDE Double -> let kde_russia = train' kdeparams list_russia :: KDE Double -> let kde_china = train' kdeparams list_china :: KDE Double - -> let kde_all = kde_usa <> kde_uk <> kde_france <> kde_russia <> kde_china - -The KDE is a powerful technique capable of capturing many different distributions. The draw back is that it is computationally expensive, especially when a large number of sample points are used. Fortunately, all computations in the HLearn library are easily parallelizable by applying the higher order function 'parallel'. - -We can calculate the full KDE from scratch in parallel like this: - -> let list_double_all = map fromIntegral list_all :: [Double] -> let kde_all_parA = (parallel (train' kdeparams)) list_double_all :: KDE Double - -or we can perform a parallel reduction on the kde's for each country like this: - -> let kde_all_parB = (parallel reduce) [kde_usa, kde_uk, kde_france, kde_russia, kde_china] - -Now, we'll calculate and plot the parallel version: - -> plotDistribution (genPlotParams "kde_all" kde_all) kde_all ->-- plotDistribution (genPlotParams "kde_usa" kde_usa) kde_usa - -The parallel computation takes about 16 seconds on my Core2 Duo laptop running on 2 processors, whereas the serial computation takes about 28 seconds. - -This is a considerable speedup, but we can still do better. It turns out that there is a homomorphism from the Categorical distribution to the KDE. - -> let kde_fromcat_all = cat_allB $> kdeparams -> plotDistribution (genPlotParams "kde_fromcat_all" kde_fromcat_all) kde_fromcat_all - -This computation takes less than a second and gets the exact same result as the much more expensive computations above. - -Why does this work? Well, the categorical distribution is a structure called the "free module" in disguise. - -** Modules, and the Free Module - -Modules (like groups, but unlike monoids) have not seen much attention from functional programmers. This is a shame, because they're quite handy. It turns out they will increase our performance dramatically. - -It's not super important to know the formal definition of an R-module, but here it is anyways: An R-module is a group with an additional property: it can be "multiplied" by any element of the ring R. This is a generalization of vector spaces because R need only be a ring instead of a field. (Rings do not necessarily have multiplicative inverses.) - -Here's an example. - -Let's say I have a vector: - -> let vec = [1,2,3,4,5] :: [Int] - -I can perform scalar multiplication on that vector like this: - -> let vec2 = 3 .* vec - -which as you matlab users might expect results in: - -[3,6,9,12,15] - -Our next example is the "free module." A "free" structure is one that obeys only the axioms of the structure and nothing else. Functional programmers are very familiar with the free monoid, i.e. the list data type. The free Z-module is like a beefed up list. Instead of just storing the elements in a list, it also stores the number of times that element occurred. This lets us greatly reduce the memory required to store a repetitive data set. - -In HLearn, we represent the free module over a ring r with the data type: - -:: FreeMod r a - -where a is the type of elements to be stored in the free module. We can convert our lists into free modules using the function list2module like this: - -> let module_usa = list2module list_usa -> let module_uk = list2module list_uk -> let module_france = list2module list_france -> let module_russia = list2module list_russia -> let module_china = list2module list_china - -Because modules are also groups, we can combine them like so: - -> let module_allA = module_usa <> module_uk <> module_france <> module_russia <> module_china - -or, we could train them from scratch: - -> let module_allB = list2module list_all - -Because generating a free module is a homomorphism, we have that: - -module_allA == module_allB - -But what does the FreeModule actually look like? Let's print it to find out: - -> print module_usa - -FreeMod (fromList [(100,768),(150,200),(300,250),(335,249),(340,50),(455,384),(1200,50)]) - -This is much more compact! So this is the take away: The Free Module makes repetitive data sets easier to work with. - -** Module distributions - -The Categorical distribution and the KDE both have this module structure. This gives us two cool properties for free. - -First, we can train these distributions directly from the Free Module. That is: - -> let cat_module_all = train module_allB :: Categorical Int Double -> let kde_module_all = train' kdeparams module_allB :: KDE Double - -and now, - -cat_mod_all == cat_all -kde_mod_all == kde_all == kde_cat_all - -Second, if a distribution is a module, we can weight the importance of our data points. Let's say we're a North Korean general, and we're planning our nuclear strategy. The US and North Korea have a very strained relationship in the nuclear department. It is much more likely that the US will try to nuke the DPRK than China will. And modules let us model this! - -> let threats_dprk = 20 .* kde_usa -> <> 10 .* kde_uk -> <> 5 .* kde_france -> <> 2 .* kde_russia -> <> 1 .* kde_china -> -> plotDistribution (genPlotParams "threats_dprk" threats_dprk) threats_dprk - -Or, if we're an American general, then we might say: - -> let threats_usa = 1 .* kde_russia -> <> 5 .* kde_china -> -> plotDistribution (genPlotParams "threats_usa" threats_usa) threats_usa - -Oh, and since we've already calculated all of the kde_country variables before, these computations take virtually no time at all to compute. - -** Bonus Homework Questions - -If you want to try out the HLearn library, here's two questions you can try to answer: - -1) What's the probability of a submarine launched ballistic missile (SLBM) having a - -In our next post, we'll go into more detail about the mathematical plumbing that makes all this possible. Then we'll start talking about Bayesian classification and full-on machine learning. - -> print "done!" \ No newline at end of file diff --git a/examples/old/future-examples/blogcensus.lhs b/examples/old/future-examples/blogcensus.lhs deleted file mode 100644 index d077fab..0000000 --- a/examples/old/future-examples/blogcensus.lhs +++ /dev/null @@ -1,27 +0,0 @@ ->{-# LANGUAGE DataKinds #-} ->{-# LANGUAGE TypeFamilies #-} ->{-# LANGUAGE BangPatterns #-} ->import HLearn.Algebra ->import HLearn.Models.Distributions ->import HLearn.Models.Classifiers.Bayes - ->data Person = Person -> { age :: !Double -> , workclass :: !String -> , fnlwgt :: !Double -> , education :: !String -> , educationNum :: !Double -> , maritalStatus :: !String -> , occupation :: !String -> , relationship :: !String -> , race :: !String -> , capitalGain :: !Double -> , capitalLoss :: !Double -> , hoursPerWeek :: !Double -> , nativeCountry :: !String -> , income :: !String -> } - ->instance Trainable Person where -> type GetHList Person = HList '[Double,String,Double,String,Double,String,String,String,String,Double,Double,Double,String] -> getHList p = age p:::workclass p:::fnlwgt p:::education p:::educationNum p:::maritalStatus p:::occupation p:::relationship p:::race p:::capitalGain p:::capitalLoss p:::hoursPerWeek p:::nativeCountry p:::HNil diff --git a/examples/old/future-examples/blogmc.lhs b/examples/old/future-examples/blogmc.lhs deleted file mode 100644 index ad4d76c..0000000 --- a/examples/old/future-examples/blogmc.lhs +++ /dev/null @@ -1,85 +0,0 @@ - ->{-# LANGUAGE DataKinds,ConstraintKinds, GADTs #-} -> ->import Control.DeepSeq ->import Control.Monad.Random ->import Data.Number.LogFloat ->import qualified Data.ByteString.Char8 as B ->import qualified Data.StorableVector as V -> ->import HLearn.Algebra ->import HLearn.Models.Markov.MarkovChain ->import qualified Control.ConstraintKinds as CK ->import Control.Parallel.Strategies ->import System.IO.Unsafe ->import Control.Concurrent - -One simple use for Markov chains is analyzing sequences of DNA. -DNA consists of a sequence of four base pairs, represented by the letters A, G, C, and T. -These letters are the data points; and a data set is a string. -A very, very short strand of DNA might look something like: - ->dna_short = "AGCTGCATAGCGCGATTACGATACG" - -We can train a Markov chain from these data points with the command: - ->mc1 = train dna_short :: MarkovChain 2 Char Double - -There's a few interesting tidbits here. -Working from right to left, the Double specifies how we will be internally representing our probabilities; -the Char is the type of our data points; -and the number three is called the "order" of our Markov chain. -In a Markov chain, we make the assumption that any given data point is dependent only on some previous number of data points. -In this case, we're only dependent on the 2 previous points. - -There are two main ways to use a Markov chain model. -The first is to generate new data. - ->fakedna1 = evalRand (replicateSamples 1000 mc1 "") $ mkStdGen 1 - -This generates the following strand of dna: - -ACGCGCGCGACGCGCGCGCGCTGCGCGCGCGCTATTAGATAGCGATGCGACGCGCGCTATAGCGCGACGCGCGCGACGCGCGCGCTGATATGCTGCTTAT -ATATATGCTAGCTGCTAGCGCGCGCGATGAGCGCGCTAGACGCGCTGCGCGCGCGCGACGCGCGACGCGCGCGCGCGCTAGCGCGCGCTAGCGCGATTTA -GCGCGCTATACGCGCGCGCGCGCGCGCGCTTATACGCTATATAGCGCGATTACGCGCGCGCGCGCGCGCTAGATAGCGATATACGATATGCGCGAGAGCG -CGCGATATTGCGCGCGCTGATACTTAGCGCGCGCGAGATATAGCTGAGCGCGATGATGCGATACGCTATAGCGCGCGATATTGCGCTGCGAGCGCGCGAT -GCGCGCGAGCGATACGCGCGCGCGCGCGCGCGCGCGCGCGCTATGCTACGCGCTGCGCGCGCGCGCGATTACGCGCGCGACGCGACGATAGCGACGATAT -ATTGCGATATATTATACGACGCGCGATTTTGCGCGCGCGCGCGCGCGCGCGCTAGAGCGCGCTAGATAGCGCGCGCGCGCTTATATGCGCTACTTTACGC -GCTAGCGCGCGCGCGAGATATATTTATTAGCGCGCTAGATGCGCGCGACTGCGCGCTGATTTGATAGCGCGCGCTGACGCGCGCGCGCGCGCGCGCGATT -ATAGCGCGCGCGCGATATGCTAGAGATGCTATAGCTATGCGCGCGCGCGCTAGAGATGCGCTATATTAGCGCGCGAGCGCGATATTGACTGCGCGCGACG -CGCGCTATAGCGCGCGCGCGATATGCGCGCGCTAGAGACGATAGCGCGACGCGATGACGCGCGCTTAGCTTGCGCGCGCGCGAGCGCGCGCGCGCGAGCT -TGACTTATATTGATATAGCGCGCTACGCGCGCTAGCTGATACGCGCTATGCGCGCGCTTATGCTTATAGCGCGCGCGCTATTATTGCGCGCGCGCGCTTA - -The second way to use the model is to ask it questions. -For example, we might want to ask: given a string of dna, how likely was it to be generated by our model? - -Markov chains are different than most other models in the HLearn library because the order of our data points matters. -For example, we could shuffle around the data points inside dna1 to create the data set: - ->dna2 = "AAAAAAACCCCCCGGGGGGGTTTTT" ->mc2 = train dna2 :: MarkovChain 3 Char Double - -But this is clearly a much different data set. -This property, where order matters, means that Markov chains are non-abelian. - ->main = do -> putStrLn "Initiating IO" -> dna <- readFile "data/winegrape-chromosome.txt" -> putStr "Training model... " -> let model = train (dna) :: MarkovChain 5 Char LogFloat -> deepseq model $ putStrLn "Done." - ->parallel2 :: -> ( HomTrainer model -> , NFData model ->-- , continer ~ [] ->-- , CK.Partitionable container ->-- , CK.PartitionableConstraint container datapoint -> ) => ({-container-} [Datapoint model] -> model) -- ^ sequential batch trainer -> -> ({-container-} [Datapoint model] -> model) -- ^ parallel batch trainer ->parallel2 train = \datapoint -> ->-- F.foldl' (mappend) mempty $ parMap strat train (CK.partition n datapoint) -> reduce $ parMap strat train1dp {-(CK.partition n-} datapoint{-)-} -> where -> strat = rdeepseq -> n = unsafePerformIO $ getNumCapabilities diff --git a/examples/old/future-examples/manual_instances-junk.hs b/examples/old/future-examples/manual_instances-junk.hs deleted file mode 100644 index 4ac74fb..0000000 --- a/examples/old/future-examples/manual_instances-junk.hs +++ /dev/null @@ -1,55 +0,0 @@ -{-# LANGUAGE DataKinds #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE TypeOperators #-} -{-# LANGUAGE NamedFieldPuns #-} -{-# LANGUAGE UndecidableInstances #-} - -import qualified Data.Vector.Unboxed as VU -import GHC.TypeLits - -import HLearn.Algebra -import HLearn.Models.Distributions - -data Output = A | B - deriving (Read, Show, Eq, Ord) - -data Point = Point - { _output :: Output - , _features :: VU.Vector Double - } deriving (Read, Show, Eq, Ord) - ---------------------------------------- --- these are the instances you'll have to define manualy -data TH_output = TH_output -data TH_features (n::Nat) = TH_features - -instance DepIndex Point TH_output where - type DepIndexResult Point TH_output = Output - p # _ = _output p - -instance (SingI n) => DepIndex Point (TH_features n) where - type DepIndexResult Point (TH_features n) = Double - p # _ = (_features p) VU.! (fromIntegral $ fromSing (sing :: Sing n)) - -instance HasDepIndex Point where --- type DepIndexList Point = TH_output ': '[TH_features 0, TH_features 1, TH_features 2, TH_features 3 ] - type DepIndexList Point = TH_output ': (Replicate 100 (TH_features 0)) --- type DepIndexList Point = '[TH_output,TH_features 0,TH_features 1,TH_features 2,TH_features 3,TH_features 4,TH_features 5,TH_features 6,TH_features 7,TH_features 8,TH_features 9,TH_features 10,TH_features 11,TH_features 12,TH_features 13,TH_features 14,TH_features 15,TH_features 16,TH_features 17,TH_features 18,TH_features 19,TH_features 20,TH_features 21,TH_features 22,TH_features 23,TH_features 24,TH_features 25,TH_features 26,TH_features 27,TH_features 28,TH_features 29,TH_features 30,TH_features 31,TH_features 32,TH_features 33,TH_features 34,TH_features 35,TH_features 36,TH_features 37,TH_features 38,TH_features 39,TH_features 40,TH_features 41,TH_features 42,TH_features 43,TH_features 44,TH_features 45,TH_features 46,TH_features 47,TH_features 48,TH_features 49,TH_features 50,TH_features 51,TH_features 52,TH_features 53,TH_features 54,TH_features 55,TH_features 56,TH_features 57,TH_features 58,TH_features 59,TH_features 60,TH_features 61,TH_features 62,TH_features 63,TH_features 64,TH_features 65,TH_features 66,TH_features 67,TH_features 68,TH_features 69,TH_features 70,TH_features 71,TH_features 72,TH_features 73,TH_features 74,TH_features 75,TH_features 76,TH_features 77,TH_features 78,TH_features 79,TH_features 80,TH_features 81,TH_features 82,TH_features 83,TH_features 84,TH_features 85,TH_features 86,TH_features 87,TH_features 88,TH_features 89,TH_features 90,TH_features 91,TH_features 92,TH_features 93,TH_features 94,TH_features 95,TH_features 96,TH_features 97,TH_features 98,TH_features 99,TH_features 100,TH_features 101,TH_features 102,TH_features 103,TH_features 104,TH_features 105,TH_features 106,TH_features 107,TH_features 108,TH_features 109,TH_features 110,TH_features 111,TH_features 112,TH_features 113,TH_features 114,TH_features 115,TH_features 116,TH_features 117,TH_features 118,TH_features 119,TH_features 120,TH_features 121,TH_features 122,TH_features 123,TH_features 124,TH_features 125,TH_features 126,TH_features 127,TH_features 128,TH_features 129,TH_features 130,TH_features 131,TH_features 132,TH_features 133,TH_features 134,TH_features 135,TH_features 136,TH_features 137,TH_features 138,TH_features 139,TH_features 140,TH_features 141,TH_features 142,TH_features 143,TH_features 144,TH_features 145,TH_features 146,TH_features 147,TH_features 148,TH_features 149,TH_features 150,TH_features 151,TH_features 152,TH_features 153,TH_features 154,TH_features 155,TH_features 156,TH_features 157,TH_features 158,TH_features 159,TH_features 160,TH_features 161,TH_features 162,TH_features 163,TH_features 164,TH_features 165,TH_features 166,TH_features 167,TH_features 168,TH_features 169,TH_features 170,TH_features 171,TH_features 172,TH_features 173,TH_features 174,TH_features 175,TH_features 176,TH_features 177,TH_features 178,TH_features 179,TH_features 180,TH_features 181,TH_features 182,TH_features 183,TH_features 184,TH_features 185,TH_features 186,TH_features 187,TH_features 188,TH_features 189,TH_features 190,TH_features 191,TH_features 192,TH_features 193,TH_features 194,TH_features 195,TH_features 196,TH_features 197,TH_features 198,TH_features 199] --- depIndexList _ = 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- --- type NB = Multivariate Point --- '[ MultiCategorical '[Output] --- -- , Independent Normal '[Double,Double,Double,Double] --- -- , Independent Normal (Replicate 4 Double) --- -- , Independent Normal (Replicate 200 Double) --- , Independent Normal '[Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double] --- ] --- Double - -xs = [ Point A $ VU.replicate 200 10 - , Point B $ VU.fromList [1 .. 200] - , Point A $ VU.fromList [-200 .. -1] - , Point B $ VU.replicate 200 55 - ] - --- dist = train xs :: NB diff --git a/examples/old/future-examples/manual_instances.hs b/examples/old/future-examples/manual_instances.hs deleted file mode 100644 index 6832094..0000000 --- a/examples/old/future-examples/manual_instances.hs +++ /dev/null @@ -1,61 +0,0 @@ -{-# LANGUAGE DataKinds #-} -{-# LANGUAGE MultiParamTypeClasses #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE TypeOperators #-} -{-# LANGUAGE NamedFieldPuns #-} -{-# LANGUAGE UndecidableInstances #-} - -import qualified Data.Vector.Unboxed as VU -import GHC.TypeLits - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Distributions.Multivariate.Internal.Container -import HLearn.Models.Distributions.Multivariate.Internal.Unital - -data Output = A | B - deriving (Read, Show, Eq, Ord) - -data Point = Point - { _output :: Output - , _features :: VU.Vector Double - } deriving (Read, Show, Eq, Ord) - ---------------------------------------- --- these are the instances you'll have to define manualy -data TH_output = TH_output -data TH_features (n::Nat) = TH_features - -instance DepIndex Point TH_output where - type DepIndexResult Point TH_output = Output - p # _ = _output p - -instance (SingI n) => DepIndex Point (TH_features n) where - type DepIndexResult Point (TH_features n) = Double - p # _ = (_features p) VU.! (fromIntegral $ fromSing (sing :: Sing n)) - -instance HasDepIndex Point where --- type DepIndexList Point = TH_output ': '[TH_features 0, TH_features 1, TH_features 2, TH_features 3 ] - type DepIndexList Point = TH_output ': (Replicate 200 (TH_features 0)) --- type DepIndexList Point = '[TH_output,TH_features 0,TH_features 1,TH_features 2,TH_features 3,TH_features 4,TH_features 5,TH_features 6,TH_features 7,TH_features 8,TH_features 9,TH_features 10,TH_features 11,TH_features 12,TH_features 13,TH_features 14,TH_features 15,TH_features 16,TH_features 17,TH_features 18,TH_features 19,TH_features 20,TH_features 21,TH_features 22,TH_features 23,TH_features 24,TH_features 25,TH_features 26,TH_features 27,TH_features 28,TH_features 29,TH_features 30,TH_features 31,TH_features 32,TH_features 33,TH_features 34,TH_features 35,TH_features 36,TH_features 37,TH_features 38,TH_features 39,TH_features 40,TH_features 41,TH_features 42,TH_features 43,TH_features 44,TH_features 45,TH_features 46,TH_features 47,TH_features 48,TH_features 49,TH_features 50,TH_features 51,TH_features 52,TH_features 53,TH_features 54,TH_features 55,TH_features 56,TH_features 57,TH_features 58,TH_features 59,TH_features 60,TH_features 61,TH_features 62,TH_features 63,TH_features 64,TH_features 65,TH_features 66,TH_features 67,TH_features 68,TH_features 69,TH_features 70,TH_features 71,TH_features 72,TH_features 73,TH_features 74,TH_features 75,TH_features 76,TH_features 77,TH_features 78,TH_features 79,TH_features 80,TH_features 81,TH_features 82,TH_features 83,TH_features 84,TH_features 85,TH_features 86,TH_features 87,TH_features 88,TH_features 89,TH_features 90,TH_features 91,TH_features 92,TH_features 93,TH_features 94,TH_features 95,TH_features 96,TH_features 97,TH_features 98,TH_features 99,TH_features 100,TH_features 101,TH_features 102,TH_features 103,TH_features 104,TH_features 105,TH_features 106,TH_features 107,TH_features 108,TH_features 109,TH_features 110,TH_features 111,TH_features 112,TH_features 113,TH_features 114,TH_features 115,TH_features 116,TH_features 117,TH_features 118,TH_features 119,TH_features 120,TH_features 121,TH_features 122,TH_features 123,TH_features 124,TH_features 125,TH_features 126,TH_features 127,TH_features 128,TH_features 129,TH_features 130,TH_features 131,TH_features 132,TH_features 133,TH_features 134,TH_features 135,TH_features 136,TH_features 137,TH_features 138,TH_features 139,TH_features 140,TH_features 141,TH_features 142,TH_features 143,TH_features 144,TH_features 145,TH_features 146,TH_features 147,TH_features 148,TH_features 149,TH_features 150,TH_features 151,TH_features 152,TH_features 153,TH_features 154,TH_features 155,TH_features 156,TH_features 157,TH_features 158,TH_features 159,TH_features 160,TH_features 161,TH_features 162,TH_features 163,TH_features 164,TH_features 165,TH_features 166,TH_features 167,TH_features 168,TH_features 169,TH_features 170,TH_features 171,TH_features 172,TH_features 173,TH_features 174,TH_features 175,TH_features 176,TH_features 177,TH_features 178,TH_features 179,TH_features 180,TH_features 181,TH_features 182,TH_features 183,TH_features 184,TH_features 185,TH_features 186,TH_features 187,TH_features 188,TH_features 189,TH_features 190,TH_features 191,TH_features 192,TH_features 193,TH_features 194,TH_features 195,TH_features 196,TH_features 197,TH_features 198,TH_features 199] - -type NB = Multivariate Point - '[ MultiCategorical '[Output] --- -- , Independent Normal '[Double,Double,Double,Double] - , Independent Normal (Replicate 200 Double) --- , Dependent MultiNormal (Replicate 500 Double) --- , Dependent MultiNormal '[Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double,Double] - ] - Double - -mkIndepNormal :: Int -> String -mkIndepNormal 0 = "Unital Double" -mkIndepNormal i = "Container Normal Double ("++(mkIndepNormal (i-1))++") Double" - -xs = [ Point A $ VU.replicate 500 10 - , Point B $ VU.fromList [1 .. 500] - , Point A $ VU.fromList [(-500) .. (-1)] - , Point B $ VU.replicate 500 55 - ] - --- dist = train xs :: NB diff --git a/examples/old/future-examples/tic-tac-toe.hs b/examples/old/future-examples/tic-tac-toe.hs deleted file mode 100644 index c9f0314..0000000 --- a/examples/old/future-examples/tic-tac-toe.hs +++ /dev/null @@ -1,62 +0,0 @@ -{-# LANGUAGE TypeOperators #-} -{-# LANGUAGE DataKinds #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE MultiParamTypeClasses #-} - -import qualified Data.Array as A -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -data Player = X | O - deriving (Read,Show,Eq,Ord) - -data Square = Empty | Taken Player - deriving (Read,Show,Eq,Ord) - -newtype TTT = TTT - { board :: A.Array (Int,Int) Square - } - deriving (Read,Show,Eq,Ord) - -newgame :: TTT -newgame = TTT $ A.listArray ((0,0),(2,2)) $ repeat Empty - -move :: TTT -> (Int,Int) -> Player -> TTT -move ttt pos player = TTT $ board ttt A.// [(pos,Taken player)] - -winner :: TTT -> Maybe Player -winner (TTT board) = if didOwin - then Just O - else if didXwin - then Just X - else Nothing - where - didXwin = or $ map (all (==Taken X)) posL - didOwin = or $ map (all (==Taken O)) posL - posL = map (map (board A.!)) positionL - positionL = [ [(i,j) | i<-[0,1,2]] | j<-[0,1,2] ] - ++[ [(j,i) | i<-[0,1,2]] | j<-[0,1,2] ] - ++[ [(i,i) | i<-[0,1,2]] ] - ++[ [(i,2-i) | i<-[0,1,2]] ] - -------------------------------------------------------------------------------- --- learning - --- instance Morphism TTT (NoParams TTT) (HVector ShowBox [Square,Square,Square,Square,Square,Square,Square,Square,Square]) --- where --- --- morph' ttt _ = vec undefined $ list2hlist $ A.elems $ board ttt --- --- -- instance Morphism TTT (NoParams TTT) (HVector ShowBox (Replicate Square 9)) --- --- data VarType = Discrete | Continuous --- --- -- class --- --- -- class HVector2Multivariate --- --- ------------------------------------------------------------------------------- --- -- junk --- diff --git a/examples/old/runtimes/runtimes.hs b/examples/old/runtimes/runtimes.hs deleted file mode 100644 index bf1a8cb..0000000 --- a/examples/old/runtimes/runtimes.hs +++ /dev/null @@ -1,125 +0,0 @@ -{-# LANGUAGE QuasiQuotes,OverlappingInstances #-} - -import Control.Monad -import Criterion.Types -import Criterion.Measurement - -import HLearn.Algebra hiding (Product) -import HLearn.Evaluation.RSquared -import HLearn.Models.Classifiers.Common hiding (Regression(..)) -import HLearn.Models.Regression -import HLearn.Models.Regression.Parsing - -fib :: Double->Double -fib 1 = 1 -fib 2 = 1 -fib n = fib (n-1) + fib (n-2) - -main = do --- let runtimeL = [1.1,2.3,3.1,4.1,5.8,6.8] - - let inputL = concat $ replicate 1 [1..35] - runtimeL <- sequence $ map (\x -> time_ $ run (nf fib x) 1) inputL - let dataset = zip runtimeL inputL - - let model = train dataset :: Product - '[ Regression (Just Linear) (Just [expr| 1+x |]) (Double,Double) - , Regression (Just Linear) (Just [expr| 1+x^2 |]) (Double,Double) - , Regression (Just Linear) (Just [expr| 1+x^3 |]) (Double,Double) - , Regression (Just Linear) (Just [expr| 1+x^4 |]) (Double,Double) - - , Regression (Just Linear) (Just [expr| 1+log x |]) (Double,Double) - , Regression (Just Linear) (Just [expr| 1+(log x)*x |]) (Double,Double) - - - , Regression (Just Linear) (Just [expr| 1+2^x |]) (Double,Double) - , Regression (Just Linear) (Just [expr| 1+(1/2)^x |]) (Double,Double) - - , Regression (Just Linear) (Just [expr| 1+x^x |]) (Double,Double) - ] - - - - sequence_ $ hlist2list $ hmap (RunTest dataset) $ unProduct model - - putStrLn "done" - - -type ProblemGen a = Int -> a - -intMaker :: ProblemGen Int -intMaker = id - -data RunTest = RunTest [(Double,Double)] -instance - ( Floating (Label (Datapoint m)) - , Show (Label (Datapoint m)) - , Show m - , NumDP m - , Classifier m - , Ring m ~ Label (Datapoint m) - , Datapoint m ~ (Double,Double) - ) => Function RunTest m (IO ()) - where - function (RunTest xs) m = do - let r = rsquared m xs - let str = show m - putStrLn $ str ++ replicate (50-length str) ' ' ++ "R^2 = " ++ show r - -runtest m xs = do - let r = rsquared m xs - let str = show m - putStrLn $ str ++ replicate (50 - length str) ' ' ++ "R^2 = " ++ show r - -instance HasRing Double where - type Ring Double = Double - -------------------------------------------------------------------------------- - -instance HomTrainer (HList '[]) where - type Datapoint (HList '[]) = () - train1dp _ = HNil - -instance - ( HomTrainer x - , HomTrainer (HList xs) - , HCons (Datapoint x) (Datapoint (HList xs)) ~ HList ( t ': ts) - , t ~ Datapoint x - , HList ts ~ Datapoint (HList xs) - ) => HomTrainer (HList (x ': xs)) - where - type Datapoint (HList (x ': xs)) = Datapoint x `HCons` Datapoint (HList xs) - train1dp (x ::: xs) = train1dp x ::: train1dp xs - ---------------------------------------- - -newtype Product (xs :: [*]) = Product { unProduct :: HList xs } - -deriving instance (Show (HList xs)) => Show (Product xs) - -instance Monoid (Product '[]) where - mempty = Product HNil - mappend a b = Product HNil - -instance (Monoid x, Monoid (HList xs)) => Monoid (Product (x ': xs)) where - mempty = Product $ mempty:::mempty - mappend (Product (a:::as)) (Product (b:::bs)) = Product $ a<>b ::: as<>bs - -instance - ( HomTrainer (Product xs) - , Monoid (HList xs) - , HomTrainer x - , Datapoint x ~ Datapoint (Product xs) - ) => HomTrainer (Product (x ': xs)) - where - type Datapoint (Product (x ': xs)) = Datapoint x - train1dp x = Product $ train1dp x ::: (unProduct $ (train1dp x :: Product xs)) - -instance - ( HomTrainer x - ) => HomTrainer (Product '[x]) where - type Datapoint (Product '[x]) = Datapoint x - train1dp dp = Product $ train1dp dp ::: HNil - -instance HMap f (HList xs) (HList ys) => HMap f (Product xs) (Product ys) where - hmap f (Product xs) = Product $ hmap f xs diff --git a/examples/old/spam_classification/README b/examples/old/spam_classification/README deleted file mode 100644 index b49a9ae..0000000 --- a/examples/old/spam_classification/README +++ /dev/null @@ -1,2 +0,0 @@ -$ ghc --make perceptron.hs -$ ./perceptron train.data test.data diff --git a/examples/old/spam_classification/perceptron.hs b/examples/old/spam_classification/perceptron.hs deleted file mode 100644 index f73fdd0..0000000 --- a/examples/old/spam_classification/perceptron.hs +++ /dev/null @@ -1,88 +0,0 @@ --- Very simple perceptron classifier to classify spam mails --- Dataset from UCI Machine Learning Repository --- Num of features - 57 --- Number of training examples - 4501 --- Number of test examples - 100 - --------------------------------------------------- --- --- --- usage :: ./perceptron train.data test.data --- --- --- --------------------------------------------------- - -{-# LANGUAGE BangPatterns #-} -{-# OPTIONS_GHC -O2 -optc-O2 #-} -{-# OPTIONS_GHC -fno-warn-incomplete-uni-patterns #-} -{-# LANGUAGE TypeSynonymInstances #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE MultiParamTypeClasses #-} - -import HLearn.Algebra -import HLearn.Models.Classifiers -import qualified Data.ByteString.Lazy.Char8 as B -import Data.List.Split -import System.Environment - -newtype DataPoint = DataPoint [Double] - deriving (Read,Show,Eq,Ord) - -instance Abelian DataPoint -instance Monoid DataPoint where - mempty = DataPoint [0, 0] - (DataPoint a) `mappend` (DataPoint b) = DataPoint $ zipWith (+) a b - -instance Group DataPoint where - inverse (DataPoint xs) = DataPoint $ map negate xs - -instance HasRing DataPoint where - type Ring DataPoint = Double - -instance Module DataPoint where - r .* (DataPoint xs) = DataPoint $ fmap (r*) xs - -instance MetricSpace DataPoint where - distance (DataPoint a) (DataPoint b) = sqrt . sum . map (^2) $ zipWith (-) a b - -toDouble :: [String] -> [Double] -toDouble = map read - -logNormalize :: Double -> Double -logNormalize x = log (x + 0.1) - -parseText :: B.ByteString -> (Int, DataPoint) -parseText bstr = (label, features) - where - line = splitOn "," (B.unpack bstr) - label = read (head line) :: Int - -- features = DataPoint $ toDouble (tail line) - features = DataPoint $ map logNormalize $ toDouble (tail line) - -accuracy :: [(Int,Int)] -> Int -accuracy [] = 0 -accuracy ((actual_label, expected_label):labels) - | actual_label == expected_label = 1 + accuracy labels - | otherwise = accuracy labels - -classifyInstance :: (Perceptron Int DataPoint) -> (Int, DataPoint) -> (Int,Int) -classifyInstance m inst = (actual_label, predicted_label) - where - actual_label = fst inst - predicted_label = classify m $ snd inst - -main = do - args <- getArgs - - let trainfile_name = head args - let testfile_name = last args - - train_lines <- B.lines `fmap` B.readFile trainfile_name - let instances = map parseText train_lines - - let m = train instances :: Perceptron Int DataPoint - - test_lines <- B.lines `fmap` B.readFile testfile_name - let tests = map parseText test_lines - - let results = map (classifyInstance m) tests - print $ (fromIntegral (accuracy results)) / (fromIntegral (length results)) diff --git a/examples/old/spam_classification/test.data b/examples/old/spam_classification/test.data deleted file mode 100644 index eb27255..0000000 --- a/examples/old/spam_classification/test.data +++ /dev/null @@ -1,100 +0,0 @@ -1 ,0.23,0.29,0.64,0,0.17,0.17,0.11,0.05,0.05,0.47,0.11,1.17,0.47,0.05,0.17,0.05,0.11,0.29,3.93,0,2.05,0,0.47,0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.133,0,0.492,0.338,0.092,6.033,87,1460 -0 ,0,0,0,0,0,0,0,0,0,0,0,3.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.81,0,0.232,0.116,0,0,0,0,1.976,9,83 -0 ,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,1.96,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3.92,0,0,3.92,0,0,0,0,0,0,0,0,0,1.647,4,28 -0 ,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1.33,0,1.33,0,0,0,4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.211,0,0,0.211,0,0,1.38,4,29 -0 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+++ /dev/null @@ -1,58 +0,0 @@ -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE DataKinds #-} - -import Data.Csv -import System.IO -import qualified Data.ByteString.Lazy.Char8 as BS -import qualified Data.Map as Map -import qualified Data.Vector as V - -import HLearn.Algebra -import HLearn.NPHard.Scheduling -import HLearn.NPHard.BinPacking - -data Task = Task - { taskid :: String - , cost :: Double - } - deriving (Read,Show,Eq) - -instance HasRing Task where - type Ring (Task) = Double - -instance Norm Task where - magnitude = cost - -instance Labeled Task where - label = taskid - -instance Ord Task where - compare t1 t2 = compare (cost t1) (cost t2) - -mkdataset l xs = [ Task ("$t_{"++l++show i++"}$") t | (i,t) <- zip [1..] xs] - -fig1_tasks = [2,2.1,1.5,0.65,0.6,0.7,0.8,0.9,1.3,1.7] -fig1_model = train (mkdataset "" fig1_tasks) :: Scheduling 4 Task -fig1 = putStr $ visualize 4 $ schedule fig1_model - - - -fig_binpack_tasks = Map.fromList - [(1,[Task {taskid = "$t_{6}$", cost = 0.7},Task {taskid = "$t_{2}$", cost = 2.1}]) - ,(2,[Task {taskid = "$t_{7}$", cost = 0.8},Task {taskid = "$t_{1}$", cost = 2.0}]) - ,(3,[Task {taskid = "$t_{8}$", cost = 0.9},Task {taskid = "$t_{10}$", cost = 1.7}]) - ,(4,[Task {taskid = "$t_{9}$", cost = 1.3},Task {taskid = "$t_{3}$", cost = 1.5}]) - ,(5,[Task {taskid = "$t_{5}$", cost = 0.6},Task {taskid = "$t_{4}$", cost = 0.65}]) - ] -fig_binpack = putStr $ visualize 3.6 fig_binpack_tasks - -fig_mon_tasksA = [2.1,2.7,3.1,1.7,1.6,0.6] -fig_mon_modelA = train (mkdataset "a," fig_mon_tasksA) :: Scheduling 3 Task -fig_monA = putStr $ visualize 7 $ schedule fig_mon_modelA - -fig_mon_tasksB = [0.6,2.5,1.0,0.7,0.65,0.7,0.9,0.85] -fig_mon_modelB = train (mkdataset "b," fig_mon_tasksB) :: Scheduling 3 Task -fig_monB = putStr $ visualize 7 $ schedule fig_mon_modelB - -fig_mon_model = fig_mon_modelA<>fig_mon_modelB -fig_mon = putStr $ visualize 7 $ schedule fig_mon_model diff --git a/examples/old/tmr/tmr.lhs b/examples/old/tmr/tmr.lhs deleted file mode 120000 index 6f13f31..0000000 --- a/examples/old/tmr/tmr.lhs +++ /dev/null @@ -1 +0,0 @@ -tmr.tex \ No newline at end of file diff --git a/examples/old/weka-cv/Readme.md b/examples/old/weka-cv/Readme.md deleted file mode 100644 index b79d47d..0000000 --- a/examples/old/weka-cv/Readme.md +++ /dev/null @@ -1,20 +0,0 @@ -This directory contains the details you need to reproduce HLearn outperforming Weka at cross-validation. - -First, you must get the appropriate version of the HLearn library from hackage. You will need GHC >= 7.6. - -> cabal install HLearn-2.0.0 -> cabal install cassava - -Next, compile the hlearn-cv.lhs file. - -> ghc -O2 hlearn-cv.lhs - -This file performs the experiments for HLearn, and the weka-cv.sh file performs the experiments for Weka. You may need to adjust some of the settings inside the weka-cv.sh file to point to the correct install locations of Java and Weka for your system. - -The files ending in .csv or .arff are the dataset files. It's easier for HLearn to parse the CSV, but Weka like's Arff, so all the Arffs were created from the CSVs using the csv2arff.sh tool. The full data set is in adult.csv, and the adult-X.csv files contain only the first X entries. These are used for testing the scalability of leave-one-out cross-validation. - -Finally, run the timing.sh file. The run times will spit out to the screen. Easy peasy. - -> ./timings.sh - -The full blown leave-one-out cross-validation for weka is commented out because it takes so long. Uncomment only if you're feeling really bored! diff --git a/examples/old/weka-cv/hlearn-cv.lhs b/examples/old/weka-cv/hlearn-cv.lhs deleted file mode 100644 index 7098e21..0000000 --- a/examples/old/weka-cv/hlearn-cv.lhs +++ /dev/null @@ -1,123 +0,0 @@ -This program performs fast cross-validation on the Census Income data set in CSV format. -It's main purpose is for run time comparison with Weka. - -> {-# LANGUAGE DataKinds #-} -> {-# LANGUAGE TypeFamilies #-} -> {-# LANGUAGE BangPatterns #-} -> {-# LANGUAGE TemplateHaskell #-} -> -> import Control.Applicative -> import Control.Monad -> import Control.Monad.Random -> import Data.Csv -> import qualified Data.Vector as V -> import qualified Data.ByteString.Lazy.Char8 as BS -> import System.Environment -> -> import HLearn.Algebra -> import HLearn.Models.Distributions -> import HLearn.Models.Classifiers.Bayes -> import HLearn.Models.Classifiers.Common -> import HLearn.Evaluation.CrossValidation - -We represent a data point with the data type Person. -The label is _income, and everything else is the attributes. -Sometimes, we will want to have a Person that has no label, but we will never want a Person with no attributes. -That's why _income is not strict, but everything else is. - -> data Person = Person -> { _income :: String -> , _workclass :: !String -> , _education :: !String -> , _maritalStatus :: !String -> , _occupation :: !String -> , _relationship :: !String -> , _race :: !String -> , _sex :: !String -> , _nativeCountry :: !String -> , _age :: !Double -> , _fnlwgt :: !Double -> , _educationNum :: !Double -> , _capitalGain :: !Double -> , _capitalLoss :: !Double -> , _hoursPerWeek :: !Double -> } -> deriving (Read,Show,Eq,Ord) -> makeTypeLenses ''Person -> -> data TestData = TestData { _a :: String, _b :: Int, _c :: Double } -> makeTypeLenses ''TestData -> instance Labeled TestData where -> type Label TestData = String -> type Attributes TestData = TestData -> getLabel = _a -> getAttributes = id -> testxs = [] :: [TestData] -> testm = train testxs :: Bayes TH_a TestDist -> testd = train testxs :: TestDist -> type TestDist = Multivariate TestData '[ MultiCategorical '[String], Independent Categorical '[Int], Independent Normal '[Double]] Double - -All data points for supervised learning must implement the LabeledAttributes class. -Things are in a relatively "hacked together" state right now, and in the future this should become much cleaner. - -> instance Labeled Person where -> type Label Person = String -> type Attributes Person = Person -> getLabel = _income -> getAttributes p = p - -This is how we convert from the CSV format into our Person data type. -Person and the CSV do not have fields in the same order. -The ordering of Person's fields makes defining the Naive Bayes distribution type easier. - -> instance FromRecord Person where -> parseRecord v -> | V.length v >= 14 = Person <$> -> v .! 14 <*> -> v .! 1 <*> -> v .! 3 <*> -> v .! 5 <*> -> v .! 6 <*> -> v .! 7 <*> -> v .! 8 <*> -> v .! 9 <*> -> v .! 13<*> -> v .! 0 <*> -> v .! 2 <*> -> v .! 4 <*> -> v .! 10<*> -> v .! 11<*> -> v .! 12 -> | otherwise = error "mzero" - -This is the type for our Bayesian classification. -It says that everything is independent of everything else, except for income which is dependent on everything. -It is easy to do non-naive Bayesian learning by specifying a more complicated dependence structure. -See this tutorial http://izbicki.me/blog/markov-networks-monoids-and-futurama for more details on how. - -> type NB = Bayes TH_income NBDist -> -> type NBDist = (Multivariate Person -> '[ MultiCategorical '[String] -> , Independent Categorical (Replicate 8 String) -> , Independent Normal (Replicate 6 Double) -> ] Double) - -Our main function just loads the csv file and calls the group cross-validation function. -We have to print the mean and variance to force the result because of Haskell's laziness. - -> main = do -> [file,foldstr] <- getArgs -> putStrLn $ "HLearn cross-validation of"++file++"with "++foldstr++" folds" -> let numfolds = read foldstr :: Int -> putStr "loading file... " -> Right rawdata <- fmap (fmap V.toList . decode True) $ BS.readFile file -> :: IO (Either String [Person]) -> putStrLn "done." -> --let model = train rawdata :: NB -> --let res= crossValidate_group (folds numfolds rawdata) (errorRate :: LossFunction NB) -> let res= evalRandIO $ crossValidate_group (kfold numfolds) errorRate rawdata (undefined::NB) -> putStrLn $ "mean = "++show (mean res) -> putStrLn $ "variance = "++show (variance res) - -Awesome! diff --git a/examples/optimization/ghc.sh b/examples/optimization/ghc.sh deleted file mode 100755 index cf6e156..0000000 --- a/examples/optimization/ghc.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -#prof="-prof" -prof="" -opt="-O2" -sandbox="-no-user-package-db -package-db ../../.cabal-sandbox/x86_64-linux-ghc-7.8.2-packages.conf.d" -core="-ddump-simpl -dsuppress-module-prefixes -dsuppress-uniques -dsuppress-uniques -dsuppress-type-applications -dsuppress-idinfo -keep-s-file -keep-llvm-files" - -ghc -fforce-recomp $prof $opt $sandbox $core "$1" > "$1.core" diff --git a/examples/optimization/optimization.hs b/examples/optimization/optimization.hs deleted file mode 100644 index 0b6cefd..0000000 --- a/examples/optimization/optimization.hs +++ /dev/null @@ -1,72 +0,0 @@ -{-# LANGUAGE RebindableSyntax,NoImplicitPrelude,OverloadedLists #-} - -import SubHask -import SubHask.Algebra.HMatrix - -import HLearn.History -import HLearn.History.DisplayMethods -import HLearn.Optimization.Common -import HLearn.Optimization.GradientDescent --- import HLearn.Optimization.StochasticGradientDescent -import HLearn.Optimization.LineMinimization.Univariate -import HLearn.Optimization.LineMinimization.Multivariate - -import qualified Data.Vector.Storable as VS -import qualified Data.Vector.Generic as VG -import System.IO - -sphere :: (Ring a, VG.Vector v a) => v a -> a -sphere v = VG.foldl' (+) zero $ VG.map (\x -> x*x) v - -sphere' :: (Module (v a), VG.Vector v a) => v a -> v a -sphere' v = 2*.v - -rosenbrock :: (Ring a, VG.Vector v a) => v a -> a -rosenbrock v = go 0 0 - where - go i tot = if i==VG.length v-1 - then tot - else go (i+1) $ 100*x*x + (v VG.! i - 1)*(v VG.! i -1) - where - x =(v VG.! (i+1) - (v VG.! i)*(v VG.! i)) - -rosenbrock' :: (Ring a, VG.Vector v a) => v a -> v a -rosenbrock' v = VG.imap go v - where - go i x = if i==VG.length v-1 - then pt2 - else if i== 0 - then pt1 - else pt1+pt2 - where - pt1 = 400*x*x*x - 400*xp1*x + 2*x -2 - pt2 = 200*x - 200*xm1*xm1 - xp1 = v VG.! i+1 - xm1 = v VG.! i-1 - -main = do - let { f=sphere; f'=sphere' } --- let { f=rosenbrock; f'=rosenbrock' } - - runDynamicHistory - ( summaryStatistics - === linearTrace - === mkOptimizationPlot - (undefined::ConjugateGradientDescent (Vector Double)) - f - "optplot.dat" - ) --- ( linearTrace ) - ( conjugateGradientDescent_ - ( lineSearchBrent ( brentTollerance 1e-12 || maxIterations 2 ) ) --- ( backtracking ( maxIterations 5) ) --- ( backtracking ( amijo (1e-4::Double) ) ) - polakRibiere - f - f' - ( [1..10] :: VS.Vector Double ) - ( maxIterations 5 --- || multiplicativeTollerance 1e-12 - ) - ) - putStrLn "done." diff --git a/executables/README.md b/executables/README.md new file mode 100644 index 0000000..920be7f --- /dev/null +++ b/executables/README.md @@ -0,0 +1,5 @@ +HLearn comes with a number of executable commands. +These executables provide a convenient interface to HLearn for shell scripts. + +Currently, only [hlearn-allknn](/executables/hlearn-allknn) is complete. +It provides a fast method for nearest neighbor queries. diff --git a/src/exec/hlearn-allknn.hs b/executables/hlearn-allknn/Main.hs similarity index 57% rename from src/exec/hlearn-allknn.hs rename to executables/hlearn-allknn/Main.hs index da78ad7..2cd03ba 100644 --- a/src/exec/hlearn-allknn.hs +++ b/executables/hlearn-allknn/Main.hs @@ -14,14 +14,13 @@ {-# LANGUAGE UndecidableInstances #-} {-# LANGUAGE ConstraintKinds #-} {-# LANGUAGE RebindableSyntax #-} -{-# LANGUAGE ForeignFunctionInterface #-} +{-# LANGUAGE PartialTypeSignatures #-} -import Control.Monad import Control.Monad.Random hiding (fromList) -import Data.List (zip,intersperse,init,tail,isSuffixOf,sortBy) +import Data.List (zip,zipWith,intersperse,init,tail,isSuffixOf,sortBy) import Data.Maybe (fromJust) -import qualified Data.Vector.Generic as VG import Data.Version +import GHC.Exts (inline) import Numeric import System.Console.CmdArgs.Implicit import System.Exit @@ -29,61 +28,52 @@ import System.IO import qualified Prelude as P import SubHask +import SubHask.Algebra.Array import SubHask.Algebra.Container import SubHask.Algebra.Ord import SubHask.Algebra.Parallel +import SubHask.Algebra.Vector +import SubHask.Algebra.Vector.FFI import SubHask.Compatibility.Containers -import SubHask.Compatibility.Vector.HistogramMetrics -import SubHask.Compatibility.Vector.Lebesgue --- import SubHask.Monad -import Data.Params - -import HLearn.Data.Graph -import HLearn.Data.Image +-- import HLearn.Data.Graph +-- import HLearn.Data.Image import HLearn.Data.LoadData import HLearn.Data.SpaceTree -import HLearn.Data.SpaceTree.CoverTree hiding (head,tail) -import HLearn.Data.SpaceTree.Algorithms.NearestNeighbor -import HLearn.Data.UnsafeVector -import HLearn.Evaluation.CrossValidation +-- import HLearn.Data.SpaceTree.CoverTree +import HLearn.Data.SpaceTree.CoverTree_Specialized +import HLearn.Data.SpaceTree.CoverTree.Unsafe +-- import HLearn.Data.SpaceTree.Algorithms +import HLearn.Data.SpaceTree.Algorithms_Specialized import HLearn.History.Timing -import HLearn.Models.Distributions.Common -import HLearn.Models.Distributions.Univariate.Normal +import HLearn.Models.Distributions import Paths_HLearn - -import qualified Prelude as P -import Control.Concurrent -import Control.Parallel -import Control.Parallel.Strategies -import System.IO.Unsafe - -import Foreign.Ptr - ------------------------------------------------------------------------------- -- command line parameters data Params = Params - { k :: Int - , kForceSlow :: Bool - - , data_format :: DataFormat + { data_format :: DataFormat , reference_file :: Maybe String , query_file :: Maybe String , distances_file :: String , neighbors_file :: String + , k :: Int + , kForceSlow :: Bool + + , maxrefdp :: Maybe Int , seed :: Maybe Int - , train_method :: TrainMethod + , treetype :: TreeType + , arrtype :: ArrType + , foldtype :: FoldType , adopt_children :: Bool , train_sequential :: Bool , cache_dists :: Bool - , pca_data :: Bool - , varshift_data :: Bool + , rotate :: DataRotate , searchEpsilon :: Float , expansionRatio :: Float @@ -96,15 +86,26 @@ data Params = Params } deriving (Show, Data, Typeable) -data TrainMethod - = TrainInsert_Orig - | TrainInsert_NoSort - | TrainInsert_Sort - | TrainInsert_Parent - | TrainInsert_Ancestor +data TreeType + = Orig + | Simplified + | Sort + | Parent + | Ancestor | TrainMonoid deriving (Read,Show,Data,Typeable) +data ArrType + = Boxed + | Unboxed + | List + deriving (Read,Show,Data,Typeable) + +data FoldType + = FoldSort + | Fold + deriving (Show, Data, Typeable) + data PackMethod = NoPack | PackCT @@ -129,69 +130,93 @@ data DataFormat | DF_String deriving (Read,Show,Data,Typeable) +data DataRotate + = Variance + | PCA + | NoRotate + deriving (Read,Show,Data,Typeable) + allknnParams = Params { k = 1 &= help "Number of nearest neighbors to find" + &= groupname "Not currently implemented" + + , searchEpsilon = 1 + &= help "Instead of finding points with the closest distance, settle for finding points whose distance is within searchepsilon*closest" + &= groupname "Normal use" , reference_file = def + &= groupname "Normal use" &= help "Reference data set" + &= name "r" &= typFile , data_format = DF_CSV + &= groupname "Normal use" &= help "file format of data files" , query_file = def + &= groupname "Normal use" &= help "Query data set" + &= name "q" &= typFile , distances_file = "distances_hlearn.csv" + &= groupname "Normal use" &= help "File to output distances into" + &= name "d" &= typFile , neighbors_file = "neighbors_hlearn.csv" + &= groupname "Normal use" &= help "File to output the neighbors into" + &= name "n" &= typFile - , searchEpsilon = 0 - &= help "" - &= groupname "Approximations" - , expansionRatio = 1.3 + &= groupname "Optimizations" &= help "" - &= groupname "Approximations" , packMethod = PackCT + &= groupname "Optimizations" &= help "Specifies which method to use for cache layout of the covertree" - &= groupname "Tree structure optimizations" + &= groupname "Optimizations" , sortMethod = NumDP_Distance + &= groupname "Optimizations" &= help "What order should the children be sorted in?" , kForceSlow = False + &= groupname "Optimizations" &= help "Don't use precompiled k function; use the generic one" , train_sequential = False + &= groupname "Optimizations" &= help "don't train the tree in parallel; this may *slightly* speed up the nearest neighbor search at the expense of greatly slowing tree construction" - , train_method = TrainInsert_NoSort + , treetype = Ancestor + &= groupname "Optimizations" &= help "which method to use to construct the cover tree?" + , arrtype = Unboxed + &= groupname "Optimizations" + &= help "boxed or unboxed arrays?" + + , foldtype = FoldSort + &= groupname "Optimizations" + &= help "should we sort the data points before folding over them?" + , adopt_children = False + &= groupname "Optimizations" &= help "move children to uncle nodes when they're closer than parents" , cache_dists = True + &= groupname "Optimizations" &= help "pre-calculate the maximum distance from any node dp to all of its descendents; speeds up queries at the expense of O(n log n) overhead in construction" - , pca_data = False - &= groupname "Data Preprocessing" - &= help "Rotate the data points using the PCA transform. Speeds up nearest neighbor searches, but computing the PCA can be expensive in many dimensions." - &= name "pca" - &= explicit - - , varshift_data = True - &= help "Sort the attributes according to their variance. Provides almost as much speed up as the PCA transform during neighbor searches, but much less expensive to compute." - &= name "varshift" - &= explicit + , rotate = Variance + &= groupname "Optimizations" + &= help "Rotate the data points. May speed up nearest neighbor queries at the expense of longer preprocessing time." , verbose = False &= help "Print tree statistics (takes some extra time)" @@ -223,42 +248,51 @@ main = do when (reference_file params == Nothing) $ error "must specify a reference file" - when (searchEpsilon params < 0) $ - error "search epsilon must be >= 0" + when (searchEpsilon params < 1) $ + error "search epsilon must be >= 1" -- load the data let filepath = fromJust $ reference_file params - let k = Proxy::Proxy (Static 1) + let k = Proxy::Proxy 1 case data_format params of DF_CSV -> do - let ct = Proxy::Proxy (CoverTree_ (Static (13/10)) Array UnboxedArray) --- let ct = Proxy::Proxy (CoverTree_ (Static (13/10)) Array Array) - dp = Proxy::Proxy (Labeled' (L2 UnboxedVector Float) Int) - - let {-# INLINE loadfile_dfcsv #-} + let {-# INLINE loadfile_dfcsv #-} -- prevents space leaks loadfile_dfcsv filepath = do - let dataparams = DataParams - { datafile = filepath - , labelcol = Nothing - , pca = pca_data params - , varshift = varshift_data params - } - rs :: Array (L2 UnboxedVector Float) <- loaddata dataparams - when (size rs > 0) $ do - putStrLn $ " numdim: " ++ show ( VG.length $ rs VG.! 0 ) - putStrLn "" - setptsize $ VG.length $ VG.head rs - - return $ VG.zipWith Labeled' rs $ VG.fromList [0::Int ..] --- return rs - - allknn params loadfile_dfcsv ct dp k + rs :: BArray (UVector "dyn" Float) <- loadCSV filepath + + rs' <- case rotate params of + + PCA -> error "PCARotate temporarily removed" + + Variance -> disableMultithreading $ do + let shuffleMap = mkShuffleMap rs + time "mkShuffleMap" shuffleMap + time "varshifting data" $ map (apShuffleMap shuffleMap) $ toList rs + + NoRotate -> return $ toList rs + + return $ fromList $ zipWith Labeled' rs' [0::Int ..] + + let dp = Proxy::Proxy (Labeled' (UVector "dyn" Float) Int) + + case arrtype params of + Unboxed -> do + let ct = Proxy::Proxy UCoverTree + allknn params loadfile_dfcsv ct dp k + +-- Boxed -> do +-- let ct = Proxy::Proxy BCoverTree +-- allknn params loadfile_dfcsv ct dp k +-- +-- List -> do +-- let ct = Proxy::Proxy (CoverTree_ () [] UArray) +-- allknn params loadfile_dfcsv ct dp k {- DF_Images -> do - let ct = Proxy::Proxy (CoverTree_ (Static (13/10)) Array Array) + let ct = Proxy::Proxy (CoverTree_ (Static (13/10)) BArray BArray) dp = Proxy::Proxy (Labeled' (ColorSig Float) FilePath) let {-# INLINE loaddata #-} @@ -272,7 +306,7 @@ main = do DF_PLG -> do - let ct = Proxy::Proxy (CoverTree_ (Static (13/10)) Array Array) + let ct = Proxy::Proxy (CoverTree_ (Static (13/10)) BArray BArray) dp = Proxy::Proxy (Labeled' Graph FilePath) let {-# INLINE loaddata #-} @@ -290,31 +324,19 @@ main = do {-# INLINE allknn #-} allknn :: forall k exprat childC leafC dp l proxy1 proxy2 proxy3. ( ValidCT exprat childC leafC (Labeled' dp l) - , ValidCT exprat childC leafC dp - , P.Fractional (Scalar dp) - , Param_k (NeighborList k dp) - , Param_k (NeighborList k (Labeled' dp l)) , RationalField (Scalar dp) - , ValidNeighbor dp - , ValidNeighbor (Labeled' dp l) + , Bounded (Scalar dp) + , Unboxable (Labeled' dp l) - , VG.Vector childC Int - , VG.Vector childC Bool - , P.Ord l + , Ord l , NFData l + , NFData (childC (CoverTree_ exprat childC leafC (Labeled' dp l))) , Show l --- , Semigroup (CoverTree_ exprat childC leafC (Labeled' dp l)) - --- , exprat ~ (13/10) --- , childC ~ Array --- , leafC ~ UnboxedArray --- , dp ~ L2 UnboxedVector Float ) => Params - -> (FilePath -> IO (Array (Labeled' dp l))) --- -> (FilePath -> IO (Array dp)) + -> (FilePath -> IO (UArray (Labeled' dp l))) -> proxy1 (CoverTree_ exprat childC leafC) -> proxy2 (Labeled' dp l) - -> proxy3 k + -> Proxy (1::Nat) -> IO () allknn params loaddata _ _ _ = do @@ -330,30 +352,39 @@ allknn params loaddata _ _ _ = do Just n -> evalRand (shuffle rs_take) (mkStdGen n) -- build the trees - reftree <- buildTree params rs_shuffle :: IO (CoverTree_ exprat childC leafC (Labeled' dp l)) + reftree :: CoverTree_ exprat childC leafC (Labeled' dp l) + <- buildTree params rs_shuffle (querytree,qs) <- case query_file params of Nothing -> return $ (reftree,rs) Just qfile -> do - when (qfile=="/dev/null") $ do - exitSuccess - qs <- loaddata qfile querytree <- buildTree params qs return (querytree, qs) -- do knn search - let res = unsafeParallelInterleaved - ( findAllNeighbors (convertRationalField $ searchEpsilon params) reftree ) + let method = case foldtype params of + FoldSort -> findNeighbor + Fold -> findNeighbor_NoSort + + let epsilon = convertRationalField $ searchEpsilon params + +-- let res = unsafeParallelInterleaved + let res = parallelInterleaved + ( fromList . map (\dp -> (dp, method epsilon reftree dp) ) ) ( stToList querytree ) - :: Seq (Labeled' dp l, NeighborList k (Labeled' dp l)) + :: ParList (Labeled' dp l, Neighbor (Labeled' dp l)) time "computing parFindNeighborMap" res + -- FIXME: + -- The ParList type is an obscure list type designed to have an O(1) monoid operation. + -- We use it here because this is important for parallelism. + -- I can make this code much simpler/more idiomatic by adding a "Partionable" instance to "CoverTree". - -- output to files + -- sort results into original order let sortedResults :: [[Neighbor (Labeled' dp l)]] sortedResults - = map (getknnL . snd) - . sortBy (\(Labeled' _ y1,_) (Labeled' _ y2,_) -> P.compare y1 y2) + = map (\x -> [snd x]) + . sortBy (\(Labeled' _ y1,_) (Labeled' _ y2,_) -> compare y1 y2) $ toList res time "sorting results" sortedResults @@ -375,59 +406,33 @@ allknn params loaddata _ _ _ = do putStrLn "done" -{-# RULES - --- "subhask/eqVectorDouble" (==) = eqVectorDouble --- "subhask/eqVectorFloat" (==) = eqVectorFloat --- "subhask/eqVectorInt" (==) = eqVectorInt --- "subhask/eqUnboxedVectorDouble" (==) = eqUnboxedVectorDouble --- "subhask/eqUnboxedVectorFloat" (==) = eqUnboxedVectorFloat --- "subhask/eqUnboxedVectorInt" (==) = eqUnboxedVectorInt - --- "subhask/distance_l2_float_unboxed" distance = distance_l2_float_unboxed --- "subhask/isFartherThan_l2_float_unboxed" isFartherThanWithDistanceCanError=isFartherThan_l2_float_unboxed --- "subhask/distance_l2_m128_unboxed" distance = distance_l2_m128_unboxed --- "subhask/isFartherThan_l2_m128_unboxed" isFartherThanWithDistanceCanError=isFartherThan_l2_m128_unboxed - --- "subhask/distance_l2_m128_storable" distance = distance_l2_m128_storable --- "subhask/distance_l2_m128d_storable" distance = distance_l2_m128d_storable --- "subhask/isFartherThan_l2_m128_storable" isFartherThanWithDistanceCanError=isFartherThan_l2_m128_storable --- "subhask/isFartherThan_l2_m128d_storable" isFartherThanWithDistanceCanError=isFartherThan_l2_m128d_storable - - #-} - -- | Gives us many possible ways to construct our cover trees based on the input parameters. -- This is important so we can compare their runtime features. buildTree :: forall exprat childC leafC dp. ( ValidCT exprat childC leafC dp - , VG.Vector childC Bool - , VG.Vector childC Int - --- , Semigroup (CoverTree_ exprat childC leafC dp) --- , exprat ~ (13/10) --- , childC ~ Array --- , leafC ~ UnboxedArray --- , dp ~ L2 UnboxedVector Float + , Unboxable dp + , Unbox dp + , NFData (childC (CoverTree_ exprat childC leafC dp)) ) => Params - -> Array dp + -> UArray dp -> IO (CoverTree_ exprat childC leafC dp) buildTree params xs = do - setexpratIORef $ P.toRational $ expansionRatio params + setExprat $ P.toRational $ expansionRatio params - let trainmethod = case train_method params of - TrainInsert_Orig -> trainInsertOrig - TrainInsert_NoSort -> trainInsertNoSort - TrainInsert_Sort -> trainInsert addChild_nothing - TrainInsert_Parent -> trainInsert addChild_parent - TrainInsert_Ancestor -> trainInsert addChild_ancestor --- TrainMonoid -> trainMonoid + let trainmethod = case treetype params of + Orig -> inline trainInsertOrig + Simplified -> inline trainInsertNoSort + Sort -> inline trainInsert addChild_nothing + Parent -> inline trainInsert addChild_parent + Ancestor -> inline trainInsert addChild_ancestor + TrainMonoid -> inline trainMonoid - let (Just' reftree) = {-parallel-} trainmethod $ toList xs + let (Just' reftree) = parallel trainmethod $ toList xs time "building tree" reftree - -- Everything below here uses a sequential algorithm (may change in the future). - -- These run faster if we disable multithreading. + -- Everything below here uses a sequential algorithm. + -- These run faster if we disable multithreading in GHC 7.10. disableMultithreading $ do let reftree_adopt = if adopt_children params @@ -468,8 +473,6 @@ buildTree params xs = do -- Should this be moved into the SpaceTree file? printTreeStats :: ( ValidCT exprat childC leafC dp - , VG.Vector childC Bool - , VG.Vector childC Int ) => String -> CoverTree_ exprat childC leafC dp -> IO () @@ -503,6 +506,111 @@ printTreeStats str t = do putStr (str++" tightCover...") >> hFlush stdout >> putStrLn (show $ invariant_CoverTree_tightCovering t) putStr (str++" separating...") >> hFlush stdout >> putStrLn (show $ invariant_CoverTree_separating t) putStr (str++" maxDescDist..") >> hFlush stdout >> putStrLn (show $ invariant_CoverTree_maxDescendentDistance t) - putStr (str++" leveled......") >> hFlush stdout >> putStrLn (show $ property_leveled t) +-- putStr (str++" leveled......") >> hFlush stdout >> putStrLn (show $ property_leveled t) putStrLn "" + +-------------------------------------------------------------------------------- +-- FIXME: +-- The code below should find a better home in subhask somewhere. + +{-# RULES + +"subhask/parallelInterleaved" forall (f :: a -> ParList b). parallel f = parallelInterleaved f + + #-} + +-- | See note above about ParList. +newtype ParList a = ParList [[a]] + deriving (Read,Show,NFData) + +mkMutable [t| forall a. ParList a |] + +type instance Scalar (ParList a) = Int +type instance Logic (ParList a) = Bool +type instance Elem (ParList a) = a + +instance (Eq a, Arbitrary a) => Arbitrary (ParList a) where + arbitrary = P.fmap fromList arbitrary + +instance Normed (ParList a) where + {-# INLINE size #-} + size (ParList xs) = sum $ map length xs + +instance Eq a => Eq_ (ParList a) where + {-# INLINE (==) #-} + (ParList a1)==(ParList a2) = a1==a2 + +instance POrd a => POrd_ (ParList a) where + {-# INLINE inf #-} + inf (ParList a1) (ParList a2) = ParList [inf (P.concat a1) (P.concat a2)] + +instance POrd a => MinBound_ (ParList a) where + {-# INLINE minBound #-} + minBound = ParList [] + +instance Semigroup (ParList a) where + {-# INLINE (+) #-} + (ParList a1)+(ParList a2) = ParList $ a1 + a2 + +instance Monoid (ParList a) where + {-# INLINE zero #-} + zero = ParList [] + +instance Abelian (ParList a) + +-- instance Eq a => Container (ParList a) where +-- {-# INLINE elem #-} +-- elem e (ParList a) = or $ map (elem e) + +instance Constructible (ParList a) where + {-# INLINE cons #-} + {-# INLINE snoc #-} + {-# INLINE singleton #-} + {-# INLINE fromList1 #-} + cons e (ParList []) = ParList [[e]] + cons e (ParList (x:xs)) = ParList ((e:x):xs) + + singleton e = ParList [[e]] + + fromList1 x xs = ParList [x:xs] + +instance ValidEq a => Foldable (ParList a) where + + {-# INLINE toList #-} + toList (ParList a) = P.concat a + +-- {-# INLINE uncons #-} +-- uncons (ParList a) = if ParList.null a +-- then Nothing +-- else Just (ParList.index a 0, ParList $ ParList.drop 1 a) +-- +-- {-# INLINE unsnoc #-} +-- unsnoc (ParList e) = if ParList.null e +-- then Nothing +-- else Just (ParList $ ParList.take (ParList.length e-1) e, ParList.index e 0) + +-- foldMap f (ParList a) = F.foldMap f a +-- +-- {-# INLINE foldr #-} +-- {-# INLINE foldr' #-} +-- {-# INLINE foldr1 #-} +-- foldr f e (ParList a) = F.foldr f e a +-- foldr' f e (ParList a) = F.foldr' f e a +-- foldr1 f (ParList a) = F.foldr1 f a +-- -- foldr1' f (ParList a) = F.foldr1' f a +-- +-- {-# INLINE foldl #-} +-- {-# INLINE foldl' #-} +-- {-# INLINE foldl1 #-} +-- foldl f e (ParList a) = F.foldl f e a +-- foldl' f e (ParList a) = F.foldl' f e a +-- foldl1 f (ParList a) = F.foldl1 f a +-- -- foldl1' f (ParList a) = F.foldl1' f a + +instance (ValidEq a) => Partitionable (ParList a) where + {-# INLINABLE partition #-} + partition n (ParList xs) = map (\x -> ParList [x]) $ partition n $ P.concat xs + + {-# INLINABLE partitionInterleaved #-} + partitionInterleaved n (ParList xs) = map (\x -> ParList [x]) $ partitionInterleaved n $ P.concat xs diff --git a/hlearn.cabal b/hlearn.cabal deleted file mode 100644 index 8bdf526..0000000 --- a/hlearn.cabal +++ /dev/null @@ -1,351 +0,0 @@ -Name: HLearn -Version: 2.0.0.0 -Synopsis: Homomorphic machine learning for classification, regression, distributions, etc. -Description: Homomorphic machine learning description. -Category: Data Mining, Machine Learning, Algorithms, Data structures -License: BSD3 -License-file: LICENSE -Author: Mike izbicki -Maintainer: mike@izbicki.me -Build-Type: Simple -Cabal-Version: >=1.8 -homepage: http://github.com/mikeizbicki/HLearn/ -bug-reports: http://github.com/mikeizbicki/HLearn/issues - -Library - Build-Depends: - -- common dependencies - base >= 3 && < 5, - ghc-prim, - template-haskell, - - -- control - containers >= 0.5, - unordered-containers>=0.2.4, - vector >= 0.10, - array >= 0.4, - lens >= 4.0, - primitive >= 0.5, - vector-algorithms >= 0.5.4, - vector-th-unbox >= 0.2, - list-extras >= 0.4, - parallel >= 3.2, - deepseq >= 1.3, - mtl >= 2.1.2, - - pipes >= 4.1.2, - ansi-terminal >= 0.6.1.1, - directory >= 1.2, - - -- my packages - ConstraintKinds >= 1.2.0, - typeparams >= 0.0.6, - subhask >= 0.1, --- simd >= 0.1.0.1, - - -- math dependencies --- erf >= 2.0, --- gamma >= 0.9, --- logfloat >= 0.12, --- hmatrix == 0.15.2.2, - hmatrix >= 0.16.0.5, - MonadRandom >= 0.1, - - -- are these needed? - statistics >= 0.10, - random >= 1.0.1, - hashable >= 1.1.2.5, - heap >= 1.0.0, - dlist >= 0.5, - split >= 0.2.2, - semigroups >= 0.9, - memoize >= 0.6, - cassava >= 0.2.2, - strict >= 0.3.2, - normaldistribution >= 1.1.0.3, - time >= 1.4.2, - - bytestring >= 0.10.4, - - -- tests - QuickCheck >= 2.6, - - -- visualization - - diagrams-svg >= 0.6, - diagrams-lib >= 0.6, - process >= 1.1, - graphviz >= 2999.16 - - hs-source-dirs: - src - - ghc-options: - -O --- -O2 - -funbox-strict-fields - -fcontext-stack=100 - --- -ddump-to-file --- --- -ddump-simpl --- -ddump-simpl-stats --- -dsuppress-module-prefixes --- -dsuppress-uniques --- -dsuppress-idinfo --- -dsuppress-coercions --- -dsuppress-type-applications --- -ddump-rule-firings --- -ddump-rule-rewrites --- -ddump-rules --- -dppr-debug - --- -ddump-cmm - - Exposed-modules: - - HLearn.History --- HLearn.History.DisplayMethods - HLearn.History.Timing - - HLearn.Data.Graph - HLearn.Data.Image - HLearn.Data.LoadData - HLearn.Data.SpaceTree - HLearn.Data.SpaceTree.CoverTree - HLearn.Data.SpaceTree.Algorithms.NearestNeighbor --- HLearn.Data.SpaceTree.Algorithms.RangeSearch - HLearn.Data.UnsafeVector - --- HLearn.Evaluation.CrossValidation --- --- HLearn.Models.Classifiers --- HLearn.Models.Classifiers.Common --- HLearn.Models.Classifiers.LinearClassifier --- -- HLearn.Models.Classifiers.KernelizedLinearClassifier --- HLearn.Models.Classifiers.NearestNeighbor --- HLearn.Models.Classifiers.NaiveNN --- HLearn.Models.Classifiers.Perceptron --- HLearn.Models.Classifiers.Centroid - --- HLearn.Metrics.Mahalanobis --- HLearn.Metrics.Mahalanobis.ITML --- HLearn.Metrics.Mahalanobis.Lego --- HLearn.Metrics.Mahalanobis.LegoPaper --- HLearn.Metrics.Mahalanobis.Mega --- HLearn.Metrics.Mahalanobis.Normal --- - HLearn.Models.Distributions - --- HLearn.Optimization.Amoeba --- HLearn.Optimization.Conic --- HLearn.Optimization.StepSize --- HLearn.Optimization.StochasticGradientDescent - HLearn.Optimization.Multivariate - HLearn.Optimization.Univariate - --- HLearn.Optimization.StepSize.Linear --- HLearn.Optimization.StepSize.Const --- HLearn.Optimization.StepSize.AlmeidaLanglois - --- HLearn.NPHard.Scheduling --- HLearn.NPHard.BinPacking - - Other-modules: - - Extensions: - FlexibleInstances - FlexibleContexts - MultiParamTypeClasses - FunctionalDependencies - UndecidableInstances - ScopedTypeVariables - BangPatterns - TypeOperators - GeneralizedNewtypeDeriving - TypeFamilies - StandaloneDeriving - GADTs - KindSignatures - ConstraintKinds - DeriveDataTypeable - RankNTypes - ImpredicativeTypes - - DataKinds - PolyKinds - AutoDeriveTypeable - TemplateHaskell - QuasiQuotes - RebindableSyntax - NoImplicitPrelude - UnboxedTuples - MagicHash - PolymorphicComponents - ExplicitNamespaces - EmptyDataDecls - --- executable hlearn-linear --- main-is: --- hlearn-linear.hs --- --- hs-source-dirs: --- src/exec --- --- ghc-options: --- -O --- -funbox-strict-fields --- --- build-depends: --- --- base >= 4.7, --- HLearn >= 2.0, --- subhask >= 0.1, --- --- vector >= 0.10.11, --- hmatrix >= 0.15.2.2, --- containers >= 0.5.5.1, --- --- cmdargs >= 0.10.7, --- cassava >= 0.4.1, --- bytestring >= 0.10.4, --- deepseq >= 1.3.0.2, --- QuickCheck >= 2.7.5, --- parallel >= 3.2.0.4, --- time >= 1.4.2, --- --- vector-algorithms >= 0.6.0.1, --- MonadRandom >= 0.1.13, --- list-extras >= 0.4.1.3 --- --- c-sources: --- cbits/emd.c --- cbits/ct.c --- --- cc-options: --- -- optimization options --- -O3 --- -msse2 --- -- -mavx --- -ffast-math --- -fassociative-math --- -ftree-vectorize --- -- -march=native --- -mfpmath=sse --- --- -- opencv options --- -fPIC --- --- extra-libraries: --- opencv_core --- opencv_highgui --- opencv_imgproc --- opencv_imgcodecs - --- executable hlearn-embed --- main-is: --- hlearn-embed.hs --- --- hs-source-dirs: --- src/exec --- --- ghc-options: --- -threaded --- -rtsopts --- --- -O --- -funbox-strict-fields --- -fexcess-precision --- -fcontext-stack=100 --- --- -fllvm --- -optlo-O3 --- -optlo-enable-fp-mad --- -optlo-enable-no-infs-fp-math --- -optlo-enable-no-nans-fp-math --- -optlo-enable-unsafe-fp-math --- --- build-depends: --- --- base >=4.7, --- HLearn >=2.0, --- typeparams >=0.0.6, --- subhask >=0.0.1, --- --- MonadRandom >=0.1.13, --- mtl >=2.1.3 - --- executable hlearn-allknn --- main-is: --- hlearn-allknn.hs --- --- hs-source-dirs: --- src/exec --- --- ghc-options: --- -- -Wall --- -threaded --- -rtsopts --- -- -with-rtsopts=-N --- --- -O --- -- -O2 --- -funbox-strict-fields --- -fexcess-precision --- -fcontext-stack=100 --- --- -fllvm --- -optlo-O3 --- -optlo-enable-fp-mad --- -optlo-enable-no-infs-fp-math --- -optlo-enable-no-nans-fp-math --- -optlo-enable-unsafe-fp-math --- --- -ddump-to-file --- --- -- -ddump-rule-firings --- -- -ddump-rule-rewrites --- -- -ddump-rules --- -- -ddump-simpl --- -- -ddump-simpl-stats --- -- -dppr-debug --- -- -dsuppress-module-prefixes --- -- -dsuppress-uniques --- -- -dsuppress-idinfo --- -- -dsuppress-coercions --- -- -dsuppress-type-applications --- --- -- -ddump-cmm --- --- build-depends: --- --- base >=4.7, --- HLearn >=2.0, --- ConstraintKinds >=1.3, --- -- simd >=0.1, --- typeparams >=0.0.6, --- subhask >=0.0.1, --- --- directory >= 1.2, --- filepath >= 1.3.0.2, --- deepseq >=1.3, --- cassava >=0.3, --- containers >=0.5, --- unordered-containers >=0.2.4, --- vector >=0.10, --- bytestring >=0.10, --- tagged >=0.7, --- reflection >=1.3, --- time >=1.4, --- cmdargs >=0.10, --- QuickCheck >=2.6, --- parallel >=3.2, --- MonadRandom >=0.1, --- vector-algorithms >= 0.5, --- strict >=0.3, --- vector-th-unbox >= 0.2, --- primitive >= 0.5, --- deepseq >= 1.3, --- -- hmatrix == 0.15.2.2 --- hmatrix >= 0.16.0.5 --- diff --git a/install/README.md b/install/README.md new file mode 100644 index 0000000..e5cf4af --- /dev/null +++ b/install/README.md @@ -0,0 +1,8 @@ +I use these scripts to install HLearn on AWS Ubuntu Precise machines (e.g. using [this AMI](https://us-west-2.console.aws.amazon.com/ec2/home?region=us-west-2#LaunchInstanceWizard:ami=ami-17b0b127)) and for testing with Travis CI (which also uses Ubuntu Precise). +The `ubuntu-precise.sh` script installs just the componenets needed for HLearn, whereas the `ubuntu-precise-extras.sh` script installs all the related packages needed to run the benchmarks and tests. +It should be pretty straightforward to adapt these scripts to whatever your preferred OS is. + +To install on an Ubuntu Precise machine, pipe the script to a shell instance using: +``` +curl https://raw.githubusercontent.com/mikeizbicki/HLearn/travis/install/ubuntu-precise.sh | sh +``` diff --git a/install/ubuntu-precise-extras.sh b/install/ubuntu-precise-extras.sh new file mode 100644 index 0000000..3a47c9c --- /dev/null +++ b/install/ubuntu-precise-extras.sh @@ -0,0 +1,85 @@ +#!/bin/bash +cd +set -e + +# installing julia +#sudo add-apt-repository -y ppa:staticfloat/juliareleases +#sudo add-apt-repository -y ppa:staticfloat/julia-deps +##sudo add-apt-repository -y 'deb http://ppa.launchpad.net/staticfloat/juliareleases/ubuntu precise main' +#sudo apt-get update +#sudo apt-get install -qq julia + +# the julia package is not compatible with python's numpy due to conflicting lapack dependencies +wget https://github.com/JuliaLang/julia/releases/download/v0.3.10/julia-0.3.10_c8ceeefcc1.tar.gz +tar -xf julia-0.3.10_c8ceeefcc1.tar.gz +cd julia +make -j5 +sudo make install +export PATH="/home/ubuntu/julia/julia-0.3.10/bin:$PATH" +cd + +echo "Pkg.add(\"KDTrees\")" > setup.julia +julia setup.julia + +# install R +sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 +sudo add-apt-repository -y 'deb http://streaming.stat.iastate.edu/CRAN/bin/linux/ubuntu precise/' +sudo apt-get update +sudo apt-get install -qq r-base r-base-dev + +echo "install.packages(\"FNN\",repos=\"http://cran.us.r-project.org\")" > setup.R +sudo Rscript setup.R + +# install scikit +sudo apt-get install -qq subversion +sudo apt-get install -qq python-dev python-pip +sudo pip install numpy +sudo pip install scipy +sudo pip install sklearn + +# install flann +sudo apt-get install -qq cmake unzip make +wget http://www.cs.ubc.ca/research/flann/uploads/FLANN/flann-1.8.4-src.zip +unzip flann-1.8.4-src.zip +cd flann-1.8.4-src +mkdir build +cd build +cmake .. +make -j5 +sudo make install +cd + +# install mlpack +sudo apt-get install -qq libxml2-dev +sudo apt-get install -qq libboost-all-dev libboost-program-options-dev libboost-test-dev libboost-random-dev +sudo apt-get install -qq doxygen + +wget http://downloads.sourceforge.net/project/boost/boost/1.58.0/boost_1_58_0.tar.gz +tar -xf boost_1_58_0.tar.gz +cd boost_1_58_0 +./booststrap.sh +./b2 +sudo ./b2 install +cd + +wget http://sourceforge.net/projects/arma/files/armadillo-5.200.2.tar.gz +tar -xf armadillo-5.200.2.tar.gz +cd armadillo-5.200.2 +cmake .. +make -j5 +sudo make install +cd + +http://mlpack.org/files/mlpack-1.0.12.tar.gz +tar -xf mlpack-1.0.12.tar.gz +cd mlpack-1.0.12 +mkdir build +cd build +cmake .. +make -j5 +sudo make install +cd + +# weka +sudo apt-get install -qq openjdk-7-jdk +sudo apt-get install -qq weka diff --git a/install/ubuntu-precise.sh b/install/ubuntu-precise.sh new file mode 100755 index 0000000..a720464 --- /dev/null +++ b/install/ubuntu-precise.sh @@ -0,0 +1,66 @@ +#!/bin/bash + +set -e + +# travis automatically sets these variables; +# this is a hacked test to check if we're running on travis, +# if not, we need to set them manually +if [ -z "$CABALVER" ]; then + + # set environment variables + CABALVER=1.22 + GHCVER=7.10.2 + LLVMVER=3.5 + + # install git + sudo apt-get update -qq + sudo apt-get install -qq git + + git config --global user.name 'InstallScript' + git config --global user.email 'installscript@izbicki.me' + + # get hlearn code + git clone https://github.com/mikeizbicki/hlearn + cd hlearn + git submodule update --init --recursive subhask +fi + +# install numeric deps +sudo apt-get install -qq libatlas3gf-base +sudo apt-get install -qq libblas-dev liblapack-dev + +# update g++ version (required for llvm) +sudo add-apt-repository -y ppa:ubuntu-toolchain-r/test +sudo apt-get update -qq +sudo apt-get install -qq g++-4.8 +sudo update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-4.8 90 + +# update llvm +wget -O - http://llvm.org/apt/llvm-snapshot.gpg.key|sudo apt-key add - +sudo add-apt-repository "deb http://llvm.org/apt/precise/ llvm-toolchain-precise main" +sudo add-apt-repository "deb http://llvm.org/apt/precise/ llvm-toolchain-precise-$LLVMVER main" +sudo apt-get update +sudo apt-get install -y llvm-$LLVMVER llvm-$LLVMVER-dev +sudo ln -s /usr/bin/opt-$LLVMVER /usr/bin/opt +sudo ln -s /usr/bin/llc-$LLVMVER /usr/bin/llc +export PATH="/usr/bin:$PATH" + +# install haskell bits +sudo add-apt-repository -y ppa:hvr/ghc +sudo apt-get update -qq +sudo apt-get install -qq cabal-install-$CABALVER ghc-$GHCVER +export PATH="~/.cabal/bin:/opt/ghc/$GHCVER/bin:/opt/cabal/$CABALVER/bin:$PATH" + +# install the local version of subhask +cd subhask +cabal update +cabal install -j4 +cd .. + +# install hlearn +cabal --version +echo "$(ghc --version) [$(ghc --print-project-git-commit-id 2> /dev/null || echo '?')]" +cabal install -j4 --only-dependencies --enable-tests --enable-benchmarks +cabal install || cabal install --disable-optimization + +echo "Optimization: False" >> /home/travis/.cabal/config diff --git a/scripts/allknn-comparelibs/runtest.sh b/scripts/allknn-comparelibs/runtest.sh deleted file mode 100755 index 73a5420..0000000 --- a/scripts/allknn-comparelibs/runtest.sh +++ /dev/null @@ -1,47 +0,0 @@ -#!/bin/bash - - -curdir=$(pwd) -tmpdir="$(basename $1)-$(date +%Y-%m-%d--%H-%M-%S)" -cd $(dirname $0) -mkdir "$tmpdir" -cd "$tmpdir" - -INPUT="$curdir/$1" - -TIME="/usr/bin/time -f %E " - - #"$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.2.csv --distances-file=distances_hlearn.2.csv +RTS -K1000M -N3" \ - #"$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.3.csv --distances-file=distances_hlearn.3.csv +RTS -K1000M -N2" \ - #"$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.4.csv --distances-file=distances_hlearn.4.csv +RTS -K1000M -N1" \ -for cmd in \ - "$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.1.csv --distances-file=distances_hlearn.1.csv +RTS -K1000M -N1" \ - "$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.1.csv --distances-file=distances_hlearn.1.csv --train-method=trainmonoid +RTS -K1000M -N1" \ - "$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.1.csv --distances-file=distances_hlearn.1.csv --train-method=traininsert_sort +RTS -K1000M -N1" \ - "$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.1.csv --distances-file=distances_hlearn.1.csv --train-method=traininsert_parent +RTS -K1000M -N1" \ - "$curdir/hlearn-allknn --varshift -r $INPUT -k 1 -n neighbors_hlearn.1.csv --distances-file=distances_hlearn.1.csv --train-method=traininsert_ancestor +RTS -K1000M -N1" \ - #"allknn -r $INPUT -k 1 -n neighbors_mlpack.1.csv -d distances_mlpack.1.csv -S " \ - #"allknn -r $INPUT -k 1 -n neighbors_mlpack.2.csv -d distances_mlpack.2.csv " \ - #"allknn -r $INPUT -k 1 -n neighbors_mlpack.3.csv -d distances_mlpack.3.csv -c S" \ - #"allknn -r $INPUT -k 1 -n neighbors_mlpack.4.csv -d distances_mlpack.4.csv -c " \ - #"../allknn.R $INPUT 1 cover_tree" \ - #"../allknn.R $INPUT 1 kd_tree " - #"allknn -r $INPUT -k 1 -v -N -n tmp/neighbors_mlpack.csv -d tmp/distances_mlpack.csv " -do - echo "$cmd" - sanitizedcmd=$(tr ' /' '__' <<< "$cmd") - runtime=$($TIME $cmd 2>&1 >> "stdout.$sanitizedcmd") - echo "$runtime" >> "stderr.$sanitizedcmd" - runtime=$(tail -1 <<< "$runtime") - runseconds=$(../time2sec.hs $runtime) - - echo runtime=$runtime - echo runseconds=$runseconds - - echo "$sanitizedcmd $runtime $runseconds" >> results -done - -echo "--------------------------------------------------------------------------------" -echo " results:" -echo "--------------------------------------------------------------------------------" -cat results diff --git a/scripts/allknn-treetypes/runtest.sh b/scripts/allknn-treetypes/runtest.sh deleted file mode 100755 index b2ec476..0000000 --- a/scripts/allknn-treetypes/runtest.sh +++ /dev/null @@ -1,57 +0,0 @@ -#!/bin/bash - -if [ -z "$1" ]; then - echo "usage: $0 dataset [optionalparams]" - echo "the dataset param is required; all other params get passed directly to hlearn-allknn" - exit -1 -fi - -distanceName="l2_" -#distanceName="emd_" - -K=1 - -curdir=$(pwd) -tmpdir="$(basename $1)-$(date +%Y-%m-%d--%H-%M-%S)" -cd $(dirname $0) -mkdir "$tmpdir" -cd "$tmpdir" - -echo "--------------------------------------------------------------------------------" -echo "tempdir=$0/$tmpdir" -echo "--------------------------------------------------------------------------------" - -hlearn_neighbors="./neighbors_hlearn.csv" -hlearn_distances="./distances_hlearn.csv" - -#verbose="--verbose" -optimization="--varshift" - -#methodlist="trainmonoid traininsert_nosort traininsert_sort traininsert_parent traininsert_ancestor traininsert_orig" -methodlist="traininsert_orig" - -touch results - -for method in $methodlist; do - echo -e "---\n$method\n---\n" - "hlearn-allknn" --train-method="$method" -k $K -r "$curdir/$1" $2 $3 $4 $5 $optimization $verbose +RTS -K1000M -p -s 2>&1 | tee out.$method - - mv ./hlearn-allknn.prof ./prof.$method - - - searchdistance=$(sed -n -e '/^ findEpsilon/,/^ [^ ]/ p' prof.$method | grep "$distanceName" | awk '{ print $4 }' | awk '{s+=$1} END {print s}') - traindistance=$(sed -n -e '/^ train/,/^ [^ ]/ p' prof.$method | grep "$distanceName" | awk '{ print $4 }' | awk '{s+=$1} END {print s}') - echo "searchdistance=$searchdistance" - echo "traindistance=$traindistance" - totaldistance=$(($searchdistance+$traindistance)) - - echo "$method $traindistance $searchdistance $totaldistance" - echo "$method $traindistance $searchdistance $totaldistance" >> results -done - -echo -echo "--------------------------------------------------------------------------------" -echo "tempdir=$0/$tmpdir" -echo -cat results -echo "--------------------------------------------------------------------------------" diff --git a/scripts/allknn-verify/runtest.sh b/scripts/allknn-verify/runtest.sh deleted file mode 100755 index 63f4752..0000000 --- a/scripts/allknn-verify/runtest.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash - -K=1 - -#tmpdir=$(mktemp --tmpdir -d) -tmpdir="." -echo "--------------------------------------------------------------------------------" -echo "tempdir=$tmpdir" -echo "--------------------------------------------------------------------------------" -hlearn_neighbors="$tmpdir/neighbors_hlearn.csv" -hlearn_distances="$tmpdir/distances_hlearn.csv" -mlpack_neighbors="$tmpdir/neighbors_mlpack.csv" -mlpack_distances="$tmpdir/distances_mlpack.csv" - -#verbose="--verbose" -optimization="--varshift" -#method="--train-method=traininsert_ancestor" -#method="--train-method=traininsert_orig" - -time hlearn-allknn -k $K -r $@ $optimization $method $verbose -n "$hlearn_neighbors" --distances-file="$hlearn_distances" +RTS -K1000M -N1 -#time hlearn-allknn -k $K -r $1 $optimization $method $verbose -n "$hlearn_neighbors" --distances-file="$hlearn_distances" +RTS -K1000M -N - -time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -S -#time allknn -r $1 -n "$mlpack_neighbors" -d "$mlpack_distances" -k $K -v -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -c -S -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -c - -#time allknn -r $1 -n $mlpack_neighbors -d $mlpack_distances -k $K -v -N - -echo "-------------------------------------" -#head neighbors_hlearn.csv -#echo "---" -#head neighbors_mlpack.csv -echo "num differences: " `diff $hlearn_neighbors $mlpack_neighbors | wc -l` " / " `cat $1 | wc -l` -diff $hlearn_neighbors $mlpack_neighbors | head diff --git a/scripts/knn-profiling-datasets/runtest.sh b/scripts/knn-profiling-datasets/runtest.sh deleted file mode 100755 index 740af6c..0000000 --- a/scripts/knn-profiling-datasets/runtest.sh +++ /dev/null @@ -1,38 +0,0 @@ -#!/bin/bash - -# $1 = the dataset to test -# -# ${@:2} = all other arguments, which get passed to $knn - -knn="~/proj/hlearn/dist/build/hlearn-allknn/hlearn-allknn" - -tmpdir=$(mktemp -d) -curdir=$(pwd) - -dataset=$(basename "$1") - -echo "hlearn-allknn -r \"$1\" ${@:2}" - -cd "$tmpdir" -#$knn -r "$1" ${@:2} --verbose +RTS -p > stdout 2> stderr -/home/user/proj/hlearn/dist/build/hlearn-allknn/hlearn-allknn -r "$1" ${@:2} --verbose +RTS -p > stdout 2> stderr -cd "$curdir" - -params="params=$(tr ' ' '_' <<< "${@:2}")" -if [ ! -d "$dataset" ]; then - mkdir "$dataset" -fi - -if [ -d "$dataset/$params" ]; then - mv "$dataset/$params" "$dataset/$params.old.$(date +%Y-%m-%d--%H-%M-%S)" -fi -mv "$tmpdir" "./${dataset}/${params}" - -#if [ ! -d "$params" ]; then - #mkdir "$params" -#fi -# -#if [ -d "$params/$dataset" ]; then - #mv "$params/$dataset" "$params/$dataset.old.$(date +%Y-%m-%d--%H-%M-%S)" -#fi -#mv "$tmpdir" "./${params}/${dataset}" diff --git a/scripts/knn-profiling-datasets/runtests.sh b/scripts/knn-profiling-datasets/runtests.sh deleted file mode 100755 index 2593ee4..0000000 --- a/scripts/knn-profiling-datasets/runtests.sh +++ /dev/null @@ -1,35 +0,0 @@ -#!/bin/bash - -datasetdir="/home/user/proj/hlearn/datasets" - -datasets_random=" - random/dataset-10000x20.csv - " -datasets_docword=" - uci/docword.kos.txt - uci/docword.nips.txt - uci/docword.enron.txt - uci/docword.nytimes.txt - uci/vocab.kos.txt - uci/vocab.nips.txt - uci/vocab.enron.txt - uci/vocab.nytimes.txt - " - -datasets=" - random/dataset-10000x2.csv - " - -echo "running tests in parallel" - -for dataset in $datasets; do - for sigma in 1 10 100 1000 10000 100000 1000000 10000000 100000000 1000000000; do - echo "--sigma2-rbf=$sigma" - done | parallel -P 2 ./runtest.sh "$datasetdir/$dataset" {} -done - -#for exprat in 1.1 1.2 1.25 1.3 1.35 1.4 1.5 1.6 2.0 4.0; do - #echo "--expansionratio=$exprat" -#for exprat in 1 2 3 4 5 6 7 8 9 10; do - #echo "--searchepsilon=$exprat" -#done | parallel -P 2 ./runtest.sh "$datasetdir/uci/vocab.enron.txt" {} $@ diff --git a/scripts/knn-profiling-random/parsedata.sh b/scripts/knn-profiling-random/parsedata.sh deleted file mode 100755 index 4c25988..0000000 --- a/scripts/knn-profiling-random/parsedata.sh +++ /dev/null @@ -1,248 +0,0 @@ -#!/bin/bash - -if [ -z "$1" ]; then - echo "ERROR: $1 must be a directory" >&2 - exit 1 -fi - -############################################################################### -# one run only - -if [ -z "$2" ]; then - - cd "$1" - - datasets=$( ( - for i in stdout-*; do - cut -d'-' -f 3 <<< "$i" - done - ) | uniq ) - -# FIXME: pretty sure this is not handling floating point results properly - function average() { - awk 'NR == 1 { max=$1; min=$1; sum=0 } - { if ($1>max) max=$1; if ($1max) max=$1; if ($1 datafile-ave - for dataset in $datasets; do - echo "dataset=$dataset" - numdp=$(cut -d'x' -f1 <<< $dataset) - numdim=$(cut -d'x' -f2 <<< $dataset) - printf "$numdp $numdim " >> datafile-ave - - functionL="stMaxDepth stNumSingletons stNumLeaves stMaxLeaves stMaxChildren ctMovableNodes ctBetterMovableNodes" - for function in $functionL; do - extractFromStdout "stdout-dataset-${dataset}-*" $function >> datafile-ave - done - - ( for file in prof-dataset-${dataset}-*; do - extractFromProfiler $file l2_isFartherThanWithDistance - done - ) | average >> datafile-ave - - echo >> datafile-ave - done - sort -k2 -n datafile-ave > datafile-ave-sorted - -####################################### -# datafile-all - - echo "" > datafile-all - for file in stdout-*; do - dataset=$(cut -d'-' -f 3 <<< "$file") - extension="$(cut -d'-' -f 3 <<< $file)-$(cut -d'-' -f4 <<< $file)" - echo "extension=$extension" - numdp=$(cut -d'x' -f1 <<< $dataset) - numdim=$(cut -d'x' -f2 <<< $dataset) - printf "$numdp $numdim " >> datafile-all - - functionL="stMaxDepth stNumSingletons stNumLeaves stMaxLeaves stMaxChildren ctMovableNodes ctBetterMovableNodes" - for function in $functionL; do - extractFromStdout "stdout-dataset-$extension" $function >> datafile-all - done - - extractFromProfiler "prof-dataset-$extension" l2_isFartherThanWithDistance >> datafile-all - - echo >> datafile-all - done - -####################################### -# gnuplot - echo " - set terminal postscript enhanced - - unset key - set y2label 'number of distance comparisons' - set y2tics - set y2range [0:] - set xtics nomirror - set ytics nomirror - set yrange [0:] - f(x) = a*x + b - -################### - - set output 'graphs-dim.ps' - set xlabel 'dimension' - - set ylabel 'maximum fanout of a node' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 2:7 lt 1 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'nodes that can be moved to a new parent (dashed => new parent is better)' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 2:8 lt 1 lw 4 lc rgb '#ff0000' w lines,\ - 'datafile-ave-sorted' u 2:9 lt 2 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'maximum tree depth' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 2:3 lt 1 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'nodes with exactly one child' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 2:4 lt 1 lw 4 lc rgb '#ff0000' w lines - - unset y2tics - unset y2label - - set xlabel 'number of dimensions' - set ylabel 'number of distance comparisons' - fit f(x) 'datafile-all' u 2:10 via a, b - plot 'datafile-all' u 2:10 w points lt 1 lc rgb '#ff0000',\ - f(x) lt 1 lc rgb '#ff0000' - - set xlabel 'maximum fanout' - fit f(x) 'datafile-all' u 7:10 via a, b - plot 'datafile-all' u 7:10 w points lt 1 lc rgb '#ff0000',\ - f(x) lt 1 lc rgb '#ff0000' - - set xlabel 'maximum depth' - fit f(x) 'datafile-all' u 3:10 via a, b - plot 'datafile-all' u 3:10 w points lt 1 lc rgb '#ff0000',\ - f(x) lt 1 lc rgb '#ff0000' - -################### - - set y2label 'number of distance comparisons' - set y2tics - set y2range [0:] - - set output 'graphs-numdp.ps' - set xlabel 'number of datapoints' - - set ylabel 'maximum fanout of a node' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 1:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 1:7 lt 1 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'nodes that can be moved to a new parent (dashed => new parent is better)' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 1:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 1:8 lt 1 lw 4 lc rgb '#ff0000' w lines,\ - 'datafile-ave-sorted' u 1:9 lt 2 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'maximum tree depth' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 1:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 1:3 lt 1 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'nodes with exactly one child' tc rgb '#ff0000' - plot 'datafile-ave-sorted' u 1:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - 'datafile-ave-sorted' u 1:4 lt 1 lw 4 lc rgb '#ff0000' w lines - -######### - - unset y2tics - unset y2label - - set xlabel 'number of datapoints' - set ylabel 'number of distance comparisons' - fit f(x) 'datafile-all' u 1:10 via a, b - plot 'datafile-all' u 1:10 w points lt 1 lc rgb '#ff0000',\ - f(x) lt 1 lc rgb '#ff0000' - - set xlabel 'maximum fanout' - fit f(x) 'datafile-all' u 7:10 via a, b - plot 'datafile-all' u 7:10 w points lt 1 lc rgb '#ff0000',\ - f(x) lt 1 lc rgb '#ff0000' - - set xlabel 'maximum depth' - fit f(x) 'datafile-all' u 3:10 via a, b - plot 'datafile-all' u 3:10 w points lt 1 lc rgb '#ff0000',\ - f(x) lt 1 lc rgb '#ff0000' - - - " > mkplots.gnu - - gnuplot mkplots.gnu -fi - -############################################################################### -# two runs - -if [ ! -z "$2" ]; then - #./parsedata.sh "$1" - #./parsedata.sh "$2" - - echo " - set terminal postscript enhanced - - unset key - set y2label 'number of distance comparisons' - set y2tics - set y2range [0:] - set xrange [0:12] - set xtics nomirror - set ytics nomirror - set yrange [0:] - f(x) = a*x + b - - ################### - - set output 'graphs-dim.ps' - set xlabel 'dimension' - - set ylabel 'maximum fanout of a node' tc rgb '#ff0000' - plot '$1/datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$2/datafile-ave-sorted' u 2:10 lt 2 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$1/datafile-ave-sorted' u 2:7 lt 1 lw 4 lc rgb '#ff0000' w lines,\ - '$2/datafile-ave-sorted' u 2:7 lt 2 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'nodes that can be moved to a new parent (pink => new parent is better)' tc rgb '#ff0000' - plot '$1/datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$2/datafile-ave-sorted' u 2:10 lt 2 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$1/datafile-ave-sorted' u 2:8 lt 1 lw 4 lc rgb '#ff0000' w lines,\ - '$1/datafile-ave-sorted' u 2:9 lt 1 lw 4 lc rgb '#ff7777' w lines - - set ylabel 'maximum tree depth' tc rgb '#ff0000' - plot '$1/datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$2/datafile-ave-sorted' u 2:10 lt 2 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$1/datafile-ave-sorted' u 2:3 lt 1 lw 4 lc rgb '#ff0000' w lines,\ - '$2/datafile-ave-sorted' u 2:3 lt 2 lw 4 lc rgb '#ff0000' w lines - - set ylabel 'nodes with exactly one child' tc rgb '#ff0000' - plot '$1/datafile-ave-sorted' u 2:10 lt 1 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$2/datafile-ave-sorted' u 2:10 lt 2 lw 4 lc rgb '#000000' w lines axes x1y2,\ - '$1/datafile-ave-sorted' u 2:4 lt 1 lw 4 lc rgb '#ff0000' w lines,\ - '$2/datafile-ave-sorted' u 2:4 lt 2 lw 4 lc rgb '#ff0000' w lines - - " > mkplots.gnu - gnuplot mkplots.gnu -fi diff --git a/scripts/knn-profiling-random/runtests.sh b/scripts/knn-profiling-random/runtests.sh deleted file mode 100755 index 0f27383..0000000 --- a/scripts/knn-profiling-random/runtests.sh +++ /dev/null @@ -1,23 +0,0 @@ -#!/bin/bash - -hlearndir="../.." -#numdpL="10000 15000 20000 25000 30000 35000 40000 45000 50000 60000 70000 80000 90000 100000" -numdpL="10000" -#dimL="10" -dimL="1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20" - -resdir="results-$(date +%Y-%m-%d--%H-%M-%S)" -echo "resdir=$resdir" -mkdir "$resdir" - -for numdp in $numdpL; do - for dim in $dimL; do - for test in a b c d e f g h i j; do - dataset="dataset-${numdp}x$dim-$test.csv" - echo "running test $dataset" - $hlearndir/datasets/random/DataGenerator $numdp $dim > $hlearndir/datasets/random/$dataset - $hlearndir/dist/build/hlearn-allknn/hlearn-allknn -r $hlearndir/datasets/random/$dataset --verbose +RTS -p > $resdir/stdout-$dataset 2> $resdir/stderr-$dataset - mv hlearn-allknn.prof "$resdir/prof-$dataset" - done - done -done diff --git a/scripts/linear/linear.sh b/scripts/linear/linear.sh deleted file mode 100755 index c1adfe5..0000000 --- a/scripts/linear/linear.sh +++ /dev/null @@ -1,115 +0,0 @@ - -test=$1 -data=$2 - -case "$1" in - mixture ) echo "testing MonoidMixture";; - numdp ) echo "testing number of datapoints";; - - * ) echo "test [$1] not supported";; -esac - -case "$data" in - wine ) col=0; file="datasets/uci/wine.csv";; - wdbc ) col=0; file="datasets/uci/wdbc-mod2.csv";; - wpbc ) col=0; file="datasets/uci/wpbc-mod2.csv";; - sonar ) col=60; file="datasets/uci/sonar.all-data";; - ion ) col=0; file="datasets/uci/ionosphere.csv";; - pima ) col=8; file="datasets/uci/pima-indians-diabetes.csv";; - magic ) col=10; file="datasets/uci/magic04.data";; - haberman ) col=3; file="datasets/uci/haberman.data";; - - optdigits ) col=64; file="datasets/uci/optdigits.train.data";; - pendigits ) col=16; file="datasets/uci/pendigits.train.data";; - winered ) col=11; file="datasets/uci/winequality-red.csv";; - winewhite ) col=11; file="datasets/uci/winequality-white.csv";; - - *) echo "dataset [$data] is not recognized... exiting";; -esac - - -# create param line arguments -array=( $@ ) -len=${#array[@]} -cmdargs="--paramseed=0 --cvfolds=2 --cvreps=10 ${array[@]:2:$len}" -echo "Command line args: $cmdargs" - -# create tmp directories if they don't exist -[ ! -d "tmp" ] && mkdir "tmp" -[ ! -d "tmp/$test" ] && mkdir "tmp/$test" - -# rename old output files; delete very old files -output="tmp/$test/$data $cmdargs" -[ -e "$output" ] && mv "$output" "$output.old" - -# run tests -case "$test" in - - numdp ) - output1="$output --monoidsplits=2 --monoidtype=MappendAverage" - output2="$output --monoidsplits=2 --monoidtype=MappendTaylor" - output3="$output --monoidsplits=2 --monoidtype=MappendUpperBound" - output4="$output --monoidsplits=1" - [ -e "$output1" ] && mv "$output1" "$output1.old" - [ -e "$output2" ] && mv "$output2" "$output2.old" - [ -e "$output3" ] && mv "$output3" "$output3.old" - - for i in {10..200..10} {200..1000..50}; do - echo test number $i - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --maxdp=$i \ - --monoidtype="MappendAverage" \ - --monoidsplits=2 \ - $cmdargs \ - >> "$output1" - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --maxdp=$i \ - --monoidtype="MappendTaylor" \ - --monoidsplits=2 \ - $cmdargs \ - >> "$output2" - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --maxdp=$i \ - --monoidtype="MappendUpperBound" \ - --monoidsplits=2 \ - $cmdargs \ - >> "$output3" - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --maxdp=$i \ - --monoidsplits=1 \ - $cmdargs \ - >> "$output4" - done - ;; - - mixture ) - output1="$output --monoidtype=MixtureAveTaylor" - output2="$output --monoidtype=MixtureAveUpper" - output3="$output --monoidtype=MixtureUpperTaylor" - [ -e "$output1" ] && mv "$output1" "$output1.old" - [ -e "$output2" ] && mv "$output2" "$output2.old" - [ -e "$output3" ] && mv "$output3" "$output3.old" - for i in -50 -40 -30 -20 -10 -8 -6 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 16 18 20 30 40 50; do - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --monoidtype="MixtureAveTaylor ($i % 10)" \ - $cmdargs \ - >> "$output1" - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --monoidtype="MixtureAveUpper ($i % 10)" \ - $cmdargs \ - >> "$output2" - nice -n 19 ./dist/build/hlearn-linear/hlearn-linear \ - -d "$file" -l "$col" \ - --monoidtype="MixtureUpperTaylor ($i % 10)" \ - $cmdargs \ - >> "$output3" - done - ;; - -esac diff --git a/src/HLearn/Classifiers/Linear.hs b/src/HLearn/Classifiers/Linear.hs new file mode 100644 index 0000000..d6bee0d --- /dev/null +++ b/src/HLearn/Classifiers/Linear.hs @@ -0,0 +1,182 @@ +module HLearn.Classifiers.Linear + where + +import SubHask +import SubHask.Category.Trans.Derivative +import SubHask.Compatibility.Containers + +import HLearn.History +import HLearn.Optimization.Univariate +import HLearn.Optimization.Multivariate + +import qualified Prelude as P +import qualified Data.List as L +import Debug.Trace + +-------------------------------------------------------------------------------- + +-- | The data type to represent arbitrary . +-- Important examples include least squares regression, logistic regression, and support vector machines. +-- In statistics, these models are called . +data GLM x y = GLM + { weights :: Map' y x + , numdp :: Scalar x + } + +deriving instance (Show x, Show y, Show (Scalar x)) => Show (GLM x y) + +type instance Scalar (GLM x y) = Scalar x + +-------------------------------------------------------------------------------- + +-- type Foldable' xs x = (Foldable xs, Elem xs~x, Scalar xs~Int) + +type IsFoldable xs x = {-forall xs.-} (Foldable xs, Elem xs~x, Scalar xs~Int) + +{-# INLINEABLE trainLogisticRegression #-} +trainLogisticRegression :: + ( Ord y + , Show y + , Hilbert x + , BoundedField (Scalar x) + ) => Scalar x -- ^ regularization parameter + -> IsFoldable xys (Labeled' x y) => xys -- ^ dataset + -> ( cxt (LineBracket (Scalar x)) + , cxt (Iterator_cgd x) + , cxt (Iterator_cgd (Scalar x)) + , cxt (Iterator_brent (Scalar x)) + , cxt (Backtracking x) + , cxt (Map' y x) + , cxt Int + ) => History cxt (GLM x y) +trainLogisticRegression lambda xs = trainGLM_ + ( fminunc_cgd_ +-- hestenesStiefel +-- polakRibiere +-- steepestDescent + fletcherReeves + (lineSearch_brent (stop_brent 1e-6 || maxIterations 50 || noProgress || fx1grows)) +-- (backtracking (strongCurvature 1e-10)) +-- (backtracking fx1grows) + (mulTolerance 1e-9 || maxIterations 50 || noProgress {-|| fx1grows-}) + ) + loss_logistic + lambda + (toList xs) + +{-# INLINEABLE trainGLM_ #-} +trainGLM_ :: forall xys x y cxt opt. + ( Ord y + , Show y + , Hilbert x + ) => Has_x1 opt x => (x -> C1 (x -> Scalar x) -> forall s. History_ cxt s (opt x)) -- ^ optimization method + -> (y -> Labeled' x y -> C2 (x -> Scalar x)) -- ^ loss function + -> Scalar x -- ^ regularization parameter + -> [Labeled' x y] -- ^ dataset + -> History cxt (GLM x y) +trainGLM_ optmethod loss lambda xs = do +-- let ys = fromList $ map yLabeled' $ toList xs :: Set y +-- ws <- fmap sum $ mapM go (toList ys) +-- return $ GLM +-- { weights = ws +-- , numdp = fromIntegral $ length xs +-- } + let Just (y0,ys) = uncons (fromList $ map yLabeled' $ toList xs :: Set y) + ws <- fmap sum $ mapM go (toList ys) + return $ GLM + { weights = insertAt y0 zero ws + , numdp = fromIntegral $ length xs + } + where + go :: Show y => y -> forall s. History_ cxt s (Map' y x) + go y0 = beginFunction ("trainGLM("++show y0++")") $ do + w <- fmap x1 $ optmethod zero (totalLoss y0) + return $ singletonAt y0 w + + totalLoss :: y -> C1 (x -> Scalar x) + totalLoss y0 = unsafeProveC1 f f' + where + g = foldMap (loss y0) xs + + f w = ( g $ w) + lambda*size w + f' w = (derivative g $ w) + lambda*.w + + +-------------------------------------------------------------------------------- +-- loss functions + +classify :: (Ord y, Hilbert x) => GLM x y -> x -> y +classify (GLM ws _) x + = fst + $ P.head + $ L.sortBy (\(_,wx1) (_,wx2) -> compare wx2 wx1) + $ toIxList + $ imap (\_ w -> w<>x) ws + +validate :: + ( Ord y + , Hilbert x + , cat <: (->) + ) => (y -> Labeled' x y -> (x `cat` Scalar x)) -> [Labeled' x y] -> GLM x y -> Scalar x +validate loss xs model@(GLM ws _) = (sum $ map go xs) -- /(fromIntegral $ length xs) + where + go xy@(Labeled' x y) = loss y' xy $ ws!y' + where + y' = classify model x + +{-# INLINEABLE loss_01 #-} +loss_01 :: (HasScalar x, Eq y) => y -> Labeled' x y -> x -> Scalar x +loss_01 y0 (Labeled' x y) = indicator $ y0/=y + +{-# INLINEABLE loss_squared #-} +loss_squared :: (Hilbert x, Eq y) => y -> Labeled' x y -> C2 (x -> Scalar x) +loss_squared y0 (Labeled' x y) = unsafeProveC2 f f' f'' + where + labelscore = bool2num $ y0==y + + f w = 0.5 * (w<>x-labelscore)**2 + f' w = x.*(w<>x-labelscore) + f'' w = x> y -> Labeled' x y -> C2 (x -> Scalar x) +loss_logistic y0 (Labeled' x y) = unsafeProveC2 f f' f'' + where + labelscore = bool2num $ y0==y + + f w = logSumOfExp2 zero $ -labelscore*w<>x + f' w = -labelscore*(1-invlogit (labelscore*w<>x)) *. x + f'' w = x>x)) + +{-# INLINEABLE loss_hinge #-} +loss_hinge :: (Hilbert x, Eq y) => y -> Labeled' x y -> C2 (x -> Scalar x) +loss_hinge y0 (Labeled' x y) = unsafeProveC2 f f' f'' + where + labelscore = bool2num $ y0==y + + f w = max 0 $ 1 -labelscore*w<>x + f' w = if x<>w > 1 then zero else -labelscore*.x + f'' w = zero + +---------------------------------------- +-- helpers + +bool2num True = 1 +bool2num False = -1 + +invlogit x = 1 / (1 + exp (-x)) + +-- | calculates log . sum . map exp in a numerically stable way +logSumOfExp xs = m + log (sum [ exp $ x-m | x <- xs ] ) + where + m = maximum xs + +-- | calculates log $ exp x1 + exp x2 in a numerically stable way +logSumOfExp2 x1 x2 = big + log ( exp (small-big) + 1 ) + where + big = max x1 x2 + small = min x1 x2 + +-- logSumOfExp2 x1 x2 = m + log ( exp (x1-m) + exp (x2-m) ) +-- where +-- m = max x1 x2 diff --git a/src/HLearn/Data/Graph.hs b/src/HLearn/Data/Graph.hs index 70c46a2..5912dc9 100644 --- a/src/HLearn/Data/Graph.hs +++ b/src/HLearn/Data/Graph.hs @@ -139,8 +139,8 @@ distance' g1 g2 = distance (graph g1) (graph g2) data Graph_ = Graph_ { transitionMatrix :: !(Matrix Double) - , startVec :: !(Vector Double) - , stopVec :: !(Vector Double) + , startVec :: !(DynVector 0 Double) + , stopVec :: !(DynVector 0 Double) } deriving (Show) diff --git a/src/HLearn/Data/Image.hs b/src/HLearn/Data/Image.hs index dd5115b..21a3044 100644 --- a/src/HLearn/Data/Image.hs +++ b/src/HLearn/Data/Image.hs @@ -10,7 +10,6 @@ module HLearn.Data.Image where import SubHask -import SubHask.Compatibility.Vector import SubHask.TemplateHaskell.Deriving import qualified Data.Vector as V diff --git a/src/HLearn/Data/LoadData.hs b/src/HLearn/Data/LoadData.hs index deaea5c..ab62f40 100644 --- a/src/HLearn/Data/LoadData.hs +++ b/src/HLearn/Data/LoadData.hs @@ -1,58 +1,38 @@ +-- | This module handles loading data from disk. module HLearn.Data.LoadData where -import Control.DeepSeq --- import Control.Monad -import Control.Monad.ST -import Data.Maybe -import qualified Data.List as L -import qualified Data.Set as Set -import qualified Data.Vector as V -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Mutable as VM -import qualified Data.Vector.Generic.Mutable as VGM -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector.Unboxed.Mutable as VUM -import qualified Data.Vector.Storable as VS -import qualified Data.Vector.Storable.Mutable as VSM -import qualified Data.ByteString.Lazy.Char8 as BS -import qualified Data.Vector.Algorithms.Intro as Intro --- import System.Mem -import System.IO -import System.Directory --- import System.FilePath.Posix -import qualified Numeric.LinearAlgebra as LA -import qualified Numeric.LinearAlgebra.Devel as LA - -import HLearn.Models.Distributions.Common -import HLearn.Models.Distributions.Univariate.Normal --- import Test.QuickCheck hiding (verbose,sample,label) --- import Control.Parallel.Strategies - -import Data.List (take,drop,zipWith) - import SubHask +import SubHask.Algebra.Array import SubHask.Algebra.Container import SubHask.Algebra.Parallel import SubHask.Compatibility.ByteString import SubHask.Compatibility.Cassava import SubHask.Compatibility.Containers -import SubHask.Compatibility.Vector.Lebesgue import SubHask.TemplateHaskell.Deriving -import HLearn.Data.UnsafeVector import HLearn.History.Timing -import HLearn.Models.Classifiers.Common +import HLearn.Models.Distributions -import Debug.Trace -import Prelude (asTypeOf,unzip,head) import qualified Prelude as P +import Prelude (asTypeOf,unzip,head,take,drop,zipWith) +import Control.Monad.ST +import qualified Data.List as L +import Data.Maybe +import System.Directory +import System.IO -------------------------------------------------------------------------------- +{- +FIXME: +This code was written a long time ago to assist with the Cover Tree ICML paper. +It needs to be updated to use the new subhask interface. +This should be an easy project. + -- | This loads files in the format used by the BagOfWords UCI dataset. -- See: https://archive.ics.uci.edu/ml/machine-learning-databases/bag-of-words/readme.txt -loadBagOfWords :: FilePath -> IO (Array (Map' Int Float)) +loadBagOfWords :: FilePath -> IO (BArray (Map' Int Float)) loadBagOfWords filepath = do hin <- openFile filepath ReadMode numdp :: Int <- liftM read $ hGetLine hin @@ -68,17 +48,17 @@ loadBagOfWords filepath = do hClose hin VG.unsafeFreeze ret +-} -- | Loads a dataset of strings in the unix words file format (i.e. one word per line). -- This format is also used by the UCI Bag Of Words dataset. -- See: https://archive.ics.uci.edu/ml/machine-learning-databases/bag-of-words/readme.txt -loadWords :: (Monoid dp, Elem dp~Char, Eq dp, Constructible dp) => FilePath -> IO (Array dp) +loadWords :: (Monoid dp, Elem dp~Char, Eq dp, Constructible dp) => FilePath -> IO (BArray dp) loadWords filepath = do hin <- openFile filepath ReadMode contents <- hGetContents hin return $ fromList $ map fromList $ L.lines contents - -------------------------------------------------------------------------------- -- | Returns all files in a subdirectory (and all descendant directories). @@ -118,7 +98,7 @@ loadDirectory :: -> (FilePath -> Bool) -- ^ function to filter out invalid filenames -> (a -> Bool) -- ^ function to filter out malformed results -> FilePath -- ^ directory to load data from - -> IO (Array (Labeled' a FilePath)) -- ^ + -> IO (BArray (Labeled' a FilePath)) -- ^ loadDirectory numdp loadFile validFilepath validResult dirpath = {-# SCC loadDirectory #-} do files <- timeIO "getDirectoryContentsRecursive" $ do @@ -137,35 +117,18 @@ loadDirectory numdp loadFile validFilepath validResult dirpath = {-# SCC loadDir return $ L.filter (validResult . xLabeled') xs putStrLn $ " numdp: " ++ show (length files) --- when debug $ do --- forM files $ \file -> do --- putStrLn file return $ fromList results -------------------- - - --------------------------------------------------------------------------------- - -data DataParams = DataParams - { datafile :: String - , labelcol :: Maybe Int - , pca :: Bool - , varshift :: Bool - } - --- head x = case uncons x of --- Nothing -> error "head on empty" --- Just (x,_) -> x - +-- | Load a CSV file containing numeric attributes. {-# INLINABLE loadCSV #-} loadCSV :: ( NFData a , FromRecord a + , FiniteModule a , Eq a , Show (Scalar a) - ) => FilePath -> IO (Array a) + ) => FilePath -> IO (BArray a) loadCSV filepath = do bs <- timeIO ("loading ["++filepath++"]") $ readFileByteString filepath @@ -178,192 +141,146 @@ loadCSV filepath = do Left str -> error $ "failed to parse CSV file " ++ filepath ++ ": " ++ L.take 1000 str putStrLn " dataset info:" - putStrLn $ " num dp: " ++ show (size rs) + putStrLn $ " num dp: " ++ show ( size rs ) + putStrLn $ " numdim: " ++ show ( dim $ rs!0 ) putStrLn "" return rs -{-# INLINABLE loadCSVLabeled #-} -loadCSVLabeled :: - ( Read k - , Read v2 - , v ~ (v1 v2) - , VG.Vector v1 v2 - , Eq v - , NFData (v1 v2) - , NFData k - ) => FilePath -> Int -> IO (Array (Labeled' v k)) -loadCSVLabeled filepath n = do - xs :: [[String]] - <- fmap toList $ loadCSV filepath - - let ks = map (head . drop n) xs - vs = map (take n + drop (n+1)) xs - - x <- return $ fromList $ zipWith Labeled' (map (listToVector . map read) vs) (map read ks) - deepseq x $ trace "loaded" $ return x - -{-# INLINABLE loaddata #-} -loaddata :: - ( VG.Vector v f - , NFData (v f) - , NFData f - , FromRecord (v f) - , Eq (v f) - , Ord f - , POrd_ (v f) --- , Normed (v f) - , Show (Scalar (v f)) - , Real f - , f~Float - , VUM.Unbox f - ) => DataParams -> IO (Array (v f)) -loaddata params = do - rs <- loadCSV $ datafile params - - -- These algorithms are implemented sequentially, - -- so they run faster if we temporarily disable the RTS multithreading - disableMultithreading $ do - - rs' <- if pca params - then time "calculating PCA" $ VG.convert $ rotatePCA rs - else return rs - - let shuffleMap = mkShuffleMap $ VG.map ArrayT rs' - time "mkShuffleMap" shuffleMap - - rs'' <- if varshift params - then time "varshifting data" $ VG.map (shuffleVec shuffleMap) rs' - else return rs' - - return $ rs'' - +-- | FIXME: this should be combined with the CSV function above +loadCSVLabeled' :: + ( NFData x + , FromRecord x + , FiniteModule x + , Eq x + , Show (Scalar x) + , Read (Scalar x) + ) => Int -- ^ column of csv file containing the label + -> FilePath -- ^ path to csv file + -> IO (BArray (Labeled' x (Lexical String))) +loadCSVLabeled' col filepath = do -------------------------------------------------------------------------------- --- data preprocessing + bs <- timeIO ("loading ["++filepath++"]") $ readFileByteString filepath --- | calculate the variance of each column, then sort so that the highest variance is first -{-# INLINABLE mkShuffleMap #-} -mkShuffleMap :: forall v a s. - ( VG.Vector v a - , Elem (v a) ~ a - , Foldable (v a) - , Real a - , Ord a - , VU.Unbox a - , Eq_ (v a) - , ClassicalLogic (v a) - , NFData a - , a~Float - ) => Array (v a) -> UnboxedArray Int -mkShuffleMap v = if VG.length v == 0 - then empty - else runST ( do - let numdim = VG.length (v VG.! 0) + let rse = decode NoHeader bs + time "parsing csv file" rse - -- Use an efficient 1-pass algorithm for the variance. - -- This is much faster (due to cache issues) than 2-pass algorithms on large datasets. - -- Since this is only used as a speed heuristic, perfect numerical stability doesn't matter. - -- See http://www.cs.berkeley.edu/~mhoemmen/cs194/Tutorials/variance.pdf - let varV = VG.map ((\(_,_,q) -> q) . VG.foldl' go (1,0,0)) v - go (k,mk,qk) x = (k+1,mk',qk') - where - mk'=mk+(x-mk)/k - qk'=qk+(x-mk)*(x-mk) + rs :: BArray (BArray String) <- case rse of + Right rs -> return rs + Left str -> error $ "failed to parse CSV file " ++ filepath ++ ": " ++ L.take 1000 str - sortV = VG.zip (VG.fromList [0..numdim-1::Int]) - $ VG.convert varV :: UnboxedArray (Int, a) + let ret = fromList $ map go $ toList rs - msortV <- VG.unsafeThaw sortV - Intro.sortBy (\(_,v2) (_,v1) -> compare v1 v2) msortV - sortV' <- VG.unsafeFreeze msortV + putStrLn " dataset info:" + putStrLn $ " num dp: " ++ show ( size ret ) + putStrLn $ " numdim: " ++ show ( dim $ xLabeled' $ ret!0 ) + putStrLn "" - return $ VG.map fst sortV' - ) + return ret --- let numdim = VG.length (v VG.! 0) --- numdpf = fromIntegral $ VG.length v + where + go arr = Labeled' x y + where + y = Lexical $ arr!col + x = unsafeToModule $ map read $ take (col) arrlist ++ drop (col+1) arrlist --- let m1V = VG.foldl1 (VG.zipWith (+)) v --- m2V = VG.foldl1 (VG.zipWith (+)) $ VG.map (VG.map (\x -> x*x)) v --- varV = VG.zipWith (\m1 m2 -> m2/numdpf-m1/numdpf*m1/numdpf) m1V m2V + arrlist = toList arr --- sortV = VG.zip (VG.fromList [0..numdim-1::Int]) --- $ VG.convert varV :: UnboxedArray (Int, a) --- --- msortV <- VG.unsafeThaw sortV --- Intro.sortBy (\(_,v2) (_,v1) -> compare v1 v2) msortV --- sortV' <- VG.unsafeFreeze msortV +------------------------------------------------------------------------------- +-- data preprocessing -- --- return $ VG.map fst sortV' --- ) - --- meanV :: VUM.MVector s a <- VGM.new numdim --- varV :: VUM.MVector s a <- VGM.new numdim --- let go (-1) = return () --- go i = do --- let go_inner (-1) = return () --- go_inner j = do --- let tmp = v VG.! i VG.! j --- meanVtmp <- (+tmp ) `liftM` VGM.read meanV j --- varVtmp <- (+tmp*tmp) `liftM` VGM.read varV j --- VUM.write meanV +-- FIXME: +-- Find a better location for all this code. + +-- | Uses an efficient 1-pass algorithm to calculate the mean variance. +-- This is much faster than the 2-pass algorithms on large datasets, +-- but has (slightly) worse numeric stability. -- --- let xs = fmap (VG.! i) v --- dist = train xs :: Normal a a --- var = variance dist --- VGM.write varV i (i,var) --- go (i-1) +-- See http://www.cs.berkeley.edu/~mhoemmen/cs194/Tutorials/variance.pdf for details. +{-# INLINE meanAndVarianceInOnePass #-} +meanAndVarianceInOnePass :: (Foldable xs, Field (Elem xs)) => xs -> (Elem xs, Elem xs) +meanAndVarianceInOnePass ys = + {-# SCC meanAndVarianceInOnePass #-} + case uncons ys of + Nothing -> error "meanAndVarianceInOnePass on empty container" + Just (x,xs) -> (\(k,m,v) -> (m,v/(k-1))) $ foldl' go (2,x,0) xs + where + go (k,mk,qk) x = (k+1,mk',qk') + where + mk'=mk+(x-mk)/k + qk'=qk+(k-1)*(x-mk)*(x-mk)/k --- msortV :: VUM.MVector s (Int, a) <- VGM.new numdim +-- | A wrapper around "meanAndVarianceInOnePass" +{-# INLINE varianceInOnePass #-} +varianceInOnePass :: (Foldable xs, Field (Elem xs)) => xs -> Elem xs +varianceInOnePass = snd . meanAndVarianceInOnePass + +-- | Calculate the variance of each column, then sort so that the highest variance is first. +-- This can be useful for preprocessing data. -- --- let go (-1) = return () --- go i = do --- -- let !xs = fmap (VG.! i) v --- let !xs = fmap (`VG.unsafeIndex` i) v --- !dist = train xs :: Normal a a --- !var = variance dist --- VGM.write msortV i (i,var) --- go (i-1) --- go (numdim-1) - --- forM [0..numdim-1] $ \i -> do --- let xs = fmap (VG.! i) v --- dist = train xs :: Normal a a --- var = variance dist --- VGM.write varV i (i,var) - --- mkShuffleMap xs --- = fromList --- $ map fst --- $ L.sortBy (\(a,_) (b,_) -> compare a b) --- $ zip [0..] --- $ map (variance . trainNormal) --- $ L.transpose --- $ map toList --- $ toList xs - -{-# INLINABLE shuffleVec #-} +-- NOTE: +-- The git history has a lot of versions of this function with different levels of efficiency. +-- I need to write a blog post about how all the subtle haskellisms effect the runtime. +{-# INLINABLE mkShuffleMap #-} +mkShuffleMap :: forall v. + ( FiniteModule v + , VectorSpace v + , Unboxable v + , Unboxable (Scalar v) + , Eq v + , Elem (SetElem v (Elem v)) ~ Elem v + , Elem (SetElem v (Scalar (Elem v))) ~ Scalar (Elem v) + , IxContainer (SetElem v (Elem v)) + ) => BArray v -> UArray Int +mkShuffleMap vs = {-# SCC mkShuffleMap #-} if size vs==0 + then error "mkShuffleMap: called on empty array" + else runST ( do + -- FIXME: + -- @smalldata@ should be a random subsample of the data. + -- The size should also depend on the dimension. + let smalldata = P.take 1000 $ toList vs + +-- let variances = fromList +-- $ values +-- $ varianceInOnePass +-- $ VG.map Componentwise vs +-- :: BArray (Scalar v) + + let variances + = imap (\i _ -> varianceInOnePass $ (imap (\_ -> (!i)) smalldata)) + $ values + $ vs!0 + :: [Scalar v] + + return + $ fromList + $ map fst + $ L.sortBy (\(_,v1) (_,v2) -> compare v2 v1) + $ imap (,) + $ variances + ) + -- | apply the shufflemap to the data set to get a better ordering of the data -shuffleVec :: VG.Vector v a => UnboxedArray Int -> v a -> v a -shuffleVec vmap v = VG.generate (VG.length vmap) $ \i -> v `VG.unsafeIndex` (vmap `VG.unsafeIndex` i) --- shuffleVec vmap v = VG.generate (VG.length vmap) $ \i -> v VG.! (vmap VG.! i) +{-# INLINABLE apShuffleMap #-} +apShuffleMap :: forall v. FiniteModule v => UArray Int -> v -> v +apShuffleMap vmap v = unsafeToModule xs + where + xs :: [Scalar v] + xs = generate1 (size vmap) $ \i -> v!(vmap!i) --- shuffleVec vmap v = runST $ do --- ret <- VGM.new (VG.length v) --- --- let go (-1) = return () --- go i = do --- VGM.write ret i $ v VG.! (vmap VG.! i) --- go (i-1) --- go (VG.length v-1) --- --- -- forM [0..VG.length v-1] $ \i -> do --- -- VGM.write ret i $ v VG.! (vmap VG.! i) --- VG.freeze ret +{-# INLINABLE generate1 #-} +generate1 :: (Monoid v, Constructible v) => Int -> (Int -> Elem v) -> v +generate1 n f = if n <= 0 + then zero + else fromList1N n (f 0) (map f [1..n-1]) + +{- +FIXME: +All this needs to be reimplemented using the subhask interface. +This requires fixing some of the features of subhask's linear algebra system. -{-# INLINABLE meanCenter #-} -- | translate a dataset so the mean is zero +{-# INLINABLE meanCenter #-} meanCenter :: ( VG.Vector v1 (v2 a) , VG.Vector v2 a @@ -373,8 +290,8 @@ meanCenter dps = {-# SCC meanCenter #-} VG.map (\v -> VG.zipWith (-) v meanV) dp where meanV = {-# SCC meanV #-} VG.map (/ fromIntegral (VG.length dps)) $ VG.foldl1' (VG.zipWith (+)) dps -{-# INLINABLE rotatePCA #-} -- | rotates the data using the PCA transform +{-# INLINABLE rotatePCA #-} rotatePCA :: ( VG.Vector container dp , VG.Vector container [Float] @@ -399,7 +316,7 @@ rotatePCA dps' = {-# SCC rotatePCA #-} VG.map rotate dps gramMatrix = {-# SCC gramMatrix #-} foldl1' (P.+) [ let dp' = VG.convert dp in LA.asColumn dp' LA.<> LA.asRow dp' | dp <- VG.toList dps ] -gramMatrix_ :: (Ring a, Storable a) => [Vector a] -> LA.Matrix a +gramMatrix_ :: (Ring a, Storable a) => [VS.Vector a] -> LA.Matrix a gramMatrix_ xs = runST ( do let dim = VG.length (head xs) m <- LA.newMatrix 0 dim dim @@ -431,15 +348,4 @@ rotatePCADouble dps' = VG.map rotate dps gramMatrix = LA.trans tmpm LA.<> tmpm where tmpm = LA.fromLists (VG.toList $ VG.map VG.toList dps) - -------------------------------------------------------------------------------- --- tests - -v1 = VS.fromList [1,2,3] -v2 = VS.fromList [1,3,4] -v3 = VS.fromList [1,5,6] -v4 = VS.fromList [0,0,1] -vs = V.fromList [v1,v2,v3,v4] :: V.Vector (VS.Vector Float) - -dist a b = distance (L2 a) (L2 b) - + -} diff --git a/src/HLearn/Data/SpaceTree.hs b/src/HLearn/Data/SpaceTree.hs index 274ef82..eed23ff 100644 --- a/src/HLearn/Data/SpaceTree.hs +++ b/src/HLearn/Data/SpaceTree.hs @@ -1,16 +1,23 @@ -{-# LANGUAGE DataKinds,MagicHash,UnboxedTuples #-} - +-- | The space tree abstraction was pioneered by the library. +-- It provides a generic interface for all tree structures that support nearest neighbor queries. +-- The paper gives full details. +-- +-- FIXME: +-- Should this interface be incorporated into subhask's "Container" class hierarchy? +-- +-- FIXME: +-- There is a close relation between the pruning folds described in the MLPack paper and the theory of ana/catamorphisms that haskellers love. +-- Making this explicit would require some serious work, but would (maybe) provide an even simpler interface. module HLearn.Data.SpaceTree ( -- * Type classes SpaceTree (..) - , Weighted# - -- * Generic algorithms - , stMaxDescendentDistance , stToList -- , stToListW + , stHasNoChildren + , stChildrenList , stDescendents , stNumDp , stNumNodes @@ -27,52 +34,25 @@ module HLearn.Data.SpaceTree , stMaxDepth , stNumSingletons , stExtraLeaves - - -- * Pruning folds - - -- ** Single tree - - , prunefoldinit - , prunefold - , prunefoldA - , prunefoldB - , prunefoldB_CanError --- , prunefoldB_CanError_sort - , prunefoldC - , prunefoldD --- , prunefoldM - , noprune - ) where -import Debug.Trace - import qualified Data.List as L -import qualified Data.Vector as V -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector.Generic as VG import Prelude (map) import SubHask +import SubHask.Algebra.Array import SubHask.Monad import SubHask.Compatibility.Containers -import HLearn.Models.Distributions.Univariate.Normal +import HLearn.Models.Distributions ------------------------------------------------------------------------------- -- SpaceTree -type Weighted# dp = (# Scalar dp, dp #) - -fst# :: (# a,b #) -> a -fst# (# a,b #) = a - -snd# :: (# a,b #) -> b -snd# (# a,b #) = b - class ( Metric dp + , Bounded (Scalar dp) , Logic (t dp) ~ Bool , Logic dp ~ Bool , Logic (LeafContainer t dp) ~ Bool @@ -80,7 +60,7 @@ class , Logic (ChildContainer t (t dp)) ~ Bool , Eq_ (t dp) , Eq_ (ChildContainer t (t dp)) - , Scalar dp ~ Scalar (t dp) +-- , Scalar dp ~ Scalar (t dp) , Elem (ChildContainer t (t dp)) ~ t dp , Elem (LeafContainer t dp) ~ dp , Constructible (LeafContainer t dp) @@ -89,79 +69,50 @@ class , Normed (ChildContainer t (t dp)) , Foldable (LeafContainer t dp) , Foldable (ChildContainer t (t dp)) --- , Container (LeafContainer t dp) --- , Container (LeafContainer t (t dp)) - --- , Constructible (t dp) , dp ~ Elem (t dp) ) => SpaceTree t dp where type LeafContainer t :: * -> * - type LeafContainer t = V.Vector + type LeafContainer t = BArray type ChildContainer t :: * -> * - type ChildContainer t = V.Vector - - {-# INLINABLE stMinDistance #-} - {-# INLINABLE stMaxDistance #-} - stMinDistance :: t dp -> t dp -> Scalar dp - stMinDistance t1 t2 = fst# (stMinDistanceWithDistance t1 t2) - stMaxDistance :: t dp -> t dp -> Scalar dp - stMaxDistance t1 t2 = fst# (stMaxDistanceWithDistance t1 t2) + type ChildContainer t = BArray - stMinDistanceWithDistance :: t dp -> t dp -> (# Scalar dp, Scalar dp #) - stMaxDistanceWithDistance :: t dp -> t dp -> (# Scalar dp, Scalar dp #) - - {-# INLINABLE stMinDistanceDp #-} - {-# INLINABLE stMaxDistanceDp #-} - stMinDistanceDp :: t dp -> dp -> Scalar dp - stMinDistanceDp t dp = fst# (stMinDistanceDpWithDistance t dp) - stMaxDistanceDp :: t dp -> dp -> Scalar dp - stMaxDistanceDp t dp = fst# (stMaxDistanceDpWithDistance t dp) - - stIsMinDistanceDpFartherThanWithDistanceCanError - :: CanError (Scalar dp) => t dp -> dp -> Scalar dp -> Scalar dp - stIsMaxDistanceDpFartherThanWithDistanceCanError - :: CanError (Scalar dp) => t dp -> dp -> Scalar dp -> Scalar dp - - stMinDistanceDpWithDistance :: t dp -> dp -> (# Scalar dp, Scalar dp #) - stMaxDistanceDpWithDistance :: t dp -> dp -> (# Scalar dp, Scalar dp #) - - stMinDistanceDpFromDistance :: t dp -> dp -> Scalar dp -> Scalar dp - stMaxDistanceDpFromDistance :: t dp -> dp -> Scalar dp -> Scalar dp - - stHasNode :: t dp -> Bool stChildren :: t dp -> ChildContainer t (t dp) + stLeaves :: t dp -> LeafContainer t dp + stNode :: t dp -> dp stWeight :: t dp -> Scalar dp - {-# INLINABLE stHasNoChildren #-} - stHasNoChildren :: t dp -> Bool - stHasNoChildren t = isEmpty $ stChildren t + {-# INLINE stNodeW #-} + stNodeW :: t dp -> Labeled' dp (Scalar dp) + stNodeW t = Labeled' (stNode t) (stWeight t) - {-# INLINABLE stChildrenList #-} - stChildrenList :: t dp -> [t dp] - stChildrenList t = toList $ stChildren t + {-# INLINABLE stMaxDescendentDistance #-} + stMaxDescendentDistance :: t dp -> Scalar dp + stMaxDescendentDistance t = maxBound - {-# INLINABLE stLeaves #-} - stLeaves :: t dp -> LeafContainer t dp - stLeaves t = zero +------------------------------------------------------------------------------- - {-# INLINABLE stNodeW #-} - stNodeW :: t dp -> Weighted# dp - stNodeW t = (# stWeight t, stNode t #) +instance + ( Metric dp + , Logic dp~Bool + , Bounded (Scalar dp) + ) => SpaceTree BArray dp + where + + type LeafContainer BArray = BArray + type ChildContainer BArray = BArray - ro :: t dp -> Scalar dp - lambda :: t dp -> Scalar dp + stChildren _ = empty + stLeaves = id + stNode = (!0) + stWeight = 1 ------------------------------------------------------------------------------- -- generic algorithms -{-# INLINABLE stMaxDescendentDistance #-} -stMaxDescendentDistance :: (Eq dp, SpaceTree t dp) => t dp -> Scalar dp -stMaxDescendentDistance t = maximum_ 0 $ map (distance (stNode t)) $ stDescendents t - {-# INLINABLE stToSeqDFS #-} stToSeqDFS :: SpaceTree t dp => t dp -> Seq dp stToSeqDFS t @@ -210,6 +161,14 @@ stToList = toList . stToSeqDFS -- then xs -- else (stNode t,getTag t) : xs +{-# INLINABLE stHasNoChildren #-} +stHasNoChildren :: SpaceTree t dp => t dp -> Bool +stHasNoChildren t = isEmpty $ stChildren t + +{-# INLINABLE stChildrenList #-} +stChildrenList :: SpaceTree t dp => t dp -> [t dp] +stChildrenList t = toList $ stChildren t + {-# INLINABLE stDescendents #-} stDescendents :: SpaceTree t dp => t dp -> [dp] stDescendents t = case tailMaybe $ go t of @@ -329,187 +288,3 @@ stExtraLeaves t = if stHasNoChildren t + if supremum $ map (\c -> stNode c==stNode t && stHasNoChildren c) $ stChildrenList t then 1 else 0 - -------------------------------------------------------------------------------- --- pruning folds - ---------------------------------------- --- single tree algs - -{-# INLINABLE prunefoldinit #-} -prunefoldinit :: SpaceTree t dp => (t dp -> res) -> (res -> t dp -> Bool) -> (dp -> res -> res) -> t dp -> res -prunefoldinit init prune f t = foldl' - (prunefold prune f) - (init t) - (stChildrenList t) - -{-# INLINABLE prunefold #-} -prunefold :: SpaceTree t a => (b -> t a -> Bool) -> (a -> b -> b) -> b -> t a -> b -prunefold prune f b t = if prune b t - then b - else if stHasNoChildren t - then b' - else foldl' (prunefold prune f) b' (stChildrenList t) - where - b' = f (stNode t) b - -{-# INLINABLE prunefoldW #-} -prunefoldW :: SpaceTree t a => (b -> t a -> Bool) -> (Weighted# a -> b -> b) -> b -> t a -> b -prunefoldW prune f b t = if prune b t - then b - else if stHasNoChildren t - then b' - else foldl' (prunefoldW prune f) b' (stChildrenList t) - where - b' = f (stNodeW t) b - -{-# INLINABLE prunefoldA #-} -prunefoldA :: SpaceTree t a => (t a -> b -> Maybe' b) -> b -> t a -> b -prunefoldA !f !b !t = {-# SCC prunefoldA #-} case f t b of - Nothing' -> b - Just' b' -> foldl' (prunefoldA f) b' (stChildren t) - -{-# INLINABLE prunefoldB #-} -prunefoldB :: SpaceTree t a => (a -> b -> b) -> (t a -> b -> Maybe' b) -> b -> t a -> b -prunefoldB !f1 !f2 !b !t = {-# SCC prunefoldB #-} case f2 t b of - Nothing' -> {-# SCC prunefoldB_Nothing #-} b - Just' b' -> {-# SCC prunefoldB_Just #-} foldl' (prunefoldB f1 f2) b'' (stChildren t) - where - b'' = {-# SCC b'' #-} foldr' f1 b' (stLeaves t) - --- {-# INLINABLE prunefoldB_CanError #-} --- prunefoldB_CanError :: (SpaceTree t a, CanError b) => --- (a -> b -> b) -> (t a -> b -> b) -> b -> t a -> b --- prunefoldB_CanError !f1 !f2 !b !t = go b t --- where --- go !b !t = {-# SCC prunefoldB_CanError #-} if isError res --- then {-# SCC prunefoldB_CanError_Nothing #-} b --- else {-# SCC prunefoldB_CanError_Just #-} --- ckfoldl' go b'' (stChildren t) --- where --- ckfoldl' a = {-# SCC ckfoldl #-} CK.foldl' a --- !res = f2 t b --- !b'' = {-# SCC b'' #-} ckfoldl' (flip f1) res (stLeaves t) - -{-# INLINABLE prunefoldB_CanError #-} --- {-# INLINE prunefoldB_CanError #-} -prunefoldB_CanError :: (SpaceTree t a, CanError b) => - (a -> b -> b) -> (t a -> b -> b) -> b -> t a -> b -prunefoldB_CanError !f1 !f2 !b !t = {-# SCC prunefoldB_CanError_start #-} go t b - where - go !t !b = {-# SCC prunefoldB_CanError_if #-} if isError res - then {-# SCC prunefoldB_CanError_Nothing #-} b - else {-# SCC prunefoldB_CanError_Just #-} - foldr' go b'' $ {-# SCC prunefoldB_CanError_stChildren #-} stChildren t - where - res = {-# SCC res #-} f2 t b - b'' = {-# SCC b'' #-} foldr' f1 res (stLeaves t) - -{-# INLINABLE prunefoldB_CanError_sort #-} -prunefoldB_CanError_sort :: (SpaceTree t a, CanError b) => - a -> (a -> b -> b) -> (Scalar a -> t a -> b -> b) -> b -> t a -> b -prunefoldB_CanError_sort !query !f1 !f2 !b !t = {-# SCC prunefoldB_CanError_sort #-} - go ( distance (stNode t) query, t ) b - where - go !( dist,t ) !b = if isError res - then b - else foldr' go b'' children' - where - res = f2 dist t b - b'' = foldr' f1 res (stLeaves t) - - children' - = {-# SCC children' #-} qsortHalf (\( d1,_ ) ( d2,_ ) -> compare d2 d1) - $ map (\x -> ( distance (stNode x) query, x )) - $ toList - $ stChildren t - --- | This is a version of quicksort that only descends on its lower half. --- That is, it only "approximately" sorts a list. --- It is modified from http://en.literateprograms.org/Quicksort_%28Haskell%29 -{-# INLINABLE qsortHalf #-} -qsortHalf :: (a -> a -> Ordering) -> [a] -> [a] -qsortHalf cmp x = go x [] - where - go [] y = y - go [x] y = x:y - go (x:xs) y = part xs [] [x] [] - where - part [] l e g = go l (e ++ g ++ y) - part (z:zs) l e g = case cmp z x of - GT -> part zs l e (z:g) - LT -> part zs (z:l) e g - EQ -> part zs l (z:e) g - --- sortBy cmp = mergeAll . sequences --- where --- sequences (a:b:xs) --- | a `cmp` b == GT = descending b [a] xs --- | otherwise = ascending b (a:) xs --- sequences xs = [xs] --- --- descending a as (b:bs) --- | a `cmp` b == GT = descending b (a:as) bs --- descending a as bs = (a:as): sequences bs --- --- ascending a as (b:bs) --- | a `cmp` b /= GT = ascending b (\ys -> as (a:ys)) bs --- ascending a as bs = as [a]: sequences bs --- --- mergeAll [x] = x --- mergeAll xs = mergeAll (mergePairs xs) --- --- mergePairs (a:b:xs) = merge a b: mergePairs xs --- mergePairs xs = xs --- --- merge as@(a:as') bs@(b:bs') --- | a `cmp` b == GT = b:merge as bs' --- | otherwise = a:merge as' bs --- merge [] bs = bs --- merge as [] = as - -{-# INLINABLE prunefoldD #-} --- {-# INLINE prunefoldD #-} -prunefoldD :: (SpaceTree t a, CanError b, Monoid b) => - (a -> b -> b) -> ( (# a,Scalar a #) -> b -> b) -> b -> t a -> b -prunefoldD !f1 !f2 !b !t = {-# SCC prunefoldB_CanError_start #-} go t b - where - go !t !b = if isError res - then b - else foldr' go b'' $ stChildren t - where - !res = f2' t b - !b'' = foldr' f1 res (stLeaves t) - - f2' t b = f2 (# stNode t, lambda t #) b - --- !b'' = foldr' f2' res (stLeaves t) - --- f2' dp b = if isError tmp --- then b --- else tmp --- where tmp = f2 (singleton dp) b - - - -{-# INLINABLE prunefoldC #-} -prunefoldC :: - ( SpaceTree t a - , CanError b - ) => (a -> b -> b) -> b -> t a -> b -prunefoldC !f !b !t = go t b - where - go !t !b = if isError res - then b - else foldr' go b'' $ stChildren t - where - res = f (stNode t) b - b'' = foldr' f1 res $ stLeaves t - - f1 a b = if isError a' then b else a' - where a' = f a b - -{-# INLINE noprune #-} -noprune :: b -> a -> Bool -noprune _ _ = False - diff --git a/src/HLearn/Data/SpaceTree/Algorithms.hs b/src/HLearn/Data/SpaceTree/Algorithms.hs new file mode 100644 index 0000000..b223e55 --- /dev/null +++ b/src/HLearn/Data/SpaceTree/Algorithms.hs @@ -0,0 +1,118 @@ +-- | This module contains algorithms for efficient operations over space trees. +-- Currently, only nearest neighbor queries are implemented. +-- It would be easy to implement more query types, however. +-- If there is another query type you want supported, ask me and I'll implement it for you. +-- +-- The paper gives full details on possible queries. +module HLearn.Data.SpaceTree.Algorithms + ( + + Neighbor (..) + + , findNeighbor + , findNeighbor_NoSort + ) + where + +import GHC.Exts (inline) +import Data.List (sortBy) + +import SubHask +import SubHask.Algebra.Array +import SubHask.Algebra.Container +import SubHask.Algebra.Vector +import SubHask.Compatibility.Containers +import SubHask.Monad +import SubHask.TemplateHaskell.Deriving + +import HLearn.Data.SpaceTree + +------------------------------------------------------------------------------- + +data Neighbor dp = Neighbor + { neighbor :: !dp + , neighborDistance :: !(Scalar dp) + } + +deriving instance (Show dp, Show (Scalar dp)) => Show (Neighbor dp) + +instance (NFData dp, NFData (Scalar dp)) => NFData (Neighbor dp) where + rnf (Neighbor _ _) = () + +type instance Logic (Neighbor dp) = Bool + +instance (Eq dp, Eq (Scalar dp)) => Eq_ (Neighbor dp) where + (Neighbor dp1 dist1)==(Neighbor dp2 dist2) = dist1==dist2 && dp1==dp2 + +---------------------------------------- + +-- | Find the nearest neighbor of a node. +-- +-- NOTE: +-- If we remove the call to "inline" on "foldr'", +-- GHC 7.10 will pass dictionaries and everything becomes very slow. +{-# INLINE findNeighbor #-} +findNeighbor :: + ( SpaceTree t dp + , Bounded (Scalar dp) + ) => Scalar dp -> t dp -> dp -> Neighbor dp +findNeighbor ε t q = + {-# SCC findNeighbor #-} + go (Labeled' t startdist) startnode + where + startnode = if startdist == 0 + then Neighbor q maxBound + else Neighbor (stNode t) startdist + + startdist = distance (stNode t) q + + go (Labeled' t dist) (Neighbor n distn) = if dist*ε > maxdist + then Neighbor n distn + else inline foldr' go leafres + $ sortBy (\(Labeled' _ d1) (Labeled' _ d2) -> compare d2 d1) + $ map (\t' -> Labeled' t' (distanceUB q (stNode t') (distnleaf+stMaxDescendentDistance t))) + $ toList + $ stChildren t + where + leafres@(Neighbor _ distnleaf) = inline foldr' + (\dp n@(Neighbor _ distn') -> cata dp (distanceUB q dp distn') n) + (cata (stNode t) dist (Neighbor n distn)) + (stLeaves t) + + maxdist = distn+stMaxDescendentDistance t + + cata !dp !dist (Neighbor n distn) = + if dist==0 || dist>distn + then Neighbor n distn + else Neighbor dp dist + +---------------------------------------- + +-- | Find the nearest neighbor of a node. +-- Internally, this function does not sort the distances of the children before descending. +-- In some (rare) cases this reduces the number of distance comparisons. +{-# INLINE findNeighbor_NoSort #-} +findNeighbor_NoSort :: + ( SpaceTree t dp + , Bounded (Scalar dp) + ) => Scalar dp -> t dp -> dp -> Neighbor dp +findNeighbor_NoSort ε t q = + {-# SCC findNeighbor_NoSort #-} + go t (Neighbor q maxBound) + where + go t res@(Neighbor _ distn) = if dist*ε > maxdist + then res + else inline foldr' go leafres $ stChildren t + where + leafres = inline foldr' + (\dp n@(Neighbor _ distn') -> cata dp (distanceUB q dp distn') n) + (cata (stNode t) dist res) + (stLeaves t) + + dist = distanceUB q (stNode t) maxdist + maxdist = distn+stMaxDescendentDistance t + + cata !dp !dist (Neighbor n distn) = + if dist==0 || dist>distn + then Neighbor n distn + else Neighbor dp dist diff --git a/src/HLearn/Data/SpaceTree/Algorithms/NearestNeighbor.hs b/src/HLearn/Data/SpaceTree/Algorithms/NearestNeighbor.hs index 749a70c..e26f03b 100644 --- a/src/HLearn/Data/SpaceTree/Algorithms/NearestNeighbor.hs +++ b/src/HLearn/Data/SpaceTree/Algorithms/NearestNeighbor.hs @@ -1,403 +1,139 @@ -{-# LANGUAGE DataKinds,UnboxedTuples,MagicHash,TemplateHaskell,RankNTypes,TupleSections #-} - -module HLearn.Data.SpaceTree.Algorithms.NearestNeighbor +-- | This module contains algorithms for efficient operations over space trees. +-- Currently, only nearest neighbor queries are implemented. +-- It would be easy to implement more query types, however. +-- If there is another query type you want supported, ask me and I'll implement it for you. +-- +-- The paper gives full details on possible queries. +module HLearn.Data.SpaceTree.Algorithms ( - -- * data types Neighbor (..) , ValidNeighbor (..) - , NeighborList (..) - , nlSingleton - , getknnL - , nlMaxDist - - , Param_k - , _k - - -- * functions - , findAllNeighbors - , findAllNeighbors' - - , findNeighborList - , findEpsilonNeighborListWith + , findNeighbor + , findNeighbor_NoSort ) where -import qualified Prelude as P -import Data.Strict.Tuple (Pair(..)) +import GHC.Exts (inline) +import Data.List (sortBy) import SubHask +import SubHask.Algebra.Array import SubHask.Algebra.Container +import SubHask.Algebra.Vector import SubHask.Compatibility.Containers -import SubHask.Compatibility.Vector -import SubHask.Compatibility.Vector.Lebesgue import SubHask.Monad import SubHask.TemplateHaskell.Deriving import HLearn.Data.SpaceTree -import Data.Params - ------------------------------------------------------------------------------- data Neighbor dp = Neighbor { neighbor :: !dp , neighborDistance :: !(Scalar dp) --- { neighbor :: {-#UNPACK#-}!(L2 UnboxedVector Float) --- , neighborDistance :: {-#UNPACK#-}!Float } -type instance Scalar (Neighbor dp) = Bool -type instance Logic (Neighbor dp) = Bool - -type ValidNeighbor dp = - ( Metric dp - , Bounded (Scalar dp) - , CanError (Scalar dp) - , Logic dp~Bool --- , dp ~ (L2 UnboxedVector Float) - ) - -deriving instance (Read dp, Read (Scalar dp)) => Read (Neighbor dp) deriving instance (Show dp, Show (Scalar dp)) => Show (Neighbor dp) -instance Eq (Scalar dp) => Eq_ (Neighbor dp) where - {-# INLINE (==) #-} - a == b = neighborDistance a == neighborDistance b - --- instance Ord (Scalar dp) => Ord (Neighbor dp) where --- compare a b = compare (neighborDistance a) (neighborDistance b) - instance (NFData dp, NFData (Scalar dp)) => NFData (Neighbor dp) where rnf (Neighbor _ _) = () --- rnf n = deepseq (neighbor n) $ rnf (neighborDistance n) - ------------------------------------------------------------------------------- - -data NeighborList (k :: Config Nat) dp - = NL_Nil - | NL_Cons {-#UNPACK#-}!(Neighbor dp) !(NeighborList k dp) --- | NL_Err - -mkParams ''NeighborList - --- | update the distances in the NeighborList based on a new data point -resetNL :: ValidNeighbor dp => dp -> NeighborList k dp -> NeighborList k dp -resetNL p NL_Nil = NL_Nil -resetNL p (NL_Cons (Neighbor q _) nl) - = NL_Cons (Neighbor q $ distance p q) $ resetNL p nl - -type instance Logic (NeighborList k dp) = Bool - -deriving instance (Read dp, Read (Scalar dp)) => Read (NeighborList k dp) -deriving instance (Show dp, Show (Scalar dp)) => Show (NeighborList k dp) - -instance (NFData dp, NFData (Scalar dp)) => NFData (NeighborList k dp) where - rnf NL_Nil = () --- rnf NL_Err = () - rnf (NL_Cons n ns) = () --- rnf (NL_Cons n ns) = deepseq n $ rnf ns - -instance (ValidNeighbor dp, Eq_ dp) => Eq_ (NeighborList k dp) where - (NL_Cons x xs) == (NL_Cons y ys) = x==y && xs==ys - NL_Nil == NL_Nil = True --- NL_Err == NL_Err = True - _ == _ = False - -property_orderedNeighborList :: (Logic dp~Bool, Metric dp) => NeighborList k dp -> Bool -property_orderedNeighborList NL_Nil = True -property_orderedNeighborList (NL_Cons n NL_Nil) = True -property_orderedNeighborList (NL_Cons n (NL_Cons n2 ns)) = if neighborDistance n < neighborDistance n2 - then property_orderedNeighborList (NL_Cons n2 ns) - else False - -{-# INLINE nlSingleton #-} -nlSingleton :: - ( ValidNeighbor dp - ) => Neighbor dp -> NeighborList k dp -nlSingleton !n = NL_Cons n NL_Nil - --- {-# INLINE mkNeighborList #-} --- mkNeighborList :: --- ( ValidNeighbor dp --- ) => dp -> Scalar dp -> NeighborList k dp --- mkNeighborList !dp !dist = NL_Cons (Neighbor dp dist) NL_Nil - -{-# INLINE getknnL #-} -getknnL :: - ( ValidNeighbor dp - ) => NeighborList k dp -> [Neighbor dp] -getknnL NL_Nil = [] -getknnL (NL_Cons n ns) = n:getknnL ns --- getknnL NL_Err = error "getknnL: NL_Err" - -{-# INLINE nlMaxDist #-} -nlMaxDist :: - ( ValidNeighbor dp - ) => NeighborList k dp -> Scalar dp -nlMaxDist !nl = go nl - where - go (NL_Cons n NL_Nil) = neighborDistance n - go (NL_Cons n ns) = go ns - go NL_Nil = maxBound --- go NL_Err = maxBound - -instance CanError (NeighborList k dp) where - {-# INLINE errorVal #-} - errorVal = NL_Nil --- errorVal = NL_Err - {-# INLINE isError #-} - isError NL_Nil = True --- isError NL_Err = True - isError _ = False - -instance --- ( KnownNat k - ( ViewParam Param_k (NeighborList k dp) - , Metric dp - , Eq dp - , ValidNeighbor dp - ) => Monoid (NeighborList k dp) - where - - {-# INLINE zero #-} - zero = NL_Nil - -instance - ( ViewParam Param_k (NeighborList k dp) - , Metric dp - , Eq dp - , ValidNeighbor dp - ) => Semigroup (NeighborList k dp) - where - - {-# INLINE (+) #-} --- nl1 + NL_Err = nl1 --- NL_Err + nl2 = nl2 - nl1 + NL_Nil = nl1 - NL_Nil + nl2 = nl2 - nl1 + nl2 = {-# SCC notNiL #-} ret - where - ret = go nl1 nl2 (viewParam _k nl1) - - go _ _ 0 = NL_Nil - go (NL_Cons n1 ns1) (NL_Cons n2 ns2) k = if neighborDistance n1 > neighborDistance n2 - then NL_Cons n2 $ go (NL_Cons n1 ns1) ns2 (k-1) - else NL_Cons n1 $ go ns1 (NL_Cons n2 ns2) (k-1) - go NL_Nil (NL_Cons n2 ns2) k = NL_Cons n2 $ go NL_Nil ns2 (k-1) - go (NL_Cons n1 ns1) NL_Nil k = NL_Cons n1 $ go ns1 NL_Nil (k-1) - go NL_Nil NL_Nil k = NL_Nil +type instance Logic (Neighbor dp) = Bool -{-# INLINE nlAddNeighbor #-} -nlAddNeighbor :: forall k dp. - ( ViewParam Param_k (NeighborList k dp) - , ValidNeighbor dp - ) => NeighborList k dp -> Neighbor dp -> NeighborList k dp --- nlAddNeighbor NL_Nil n' = NL_Cons n' NL_Nil --- nlAddNeighbor (NL_Cons n NL_Nil) n' = if neighborDistance n' > neighborDistance n --- then NL_Cons n' NL_Nil --- else NL_Cons n NL_Nil +instance (Eq dp, Eq (Scalar dp)) => Eq_ (Neighbor dp) where + (Neighbor dp1 dist1)==(Neighbor dp2 dist2) = dist1==dist2 && dp1==dp2 -nlAddNeighbor !nl !n = {-# SCC nlAddNeighbor #-} nl + NL_Cons n NL_Nil +---------------------------------------- --- mappend (NeighborList (x:.xs) ) (NeighborList (y:.ys) ) = {-# SCC mappend_NeighborList #-} case k of --- 1 -> if x < y then NeighborList (x:.Strict.Nil) else NeighborList (y:.Strict.Nil) --- otherwise -> NeighborList $ Strict.take k $ interleave (x:.xs) (y:.ys) --- where --- k=fromIntegral $ natVal (Proxy :: Proxy k) +-- | Find the nearest neighbor of a node. -- --- interleave !xs Strict.Nil = xs --- interleave Strict.Nil !ys = ys --- interleave (x:.xs) (y:.ys) = case compare x y of --- LT -> x:.(interleave xs (y:.ys)) --- GT -> y:.(interleave (x:.xs) ys) --- EQ -> if neighbor x == neighbor y --- then x:.interleave xs ys --- else x:.(y:.(interleave xs ys)) - -------------------------------------------------------------------------------- --- single tree - -{-# INLINABLE findNeighborList #-} -findNeighborList :: --- ( KnownNat k - ( ViewParam Param_k (NeighborList k dp) - , SpaceTree t dp - , Eq dp - , Real (Scalar dp) - , CanError (Scalar dp) - , ValidNeighbor dp - ) => t dp -> dp -> NeighborList k dp -findNeighborList !t !query = findEpsilonNeighborListWith zero zero t query - -{-# INLINABLE findEpsilonNeighborListWith #-} -findEpsilonNeighborListWith :: --- ( KnownNat k - ( ViewParam Param_k (NeighborList k dp) - , SpaceTree t dp - , Eq dp - , Real (Scalar dp) - , CanError (Scalar dp) - , ValidNeighbor dp - ) => NeighborList k dp -> Scalar dp -> t dp -> dp -> NeighborList k dp -findEpsilonNeighborListWith !knn !epsilon !t !query = - {-# SCC findEpsilonNeighborListWith #-} --- prunefoldC (knn_catadp smudge query) knn t --- prunefoldB_CanError_sort query (knn_catadp smudge query) (knn_cata_dist smudge query) knn t - prunefoldB_CanError (knn_catadp smudge query) (knn_cata smudge query) knn t --- prunefoldD (knn_catadp smudge query) (knn_cata2 smudge query) knn t - where - smudge = 1/(1+epsilon) - -{-# INLINABLE knn_catadp #-} --- {-# INLINE knn_catadp #-} -knn_catadp :: forall k dp. --- ( KnownNat k - ( ViewParam Param_k (NeighborList k dp) - , Metric dp - , Eq dp - , CanError (Scalar dp) - , ValidNeighbor dp - ) => Scalar dp -> dp -> dp -> NeighborList k dp -> NeighborList k dp -knn_catadp !smudge !query !dp !knn = {-# SCC knn_catadp #-} - -- dist==0 is equivalent to query==dp, - -- but we have to calculate dist anyways so it's faster - if dist==0 || dist>bound --- if dist==0 || isError dist - then knn - else nlAddNeighbor knn $ Neighbor dp dist - where - dist = distanceUB dp query bound - bound = smudge*nlMaxDist knn --- dist = isFartherThanWithDistanceCanError dp query --- $ nlMaxDist knn * smudge - --- {-# INLINABLE knn_cata #-} -{-# INLINE knn_cata #-} -knn_cata :: forall k t dp. - ( ViewParam Param_k (NeighborList k dp) - , SpaceTree t dp - , Real (Scalar dp) - , Eq dp - , CanError (Scalar dp) - , ValidNeighbor dp - ) => Scalar dp -> dp -> t dp -> NeighborList k dp -> NeighborList k dp -knn_cata !smudge !query !t !knn = {-# SCC knn_cata #-} - if dist==0 - then if isError knn - then nlSingleton $ Neighbor (stNode t) maxBound - else knn - else if isError dist - then errorVal - else nlAddNeighbor knn $ Neighbor (stNode t) dist - where - dist = stIsMinDistanceDpFartherThanWithDistanceCanError t query - $ nlMaxDist knn * smudge - -{-# INLINABLE prunefoldB_CanError_sort #-} -prunefoldB_CanError_sort :: - ( SpaceTree t a - , ValidNeighbor a - , b ~ NeighborList k a - , ClassicalLogic a - , CanError (Scalar a) - , Bounded (Scalar a) - ) => - a -> (a -> b -> b) -> (Scalar a -> t a -> b -> b) -> b -> t a -> b -prunefoldB_CanError_sort !query !f1 !f2 !b !t = {-# SCC prunefoldB_CanError_sort #-} - go ( distance (stNode t) query :!: t ) b +-- NOTE: +-- If we remove the call to "inline" on "foldr'", +-- GHC 7.10 will pass dictionaries and everything becomes very slow. +{-# INLINE findNeighbor #-} +findNeighbor :: + ( SpaceTree t dp + , Bounded (Scalar dp) + ) => t dp -> dp -> Neighbor dp +findNeighbor t q = + {-# SCC findNeighbor #-} + go (Labeled' t startdist) startnode where - go !( dist :!: t ) !b = if isError res - then b - else foldr' go b'' children' - where - res = f2 dist t b - b'' = foldr' f1 res (stLeaves t) - - children' - = {-# SCC children' #-} qsortHalf (\( d1 :!: _ ) ( d2 :!: _ ) -> compare d2 d1) - $ map (\x -> ( stIsMinDistanceDpFartherThanWithDistanceCanError x query maxdist - :!: x )) --- $ map (\x -> ( distanceUB (stNode x) query (lambda t+maxdist), x )) --- $ map (\x -> ( distance (stNode x) query , x )) - $ toList - $ stChildren t - - maxdist = nlMaxDist b'' - --- | This is a version of quicksort that only descends on its lower half. --- That is, it only "approximately" sorts a list. --- It is modified from http://en.literateprograms.org/Quicksort_%28Haskell%29 -{-# INLINABLE qsortHalf #-} -qsortHalf :: (a -> a -> Ordering) -> [a] -> [a] -qsortHalf !cmp !x = {-# SCC qsortHalf #-} go x [] + startnode = if startdist == 0 + then Neighbor q maxBound + else Neighbor (stNode t) startdist + + startdist = distance (stNode t) q + + go (Labeled' t dist) (Neighbor n distn) = if dist > maxdist + then Neighbor n distn + else inline foldr' go leafres + $ sortBy (\(Labeled' _ d1) (Labeled' _ d2) -> compare d2 d1) + $ map (\t' -> Labeled' t' (distanceUB q (stNode t') (distnleaf+stMaxDescendentDistance t))) + $ toList + $ stChildren t + where + leafres@(Neighbor _ distnleaf) = inline foldr' + (\dp n@(Neighbor _ distn') -> cata dp (distanceUB q dp distn') n) + (cata (stNode t) dist (Neighbor n distn)) + (stLeaves t) + + maxdist = distn+stMaxDescendentDistance t + + cata !dp !dist (Neighbor n distn) = + if dist==0 || dist>distn + then Neighbor n distn + else Neighbor dp dist + +-- {-# INLINE findAllNeighbors #-} +-- findAllNeighbors :: +-- ( SpaceTree t dp +-- , Bounded (Scalar dp) +-- ) => Scalar dp +-- -> t dp +-- -> [dp] +-- -> All Constructible0 ( dp, Neighbor dp ) +-- findAllNeighbors epsilon rtree qs = fromList $ map (\dp -> (dp, findNeighbor dp rtree)) qs + +---------------------------------------- + +-- | Find the nearest neighbor of a node. +-- Internally, this function does not sort the distances of the children before descending. +-- In some (rare) cases this reduces the number of distance comparisons. +{-# INLINE findNeighbor_NoSort #-} +findNeighbor_NoSort :: + ( SpaceTree t dp + , Bounded (Scalar dp) + ) => t dp -> dp -> Neighbor dp +findNeighbor_NoSort t q = + {-# SCC findNeighbor_NoSort #-} + go t (Neighbor q maxBound) where - go [] !y = y - go [x] !y = x:y - go (x:xs) !y = part xs [] [x] [] + go t res@(Neighbor _ distn) = if dist > maxdist + then res + else inline foldr' go leafres $ stChildren t where - part [] !l !e !g = go l (e ++ g ++ y) - part (z:zs) !l !e !g = case cmp z x of - GT -> part zs l e (z:g) - LT -> part zs (z:l) e g - EQ -> part zs l (z:e) g - -{-# INLINABLE knn_cata_dist #-} -knn_cata_dist :: forall k t dp. - ( ViewParam Param_k (NeighborList k dp) - , SpaceTree t dp - , Real (Scalar dp) - , Eq dp - , CanError (Scalar dp) - , ValidNeighbor dp - ) => Scalar dp -> dp -> Scalar dp -> t dp -> NeighborList k dp -> NeighborList k dp -knn_cata_dist !smudge !query !dist !t !knn = {-# SCC knn_cata #-} - if dist==0 - then if isError knn - then nlSingleton $ Neighbor (stNode t) maxBound - else knn --- else if dist - lambda t > nlMaxDist knn * smudge -- isError dist - else if isError dist - then errorVal - else nlAddNeighbor knn $ Neighbor (stNode t) dist --- where --- dist = stIsMinDistanceDpFartherThanWithDistanceCanError t query --- $ nlMaxDist knn * smudge - ---------------------------------------- - -{-# INLINABLE findAllNeighbors #-} -findAllNeighbors :: forall k dp t. - ( ViewParam Param_k (NeighborList k dp) - , SpaceTree t dp - , NFData (Scalar dp) - , NFData dp - , Real (Scalar dp) - , CanError (Scalar dp) - , ValidNeighbor dp - ) => Scalar dp -> t dp -> [dp] -> Seq (dp,NeighborList k dp) -findAllNeighbors epsilon rtree qs = reduce $ map - (\dp -> singleton (dp, findEpsilonNeighborListWith zero epsilon rtree dp)) - qs - -{-# INLINABLE findAllNeighbors' #-} -findAllNeighbors' :: forall k dp t. - ( ViewParam Param_k (NeighborList k dp) - , SpaceTree t dp - , NFData (Scalar dp) - , NFData dp - , Real (Scalar dp) - , CanError (Scalar dp) - , ValidNeighbor dp - ) => Scalar dp -> t dp -> [dp] -> Seq (Labeled' dp (NeighborList k dp)) -findAllNeighbors' epsilon rtree qs = fromList $ map - (\dp -> mkLabeled' dp $ findEpsilonNeighborListWith zero epsilon rtree dp) - qs --- findAllNeighbors' epsilon rtree qs = reduce $ map --- (\dp -> singleton $ mkLabeled' dp $ findEpsilonNeighborListWith zero epsilon rtree dp) --- qs - -mkLabeled' :: x -> y -> Labeled' x y -mkLabeled' x y = Labeled' x y + leafres = inline foldr' + (\dp n@(Neighbor _ distn') -> cata dp (distanceUB q dp distn') n) + (cata (stNode t) dist res) + (stLeaves t) + + dist = distanceUB q (stNode t) maxdist + maxdist = distn+stMaxDescendentDistance t + + cata !dp !dist (Neighbor n distn) = + if dist==0 || dist>distn + then Neighbor n distn + else Neighbor dp dist + +-- {-# INLINE findAllNeighbors_NoSort #-} +-- findAllNeighbors_NoSort :: +-- ( SpaceTree t dp +-- , Bounded (Scalar dp) +-- ) => Scalar dp +-- -> t dp +-- -> [dp] +-- -> All Constructible0 ( dp, Neighbor dp ) +-- findAllNeighbors_NoSort epsilon rtree qs = fromList $ map (\dp -> (dp, findNeighbor_NoSort dp rtree)) qs diff --git a/src/HLearn/Data/SpaceTree/Algorithms_Specialized.hs b/src/HLearn/Data/SpaceTree/Algorithms_Specialized.hs new file mode 100644 index 0000000..32c4d13 --- /dev/null +++ b/src/HLearn/Data/SpaceTree/Algorithms_Specialized.hs @@ -0,0 +1,125 @@ +-- | This module contains algorithms for efficient operations over space trees. +-- Currently, only nearest neighbor queries are implemented. +-- It would be easy to implement more query types, however. +-- If there is another query type you want supported, ask me and I'll implement it for you. +-- +-- The paper gives full details on possible queries. +module HLearn.Data.SpaceTree.Algorithms_Specialized + ( + + Neighbor (..) + , ValidNeighbor (..) + + , findNeighbor + , findNeighbor_NoSort + ) + where + +import GHC.Exts (inline) +import Data.List (sortBy) + +import SubHask +import SubHask.Algebra.Array +import SubHask.Algebra.Container +import SubHask.Algebra.Vector +import SubHask.Compatibility.Containers +import SubHask.Monad +import SubHask.TemplateHaskell.Deriving + +import HLearn.Data.SpaceTree + +------------------------------------------------------------------------------- + +data Neighbor dp = Neighbor +-- { neighbor :: !dp +-- , neighborDistance :: !(Scalar dp) + { neighbor :: !(Labeled' (UVector "dyn" Float) Int) + , neighborDistance :: !Float + } + +type ValidNeighbor dp = dp~(Labeled' (UVector "dyn" Float) Int) + +deriving instance (Show dp, Show (Scalar dp)) => Show (Neighbor dp) + +instance (NFData dp, NFData (Scalar dp)) => NFData (Neighbor dp) where + rnf (Neighbor _ _) = () + +type instance Logic (Neighbor dp) = Bool + +instance (Eq dp, Eq (Scalar dp)) => Eq_ (Neighbor dp) where + (Neighbor dp1 dist1)==(Neighbor dp2 dist2) = dist1==dist2 && dp1==dp2 + +---------------------------------------- + +-- | Find the nearest neighbor of a node. +-- +-- NOTE: +-- If we remove the call to "inline" on "foldr'", +-- GHC 7.10 will pass dictionaries and everything becomes very slow. +{-# INLINE findNeighbor #-} +findNeighbor :: + ( SpaceTree t dp + , Bounded (Scalar dp) + , ValidNeighbor dp + ) => Scalar dp -> t dp -> dp -> Neighbor dp +findNeighbor ε t q = + {-# SCC findNeighbor #-} + go (Labeled' t startdist) startnode + where + startnode = if startdist == 0 + then Neighbor q maxBound + else Neighbor (stNode t) startdist + + startdist = distance (stNode t) q + + go (Labeled' t dist) (Neighbor n distn) = if dist*ε > maxdist + then Neighbor n distn + else inline foldr' go leafres + $ sortBy (\(Labeled' _ d1) (Labeled' _ d2) -> compare d2 d1) + $ map (\t' -> Labeled' t' (distanceUB q (stNode t') (distnleaf+stMaxDescendentDistance t))) + $ toList + $ stChildren t + where + leafres@(Neighbor _ distnleaf) = inline foldr' + (\dp n@(Neighbor _ distn') -> cata dp (distanceUB q dp distn') n) + (cata (stNode t) dist (Neighbor n distn)) + (stLeaves t) + + maxdist = distn+stMaxDescendentDistance t + + cata !dp !dist (Neighbor n distn) = + if dist==0 || dist>distn + then Neighbor n distn + else Neighbor dp dist + +---------------------------------------- + +-- | Find the nearest neighbor of a node. +-- Internally, this function does not sort the distances of the children before descending. +-- In some (rare) cases this reduces the number of distance comparisons. +{-# INLINE findNeighbor_NoSort #-} +findNeighbor_NoSort :: + ( SpaceTree t dp + , Bounded (Scalar dp) + , ValidNeighbor dp + ) => Scalar dp -> t dp -> dp -> Neighbor dp +findNeighbor_NoSort ε t q = + {-# SCC findNeighbor_NoSort #-} + go t (Neighbor q maxBound) + where + go t res@(Neighbor _ distn) = if dist*ε > maxdist + then res + else inline foldr' go leafres $ stChildren t + where + leafres = inline foldr' + (\dp n@(Neighbor _ distn') -> cata dp (distanceUB q dp distn') n) + (cata (stNode t) dist res) + (stLeaves t) + + dist = distanceUB q (stNode t) maxdist + maxdist = distn+stMaxDescendentDistance t + + cata !dp !dist (Neighbor n distn) = + if dist==0 || dist>distn + then Neighbor n distn + else Neighbor dp dist diff --git a/src/HLearn/Data/SpaceTree/CoverTree.hs b/src/HLearn/Data/SpaceTree/CoverTree.hs index 60f649e..3472f06 100644 --- a/src/HLearn/Data/SpaceTree/CoverTree.hs +++ b/src/HLearn/Data/SpaceTree/CoverTree.hs @@ -1,55 +1,94 @@ -{-# LANGUAGE DataKinds #-} - +-- | This module provides an interface to the cover tree data structure. +-- Cover trees are used for fast nearest neighbor queries. +-- They are similar to kd-trees, but they are faster in high dimensions and can be used on more types of data. +-- In particular, they can be used on anything that is an instance of "Metric". +-- +-- See the paper for a detailed explanation of the theory. +-- I'm happy to answer any questions you have about the implementation details here. module HLearn.Data.SpaceTree.CoverTree + ( BCoverTree + , UCoverTree + , CoverTree_ + , ValidCT + + -- * Debug interface + -- | The functions below are not intended for standard use. + -- They provide an advanced interface to assist with development. + -- Instead, you should use the functions from the "Container" hierarchy of classes provided by subhask. + + -- ** Optimizations + , ctAdoptNodes + , setMaxDescendentDistance + , sortChildren + , cmp_numdp_distance + , cmp_numdp_distance' + , cmp_distance_numdp + , cmp_distance_numdp' + , setLeaves + , packCT + + -- ** Alternative Construction Methods + , trainMonoid + + , trainInsertOrig + , trainInsertNoSort + , trainInsert + + , insertCTOrig + , insertCTNoSort + , insertCT + + , AddChildMethod + , addChild_nothing + , addChild_parent + , addChild_ancestor + + -- ** Performance Measures + , ctAveCoverRatio + , ctMaxCoverRatio + , ctMovableParents + , ctBetterMovableParents + , ctMovableNodes + , ctBetterMovableNodes + + -- ** Invariants + , invariant_CoverTree_covering + , invariant_CoverTree_tightCovering + , invariant_CoverTree_maxDescendentDistance + , invariant_CoverTree_separating + ) where -import Debug.Trace import qualified Data.List as L -import Data.Either import qualified Prelude as P -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector as V - -import Data.Params - import SubHask import SubHask.Monad +import SubHask.Algebra.Array import SubHask.Algebra.Container +import SubHask.Algebra.Ord +import SubHask.Algebra.Vector import SubHask.Compatibility.Containers -import SubHask.Compatibility.Vector -import SubHask.Compatibility.Vector.Lebesgue -import HLearn.Data.UnsafeVector - import HLearn.Data.SpaceTree -import HLearn.Models.Distributions.Univariate.Normal - -import Diagrams.Prelude hiding (distance,trace,query,connect,Semigroup,(<>),Scalar,Monoid,size) -import qualified Diagrams.Prelude as D -import Diagrams.Backend.SVG hiding (size) +import HLearn.Data.SpaceTree.CoverTree.Unsafe +import HLearn.Models.Distributions ---------- -import System.IO.Unsafe -import Data.IORef - -{-# NOINLINE expratIORef #-} -expratIORef = unsafePerformIO $ newIORef (1.3::Rational) - -setexpratIORef :: Rational -> P.IO () -setexpratIORef r = writeIORef expratIORef r - -{-# INLINABLE exprat_ #-} -exprat_ :: Field r => r -exprat_ = fromRational $ unsafePerformIO $ readIORef expratIORef +import Debug.Trace ------------------------------------------------------------------------------- -type CoverTree dp = CoverTree_ (Static (13/10)) Array Array dp +-- | The type of boxed cover trees. +type BCoverTree = CoverTree_ () BArray BArray +-- | The type of unboxed cover trees. +-- Typically more than 2x faster than boxed cover trees. +type UCoverTree = CoverTree_ () BArray UArray + +-- | A generic type of cover tree that let's the user specify a number of internal parameters about how memory is represented internally. +-- You probably don't want to use this type. data CoverTree_ - ( exprat :: Config Frac ) + ( exprat :: * ) -- FIXME: should be "Frac" from typeparams ( childC :: * -> * ) ( leafC :: * -> * ) ( dp :: * ) @@ -61,51 +100,27 @@ data CoverTree_ , maxDescendentDistance :: !(Scalar dp) , children :: !(childC (CoverTree_ exprat childC leafC dp)) , leaves :: !(leafC dp) +-- { nodedp :: {-#UNPACK#-}!(Labeled' (UVector "dyn" Float) Int) -- , nodeWeight :: {-#UNPACK#-}!Float -- , level :: {-#UNPACK#-}!Int -- , numdp :: {-#UNPACK#-}!Float -- , maxDescendentDistance :: {-#UNPACK#-}!Float --- , children :: {-#UNPACK#-}!(Array (CoverTree_ exprat childC leafC dp)) --- , leaves :: {-#UNPACK#-}!(UnboxedArray dp) +-- , children :: {-#UNPACK#-}!(BArray (CoverTree_ exprat childC leafC dp)) +-- , leaves :: {-#UNPACK#-}!(UArray dp) } -mkParams ''CoverTree_ +mkMutable [t| forall a b c d. CoverTree_ a b c d |] type instance Scalar (CoverTree_ exprat childC leafC dp) = Scalar dp type instance Logic (CoverTree_ exprat childC leafC dp) = Bool type instance Elem (CoverTree_ exprat childC leafC dp) = dp - -instance ValidCT exprat childC leafC dp => Normed (CoverTree_ exprat childC leafC dp) where - size = numdp - -instance ValidCT exprat childC leafC dp => Eq_ (CoverTree_ exprat childC leafC dp) where - ct1==ct2 = stToList ct1==stToList ct2 - -deriving instance - ( Show (childC (CoverTree_ exprat childC leafC dp)) - , Show (leafC dp) - , Show dp - , Show (Scalar dp) - , ValidCT exprat childC leafC dp - ) => Show (CoverTree_ exprat childC leafC dp) - -instance - ( NFData (childC (CoverTree_ exprat childC leafC dp)) - , NFData (leafC dp) - , NFData dp - , NFData (Scalar dp) - ) => NFData (CoverTree_ exprat childC leafC dp) where - rnf ct = deepseq (nodedp ct) - $ deepseq (nodeWeight ct) - $ deepseq (level ct) - $ deepseq (numdp ct) - $ deepseq (maxDescendentDistance ct) - $ deepseq (children ct) - $ deepseq (leaves ct) - $ rnf () - +type instance SetElem (CoverTree_ exprat childC leafC dp) dp' = CoverTree_ exprat childC leafC dp' -- | This type alias simplifies all our type signatures. +-- It ensures that a "CoverTree_"'s parameters are sufficient to carry out the necessary operations. +-- +-- FIXME: +-- Trim this set of constraints to be more manageable. -- -- FIXME: -- There is a much smaller subset of these that is actually needed, @@ -115,25 +130,25 @@ type ValidCT exprat childC leafC dp = , Foldable (leafC dp) , Constructible (childC (CoverTree_ exprat childC leafC dp)) , Constructible (leafC dp) + , IxContainer (childC (CoverTree_ exprat childC leafC dp)) + , Sliceable (leafC dp) , Normed (childC (CoverTree_ exprat childC leafC dp)) , Normed (leafC dp) , Elem (childC (CoverTree_ exprat childC leafC dp)) ~ CoverTree_ exprat childC leafC dp , Elem (leafC dp) ~ dp - , VG.Vector leafC dp - , VG.Vector leafC (Scalar dp) , Metric dp , QuotientField (Scalar dp) Int , Real (Scalar dp) , Bounded (Scalar dp) - , CanError (Scalar dp) +-- , CanError (Scalar dp) , Logic dp ~ Bool , Logic (CoverTree_ exprat childC leafC dp) ~ Bool , Logic (Scalar (leafC dp)) ~ Bool , NFData (CoverTree_ exprat childC leafC dp) , HasScalar dp , Eq_ (childC (CoverTree_ exprat childC leafC dp)) - - , Param_exprat (CoverTree_ exprat childC leafC dp) + , Index (leafC dp) ~ Int + , Scalar (childC (CoverTree_ exprat childC leafC dp)) ~ Int , ClassicalLogic (leafC dp) , ClassicalLogic (leafC (CoverTree_ exprat childC leafC dp)) @@ -141,12 +156,16 @@ type ValidCT exprat childC leafC dp = , Container (leafC dp) , Container (childC (CoverTree_ exprat childC leafC dp)) + -- ghc7.10 + , Elem (childC (CoverTree_ exprat childC leafC dp)) ~ (CoverTree_ exprat childC leafC dp) + , Scalar (childC (CoverTree_ exprat childC leafC dp)) ~ Scalar (leafC dp) + , Hilbert (Scalar (Elem (childC (Scalar dp)))) + , FiniteModule (Scalar (Elem (childC (Scalar dp)))) + -- unpack --- , Scalar dp~Float --- , childC~Array --- , leafC~UnboxedArray --- , VU.Unbox dp --- , VU.Unbox (Scalar dp) +-- , dp ~ Labeled' (UVector "dyn" Float) Int +-- , childC~BArray +-- , leafC~UArray -- these constraints come from hlearn-allknn , Scalar (leafC dp) ~ Scalar (childC (CoverTree_ exprat childC leafC dp)) @@ -158,7 +177,6 @@ type ValidCT exprat childC leafC dp = , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) , NFData (Scalar dp) , NFData dp --- , Ord dp -- debugging constraints , Show (Scalar dp) @@ -167,17 +185,11 @@ type ValidCT exprat childC leafC dp = , Show (childC (CoverTree_ exprat childC leafC dp)) , Show (Scalar (childC (CoverTree_ exprat childC leafC dp))) , Show (Scalar (leafC dp)) - - - , VG.Vector childC (CoverTree_ exprat childC leafC dp) - , VG.Vector childC (Scalar dp) - --- , exprat ~ (13/10) --- , childC ~ Array --- , leafC ~ UnboxedArray --- , dp ~ Labeled' (L2 UnboxedVector Float) Int ) +------------------------------------------------------------------------------- +-- type classes + instance ( ValidCT exprat childC leafC dp ) => SpaceTree (CoverTree_ exprat childC leafC) dp @@ -186,641 +198,302 @@ instance type ChildContainer (CoverTree_ exprat childC leafC ) = childC type LeafContainer (CoverTree_ exprat childC leafC ) = leafC - {-# INLINABLE stMinDistanceWithDistance #-} - {-# INLINABLE stMaxDistanceWithDistance #-} - - stMinDistanceWithDistance !ct1 !ct2 = - (# dist-(maxDescendentDistance ct1)-(maxDescendentDistance ct2), dist #) - where dist = distance (nodedp ct1) (nodedp ct2) - - stMaxDistanceWithDistance !ct1 !ct2 = - (# dist+(maxDescendentDistance ct1)+(maxDescendentDistance ct2), dist #) - where dist = distance (nodedp ct1) (nodedp ct2) + stChildren = children + stLeaves = leaves + stNode = nodedp + stWeight = nodeWeight - {-# INLINABLE stMinDistanceDpWithDistance #-} - {-# INLINABLE stMaxDistanceDpWithDistance #-} + stMaxDescendentDistance = maxDescendentDistance - stMinDistanceDpWithDistance !ct !dp = - (# dist - maxDescendentDistance ct, dist #) - where dist = distance (nodedp ct) dp +---------------------------------------- +-- misc type classes - stMaxDistanceDpWithDistance !ct !dp = - (# dist + maxDescendentDistance ct, dist #) - where dist = distance (nodedp ct) dp +deriving instance + ( Show (childC (CoverTree_ exprat childC leafC dp)) + , Show (leafC dp) + , Show dp + , Show (Scalar dp) + , ValidCT exprat childC leafC dp + ) => Show (CoverTree_ exprat childC leafC dp) - {-# INLINABLE stIsMinDistanceDpFartherThanWithDistanceCanError #-} - {-# INLINABLE stIsMaxDistanceDpFartherThanWithDistanceCanError #-} +instance + ( NFData (childC (CoverTree_ exprat childC leafC dp)) + , NFData (leafC dp) + , NFData dp + , NFData (Scalar dp) + ) => NFData (CoverTree_ exprat childC leafC dp) where + rnf ct = deepseq (nodedp ct) + $ deepseq (nodeWeight ct) + $ deepseq (level ct) + $ deepseq (numdp ct) + $ deepseq (maxDescendentDistance ct) + $ deepseq (children ct) + $ deepseq (leaves ct) + $ rnf () - stIsMinDistanceDpFartherThanWithDistanceCanError !ct !dp !b = - isFartherThanWithDistanceCanError (nodedp ct) dp (b+maxDescendentDistance ct) - where - isFartherThanWithDistanceCanError p q b = if d > b then errorVal else d - where d = distanceUB p q b +instance + ( Arbitrary dp + , ValidCT exprat childC leafC dp + ) => Arbitrary (CoverTree_ exprat childC leafC dp) where + arbitrary = do + x <- arbitrary + xs <- arbitrary + return $ fromList1 x xs - stIsMaxDistanceDpFartherThanWithDistanceCanError !ct !dp !b = - isFartherThanWithDistanceCanError (nodedp ct) dp (b-maxDescendentDistance ct) - where - isFartherThanWithDistanceCanError p q b = if d > b then errorVal else d - where d = distanceUB p q b +---------------------------------------- +-- comparison - {-# INLINABLE stMinDistanceDpFromDistance #-} - {-# INLINABLE stMaxDistanceDpFromDistance #-} +instance ValidCT exprat childC leafC dp => Eq_ (CoverTree_ exprat childC leafC dp) where + ct1==ct2 = stToList ct1==stToList ct2 - stMinDistanceDpFromDistance !ct !dp !dist = dist-maxDescendentDistance ct - stMaxDistanceDpFromDistance !ct !dp !dist = dist+maxDescendentDistance ct +-- FIXME: need proper MultiSet type to implement +-- +-- instance +-- ( ValidCT exprat childC leafC dp +-- ) => POrd_ (Maybe' (CoverTree_ exprat childC leafC dp)) +-- where +-- +-- inf ct1 ct2 = trainInsert addChild_ancestor +-- $ inf (convertContainer ct1) (convertContainer ct2 :: Set dp) +-- where +-- convertContainer = fromList . stToList +-- +-- instance +-- ( ValidCT exprat childC leafC dp +-- ) => Lattice_ (Maybe' (CoverTree_ exprat childC leafC dp)) +-- where +-- +-- sup ct1 ct2 = trainInsert addChild_ancestor +-- $ sup (convertContainer ct1) (convertContainer ct2 :: Set dp) +-- where +-- convertContainer = fromList . stToList - {-# INLINABLE stChildren #-} - {-# INLINABLE stNode #-} - {-# INLINABLE stLeaves #-} - {-# INLINABLE stHasNode #-} - stChildren = children - stLeaves = leaves - stNode = nodedp - stWeight = nodeWeight - stHasNode _ = True +---------------------------------------- +-- algebra - {-# INLINABLE ro #-} - ro _ = 0 +instance ValidCT exprat childC leafC dp => Normed (CoverTree_ exprat childC leafC dp) where + size = numdp - {-# INLINABLE lambda #-} - lambda !ct = maxDescendentDistance ct +---------------------------------------- +-- container ---------------------------------------- +instance + ( ValidCT exprat childC leafC dp + ) => Constructible (CoverTree_ exprat childC leafC dp) + where -{-# INLINABLE indicator #-} -indicator :: Ring r => Bool -> r -indicator True = 1 -indicator False = 0 + singleton = singletonCT + fromList1 x xs = fromJust' $ {-packCT $ setLeaves 0 $-} trainInsert addChild_ancestor (x:xs) + -- FIXME: + -- We can't pack the cover tree because insertion and (+) currently ignore datapoints in the leafContainer --- | returns a measure of how "spread out" descendent points are -{-# INLINABLE ctMaxCoverRatio #-} -ctMaxCoverRatio :: +instance ( ValidCT exprat childC leafC dp - , Scalar (childC (CoverTree_ exprat childC leafC dp)) ~ Scalar (leafC dp) - ) => CoverTree_ exprat childC leafC dp -> Scalar dp -ctMaxCoverRatio ct = if size (children ct) + size (leaves ct) > 0 - then maximum - $ (stMaxDescendentDistance ct / coverdist ct) - : map ctMaxCoverRatio (toList $ children ct) - else 0 + ) => Container (CoverTree_ exprat childC leafC dp) + where --- | returns a measure of how "spread out" descendent points are -{-# INLINABLE ctAveCoverRatio #-} -ctAveCoverRatio :: - ( ValidCT exprat childC leafC dp - , Scalar (childC (CoverTree_ exprat childC leafC dp)) ~ Scalar (leafC dp) - ) => CoverTree_ exprat childC leafC dp -> Normal (Scalar dp) -ctAveCoverRatio ct = if size (children ct) + size (leaves ct) > 0 - then train1Normal (stMaxDescendentDistance ct / coverdist ct) - + reduce (map ctAveCoverRatio $ toList $ children ct) - else zero + -- FIXME: use the covertree's structure! + elem e ct = elem e $ stToList ct --- | counts the number of parents in the tree that have nephews that could be placed under them -{-# INLINABLE ctMovableParents #-} -ctMovableParents :: - ( ValidCT exprat childC leafC dp - , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) - , Integral (Scalar (leafC dp)) - ) => CoverTree_ exprat childC leafC dp -> Int -ctMovableParents ct - = sum (map movableChildren $ toList $ children ct) - + sum (map ctMovableParents $ toList $ children ct) - where - movableChildren c - = sum (map totalParentsOfChildren ( toList $ children c)) - + sum (map totalParentsOfChildren (map singletonCT $ toList $ leaves c)) - - (fromIntegral $ toInteger $ size $ children c) - - (fromIntegral $ toInteger $ size $ leaves c) +-- FIXME: +-- We need to make subhask's hierarchy a bit more generic before we can make cover trees foldable. +-- +-- instance +-- ( ValidCT exprat childC leafC dp +-- ) => Foldable (CoverTree_ exprat childC leafC dp) +-- where +-- toList = stToList - totalParentsOfChildren c - = sum - $ map (\p -> indicator $ distance (nodedp c) (nodedp p) <= sepdist ct) - $ toList (children ct) - ++ toList (map singletonCT $ toList $ leaves ct) +------------------------------------------------------------------------------- +-- construction --- | counts the number of parents in the tree that have nephews that would be better placed under them -{-# INLINABLE ctBetterMovableParents #-} -ctBetterMovableParents :: - ( ValidCT exprat childC leafC dp - , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) - , Integral (Scalar (leafC dp)) - ) => CoverTree_ exprat childC leafC dp -> Int -ctBetterMovableParents ct - = sum (map betterMovableChildren $ toList $ children ct) - + sum (map ctBetterMovableParents $ toList $ children ct) - where - betterMovableChildren c - = sum (map (totalBetterParentsOfChildren c) ( toList $ children c)) - + sum (map (totalBetterParentsOfChildren c) (map singletonCT $ toList $ leaves c)) - - (fromIntegral $ toInteger $ size $ children c) - - (fromIntegral $ toInteger $ size $ leaves c) +{-# INLINABLE singletonCT #-} +singletonCT :: ValidCT exprat childC leafC dp => dp -> CoverTree_ exprat childC leafC dp +singletonCT dp = Node + { nodedp = dp + , nodeWeight = 1 + , level = minBound + , numdp = 1 + , children = empty + , leaves = empty + , maxDescendentDistance = 0 + } - totalBetterParentsOfChildren realparent c - = sum - $ map (\p -> indicator $ distance (nodedp c) (nodedp p) - <= distance (nodedp c) (nodedp realparent)) - $ toList (children ct) - ++ toList (map singletonCT $ toList $ leaves ct) +---------------------------------------- + +-- | Construct a cover tree using the original algorithm. +{-# INLINABLE trainInsertOrig #-} +trainInsertOrig :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainInsertOrig xs = {-# SCC trainInsertOrig #-}case uncons xs of + Nothing -> Nothing' + Just (dp,dps) -> Just' $ rmSingletons $ foldr' insertCTOrig (singletonCT dp) dps --- | counts the number of nodes in the tree with uncles they could be moved under -{-# INLINABLE ctMovableNodes #-} -ctMovableNodes :: - ( ValidCT exprat childC leafC dp - , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) - , Integral (Scalar (leafC dp)) - ) => CoverTree_ exprat childC leafC dp -> Int -ctMovableNodes ct - = sum (map movableChildren $ toList $ children ct) - + sum (map ctMovableNodes $ toList $ children ct) where - movableChildren c - = sum (map totalNodesOfChildren ( toList $ children c)) - + sum (map totalNodesOfChildren (map singletonCT $ toList $ leaves c)) --- - (fromIntegral $ toInteger $ size $ children c) --- - (fromIntegral $ toInteger $ size $ leaves c) + -- Removes nodes that have only a single child that contains the same datapoint. + -- These singleton nodes are not supposed to be included in the original cover tree's expicit representation, + -- but the way our insertion method works it is easiest to add them then remove them at the end of insertion. + -- The new insertion methods can't create singleton nodes. + rmSingletons :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + rmSingletons ct = if isSingleton + then rmSingletons onlyChild + else ct + { children = fromList $ map rmSingletons $ toList $ children ct + } + where + isSingleton = size (children ct) == 1 + && nodedp onlyChild == nodedp ct - totalNodesOfChildren c - = indicator - $ (> (1::Int)) - $ sum - $ map (\p -> indicator $ distance (nodedp c) (nodedp p) < sepdist ct) - $ toList (children ct) - ++ toList (map singletonCT $ toList $ leaves ct) + onlyChild = P.head $ toList $ children ct --- | counts the number of nodes in the tree with uncles they would be better placed under -{-# INLINABLE ctBetterMovableNodes #-} -ctBetterMovableNodes :: +-- | Insert a node into a cover tree using the original algorithm. +{-# INLINABLE insertCTOrig #-} +insertCTOrig :: ( ValidCT exprat childC leafC dp - , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) - , Integral (Scalar (leafC dp)) - ) => CoverTree_ exprat childC leafC dp -> Int -ctBetterMovableNodes ct - = sum (map betterMovableChildren $ toList $ children ct) - + sum (map ctBetterMovableNodes $ toList $ children ct) - where - betterMovableChildren c - = sum (map (totalBetterNodesOfChildren c) ( toList $ children c)) - + sum (map (totalBetterNodesOfChildren c) (map singletonCT $ toList $ leaves c)) --- - (fromIntegral $ toInteger $ size $ children c) --- - (fromIntegral $ toInteger $ size $ leaves c) + ) => dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +insertCTOrig dp ct = insertCTOrig_ dp ct (distance dp (nodedp ct)) - totalBetterNodesOfChildren realparent c - = indicator - $ or - $ map (\p -> if distance (nodedp c) (nodedp p) - < distance (nodedp c) (nodedp realparent) --- then trace (" distance c p = "++show (distance (nodedp c) (nodedp p)) --- ++"; distance c realparent = "++show (distance (nodedp c) (nodedp realparent)) --- ++"; c = "++show (nodedp c) --- ++"; p = "++show (nodedp p) --- ++"; realparent = "++show (nodedp c) --- ) - then --trace (" ct = "++show ct) - True - else False - ) - $ toList (children ct) - ++ toList (map singletonCT $ toList $ leaves ct) - -------------------------------------------------------------------------------- --- tests - -invariant_CoverTree_covering :: - ( ValidCT exprat childC leafC dp - , Elem (childC (Scalar dp)) ~ Scalar dp - , Elem (childC Bool) ~ Bool - , Foldable (childC Bool) - , Foldable (childC (Scalar dp)) - , VG.Vector childC Bool - ) => CoverTree_ exprat childC leafC dp -> Bool -invariant_CoverTree_covering node - = and (map invariant_CoverTree_covering $ stChildrenList node) --- && and (map (\child -> distance (nodedp node) (nodedp child) <= 1.1*coverdist node) $ stChildrenList node) - && and (map (\child -> distance (nodedp node) (nodedp child) <= coverdist node) $ stChildrenList node) - -invariant_CoverTree_tightCovering :: +-- | Helper function required for GHC's optimizer to work properly. +{-# INLINABLE insertCTOrig_ #-} +insertCTOrig_ :: forall exprat childC leafC dp. ( ValidCT exprat childC leafC dp - , Elem (childC (Scalar dp)) ~ Scalar dp - , Elem (childC Bool) ~ Bool - , Foldable (childC Bool) - , Foldable (childC (Scalar dp)) - , VG.Vector childC Bool - ) => CoverTree_ exprat childC leafC dp -> Bool -invariant_CoverTree_tightCovering node - = and (map invariant_CoverTree_tightCovering $ stChildrenList node) - && and (map (\dp -> distance dp (nodedp node) <= coverdist node) $ stDescendents node) + ) => dp + -> CoverTree_ exprat childC leafC dp + -> Scalar dp + -> CoverTree_ exprat childC leafC dp +insertCTOrig_ !dp !ct !dist = {-# SCC insertCTOrig_ #-} + if dist > coverdist ct -invariant_CoverTree_maxDescendentDistance :: - ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp -> Bool -invariant_CoverTree_maxDescendentDistance node - = and (map invariant_CoverTree_maxDescendentDistance $ stChildrenList node) --- && and (map (\dp -> distance dp (nodedp node) <= 1.1 * maxDescendentDistance node) $ stDescendents node) - && and (map (\dp -> distance dp (nodedp node) <= maxDescendentDistance node) $ stDescendents node) + -- ct can't cover dp, so create a new node at dp that covers ct + then Node + { nodedp = dp + , nodeWeight = 1 + , level = dist2level_up (Proxy::Proxy exprat) dist + , numdp = numdp ct+1 + , maxDescendentDistance = dist+maxDescendentDistance ct + , children = singleton + $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) + $ ct + , leaves = empty + } -invariant_CoverTree_separating :: - ( ValidCT exprat childC leafC dp - , Elem (childC Bool) ~ Bool - , Foldable (childC Bool) - , Foldable (childC (Scalar dp)) - , VG.Vector childC Bool - ) => CoverTree_ exprat childC leafC dp -> Bool -invariant_CoverTree_separating node - = minimum ( P.filter (>0) - $ map (\(x,y) -> distance (nodedp x) (nodedp y)) - $ cartesianProduct (toList $ children node) (toList $ children node) - ) - >= 0.9*sepdist node --- >= sepdist node - && and (VG.map invariant_CoverTree_separating $ children node) - where - cartesianProduct :: [a] -> [b] -> [(a,b)] - cartesianProduct xs ys = P.concatMap (\y -> map (\x -> (x,y)) xs) ys + -- insert dp underneath ct + else ct + { numdp = numdp ct+1 + , maxDescendentDistance = max dist (maxDescendentDistance ct) + , children = fromList $ go [] $ toList $ children ct + } --- | FIXME: is this needed? -property_leveled :: - ( ValidCT exprat childC leafC dp - , VG.Vector childC Bool - , VG.Vector childC Int - ) => CoverTree_ exprat childC leafC dp -> Bool -property_leveled node - = VG.all (== VG.head xs) xs - && VG.and (VG.map property_leveled $ children node) - where - xs = VG.map level $ children node + where + go !acc (x:xs) = if isFartherThan (nodedp x) dp (sepdist ct) + then go (x:acc) xs + else acc+((insertCTOrig dp x):xs) -------------------------------------------------------------------------------- --- optimization helpers + go !acc [] = if dist >= sepdist ct --- | uncles adopt all children that are closer to them than their parents -{-# INLINABLE ctAdoptNodes #-} -ctAdoptNodes :: - ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp -ctAdoptNodes ct = ct { children = fromList $ map ctAdoptNodes $ go [] $ toList $ children ct } - where - go acc [] = acc - go acc (x:xs) = go (x { children = (fromList accchildren+fromList xschildren+children x)}:acc') xs' - where - betterMoveNode y c = distance (nodedp x) (nodedp c) < distance (nodedp y) (nodedp c) + -- far from root, so just insert the node + then ((singletonCT dp) { level = level ct-1 }):acc - extractChildren y = ( oldchildren, y { children = fromList newchildren } ) + -- close to root, so add new lower root level + else insertCTOrig_ dp ct' dist:acc where - (oldchildren,newchildren) = L.partition (betterMoveNode y) $ toList $ children y + ct' = (singletonCT (nodedp ct)) + { level = level ct-1 + , numdp = 0 + , nodeWeight = 0 + } - extractChildrenL ys = foldl' ecgo ([],[]) ys - where - ecgo (oldchildL, newchildL) y = (oldchildren++oldchildL,newparent:newchildL) - where - (oldchildren, newparent) = extractChildren y +---------------------------------------- - (accchildren, acc') = extractChildrenL acc - (xschildren, xs') = extractChildrenL xs +-- | This corresponds to the simplified cover tree in the ICML paper. +{-# INLINABLE trainInsertNoSort #-} +trainInsertNoSort :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainInsertNoSort xs = {-# SCC trainInsertNoSort #-}case uncons xs of + Nothing -> Nothing' + Just (dp,dps) -> Just' $ foldr' insertCTNoSort (singletonCT dp) dps --- | Sets the "maxDescendentDistance" parameter to the tightest bound possible. --- This makes future queries of the tree more efficient---we can prune off more traversals, resulting in fewer distance computations. --- --- FIXME: --- This version uses a branch-and-bound recursion that is much faster than the previous naive implementation. --- But it would be cooler (and faster?) if we used a find farthest neighbor method in the SpaceTree framework. -{-# INLINABLE setMaxDescendentDistance #-} -setMaxDescendentDistance :: +-- | This corresponds to the simplified cover tree in the ICML paper. +{-# INLINABLE insertCTNoSort #-} +insertCTNoSort :: forall exprat childC leafC dp. ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp + ) => dp -> CoverTree_ exprat childC leafC dp -setMaxDescendentDistance ct = {-# SCC setMaxDescendentDistance #-} ct - { children = children' - , maxDescendentDistance = max leavesMaxDist $ go 0 (toList $ children') - } - where - - children' = fromList $ map setMaxDescendentDistance $ toList $ children ct - - leavesMaxDist = maximum $ map (distance (nodedp ct)) $ toList $ leaves ct - - go curmax [] = curmax - go curmax (x:xs) = go curmax' xs - where - curmax' = if dist + maxDescendentDistance x <= curmax - then curmax - else go (maximum [dist,leavesMaxDist,curmax]) (toList $ children x) - - leavesMaxDist = maximum $ map (distance (nodedp ct)) $ toList $ leaves x - - dist = distance (nodedp x) (nodedp ct) - ---------------------------------------- + -> CoverTree_ exprat childC leafC dp +insertCTNoSort dp ct = {-# SCC insertCTNoSort #-} insertCTNoSort_ dp ct $ distance dp (nodedp ct) -{-# INLINABLE sortChildren #-} -sortChildren :: +-- | Helper function required for GHC's optimizer to work properly. +{-# INLINABLE insertCTNoSort_ #-} +insertCTNoSort_ :: forall exprat childC leafC dp. ( ValidCT exprat childC leafC dp - ) => ( CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp - -> Ordering - ) + ) => dp -> CoverTree_ exprat childC leafC dp + -> Scalar dp -> CoverTree_ exprat childC leafC dp -sortChildren cmp ct = ct - { children = fromList $ L.sortBy (cmp ct) $ map (sortChildren cmp) $ toList $ children ct - } +insertCTNoSort_ dp ct dist = {-# SCC insertCTNoSort_ #-} + if dist > coverdist ct -cmp_numdp_distance ct a b - = compare (numdp a) (numdp b) - + compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + -- ct can't cover dp, so create a new node at dp that covers ct + then Node + { nodedp = dp + , nodeWeight = 1 + , level = dist2level_up (Proxy::Proxy exprat) dist + , numdp = numdp ct+1 + , maxDescendentDistance = dist+maxDescendentDistance ct + , children = singleton + $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) + $ ct + , leaves = empty + } -cmp_numdp_distance' ct b a - = compare (numdp a) (numdp b) - + compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + -- insert dp underneath ct + else ct + { numdp = numdp ct+1 + , maxDescendentDistance = max dist (maxDescendentDistance ct) + , children = {-# SCC conv #-} fromList $ go [] $ toList $ children ct + } -cmp_distance_numdp ct a b - = compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) - + compare (numdp a) (numdp b) + where + go !acc [] = {-# SCC go #-} ((singletonCT dp) { level = level ct-1 }):acc + go !acc (x:xs) = {-# SCC go #-} if isFartherThan (nodedp x) dp (sepdist ct) + then go (x:acc) xs + else acc+((insertCTNoSort dp x):xs) -cmp_distance_numdp' ct b a - = compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) - + compare (numdp a) (numdp b) +---------------------------------------- ---------------------------------------- +-- | Provides a generic insertion method that lets us enforce a whole suite of invariants related to the nearest ancestor invariant of the ICML paper. +{-# INLINABLE trainInsert #-} +trainInsert :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => AddChildMethod exprat childC leafC (Elem xs) + -> xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainInsert addChild xs = {-# SCC trainInsert #-} case uncons xs of + Nothing -> Nothing' + Just (dp,dps) -> Just' $ foldr' (insertCT addChild) (singletonCT dp) $ toList dps --- | This function moves subtrees that have @n@ children or less from @childC@ to @leafC@. --- The @leafC@ container stores data points directly. --- This makes traversals faster and greatly improves runtimes. -{-# INLINABLE setLeaves #-} -setLeaves :: +-- | Insert a single data point into the cover tree. +{-# INLINABLE insertCT #-} +insertCT :: forall exprat childC leafC dp. ( ValidCT exprat childC leafC dp - ) => Int - -> CoverTree_ exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp + -> dp -> CoverTree_ exprat childC leafC dp -setLeaves n ct = {-# SCC setLeaves #-} if stNumNodes ct > n - then ct - { children = fromList $ map (setLeaves n) $ L.filter (not . stHasNoChildren) $ toList $ children ct - , leaves = fromList $ map nodedp $ L.filter stHasNoChildren $ toList $ children ct - } - else ct - { children = zero - , leaves = fromList $ stToList ct - } - --- | This function puts the data points into a van Embde Boas layout. --- This is a cache oblivious layout that causes queries to have fewer cache misses (and hence run faster). --- Any modifications to the cover tree after calling "PackCT" will result in a slow degradation of cache performance. -{-# INLINABLE packCT #-} -packCT :: forall exprat childC leafC dp. - ( ValidCT exprat childC leafC dp - , VG.Vector leafC dp - ) => CoverTree_ exprat childC leafC dp -> CoverTree_ exprat childC leafC dp ---packCT ct = {-# SCC packCT #-} deepseq (stToList ct) $ ct - -packCT ct = {-# SCC packCT #-} snd $ go 0 ct - where - dpvec :: leafC dp - dpvec = VG.fromList $ stToList ct - go !i !t = {-# SCC packCT_go #-} ( i',t - { nodedp = dpvec `VG.unsafeIndex` i - , leaves = VG.unsafeSlice (i+1) (VG.length $ leaves t) dpvec - , children = fromList children' - } ) - where - (i',children') = {-# SCC mapAccumL #-} L.mapAccumL - go - (i+1+VG.length (leaves t)) - (toList $ children t) - -------------------------------------------------------------------------------- - -{- --- FIXME: add proper container hierarchy -instance - ( ValidCT exprat childC leafC dp - ) => Container (CoverTree_ exprat childC leafC dp) - where - - -- FIXME: use the covertree's structure! - elem e ct = elem e $ stToList ct - notElem = not elem - --- instance --- ( ValidCT exprat childC leafC dp --- ) => Container (CoverTree_ exprat childC leafC dp) - -instance - ( ValidCT exprat childC leafC dp - ) => Constructible (CoverTree_ exprat childC leafC dp) - where - - singleton = singletonCT - -instance - ( ValidCT exprat childC leafC dp - ) => Constructible (CoverTree_ exprat childC leafC dp) - -instance - ( ValidCT exprat childC leafC dp - ) => POrd_ (CoverTree_ exprat childC leafC dp) - where - - inf ct1 ct2 = error "inf ValidCT undefined" - - -instance - ( ValidCT exprat childC leafC dp - ) => Foldable (CoverTree_ exprat childC leafC dp) - where - toList = stToList - -instance - ( ValidCT exprat childC leafC dp - ) => Monoid (CoverTree_ exprat childC leafC dp) --} - -------------------------------------------------------------------------------- --- construction - -{-# INLINABLE singletonCT #-} -singletonCT :: ValidCT exprat childC leafC dp => dp -> CoverTree_ exprat childC leafC dp -singletonCT dp = Node - { nodedp = dp - , nodeWeight = 1 - , level = minBound - , numdp = 1 - , children = empty - , leaves = empty - , maxDescendentDistance = 0 - } - ----------------------------------------- - -{-# INLINABLE trainInsertOrig #-} -trainInsertOrig :: - ( ValidCT exprat childC leafC (Elem xs) - , Foldable xs - ) => xs - -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) -trainInsertOrig xs = {-# SCC trainInsertOrig #-}case uncons xs of - Nothing -> Nothing' - Just (dp,dps) -> Just' $ rmSingletons $ foldr' insertCTOrig (singletonCT dp) dps - --- | This function is a helper function for trainInsertOrig. --- It deletes any "singleton" nodes in the tree. --- These are nodes that have only a single child, and that child is equal to the node. --- This can't occur in the new insertion methods; --- it only occurs within "trainInsertorig". -{-# INNLINABLE rmSingletons #-} -rmSingletons :: - ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp -rmSingletons ct = if isSingleton - then {-trace "rm" $-} rmSingletons onlyChild - else ct - { children = fromList $ map rmSingletons $ toList $ children ct - } - where - isSingleton = size (children ct) == 1 - && nodedp onlyChild == nodedp ct - - onlyChild = head $ toList $ children ct - - -{-# INLINABLE insertCTOrig #-} -insertCTOrig :: - ( ValidCT exprat childC leafC dp - ) => dp - -> CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp -insertCTOrig dp ct = insertCTOrig_ dp ct (distance dp (nodedp ct)) - -{-# INLINABLE insertCTOrig_ #-} -insertCTOrig_ :: forall exprat childC leafC dp. - ( ValidCT exprat childC leafC dp - ) => dp - -> CoverTree_ exprat childC leafC dp - -> Scalar dp - -> CoverTree_ exprat childC leafC dp -insertCTOrig_ !dp !ct !dist = {-# SCC insertCTOrig_ #-} - if dist > coverdist ct - - -- ct can't cover dp, so create a new node at dp that covers ct - then Node - { nodedp = dp - , nodeWeight = 1 - , level = dist2level_up (Proxy::Proxy exprat) dist - , numdp = numdp ct+1 - , maxDescendentDistance = dist+maxDescendentDistance ct - , children = singleton - $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) - $ ct - , leaves = empty - } - - -- insert dp underneath ct - else ct - { numdp = numdp ct+1 - , maxDescendentDistance = max dist (maxDescendentDistance ct) - , children = fromList $ go [] $ toList $ children ct - } - - where - go !acc (x:xs) = if isFartherThan (nodedp x) dp (sepdist ct) - then go (x:acc) xs - else acc+((insertCTOrig dp x):xs) - - go !acc [] = if dist >= sepdist ct - - -- far from root, so just insert the node - then ((singletonCT dp) { level = level ct-1 }):acc - - -- close to root, so add new lower root level - else insertCTOrig_ dp ct' dist:acc - where - ct' = (singletonCT (nodedp ct)) - { level = level ct-1 - , numdp = 0 - , nodeWeight = 0 - } - ----------------------------------------- - -{-# INLINABLE trainInsertNoSort #-} -trainInsertNoSort :: - ( ValidCT exprat childC leafC (Elem xs) - , Foldable xs - ) => xs - -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) -trainInsertNoSort xs = {-# SCC trainInsertNoSort #-}case uncons xs of - Nothing -> Nothing' - Just (dp,dps) -> Just' $ foldr' insertCTNoSort (singletonCT dp) dps - -{-# INLINABLE insertCTNoSort #-} -insertCTNoSort :: forall exprat childC leafC dp. - ( ValidCT exprat childC leafC dp - ) => dp - -> CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp -insertCTNoSort dp ct = {-# SCC insertCTNoSort #-} insertCTNoSort_ dp ct $ distance dp (nodedp ct) - -{-# INLINABLE insertCTNoSort_ #-} -insertCTNoSort_ :: forall exprat childC leafC dp. - ( ValidCT exprat childC leafC dp - ) => dp - -> CoverTree_ exprat childC leafC dp - -> Scalar dp - -> CoverTree_ exprat childC leafC dp -insertCTNoSort_ dp ct dist = {-# SCC insertCTNoSort_ #-} - if dist > coverdist ct - - -- ct can't cover dp, so create a new node at dp that covers ct - then Node - { nodedp = dp - , nodeWeight = 1 - , level = dist2level_up (Proxy::Proxy exprat) dist - , numdp = numdp ct+1 - , maxDescendentDistance = dist+maxDescendentDistance ct - , children = singleton - $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) - $ ct - , leaves = empty - } - - -- insert dp underneath ct - else ct - { numdp = numdp ct+1 - , maxDescendentDistance = max dist (maxDescendentDistance ct) - , children = fromList $ go [] $ toList $ children ct - } - - where - go !acc [] = ((singletonCT dp) { level = level ct-1 }):acc - go !acc (x:xs) = if isFartherThan (nodedp x) dp (sepdist ct) - then go (x:acc) xs - else acc+((insertCTNoSort dp x):xs) - ----------------------------------------- - -{-# INLINABLE trainInsert #-} -trainInsert :: - ( ValidCT exprat childC leafC (Elem xs) - , Foldable xs - ) => AddChildMethod exprat childC leafC (Elem xs) - -> xs - -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) -trainInsert addChild xs = {-# SCC trainInsert #-} case uncons xs of - Nothing -> Nothing' - Just (dp,dps) -> Just' $ foldr' (insertCT addChild) (singletonCT dp) $ toList dps - --- | Insert a single data point into the cover tree. -{-# INLINABLE insertCT #-} -insertCT :: forall exprat childC leafC dp. - ( ValidCT exprat childC leafC dp - ) => AddChildMethod exprat childC leafC dp - -> dp - -> CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp -insertCT addChild dp ct = - insertCT_ addChild dp ct (distance dp $ nodedp ct) +insertCT addChild dp ct = + insertCT_ addChild dp ct (distance dp $ nodedp ct) -- | This function is exactly the same as insertCT. -- We need to provide a different function, however, for performance reasons. @@ -849,7 +522,7 @@ insertCT_ :: forall exprat childC leafC dp. insertCT_ addChild dp ct dist = {-# SCC insertCT_ #-} if dist > coverdist ct -- ct can't cover dp, so create a new node at dp that covers ct - then {-# SCC insertCT_greater #-} Node + then Node { nodedp = dp , nodeWeight = 1 , level = dist2level_up (Proxy::Proxy exprat) dist @@ -862,23 +535,53 @@ insertCT_ addChild dp ct dist = {-# SCC insertCT_ #-} } -- insert dp underneath ct - else {-# SCC insertCT_under #-}ct + else ct { numdp = numdp ct+1 , maxDescendentDistance = max dist (maxDescendentDistance ct) , children = fromList $ go [] childrendists } where - childrendists = {-# SCC childrendists #-} - map (\x -> (distance dp (nodedp x), x)) $ toList $ children ct + childrendists = map (\x -> (distance dp (nodedp x), x)) $ toList $ children ct + + mindist = minimum $ map fst childrendists + + go !acc [] = addChild dp ct + go !acc ((xdist,x):xs) = if xdist == mindist && xdist <= coverdist x + then insertCT_ addChild dp x xdist:(acc+map snd xs) + else go (x:acc) xs + +{- + - FIXME: + - The code below enforces a "repulsion" invariant. + - This speeds up nearest neighbor queries in practice. + - But this implementation breaks the ancestor invariant. + - It shouldn't be too hard to fix, + - but I'm pretty sick of thinking about cover trees right now and don't want to do it. - mindist = {-# SCC mindist #-} minimum $ map fst childrendists + childrendists = map (\x -> (distance dp (nodedp x), x)) $ toList $ children ct - go !acc [] = {-# SCC go_addChild #-} addChild dp ct - go !acc ((dist,x):xs) = if dist == mindist && dist <= coverdist x - then insertCT_ addChild dp x dist:(acc+map snd xs) + (mindist:mindist2:_) = (sort $ map fst childrendists)++[maxBound,maxBound] + + go !acc [] = addChild dp ct + go !acc ((xdist,x):xs) = if xdist == mindist && xdist <= coverdist x + then insertswapper x xdist:(acc+map snd xs) else go (x:acc) xs + insertswapper x xdist = if mindist2 > sepdist ct +-- && distance (nodedp ct) dp > distance (nodedp ct) (nodedp x) +-- && distance (nodedp ct) dp > sepdist ct + && maximum dpdists > maximum xdists + && maximum xchilddists < coverdist x + && and (map (\t -> size (stChildrenList t)==0) $ stChildrenList x) + then insertCT_ addChild (nodedp x) (x {nodedp=dp}) xdist + else insertCT_ addChild dp x xdist + where + dpdists = map (\x -> distance dp (nodedp x)) $ toList $ children ct + xdists = map (\x' -> distance (nodedp x) (nodedp x')) $ toList $ children ct + xchilddists = map (distance dp . nodedp) (stChildrenList x) +-} + -- | These functions control the invariants we maintain while performing insertions into the cover tree. -- The input data point will be directly inserted as one of the children of the root of the tree. -- Therefore, we require it satisfy the separating and covering conditions (and any other applicable conditions). @@ -921,17 +624,17 @@ addChild_parent dp ct = {-# SCC addChild_parent #-} ret:acc' ret=foldr' (insertCT_internal addChild_parent) ct' $ concat $ map stToList dps {-# INLINABLE addChild_ancestor #-} -addChild_ancestor :: +addChild_ancestor :: forall exprat childC leafC dp k. ( ValidCT exprat childC leafC dp ) => AddChildMethod exprat childC leafC dp addChild_ancestor dp ct = {-# SCC addChild_ancestor #-} --- FIXME: it would be more efficient to use a proper monoid instance --- toList $ children $ foldl' (+) ct' dps - toList $ children $ foldr' (insertCT addChild_ancestor) ct' $ concat $ map stToList dps + toList $ children $ foldr' (insertCT addChild_ancestor) ct' (concat $ map stToList dps) where - (acc',dps) = rmCloseChildren addChild_ancestor dp $ toList $ children ct + ( acc', dps ) = rmCloseChildren addChild_ancestor dp $ toList $ children ct + + ct' :: CoverTree_ exprat childC leafC dp ct' = ct - { children = ((singletonCT dp) { level = level ct-1 }) `cons` fromList acc' + { children = fromList $ ((singletonCT dp) { level = level ct-1 }) : acc' } {-# INLINABLE rmCloseChildren #-} @@ -979,11 +682,6 @@ extractCloseChildren addChild dp root = {-# SCC extractCloseChildren #-} dist_ct_root = distance (nodedp ct) (nodedp root) dist_ct_dp = distance (nodedp ct) dp -justs' :: [Maybe' a] -> [a] -justs' [] = [] -justs' (Nothing':xs) = justs' xs -justs' (Just' x:xs) = x:justs' xs - {-# INLINABLE moveLeafToRoot #-} moveLeafToRoot :: forall exprat childC leafC dp. ( ValidCT exprat childC leafC dp @@ -1000,358 +698,495 @@ moveLeafToRoot ct = if stHasNoChildren ct where (leaf,ct') = rmleaf ct -{- -{-# INLINABLE rmleaf #-} -rmleaf :: - ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp - -> ( CoverTree_ exprat childC leafC dp - , CoverTree_ exprat childC leafC dp - ) -rmleaf ct = {-# SCC rmleaf #-} if stHasNoChildren (head childL) - then ( head childL - , ct - { numdp = numdp ct-nodeWeight (head childL) - , children = tail childL - } - ) - else ( itrleaf - , ct - { numdp = numdp ct-nodeWeight (head childL) - , children = itrtree `cons` tail childL - } - ) +-------------------------------------------------------------------------------- + +{-# INLINABLE trainMonoid #-} +trainMonoid :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainMonoid xs = {-# SCC trainMonoid #-} foldtree1 $ map (Just' . singletonCT) $ toList xs + +instance + ( ValidCT exprat childC leafC dp + ) => Semigroup (CoverTree_ exprat childC leafC dp) + where + + {-# INLINABLE (+) #-} + ct1 + ct2 = {-# SCC semigroup_CoverTree #-} case ctmerge_ ct1_ ct2_ dist of + (ct, []) -> ct + (ct, xs) -> {-# SCC sg_foldr #-} foldr' insertCTNoSort ct $ concat $ map stToList xs + + where + dist = distance (nodedp ct1) (nodedp ct2) + + maxlevel = maximum + [ level ct1 + , level ct2 + , dist2level_down (Proxy::Proxy exprat) dist + ] + + ct1_ = if level ct1 < maxlevel then raiseRootLevel maxlevel ct1 else ct1 + ct2_ = if level ct2 < maxlevel then raiseRootLevel maxlevel ct2 else ct2 + +-- | If the following prerequisites are met: +-- +-- > level ct1==level ct2 +-- +-- > distance (nodedp ct1) (nodedp ct2) < coverdist ct1 +-- +-- then all output covertrees will be valid. +-- The root of ct1 is guaranteed to not change. +{-# INLINABLE ctmerge_ #-} +ctmerge_ :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + -> Scalar dp + -> ( CoverTree_ exprat childC leafC dp + , [CoverTree_ exprat childC leafC dp] + ) +ctmerge_ ct1 ct2 dist = + + {-# SCC ctmerge_ #-} + ( ct' , notCoveredChildren+leftovers' ) + where + -- remove the children of ct2 that can't be covered by ct1 ("notCoveredChildren"); + -- these will be handled separately in (+) + (coveredChildren,notCoveredChildren) = {-# SCC isCovered #-} + L.partition isCovered $ stChildrenList ct2 + where + isCovered x = not $ isFartherThan (nodedp ct1) (nodedp x) (coverdist ct1) + + -- sepcovChildren is not a part of children' because we loop through children' in go + -- adding in sepcovChildren would result in extra iterations + (children',sepcovChildren,leftovers) + = {-# SCC sg_foldl #-} foldl' (go []) (stChildrenList ct1,[],[]) coveredChildren + where + -- merge covChild with a child of ct1' within the separating distance + go tot (x:xs,ys,zs) covChild = if godist <= sepdist ct1 + then let (x',zs') = ctmerge_ x covChild godist + in (x':tot++xs,ys,zs'++zs) + else go (x:tot) (xs,ys,zs) covChild + where + godist = distance (nodedp x) (nodedp covChild) + + -- nothing within separating distance, so add covChild as new child + go tot ([],ys,zs) covChild = (tot,covChild:ys,zs) + + -- update children, insert root of ct2 into ct1 + ct_minusleftovers = {-# SCC minusleftover #-} insertCTNoSort_ + (nodedp ct2) + ( ct1 + { children = fromList $ children' + sepcovChildren + , numdp = sum $ map numdp $ children' + sepcovChildren + , maxDescendentDistance = coverdist ct1*2 + } ) + dist + + -- insert any leftovers into the tree if they fit + (ct',leftovers') = {-# SCC part4 #-} foldl' go (ct_minusleftovers,[]) leftovers + where + go (ct,xs) x = if level ct >= level x && godist <= coverdist ct + then (goct, goleftovers++xs) + else (ct,x:xs) + where + godist = distance (nodedp ct) (nodedp x) + (goct,goleftovers) = ctmerge_ ct (raiseRootLevel (level ct) x) godist + +-- | FIXME: does this work if there are leaves present? +{-# INLINABLE raiseRootLevel #-} +raiseRootLevel :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => Int + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +raiseRootLevel i ct + | stHasNoChildren ct = {-# SCC raiseRootLevel #-}ct { level = i } + | i < level ct = error + $ "\nraiseRootLevel: target level less than current tree level" + + "; i="+show i + + "; level ct="+show (level ct) + + "; nodedp ct="+show (nodedp ct) + | i == level ct = {-# SCC raiseRootLevel #-}ct + | i > level ct = {-# SCC raiseRootLevel #-} raiseRootLevel i $ leaf + { level = level ct'+1 + , numdp = numdp ct'+nodeWeight leaf + , maxDescendentDistance = distance (nodedp leaf) (nodedp ct) + maxDescendentDistance ct' + , children = singleton ct' + } + where + (leaf,ct') = rmleaf ct + +{-# INLINABLE rmleaf #-} +rmleaf :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> ( CoverTree_ exprat childC leafC dp + , CoverTree_ exprat childC leafC dp + ) +rmleaf ct = {-# SCC rmleaf #-} if stHasNoChildren head + then ( head + , ct + { numdp = numdp ct-nodeWeight head + , children = tail + } + ) + else ( itrleaf + , ct + { numdp = numdp ct-nodeWeight head + , children = itrtree `cons` tail + } + ) + where + (itrleaf,itrtree) = rmleaf head + + childL = children ct + (head,tail) = case uncons childL of Just x -> x + +-------------------------------------------------------------------------------- +-- Performance measurements + +-- | returns a measure of how "spread out" descendent points are +{-# INLINABLE ctMaxCoverRatio #-} +ctMaxCoverRatio :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp -> Scalar dp +ctMaxCoverRatio ct = if size (children ct) + size (leaves ct) > 0 + then maximum + $ (stMaxDescendentDistance ct / coverdist ct) + : map ctMaxCoverRatio (toList $ children ct) + else 0 + +-- | returns a measure of how "spread out" descendent points are +{-# INLINABLE ctAveCoverRatio #-} +ctAveCoverRatio :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp -> Normal (Scalar dp) +ctAveCoverRatio ct = if size (children ct) + size (leaves ct) > 0 + then train1Normal (stMaxDescendentDistance ct / coverdist ct) + + reduce (map ctAveCoverRatio $ toList $ children ct) + else zero + +-- | counts the number of parents in the tree that have nephews that could be placed under them +{-# INLINABLE ctMovableParents #-} +ctMovableParents :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctMovableParents ct + = sum (map movableChildren $ toList $ children ct) + + sum (map ctMovableParents $ toList $ children ct) + where + movableChildren c + = sum (map totalParentsOfChildren ( toList $ children c)) + + sum (map totalParentsOfChildren (map singletonCT $ toList $ leaves c)) + - (fromIntegral $ toInteger $ size $ children c) + - (fromIntegral $ toInteger $ size $ leaves c) + + totalParentsOfChildren c + = sum + $ map (\p -> indicator $ distance (nodedp c) (nodedp p) <= sepdist ct) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +-- | counts the number of parents in the tree that have nephews that would be better placed under them +{-# INLINABLE ctBetterMovableParents #-} +ctBetterMovableParents :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctBetterMovableParents ct + = sum (map betterMovableChildren $ toList $ children ct) + + sum (map ctBetterMovableParents $ toList $ children ct) + where + betterMovableChildren c + = sum (map (totalBetterParentsOfChildren c) ( toList $ children c)) + + sum (map (totalBetterParentsOfChildren c) (map singletonCT $ toList $ leaves c)) + - (fromIntegral $ toInteger $ size $ children c) + - (fromIntegral $ toInteger $ size $ leaves c) + + totalBetterParentsOfChildren realparent c + = sum + $ map (\p -> indicator $ distance (nodedp c) (nodedp p) + <= distance (nodedp c) (nodedp realparent)) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +-- | counts the number of nodes in the tree with uncles they could be moved under +{-# INLINABLE ctMovableNodes #-} +ctMovableNodes :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctMovableNodes ct + = sum (map movableChildren $ toList $ children ct) + + sum (map ctMovableNodes $ toList $ children ct) + where + movableChildren c + = sum (map totalNodesOfChildren ( toList $ children c)) + + sum (map totalNodesOfChildren (map singletonCT $ toList $ leaves c)) + + totalNodesOfChildren c + = indicator + $ (> (1::Int)) + $ sum + $ map (\p -> indicator $ distance (nodedp c) (nodedp p) < sepdist ct) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +-- | Counts the number of nodes in the tree with uncles they would be better placed under +{-# INLINABLE ctBetterMovableNodes #-} +ctBetterMovableNodes :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctBetterMovableNodes ct + = sum (map betterMovableChildren $ toList $ children ct) + + sum (map ctBetterMovableNodes $ toList $ children ct) + where + betterMovableChildren c + = sum (map (totalBetterNodesOfChildren c) ( toList $ children c)) + + sum (map (totalBetterNodesOfChildren c) (map singletonCT $ toList $ leaves c)) + + totalBetterNodesOfChildren realparent c + = indicator + $ or + $ map (\p -> distance (nodedp c) (nodedp p) + < distance (nodedp c) (nodedp realparent) + ) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +------------------------------------------------------------------------------- +-- tests + +invariant_CoverTree_covering :: + ( ValidCT exprat childC leafC dp + , Elem (childC (Scalar dp)) ~ Scalar dp + , Elem (childC Bool) ~ Bool + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_covering node + = and (map invariant_CoverTree_covering $ stChildrenList node) + && and (map (\child -> distance (nodedp node) (nodedp child) <= coverdist node) $ stChildrenList node) + +invariant_CoverTree_tightCovering :: + ( ValidCT exprat childC leafC dp + , Elem (childC (Scalar dp)) ~ Scalar dp + , Elem (childC Bool) ~ Bool + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_tightCovering node + = and (map invariant_CoverTree_tightCovering $ stChildrenList node) + && and (map (\dp -> distance dp (nodedp node) <= coverdist node) $ stDescendents node) + +invariant_CoverTree_maxDescendentDistance :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_maxDescendentDistance node + = and (map invariant_CoverTree_maxDescendentDistance $ stChildrenList node) + && and (map (\dp -> distance dp (nodedp node) <= maxDescendentDistance node) $ stDescendents node) + +invariant_CoverTree_separating :: + ( ValidCT exprat childC leafC dp + , Elem (childC Bool) ~ Bool + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_separating node + = minimum ( P.filter (>0) + $ map (\(x,y) -> distance (nodedp x) (nodedp y)) + $ cartesianProduct (toList $ children node) (toList $ children node) + ) + >= sepdist node + && and (map invariant_CoverTree_separating $ toList $ children node) + where + cartesianProduct :: [a] -> [b] -> [(a,b)] + cartesianProduct xs ys = P.concatMap (\y -> map (\x -> (x,y)) xs) ys + +{- +-- | FIXME: +-- This needs to be converted to use subhask notation. +property_leveled :: + ( ValidCT exprat childC leafC dp + , VG.Vector childC Bool + , VG.Vector childC Int + , VG.Vector childC (CoverTree_ exprat childC leafC dp) + ) => CoverTree_ exprat childC leafC dp -> Bool +property_leveled node + = VG.all (== VG.P.head xs) xs + && VG.and (VG.map property_leveled $ children node) where - (itrleaf,itrtree) = rmleaf $ head childL - childL = children ct + xs = VG.map level $ children node -} --------------------------------------------------------------------------------- - --- instance --- ( ValidCT exprat childC leafC dp --- ) => Abelian (CoverTree_ exprat childC leafC dp) - --- FIXME: --- This specialize pragma never fires in GHC 7.8.2 --- This is possibly related to https://ghc.haskell.org/trac/ghc/ticket/8848 and http://stackoverflow.com/questions/18792388/haskell-ghc-specialize-causes-rule-left-hand-side-too-complicated-to --- --- {-# SPECIALIZE trainMonoid :: --- ( --KnownFrac exprat --- ) => [ L2 UnboxedVector Float ] --- -> Maybe' (CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)) --- #-} --- - -{-# INLINABLE trainMonoid #-} --- trainMonoid :: --- ( ValidCT exprat childC leafC (Elem xs) --- , Foldable xs --- ) => xs --- -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) -trainMonoid :: - ( --KnownFrac exprat --- ) => [ L2 UnboxedVector Float ] --- -> Maybe' (CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)) - ) => [ Labeled' (L2 UnboxedVector Float) Int ] - -> Maybe' (CoverTree_ (Static (13/10)) Array UnboxedArray (Labeled' (L2 UnboxedVector Float) Int)) -trainMonoid xs = {-# SCC trainMonoid #-} foldtree1 $ map (Just' . singletonCT) $ toList xs +------------------------------------------------------------------------------- +-- optimization helpers -instance +-- | uncles adopt all children that are closer to them than their parents +{-# INLINABLE ctAdoptNodes #-} +ctAdoptNodes :: ( ValidCT exprat childC leafC dp - ) => Semigroup (CoverTree_ exprat childC leafC dp) --- instance Semigroup (CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)) --- instance Semigroup (CoverTree_ (13/10) Array UnboxedArray (Labeled' (L2 UnboxedVector Float) Int)) - where + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +ctAdoptNodes ct = ct { children = fromList $ map ctAdoptNodes $ go [] $ toList $ children ct } + where + go acc [] = acc + go acc (x:xs) = go (x { children = (fromList accchildren+fromList xschildren+children x)}:acc') xs' + where + betterMoveNode y c = distance (nodedp x) (nodedp c) < distance (nodedp y) (nodedp c) - {-# INLINABLE (+) #-} --- {-# INLINE (+) #-} - ct1 + ct2 = {-# SCC semigroup_CoverTree #-} case ctmerge_ ct1_ ct2_ dist of - (ct, []) -> ct - (ct, xs) -> {-# SCC sg_foldr #-} foldr' insertCTNoSort ct $ concat $ map stToList xs --- (ct, xs) -> foldr' (insertCT addChild_nothing) ct $ concat $ map stToList xs --- (ct, xs) -> foldr' (insertCT_internal addChild_nothing) ct $ concat $ map stToList xs --- (ct, xs) -> foldr' (insertCT_internal addChild_ancestor) ct $ concat $ map stToList xs + extractChildren y = ( oldchildren, y { children = fromList newchildren } ) + where + (oldchildren,newchildren) = L.partition (betterMoveNode y) $ toList $ children y - where - dist = distance (nodedp ct1) (nodedp ct2) + extractChildrenL ys = foldl' ecgo ([],[]) ys + where + ecgo (oldchildL, newchildL) y = (oldchildren++oldchildL,newparent:newchildL) + where + (oldchildren, newparent) = extractChildren y - maxlevel = maximum - [ level ct1 - , level ct2 - , dist2level_down (Proxy::Proxy (13/10)) dist --- , dist2level_down (Proxy::Proxy exprat) dist - ] + (accchildren, acc') = extractChildrenL acc + (xschildren, xs') = extractChildrenL xs - ct1_ = if level ct1 < maxlevel then raiseRootLevel maxlevel ct1 else ct1 - ct2_ = if level ct2 < maxlevel then raiseRootLevel maxlevel ct2 else ct2 +-- | Sets the "maxDescendentDistance" parameter to the tightest bound possible. +-- This makes future queries of the tree more efficient---we can prune off more traversals, resulting in fewer distance computations. +-- +-- FIXME: +-- This version uses a branch-and-bound recursion that is much faster than the previous naive implementation. +-- But it would be cooler (and faster?) if we used a find farthest neighbor method in the SpaceTree framework. +{-# INLINABLE setMaxDescendentDistance #-} +setMaxDescendentDistance :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +setMaxDescendentDistance ct = {-# SCC setMaxDescendentDistance #-} ct + { children = children' + , maxDescendentDistance = max leavesMaxDist $ go 0 (toList $ children') + } + where -{- - ct1 + ct2 = {-# SCC semigroup_CoverTree #-} --- trace "semigroup" $ - if level ct1_ == level ct2_ - then {-# SCC plus_then #-} {-trace " then" $ -} case ctmerge_ ct1_ ct2_ dist of - (ct, []) -> ct - (ct, xs) -> {-trace (" |xs|="++show (size xs)) $-} foldr' (insertCT addChild_nothing) ct $ concat $ map stToList xs - else {-# SCC plus_else #-} {-trace " else" $ -} ct1_ - { children = fromList $ go [] (stChildrenList ct1_) - , numdp = numdp ct1_ + numdp ct2_ - , maxDescendentDistance = maxDescendentDistance ct2_ + dist - } + children' = fromList $ map setMaxDescendentDistance $ toList $ children ct - where - go ret [] = (raiseRootLevel (maxlevel-1) ct2_):ret - go ret (x:xs) = if isFartherThan (nodedp x) (nodedp ct2_) (coverdist x) - then go (x:ret) xs - else (x+ct2_):(ret++xs) + leavesMaxDist = maximum $ map (distance (nodedp ct)) $ toList $ leaves ct - dist = distance (nodedp ct1) (nodedp ct2) + go curmax [] = curmax + go curmax (x:xs) = go curmax' xs + where + curmax' = if dist + maxDescendentDistance x <= curmax + then curmax + else go (maximum [dist,leavesMaxDist,curmax]) (toList $ children x) - maxlevel = maximum - [ level ct1 - , level ct2 - , dist2level_down (Proxy::Proxy exprat) dist - ] + leavesMaxDist = maximum $ map (distance (nodedp ct)) $ toList $ leaves x - (ct1_,ct2_) = if level ct1 >= level ct2 - then (raiseRootLevel maxlevel ct1,ct2) - else (raiseRootLevel maxlevel ct2,ct1) --} + dist = distance (nodedp x) (nodedp ct) --- | If the following prerequisites are met: --- --- > level ct1==level ct2 --- --- > distance (nodedp ct1) (nodedp ct2) < coverdist ct1 --- --- then all output covertrees will be valid. --- The root of ct1 is guaranteed to not change. +--------------------------------------- -{-# INLINABLE ctmerge_ #-} --- {-# INLINE ctmerge_ #-} -ctmerge_ :: forall exprat childC leafC dp. - ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp - -> CoverTree_ exprat childC leafC dp - -> Scalar dp - -> ( CoverTree_ exprat childC leafC dp - , [CoverTree_ exprat childC leafC dp] +{-# INLINABLE sortChildren #-} +sortChildren :: + ( ValidCT exprat childC leafC dp + ) => ( CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + -> Ordering ) --- ctmerge_ --- :: (CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)) --- -> (CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)) --- -> Float --- -> ( (CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)) --- , [CoverTree_ (13/10) Array UnboxedArray (L2 UnboxedVector Float)] --- ) --- ctmerge_ --- :: (CoverTree_ (13/10) Array UnboxedArray (Labeled' (L2 UnboxedVector Float) Int)) --- -> (CoverTree_ (13/10) Array UnboxedArray (Labeled' (L2 UnboxedVector Float) Int)) --- -> Float --- -> ( (CoverTree_ (13/10) Array UnboxedArray (Labeled' (L2 UnboxedVector Float) Int)) --- , [CoverTree_ (13/10) Array UnboxedArray (Labeled' (L2 UnboxedVector Float) Int)] --- ) -ctmerge_ ct1 ct2 dist = --- assert "ctmerge_ level ==" (level ct1==level ct2) $ --- assert "ctmerge_ covdist <" (distance (nodedp ct1) (nodedp ct2) <= coverdist ct1) $ - - {-# SCC ctmerge_ #-} - ( ct' , notCoveredChildren+leftovers' ) - where - -- remove the children of ct2 that can't be covered by ct1 ("notCoveredChildren"); - -- these will be handled separately in (+) - (coveredChildren,notCoveredChildren) = {-# SCC isCovered #-} - L.partition isCovered $ stChildrenList ct2 - where - isCovered x = not $ isFartherThan (nodedp ct1) (nodedp x) (coverdist ct1) - - -- sepcovChildren is not a part of children' because we loop through children' in go - -- adding in sepcovChildren would result in extra iterations - (children',sepcovChildren,leftovers) - = {-# SCC sg_foldl #-} foldl' (go []) (stChildrenList ct1,[],[]) coveredChildren - where - -- merge covChild with a child of ct1' within the separating distance - go tot (x:xs,ys,zs) covChild = if godist <= sepdist ct1 - then let (x',zs') = ctmerge_ x covChild godist - in (x':tot++xs,ys,zs'++zs) - else go (x:tot) (xs,ys,zs) covChild - where - godist = distance (nodedp x) (nodedp covChild) + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +sortChildren cmp ct = ct + { children = fromList $ L.sortBy (cmp ct) $ map (sortChildren cmp) $ toList $ children ct + } - -- nothing within separating distance, so add covChild as new child - go tot ([],ys,zs) covChild = (tot,covChild:ys,zs) +cmp_numdp_distance ct a b + = compare (numdp a) (numdp b) + + compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) - -- update children, insert root of ct2 into ct1 - ct_minusleftovers = {-# SCC minusleftover #-} insertCTNoSort_ --- ct_minusleftovers = insertCT_internal addChild_nothing --- ct_minusleftovers = insertCT_internal addChild_ancestor - (nodedp ct2) - ( ct1 - { children = fromList $ children' + sepcovChildren - , numdp = sum $ map numdp $ children' + sepcovChildren - , maxDescendentDistance = coverdist ct1*2 - } ) - dist +cmp_numdp_distance' ct b a + = compare (numdp a) (numdp b) + + compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) - -- insert any leftovers into the tree if they fit - (ct',leftovers') = {-# SCC part4 #-} foldl' go (ct_minusleftovers,[]) leftovers - where - go (ct,xs) x = if level ct >= level x && godist <= coverdist ct - then (goct, goleftovers++xs) - else (ct,x:xs) - where - godist = distance (nodedp ct) (nodedp x) - (goct,goleftovers) = ctmerge_ ct (raiseRootLevel (level ct) x) godist +cmp_distance_numdp ct a b + = compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + + compare (numdp a) (numdp b) -head x = ret - where - (Just ret) = headMaybe x +cmp_distance_numdp' ct b a + = compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + + compare (numdp a) (numdp b) -tail x = ret - where - (Just ret) = tailMaybe x +--------------------------------------- --- | FIXME: does this work if there are leaves present? -{-# INLINABLE raiseRootLevel #-} -raiseRootLevel :: forall exprat childC leafC dp. +-- | This function moves subtrees that have @n@ children or less from @childC@ to @leafC@. +-- The @leafC@ container stores data points directly. +-- This makes traversals faster and greatly improves runtimes. +{-# INLINABLE setLeaves #-} +setLeaves :: ( ValidCT exprat childC leafC dp ) => Int -> CoverTree_ exprat childC leafC dp -> CoverTree_ exprat childC leafC dp -raiseRootLevel i ct - | stHasNoChildren ct = {-# SCC raiseRootLevel #-}ct { level = i } - | i < level ct = error "raiseRootLevel: target level less than current tree level" - | i == level ct = {-# SCC raiseRootLevel #-}ct - | i > level ct = {-# SCC raiseRootLevel #-} raiseRootLevel i $ leaf - { level = level ct'+1 - , numdp = numdp ct'+nodeWeight leaf - , maxDescendentDistance = distance (nodedp leaf) (nodedp ct) + maxDescendentDistance ct' - , children = singleton ct' +setLeaves n ct = {-# SCC setLeaves #-} if stNumNodes ct > n + then ct + { children = fromList $ map (setLeaves n) $ L.filter (not . stHasNoChildren) $ toList $ children ct + , leaves = fromList $ map nodedp $ L.filter stHasNoChildren $ toList $ children ct + } + else ct + { children = zero + , leaves = fromList $ stToList ct } - where - (leaf,ct') = rmleaf ct -{-# INLINABLE rmleaf #-} -rmleaf :: - ( ValidCT exprat childC leafC dp - ) => CoverTree_ exprat childC leafC dp - -> ( CoverTree_ exprat childC leafC dp - , CoverTree_ exprat childC leafC dp - ) -rmleaf ct = {-# SCC rmleaf #-} if stHasNoChildren (head childL) - then ( head childL - , ct - { numdp = numdp ct-nodeWeight (head childL) - , children = tail childL - } - ) - else ( itrleaf - , ct - { numdp = numdp ct-nodeWeight (head childL) - , children = itrtree `cons` tail childL - } - ) +-- | This function puts the data points into a van Embde Boas layout. +-- This is a cache oblivious layout that causes queries to have fewer cache misses (and hence run faster). +-- Any modifications to the cover tree after calling "PackCT" will result in a slow degradation of cache performance. +{-# INLINABLE packCT #-} +packCT :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +packCT ct = {-# SCC packCT #-} snd $ go 0 ct where - (itrleaf,itrtree) = rmleaf $ head childL - childL = children ct + dpvec :: leafC dp + dpvec = fromList $ stToList ct ---------------------------------------- + go !i !t = {-# SCC packCT_go #-} ( i',t + { nodedp = dpvec!i + , leaves = slice (i+1) (length $ leaves t) dpvec + , children = fromList children' + } ) + where + (i',children') = {-# SCC mapAccumL #-} L.mapAccumL + go + (i+1+length (leaves t)) + (toList $ children t) + +------------------------------------------------------------------------------- +-- helper functions coverdist :: (QuotientField (Scalar dp) Int, Real (Scalar dp)) => CoverTree_ exprat childC leafC dp -> Scalar dp -coverdist node = sepdist node*exprat_ +coverdist node = sepdist node*getExprat sepdist :: forall exprat childC leafC dp. (QuotientField (Scalar dp) Int, Real (Scalar dp)) => CoverTree_ exprat childC leafC dp -> Scalar dp -sepdist ct = exprat_**(fromIntegral $ level ct) +sepdist ct = getExprat**(fromIntegral $ level ct) level2sepdist :: (QuotientField f r, Real f) => r -> f -level2sepdist i = exprat_**(fromIntegral i) +level2sepdist i = getExprat**(fromIntegral i) level2coverdist :: (QuotientField f r, Real f) => r -> f level2coverdist i = level2sepdist (i+1) dist2level_down :: forall exprat num. ({-KnownFrac exprat, -}Real num, QuotientField num Int) => Proxy exprat -> num -> Int -dist2level_down _ d = floor $ log d / log exprat_ +dist2level_down _ d = floor $ log d / log getExprat dist2level_up :: forall exprat num. ({-KnownFrac exprat,-} Real num, QuotientField num Int) => Proxy exprat -> num -> Int -dist2level_up _ d = ceiling $ log d / log exprat_ - - -------------------------------------------------------------------------------- - -{- --- drawCT :: --- ( ValidCT exprat childC leafC dp --- , VG.Vector childC (QDiagram SVG R2 Any) --- , Integral (Scalar (leafC dp)) --- , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) --- ) => P.FilePath --- -> CoverTree_ exprat childC leafC dp --- -> IO () -drawCT path ct = renderSVG path (Dims 500 300) (diagramCT_ 0 ct) - - --- diagramCT node = diagramCT_ 0 node - --- type instance Scalar R2 = Double - --- diagramCT_ :: --- ( ValidCT exprat childC leafC dp --- ) => Int --- -> CoverTree_ exprat childC leafC dp --- -> Diagram a R2 -diagramCT_ (depth::Int) tree - = mkConnections $ - ( named (label++show depth) $ fontSize (Global 0.01) $ - ( - (text label D.<> strutY 0.5) - === (text (show (sepdist tree)) D.<> strutY 0.5) - -- === (text (show (maxDescendentDistance tree)) <> strutY 0.5) - ) - D.<> circle 1 # fc nodecolor - ) - === (pad 1.05 $ centerName (label++show (depth+1)) $ - VG.foldr (|||) mempty $ VG.map (diagramCT_ (depth+1)) $ children tree) - - where - label = intShow $ nodedp tree - nodecolor = if ctBetterMovableNodes tree==0 --nodeWeight tree > 0 - then red - else lightblue - - mkConnections = - D.connect (label++show depth) (label++show (depth+1)) - . apList (fmap - (\key -> D.connect (label++show depth) (intShow key++show (depth+1))) - (map nodedp $ toList $ children tree) - ) - -centerName name = withName name $ \b a -> moveOriginTo (location b) a - -apList :: [a -> a] -> a -> a -apList [] a = a -apList (x:xs) a = apList xs (x a) - -intShow :: Show a => a -> String -intShow a = P.filter go $ show a - where - go x - | x=='.' = True - | x==',' = True - | x=='-' = True - | x=='1' = True - | x=='2' = True - | x=='3' = True - | x=='4' = True - | x=='5' = True - | x=='6' = True - | x=='7' = True - | x=='8' = True - | x=='9' = True - | x=='0' = True - | otherwise = False - --} +dist2level_up _ d = ceiling $ log d / log getExprat diff --git a/src/HLearn/Data/SpaceTree/CoverTree/Unsafe.hs b/src/HLearn/Data/SpaceTree/CoverTree/Unsafe.hs new file mode 100644 index 0000000..ebb71b8 --- /dev/null +++ b/src/HLearn/Data/SpaceTree/CoverTree/Unsafe.hs @@ -0,0 +1,25 @@ +-- | This module let's us tune the cover tree's expansion ratio. +-- You might be able to get around 5% better performance by tuning this value to your specific application. +-- But you probably don't want to do this. +module HLearn.Data.SpaceTree.CoverTree.Unsafe + ( setExprat + , getExprat + ) + where + +import SubHask +import System.IO.Unsafe +import Data.IORef + +-------------------------------------------------------------------------------- + +{-# NOINLINE expratIORef #-} +expratIORef = unsafePerformIO $ newIORef (1.3::Rational) + +setExprat :: Rational -> IO () +setExprat r = writeIORef expratIORef r + +{-# INLINABLE getExprat #-} +getExprat :: Field r => r +getExprat = fromRational $ unsafePerformIO $ readIORef expratIORef + diff --git a/src/HLearn/Data/SpaceTree/CoverTree_Specialized.hs b/src/HLearn/Data/SpaceTree/CoverTree_Specialized.hs new file mode 100644 index 0000000..abba153 --- /dev/null +++ b/src/HLearn/Data/SpaceTree/CoverTree_Specialized.hs @@ -0,0 +1,1148 @@ +-- | This module provides an interface to the cover tree data structure. +-- Cover trees are used for fast nearest neighbor queries. +-- They are similar to kd-trees, but they are faster in high dimensions and can be used on more types of data. +-- In particular, they can be used on anything that is an instance of "Metric". +-- +-- See the paper for a detailed explanation of the theory. +-- I'm happy to answer any questions you have about the implementation details here. +module HLearn.Data.SpaceTree.CoverTree_Specialized + ( BCoverTree + , UCoverTree + , CoverTree_ + , ValidCT + + -- * Debug interface + -- | The functions below are not intended for standard use. + -- They provide an advanced interface to assist with development. + -- Instead, you should use the functions from the "Container" hierarchy of classes provided by subhask. + + -- ** Optimizations + , ctAdoptNodes + , setMaxDescendentDistance + , sortChildren + , cmp_numdp_distance + , cmp_numdp_distance' + , cmp_distance_numdp + , cmp_distance_numdp' + , setLeaves + , packCT + + -- ** Alternative Construction Methods + , trainMonoid + + , trainInsertOrig + , trainInsertNoSort + , trainInsert + + , insertCTOrig + , insertCTNoSort + , insertCT + + , AddChildMethod + , addChild_nothing + , addChild_parent + , addChild_ancestor + + -- ** Performance Measures + , ctAveCoverRatio + , ctMaxCoverRatio + , ctMovableParents + , ctBetterMovableParents + , ctMovableNodes + , ctBetterMovableNodes + + -- ** Invariants + , invariant_CoverTree_covering + , invariant_CoverTree_tightCovering + , invariant_CoverTree_maxDescendentDistance + , invariant_CoverTree_separating + ) + where + +import qualified Data.List as L +import qualified Prelude as P + +import SubHask +import SubHask.Monad +import SubHask.Algebra.Array +import SubHask.Algebra.Container +import SubHask.Algebra.Ord +import SubHask.Algebra.Vector +import SubHask.Compatibility.Containers + +import HLearn.Data.SpaceTree +import HLearn.Data.SpaceTree.CoverTree.Unsafe +import HLearn.Models.Distributions + +------------------------------------------------------------------------------- + +-- | The type of boxed cover trees. +type BCoverTree = CoverTree_ () BArray BArray + +-- | The type of unboxed cover trees. +-- Typically more than 2x faster than boxed cover trees. +type UCoverTree = CoverTree_ () BArray UArray + +-- | A generic type of cover tree that let's the user specify a number of internal parameters about how memory is represented internally. +-- You probably don't want to use this type. +data CoverTree_ + ( exprat :: * ) -- FIXME: should be "Frac" from typeparams + ( childC :: * -> * ) + ( leafC :: * -> * ) + ( dp :: * ) + = Node +-- { nodedp :: !dp +-- , level :: {-#UNPACK#-}!Int +-- , nodeWeight :: !(Scalar dp) +-- , numdp :: !(Scalar dp) +-- , maxDescendentDistance :: !(Scalar dp) +-- , children :: !(childC (CoverTree_ exprat childC leafC dp)) +-- , leaves :: !(leafC dp) + { nodedp :: {-#UNPACK#-}!(Labeled' (UVector "dyn" Float) Int) + , nodeWeight :: {-#UNPACK#-}!Float + , level :: {-#UNPACK#-}!Int + , numdp :: {-#UNPACK#-}!Float + , maxDescendentDistance :: {-#UNPACK#-}!Float + , children :: {-#UNPACK#-}!(BArray (CoverTree_ exprat childC leafC dp)) + , leaves :: {-#UNPACK#-}!(UArray dp) + } + +mkMutable [t| forall a b c d. CoverTree_ a b c d |] + +type instance Scalar (CoverTree_ exprat childC leafC dp) = Scalar dp +type instance Logic (CoverTree_ exprat childC leafC dp) = Bool +type instance Elem (CoverTree_ exprat childC leafC dp) = dp +type instance SetElem (CoverTree_ exprat childC leafC dp) dp' = CoverTree_ exprat childC leafC dp' + +-- | This type alias simplifies all our type signatures. +-- It ensures that a "CoverTree_"'s parameters are sufficient to carry out the necessary operations. +-- +-- FIXME: +-- Trim this set of constraints to be more manageable. +-- +-- FIXME: +-- There is a much smaller subset of these that is actually needed, +-- but GHC's type system isn't strong enough to express this subset. +type ValidCT exprat childC leafC dp = + ( Foldable (childC (CoverTree_ exprat childC leafC dp)) + , Foldable (leafC dp) + , Constructible (childC (CoverTree_ exprat childC leafC dp)) + , Constructible (leafC dp) + , IxContainer (childC (CoverTree_ exprat childC leafC dp)) + , Sliceable (leafC dp) + , Normed (childC (CoverTree_ exprat childC leafC dp)) + , Normed (leafC dp) + , Elem (childC (CoverTree_ exprat childC leafC dp)) ~ CoverTree_ exprat childC leafC dp + , Elem (leafC dp) ~ dp + , Metric dp + , QuotientField (Scalar dp) Int + , Real (Scalar dp) + , Bounded (Scalar dp) +-- , CanError (Scalar dp) + , Logic dp ~ Bool + , Logic (CoverTree_ exprat childC leafC dp) ~ Bool + , Logic (Scalar (leafC dp)) ~ Bool + , NFData (CoverTree_ exprat childC leafC dp) + , HasScalar dp + , Eq_ (childC (CoverTree_ exprat childC leafC dp)) + , Index (leafC dp) ~ Int + , Scalar (childC (CoverTree_ exprat childC leafC dp)) ~ Int + + , ClassicalLogic (leafC dp) + , ClassicalLogic (leafC (CoverTree_ exprat childC leafC dp)) + , ClassicalLogic (childC (CoverTree_ exprat childC leafC dp)) + , Container (leafC dp) + , Container (childC (CoverTree_ exprat childC leafC dp)) + + -- ghc7.10 + , Elem (childC (CoverTree_ exprat childC leafC dp)) ~ (CoverTree_ exprat childC leafC dp) + , Scalar (childC (CoverTree_ exprat childC leafC dp)) ~ Scalar (leafC dp) + , Hilbert (Scalar (Elem (childC (Scalar dp)))) + , FiniteModule (Scalar (Elem (childC (Scalar dp)))) + + -- unpack + , dp ~ Labeled' (UVector "dyn" Float) Int + , childC~BArray + , leafC~UArray + + -- these constraints come from hlearn-allknn + , Scalar (leafC dp) ~ Scalar (childC (CoverTree_ exprat childC leafC dp)) + , Elem (childC Bool) ~ Bool + , Elem (childC (Scalar dp)) ~ Scalar dp + , Elem (leafC dp) ~ dp + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , NFData (Scalar dp) + , NFData dp + + -- debugging constraints + , Show (Scalar dp) + , Show dp + , Show (leafC dp) + , Show (childC (CoverTree_ exprat childC leafC dp)) + , Show (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Show (Scalar (leafC dp)) + ) + +------------------------------------------------------------------------------- +-- type classes + +instance + ( ValidCT exprat childC leafC dp + ) => SpaceTree (CoverTree_ exprat childC leafC) dp + where + + type ChildContainer (CoverTree_ exprat childC leafC ) = childC + type LeafContainer (CoverTree_ exprat childC leafC ) = leafC + + stChildren = children + stLeaves = leaves + stNode = nodedp + stWeight = nodeWeight + + stMaxDescendentDistance = maxDescendentDistance + +---------------------------------------- +-- misc type classes + +deriving instance + ( Show (childC (CoverTree_ exprat childC leafC dp)) + , Show (leafC dp) + , Show dp + , Show (Scalar dp) + , ValidCT exprat childC leafC dp + ) => Show (CoverTree_ exprat childC leafC dp) + +instance + ( NFData (childC (CoverTree_ exprat childC leafC dp)) + , NFData (leafC dp) + , NFData dp + , NFData (Scalar dp) + ) => NFData (CoverTree_ exprat childC leafC dp) where + rnf ct = deepseq (nodedp ct) + $ deepseq (nodeWeight ct) + $ deepseq (level ct) + $ deepseq (numdp ct) + $ deepseq (maxDescendentDistance ct) + $ deepseq (children ct) + $ deepseq (leaves ct) + $ rnf () + +---------------------------------------- +-- comparison + +instance ValidCT exprat childC leafC dp => Eq_ (CoverTree_ exprat childC leafC dp) where + ct1==ct2 = stToList ct1==stToList ct2 + +-- FIXME: need proper MultiSet type to implement +-- +-- instance +-- ( ValidCT exprat childC leafC dp +-- ) => POrd_ (Maybe' (CoverTree_ exprat childC leafC dp)) +-- where +-- +-- inf ct1 ct2 = trainInsert addChild_ancestor +-- $ inf (convertContainer ct1) (convertContainer ct2 :: Set dp) +-- where +-- convertContainer = fromList . stToList +-- +-- instance +-- ( ValidCT exprat childC leafC dp +-- ) => Lattice_ (Maybe' (CoverTree_ exprat childC leafC dp)) +-- where +-- +-- sup ct1 ct2 = trainInsert addChild_ancestor +-- $ sup (convertContainer ct1) (convertContainer ct2 :: Set dp) +-- where +-- convertContainer = fromList . stToList + +---------------------------------------- +-- algebra + +instance ValidCT exprat childC leafC dp => Normed (CoverTree_ exprat childC leafC dp) where + size = numdp + +---------------------------------------- +-- container + +instance + ( ValidCT exprat childC leafC dp + ) => Constructible (CoverTree_ exprat childC leafC dp) + where + + singleton = singletonCT + fromList1 x xs = fromJust' $ trainInsert addChild_ancestor (x:xs) + +instance + ( ValidCT exprat childC leafC dp + ) => Container (CoverTree_ exprat childC leafC dp) + where + + -- FIXME: use the covertree's structure! + elem e ct = elem e $ stToList ct + +-- FIXME: +-- We need to make subhask's hierarchy a bit more generic before we can make cover trees foldable. +-- +-- instance +-- ( ValidCT exprat childC leafC dp +-- ) => Foldable (CoverTree_ exprat childC leafC dp) +-- where +-- toList = stToList + +------------------------------------------------------------------------------- +-- construction + +{-# INLINABLE singletonCT #-} +singletonCT :: ValidCT exprat childC leafC dp => dp -> CoverTree_ exprat childC leafC dp +singletonCT dp = Node + { nodedp = dp + , nodeWeight = 1 + , level = minBound + , numdp = 1 + , children = empty + , leaves = empty + , maxDescendentDistance = 0 + } + +---------------------------------------- + +-- | Construct a cover tree using the original algorithm. +{-# INLINABLE trainInsertOrig #-} +trainInsertOrig :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainInsertOrig xs = {-# SCC trainInsertOrig #-}case uncons xs of + Nothing -> Nothing' + Just (dp,dps) -> Just' $ rmSingletons $ foldr' insertCTOrig (singletonCT dp) dps + + where + -- Removes nodes that have only a single child that contains the same datapoint. + -- These singleton nodes are not supposed to be included in the original cover tree's expicit representation, + -- but the way our insertion method works it is easiest to add them then remove them at the end of insertion. + -- The new insertion methods can't create singleton nodes. + rmSingletons :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + rmSingletons ct = if isSingleton + then rmSingletons onlyChild + else ct + { children = fromList $ map rmSingletons $ toList $ children ct + } + where + isSingleton = size (children ct) == 1 + && nodedp onlyChild == nodedp ct + + onlyChild = P.head $ toList $ children ct + +-- | Insert a node into a cover tree using the original algorithm. +{-# INLINABLE insertCTOrig #-} +insertCTOrig :: + ( ValidCT exprat childC leafC dp + ) => dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +insertCTOrig dp ct = insertCTOrig_ dp ct (distance dp (nodedp ct)) + +-- | Helper function required for GHC's optimizer to work properly. +{-# INLINABLE insertCTOrig_ #-} +insertCTOrig_ :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => dp + -> CoverTree_ exprat childC leafC dp + -> Scalar dp + -> CoverTree_ exprat childC leafC dp +insertCTOrig_ !dp !ct !dist = {-# SCC insertCTOrig_ #-} + if dist > coverdist ct + + -- ct can't cover dp, so create a new node at dp that covers ct + then Node + { nodedp = dp + , nodeWeight = 1 + , level = dist2level_up (Proxy::Proxy exprat) dist + , numdp = numdp ct+1 + , maxDescendentDistance = dist+maxDescendentDistance ct + , children = singleton + $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) + $ ct + , leaves = empty + } + + -- insert dp underneath ct + else ct + { numdp = numdp ct+1 + , maxDescendentDistance = max dist (maxDescendentDistance ct) + , children = fromList $ go [] $ toList $ children ct + } + + where + go !acc (x:xs) = if isFartherThan (nodedp x) dp (sepdist ct) + then go (x:acc) xs + else acc+((insertCTOrig dp x):xs) + + go !acc [] = if dist >= sepdist ct + + -- far from root, so just insert the node + then ((singletonCT dp) { level = level ct-1 }):acc + + -- close to root, so add new lower root level + else insertCTOrig_ dp ct' dist:acc + where + ct' = (singletonCT (nodedp ct)) + { level = level ct-1 + , numdp = 0 + , nodeWeight = 0 + } + +---------------------------------------- + +-- | This corresponds to the simplified cover tree in the ICML paper. +{-# INLINABLE trainInsertNoSort #-} +trainInsertNoSort :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainInsertNoSort xs = {-# SCC trainInsertNoSort #-}case uncons xs of + Nothing -> Nothing' + Just (dp,dps) -> Just' $ foldr' insertCTNoSort (singletonCT dp) dps + +-- | This corresponds to the simplified cover tree in the ICML paper. +{-# INLINABLE insertCTNoSort #-} +insertCTNoSort :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +insertCTNoSort dp ct = {-# SCC insertCTNoSort #-} insertCTNoSort_ dp ct $ distance dp (nodedp ct) + +-- | Helper function required for GHC's optimizer to work properly. +{-# INLINABLE insertCTNoSort_ #-} +insertCTNoSort_ :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => dp + -> CoverTree_ exprat childC leafC dp + -> Scalar dp + -> CoverTree_ exprat childC leafC dp +insertCTNoSort_ dp ct dist = {-# SCC insertCTNoSort_ #-} + if dist > coverdist ct + + -- ct can't cover dp, so create a new node at dp that covers ct + then Node + { nodedp = dp + , nodeWeight = 1 + , level = dist2level_up (Proxy::Proxy exprat) dist + , numdp = numdp ct+1 + , maxDescendentDistance = dist+maxDescendentDistance ct + , children = singleton + $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) + $ ct + , leaves = empty + } + + -- insert dp underneath ct + else ct + { numdp = numdp ct+1 + , maxDescendentDistance = max dist (maxDescendentDistance ct) + , children = {-# SCC conv #-} fromList $ go [] $ toList $ children ct + } + + where + go !acc [] = {-# SCC go #-} ((singletonCT dp) { level = level ct-1 }):acc + go !acc (x:xs) = {-# SCC go #-} if isFartherThan (nodedp x) dp (sepdist ct) + then go (x:acc) xs + else acc+((insertCTNoSort dp x):xs) + +---------------------------------------- + +-- | Provides a generic insertion method that lets us enforce a whole suite of invariants related to the nearest ancestor invariant of the ICML paper. +{-# INLINABLE trainInsert #-} +trainInsert :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => AddChildMethod exprat childC leafC (Elem xs) + -> xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainInsert addChild xs = {-# SCC trainInsert #-} case uncons xs of + Nothing -> Nothing' + Just (dp,dps) -> Just' $ foldr' (insertCT addChild) (singletonCT dp) $ toList dps + +-- | Insert a single data point into the cover tree. +{-# INLINABLE insertCT #-} +insertCT :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp + -> dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +insertCT addChild dp ct = + insertCT_ addChild dp ct (distance dp $ nodedp ct) + +-- | This function is exactly the same as insertCT. +-- We need to provide a different function, however, for performance reasons. +-- There's many weird layers of recursion going on in these functions, +-- and this was preventing GHC from inlining/specializing these functions. +-- Now, insertCT is not recussive, so GHC can trivially inline it. +{-# INLINABLE insertCT_internal #-} +insertCT_internal :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp + -> dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +insertCT_internal addChild dp ct = + insertCT_ addChild dp ct (distance dp $ nodedp ct) + +-- | Like "insertCT", but this function also takes the distance between the data point and root of the cover tree to avoid recomputation. +{-# INLINABLE insertCT_ #-} +insertCT_ :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp + -> dp + -> CoverTree_ exprat childC leafC dp + -> Scalar dp + -> CoverTree_ exprat childC leafC dp +insertCT_ addChild dp ct dist = {-# SCC insertCT_ #-} + if dist > coverdist ct + -- ct can't cover dp, so create a new node at dp that covers ct + then Node + { nodedp = dp + , nodeWeight = 1 + , level = dist2level_up (Proxy::Proxy exprat) dist + , numdp = numdp ct+1 + , maxDescendentDistance = dist+maxDescendentDistance ct + , children = singleton + $ raiseRootLevel (dist2level_down (Proxy::Proxy exprat) dist) + $ ct + , leaves = empty + } + + -- insert dp underneath ct + else ct + { numdp = numdp ct+1 + , maxDescendentDistance = max dist (maxDescendentDistance ct) + , children = fromList $ go [] childrendists + } + + where + childrendists = map (\x -> (distance dp (nodedp x), x)) $ toList $ children ct + + mindist = minimum $ map fst childrendists + + go !acc [] = addChild dp ct + go !acc ((dist,x):xs) = if dist == mindist && dist <= coverdist x + then insertCT_ addChild dp x dist:(acc+map snd xs) + else go (x:acc) xs + +-- | These functions control the invariants we maintain while performing insertions into the cover tree. +-- The input data point will be directly inserted as one of the children of the root of the tree. +-- Therefore, we require it satisfy the separating and covering conditions (and any other applicable conditions). +-- The function may (possibly) move points from other nodes into this new child to enforce other invariants. +-- The return value is the list of new children for the root node after the insertion has taken place. +-- +-- NOTE: +-- +-- We could make this type synonym have a much "prettier" interface like so: +-- +-- > type AddChildMethod dp = forall exprat childC leafC. +-- > ValidCT exprat childC leafC dp +-- > => dp +-- > -> CoverTree_ exprat childC leafC dp +-- > -> [CoverTree_ exprat childC leafC dp] +-- +-- But this has devastating effects on performance. +-- It combines with the weird recursive calls to prevent inlining, which results in >10x slow downs. +type AddChildMethod exprat childC leafC dp + = dp + -> CoverTree_ exprat childC leafC dp + -> [CoverTree_ exprat childC leafC dp] + +{-# INLINABLE addChild_nothing #-} +addChild_nothing :: + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp +addChild_nothing dp ct = cons + ( (singletonCT dp) { level = level ct-1} ) + (toList $ children ct) + +{-# INLINABLE addChild_parent #-} +addChild_parent :: + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp +addChild_parent dp ct = {-# SCC addChild_parent #-} ret:acc' + where + (acc',dps) = rmCloseChildren addChild_parent dp $ toList $ children ct + ct'=(singletonCT dp) { level = level ct-1 } + ret=foldr' (insertCT_internal addChild_parent) ct' $ concat $ map stToList dps + +{-# INLINABLE addChild_ancestor #-} +addChild_ancestor :: forall exprat childC leafC dp k. + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp +addChild_ancestor dp ct = {-# SCC addChild_ancestor #-} + toList $ children $ foldr' (insertCT addChild_ancestor) ct' (concat $ map stToList dps) + where + ( acc', dps ) = rmCloseChildren addChild_ancestor dp $ toList $ children ct + + ct' :: CoverTree_ exprat childC leafC dp + ct' = ct + { children = fromList $ ((singletonCT dp) { level = level ct-1 }) : acc' + } + +{-# INLINABLE rmCloseChildren #-} +rmCloseChildren :: + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp + -> dp + -> [CoverTree_ exprat childC leafC dp] + -> ( [CoverTree_ exprat childC leafC dp] + , [CoverTree_ exprat childC leafC dp] + ) +rmCloseChildren addChild dp cts = {-# SCC rmCloseChildren #-} + (map fst xs, P.concat $ map snd xs) + where + xs = map (extractCloseChildren addChild dp) cts + +{-# INLINABLE extractCloseChildren #-} +extractCloseChildren :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => AddChildMethod exprat childC leafC dp + -> dp + -> CoverTree_ exprat childC leafC dp + -> ( CoverTree_ exprat childC leafC dp + , [CoverTree_ exprat childC leafC dp] + ) +extractCloseChildren addChild dp root = {-# SCC extractCloseChildren #-} + case go root of + (Just' x,xs) -> (x,xs) + where + go ct = if dist_ct_root + maxDescendentDistance ct < dist_ct_dp + then (Just' ct,[]) + else if dist_ct_root > dist_ct_dp + then (Nothing', [ct]) + else + ( Just' $ ct + { children = fromList children' + , numdp = nodeWeight ct + sum (map numdp children') + } + , concat $ map snd allbabies + ) + where + allbabies = map go $ toList $ children ct + children' = justs' $ map fst allbabies + + dist_ct_root = distance (nodedp ct) (nodedp root) + dist_ct_dp = distance (nodedp ct) dp + +{-# INLINABLE moveLeafToRoot #-} +moveLeafToRoot :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> Maybe' (CoverTree_ exprat childC leafC dp) +moveLeafToRoot ct = if stHasNoChildren ct + then Nothing' + else Just' $ ct + { nodedp = nodedp leaf + , numdp = numdp ct - numdp leaf + , children = children ct' + , maxDescendentDistance = coverdist ct*2 + } + where + (leaf,ct') = rmleaf ct + +-------------------------------------------------------------------------------- + +{-# INLINABLE trainMonoid #-} +trainMonoid :: + ( ValidCT exprat childC leafC (Elem xs) + , Foldable xs + ) => xs + -> Maybe' (CoverTree_ exprat childC leafC (Elem xs)) +trainMonoid xs = {-# SCC trainMonoid #-} foldtree1 $ map (Just' . singletonCT) $ toList xs + +instance + ( ValidCT exprat childC leafC dp + ) => Semigroup (CoverTree_ exprat childC leafC dp) + where + + {-# INLINABLE (+) #-} + ct1 + ct2 = {-# SCC semigroup_CoverTree #-} case ctmerge_ ct1_ ct2_ dist of + (ct, []) -> ct + (ct, xs) -> {-# SCC sg_foldr #-} foldr' insertCTNoSort ct $ concat $ map stToList xs + + where + dist = distance (nodedp ct1) (nodedp ct2) + + maxlevel = maximum + [ level ct1 + , level ct2 + , dist2level_down (Proxy::Proxy exprat) dist + ] + + ct1_ = if level ct1 < maxlevel then raiseRootLevel maxlevel ct1 else ct1 + ct2_ = if level ct2 < maxlevel then raiseRootLevel maxlevel ct2 else ct2 + +-- | If the following prerequisites are met: +-- +-- > level ct1==level ct2 +-- +-- > distance (nodedp ct1) (nodedp ct2) < coverdist ct1 +-- +-- then all output covertrees will be valid. +-- The root of ct1 is guaranteed to not change. +{-# INLINABLE ctmerge_ #-} +ctmerge_ :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + -> Scalar dp + -> ( CoverTree_ exprat childC leafC dp + , [CoverTree_ exprat childC leafC dp] + ) +ctmerge_ ct1 ct2 dist = + + {-# SCC ctmerge_ #-} + ( ct' , notCoveredChildren+leftovers' ) + where + -- remove the children of ct2 that can't be covered by ct1 ("notCoveredChildren"); + -- these will be handled separately in (+) + (coveredChildren,notCoveredChildren) = {-# SCC isCovered #-} + L.partition isCovered $ stChildrenList ct2 + where + isCovered x = not $ isFartherThan (nodedp ct1) (nodedp x) (coverdist ct1) + + -- sepcovChildren is not a part of children' because we loop through children' in go + -- adding in sepcovChildren would result in extra iterations + (children',sepcovChildren,leftovers) + = {-# SCC sg_foldl #-} foldl' (go []) (stChildrenList ct1,[],[]) coveredChildren + where + -- merge covChild with a child of ct1' within the separating distance + go tot (x:xs,ys,zs) covChild = if godist <= sepdist ct1 + then let (x',zs') = ctmerge_ x covChild godist + in (x':tot++xs,ys,zs'++zs) + else go (x:tot) (xs,ys,zs) covChild + where + godist = distance (nodedp x) (nodedp covChild) + + -- nothing within separating distance, so add covChild as new child + go tot ([],ys,zs) covChild = (tot,covChild:ys,zs) + + -- update children, insert root of ct2 into ct1 + ct_minusleftovers = {-# SCC minusleftover #-} insertCTNoSort_ + (nodedp ct2) + ( ct1 + { children = fromList $ children' + sepcovChildren + , numdp = sum $ map numdp $ children' + sepcovChildren + , maxDescendentDistance = coverdist ct1*2 + } ) + dist + + -- insert any leftovers into the tree if they fit + (ct',leftovers') = {-# SCC part4 #-} foldl' go (ct_minusleftovers,[]) leftovers + where + go (ct,xs) x = if level ct >= level x && godist <= coverdist ct + then (goct, goleftovers++xs) + else (ct,x:xs) + where + godist = distance (nodedp ct) (nodedp x) + (goct,goleftovers) = ctmerge_ ct (raiseRootLevel (level ct) x) godist + +-- | FIXME: does this work if there are leaves present? +{-# INLINABLE raiseRootLevel #-} +raiseRootLevel :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => Int + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +raiseRootLevel i ct + | stHasNoChildren ct = {-# SCC raiseRootLevel #-}ct { level = i } + | i < level ct = error + $ "\nraiseRootLevel: target level less than current tree level" + + "; i="+show i + + "; level ct="+show (level ct) + + "; nodedp ct="+show (nodedp ct) + | i == level ct = {-# SCC raiseRootLevel #-}ct + | i > level ct = {-# SCC raiseRootLevel #-} raiseRootLevel i $ leaf + { level = level ct'+1 + , numdp = numdp ct'+nodeWeight leaf + , maxDescendentDistance = distance (nodedp leaf) (nodedp ct) + maxDescendentDistance ct' + , children = singleton ct' + } + where + (leaf,ct') = rmleaf ct + +{-# INLINABLE rmleaf #-} +rmleaf :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> ( CoverTree_ exprat childC leafC dp + , CoverTree_ exprat childC leafC dp + ) +rmleaf ct = {-# SCC rmleaf #-} if stHasNoChildren head + then ( head + , ct + { numdp = numdp ct-nodeWeight head + , children = tail + } + ) + else ( itrleaf + , ct + { numdp = numdp ct-nodeWeight head + , children = itrtree `cons` tail + } + ) + where + (itrleaf,itrtree) = rmleaf head + + childL = children ct + (head,tail) = case uncons childL of Just x -> x + +-------------------------------------------------------------------------------- +-- Performance measurements + +-- | returns a measure of how "spread out" descendent points are +{-# INLINABLE ctMaxCoverRatio #-} +ctMaxCoverRatio :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp -> Scalar dp +ctMaxCoverRatio ct = if size (children ct) + size (leaves ct) > 0 + then maximum + $ (stMaxDescendentDistance ct / coverdist ct) + : map ctMaxCoverRatio (toList $ children ct) + else 0 + +-- | returns a measure of how "spread out" descendent points are +{-# INLINABLE ctAveCoverRatio #-} +ctAveCoverRatio :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp -> Normal (Scalar dp) +ctAveCoverRatio ct = if size (children ct) + size (leaves ct) > 0 + then train1Normal (stMaxDescendentDistance ct / coverdist ct) + + reduce (map ctAveCoverRatio $ toList $ children ct) + else zero + +-- | counts the number of parents in the tree that have nephews that could be placed under them +{-# INLINABLE ctMovableParents #-} +ctMovableParents :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctMovableParents ct + = sum (map movableChildren $ toList $ children ct) + + sum (map ctMovableParents $ toList $ children ct) + where + movableChildren c + = sum (map totalParentsOfChildren ( toList $ children c)) + + sum (map totalParentsOfChildren (map singletonCT $ toList $ leaves c)) + - (fromIntegral $ toInteger $ size $ children c) + - (fromIntegral $ toInteger $ size $ leaves c) + + totalParentsOfChildren c + = sum + $ map (\p -> indicator $ distance (nodedp c) (nodedp p) <= sepdist ct) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +-- | counts the number of parents in the tree that have nephews that would be better placed under them +{-# INLINABLE ctBetterMovableParents #-} +ctBetterMovableParents :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctBetterMovableParents ct + = sum (map betterMovableChildren $ toList $ children ct) + + sum (map ctBetterMovableParents $ toList $ children ct) + where + betterMovableChildren c + = sum (map (totalBetterParentsOfChildren c) ( toList $ children c)) + + sum (map (totalBetterParentsOfChildren c) (map singletonCT $ toList $ leaves c)) + - (fromIntegral $ toInteger $ size $ children c) + - (fromIntegral $ toInteger $ size $ leaves c) + + totalBetterParentsOfChildren realparent c + = sum + $ map (\p -> indicator $ distance (nodedp c) (nodedp p) + <= distance (nodedp c) (nodedp realparent)) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +-- | counts the number of nodes in the tree with uncles they could be moved under +{-# INLINABLE ctMovableNodes #-} +ctMovableNodes :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctMovableNodes ct + = sum (map movableChildren $ toList $ children ct) + + sum (map ctMovableNodes $ toList $ children ct) + where + movableChildren c + = sum (map totalNodesOfChildren ( toList $ children c)) + + sum (map totalNodesOfChildren (map singletonCT $ toList $ leaves c)) + + totalNodesOfChildren c + = indicator + $ (> (1::Int)) + $ sum + $ map (\p -> indicator $ distance (nodedp c) (nodedp p) < sepdist ct) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +-- | Counts the number of nodes in the tree with uncles they would be better placed under +{-# INLINABLE ctBetterMovableNodes #-} +ctBetterMovableNodes :: + ( ValidCT exprat childC leafC dp + , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) + , Integral (Scalar (leafC dp)) + ) => CoverTree_ exprat childC leafC dp -> Int +ctBetterMovableNodes ct + = sum (map betterMovableChildren $ toList $ children ct) + + sum (map ctBetterMovableNodes $ toList $ children ct) + where + betterMovableChildren c + = sum (map (totalBetterNodesOfChildren c) ( toList $ children c)) + + sum (map (totalBetterNodesOfChildren c) (map singletonCT $ toList $ leaves c)) + + totalBetterNodesOfChildren realparent c + = indicator + $ or + $ map (\p -> distance (nodedp c) (nodedp p) + < distance (nodedp c) (nodedp realparent) + ) + $ toList (children ct) + ++ toList (map singletonCT $ toList $ leaves ct) + +------------------------------------------------------------------------------- +-- tests + +invariant_CoverTree_covering :: + ( ValidCT exprat childC leafC dp + , Elem (childC (Scalar dp)) ~ Scalar dp + , Elem (childC Bool) ~ Bool + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_covering node + = and (map invariant_CoverTree_covering $ stChildrenList node) + && and (map (\child -> distance (nodedp node) (nodedp child) <= coverdist node) $ stChildrenList node) + +invariant_CoverTree_tightCovering :: + ( ValidCT exprat childC leafC dp + , Elem (childC (Scalar dp)) ~ Scalar dp + , Elem (childC Bool) ~ Bool + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_tightCovering node + = and (map invariant_CoverTree_tightCovering $ stChildrenList node) + && and (map (\dp -> distance dp (nodedp node) <= coverdist node) $ stDescendents node) + +invariant_CoverTree_maxDescendentDistance :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_maxDescendentDistance node + = and (map invariant_CoverTree_maxDescendentDistance $ stChildrenList node) + && and (map (\dp -> distance dp (nodedp node) <= maxDescendentDistance node) $ stDescendents node) + +invariant_CoverTree_separating :: + ( ValidCT exprat childC leafC dp + , Elem (childC Bool) ~ Bool + , Foldable (childC Bool) + , Foldable (childC (Scalar dp)) + ) => CoverTree_ exprat childC leafC dp -> Bool +invariant_CoverTree_separating node + = minimum ( P.filter (>0) + $ map (\(x,y) -> distance (nodedp x) (nodedp y)) + $ cartesianProduct (toList $ children node) (toList $ children node) + ) + >= sepdist node + && and (map invariant_CoverTree_separating $ toList $ children node) + where + cartesianProduct :: [a] -> [b] -> [(a,b)] + cartesianProduct xs ys = P.concatMap (\y -> map (\x -> (x,y)) xs) ys + +{- +-- | FIXME: +-- This needs to be converted to use subhask notation. +property_leveled :: + ( ValidCT exprat childC leafC dp + , VG.Vector childC Bool + , VG.Vector childC Int + , VG.Vector childC (CoverTree_ exprat childC leafC dp) + ) => CoverTree_ exprat childC leafC dp -> Bool +property_leveled node + = VG.all (== VG.P.head xs) xs + && VG.and (VG.map property_leveled $ children node) + where + xs = VG.map level $ children node +-} + +------------------------------------------------------------------------------- +-- optimization helpers + +-- | uncles adopt all children that are closer to them than their parents +{-# INLINABLE ctAdoptNodes #-} +ctAdoptNodes :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +ctAdoptNodes ct = ct { children = fromList $ map ctAdoptNodes $ go [] $ toList $ children ct } + where + go acc [] = acc + go acc (x:xs) = go (x { children = (fromList accchildren+fromList xschildren+children x)}:acc') xs' + where + betterMoveNode y c = distance (nodedp x) (nodedp c) < distance (nodedp y) (nodedp c) + + extractChildren y = ( oldchildren, y { children = fromList newchildren } ) + where + (oldchildren,newchildren) = L.partition (betterMoveNode y) $ toList $ children y + + extractChildrenL ys = foldl' ecgo ([],[]) ys + where + ecgo (oldchildL, newchildL) y = (oldchildren++oldchildL,newparent:newchildL) + where + (oldchildren, newparent) = extractChildren y + + (accchildren, acc') = extractChildrenL acc + (xschildren, xs') = extractChildrenL xs + +-- | Sets the "maxDescendentDistance" parameter to the tightest bound possible. +-- This makes future queries of the tree more efficient---we can prune off more traversals, resulting in fewer distance computations. +-- +-- FIXME: +-- This version uses a branch-and-bound recursion that is much faster than the previous naive implementation. +-- But it would be cooler (and faster?) if we used a find farthest neighbor method in the SpaceTree framework. +{-# INLINABLE setMaxDescendentDistance #-} +setMaxDescendentDistance :: + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +setMaxDescendentDistance ct = {-# SCC setMaxDescendentDistance #-} ct + { children = children' + , maxDescendentDistance = max leavesMaxDist $ go 0 (toList $ children') + } + where + + children' = fromList $ map setMaxDescendentDistance $ toList $ children ct + + leavesMaxDist = maximum $ map (distance (nodedp ct)) $ toList $ leaves ct + + go curmax [] = curmax + go curmax (x:xs) = go curmax' xs + where + curmax' = if dist + maxDescendentDistance x <= curmax + then curmax + else go (maximum [dist,leavesMaxDist,curmax]) (toList $ children x) + + leavesMaxDist = maximum $ map (distance (nodedp ct)) $ toList $ leaves x + + dist = distance (nodedp x) (nodedp ct) + +--------------------------------------- + +{-# INLINABLE sortChildren #-} +sortChildren :: + ( ValidCT exprat childC leafC dp + ) => ( CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp + -> Ordering + ) + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +sortChildren cmp ct = ct + { children = fromList $ L.sortBy (cmp ct) $ map (sortChildren cmp) $ toList $ children ct + } + +cmp_numdp_distance ct a b + = compare (numdp a) (numdp b) + + compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + +cmp_numdp_distance' ct b a + = compare (numdp a) (numdp b) + + compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + +cmp_distance_numdp ct a b + = compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + + compare (numdp a) (numdp b) + +cmp_distance_numdp' ct b a + = compare (distance (nodedp ct) (nodedp a)) (distance (nodedp ct) (nodedp b)) + + compare (numdp a) (numdp b) + +--------------------------------------- + +-- | This function moves subtrees that have @n@ children or less from @childC@ to @leafC@. +-- The @leafC@ container stores data points directly. +-- This makes traversals faster and greatly improves runtimes. +{-# INLINABLE setLeaves #-} +setLeaves :: + ( ValidCT exprat childC leafC dp + ) => Int + -> CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +setLeaves n ct = {-# SCC setLeaves #-} if stNumNodes ct > n + then ct + { children = fromList $ map (setLeaves n) $ L.filter (not . stHasNoChildren) $ toList $ children ct + , leaves = fromList $ map nodedp $ L.filter stHasNoChildren $ toList $ children ct + } + else ct + { children = zero + , leaves = fromList $ stToList ct + } + +-- | This function puts the data points into a van Embde Boas layout. +-- This is a cache oblivious layout that causes queries to have fewer cache misses (and hence run faster). +-- Any modifications to the cover tree after calling "PackCT" will result in a slow degradation of cache performance. +{-# INLINABLE packCT #-} +packCT :: forall exprat childC leafC dp. + ( ValidCT exprat childC leafC dp + ) => CoverTree_ exprat childC leafC dp + -> CoverTree_ exprat childC leafC dp +packCT ct = {-# SCC packCT #-} snd $ go 0 ct + where + dpvec :: leafC dp + dpvec = fromList $ stToList ct + + go !i !t = {-# SCC packCT_go #-} ( i',t + { nodedp = dpvec!i + , leaves = slice (i+1) (length $ leaves t) dpvec + , children = fromList children' + } ) + where + (i',children') = {-# SCC mapAccumL #-} L.mapAccumL + go + (i+1+length (leaves t)) + (toList $ children t) + +------------------------------------------------------------------------------- +-- helper functions + +coverdist :: (QuotientField (Scalar dp) Int, Real (Scalar dp)) => + CoverTree_ exprat childC leafC dp -> Scalar dp +coverdist node = sepdist node*getExprat + +sepdist :: forall exprat childC leafC dp. (QuotientField (Scalar dp) Int, Real (Scalar dp)) => + CoverTree_ exprat childC leafC dp -> Scalar dp +sepdist ct = getExprat**(fromIntegral $ level ct) + +level2sepdist :: (QuotientField f r, Real f) => r -> f +level2sepdist i = getExprat**(fromIntegral i) + +level2coverdist :: (QuotientField f r, Real f) => r -> f +level2coverdist i = level2sepdist (i+1) + +dist2level_down :: forall exprat num. + ({-KnownFrac exprat, -}Real num, QuotientField num Int) => Proxy exprat -> num -> Int +dist2level_down _ d = floor $ log d / log getExprat + +dist2level_up :: forall exprat num. + ({-KnownFrac exprat,-} Real num, QuotientField num Int) => Proxy exprat -> num -> Int +dist2level_up _ d = ceiling $ log d / log getExprat diff --git a/src/HLearn/Data/SpaceTree/Diagrams.hs b/src/HLearn/Data/SpaceTree/Diagrams.hs new file mode 100644 index 0000000..3b48bc5 --- /dev/null +++ b/src/HLearn/Data/SpaceTree/Diagrams.hs @@ -0,0 +1,89 @@ +module HLearn.Data.SpaceTree.Diagrams + where + +import qualified Prelude as P + +import SubHask + +import HLearn.Data.SpaceTree +import HLearn.Models.Distributions + +import Diagrams.Prelude () +import qualified Diagrams.Prelude as D +import Diagrams.Backend.SVG hiding (size) + +-------------------------------------------------------------------------------- + +{- +-- drawCT :: +-- ( ValidCT exprat childC leafC dp +-- , VG.Vector childC (QDiagram SVG R2 Any) +-- , Integral (Scalar (leafC dp)) +-- , Integral (Scalar (childC (CoverTree_ exprat childC leafC dp))) +-- ) => P.FilePath +-- -> CoverTree_ exprat childC leafC dp +-- -> IO () +drawCT path ct = renderSVG path (Dims 500 300) (diagramCT_ 0 ct) + + +-- diagramCT node = diagramCT_ 0 node + +-- type instance Scalar R2 = Double + +-- diagramCT_ :: +-- ( ValidCT exprat childC leafC dp +-- ) => Int +-- -> CoverTree_ exprat childC leafC dp +-- -> Diagram a R2 +diagramCT_ (depth::Int) tree + = mkConnections $ + ( named (label++show depth) $ fontSize (Global 0.01) $ + ( + (text label D.<> strutY 0.5) + === (text (show (sepdist tree)) D.<> strutY 0.5) + -- === (text (show (maxDescendentDistance tree)) <> strutY 0.5) + ) + D.<> circle 1 # fc nodecolor + ) + === (pad 1.05 $ centerName (label++show (depth+1)) $ + VG.foldr (|||) mempty $ VG.map (diagramCT_ (depth+1)) $ children tree) + + where + label = intShow $ nodedp tree + nodecolor = if ctBetterMovableNodes tree==0 --nodeWeight tree > 0 + then red + else lightblue + + mkConnections = + D.connect (label++show depth) (label++show (depth+1)) + . apList (fmap + (\key -> D.connect (label++show depth) (intShow key++show (depth+1))) + (map nodedp $ toList $ children tree) + ) + +centerName name = withName name $ \b a -> moveOriginTo (location b) a + +apList :: [a -> a] -> a -> a +apList [] a = a +apList (x:xs) a = apList xs (x a) + +intShow :: Show a => a -> String +intShow a = P.filter go $ show a + where + go x + | x=='.' = True + | x==',' = True + | x=='-' = True + | x=='1' = True + | x=='2' = True + | x=='3' = True + | x=='4' = True + | x=='5' = True + | x=='6' = True + | x=='7' = True + | x=='8' = True + | x=='9' = True + | x=='0' = True + | otherwise = False + +-} diff --git a/src/HLearn/Data/UnsafeVector.hs b/src/HLearn/Data/UnsafeVector.hs deleted file mode 100644 index 6919dfc..0000000 --- a/src/HLearn/Data/UnsafeVector.hs +++ /dev/null @@ -1,237 +0,0 @@ -module HLearn.Data.UnsafeVector - ( setptsize - ) - where - -import Control.DeepSeq -import Control.Monad -import Data.IORef -import Debug.Trace -import qualified Data.Foldable as F -import Data.Primitive -import Data.Primitive.MachDeps -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Generic.Mutable as VGM -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector.Unboxed.Mutable as VUM -import System.IO.Unsafe -import qualified Data.Strict.Maybe as Strict - -import Data.Csv - -import Unsafe.Coerce -import Data.Hashable -import Data.Primitive.ByteArray -import GHC.Prim -import GHC.Int -import GHC.Types - -import SubHask hiding (Functor(..), Applicative(..), Monad(..), Then(..), fail, return, liftM, forM_) --- import SubHask.Compatibility.Vector.HistogramMetrics -import SubHask.Compatibility.Vector.Lebesgue - -------------------------------------------------------------------------------- --- unsafe globals - -{-# NOINLINE ptsizeIO #-} -ptsizeIO = unsafeDupablePerformIO $ newIORef (16::Int) - -{-# NOINLINE ptalignIO #-} -ptalignIO = unsafeDupablePerformIO $ newIORef (16::Int) - -{-# NOINLINE ptsize #-} -ptsize = unsafeDupablePerformIO $ readIORef ptsizeIO - -{-# NOINLINE ptalign #-} -ptalign = unsafeDupablePerformIO $ readIORef ptalignIO - -setptsize :: Int -> IO () -setptsize len = do - writeIORef ptsizeIO len - writeIORef ptalignIO (4::Int) - -------------------------------------------------------------------------------- --- l2 vector ops - -instance VUM.Unbox elem => VUM.Unbox (L2 VU.Vector elem) - -data instance VUM.MVector s (L2 VU.Vector elem) = UnsafeMVector - { elemsizeM :: {-#UNPACK#-} !Int - , elemsizerealM :: {-#UNPACK#-} !Int - , lenM :: {-#UNPACK#-} !Int - , vecM :: !(VUM.MVector s elem) - } - -instance - ( VG.Vector VU.Vector elem - , VGM.MVector VUM.MVector elem - ) => VGM.MVector VUM.MVector (L2 VU.Vector elem) - where - {-# INLINABLE basicLength #-} - basicLength uv = lenM uv - - {-# INLINABLE basicUnsafeSlice #-} - basicUnsafeSlice i lenM' uv = UnsafeMVector - { elemsizeM = elemsizeM uv - , elemsizerealM = elemsizerealM uv - , lenM = lenM' - , vecM = VGM.basicUnsafeSlice (i*elemsizerealM uv) (lenM'*elemsizerealM uv) $ vecM uv - } - - {-# INLINABLE basicOverlaps #-} - basicOverlaps uv1 uv2 = VGM.basicOverlaps (vecM uv1) (vecM uv2) - - {-# INLINABLE basicUnsafeNew #-} - basicUnsafeNew lenM' = do - let elemsizeM'=ptsize --- let elemsizerealM'=20 - let elemsizerealM'=ptalign*(ptsize `div` ptalign) - +if ptsize `mod` ptalign == 0 then 0 else ptalign - --- trace ("elemsizeM' = "++show elemsizeM') $ return () --- trace ("elemsizerealM' = "++show elemsizerealM') $ return () - - vecM' <- VGM.basicUnsafeNew (lenM'*elemsizerealM') - return $ UnsafeMVector - { elemsizeM=elemsizeM' - , elemsizerealM=elemsizerealM' - , lenM=lenM' - , vecM=vecM' - } - - {-# INLINABLE basicUnsafeRead #-} - basicUnsafeRead uv i = - liftM L2 $ VG.freeze $ VGM.unsafeSlice (i*elemsizerealM uv) (elemsizeM uv) (vecM uv) - - {-# INLINABLE basicUnsafeWrite #-} --- FIXME: this is probably better, but it causes GHC to panic --- basicUnsafeWrite uv loc v = go 0 --- where --- go i = if i <= elemsizerealM uv --- then do --- VGM.unsafeWrite (vecM uv) (start+i) $ v `VG.unsafeIndex` i --- go (i+1) --- else return () --- start = loc*elemsizerealM uv - - basicUnsafeWrite uv loc v = - forM_ [0..elemsizeM uv-1] $ \i -> do - let x = v `VG.unsafeIndex` i - VGM.unsafeWrite (vecM uv) (start+i) x - where - start = loc*elemsizerealM uv - - {-# INLINABLE basicUnsafeCopy #-} - basicUnsafeCopy v1 v2 = VGM.basicUnsafeCopy (vecM v1) (vecM v2) - - {-# INLINABLE basicUnsafeMove #-} - basicUnsafeMove v1 v2 = VGM.basicUnsafeMove (vecM v1) (vecM v2) - --- {-# INLINABLE basicSet #-} --- basicSet v x = VGM.basicSet (vecM v) x - -------------------------------------------------------------------------------- --- immutable vector - -data instance VU.Vector (L2 VU.Vector elem) = UnsafeVector - { elemsize :: {-# UNPACK #-} !Int - , elemsizereal :: {-# UNPACK #-} !Int - , len :: {-# UNPACK #-} !Int - , vec :: !(L2 VU.Vector elem) - } - -instance - ( VG.Vector VU.Vector elem - , VGM.MVector VUM.MVector elem - ) => VG.Vector VU.Vector (L2 VU.Vector elem) - where - - {-# INLINABLE basicUnsafeFreeze #-} - basicUnsafeFreeze uv = do - vec' <- VG.basicUnsafeFreeze (vecM uv) - return $ UnsafeVector - { elemsize = elemsizeM uv - , elemsizereal = elemsizerealM uv - , len = lenM uv - , vec = L2 vec' - } - - {-# INLINABLE basicUnsafeThaw #-} - basicUnsafeThaw uv = do - vecM' <- VG.basicUnsafeThaw (unL2 $ vec uv) - return $ UnsafeMVector - { elemsizeM = elemsize uv - , elemsizerealM = elemsizereal uv - , lenM = len uv - , vecM = vecM' - } - - {-# INLINABLE basicLength #-} - basicLength uv = len uv - - {-# INLINABLE basicUnsafeSlice #-} - basicUnsafeSlice i len' uv = uv - { len = len' - , vec = VG.basicUnsafeSlice (i*elemsizereal uv) (len'*elemsizereal uv) (vec uv) - } - - {-# INLINABLE basicUnsafeIndexM #-} - basicUnsafeIndexM uv i = return $ VG.basicUnsafeSlice (i*elemsizereal uv) (elemsize uv) (vec uv) - --- {-# INLINABLE basicUnsafeCopy #-} --- basicUnsafeCopy mv v = VG.basicUnsafeCopy (vecM mv) (vec v) - -------------------------------------------------------------------------------- --- Labeled' - -instance (VUM.Unbox x, VUM.Unbox y) => VUM.Unbox (Labeled' x y) - -newtype instance VUM.MVector s (Labeled' x y) = UMV_Labeled' (VUM.MVector s (x,y)) - -instance - ( VUM.Unbox x - , VUM.Unbox y - ) => VGM.MVector VUM.MVector (Labeled' x y) - where - - {-# INLINABLE basicLength #-} - {-# INLINABLE basicUnsafeSlice #-} - {-# INLINABLE basicOverlaps #-} - {-# INLINABLE basicUnsafeNew #-} - {-# INLINABLE basicUnsafeRead #-} - {-# INLINABLE basicUnsafeWrite #-} - {-# INLINABLE basicUnsafeCopy #-} - {-# INLINABLE basicUnsafeMove #-} - {-# INLINABLE basicSet #-} - basicLength (UMV_Labeled' v) = VGM.basicLength v - basicUnsafeSlice i len (UMV_Labeled' v) = UMV_Labeled' $ VGM.basicUnsafeSlice i len v - basicOverlaps (UMV_Labeled' v1) (UMV_Labeled' v2) = VGM.basicOverlaps v1 v2 - basicUnsafeNew len = liftM UMV_Labeled' $ VGM.basicUnsafeNew len - basicUnsafeRead (UMV_Labeled' v) i = do - (!x,!y) <- VGM.basicUnsafeRead v i - return $ Labeled' x y - basicUnsafeWrite (UMV_Labeled' v) i (Labeled' x y) = VGM.basicUnsafeWrite v i (x,y) - basicUnsafeCopy (UMV_Labeled' v1) (UMV_Labeled' v2) = VGM.basicUnsafeCopy v1 v2 - basicUnsafeMove (UMV_Labeled' v1) (UMV_Labeled' v2) = VGM.basicUnsafeMove v1 v2 - basicSet (UMV_Labeled' v1) (Labeled' x y) = VGM.basicSet v1 (x,y) - -newtype instance VU.Vector (Labeled' x y) = UV_Labeled' (VU.Vector (x,y)) - -instance - ( VUM.Unbox x - , VUM.Unbox y - ) => VG.Vector VU.Vector (Labeled' x y) - where - - {-# INLINABLE basicUnsafeFreeze #-} - {-# INLINABLE basicUnsafeThaw #-} - {-# INLINABLE basicLength #-} - {-# INLINABLE basicUnsafeSlice #-} - {-# INLINABLE basicUnsafeIndexM #-} - basicUnsafeFreeze (UMV_Labeled' v) = liftM UV_Labeled' $ VG.basicUnsafeFreeze v - basicUnsafeThaw (UV_Labeled' v) = liftM UMV_Labeled' $ VG.basicUnsafeThaw v - basicLength (UV_Labeled' v) = VG.basicLength v - basicUnsafeSlice i len (UV_Labeled' v) = UV_Labeled' $ VG.basicUnsafeSlice i len v - basicUnsafeIndexM (UV_Labeled' v) i = do - (!x,!y) <- VG.basicUnsafeIndexM v i - return $ Labeled' x y diff --git a/src/HLearn/History.hs b/src/HLearn/History.hs index a5700e7..9134ca4 100644 --- a/src/HLearn/History.hs +++ b/src/HLearn/History.hs @@ -9,29 +9,35 @@ module HLearn.History ( -- * The History Monad History + , History_ +-- , History__ , runHistory , evalHistory , traceHistory , traceAllHistory + , NoCxt + , ValidCxt + -- ** Reporting tools , Report (..) , beginFunction , report + , withMsg + , withMsgIO , iterate , currentItr -- *** stop conditions , StopCondition - , StopCondition_ , maxIterations , stopBelow - , fx1grows , mulTolerance + , fx1grows + , noProgress -- * Display Functions - , Optimizable , DisplayFunction -- ** Display each iteration @@ -76,6 +82,14 @@ import SubHask.Compatibility.Containers ------------------------------------------------------------------------------- +-- FIXME: +-- This class is obsolete and should be deleted. +-- It should be hard to do, I'm just tired right now and don't want to do the refactoring. +class (Typeable a, Show a) => Optimizable a +instance (Typeable a, Show a) => Optimizable a + +------------------------------------------------------------------------------- + -- | -- -- FIXME: Is there a way to store times in "Int"s rather than "Integer"s for more efficiency? @@ -93,15 +107,196 @@ data Report = Report } deriving Show +mkMutable [t| Report |] + +------------------------------------------------------------------------------- +-- display functions + +-- | When running a "History" monad, there are three times we might need to perform IO actions: the beginning, middle, and end. +-- This type just wraps all three of those functions into a single type. +data DisplayFunction_ cxt s = DisplayFunction + { startDisplayFunction :: IO () + , stepDisplayFunction :: forall a. cxt a => Report -> s -> a -> (s, IO ()) + , stopDisplayFunction :: s -> IO () + } + +type DisplayFunction = DisplayFunction_ Optimizable + +mkMutable [t| forall cxt s. DisplayFunction_ cxt s |] + +instance Semigroup s => Semigroup (DisplayFunction_ cxt s) where + df1+df2 = DisplayFunction + (startDisplayFunction df1+startDisplayFunction df2) + (stepDisplayFunction df1 +stepDisplayFunction df2 ) + (stopDisplayFunction df1 +stopDisplayFunction df2 ) + +instance Monoid s => Monoid (DisplayFunction_ cxt s) where + zero = DisplayFunction zero zero zero + +---------------------------------------- +-- filtering + +-- | Functions of this type are used to prevent the "stepDisplayFunction" from being called in certain situaations. +type DisplayFilter = forall a. Optimizable a => Report -> a -> Bool + +displayFilter :: Monoid s => DisplayFilter -> DisplayFunction s -> DisplayFunction s +displayFilter f df = df + { stepDisplayFunction = \r s a -> if f r a + then stepDisplayFunction df r s a + else (zero, return ()) + } + +maxReportLevel :: Int -> DisplayFilter +maxReportLevel n r _ = reportLevel r <= n + +---------------------------------------- +-- summary table + +-- | Functions of this type are used as parameters to the "dispIteration_" function. +type DisplayInfo = forall a. Optimizable a => Report -> a -> String + +-- | After each step in the optimization completes, print a single line describing what happened. +dispIteration :: Monoid s => DisplayFunction s +dispIteration = dispIteration_ (infoItr + infoType + infoDiffTime) + +-- | A more general version of "dispIteration" that let's you specify what information to display. +dispIteration_ :: forall s. Monoid s => DisplayInfo -> DisplayFunction s +dispIteration_ f = DisplayFunction zero g zero + where + -- type signature needed for -XImpredicativeTypes + g :: forall a. Optimizable a => Report -> s -> a -> (s, IO () ) + g r s a = (zero, putStrLn $ (concat $ P.replicate (reportLevel r) " - ") ++ f r a) + +-- | Pretty-print a "CPUTime". +showTime :: CPUTime -> String +showTime t = showEFloat (Just $ len-4-4) (fromIntegral t * 1e-12 :: Double) "" ++ " sec" + where + len=12 + +-- | Print a raw string. +infoString :: String -> DisplayInfo +infoString = const . const + +-- | Print the time used to complete the step. +infoDiffTime :: DisplayInfo +infoDiffTime r _ = "; " ++ showTime (cpuTimeDiff r) + +-- | Print the name of the optimization step. +infoType :: DisplayInfo +infoType _ a = "; "++if typeRep [a] == typeRep [""] + then P.init $ P.tail $ show a + else show a +-- else P.head $ P.words $ show $ typeRep [a] + +-- | Print the current iteration of the optimization. +infoItr :: DisplayInfo +infoItr r _ = "; "++show (numReports r) + +---------------------------------------- +-- summary table + +-- | Contains all the information that might get displayed by "summaryTable". +-- +-- FIXME: +-- There's a lot more information that could be included. +-- We could make "summaryTable" take parameters describing which elements to actually calculate/display. +data CountInfo = CountInfo + { numcalls :: Int + , tottime :: Integer + } + deriving Show + +type instance Logic CountInfo = Bool + +instance Eq_ CountInfo where + ci1==ci2 = numcalls ci1 == numcalls ci2 + && tottime ci1 == tottime ci2 + +avetime :: CountInfo -> Integer +avetime = round (fromIntegral tottime / fromIntegral numcalls :: CountInfo -> Double) + +-- | Call "runHistory" with this "DisplayFunction" to get a table summarizing the optimization. +-- This does not affect output during the optimization itself, only at the end. +summaryTable :: DisplayFunction (Map' (Lexical String) CountInfo) +summaryTable = DisplayFunction zero step stop + where + -- type signature needed for -XImpredicativeTypes + step :: forall a. Optimizable a + => Report + -> Map' (Lexical String) CountInfo + -> a + -> ( Map' (Lexical String) CountInfo, IO () ) + step r s a = (insertAt k ci s, return ()) + where + t = typeRep (Proxy::Proxy a) + + k = Lexical $ if t == typeRep (Proxy::Proxy String) + then unsafeCoerce a + else P.head $ P.words $ show t + + ci0 = case lookup k s of + Just x -> x + Nothing -> CountInfo + { numcalls = 0 + , tottime = 0 + } + + ci = ci0 + { numcalls = numcalls ci0+1 + , tottime = tottime ci0+cpuTimeDiff r + } + + stop :: Map' (Lexical String) CountInfo -> IO () + stop m = do + + let hline = putStrLn $ " " ++ P.replicate (maxlen_name+maxlen_count+maxlen_time+10) '-' + + hline + putStrLn $ " | " ++ padString title_name maxlen_name + ++ " | " ++ padString title_count maxlen_count + ++ " | " ++ padString title_time maxlen_time + ++ " | " + hline + forM_ (toIxList m) $ \(k,ci) -> do + putStrLn $ " | " ++ padString (unLexical k ) maxlen_name + ++ " | " ++ padString (show $ numcalls ci) maxlen_count + ++ " | " ++ padString (showTime $ tottime ci) maxlen_time + ++ " | " + hline + + where + title_name = "report name" + title_count = "number of calls" + title_time = "average time per call" + maxlen_name = maximum $ length title_name:(map (length . fst) $ toIxList m) + maxlen_count = maximum $ length title_count:(map (length . show . numcalls . snd) $ toIxList m) + maxlen_time = maximum $ length title_time: (map (length . showTime . tottime . snd) $ toIxList m) + +padString :: String -> Int -> String +padString a i = P.take i $ a ++ P.repeat ' ' + ------------------------------------------------- +-- | Every type is an instance of "NoCxt". +-- When running a "History" monad, we must always assign a value to the "cxt" variable. +-- Use "NoCxt" when you don't want to enforce any constraints. +class NoCxt a +instance NoCxt a + +-- | Applies the cxt to construct the needed constraints. +type ValidCxt (cxt :: * -> Constraint) a = + ( cxt String + , cxt a + , cxt (Scalar a) + ) + -- | A (sometimes) more convenient version of "History_" -type History a = forall s. History_ s a +type History cxt a = forall s. ValidCxt cxt a => History_ cxt s a -- | This monad internally requires -XImpredicativeTypes to thread our "DisplayFunction" throughout the code. -newtype History_ s a = History +newtype History_ cxt s a = History ( ReaderT - ( DisplayFunction s ) + ( DisplayFunction_ cxt s ) ( StateT (s,[Report]) -- ( StateT @@ -112,25 +307,27 @@ newtype History_ s a = History a ) +mkMutable [t| forall cxt s a. History_ cxt s a |] + -- | Run the "History" computation without any debugging information. -- This is the most efficient way to run an optimization. {-# INLINABLE evalHistory #-} -evalHistory :: History_ () a -> a +evalHistory :: History_ NoCxt () a -> a evalHistory = unsafePerformIO . runHistory zero -- | Run the "History" computation with a small amount of debugging information. {-# INLINABLE traceHistory #-} -traceHistory :: History_ () a -> a +traceHistory :: Optimizable a => History_ Optimizable () a -> a traceHistory = unsafePerformIO . runHistory (displayFilter (maxReportLevel 2) dispIteration) -- | Run the "History" computation with a lot of debugging information. {-# INLINABLE traceAllHistory #-} -traceAllHistory :: History_ () a -> a +traceAllHistory :: Optimizable a => History_ Optimizable () a -> a traceAllHistory = unsafePerformIO . runHistory dispIteration -- | Specify the amount of debugging information to run the "History" computation with. {-# INLINABLE runHistory #-} -runHistory :: forall s a. Monoid s => DisplayFunction s -> History_ s a -> IO a +runHistory :: forall cxt s a. (cxt a, Monoid s) => DisplayFunction_ cxt s -> History_ cxt s a -> IO a runHistory df (History hist) = {-# SCC runHistory #-} do time <- getCPUTime let startReport = Report @@ -144,7 +341,7 @@ runHistory df (History hist) = {-# SCC runHistory #-} do -- the nasty type signature below is needed for -XImpredicativeTypes (a, (s,_)) <- runStateT - ( (runReaderT :: forall m. ReaderT (DisplayFunction s) m a -> DisplayFunction s -> m a ) + ( (runReaderT :: forall m. ReaderT (DisplayFunction_ cxt s) m a -> DisplayFunction_ cxt s -> m a ) hist df ) @@ -159,7 +356,7 @@ runHistory df (History hist) = {-# SCC runHistory #-} do -- -- This is a convenient wrapper around the "report" and "collectReports" functions. {-# INLINABLE beginFunction #-} -beginFunction :: String -> History_ s a -> History_ s a +beginFunction :: cxt String => String -> History_ cxt s a -> History_ cxt s a beginFunction b ha = collectReports $ do report b collectReports ha @@ -167,7 +364,7 @@ beginFunction b ha = collectReports $ do -- | Register the parameter of type @a@ as being important for debugging information. -- This creates a new "Report" and automatically runs the appropriate "stepDisplayFunction". {-# INLINABLE report #-} -report :: forall s a. Optimizable a => a -> History_ s a +report :: forall cxt s a. cxt a => a -> History_ cxt s a report a = {-# SCC report #-} History $ do time <- liftIO getCPUTime @@ -181,8 +378,8 @@ report a = {-# SCC report #-} History $ do -- get our DisplayFunction and call it -- the cumbersome type signature is required for -XImpredicativeTypes - (f::DisplayFunction s) <- - (ask :: ReaderT (DisplayFunction s) (StateT (s, [Report]) IO) (DisplayFunction s)) + (f::DisplayFunction_ cxt s) <- + (ask :: ReaderT (DisplayFunction_ cxt s) (StateT (s, [Report]) IO) (DisplayFunction_ cxt s)) let (s1,io) = stepDisplayFunction f newReport s0 a put $ (s1, newReport:xs) @@ -192,7 +389,7 @@ report a = {-# SCC report #-} History $ do -- | Group all of the "Reports" that happen in the given computation together. -- You probably don't need to call this function directly, and instead should call "beginFunction". {-# INLINABLE collectReports #-} -collectReports :: History_ s a -> History_ s a +collectReports :: History_ cxt s a -> History_ cxt s a collectReports (History hist) = {-# SCC collectReports #-} History $ do mkLevel a <- hist @@ -216,45 +413,59 @@ collectReports (History hist) = {-# SCC collectReports #-} History $ do {-# INLINABLE currentItr #-} -currentItr :: History Int +currentItr :: History cxt Int currentItr = History $ do (_ , x:_) <- get return $ numReports x +--------------------------------------- + +withMsg :: (cxt String, NFData a) => String -> a -> History_ cxt s a +withMsg msg a = withMsgIO msg (return a) + +withMsgIO :: (cxt String, NFData a) => String -> IO a -> History_ cxt s a +withMsgIO msg ioa = do + a <- History $ liftIO ioa + report $ deepseq a $ msg + return a + +------------------------------------------------------------------------------- +-- algebra + --------------------------------------- -- monad hierarchy -instance Functor Hask (History_ s) where +instance Functor Hask (History_ cxt s) where fmap f (History s) = History (fmap f s) -instance Then (History_ s) where +instance Then (History_ cxt s) where (>>) = haskThen -instance Monad Hask (History_ s) where +instance Monad Hask (History_ cxt s) where return_ a = History $ return_ a join (History s) = History $ join (fmap (\(History s)->s) s) --------------------------------------- --- algebra hierarchy +-- math hierarchy -type instance Scalar (History_ s a) = Scalar a +type instance Scalar (History_ cxt s a) = Scalar a -instance Semigroup a => Semigroup (History_ s a) where +instance Semigroup a => Semigroup (History_ cxt s a) where ha1 + ha2 = do a1 <- ha1 a2 <- ha2 return $ a1+a2 -instance Cancellative a => Cancellative (History_ s a) where +instance Cancellative a => Cancellative (History_ cxt s a) where ha1 - ha2 = do a1 <- ha1 a2 <- ha2 return $ a1-a2 -instance Monoid a => Monoid (History_ s a) where +instance Monoid a => Monoid (History_ cxt s a) where zero = return zero -instance Group a => Group (History_ s a) where +instance Group a => Group (History_ cxt s a) where negate ha = do a <- ha return $ negate a @@ -262,71 +473,60 @@ instance Group a => Group (History_ s a) where --------------------------------------- -- comparison hierarchy -type instance Logic (History_ s a) = History_ s (Logic a) +type instance Logic (History_ cxt s a) = History_ cxt s (Logic a) -instance Eq_ a => Eq_ (History_ s a) where +instance Eq_ a => Eq_ (History_ cxt s a) where a==b = do a' <- a b' <- b return $ a'==b' -instance POrd_ a => POrd_ (History_ s a) where +instance POrd_ a => POrd_ (History_ cxt s a) where {-# INLINABLE inf #-} inf a b = do a' <- a b' <- b return $ inf a' b' -instance Lattice_ a => Lattice_ (History_ s a) where +instance Lattice_ a => Lattice_ (History_ cxt s a) where {-# INLINABLE sup #-} sup a b = do a' <- a b' <- b return $ sup a' b' -instance MinBound_ a => MinBound_ (History_ s a) where +instance MinBound_ a => MinBound_ (History_ cxt s a) where minBound = return $ minBound -instance Bounded a => Bounded (History_ s a) where +instance Bounded a => Bounded (History_ cxt s a) where maxBound = return $ maxBound -instance Heyting a => Heyting (History_ s a) where +instance Heyting a => Heyting (History_ cxt s a) where (==>) a b = do a' <- a b' <- b return $ a' ==> b' -instance Complemented a => Complemented (History_ s a) where +instance Complemented a => Complemented (History_ cxt s a) where not a = do a' <- a return $ not a' -instance Boolean a => Boolean (History_ s a) +instance Boolean a => Boolean (History_ cxt s a) ------------------------------------------------------------------------------- --- Stop conditions - -class Has_x1 opt v where x1 :: opt v -> v -class Has_fx1 opt v where fx1 :: opt v -> Scalar v - ---------------------------------------- - --- | A (sometimes) more convenient version of "StopCondition_". -type StopCondition = forall a. StopCondition_ a - --- | Functions of this type determine whether the "iterate" function should keep looping or stop. -type StopCondition_ a = a -> a -> History Bool +-- iteration -- | This function is similar in spirit to the @while@ loop of imperative languages like @C@. -- The advantage is the "DisplayFunction"s from the "History" monad get automatically (and efficiently!) threaded throughout our computation. -- "iterate" is particularly useful for implementing iterative optimization algorithms. {-# INLINABLE iterate #-} -iterate :: forall a. Optimizable a - => (a -> History a) -- ^ step function - -> a -- ^ start parameters - -> StopCondition_ a -- ^ stop conditions - -> History a -iterate step opt0 stop = {-# SCC iterate #-} do +iterate :: forall cxt a. cxt a + => (a -> History cxt a) -- ^ step function + -> StopCondition a -- ^ stop conditions + -> a -- ^ start parameters + -> History cxt a -- ^ result +iterate step stop opt0 = {-# SCC iterate #-} do report opt0 opt1 <- step opt0 go opt0 opt1 @@ -340,10 +540,21 @@ iterate step opt0 stop = {-# SCC iterate #-} do opt' <- step curopt go curopt opt' +--------------------------------------- + +class Has_x1 opt v where x1 :: opt v -> v +class Has_fx1 opt v where fx1 :: opt v -> Scalar v + +--------------------------------------- +-- stop conditions + +-- | Functions of this type determine whether the "iterate" function should keep looping or stop. +type StopCondition a = forall cxt. cxt a => a -> a -> forall s. History_ cxt s Bool + -- | Stop iterating after the specified number of iterations. -- This number is typically set fairly high (at least one hundred, possibly in the millions). -- It should probably be used on all optimizations to prevent poorly converging optimizations taking forever. -maxIterations :: Int {- ^ max number of iterations -} -> StopCondition +maxIterations :: Int {- ^ max number of iterations -} -> StopCondition a maxIterations i _ _ = History $ do (_ , x:_) <- get return $ numReports x >= i @@ -353,7 +564,7 @@ stopBelow :: ( Has_fx1 opt v , Ord (Scalar v) ) => Scalar v - -> StopCondition_ (opt v) + -> StopCondition (opt v) stopBelow threshold _ opt = return $ (fx1 opt) < threshold -- | Stop the iteration when successive function evaluations are within a given distance of each other. @@ -362,7 +573,7 @@ mulTolerance :: ( BoundedField (Scalar v) , Has_fx1 opt v ) => Scalar v -- ^ tolerance - -> StopCondition_ (opt v) + -> StopCondition (opt v) mulTolerance tol prevopt curopt = {-# SCC multiplicativeTollerance #-} do return $ (fx1 prevopt) /= infinity && left < right where @@ -370,171 +581,9 @@ mulTolerance tol prevopt curopt = {-# SCC multiplicativeTollerance #-} do right = tol*(abs (fx1 curopt) + abs (fx1 prevopt) + 1e-18) -- | Stop the optimization if our function value has grown between iterations -fx1grows :: ( Has_fx1 opt v , Ord (Scalar v) ) => StopCondition_ (opt v) -fx1grows opt0 opt1 = {-# SCC fx1grows #-} return $ fx1 opt0 < fx1 opt1 - -------------------------------------------------------------------------------- --- display functions - --- | This type synonym is a hack, that I'm not happy with. --- Any class that any display function might want just gets included here. -type Optimizable a = (Typeable a, Show a) - --- | When running a "History" monad, there are three times we might need to perform IO actions: the beginning, middle, and end. --- This type just wraps all three of those functions into a single type. -data DisplayFunction s = DisplayFunction - { startDisplayFunction :: IO () - , stepDisplayFunction :: forall a. Optimizable a => Report -> s -> a -> (s, IO ()) - , stopDisplayFunction :: s -> IO () - } - -instance Semigroup s => Semigroup (DisplayFunction s) where - df1+df2 = DisplayFunction - (startDisplayFunction df1+startDisplayFunction df2) - (stepDisplayFunction df1 +stepDisplayFunction df2 ) - (stopDisplayFunction df1 +stopDisplayFunction df2 ) - -instance Monoid s => Monoid (DisplayFunction s) where - zero = DisplayFunction zero zero zero - ----------------------------------------- --- filtering - --- | Functions of this type are used to prevent the "stepDisplayFunction" from being called in certain situaations. -type DisplayFilter = forall a. Optimizable a => Report -> a -> Bool - -displayFilter :: Monoid s => DisplayFilter -> DisplayFunction s -> DisplayFunction s -displayFilter f df = df - { stepDisplayFunction = \r s a -> if f r a - then stepDisplayFunction df r s a - else (zero, return ()) - } - -maxReportLevel :: Int -> DisplayFilter -maxReportLevel n r _ = reportLevel r <= n - ----------------------------------------- --- summary table - --- | Functions of this type are used as parameters to the "dispIteration_" function. -type DisplayInfo = forall a. Optimizable a => Report -> a -> String - --- | After each step in the optimization completes, print a single line describing what happened. -dispIteration :: Monoid s => DisplayFunction s -dispIteration = dispIteration_ (infoItr + infoType + infoDiffTime) - --- | A more general version of "dispIteration" that let's you specify what information to display. -dispIteration_ :: forall s. Monoid s => DisplayInfo -> DisplayFunction s -dispIteration_ f = DisplayFunction zero g zero - where - -- type signature needed for -XImpredicativeTypes - g :: forall a. Optimizable a => Report -> s -> a -> (s, IO () ) - g r s a = (zero, putStrLn $ (concat $ P.replicate (reportLevel r) " - ") ++ f r a) - --- | Pretty-print a "CPUTime". -showTime :: CPUTime -> String -showTime t = showEFloat (Just $ len-4-4) (fromIntegral t * 1e-12 :: Double) "" ++ " sec" - where - len=12 - --- | Print a raw string. -infoString :: String -> DisplayInfo -infoString = const . const - --- | Print the time used to complete the step. -infoDiffTime :: DisplayInfo -infoDiffTime r _ = "; " ++ showTime (cpuTimeDiff r) - --- | Print the name of the optimization step. -infoType :: DisplayInfo -infoType _ a = "; "++if typeRep [a] == typeRep [""] - then P.init $ P.tail $ show a - else P.head $ P.words $ show $ typeRep [a] - --- | Print the current iteration of the optimization. -infoItr :: DisplayInfo -infoItr r _ = "; "++show (numReports r) - ----------------------------------------- --- summary table - --- | Contains all the information that might get displayed by "summaryTable". --- --- FIXME: --- There's a lot more information that could be included. --- We could make "summaryTable" take parameters describing which elements to actually calculate/display. -data CountInfo = CountInfo - { numcalls :: Int - , tottime :: Integer - } - deriving Show - -type instance Logic CountInfo = Bool - -instance Eq_ CountInfo where - ci1==ci2 = numcalls ci1 == numcalls ci2 - && tottime ci1 == tottime ci2 - -avetime :: CountInfo -> Integer -avetime = round (fromIntegral tottime / fromIntegral numcalls :: CountInfo -> Double) - --- | Call "runHistory" with this "DisplayFunction" to get a table summarizing the optimization. --- This does not affect output during the optimization itself, only at the end. -summaryTable :: DisplayFunction (Map' (Lexical String) CountInfo) -summaryTable = DisplayFunction zero step stop - where - -- type signature needed for -XImpredicativeTypes - step :: forall a. Optimizable a - => Report - -> Map' (Lexical String) CountInfo - -> a - -> ( Map' (Lexical String) CountInfo, IO () ) - step r s a = (insertAt k ci s, return ()) - where - t = typeRep (Proxy::Proxy a) - - k = Lexical $ if t == typeRep (Proxy::Proxy String) - then unsafeCoerce a - else P.head $ P.words $ show t - - ci0 = case lookup k s of - Just x -> x - Nothing -> CountInfo - { numcalls = 0 - , tottime = 0 - } - - ci = ci0 - { numcalls = numcalls ci0+1 - , tottime = tottime ci0+cpuTimeDiff r - } - - stop :: Map' (Lexical String) CountInfo -> IO () - stop m = do - - let hline = putStrLn $ " " ++ P.replicate (maxlen_name+maxlen_count+maxlen_time+10) '-' - - hline - putStrLn $ " | " ++ padString title_name maxlen_name - ++ " | " ++ padString title_count maxlen_count - ++ " | " ++ padString title_time maxlen_time - ++ " | " - hline - forM_ (toIxList m) $ \(k,ci) -> do - putStrLn $ " | " ++ padString (unLexical k ) maxlen_name - ++ " | " ++ padString (show $ numcalls ci) maxlen_count - ++ " | " ++ padString (showTime $ tottime ci) maxlen_time - ++ " | " - hline - - where - title_name = "report name" - title_count = "number of calls" - title_time = "average time per call" - maxlen_name = maximum $ length title_name:(map (length . fst) $ toIxList m) - maxlen_count = maximum $ length title_count:(map (length . show . numcalls . snd) $ toIxList m) - maxlen_time = maximum $ length title_time: (map (length . showTime . tottime . snd) $ toIxList m) - -padString :: String -> Int -> String -padString a i = P.take i $ a ++ P.repeat ' ' +fx1grows :: ( Has_fx1 opt v , Ord (Scalar v) ) => StopCondition (opt v) +fx1grows opt0 opt1 = return $ fx1 opt0 < fx1 opt1 +-- | Stop the optimization if our function value has stopped decreasing +noProgress :: Eq (opt v) => StopCondition (opt v) +noProgress opt0 opt1 = return $ opt0 == opt1 diff --git a/src/HLearn/History/Timing.hs b/src/HLearn/History/Timing.hs index 7d8ba13..b0893eb 100644 --- a/src/HLearn/History/Timing.hs +++ b/src/HLearn/History/Timing.hs @@ -4,8 +4,9 @@ module HLearn.History.Timing where +import SubHask (NFData, deepseq) + import Prelude -import Control.DeepSeq import Data.Time.Clock import Numeric import System.CPUTime diff --git a/src/HLearn/Metrics/Generic.hs b/src/HLearn/Metrics/Generic.hs deleted file mode 100644 index 68b71f6..0000000 --- a/src/HLearn/Metrics/Generic.hs +++ /dev/null @@ -1,16 +0,0 @@ -{-# LANGUAGE PolyKinds,DataKinds #-} -module HLearn.Metrics.Generic - where - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -newtype Maximum (xs :: [* -> *]) dp = Maximum dp - -type instance Scalar (Maximum '[m] dp) = Scalar (m dp) -type instance Scalar (Maximum (m1 ': m2 ': ms) dp) = Scalar (m1 dp) - --- instance MetricSpace (m dp) => MetricSpace (Maximum '[m] dp) where --- distance (Maximum a) (Maximum b) = distance a b diff --git a/src/HLearn/Metrics/Mahalanobis.hs b/src/HLearn/Metrics/Mahalanobis.hs deleted file mode 100644 index 5130fbb..0000000 --- a/src/HLearn/Metrics/Mahalanobis.hs +++ /dev/null @@ -1,109 +0,0 @@ -module HLearn.Metrics.Mahalanobis - where - -import Control.DeepSeq -import qualified Data.Vector.Generic as VG - -import Debug.Trace - -import qualified Data.Strict as Strict -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>),dim) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -data Mahalanobis dp = Mahalanobis - { rawdp :: !dp - , moddp :: !dp - } - deriving (Read,Show,Eq,Ord) - -instance NFData dp => NFData (Mahalanobis dp) where - rnf m = deepseq (rawdp m) - $ rnf (moddp m) - -mkIdentity :: dp -> Mahalanobis dp -mkIdentity dp = Mahalanobis dp dp - ---------------------------------------- - -dim :: VG.Vector dp r => Mahalanobis (dp r) -> Int -dim m = VG.length $ rawdp m - ---------------------------------------- - -class - ( Storable (Scalar dp) - , Element (Scalar dp) - , Container Vector (Scalar dp) - , LA.Product (Scalar dp) - , Field (Scalar dp) - , Scalar dp ~ Double - ) => MatrixField dp - -instance - ( Storable (Scalar dp) - , Element (Scalar dp) - , Container Vector (Scalar dp) - , LA.Product (Scalar dp) - , Field (Scalar dp) - , Scalar dp ~ Double - ) => MatrixField dp - -class MkMahalanobis params where - type MetricDatapoint params -- = Datapoint params - mkMahalanobis :: params -> MetricDatapoint params -> Mahalanobis (MetricDatapoint params) - - -class MahalanobisMetric metric where - getMatrix :: metric -> Matrix (Scalar metric) - -applyMahalanobis :: - ( VG.Vector vec r - , MahalanobisMetric metric - , Element r - , Field r - , LA.Product r - , Scalar metric ~ r - , r~Double - ) => metric -> vec r -> vec r -applyMahalanobis metric dp = VG.fromList $ toList $ flatten $ - (mapMatrix realPart $ matFunc sqrt $ mapMatrix (:+0) $ getMatrix metric) LA.<> asColumn dp' - where - dp' = fromList $ VG.toList dp - -------------------------------------------------------------------------------- --- algebra - -type instance Scalar (Mahalanobis dp) = Scalar dp - -instance - ( RealFrac r - , Floating r - , Scalar (dp r) ~ r - , VG.Vector dp r - ) => MetricSpace (Mahalanobis (dp r)) where - {-# INLINABLE distance #-} - - distance !m1 !m2 = {-# SCC distance #-} sqrt $ go 0 (dim m1-1) - where - go tot (-1) = tot - go tot i = go (tot+(((rawdp m1) `VG.unsafeIndex` i)-((rawdp m2) `VG.unsafeIndex` i)) - *(((moddp m1) `VG.unsafeIndex` i)-((moddp m2) `VG.unsafeIndex` i))) (i-1) - --- isFartherThanWithDistance !m1 !m2 !dist = {-# SCC isFartherThanWithDistance #-} --- go 0 (dim m1-1) --- where --- dist2=dist*dist --- --- go tot (-1) = Strict.Just $ sqrt tot --- go tot i = if tot'>dist2 --- then Strict.Nothing --- else go tot' (i-1) --- where --- tot' = tot+(((rawdp m1) `VG.unsafeIndex` i)-((rawdp m2) `VG.unsafeIndex` i)) --- *(((rawdp m1) `VG.unsafeIndex` i)-((rawdp m2) `VG.unsafeIndex` i)) diff --git a/src/HLearn/Metrics/Mahalanobis/ITML.hs b/src/HLearn/Metrics/Mahalanobis/ITML.hs deleted file mode 100644 index a09fa84..0000000 --- a/src/HLearn/Metrics/Mahalanobis/ITML.hs +++ /dev/null @@ -1,119 +0,0 @@ -module HLearn.Metrics.Mahalanobis.ITML - where - -import Control.DeepSeq -import Control.Monad -import Control.Monad.ST -import Data.List -import Data.STRef -import qualified Data.Semigroup as SG -import qualified Data.Foldable as F -import qualified Data.Vector.Generic as VG -import qualified Data.Vector as V -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector.Unboxed.Mutable as VUM -import Debug.Trace - -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>)) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra -import HLearn.Metrics.Mahalanobis -import HLearn.Metrics.Mahalanobis.LegoPaper -import HLearn.Models.Distributions.Multivariate.MultiNormalFast - -------------------------------------------------------------------------------- --- data types - -newtype ITML dp = ITML - { _x :: Matrix (Scalar dp) - } - -deriving instance (Element (Scalar dp), Show (Scalar dp)) => Show (ITML dp) - -instance (Storable (Scalar dp), NFData (Scalar dp), NFData dp) => NFData (ITML dp) where - rnf mega = rnf $ _x mega - -instance MahalanobisMetric (ITML dp) where - getMatrix mega = _x mega - -------------------------------------------------------------------------------- --- algebra - -type instance Scalar (ITML dp) = Scalar dp - -type instance Scalar (Vector Double) = Double - -------------------------------------------------------------------------------- --- training - -train_ITML :: - ( VG.Vector container Double - , Scalar (container Double) ~ Double - ) => Double -> [(Double,container Double)] -> ITML (container Double) -train_ITML gamma xs = ITML . _x $ train_ITML' xs' gamma u l - where - xsL = map (\(dist,x) -> (dist v (Bool,Vector Double) -> Double -> Double -> Double -> ITML (Vector Double) -train_ITML' vec gamma u l = ITML $ runST $ do - - -- initialize mutabele variables - aref <- newSTRef $ ident dim - lambda <- VUM.replicate (VG.length vec) (0::Double) - - xsi :: VUM.MVector s Double <- VUM.new (VG.length vec) - forM [0..VG.length vec-1] $ \i -> do - if fst $ vec VG.! i - then VUM.write xsi i u - else VUM.write xsi i l - - -- perform training - forM [0..10] $ \j -> do - forM [0..VG.length vec-1] $ \i -> do - let (similar,x) = vec VG.! i - delta = if similar then 1 else -1 - - a <- readSTRef aref - let p = minElement $ asRow x LA.<> a LA.<> asColumn x - - lambda_i <- VUM.read lambda i - xsi_i <- VUM.read xsi i - - let alpha = min lambda_i (delta/2*(1/p - gamma/xsi_i)) - beta = delta*alpha/(1-delta*alpha*p) - - VUM.write xsi i $ gamma*xsi_i / (gamma+delta*alpha*xsi_i) - VUM.write lambda i $ lambda_i - alpha - - writeSTRef aref $ a + scale beta (a LA.<> asColumn x LA.<> asRow x LA.<> a) - - readSTRef aref - - where - dim = VG.length $ snd $ VG.head vec - -------------------------------------------------------------------------------- --- metric - -instance - ( VG.Vector dp r - , Scalar (dp r) ~ r - , LA.Product r - ) => MkMahalanobis (ITML (dp r)) - where - type MetricDatapoint (ITML (dp r)) = dp r - mkMahalanobis mega dp = Mahalanobis - { rawdp = dp - , moddp = VG.fromList $ toList $ flatten $ (_x mega) LA.<> asColumn v - } - where - v = fromList $ VG.toList dp - diff --git a/src/HLearn/Metrics/Mahalanobis/Lego.hs b/src/HLearn/Metrics/Mahalanobis/Lego.hs deleted file mode 100644 index 345c4d9..0000000 --- a/src/HLearn/Metrics/Mahalanobis/Lego.hs +++ /dev/null @@ -1,155 +0,0 @@ -{-# LANGUAGE DataKinds,EmptyDataDecls #-} - -module HLearn.Metrics.Mahalanobis.Lego - where - -import Control.DeepSeq -import Data.List -import Data.Proxy -import qualified Data.Semigroup as SG -import qualified Data.Foldable as F -import qualified Data.Vector.Generic as VG - -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>)) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra -import HLearn.Metrics.Mahalanobis -import HLearn.Models.Distributions.Multivariate.MultiNormalFast - -------------------------------------------------------------------------------- --- data types - -type instance Scalar (AddUnit1 (Lego' reg eta) dp) = Scalar (Lego' reg eta dp) - -instance Field (Scalar dp) => MahalanobisMetric (AddUnit1 (Lego' reg eta) dp) where - getMatrix (UnitLift1 m) = inv $ covar $ multinorm m - -type Lego reg (eta::Frac) dp = AddUnit1 (Lego' reg eta) dp - -data Lego' reg (eta::Frac) dp = Lego' - { b :: !(Matrix (Scalar dp)) - , c :: !(Matrix (Scalar dp)) - , x :: Matrix (Scalar dp) - , multinorm :: MultiNormal dp - } - -deriving instance (Element (Scalar dp), Show (Scalar dp)) => Show (Lego' reg eta dp) - -instance NFData dp => NFData (Lego' reg eta dp) where - rnf lego = deepseq b - $ deepseq c - $ rnf x - ---------------------------------------- - -data RegMatrix_ = Identity_ | InvCovar_ - -class RegMatrix m where - regmatrix :: m -> RegMatrix_ - -data Identity -instance RegMatrix Identity where - regmatrix _ = Identity_ - -data InvCovar -instance RegMatrix InvCovar where - regmatrix _ = InvCovar_ - ---------------------------------------- - -mkLego :: forall reg eta dp. - ( KnownFrac eta - , MatrixField dp - , RegMatrix reg - ) => Matrix (Scalar dp) -> Matrix (Scalar dp) -> MultiNormal dp -> Lego' reg eta dp -mkLego b c multinorm = Lego' - { b = b - , c = c - , multinorm = multinorm - , x = inv $ completeTheSquare (scale (-1) b') (scale (-eta) c) - } - where - m = case (regmatrix (undefined::reg)) of - Identity_ -> ident (rows b) - InvCovar_ -> inv $ covar multinorm - b' = scale (1/2) (m `add` scale eta b) - eta = fromRational $ fracVal (Proxy :: Proxy eta) - --- | given an equation Y^2 + BY + YB + C, solve for Y -completeTheSquare :: - ( Storable r - , Element r - , Container Vector r - , LA.Product r - , Field r - , r ~ Double - ) => Matrix r -> Matrix r -> Matrix r --- completeTheSquare b c = (sqrtm' ((b LA.<> b) `sub` c)) `sub` b -completeTheSquare b c = (sqrtm' ((scale (1/4) $ b LA.<> b) `sub` c)) `sub` b - where - sqrtm' m = cmap realPart $ matFunc sqrt $ cmap (:+0) m - -------------------------------------------------------------------------------- --- algebra - -instance (KnownFrac eta, RegMatrix reg, MatrixField dp) => SG.Semigroup (Lego' reg eta dp) where - lego1 <> lego2 = mkLego b' c' mn' - where - b' = b lego1 `add` b lego2 - c' = c lego1 `add` c lego2 - mn' = multinorm lego1 <> multinorm lego2 - -type instance Scalar (Lego' reg eta dp) = Scalar dp - -type instance Scalar (Vector r) = r - -------------------------------------------------------------------------------- --- training - -instance - ( KnownFrac eta - , RegMatrix reg - , MatrixField (Vector r) - , r ~ Scalar (dp r) - , VG.Vector dp r - ) => HomTrainer (Lego reg eta (dp r)) - where - type Datapoint (Lego reg eta (dp r)) = (r,dp r) - - train1dp (y,dp) = UnitLift1 $ mkLego (scale y zzT) (zzT LA.<> zzT) (train1dp dp) - where - zzT = asColumn dp' LA.<> asRow dp' - dp' = fromList $ VG.toList dp - -------------------------------------------------------------------------------- --- metric - -instance - ( VG.Vector dp r - , Scalar (dp r) ~ r - , RegMatrix reg - , LA.Product r - ) => MkMahalanobis (Lego reg eta (dp r)) - where - type MetricDatapoint (Lego reg eta (dp r)) = dp r - mkMahalanobis (UnitLift1 lego) dp = Mahalanobis - { rawdp = dp - , moddp = VG.fromList $ toList $ flatten $ (x lego) LA.<> asColumn v - } - where - v = fromList $ VG.toList dp - -------------------------------------------------------------------------------- --- test - -xs = - [ [1,2] - , [2,3] - , [3,4] - ] - :: [[Double]] - -mb = (2><2)[-6.5,-10,-10,-14] :: Matrix Double -mc = (2><2)[282,388,388,537] :: Matrix Double diff --git a/src/HLearn/Metrics/Mahalanobis/LegoPaper.hs b/src/HLearn/Metrics/Mahalanobis/LegoPaper.hs deleted file mode 100644 index a39c083..0000000 --- a/src/HLearn/Metrics/Mahalanobis/LegoPaper.hs +++ /dev/null @@ -1,161 +0,0 @@ -module HLearn.Metrics.Mahalanobis.LegoPaper - where - -import Control.Monad -import Control.Monad.Random hiding (fromList) -import Control.Monad.ST -import Data.Array.ST -import GHC.Arr -import qualified Data.Foldable as F -import qualified Data.Vector.Generic as VG -import Debug.Trace - -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>)) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra -import HLearn.Metrics.Mahalanobis - -------------------------------------------------------------------------------- --- data types - -data LegoPaper dp = LegoPaper - { _matrix :: Matrix (Scalar dp) - , _xs :: [(Scalar dp,dp)] - , _numdp :: Scalar dp - } - -deriving instance (Element (Scalar dp), Show(Scalar dp), Show dp) => Show (LegoPaper dp) - -type instance Scalar (LegoPaper dp) = Scalar dp - -instance MahalanobisMetric (LegoPaper dp) where - getMatrix (LegoPaper x _ _) = x - -fromSingleton :: (Element r, Container Vector r, Storable r) => Matrix r -> r -fromSingleton m = if rows m /= 1 || cols m /= 1 - then error "fromSingleton on not 1x1 matrix" - else atIndex m $ minIndex m - --- mappend1 :: (Field (Scalar dp)) => LegoPaper dp -> LegoPaper dp -> LegoPaper dp -mappend1 m1 m2 = LegoPaper - { _matrix = (scale (_numdp m1/totdp) $ _matrix m1) `LA.add` (scale (_numdp m2/totdp) $ _matrix m2) - , _numdp = _numdp m1+_numdp m2 - , _xs = _xs m1++_xs m2 - } - where - totdp = _numdp m1+_numdp m2 - -mappend2 eta m1 m2 = mappend1 m1' m2' - where - m1' = m1 { _matrix = _matrix m1'' } - m2' = m1 { _matrix = _matrix m2'' } --- m1'' = F.foldl' (add1dp_LegoPaper eta) m1 $ take 100 xs1' --- m2'' = F.foldl' (add1dp_LegoPaper eta) m2 $ take 100 xs2' - m1'' = F.foldl' (add1dp_LegoPaper eta) m1 xs1' - m2'' = F.foldl' (add1dp_LegoPaper eta) m2 xs2' - - xs1' = evalRand (shuffle $ _xs m1) (mkStdGen 10) - xs2' = evalRand (shuffle $ _xs m2) (mkStdGen 10) - --- train_LegoMonoid :: --- ( F.Foldable container --- , Functor container --- , VG.Vector vec r --- , Container Vector r --- , LA.Product r --- , Floating r --- , r ~ Scalar (vec r) --- , Show r --- , Ord r --- ) => Int -> r -> container (r,vec r) -> LegoPaper (vec r) -train_LegoMonoid monoid k1 k2 eta dps = F.foldl1 monoid ms - where - ms = map (train_LegoPaper 0 eta) dps' - dps' = take k2 [takeEvery k1 $ drop j dps | j<-[0..k1-1]] - - takeEvery n [] = [] - takeEvery n xs = head xs : (takeEvery n $ drop n xs) - -train_LegoPaper :: - ( F.Foldable container - , Functor container - , VG.Vector vec r - , Container Vector r - , LA.Product r - , Floating r - , r ~ Scalar (vec r) - , Show r - , Ord r - ) => Int -> r -> container (r,vec r) -> LegoPaper (vec r) -train_LegoPaper rep eta dps = - F.foldl' (add1dp_LegoPaper eta) reg dps' - where --- dps' = evalRand (shuffle $ concat $ replicate rep $ F.toList dps) $ mkStdGen 10 - dps' = dps - reg = LegoPaper - { _matrix = ident $ VG.length $ snd $ head $ F.toList dps - , _numdp = 0 -- fromIntegral $ length $ F.toList dps - , _xs = [] - } - -shuffle :: RandomGen g => [a] -> Rand g [a] -shuffle xs = do - let l = length xs - rands <- take l `fmap` getRandomRs (0, l-1) - let ar = runSTArray $ do - ar <- thawSTArray $ listArray (0, l-1) xs - forM_ (zip [0..(l-1)] rands) $ \(i, j) -> do - vi <- readSTArray ar i - vj <- readSTArray ar j - writeSTArray ar j vi - writeSTArray ar i vj - return ar - return (elems ar) - -add1dp_LegoPaper :: - ( Storable r - , LA.Product r - , Floating r - , Container Vector r - , VG.Vector vec r - , r ~ Scalar (vec r) - , Show r - , Ord r - ) => r -> LegoPaper (vec r) -> (r,vec r) -> LegoPaper (vec r) -add1dp_LegoPaper eta lp (y,vec) = LegoPaper - { _matrix = mat - , _numdp = _numdp lp+1 - , _xs = (y,vec):(_xs lp) - } - -- trace ("yhat="++show yhat++"; ybar="++show ybar++"; ybar_det="++show ybar_det++"; y="++show y++"; vec="++show vec++"; a="++show a) $ - where - a = _matrix lp - mat = if yhat<0.00001 - then a - else a `LA.sub` (scale (eta*(ybar-y)/(1+eta*(ybar-y)*yhat)) $ a LA.<> z LA.<> zT LA.<> a) - - vec' = VG.convert vec - zT = asRow vec' - z = asColumn vec' - yhat = fromSingleton $ zT LA.<> a LA.<> z - ybar = ybar_top / ybar_bot - ybar_bot = 2*eta*yhat - ybar_top = (eta*y*yhat - 1 + (sqrt ybar_det)) - ybar_det = (eta*y*yhat-1)**2 + 4*eta*yhat**2 - - -instance - ( VG.Vector vec r - , Scalar (vec r) ~ r - , LA.Product r - ) => MkMahalanobis (LegoPaper (vec r)) - where - type MetricDatapoint (LegoPaper (vec r)) = vec r - mkMahalanobis (LegoPaper a _ _) vec = Mahalanobis - { rawdp = vec - , moddp = VG.fromList $ toList $ flatten $ a LA.<> asColumn v - } - where - v = fromList $ VG.toList vec diff --git a/src/HLearn/Metrics/Mahalanobis/Mega.hs b/src/HLearn/Metrics/Mahalanobis/Mega.hs deleted file mode 100644 index a0d1045..0000000 --- a/src/HLearn/Metrics/Mahalanobis/Mega.hs +++ /dev/null @@ -1,299 +0,0 @@ -{-# LANGUAGE DataKinds,EmptyDataDecls #-} - -module HLearn.Metrics.Mahalanobis.Mega - where - -import Control.DeepSeq -import Control.Monad -import Control.Monad.ST -import Data.STRef -import Data.List -import qualified Data.Semigroup as SG -import qualified Data.Foldable as F -import qualified Data.Vector as V -import qualified Data.Vector.Mutable as VM -import qualified Data.Vector.Storable as VS -import qualified Data.Vector.Storable.Mutable as VSM -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Generic.Mutable as VGM -import qualified Data.Vector.Algorithms.Intro as Intro - -import Debug.Trace -import Unsafe.Coerce - -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>)) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra -import HLearn.Metrics.Mahalanobis -import HLearn.Metrics.Mahalanobis.Normal -import HLearn.Metrics.Mahalanobis.LegoPaper -import HLearn.Metrics.Mahalanobis.ITML hiding (_x) -import HLearn.Models.Distributions.Multivariate.MultiNormalFast -import qualified HLearn.Optimization.Conic as Recipe -import qualified HLearn.Optimization.Common as Recipe -import qualified HLearn.Optimization.GradientDescent as Recipe -import qualified HLearn.Optimization.Amoeba as Recipe -import qualified HLearn.Optimization.NewtonRaphson as Recipe - -------------------------------------------------------------------------------- --- data types - -newtype Mega (eta::Frac) dp = Mega - { _x :: Matrix (Scalar dp) - } - -deriving instance (Element (Scalar dp), Show (Scalar dp)) => Show (Mega eta dp) - -instance (Storable (Scalar dp), NFData (Scalar dp), NFData dp) => NFData (Mega eta dp) where - rnf mega = rnf $ _x mega - -instance MahalanobisMetric (Mega eta dp) where - getMatrix mega = _x mega - -------------------------------------------------------------------------------- --- algebra - -type instance Scalar (Mega eta dp) = Scalar dp -type instance Scalar (Matrix r) = r - -------------------------------------------------------------------------------- --- training - -mkMega :: forall container eta. - ( Scalar (container Double) ~ Double - , VG.Vector container Double - , KnownFrac eta - ) => Double - -> Double - -> [(Double,container Double)] - -> Mega eta (container Double) -mkMega etaraw eta2 !xs = Mega $ _x $ (mkMega' etaraw eta2 xs'::Mega eta (Vector Double)) - where - xs' = map (\(y,x) -> (y, fromList $ VG.toList x)) xs - --- mkMega' :: forall eta. KnownFrac eta => Double -> [(Double,Vector Double)] -> Mega eta (Vector Double) --- -- mkMega' etaraw !xs = Mega $ findMinAmoeba f init --- mkMega' etaraw !xs = Mega $ findzero a b init --- where --- -- (ITML init) = train_ITML 10 xs --- -- (LegoPaper init) = train_LegoPaper eta xs --- init = identity --- --- identity = ident (LA.dim $ snd $ head xs) --- xxt x = asColumn x LA.<> asRow x --- a = foldl1' LA.add $ map (\(_,x) -> eta `scale` kronecker (xxt x) (xxt x)) xs --- b = foldl' LA.add (vec identity) $ map (\(y,x) -> vec $ (eta*y) `scale` xxt x) xs --- --- eta = etaraw - - -mkMega' :: forall eta. KnownFrac eta => Double -> Double -> [(Double,Vector Double)] -> Mega eta (Vector Double) -mkMega' etaraw eta2 !xs = {-trace ("megamatrix="++show res) $ -} - deepseq lego $ deepseq itml $ deepseq res $ - trace ("rank q_zz="++show (LA.rank (q_zz :: Matrix Double))++"; rank a="++show (LA.rank a)) $ - trace ("lego score="++show (f lego)) $ - trace ("itml score="++show (f itml)) $ - trace ("mega score="++show (f res)) $ - trace ("trace = "++show (tr $ res)) $ - trace ("logdet = "++show (log $ det res)) $ - trace ("zAzzAz = "++show (0.5*eta*(sumElements $ (trans $ vec res) LA.<> q_zz LA.<> (vec res)))) $ - trace ("yzAz = "++show (0.5*eta*(sumElements $ scale (-2) $ q_yz * res))) $ - trace ("yy = "++show (0.5*eta*q_yy)) $ - trace ("sum = "++show (0.5*eta*((sumElements $ (trans $ vec res) LA.<> q_zz LA.<> (vec res)) - +(sumElements $ scale (-2) $ q_yz * res) - +q_yy))) $ --- trace ("eigenvalues q_zz="++show (eigenvalues q_zz)) $ --- deepseq res $ deepseq res2 $ deepseq res3 $ --- trace ("res="++show res++"\n\nres2="++show res2++"\n\nres3="++show res3) $ - Mega $ res - where - magicJumble2 0 x = x - magicJumble2 i x = magicJumble2 (i-1) --- $ trace "---------------\nunderscore" $ Recipe.newtonRaphson_constrained f_ f'_ f''_ --- $ trace "---------------\nplain" $ Recipe.newtonRaphson_constrained f f'c f'' --- $ Recipe.runOptimization (Recipe.conjugateGradientDescent f f') --- $ Recipe.newtonRaphson_constrained f f'c f'' - $ x - - magicJumble 0 x = x - magicJumble i x = magicJumble (i-1) x' - where - x_proj = Recipe.conicProjection f --- $ Recipe.newtonRaphson_constrained f f'c f'' - $ x - - fx = f x - fx_proj = f x_proj - - x' = Recipe.randomConicPersuit f $ if fx_proj < fx - then x_proj - else x - - res = --- Recipe.newtonRaphson_constrained f f'c f'' --- $ Recipe.runOptimization (Recipe.conjugateGradientDescent f f') --- $ x0 - x0 - - res1 = findzero a b x0 --- res2 = findzero a b $ Recipe.runOptimization (Recipe.conjugateGradientDescent f f' x0) - res3 = findzero a b itml - --- x0 = identity - x0 = mahalanobis - mahalanobis = getMatrix $ (train $ map snd xs :: MahalanobisParams (Vector Double)) - (ITML itml) = train_ITML 10 xs -- :: ITML (Double,Vector Double) - (LegoPaper lego _ _) = train_LegoPaper 1 0.01 xs -- :: LegoPaper (Double,Vector Double) - - identity = ident (LA.dim $ snd $ head xs) - xxt x = asColumn x LA.<> asRow x --- a = foldl1' LA.add $ map (\(_,x) -> eta `scale` kronecker (xxt x) (xxt x)) xs - a = scale eta q_zz - b = foldl' LA.add (vec identity) $ map (\(y,x) -> vec $ (eta*y) `scale` xxt x) xs - --- q_zz = foldl1' LA.add $ map (\(y,x) -> (asColumn$flatten$xxt x) LA.<> (asRow$flatten$xxt x)) xs --- q_yz = foldl1' LA.add $ map (\(y,x) -> scale (-2*y) (xxt x)) xs - q_z = sum $ map (\(y,x) -> xxt x) xs - q_zz = q_zz_mock - q_zz_lowrank = (head $ head $ toBlocks [size] [rank] u) - LA.<> (diagRect 0 (VG.slice 0 rank sigma) rank rank) - LA.<> (head $ head $ toBlocks [rank] [size] $ trans v) :: Matrix Double --- rank=rows x0 - rank=floor $ (fromIntegral size::Double)/2 - size=rows q_zz_full - (u,sigma,v) = svd q_zz_full - q_zz_full = sum $ map (\(y,x) -> kronecker (xxt x) (xxt x)) xs - q_zz_mock = kronecker zz zz - where - zz = sum $ (map (\(y,x) -> xxt x)) xs - q_yz = sum $ map (\(y,x) -> scale y (xxt x)) xs - q_yy = sum $ map (\(y,x) -> y^2) xs - q_zzk= sum $ map (\(y,x) -> kronecker (xxt x) (xxt x)) xs - - eta = etaraw --- eta = etaraw / fromIntegral (length xs) - - tr = LA.sumElements . LA.takeDiag - - f :: Matrix Double -> Double - f x = tr prod - + if det < 0 - then infinity - else (-1) * log det - + 0.5*eta* - ( (sumElements $ (trans $ vec x) LA.<> q_zz LA.<> (vec x)) - + (sumElements $ scale (-2) $ q_yz * x) - + q_yy - ) - + eta2 * (pnorm Frobenius x) - where - det = LA.det prod - prod = x LA.<> LA.inv x0 - - f_ x = tr (x LA.<> LA.inv x0) - + 0.5*eta* - ( (sumElements $ (trans $ vec x) LA.<> q_zz LA.<> (vec x)) - + (sumElements $ scale (-2) $ q_yz * x) - + q_yy - ) - f'_ x = (a LA.<> vec x) `LA.sub` b - f''_ x = a - - f' x = f'a x + scale (eta2*2) x - f'a x = reshape (rows x) $ flatten $ (vec $ (-1) `scale` inv x) `LA.add` (a LA.<> vec x) `LA.sub` b - f'b x = inv x0 - inv x + scale eta ((reshape (rows x) $ flatten $ q_zzk LA.<> vec x) - q_yz) - f'c x = (vec $ (-1) `scale` inv x) `LA.add` (a LA.<> vec x) `LA.sub` b - - f'' x = kronecker xinv xinv `LA.add` a - + scale eta2 (ident $ (rows xinv)^2) --- f'' x = reshape (rows x) $ flatten $ kronecker xinv xinv `LA.add` a - where - xinv = inv x - --- stepLineMin f f' lower mid upper - - - --- g :: Matrix Double -> Matrix Double -g :: LA.Vector Double -> Double -g x = VG.sum $ VG.map abs $ x `LA.add` (LA.fromList [2,3]) - -y0 = LA.fromList [1,2::Double] - -m1 = 2 >< 2 $ [1,2,2,4::Double] -m2 = 2 >< 2 $ [3,1,1,4::Double] - - -findzero :: Matrix Double -> Matrix Double -> Matrix Double -> Matrix Double -findzero !a !b !x0 = newtonRaphson x0 x0' 1000 --0000 - where - x0' = x0 `LA.sub` (((rows x0)><(cols x0)) $ repeat 1) - --- gradientDescent !x 0 = x --- gradientDescent x i = gradientDescent x' (i-1) --- where --- x' = --- xinv = inv x --- df = kronecker xinv xinv `LA.add` a - - newtonRaphson !x !lastx !itr = --trace ("x="++show x++"\nx'="++show x'++"\n\n") $ - if itr==0 - then trace ("WARNING: findzero probably failed to converge; diff="++show diff++"; ratio="++show ratio) x - else if diff <= 1e-8 || ratio <= 1e-8 - then x - else newtonRaphson x' x (itr-1) - where - diff = (maxElement $ cmap abs $ x `LA.sub` lastx) - ratio = (maxElement $ cmap abs $ x `LA.sub` lastx) / maxElement (cmap abs x) - xinv = inv x - f = (vec $ (-1) `scale` xinv) `LA.add` (a LA.<> vec x) `LA.sub` b - df = kronecker xinv xinv `LA.add` a --- x' = if itr > 20 --- then x `LA.sub` reshape (rows x) (flatten $ scale (0.1) df) --- else x `LA.sub` reshape (rows x) (flatten $ inv df LA.<> f) - - epsilon = 1e-13 -- 0.000000000000001 -- *(maxElement f/ fromIntegral (rows x*rows x)) --- x' = x `LA.sub` reshape (rows x) (flatten $ scale epsilon f) - x' = x `LA.sub` reshape (rows x) (flatten $ inv df LA.<> f) - -vec :: Matrix Double -> Matrix Double -vec = asColumn . flatten - -------------------------------------------------------------------------------- --- metric - -instance - ( VG.Vector dp r - , Scalar (dp r) ~ r - , LA.Product r - ) => MkMahalanobis (Mega eta (dp r)) - where - type MetricDatapoint (Mega eta (dp r)) = dp r - mkMahalanobis mega dp = Mahalanobis - { rawdp = dp - , moddp = VG.fromList $ toList $ flatten $ (_x mega) LA.<> asColumn v - } - where - v = fromList $ VG.toList dp - -------------------------------------------------------------------------------- --- test - --- xs = --- [ [1,2] --- , [2,3] --- , [3,4] --- ] --- :: [[Double]] - -xs = - [ (1, fromList [2,3]) - , (1, fromList [3,3]) - , (1, fromList [3,-2]) - ] :: [(Double,Vector Double)] - -mb = (2><2)[-6.5,-10,-10,-14] :: Matrix Double -mc = (2><2)[282,388,388,537] :: Matrix Double - diff --git a/src/HLearn/Metrics/Mahalanobis/Normal.hs b/src/HLearn/Metrics/Mahalanobis/Normal.hs deleted file mode 100644 index 7e550bd..0000000 --- a/src/HLearn/Metrics/Mahalanobis/Normal.hs +++ /dev/null @@ -1,75 +0,0 @@ -module HLearn.Metrics.Mahalanobis.Normal - where - -import Control.DeepSeq -import qualified Data.Vector.Generic as VG - -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>)) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra -import HLearn.Metrics.Mahalanobis -import HLearn.Models.Distributions.Multivariate.MultiNormalFast - -------------------------------------------------------------------------------- --- data types - -newtype MahalanobisParams dp = MahalanobisParams - { normal :: MultiNormal dp - } - -deriving instance Show (MultiNormal dp) => Show (MahalanobisParams dp) -deriving instance Read (MultiNormal dp) => Read (MahalanobisParams dp) -deriving instance Eq (MultiNormal dp) => Eq (MahalanobisParams dp) -deriving instance Ord (MultiNormal dp) => Ord (MahalanobisParams dp) -deriving instance NFData (MultiNormal dp) => NFData (MahalanobisParams dp) - -------------------------------------------------------------------------------- --- algebra - -deriving instance Monoid (MultiNormal dp) => Monoid (MahalanobisParams dp) - -type instance Scalar (MahalanobisParams dp) = Scalar dp - -instance (Field (Scalar dp)) => MahalanobisMetric (MahalanobisParams dp) where - getMatrix m = inv $ covar $ normal m - -------------------------------------------------------------------------------- --- training - -instance HomTrainer (MultiNormal dp) => HomTrainer (MahalanobisParams dp) where - type Datapoint (MahalanobisParams dp) = Datapoint (MultiNormal dp) - - train1dp dp = MahalanobisParams $ train1dp dp - train dps = MahalanobisParams $ train dps - -------------------------------------------------------------------------------- --- metric - -instance - ( VG.Vector dp r - , Scalar (dp r) ~ r - , LA.Product r - , Field r - ) => MkMahalanobis (MahalanobisParams (dp r)) - where - type MetricDatapoint (MahalanobisParams (dp r)) = dp r - mkMahalanobis (MahalanobisParams m) dp = Mahalanobis - { rawdp = dp - , moddp = VG.fromList $ toList $ flatten $ inv (covar m) LA.<> asColumn v - } - where - v = fromList $ VG.toList dp - --- mkMahalanobis :: --- ( VG.Vector dp r --- , Scalar (dp r) ~ r --- , LA.Product r --- ) => MahalanobisParams (dp r) -> dp r -> Mahalanobis (dp r) --- mkMahalanobis (MahalanobisParams m) dp = Mahalanobis --- { rawdp = dp --- , moddp = VG.fromList $ toList $ flatten $ covar m LA.<> asColumn v --- } --- where --- v = fromList $ VG.toList dp diff --git a/src/HLearn/Metrics/RandomProjection.hs b/src/HLearn/Metrics/RandomProjection.hs deleted file mode 100644 index 02af894..0000000 --- a/src/HLearn/Metrics/RandomProjection.hs +++ /dev/null @@ -1,25 +0,0 @@ -module HLearn.Metrics.RandomProjection - where - -import Control.Monad -import Control.Monad.Random -import qualified Data.Vector.Unboxed as VU -import System.Random - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -data RandomProjection (seed::Nat) (n::Nat) dp = RandomProjection - { weights :: !(VU.Vector (Ring dp)) - , rawdp :: !dp - } - --- basisVector :: RandomGen g => Int -> Rand g (VU.Vector r) -basisVector d = liftM (VU.fromList) $ replicateM d $ getRandomR (-1,1) - - -------------------------------------------------------------------------------- --- algebra - diff --git a/src/HLearn/Metrics/String.hs b/src/HLearn/Metrics/String.hs deleted file mode 100644 index d5bc2d0..0000000 --- a/src/HLearn/Metrics/String.hs +++ /dev/null @@ -1,112 +0,0 @@ -module HLearn.Metrics.String - where - -import SubHask - -import Data.Function.Memoize - -------------------------------------------------------------------------------- - -newtype Jaccard a = Jaccard a - deriving (Read,Show,Eq) - -type instance Scalar (Jaccard a) = Scalar a - -instance - ( Lattice a - , Field (Scalar a) - , Normed a - , Eq a - ) => MetricSpace (Jaccard a) - where - distance (Jaccard xs) (Jaccard ys) = 1 - abs (xs && ys) / abs (xs || ys) - -------------------------------------------------------------------------------- - -newtype Hamming a = Hamming a - deriving (Read,Show,Eq,POrd,Ord,Lattice,NFData) - -instance Semigroup a => Semigroup (Hamming a) where - (Hamming a1)+(Hamming a2)=Hamming (a1+a2) --- associativeEpsilon a = 0 - -instance Monoid a => Monoid (Hamming a) where - zero = Hamming zero - -instance Container a => Container (Hamming a) where - type Elem (Hamming a) = Elem a - type ElemConstraint (Hamming a) = ElemConstraint a - elem e (Hamming a) = elem e a - -deriving instance (Integral (Scalar a), Unfoldable a) => Unfoldable (Hamming a) -deriving instance (Integral (Scalar a), Foldable a) => Foldable (Hamming a) - -instance (Integral (Scalar a), Normed a) => Normed (Hamming a) where - abs (Hamming a) = fromIntegral $ abs a - --- FIXME --- type instance Scalar (Hamming a) = Scalar a -type instance Scalar (Hamming a) = Float - -instance - ( Foldable a - , Eq (Elem a) - , Eq a - ) => MetricSpace (Hamming a) - where - - distance (Hamming xs) (Hamming ys) = {-# distance_Hamming #-} go (toList xs) (toList ys) 0 - where - go [] [] i = i - go xs [] i = i + fromIntegral (abs xs) - go [] ys i = i + fromIntegral (abs ys) - go (x:xs) (y:ys) i = go xs ys $ i + if x==y - then 0 - else 1 - -------------------------------------------------------------------------------- - -newtype Levenshtein a = Levenshtein a - deriving (Read,Show,Eq,POrd,Ord,Lattice,NFData) - -instance Semigroup a => Semigroup (Levenshtein a) where - (Levenshtein a1)+(Levenshtein a2)=Levenshtein (a1+a2) --- associativeEpsilon a = 0 - -instance Monoid a => Monoid (Levenshtein a) where - zero = Levenshtein zero - -instance Container a => Container (Levenshtein a) where - type Elem (Levenshtein a) = Elem a - type ElemConstraint (Levenshtein a) = ElemConstraint a - elem e (Levenshtein a) = elem e a - -deriving instance (Integral (Scalar a), Unfoldable a) => Unfoldable (Levenshtein a) -deriving instance (Integral (Scalar a), Foldable a) => Foldable (Levenshtein a) - -instance (Integral (Scalar a), Normed a) => Normed (Levenshtein a) where - abs (Levenshtein a) = fromIntegral $ abs a - --- FIXME --- type instance Scalar (Levenshtein a) = Scalar a -type instance Scalar (Levenshtein a) = Float - -instance - ( Foldable a - , Memoizable (Elem a) - , Eq (Elem a) - , Eq a - ) => MetricSpace (Levenshtein a) - where - distance (Levenshtein xs) (Levenshtein ys) = {-# distance_Levenshtein #-} fromIntegral $ go (toList xs) (toList ys) - where - go = memoize2 go' - where - go' [] [] = 0 - go' xs [] = length xs - go' [] ys = length ys - go' (x:xs) (y:ys) = minimum - [ go xs (y:ys) + 1 - , go (x:xs) ys + 1 - , go xs ys + if (x/=y) then 1 else 0 - ] diff --git a/src/HLearn/Metrics/String/Hamming.hs b/src/HLearn/Metrics/String/Hamming.hs deleted file mode 100644 index cafce01..0000000 --- a/src/HLearn/Metrics/String/Hamming.hs +++ /dev/null @@ -1,33 +0,0 @@ -{-# LANGUAGE OverloadedStrings #-} -module HLearn.Metrics.String.Hamming - where - -import GHC.Exts - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -newtype Hamming str = Hamming str - deriving (Read,Show,Eq,Ord) - -instance IsString (Hamming String) where - fromString = Hamming - -------------------------------------------------------------------------------- --- metric space - -type instance Scalar (Hamming String) = Double - -instance MetricSpace (Hamming String) where - distance (Hamming xs) (Hamming ys) = fromIntegral $ go xs ys 0 - where - go :: String -> String -> Int -> Int - go [] [] i = i - go xs [] i = i + length xs - go [] ys i = i + length ys - go (x:xs) (y:ys) i = go xs ys $ i + if x==y - then 0 - else 1 - diff --git a/src/HLearn/Metrics/String/Levenshtein.hs b/src/HLearn/Metrics/String/Levenshtein.hs deleted file mode 100644 index 570a1d6..0000000 --- a/src/HLearn/Metrics/String/Levenshtein.hs +++ /dev/null @@ -1,37 +0,0 @@ -{-# LANGUAGE OverloadedStrings #-} -module HLearn.Metrics.String.Levenshtein - where - -import GHC.Exts -import Data.Function.Memoize - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -newtype Levenshtein str = Levenshtein str - deriving (Read,Show,Eq,Ord) - -instance IsString (Levenshtein String) where - fromString = Levenshtein - -------------------------------------------------------------------------------- --- metric space - -type instance Scalar (Levenshtein String) = Double - -instance MetricSpace (Levenshtein String) where - distance (Levenshtein xs) (Levenshtein ys) = fromIntegral $ lev xs ys - -lev :: String -> String -> Int -lev = memoize2 lev' - where - lev' [] [] = 0 - lev' xs [] = length xs - lev' [] ys = length ys - lev' (x:xs) (y:ys) = minimum - [ lev xs (y:ys) + 1 - , lev (x:xs) ys + 1 - , lev xs ys + if (x/=y) then 1 else 0 - ] diff --git a/src/HLearn/Models/Classifiers.hs b/src/HLearn/Models/Classifiers.hs deleted file mode 100644 index 57d4657..0000000 --- a/src/HLearn/Models/Classifiers.hs +++ /dev/null @@ -1,14 +0,0 @@ --- | This file exports the most commonly used modules within HLearn-classifiers. Most likely this is the only file you will have to import. - -module HLearn.Models.Classifiers - ( module HLearn.Models.Classifiers.Common - , module HLearn.Models.Classifiers.NearestNeighbor - , module HLearn.Models.Classifiers.Perceptron - , module HLearn.Evaluation.CrossValidation - ) - where - -import HLearn.Models.Classifiers.Common -import HLearn.Models.Classifiers.NearestNeighbor -import HLearn.Models.Classifiers.Perceptron -import HLearn.Evaluation.CrossValidation diff --git a/src/HLearn/Models/Classifiers/BayesNotWorking.hs b/src/HLearn/Models/Classifiers/BayesNotWorking.hs deleted file mode 100644 index 8931dd2..0000000 --- a/src/HLearn/Models/Classifiers/BayesNotWorking.hs +++ /dev/null @@ -1,149 +0,0 @@ -{-# LANGUAGE MultiParamTypeClasses #-} -{-# LANGUAGE FunctionalDependencies #-} -{-# LANGUAGE TypeSynonymInstances #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE UndecidableInstances #-} -{-# LANGUAGE ScopedTypeVariables #-} -{-# LANGUAGE ConstraintKinds #-} -{-# LANGUAGE TypeOperators #-} -{-# LANGUAGE ExistentialQuantification #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE DataKinds #-} -{-# LANGUAGE StandaloneDeriving #-} -{-# LANGUAGE TemplateHaskell #-} - -module HLearn.Models.Classifiers.Bayes - where - - -import Debug.Trace -import qualified Data.Map as Map -import GHC.TypeLits - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common - -------------------------------------------------------------------------------- --- data types - -data Bayes labelLens dist = Bayes - { labelDist :: !(Margin labelLens dist) - , attrDist :: !(MarginalizeOut labelLens dist) - } - -instance (Show (Margin labelLens dist), Show (MarginalizeOut labelLens dist)) => Show (Bayes labelLens dist) --- deriving (Read,Show,Eq,Ord) - -------------------------------------------------------------------------------- --- Algebra - --- instance --- ( {-Monoid (Margin labelLens dist) --- , Monoid (MarginalizeOut labelLens dist) --- -}) => Monoid (Bayes labelLens dist) --- where --- mempty = undefined -- Bayes undefined undefined -- mempty mempty --- b1 `mappend` b2 = undefined {-Bayes --- { labelDist = undefined -- labelDist b1 `mappend` labelDist b2 --- , attrDist = undefined -- attrDist b1 `mappend` attrDist b2 --- }-} - -------------------------------------------------------------------------------- --- Training - --- instance --- ( {-Datapoint dist ~ Datapoint (MarginalizeOut labelLens dist) --- , TypeFunction labelLens --- , Datapoint (Margin labelLens dist) ~ Range labelLens --- , Datapoint (MarginalizeOut labelLens dist) ~ Domain labelLens --- , Monoid (MarginalizeOut labelLens dist) --- , Monoid (Margin labelLens dist) --- , HomTrainer (MarginalizeOut labelLens dist) --- , HomTrainer (Margin labelLens dist) --- -}) => HomTrainer (Bayes labelLens dist) --- where --- type Datapoint (Bayes labelLens dist) = Datapoint dist --- train = undefined --- train1dp dp = trace "train1dp" $ Bayes --- { labelDist = undefined -- train1dp $ typefunc (undefined::labelLens) dp --- , attrDist = undefined -- train1dp dp --- } - -------------------------------------------------------------------------------- --- Classification - -instance Probabilistic (Bayes labelIndex dist) where - type Probability (Bayes labelIndex dist) = Probability dist - -instance - ( Mean (Margin labelLens dist) - , Margin labelLens dist ~ Categorical label prob - , Ord prob, Fractional prob - , prob ~ Probability (MarginalizeOut labelLens dist) - , Ord (Datapoint (Margin labelLens dist)) --- , PDF dist --- , Ord (Datapoint (ResultDistribution (Margin labelLens dist))) - , Datapoint dist ~ Datapoint (MarginalizeOut labelLens dist) - , PDF (MarginalizeOut labelLens dist) - ) => Classifier (Bayes labelLens dist) - where - type Label (Bayes labelLens dist) = Datapoint (Margin labelLens dist) - type UnlabeledDatapoint (Bayes labelLens dist) = Datapoint dist - type ResultDistribution (Bayes labelLens dist) = Margin labelLens dist - --- probabilityClassify bayes dp = (pdf (labelDist bayes) label)*(pdf (attrDist bayes) dp) - - probabilityClassify bayes dp = undefined -- Categorical $ Map.fromList $ map (\k -> (k,prob k)) labelL - where - prob k = pdf (labelDist bayes) k * pdf (attrDist bayes) dp - - Categorical labelMap = labelDist bayes - labelL = Map.keys labelMap --- probabilityClassify bayes dp = --- Categorical $ Map.mapWithKey (\label dist -> (pdf dist dp)*(pdf (labelDist bayes) label)) $ attrDist bayes - - -------------------------------------------------------------------------------- --- Test - -data Sex = Male | Female - deriving (Read,Show,Eq,Ord) - -data Human = Human - { _sex :: Sex - , _weight :: Double - , _height :: Double - , _shoesize :: Double - } -makeTypeLenses ''Human - -ds = - [ Human Male 6 180 12 - , Human Male 5.92 190 11 - , Human Male 5.58 170 12 - , Human Male 5.92 165 10 - , Human Female 5 100 6 - , Human Female 5.5 150 8 - , Human Female 5.42 130 7 - , Human Female 5.75 150 9 - ] - -dp = (6:::130:::8:::HNil)::(HList '[Double,Double,Double]) - --- model = train ds :: BC - -type BC = Bayes TH_sex - (Multivariate Human - '[ MultiCategorical '[Sex] - , Independent Normal '[Double,Double,Double] - ] Double) - -instance Monoid (Bayes label dist) - -{-dist = train (map snd ds) :: Multivariate (HList '[Double,Double,Double]) - '[ Independent Normal '[Double] - , Dependent MultiNormal '[Double,Double] - ] - Double-} - \ No newline at end of file diff --git a/src/HLearn/Models/Classifiers/BayesOld.hs b/src/HLearn/Models/Classifiers/BayesOld.hs deleted file mode 100644 index a444091..0000000 --- a/src/HLearn/Models/Classifiers/BayesOld.hs +++ /dev/null @@ -1,174 +0,0 @@ -{-# LANGUAGE MultiParamTypeClasses #-} -{-# LANGUAGE FunctionalDependencies #-} -{-# LANGUAGE TypeSynonymInstances #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE UndecidableInstances #-} -{-# LANGUAGE ScopedTypeVariables #-} -{-# LANGUAGE ConstraintKinds #-} -{-# LANGUAGE TypeOperators #-} -{-# LANGUAGE ExistentialQuantification #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE DataKinds #-} - -module HLearn.Models.Classifiers.Bayes - where - -import qualified Data.Map as Map - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common - -------------------------------------------------------------------------------- --- data types - -data Bayes dist label prob = Bayes - { labelDist :: !(Categorical label prob) - , attrDist :: !(Map.Map label (dist prob)) - } - deriving (Read,Show,Eq,Ord) - -------------------------------------------------------------------------------- --- Algebra - -instance - ( Ord label, Num prob - , Semigroup (dist prob) - ) => Semigroup (Bayes dist label prob) - where - b1 <> b2 = Bayes - { labelDist = labelDist b1 <> labelDist b2 - , attrDist = Map.unionWith (<>) (attrDist b1) (attrDist b2) - } - -instance (Semigroup (Bayes dist label prob)) => Abelian (Bayes dist label prob) - -instance - ( Ord label - , Num prob - , Semigroup (dist prob) - , Monoid (dist prob) - ) => Monoid (Bayes dist label prob) - where - mempty = Bayes mempty mempty - mappend = (<>) - -instance (Ord label, RegularSemigroup (dist prob), Num prob) => RegularSemigroup (Bayes dist label prob) where - inverse b = b - { labelDist = inverse $ labelDist b - , attrDist = Map.map inverse $ attrDist b - } - -instance - ( LeftOperator ring (dist prob) - , LeftOperator ring (Categorical label prob) - , RegularSemigroup (dist prob) - , Ord label, Num prob - ) => LeftOperator ring (Bayes dist label prob) - where - r .* b = b - { labelDist = r .* (labelDist b) - , attrDist = Map.map (r .*) (attrDist b) - } - --- instance --- ( RightOperator ring distL --- , RightOperator ring (Categorical label prob) --- , Ord label, RegularSemigroup distL, Eq distparams, Num prob --- ) => RightOperator ring (Bayes distparams distL label prob) --- where --- b *. r = b --- { labelDist = (labelDist b) *. r --- , attrDist = Map.map (*. r) (attrDist b) --- } --- -------------------------------------------------------------------------------- --- Training - -instance - ( Semigroup (dist prob) - , Ord label, Num prob, Eq prob - , HomTrainer (dist prob) - ) => HomTrainer (Bayes dist label prob) - where - type Datapoint (Bayes dist label prob) = (label,Datapoint (dist prob)) - - train1dp (label,attr) = Bayes (train1dp label) (Map.singleton label $ train1dp attr) - -------------------------------------------------------------------------------- --- Classification - -instance Probabilistic (Bayes dist label prob) where - type Probability (Bayes dist label prob) = prob - -instance - ( Ord label - , Ord prob - , Fractional prob - , Probabilistic (dist prob) - , Probability (dist (Probability (Bayes dist label prob))) ~ Probability (Bayes dist label prob) - , PDF (dist prob) - ) => Classifier (Bayes dist label prob) - where - - type Label (Bayes dist label prob) = label - type UnlabeledDatapoint (Bayes dist label prob) = Datapoint (dist prob) - - probabilityClassify bayes dp = - Categorical $ Map.mapWithKey - (\label dist -> (pdf dist dp)*(pdf (labelDist bayes) label)) $ attrDist bayes - - -data Sex = Male | Female - deriving (Read,Show,Eq,Ord) - -ds = - [ (Male, (6::Double):::(180::Double):::(12::Double):::HNil) - , (Male, 5.92:::190:::11:::HNil) - , (Male, 5.58:::170:::12:::HNil) - , (Male, 5.92:::165:::10:::HNil) - , (Female, 5:::100:::6:::HNil) - , (Female, 5.5:::150:::8:::HNil) - , (Female, 5.42:::130:::7:::HNil) - , (Female, 5.75:::150:::9:::HNil) - ] - -dp = (6:::130:::8:::HNil)::(HList '[Double,Double,Double]) - -model = train ds :: Bayes - (Multivariate (HList '[Double,Double,Double]) - '[ Independent Normal '[Double] --- , Independent Normal '[Double,Double] - , Dependent MultiNormal '[Double,Double] - ]) - Sex - Double - -dist = train (map snd ds) :: Multivariate (HList '[Double,Double,Double]) - '[ Independent Normal '[Double] - , Dependent MultiNormal '[Double,Double] - ] - Double - -{-ds = - [ ("animal","dog":::"food") - , ("animal","dog":::"water") - , ("plant","tree":::"water") - ] - -ds2 = - [ "dog":::"food" - , "dog":::"water" - , "tree":::"water" - ] - -m = train ds :: Bayes - (CategoricalParams ::: CategoricalParams) - (Categorical String Double ::: Categorical String Double) - String - Double - -m2 = train ds2 :: Multivariate - (CategoricalParams ::: CategoricalParams) - (Categorical String Double ::: Categorical String Double)-} - \ No newline at end of file diff --git a/src/HLearn/Models/Classifiers/Centroid.hs b/src/HLearn/Models/Classifiers/Centroid.hs deleted file mode 100644 index a6faa64..0000000 --- a/src/HLearn/Models/Classifiers/Centroid.hs +++ /dev/null @@ -1,53 +0,0 @@ -module HLearn.Models.Classifiers.Centroid - where - -import qualified Data.Vector.Unboxed as VU - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data structures - -data Centroid vector = Centroid - { c_numdp :: Scalar vector - , vector :: vector - } - -deriving instance (Show (Scalar vector), Show vector) => Show (Centroid vector) -deriving instance (Read (Scalar vector), Read vector) => Read (Centroid vector) -deriving instance (Eq (Scalar vector), Eq vector) => Eq (Centroid vector) -deriving instance (Ord (Scalar vector), Ord vector) => Ord (Centroid vector) - -------------------------------------------------------------------------------- --- algebra - -instance (Num (Scalar vector), Monoid vector) => Monoid (Centroid vector) where - mempty = Centroid 0 mempty - c1 `mappend` c2 = Centroid - { c_numdp = c_numdp c1 + c_numdp c2 - , vector = vector c1 <> vector c2 - } - -type instance Scalar (Centroid vector) = Scalar vector - -instance - ( MetricSpace vector - , VectorSpace vector - ) => MetricSpace (Centroid vector) - where - distance v1 v2 = distance (vector v1 /. c_numdp v1) (vector v2 /. c_numdp v2) - -------------------------------------------------------------------------------- --- model - -instance NumDP (Centroid vector) where - numdp = c_numdp - -instance - ( Monoid vector - , Num (Scalar vector) - ) => HomTrainer (Centroid vector) - where - type Datapoint (Centroid vector) = vector - - train1dp dp = Centroid { c_numdp=1, vector=dp } diff --git a/src/HLearn/Models/Classifiers/Common.hs b/src/HLearn/Models/Classifiers/Common.hs deleted file mode 100644 index 8300e88..0000000 --- a/src/HLearn/Models/Classifiers/Common.hs +++ /dev/null @@ -1,130 +0,0 @@ -{-# LANGUAGE DataKinds #-} - -module HLearn.Models.Classifiers.Common - where - -import SubHask -import SubHask.Monad -import HLearn.Models.Distributions.Common - - -------------------------------------------------------------------------------- --- Labeled datapoints - - -class - ( Scalar (Attributes dp) ~ Scalar dp - , IsScalar (Scalar dp) - ) => Labeled dp - where - - type Label dp - type Attributes dp - - getLabel :: dp -> Label dp - getAttributes :: dp -> Attributes dp - -{- ---------------------------------------- - --- instance Labeled (label,attr) where --- type Label (label,attr) = label --- type Attributes (label,attr) = attr --- --- getLabel = fst --- getAttributes = snd - ---------------------------------------- - -data MaybeLabeled label attr = MaybeLabeled - { label :: Maybe label - , attr :: attr - } - deriving (Read,Show,Typeable) - -type instance Logic (MaybeLabeled label attr) = Logic attr - -instance (NFData label, NFData attr) => NFData (MaybeLabeled label attr) where - rnf (MaybeLabeled label attr) = deepseq label $ rnf attr - -instance Eq_ attr => Eq_ (MaybeLabeled label attr) where - a==b = attr a==attr b - -{- -instance Lattice attr => Lattice (MaybeLabeled label attr) where - inf a b = inf (attr a) (attr b) - sup a b = sup (attr a) (attr b) - -instance POrd attr => POrd (MaybeLabeled label attr) where - a `pcompare` b = attr a `pcompare` attr b - -instance Ord attr => Ord (MaybeLabeled label attr) where - a `compare` b = attr a `compare` attr b - -instance (NFData label, NFData attr) => NFData (MaybeLabeled label attr) where - rnf (MaybeLabeled label attr) = deepseq label $ rnf attr --} - -noLabel :: attr -> MaybeLabeled label attr -noLabel attr = MaybeLabeled - { label = Nothing - , attr = attr - } - -instance - ( IsScalar (Scalar attr) - ) => Labeled (MaybeLabeled label attr) - where - - type Label (MaybeLabeled label attr) = Maybe label - type Attributes (MaybeLabeled label attr) = attr - - getLabel = label - getAttributes = attr - -type instance Scalar (MaybeLabeled label attr) = Scalar attr - -instance Normed attr => Normed (MaybeLabeled label attr) where - size (MaybeLabeled _ a) = size a - --- FIXME: add faster functions -instance Metric attr => Metric (MaybeLabeled label attr) where - distance (MaybeLabeled _ a1) (MaybeLabeled _ a2) = distance a1 a2 --- isFartherThan dp1 dp2 = isFartherThan (getAttributes dp1) (getAttributes dp2) --- isFartherThanWithDistance dp1 dp2 = isFartherThanWithDistance (getAttributes dp1) (getAttributes dp2) - --} -------------------------------------------------------------------------------- --- Classification - -class - ( Labeled (Datapoint model) - ) => ProbabilityClassifier model - where - type ResultDistribution model - probabilityClassify :: model -> Attributes (Datapoint model) -> ResultDistribution model - -class MarginClassifier model where - margin :: model -> Attributes (Datapoint model) -> (Scalar model, Label (Datapoint model)) - -class - ( Labeled (Datapoint model) - ) => Classifier model - where - classify :: model -> Attributes (Datapoint model) -> Label (Datapoint model) - --- | this is a default instance that any instance of Classifier should satisfy if it is also an instance of ProbabilityClassifier --- instance --- ( Label (Datapoint model) ~ Datapoint (ResultDistribution model) --- , Mean (ResultDistribution model) --- , ProbabilityClassifier model --- ) => Classifier model --- where --- classify model dp = mean $ probabilityClassify model dp - -------------------------------------------------------------------------------- --- Regression - --- | Regression is classification where the class labels are (isomorphic to) real numbers. The constraints could probably be better specified, but they're close enough for now. -class (Classifier model, Scalar model ~ Label (Datapoint model)) => Regression model -instance (Classifier model, Scalar model ~ Label (Datapoint model)) => Regression model diff --git a/src/HLearn/Models/Classifiers/Experimental/Boosting/FiniteBoost.hs b/src/HLearn/Models/Classifiers/Experimental/Boosting/FiniteBoost.hs deleted file mode 100644 index 596f995..0000000 --- a/src/HLearn/Models/Classifiers/Experimental/Boosting/FiniteBoost.hs +++ /dev/null @@ -1,90 +0,0 @@ -module HLearn.Models.Classifiers.Experimental.Boosting.FiniteBoost - where - -import qualified Data.Map as Map -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector as V - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common - -------------------------------------------------------------------------------- --- data structures - -class FiniteBoostParams p where - type BaseModel p - getModelL :: p -> [BaseModel p] - numModels :: p -> Int - -newtype FiniteBoost params = FiniteBoost - { weights :: V.Vector (Ring (BaseModel params)) - } --- deriving (Read,Show,Eq,Ord) - --- type FiniteBoost weight basemodel = RegSG2Group (FiniteBoost' weight basemodel) - -------------------------------------------------------------------------------- --- algebra - -instance - ( FiniteBoostParams params - , HasRing (BaseModel params) - ) => Monoid (FiniteBoost params) - where - mempty = FiniteBoost $ V.replicate (numModels (undefined::params)) 0 - b1 `mappend` b2 = FiniteBoost $ V.zipWith (+) (weights b1) (weights b2) - -instance - ( FiniteBoostParams params - , HasRing (BaseModel params) - ) => HasRing (FiniteBoost params) - where - type Ring (FiniteBoost params) = Ring (BaseModel params) - -instance - ( FiniteBoostParams params - , HasRing (BaseModel params) - ) => Group (FiniteBoost params) - where - inverse b = FiniteBoost $ V.map negate (weights b) - -------------------------------------------------------------------------------- --- model - -instance - ( ProbabilityClassifier (BaseModel params) - , HomTrainer (BaseModel params) - , FiniteBoostParams params - , HasRing (BaseModel params) - , Ring (BaseModel params) ~ Probability (ResultDistribution (BaseModel params)) - , Datapoint (ResultDistribution (BaseModel params)) ~ Label (Datapoint (BaseModel params)) - , PDF (ResultDistribution (BaseModel params)) - ) => HomTrainer (FiniteBoost params) - where - type Datapoint (FiniteBoost params) = Datapoint (BaseModel params) - - train1dp dp = FiniteBoost - { weights = V.generate (V.length modelV) (\i -> pdf (probabilityClassify (modelV V.! i) attr) label) - } - where - attr = getAttributes dp - label = getLabel dp - modelV = V.fromList $ getModelL (undefined :: params) - -------------------------------------------------------------------------------- --- classification - -instance - ( ProbabilityClassifier (BaseModel params) - , Ring (BaseModel params) ~ Ring (ResultDistribution (FiniteBoost params)) - , Module (ResultDistribution (FiniteBoost params)) - , FiniteBoostParams params - ) => ProbabilityClassifier (FiniteBoost params) - where - type ResultDistribution (FiniteBoost params) = ResultDistribution (BaseModel params) - - probabilityClassify (FiniteBoost weights) dp = - reduce $ V.zipWith (.*) weights (fmap (\model -> probabilityClassify model dp) modelL) - where - modelL = V.fromList $ getModelL (undefined :: params) \ No newline at end of file diff --git a/src/HLearn/Models/Classifiers/Experimental/Boosting/MonoidBoost.hs b/src/HLearn/Models/Classifiers/Experimental/Boosting/MonoidBoost.hs deleted file mode 100644 index ececf05..0000000 --- a/src/HLearn/Models/Classifiers/Experimental/Boosting/MonoidBoost.hs +++ /dev/null @@ -1,133 +0,0 @@ -{-# LANGUAGE DataKinds #-} - -module HLearn.Models.Classifiers.Experimental.Boosting.MonoidBoost - where - -import Control.Applicative -import Data.List -import qualified Data.Foldable as F -import qualified Data.Sequence as Seq -import Data.Sequence (fromList) -import GHC.TypeLits -import Debug.Trace - -import Test.QuickCheck - -import HLearn.Algebra -import HLearn.Models.Distributions.Visualization.Gnuplot -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common - -------------------------------------------------------------------------------- --- data structures - -data MonoidBoost (k::Nat) basemodel = MonoidBoost - { dataL :: Seq.Seq (Datapoint basemodel) - , modelL :: Seq.Seq basemodel - , weightL :: Seq.Seq (Ring basemodel) - , boost_numdp :: Int - } - -deriving instance (Read (Datapoint basemodel), Read (Ring basemodel), Read basemodel) => Read (MonoidBoost k basemodel) -deriving instance (Show (Datapoint basemodel), Show (Ring basemodel), Show basemodel) => Show (MonoidBoost k basemodel) -deriving instance (Eq (Datapoint basemodel), Eq (Ring basemodel), Eq basemodel) => Eq (MonoidBoost k basemodel) -deriving instance (Ord (Datapoint basemodel), Ord (Ring basemodel), Ord basemodel) => Ord (MonoidBoost k basemodel) - -instance - ( HomTrainer basemodel - , Arbitrary (Datapoint basemodel) - , SingI k - ) => Arbitrary (MonoidBoost k basemodel) - where - arbitrary = train <$> listOf arbitrary - -------------------------------------------------------------------------------- --- algebra - --- testassociativity = quickCheck ((\m1 m2 m3 -> m1<>(m2<>m3)==(m1<>m2)<>m3) --- :: MonoidBoost 3 (Normal Rational) --- -> MonoidBoost 3 (Normal Rational) --- -> MonoidBoost 3 (Normal Rational) --- -> Bool --- ) - -leave :: Int -> Seq.Seq a -> Seq.Seq a -leave k xs = Seq.drop (Seq.length xs - k) xs - -instance - ( HomTrainer basemodel - , SingI k - ) => Monoid (MonoidBoost k basemodel) - where - mempty = MonoidBoost mempty mempty mempty 0 - mb1 `mappend` mb2 = MonoidBoost - { dataL = dataL' - , modelL = modelL mb1 <> newmodel <> modelL mb2 - , weightL = mempty - , boost_numdp = boost_numdp' - } - where - boost_numdp' = boost_numdp mb1 + boost_numdp mb2 - dataL' = dataL mb1 <> dataL mb2 - - newmodel = Seq.fromList $ newmodels $ leave (2*k) (dataL mb1) <> Seq.take (2*k) (dataL mb2) - newmodels xs = if Seq.length xs >= modelsize - then (train (Seq.take modelsize xs)):(newmodels $ Seq.drop 1 xs) - else [] - - modelsize = 2*k+1 - k = fromIntegral $ fromSing (sing::Sing k) --- frontL mb = Seq.take k $ dataL mb --- backL mb = Seq.drop (Seq.length (dataL mb) - k) (dataL mb) - -------------------------------------------------------------------------------- --- model - -instance - ( SingI k - , HomTrainer basemodel - ) => HomTrainer (MonoidBoost k basemodel) - where - type Datapoint (MonoidBoost k basemodel) = Datapoint basemodel - train1dp dp = MonoidBoost - { dataL = mempty |> dp - , modelL = mempty - , weightL = mempty - , boost_numdp = 1 - } - -------------------------------------------------------------------------------- --- classification - -instance Probabilistic (MonoidBoost k basemodel) where - type Probability (MonoidBoost k basemodel) = Probability basemodel - -instance - ( ProbabilityClassifier basemodel - , Monoid (ResultDistribution basemodel) - ) => ProbabilityClassifier (MonoidBoost k basemodel) - where - type ResultDistribution (MonoidBoost k basemodel) = ResultDistribution basemodel - probabilityClassify mb dp = reduce $ fmap (flip probabilityClassify dp) $ modelL mb - -------------------------------------------------------------------------------- --- distribution - -instance - ( PDF basemodel - , Fractional (Probability basemodel) - ) => PDF (MonoidBoost k basemodel) - where - pdf mb dp = ave $ fmap (flip pdf dp) $ modelL mb - where - ave xs = (F.foldl1 (+) xs) / (fromIntegral $ Seq.length xs) - -instance - ( PlottableDistribution basemodel - , Fractional (Probability basemodel) - ) => PlottableDistribution (MonoidBoost k basemodel) - where - - samplePoints mb = concat $ map samplePoints $ F.toList $ modelL mb - plotType _ = plotType (undefined :: basemodel) - diff --git a/src/HLearn/Models/Classifiers/HTree.hs b/src/HLearn/Models/Classifiers/HTree.hs deleted file mode 100644 index 67e143b..0000000 --- a/src/HLearn/Models/Classifiers/HTree.hs +++ /dev/null @@ -1,55 +0,0 @@ -module HLearn.Models.Classifiers.HTree - where - -import Debug.Trace -import qualified Data.Map as Map - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Classification -import HLearn.Models.Classifiers.Bayes - -newtype HTreeParams distparams = HTreeParams (BayesParams distparams) - deriving (Read,Show,Eq,Ord) - -newtype HTree dist label prob = HTree (Bayes dist label prob) - deriving (Read,Show,Eq,Ord) - --- instance (Ord prob, Show (dist prob), Show label, Untup (dist prob) a b, Show b) => ProbabilityClassifier (HTree dist label prob) dp label prob where --- probabilityClassify (HTree (SGJust (Bayes' labelDist attrDist))) dp = --- trace (show $ untup $ map snd $ Map.toList attrDist) $ undefined - -debug (HTree (SGJust (Bayes' labelDist attrDist))) dp = map snd $ Map.toList attrDist - -htree = HTree model - --- class TupTranspose tup where --- tuptrans :: tup -> - --- tuptrans [a:::b] -> [a]:::[b] --- tuptrans [] = [] --- tuptrans x:xs = - -class Untup tup a b where - untup :: [tup] -> a:::[b] - -instance (Untup a a' b') => Untup (a:::b) (a':::[b']) b where - untup xs = (untup a) ::: b - where (a:::b) = untupzip xs - -instance Untup (a:::b) [a] b where - untup = untupzip - - -kindswap :: ((a:::.b) c) -> (a c):::(b c) -kindswap (a:::.b) = (a:::b) -{-instance (Untup (a c) (a' c) (b' c)) => Untup ((a:::.b) c) ((a' c):::[b' c]) (b c) where - untup xs = (untup a) ::: b - where (a:::.b) = untupzip xs - -instance Untup ((a:::.b) c) [a c] (b c) where - untup = untupzip-} - -untupzip :: [(a:::b)] -> ([a]:::[b]) -{-# INLINE untupzip #-} -untupzip = foldr (\(a:::b) ~(as:::bs) -> ((a:as):::(b:bs))) ([]:::[]) \ No newline at end of file diff --git a/src/HLearn/Models/Classifiers/HomTree.hs b/src/HLearn/Models/Classifiers/HomTree.hs deleted file mode 100644 index 3ebc2ff..0000000 --- a/src/HLearn/Models/Classifiers/HomTree.hs +++ /dev/null @@ -1,129 +0,0 @@ -module HLearn.Models.Classifiers.HomTree - where - -import Control.DeepSeq -import Data.List -import Data.List.Extras -import Debug.Trace - -import qualified Data.Vector as V -import qualified Control.ConstraintKinds as CK - -import HLearn.Algebra -import HLearn.DataContainers -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.NBayes -import HLearn.Models.Classification - -fi=fromIntegral - -------------------------------------------------------------------------------- --- HomTreeParams - -data HomTreeParams = HomTreeParams - { - } - deriving (Show, Read, Eq,Ord) - -defHomTreeParams = HomTreeParams - -instance NFData HomTreeParams where - rnf params = () - -instance (Eq label) => Model (ClassificationParams label HomTreeParams) (HomTree label) where - getparams _ = error "HomTree.getparams" - -------------------------------------------------------------------------------- --- HomTree - -data HomTree' label = HomTree' - { nbayes :: !(NBayes label) - , splitAttr :: !Int - } - -instance (NFData (NBayes label)) => NFData (HomTree' label) where - rnf (HomTree' nbayes splitAttr) = deepseq nbayes $ rnf splitAttr - -type HomTree label = RegSG2Group (HomTree' label) - -------------------------------------------------------------------------------- --- Functors - -nbayes2homtree :: NaiveBayes Int -> HomTree' Int -nbayes2homtree (SGJust nbayes) = HomTree' - { nbayes = nbayes - , splitAttr = getSplitAttr nbayes --- , splitAttr = error "nbayes2homtree: not implemented" - } - -getSplitAttr nbayes = argmax (attrEntropy nbayes) $ attrL $ dataDesc nbayes - --- attrEntropy :: (ProbabilityClassifier (NBayes label) DPS label) => NBayes label -> Int -> HLearn Double -attrEntropy :: NBayes Int -> Int -> Double -attrEntropy nb attrI = nonoverlap $ V.toList $ V.map (V.! attrI) $ attrDist nb --- do --- -- let labelL = [0,1,0,1] --- let labelL = (map (cdfInverse (labelDist nb) {-. logFloat-})) $ map (/100) [1..99] --- let sampleL = map (cdfInverse . getDist nb attrI) labelL --- let pcL = map (\sample -> probabilityClassify nb [(attrI,sample)]) sampleL --- let pcLg = groupBy (\pc1 pc2 -> (mean pc1)==(mean pc2)) pcL --- let weightL = normalizeL $ map (fi . length) pcLg --- let entropyL = map (average . map (classificationEntropy . dist2list)) pcLg --- let entropy = sum $ map (\(w,e) -> w*e) $ zip weightL entropyL --- return entropy - -average :: [Double] -> Double -average xs = (sum xs) / (fi $ length xs) - -------------------------------------------------------------------------------- --- Algebra - --- instance (Label label, Semigroup (NBayes label)) => Semigroup (HomTree label) where -instance Semigroup (HomTree' Int) where - (<>) homtree1 homtree2 = nbayes2homtree $ SGJust $ ((nbayes homtree1) <> (nbayes homtree2)) --- (<>) homtree1 homtree2 = HomTree' ((nbayes homtree1) <> (nbayes homtree2)) Nothing - --- instance (Label label, Invertible (NBayes label)) => Invertible (HomTree label) where -instance RegularSemigroup (HomTree' Int) where - inverse homtree = nbayes2homtree $ SGJust $ (inverse (nbayes homtree)) --- inverse homtree = HomTree' (inverse (nbayes homtree)) Nothing - -------------------------------------------------------------------------------- --- Training - -instance --- ( HomTrainer (ClassificationParams label (NBayesParams label)) (label,DPS) (NaiveBayes label) --- ) => HomTrainer (ClassificationParams label HomTreeParams) (label,DPS) (HomTree label) - ( {-HomTrainer (ClassificationParams Int (NBayesParams Int)) (Int,DPS) (NaiveBayes Int)-} - ) => HomTrainer (ClassificationParams Int HomTreeParams) (Int,DPS) (HomTree Int) - where - train' (ClassificationParams params datadesc) dp = - SGJust $ nbayes2homtree $ deepseq nb nb - where - nb = train' (ClassificationParams defNBayesParams datadesc) dp - -{-instance BatchTrainer HomTreeParams (HomTree Int) DPS Int where - trainBatch = trainOnline - -instance EmptyTrainer HomTreeParams (HomTree Int) Int where - emptyModel desc modelparams = HomTree - { nbayes = emptyModel desc defNBayesParams - , splitAttr = trace "WARNING: HomTree.EmptyTrainer: no splitAttr yet" $ 0 - } - -instance OnlineTrainer HomTreeParams (HomTree Int) DPS Int where - add1dp desc modelparams model dp = do - nbayes' <- add1dp desc defNBayesParams (nbayes model) dp - return $ nbayes2homtree nbayes'-} - -------------------------------------------------------------------------------- --- Classification - --- instance Classifier (HomTree Int) DPS Int where --- classify model dp = mean $ probabilityClassify model dp - --- instance (Ord prob) => ProbabilityClassifier (HomTree label) DPS label prob where -instance ProbabilityClassifier (HomTree Int) DPS Int Double where - - probabilityClassify (SGJust model) dp = probabilityClassify (nbayes model) dp' - where dp' = filter (\(attrI,val) -> attrI==splitAttr model) dp \ No newline at end of file diff --git a/src/HLearn/Models/Classifiers/KernelizedLinearClassifier.hs b/src/HLearn/Models/Classifiers/KernelizedLinearClassifier.hs deleted file mode 100644 index e96c2f7..0000000 --- a/src/HLearn/Models/Classifiers/KernelizedLinearClassifier.hs +++ /dev/null @@ -1,339 +0,0 @@ -{-# LANGUAGE DataKinds #-} -module HLearn.Models.Classifiers.KernelizedLinearClassifier - where - -import Control.DeepSeq -import Control.Lens -import Control.Monad -import Control.Monad.Random -import Data.Dynamic -import Data.List -import Data.List.Extras -import Data.Maybe -import Data.Typeable -import qualified Data.ByteString.Lazy as BS -import qualified Data.Foldable as F -import qualified Data.Map.Strict as Map -import qualified Data.Set as Set -import qualified Data.Vector as V -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Generic.Mutable as VGM -import System.IO - -import Data.Csv - -import Debug.Trace - -import HLearn.Algebra -import HLearn.Algebra.History -import HLearn.Algebra.LinearAlgebra -import HLearn.Evaluation.CrossValidation -import HLearn.Metrics.Lebesgue -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common -import HLearn.Optimization.GradientDescent -import HLearn.Optimization.NewtonRaphson -import HLearn.Optimization.QuasiNewton -import HLearn.Optimization.Common -import HLearn.Optimization.Trace - -import HLearn.Models.Classifiers.LinearClassifier (l1reg,l2reg,elasticNet,logloss,hingeloss,sumOver,logSumOfExp2,invlogit) - - -------------------------------------------------------------------------------- --- vector space - -data FreeVectorSpace r a = FreeVectorSpace (Map.Map a r) - deriving (Read,Show,Typeable) - -applyFVS :: Num r => (a -> r) -> FreeVectorSpace r a -> r -applyFVS f (FreeVectorSpace m) = foldl' (+) 0 $ map (\(a,r) -> f a * r) $ Map.assocs m - -instance (NFData r, NFData a) => NFData (FreeVectorSpace r a) where - rnf (FreeVectorSpace m) = rnf m - -------------------- - -type instance Scalar (FreeVectorSpace r a) = Scalar r - -instance (Ord a, Abelian r) => Abelian (FreeVectorSpace r a) -instance (Ord a, Monoid r) => Monoid (FreeVectorSpace r a) where - mempty = FreeVectorSpace $ Map.empty - mappend (FreeVectorSpace m1) (FreeVectorSpace m2) = FreeVectorSpace $ Map.unionWith (<>) m1 m2 - -instance (Ord a, Group r) => Group (FreeVectorSpace r a) where - inverse (FreeVectorSpace m1) = FreeVectorSpace $ Map.map inverse m1 - -instance (Ord a, Module r) => Module (FreeVectorSpace r a) where - r .* (FreeVectorSpace m) = FreeVectorSpace $ Map.map (r.*) m - -instance (Ord a, VectorSpace r) => VectorSpace (FreeVectorSpace r a) - -instance (Ord a, IsScalar r) => InnerProduct (FreeVectorSpace r a) where - inner (FreeVectorSpace m1) (FreeVectorSpace m2) = Map.foldr (+) mempty $ Map.intersectionWith (*) m1 m2 - -------------------- - -instance - ( IsScalar r - , Ord a - ) => ValidTensor1 (FreeVectorSpace r a) - where - type Tensor 0 (FreeVectorSpace r a) = r - type Tensor 1 (FreeVectorSpace r a) = FreeVectorSpace r a - mkTensor = id - -------------------- - -instance (Ord a, IsScalar r) => HomTrainer (FreeVectorSpace r a) where - type Datapoint (FreeVectorSpace r a) = a - train1dp dp = FreeVectorSpace $ Map.singleton dp 1 - -------------------------------------------------------------------------------- --- data types - -data KernelizedLinearClassifier dp = KernelizedLinearClassifier - { weights :: Map.Map (Label dp) (Scalar dp, FreeVectorSpace (Scalar dp) (Attributes dp)) - } - deriving (Typeable) - -------------------------------------------------------------------------------- - -instance Monoid (KernelizedLinearClassifier dp) where - mempty = error "KLC mempty" - mappend = error "KLC mappend" - -instance HomTrainer (KernelizedLinearClassifier dp) where - type Datapoint (KernelizedLinearClassifier dp) = dp - ---------------------------------------- - -klrtrainM :: forall container dp. - ( ValidTensor1 (Attributes dp) - , Attributes dp ~ Tensor 1 (Attributes dp) - , Labeled dp - , Ord (Label dp) - , Ord (Attributes dp) - , Ord (Scalar (Attributes dp)) - , F.Foldable container - , NFData (Tensor 1 (Attributes dp)) - , NFData (Scalar (Attributes dp)) - , Typeable (Label dp) - , Typeable (Attributes dp) - , Typeable (Scalar dp) - , Typeable dp - , Show (Scalar dp) - , Show (Attributes dp) - ) => Scalar dp - -> container dp - -> Map.Map (Label dp) (Attributes dp) - -> History (KernelizedLinearClassifier dp) -klrtrainM lambda dps weights0 = do - let emptyweights = Map.fromList $ map (\dp -> (getLabel dp,mempty)) $ F.toList dps - weights' <- collectEvents $ fmap Map.fromList $ go $ Map.assocs emptyweights - report $ KernelizedLinearClassifier - { weights = weights' - } - where - n l = fromIntegral $ length $ filter (\dp -> getLabel dp ==l) $ F.toList dps - - -- the weights for the last label are set to zero; - -- this is equivalent to running the optimization procedure, - -- but much cheaper - go ((l,w0):[]) = report [(l, (n l,mempty))] - - -- calculate the weights for label l - go ((l,w0):xs) = trace "go" $ do - opt <- conjugateGradientDescent f f' w0 - [ maxIterations 1000 - , fx1grows - , multiplicativeTollerance 1e-12 - ] - - let w1 = opt^.x1 --- fw1 = f w1 --- f'w1 = f' w1 --- f''w1 = f'' w1 - - res <- report --- $ trace ("w1="++show w1) - $ deepseq w1 - $ (l, (n l, w1)) - - fmap (res:) $ go xs - where - reg (FreeVectorSpace m) = Map.foldr (+) 0 $ Map.map (**2) m - reg' w = 2 .* w - - - loss dp w = let r=logSumOfExp2 0 $ -y dp * applyFVS (inner (getAttributes dp)) w - x=applyFVS (inner (getAttributes dp)) w - in r -- trace (" r="++show r++"; appyVFS="++show x) r - loss' dp w = (-y dp*(1-invlogit (y dp * applyFVS (inner (getAttributes dp)) w))) - .* (train1dp $ getAttributes dp :: FreeVectorSpace (Scalar dp) (Attributes dp)) - - f :: FreeVectorSpace (Scalar (Attributes dp)) (Attributes dp) -> Scalar (Attributes dp) - f w = (numdp*lambda .* reg w) <> (sumOver dps $ \dp -> loss dp w) - f' w = (numdp*lambda .* reg' w) <> (sumOver dps $ \dp -> loss' dp w) - - numdp :: Scalar dp - numdp = fromIntegral $ length $ F.toList dps - - y dp = bool2num $ getLabel dp==l - -klrtrain lambda dps = klrtrain2 lambda dps -klrtrainMsimple lambda dps = klrtrainM lambda dps mempty - -klrtrain2 :: forall dp. - ( Tensor 1 (Attributes dp) ~ Attributes dp - , ValidTensor1 (Attributes dp) - , Scalar dp ~ Double - , Ord (Attributes dp) - , Ord (Label dp) - , Labeled dp - , NFData (Attributes dp) - , Show (Attributes dp) - , Typeable dp - , Typeable (Attributes dp) - , Typeable (Label dp) - , Typeable (Scalar dp) - ) => Scalar dp -> [dp] -> KernelizedLinearClassifier dp -klrtrain2 lambda dps = traceHistory - [ traceBFGS - , traceNewtonRaphson - , traceFunk (undefined::ConjugateGradientDescent (FreeVectorSpace (Scalar dp) (Attributes dp))) - , traceKernelizedLinearClassifier (undefined::dp) - ] - $ collectEvents $ klrtrainM lambda dps mempty - - -------------------------------------------------------------------------------- --- classification - -instance - ( Labeled dp - , InnerProduct (Attributes dp) - , Ord (Scalar dp) - ) => Classifier (KernelizedLinearClassifier dp) - where - classify m attr = fst $ argmax snd $ Map.assocs $ Map.map (\(_,w) -> applyFVS (inner attr) w) $ weights m - -------------------------------------------------------------------------------- --- tests - -type DP = MaybeLabeled String (Vector Double) - -traceKernelizedLinearClassifier :: forall dp. Typeable dp => dp -> Event -> [String] -traceKernelizedLinearClassifier _ opt = case fromDynamic (dyn opt) :: Maybe (KernelizedLinearClassifier dp) of - Nothing -> [] - Just x -> - [ (head $ words $ drop 2 $ init $ init $ show $ dyn opt) --- ++"; fx1="++showDoubleLong (fx1 x) --- ++"; |f'x1|="++showDouble (innerProductNorm $ f'x1 x) --- ++"; step="++showDouble (stepSize x) --- ++"; step="++showDouble (stepSize x) --- ++"; sec="++showDouble ((fromIntegral $ stoptime opt)*1e-12) - ++"; sec="++showDouble ((fromIntegral $ runtime opt)*1e-12) - ] - -test = do - --- let {filename = "../datasets/ida/banana_data.csv"; label_index=0} --- let {filename = "../datasets/ripley/synth.train.csv"; label_index=2} --- let {filename = "../datasets/uci/haberman.data"; label_index=3} --- let {filename = "../datasets/uci/pima-indians-diabetes.data"; label_index=8} - let {filename = "../datasets/uci/wine.csv"; label_index=0} --- let {filename = "../datasets/uci/ionosphere.csv"; label_index=34} --- let {filename = "../datasets/uci/sonar.csv"; label_index=60} --- let {filename = "../datasets/uci/optdigits.train.data"; label_index=64} - - let verbose = True - - ----------------------------------- - -- load data - - xse :: Either String (V.Vector (V.Vector String)) - <- trace "loading reference dataset" $ fmap (decode HasHeader) $ BS.readFile filename - xs <- case xse of - Right rs -> return rs - Left str -> error $ "failed to parse CSV file " ++ filename ++ ": " ++ take 1000 str - - let numdp = VG.length xs - let numdim = VG.length $ xs VG.! 0 - let numlabels = Set.size $ Set.fromList $ VG.toList $ VG.map (VG.! label_index) xs - - if verbose - then do - hPutStrLn stderr " dataset info:" - hPutStrLn stderr $ " num dp: " ++ show numdp - hPutStrLn stderr $ " num dim: " ++ show numdim - hPutStrLn stderr $ " num labels: " ++ show numlabels - else return () - - ----------------------------------- - -- convert to right types - - let ys = VG.map (\x -> MaybeLabeled - { label = Just $ x VG.! label_index - , attr = VG.convert ( - VG.map read $ VG.fromList $ (:) "1" $ VG.toList $ VG.take label_index x <> VG.drop (label_index+1) x - :: V.Vector Double - ) - }) - xs - :: V.Vector DP -- (MaybeLabeled String (LA.Vector Double)) - - ----------------------------------- - -- convert to right types - --- let m = train ys :: LinearClassifier (MaybeLabeled String (LA.Vector Double)) --- deepseq m $ print $ m - - --- let runtest f = flip evalRand (mkStdGen 100) $ validate --- (repeatExperiment 1 (kfold 5)) --- errorRate --- ys --- (f (lrtrain 1e-2) :: [DP] -> LinearClassifier DP) - --- let runtest f = flip evalRand (mkStdGen 100) $ validate_monoid --- (repeatExperiment 1 (kfold 5)) --- errorRate --- ys --- (f (lrtrain 1e-2) :: [DP] -> LinearClassifier DP) --- (mappendTaylor) --- (mappendAverage) - --- let tests = --- [ do --- putStr $ show n++", " --- let res = runtest id --- putStrLn $ show (mean res)++", "++show (variance res) --- | n <- [100] --- ] --- sequence_ tests --- print $ runtest (partition 10 $ VG.toList ys) (VG.toList ys) - - let (res,hist) = unsafeRunHistory $ flip evalRandT (mkStdGen 100) $ validateM - (kfold 5) - errorRate - ys - (klrtrainMsimple 1e-3) - - --- printHistory [traceBFGS,traceNewtonRaphson,traceBrent] hist - printHistory - [ traceFunk (undefined::ConjugateGradientDescent (FreeVectorSpace (Scalar DP) (Attributes DP))) --- [ traceKernelizedLinearClassifier (undefined::DP) --- , traceBFGS --- , traceNewtonRaphson - , traceBacktracking (undefined :: FreeVectorSpace (Scalar DP) (Attributes DP)) - , traceBracket - , traceBrent --- , traceGSS - ] hist - putStrLn $ show (mean res)++","++show (variance res) - - - putStrLn "done." - diff --git a/src/HLearn/Models/Classifiers/Lame/AdaBoost.hs b/src/HLearn/Models/Classifiers/Lame/AdaBoost.hs deleted file mode 100644 index 1f768df..0000000 --- a/src/HLearn/Models/Classifiers/Lame/AdaBoost.hs +++ /dev/null @@ -1,65 +0,0 @@ -module HLearn.Models.Classifiers.Lame.AdaBoost - where - -import HLearn.Algebra -import HLearn.Models.Classification -import HLearn.Models.Ensemble -import qualified Control.ConstraintKinds as CK - -import qualified Data.Vector as V -import qualified Data.Vector.Unboxed as VU - -------------------------------------------------------------------------------- --- AdaBoost - -data AdaBoostParams baseparams prob = AdaBoostParams - { baseparams :: baseparams - } - deriving (Read,Show,Eq,Ord) - --- instance Model (AdaBoostParams basemodelparams prob) (Ensemble (AdaBoostParams basemodelparams prob) model prob) where -instance (Eq baseparams) => Model (AdaBoostParams baseparams prob) (Ensemble (AdaBoostParams baseparams prob) model prob) where - getparams (SGJust ens') = params ens' - -------------------------------------------------------------------------------- --- Training - -instance - ( Floating prob - , Ord label - , LameTrainer baseparams V.Vector (prob,(label,attr)) basemodel - , ProbabilityClassifier basemodel attr label prob - , ProbabilityClassifier (Ensemble (AdaBoostParams baseparams prob) basemodel prob) attr label prob - ) => LameTrainer - (AdaBoostParams baseparams prob) - V.Vector - (label,attr) - (Ensemble (AdaBoostParams baseparams prob) basemodel prob) - where - - lame_train' params ds = go 0 _D0 _F0 - where - m = V.length ds - _y = V.map fst ds - _x = V.map snd ds - _D0 = V.replicate m (1/fromIntegral m) :: V.Vector prob - _F0 = SGJust $ Ensemble' params mempty - - cost x = exp(-x) - cost' x = -exp(-x) - - go :: Int -> (V.Vector prob) -> (Ensemble (AdaBoostParams baseparams prob) basemodel prob) -> (Ensemble (AdaBoostParams baseparams prob) basemodel prob) - go itr _D _F = - let f_t1 = lame_train' (baseparams params) (V.zip _D0 ds) :: basemodel - _y_t1 = V.map (classify f_t1) _x - err = V.sum $ V.map (\(y0,y_t1) -> indicator $ y0==y_t1) $ V.zip _y _y_t1 - w_t1 = (1/2)*(log $ (1-err)/err) :: prob - _F_t1 = _F |> (w_t1,f_t1) - - _D_t1_raw = V.map (\(y,x) -> cost' $ bool2num $ y==classify _F_t1 x) $ V.zip _y _x - _D_t1_sum = V.sum _D_t1_raw - _D_t1 = V.map (\d -> d/_D_t1_sum) _D_t1_raw - in if itr<20 - then go (itr+1) _D_t1 _F_t1 - else _F_t1 - diff --git a/src/HLearn/Models/Classifiers/LinearClassifier.hs b/src/HLearn/Models/Classifiers/LinearClassifier.hs deleted file mode 100644 index 9e8014a..0000000 --- a/src/HLearn/Models/Classifiers/LinearClassifier.hs +++ /dev/null @@ -1,614 +0,0 @@ -{-# LANGUAGE DataKinds, RankNTypes #-} -module HLearn.Models.Classifiers.LinearClassifier - where - -import Data.Data -import Control.DeepSeq -import Control.Lens --- import Control.Monad -import Control.Monad.Random -import Data.Dynamic -import Data.List.Extras -import Data.Maybe -import Data.Typeable -import qualified Data.ByteString.Lazy as BS -import qualified Data.Map.Strict as Map -import qualified Data.Set as Set -import qualified Data.Vector as V -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Storable as VS -import Foreign.Storable -import System.IO - -import Data.Csv hiding (Field) - -import Debug.Trace - -import qualified Prelude as P -import Prelude (take,drop,map,filter,zip,head) -import SubHask hiding (Functor(..), Applicative(..), Monad(..), Then(..), fail, return, argmax) -import SubHask.Compatibility.HMatrix -import SubHask.Monad - --- import HLearn.Algebra -import HLearn.History --- import HLearn.Evaluation.CrossValidation --- import HLearn.Metrics.Lebesgue --- import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common -import HLearn.Models.Distributions.Common -import HLearn.Optimization.Common -import HLearn.Optimization.GradientDescent --- import HLearn.Optimization.NewtonRaphson --- import HLearn.Optimization.QuasiNewton --- import HLearn.Optimization.StochasticGradientDescent -import HLearn.Optimization.LineMinimization.Univariate -import HLearn.Optimization.LineMinimization.Multivariate - -bool2num :: Ring r => Bool -> r -bool2num True = 1 -bool2num False = -1 - -------------------------------------------------------------------------------- --- data types - -data LabelDecision dp = LabelDecision - { ldWeight :: !(Scalar dp) - , ldVector :: !(Attributes dp) - , ldTaylor :: (Taylor (Attributes dp)) - } - deriving (Typeable) - -data LinearClassifier dp = LinearClassifier - { weights :: Map.Map (Label dp) (LabelDecision dp) - , datapoints :: V.Vector dp - , reg :: Scalar dp - } - deriving (Typeable) - -data Taylor dp - = Taylor dp (Matrix (Scalar dp)) - | NoTaylor - deriving (Typeable) - -dptaylor (Taylor dp _) = dp -mattaylor (Taylor _ mat) = mat - -type instance Scalar (Taylor dp) = Scalar dp - -instance - ( NFData (Scalar dp) - , NFData (Label dp) - , NFData (Attributes dp) - ) => NFData (LabelDecision dp) - where - rnf ld = deepseq (ldWeight ld) - $ deepseq (ldVector ld) - $ seq (ldTaylor ld) - $ () - -instance - ( NFData (Scalar dp) - , NFData (Label dp) - , NFData (Attributes dp) - ) => NFData (LinearClassifier dp) - where - rnf lr = seq (Map.map rnf $ weights lr) () - --- instance --- ( Show (Label dp) --- , Show (Scalar dp) --- , Show (Attributes dp) --- , Storable (Scalar dp) --- ) => Show (LinearClassifier dp) --- where --- show lr = show $ Map.map (\ld -> (n,w)) $ weights lr - -------------------------------------------------------------------------------- --- training - --- mappendAverage :: --- ( VG.Vector v t --- , Attributes dp ~ v t --- , Fractional t --- , t ~ Scalar dp --- , Ord (Label dp) --- ) => LinearClassifier dp -> LinearClassifier dp -> LinearClassifier dp -mappendAverage lr1 lr2 = LinearClassifier - { weights = Map.unionWith go (weights lr1) (weights lr2) - , datapoints = V.fromList $ V.toList (datapoints lr1) ++ V.toList (datapoints lr2) - , reg = reg lr1 - } - where - go (LabelDecision n1 w1 _) (LabelDecision n2 w2 _) = LabelDecision (n1+n2) w NoTaylor - where - w = VG.zipWith (\a b -> (n1*a+n2*b)/(n1+n2)) w1 w2 - --- mappendQuadratic :: --- ( VG.Vector v t --- , Attributes dp ~ v t --- , Fractional t --- , t ~ Scalar dp --- , t ~ Scalar t --- , v ~ LA.Vector --- , LA.Field t --- , VectorSpace t --- , Typeable t --- , Show t --- , Ord t --- , Ord (Label dp) --- ) => LinearClassifier dp -> LinearClassifier dp -> LinearClassifier dp -mappendQuadratic lr1 lr2 = LinearClassifier - { weights = Map.unionWith go (weights lr1) (weights lr2) - , datapoints = V.fromList $ V.toList (datapoints lr1) ++ V.toList (datapoints lr2) - , reg = reg lr1 - } - where - go (LabelDecision n1 w1 NoTaylor) (LabelDecision n2 w2 NoTaylor) = LabelDecision (n1+n2) w1 NoTaylor - go (LabelDecision _ _ NoTaylor) a = a - go a (LabelDecision _ _ NoTaylor) = a - go (LabelDecision n1 w1 (Taylor v1 m1)) (LabelDecision n2 w2 (Taylor v2 m2)) - = LabelDecision (n1+n2) w (Taylor v' m') - where - m' = ((n1*.m1) + (n2*.m2))./(n1+n2) - v' = ((n1*.v1) + (n2*.v2))./(n1+n2) - w = reciprocal m' * v' --- reoptimize --- :: (Labeled dp, Typeable (Label dp), Typeable (Scalar dp), --- Typeable dp, Show (Label dp), Show (Scalar dp), Show dp, --- Ord (Label dp), Ord (Scalar dp), Ord dp, --- Attributes dp1 ~ Vector (Scalar dp), --- Attributes dp ~ Vector (Scalar dp), Label dp1 ~ Label dp, --- Tensor 1 (Scalar dp) ~ Scalar dp, --- Scalar (Scalar dp) ~ Scalar dp) => --- (LinearClassifier dp --- -> LinearClassifier dp -> LinearClassifier dp1) --- -> LinearClassifier dp --- -> LinearClassifier dp --- -> LinearClassifier dp --- reoptimize f lr1 lr2 = lrtrain2 (reg lr1) dps' $ Map.map go $ weights $ f lr1 lr2 --- where --- dps' = V.fromList $ V.toList (datapoints lr1) ++ V.toList (datapoints lr2) --- go (_,w,_) = w - --- traceLinearClassifier :: forall dp. Typeable dp => dp -> Report -> [String] --- traceLinearClassifier _ opt = case fromDynamic (dyn opt) :: Maybe (LinearClassifier dp) of --- Nothing -> [] --- Just x -> --- [ (head $ words $ drop 2 $ init $ init $ show $ dyn opt) --- -- ++"; fx1="++showDoubleLong (fx1 x) --- -- ++"; |f'x1|="++showDouble (innerProductNorm $ f'x1 x) --- -- ++"; step="++showDouble (stepSize x) --- -- ++"; step="++showDouble (stepSize x) --- -- ++"; sec="++showDouble ((fromIntegral $ stoptime opt)*1e-12) --- ++"; sec="++showDouble ((fromIntegral $ cpuTimeDiff opt)*1e-12) --- ] - -data MonoidType - = MappendAverage - | MappendTaylor - | MixtureUpperTaylor Rational - | MixtureAveTaylor Rational - | MixtureAveUpper Rational - | MappendUpperBound - | MappendUpperBoundCenter - deriving (Read, Show, Data, Typeable) - -monoidMixRate :: MonoidType -> Rational -monoidMixRate (MixtureUpperTaylor i) = i -monoidMixRate (MixtureAveTaylor i) = i -monoidMixRate (MixtureAveUpper i) = i -monoidMixRate _ = 0 - -trainLinearClassifier :: forall dp vec r container m. - ( Labeled dp --- , Ord (Label dp) - , Logic (Label dp)~Bool - , Eq_ (Label dp) - , Attributes dp ~ vec r - , Scalar dp ~ r - , Normed r - , Ord r - , Module r - , HasScalar dp - , OuterProductSpace (vec r) - , Hilbert (vec r) - , VectorSpace (Outer (vec r)) - , Floating r - , Foldable (container dp) - , Elem (container dp)~dp - , VG.Vector vec r - , P.Ord (Label dp) - , Eq dp - - , HistoryMonad m - , Reportable m (LinearClassifier dp) - , Reportable m (LabelDecision dp) - , Reportable m (vec r) - , Reportable m r - , Reportable m dp - , Reportable m String - , Reportable m (Label dp, LabelDecision dp) - - , Show (vec r) - , Show (Label dp) --- , Outer (vec r) ~ Matrix r - , NFData (vec r) - , Typeable (Label dp) - , Typeable container - , VG.Vector container (vec r) - , VG.Vector container dp --- , container ~ [] - ) => MonoidType - -> Scalar dp - -> C2Function (Attributes dp) -- ^ regularization function - -> (Label dp -> dp -> C2Function (Attributes dp)) -- ^ loss function - -> OptimizationMethod m dp - -> container dp - -> m (LinearClassifier dp) -trainLinearClassifier monoidtype lambda c2reg c2loss optMethod dps - = warmStartLinearClassifier monoidtype lambda c2reg c2loss optMethod dps $ zeroWeights dps - -warmStartLinearClassifier :: forall dp vec r container m. - ( Labeled dp --- , Ord (Label dp) - , Logic (Label dp)~Bool - , Eq_ (Label dp) - , Attributes dp ~ vec r - , Scalar dp ~ r - , Normed r - , Ord r - , Module r - , HasScalar dp - , OuterProductSpace (vec r) - , Hilbert (vec r) - , VectorSpace (Outer (vec r)) - , Floating r - , Foldable (container dp) - , Elem (container dp)~dp - , VG.Vector vec r - , P.Ord (Label dp) - , Eq dp - - , HistoryMonad m - , Reportable m (LinearClassifier dp) - , Reportable m (LabelDecision dp) - , Reportable m (vec r) - , Reportable m r - , Reportable m dp - , Reportable m String - , Reportable m (Label dp, LabelDecision dp) - - , Show (vec r) - , Show (Label dp) --- , Outer (vec r) ~ Matrix r - , NFData (vec r) - , Typeable (Label dp) - , Typeable container - , VG.Vector container (vec r) - , VG.Vector container dp --- , container ~ [] - ) => MonoidType - -> Scalar dp - -> C2Function (Attributes dp) -- ^ regularization function - -> (Label dp -> dp -> C2Function (Attributes dp)) -- ^ loss function - -> OptimizationMethod m dp - -> container dp - -> Map.Map (Label dp) (Attributes dp) - -> m (LinearClassifier dp) -warmStartLinearClassifier monoidtype lambda c2reg c2loss optMethod dps weights0 = trace ("weights0="++show weights0) $ do - weights' <- collectReports $ fmap Map.fromList $ go $ Map.assocs weights0 - report $ LinearClassifier - { weights = weights' - , datapoints = V.fromList $ toList dps - , reg= lambda - } - where - n :: Label dp -> Scalar dp - n l = fromIntegral $ length $ filter (\dp -> getLabel dp ==l) $ toList dps - - -- the weights for the last label are set to zero; - -- this is equivalent to running the optimization procedure, - -- but much cheaper - go ((l,w0):[]) = do - x <- report $ LabelDecision (n l) (VG.replicate (VG.length w0) 0) NoTaylor - return [(l,x)] - - -- calculate the weights for label l - go ((l,w0):xs) = do - w1 <- optMethod dps (\dp -> lambda*.c2reg+c2loss l dp) w0 - - report "next"; - - let ret = (l, LabelDecision (n l) w1 undefined) - deepseq w1 $ report ret - fmap (ret:) $ go xs - -------------------------------------------------------------------------------- - -type C2Function v = v -> (Scalar v, v, Outer v) - -type C2Loss dp = dp -> C2Function (Attributes dp) - -type OptimizationMethod m dp = - ( Reportable m (Attributes dp) - , Reportable m (Scalar (Attributes dp)) - , Reportable m dp - ) => forall container. - ( Foldable (container dp) - , dp ~ Elem (container dp) - , Typeable container - ) => container dp -> C2Loss dp -> Attributes dp -> m (Attributes dp) - -cgd :: - ( Hilbert (Attributes dp) - , BoundedField (Scalar (Attributes dp)) - , Normed (Scalar (Attributes dp)) - , Floating (Scalar (Attributes dp)) - , Ord (Scalar (Attributes dp)) - , HistoryMonad m - , Reportable m (LineBracket (Scalar (Attributes dp))) - , Reportable m (Brent (Scalar (Attributes dp))) - , Reportable m (ConjugateGradientDescent (Attributes dp)) - - , Show (Attributes dp) - , Show (Scalar (Attributes dp)) - ) => OptimizationMethod m dp -cgd dps c2f w0 = do - ret <- conjugateGradientDescent - (\w -> sumOver dps $ \dp -> (c2f dp w) ^._1) - (\w -> sumOver dps $ \dp -> (c2f dp w) ^._2) - w0 - return $ ret^.x1 - --- sgd :: --- ( VectorSpace (Attributes dp) --- , LearningRate hyperparams params (Attributes dp) --- , Normed (Scalar (Attributes dp)) --- , Floating (Scalar (Attributes dp)) --- , Ord (Scalar (Attributes dp)) --- , Labeled dp --- , Typeable params --- ) => [StopCondition (SGD V.Vector params dp (Attributes dp))] --- -> StochasticMethod V.Vector dp (Attributes dp) --- -> hyperparams (Attributes dp) --- -> OptimizationMethod dp --- sgd stop sm lr dps c2f w0 = do --- ret <- stochasticGradientDescent --- sm --- lr --- ( V.fromList $ F.toList dps ) --- (\dp w -> c2f dp w ^. _2) --- w0 --- stop --- return $ ret^.x1 - -------------------- - -l1reg :: forall vec r. - ( VG.Vector vec r - , r ~ Scalar (vec r) - , Field r - , Normed r - , Ord r - , IsScalar r - , OuterProductSpace (vec r) - ) => C2Function (vec r) -l1reg w = - ( VG.foldl1' (+) $ VG.map abs w - , VG.map (\i -> if i>=0 then 1 else -1) w - , let z = VG.replicate (VG.length w) 0 :: vec r in z >< z - ) - --- l2reg :: (Storable r, IsScalar r) => C2Function (Vector r) -l2reg w = - ( VG.sum $ VG.map (**2) w - , VG.map (*2) w - , 2 - ) - --- elasticNet :: (Storable r, Ord r, IsScalar r) => C2Function (Vector r) -elasticNet w = - ( l1^._1 + eta*.l2^._1 - , l1^._2 + eta*.l2^._2 - , l1^._3 + eta*.l2^._3 - ) - where - eta = 0.85 - l1 = l1reg w - l2 = l2reg w - -------------------- - --- logloss :: --- ( Hilbert (Attributes dp) --- , Ord (Scalar dp) --- , Ord (Scalar (Attributes dp)) --- , Labeled dp --- , Eq (Label dp) --- ) => Label dp -> dp -> C2Function (Attributes dp) -logloss label dp w = - ( logSumOfExp2 zero $ -y dp * w <> x - , (-y dp*(1-invlogit (y dp * w <> x))) *. x - , x >< x ./ (1+exp(-y dp* w<>x)) - ) - where - x = getAttributes dp - y dp = bool2num $ getLabel dp==label - --- hingeloss :: --- ( Attributes dp ~ LA.Vector r --- , LA.Field r --- , IsScalar r --- , Ord r --- , VectorSpace r --- , Labeled dp --- , Eq (Label dp) --- , ValidTensor (Attributes dp) --- ) => Label dp -> dp -> C2Function (Attributes dp) -hingeloss label dp w = - ( max 0 $ 1 - y dp * (getAttributes dp) <> w - , if (getAttributes dp) <> w > 1 - then VG.map (const 0) w - else (-y dp) *. getAttributes dp - , (VG.map (*0) w) >< (VG.map (*0) w) - - ) - where - y dp = bool2num $ getLabel dp==label - -------------------------------------------------------------------------------- - -invlogit x = 1 / (1 + exp (-x)) - --- | calculates log . sum . map exp in a numerically stable way -logSumOfExp xs = m + log (sum [ exp $ x-m | x <- xs ] ) - where - m = maximum xs - --- | calculates log $ exp x1 + exp x2 in a numerically stable way -logSumOfExp2 x1 x2 = m + log ( exp (x1-m) + exp (x2-m) ) - where - m = max x1 x2 - --- sumOver :: (F.Foldable container, Semigroup r) => container x -> (x -> r) -> r -sumOver :: (x~Elem (container x),Foldable (container x), Monoid r) => container x -> (x -> r) -> r -sumOver xs f = foldl' (\a b -> a + f b) zero xs - - --- zeroWeights :: forall dp vec r container. --- ( Ord dp --- , Ord (Label dp) --- , Labeled dp --- , Attributes dp ~ vec r --- , VG.Vector vec r --- , Floating r --- , Monoid r --- , r ~ Scalar dp --- , Ord r --- , r ~ Scalar (vec r) --- , InnerProduct (vec r) --- , Show (Label dp) --- , Show (vec r) --- , Show r --- , F.Foldable container --- ) => container dp -> Map.Map (Label dp) (Attributes dp) -zeroWeights dps = Map.fromList [ (label,VG.replicate dim zero) | label <- labels ] - where - labels = map getLabel $ toList dps - dim = VG.length $ getAttributes $ head $ toList dps - --- centroidtrain :: forall dp vec r container. --- ( Ord dp --- , Ord (Label dp) --- , Labeled dp --- , Attributes dp ~ vec r --- , VG.Vector vec r --- , Floating r --- , Monoid r --- , r ~ Scalar dp --- , Ord r --- , r ~ Scalar (vec r) --- , InnerProduct (vec r) --- , Show (Label dp) --- , Show r --- , Show (vec r) --- , F.Foldable container --- ) => container dp -> LinearClassifier dp --- centroidtrain dps = LinearClassifier --- { weights = Map.fromList $ go $ Map.assocs $ zeroWeights dps --- , datapoints = V.fromList $ F.toList dps --- , reg =0 --- } --- where --- n l = fromIntegral $ length $ filter (\dp -> getLabel dp == l) $ F.toList dps --- --- go [] = [] --- go ((l,w0):xs) = (l,LabelDecision (n l) w' NoTaylor):go xs --- where --- w' = (foldl1 (+) $ map getAttributes $ filter (\dp -> getLabel dp==l) $ F.toList dps) /. n l - --- nbtrain :: forall dp vec r container. --- ( Ord dp --- , Ord (Label dp) --- , Labeled dp --- , Attributes dp ~ vec r --- , VG.Vector vec r --- , Floating r --- , Monoid r --- , r ~ Scalar dp --- , Ord r --- , r ~ Scalar (vec r) --- , InnerProduct (vec r) --- , Show (Label dp) --- , Show r --- , Show (vec r) --- , F.Foldable container --- ) => container dp -> LinearClassifier dp --- nbtrain dps = LinearClassifier --- { weights = Map.fromList $ go $ Map.assocs $ zeroWeights dps --- , datapoints = V.fromList $ F.toList dps --- , reg =0 --- } --- where --- n l = fromIntegral $ length $ filter (\dp -> getLabel dp ==l) $ F.toList dps --- --- go [] = [] --- go ((l,w0):xs) = (l, LabelDecision (n l) w' NoTaylor):go xs --- where --- w' = (VG.convert $ VG.map (\(n,t) -> mean n/(variance t+1e-6)) normV) --- VG/./ [(0,bias)] --- --- bias = -sumOver normV (\(n,t) -> (mean n*mean n)/(2*(variance t+1e-6))) --- --- normV = VG.zip (fromJust $ Map.lookup l gaussianMap) gaussianTot --- --- gaussianTot = foldl1 (VG.zipWith (+)) $ Map.elems gaussianMap --- --- gaussianMap = Map.fromListWith (VG.zipWith (+)) --- [ ( getLabel dp --- , V.map (train1dp :: r -> Normal r r) $ VG.convert $ getAttributes dp --- ) --- | dp <- F.toList dps --- ] - -------------------------------------------------------------------------------- --- classification - --- instance Monoid (LinearClassifier dp) where --- mappend = undefined --- zero = undefined - -type instance Scalar (LinearClassifier dp) = Scalar dp - --- instance HomTrainer (LinearClassifier dp) where --- type Datapoint (LinearClassifier dp) = dp --- train = undefined - -instance --- ( Labeled dp --- , Scalar (Attributes dp) ~ Scalar dp --- , Hilbert (Attributes dp) --- , Floating (Scalar dp) --- , Ord (Scalar dp) --- , Attributes dp ~ vec (Scalar dp) --- , VG.Vector vec (Scalar dp) --- , Show (Scalar dp) --- , Show (Label dp) - ( Labeled dp - , P.Ord (Scalar (Datapoint (LinearClassifier dp))) - , Hilbert (Attributes (Datapoint (LinearClassifier dp))) - ) => Classifier (LinearClassifier dp) - where - classify m attr - = (fst::(a,b) -> a) - $ argmax (\(l,s) -> s) - $ Map.assocs - $ Map.map (\ld -> ldVector ld <> attr) - $ weights m - -type instance Datapoint (LinearClassifier dp) = dp - -------------------------------------------------------------------------------- --- test - diff --git a/src/HLearn/Models/Classifiers/NBayes.hs b/src/HLearn/Models/Classifiers/NBayes.hs deleted file mode 100644 index 4698727..0000000 --- a/src/HLearn/Models/Classifiers/NBayes.hs +++ /dev/null @@ -1,167 +0,0 @@ -module HLearn.Models.Classifiers.NBayes - ( NBayes (..) - , NaiveBayes - , NBayesParams (..), defNBayesParams --- , file2nbayes, nbayes2file - , getDist, labelProb - ) - where - -import Control.DeepSeq -import Control.Monad.ST.Strict -import Control.Monad.Primitive -import Data.List -import Data.List.Extras -import Data.STRef --- import Data.Vector.Binary -import qualified Data.Map as Map -import qualified Data.Vector as V -import qualified Data.Vector.Fusion.Stream as Stream -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Generic.Mutable as VGM -import qualified Data.Vector.Mutable as VM -import qualified Data.ByteString as BS -import Debug.Trace --- import qualified Numeric.Algebra as Alg -import System.IO -import Test.QuickCheck - -import HLearn.Algebra -import HLearn.DataContainers -import HLearn.Models.Classification -import HLearn.Models.Distributions -import HLearn.Models.DistributionContainer - -instance NFData a => NFData (V.Vector a) where - rnf v = V.foldl' (\x y -> y `deepseq` x) () v - -------------------------------------------------------------------------------- --- NBayesParams - -data NBayesParams datatype = NBayesParams - deriving (Read,Show,Eq) - -instance (Label label) => Model (ClassificationParams label (NBayesParams label)) (NaiveBayes label) where - getparams (SGJust model) = ClassificationParams NBayesParams (dataDesc model) - -instance NFData (NBayesParams datatype) where - rnf params = () - -defNBayesParams = NBayesParams - -------------------------------------------------------------------------------- --- NBayes - -type NaiveBayes label = RegSG2Group (NBayes label) - -data NBayes label = NBayes - { dataDesc :: !(DataDesc label) - , labelDist :: !(Categorical label Double) - , attrDist :: !(V.Vector (V.Vector DistContainer)) -- ^ The inner vector corresponds to attributes and the outer vector labels - } - deriving ({-Read,-}Show) - -getDist :: NBayes Int -> Int -> Int -> DistContainer -getDist nb attrI label = (attrDist nb) V.! label V.! attrI - --- labelProb :: NBayes Int -> Int -> LogFloat -labelProb :: NBayes Int -> Int -> Double -labelProb = pdf . labelDist - --- instance (Label label) => Model (NBayes label) label where --- datadesc = dataDesc - -instance (NFData label) => NFData (NBayes label) where - rnf nb = seq (rnf $ attrDist nb) $ seq (rnf $ dataDesc nb) (rnf $ labelDist nb) - -------------------------------------------------------------------------------- --- Algebra - -instance (Label label) => Semigroup (NBayes label) where - (<>) a b = - if (dataDesc a)/=(dataDesc b) - then error $ "mappend.NBayes: cannot combine nbayes with different sizes! lhs="++(show $ dataDesc a)++"; rhs="++(show $ dataDesc b) - else NBayes - { dataDesc = dataDesc a - , labelDist = (labelDist a) <> (labelDist b) - , attrDist = V.zipWith (V.zipWith mappend) (attrDist a) (attrDist b) - } - -instance (Label label) => RegularSemigroup (NBayes label) where - inverse nb = nb - { labelDist = inverse $ labelDist nb - , attrDist = V.map (V.map inverse) $ attrDist nb - } - -------------------------------------------------------------------------------- --- Training - --- instance (Label label) => HomTrainer (NBayesParams,DataDesc label) (LDPS label) (NaiveBayes label) where -instance HomTrainer (ClassificationParams Int (NBayesParams Int)) (LDPS Int) (NaiveBayes Int) where - train1dp' (ClassificationParams NBayesParams desc) (label,dp) = SGJust $ NBayes - { dataDesc = desc - , labelDist = train1dp label - , attrDist = emptyvecs V.// [(label,newLabelVec)] - } - where - emptyvecs = V.fromList [V.fromList [mempty | y<-[1..numAttr desc]] | x<-[1..numLabels desc]] - newLabelVec = V.accum add1dp (emptyvecs V.! label) dp - -emptyNBayes :: (Ord label) => DataDesc label -> NBayes label -emptyNBayes desc = NBayes - { dataDesc = desc - , labelDist = mempty - , attrDist = V.fromList [V.fromList [mempty | y<-[1..numAttr desc]] | x<-[1..numLabels desc]] - } - -{-instance (OnlineTrainer NBayesParams (NBayes label) datatype label) => - BatchTrainer NBayesParams (NBayes label) datatype label - where - - trainBatch = trainOnline - -instance (Label label) => EmptyTrainer NBayesParams (NBayes label) label where - emptyModel desc NBayesParams = NBayes - { dataDesc = desc - , labelDist = mempty - , attrDist = V.fromList [V.fromList [mempty | y<-[1..numAttr desc]] | x<-[1..numLabels desc]] - } - -instance OnlineTrainer NBayesParams (NBayes Int) DPS Int where --- add1dp desc NBayesUndefined (label,dp) = add1dp desc (emptyNBayes desc) (label,dp) - add1dp desc modelparams nb (label,dp) = return $ - nb { labelDist = add1sample (labelDist nb) label - , attrDist = (attrDist nb) V.// [(label,newLabelVec)] - } - where - newLabelVec = V.accum add1sample (attrDist nb V.! label) dp-} - - -------------------------------------------------------------------------------- --- Classification - --- instance Classifier (NaiveBayes Int) DPS Int where --- -- classify model dp = fst $ argmaxBy compare snd $ probabilityClassify model dp --- classify (SGJust model) dp = mostLikely $ probabilityClassify model dp - -instance ProbabilityClassifier (NaiveBayes Int) DPS Int Double where - probabilityClassify (SGJust nb) dp = probabilityClassify nb dp - -instance ProbabilityClassifier (NBayes Int) DPS Int Double where - probabilityClassify nb dp = unweight $ train {-CategoricalParams -}answer - {-normedAnswer-} - where - labelProbGivenDp label = (labelProbGivenNothing label)*(dpProbGivenLabel label) - labelProbGivenNothing label = pdf (labelDist nb) label - dpProbGivenLabel label = foldl (*) ({-logFloat-} (1::Double)) (attrProbL label) - attrProbL label = [ pdf (attrDist nb V.! label V.! attrIndex) di | (attrIndex,di) <- dp] - - answer = [ (labelProbGivenDp label, label) | label <- [0..(numLabels $ dataDesc nb)-1]] - normedAnswer = zip [0..] $ normalizeL [labelProbGivenDp label | label <- [0..(numLabels $ dataDesc nb)-1]] - - -normalizeL :: (Fractional a) => [a] -> [a] -normalizeL xs = map (/s) xs - where - s = sum xs - \ No newline at end of file diff --git a/src/HLearn/Models/Classifiers/NaiveNN.hs b/src/HLearn/Models/Classifiers/NaiveNN.hs deleted file mode 100644 index 609e474..0000000 --- a/src/HLearn/Models/Classifiers/NaiveNN.hs +++ /dev/null @@ -1,87 +0,0 @@ -{-# LANGUAGE DataKinds #-} - -module HLearn.Models.Classifiers.NaiveNN - where - -import Control.Applicative -import qualified Data.Foldable as F -import Data.List -import Data.Proxy - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common - -------------------------------------------------------------------------------- --- data structures - -newtype NaiveNN (k::Nat) container dp = NaiveNN - { getcontainer :: container dp } --- deriving (Read,Show,Eq,Ord,Semigroup,Monoid,RegularSemigroup) - -deriving instance (Show (container dp)) => Show (NaiveNN k container dp) - -------------------------------------------------------------------------------- --- algebra - -instance (Monoid (container dp)) => Monoid (NaiveNN k container dp) where - mempty = NaiveNN mempty - mappend nn1 nn2 = NaiveNN $ getcontainer nn1 `mappend` getcontainer nn2 - -------------------------------------------------------------------------------- --- model - -instance - ( Applicative container - , Monoid (container ldp) - ) => HomTrainer (NaiveNN k container ldp) - where - type Datapoint (NaiveNN k container ldp) = ldp - train1dp ldp = NaiveNN $ pure ldp - -------------------------------------------------------------------------------- --- classification - -instance (Probabilistic (NaiveNN k container ldp)) where - type Probability (NaiveNN k container ldp) = Double - -neighborList :: - ( F.Foldable container - , MetricSpace ldp - , Ord (Scalar ldp) - ) => ldp -> NaiveNN k container ldp -> [ldp] -neighborList dp (NaiveNN dps) = sortBy f $ F.toList dps - where --- f (_,dp1) (_,dp2) = compare (distance dp dp1) (distance dp dp2) - f dp1 dp2 = compare (distance dp dp1) (distance dp dp2) - - -instance - ( Ord label - , ldp ~ MaybeLabeled maybelabel attr - , Labeled ldp - , label ~ Label ldp - , F.Foldable container - , MetricSpace ldp - , Ord (Scalar ldp) - , KnownNat k - ) => ProbabilityClassifier (NaiveNN k container ldp) - where - type ResultDistribution (NaiveNN k container ldp) = Categorical (Scalar (Attributes ldp)) (Label ldp) - - probabilityClassify nn dp = train (map getLabel $ take k $ neighborList (noLabel dp) nn) - where - k = fromIntegral $ natVal (Proxy::Proxy k) - -instance - ( ProbabilityClassifier (NaiveNN k container ldp) - , label ~ Label ldp - , dp ~ Attributes ldp - , Ord (Scalar dp) - , MetricSpace dp - , F.Foldable container - , Ord label - ) => Classifier (NaiveNN k container ldp) - where - - classify nn dp = mean $ probabilityClassify nn dp diff --git a/src/HLearn/Models/Classifiers/NearestNeighbor.hs b/src/HLearn/Models/Classifiers/NearestNeighbor.hs deleted file mode 100644 index ee48002..0000000 --- a/src/HLearn/Models/Classifiers/NearestNeighbor.hs +++ /dev/null @@ -1,119 +0,0 @@ -{-# LANGUAGE DataKinds #-} - -module HLearn.Models.Classifiers.NearestNeighbor - ( KNearestNeighbor (..) - ) - where - -import Control.Applicative -import qualified Data.Foldable as F -import Data.List -import Data.Maybe -import Debug.Trace - -import HLearn.Algebra -import HLearn.Algebra.LinearAlgebra -import HLearn.DataStructures.SpaceTree -import HLearn.DataStructures.SpaceTree.Algorithms.NearestNeighbor -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common - -import HLearn.DataStructures.CoverTree -import HLearn.Metrics.Lebesgue -import qualified Data.Vector as V -import qualified Data.Vector.Unboxed as VU - -------------------------------------------------------------------------------- --- data structures - -newtype KNearestNeighbor tree (k::Nat) (dp:: *) = KNearestNeighbor - { gettree :: tree dp - } - deriving (Read,Show,Eq,Ord,Monoid,Group,Abelian) - -------------------------------------------------------------------------------- --- algebra - -type instance Scalar (KNearestNeighbor tree k dp) = Scalar (tree dp) - -------------------------------------------------------------------------------- --- model - -instance - ( HomTrainer (tree dp) - , Datapoint (tree dp) ~ dp - ) => HomTrainer (KNearestNeighbor tree k dp) - where - type Datapoint (KNearestNeighbor tree k dp) = dp - - train1dp dp = KNearestNeighbor $ train1dp dp - train dps = KNearestNeighbor $ train dps - add1dp m dp = KNearestNeighbor $ add1dp (gettree m) dp - addBatch m dps = KNearestNeighbor $ addBatch (gettree m) dps - -------------------------------------------------------------------------------- --- classification - -instance Probabilistic (KNearestNeighbor tree k dp) where - type Probability (KNearestNeighbor tree k dp) = Scalar (KNearestNeighbor tree k dp) - -{- -instance - ( dp ~ MaybeLabeled label attr - , IsScalar (Scalar dp) - , SpaceTree tree dp - , KnownNat k - , Eq dp - , Ord (Label dp) - , Scalar (tree dp) ~ Scalar dp - , Floating (Scalar dp) - , CanError (Scalar dp) - , Show attr - , Show (Scalar dp) - , Show label - ) => ProbabilityClassifier (KNearestNeighbor tree k dp) - where - type ResultDistribution (KNearestNeighbor tree k dp) = - Categorical (Probability (KNearestNeighbor tree k dp)) (Label dp) - - probabilityClassify m dp = --trace ("length res="++show (length $ getknnL res)++"; dp="++show dp++";\nres="++show res++"\n\n") $ - train . map (getLabel . neighbor) $ getknnL res --- probabilityClassify m dp = reduce . map (\dp -> (1+1/neighborDistance dp) .* train1dp (getLabel $ neighbor dp)) $ getknnL res - where - res = findNeighborList (gettree m) (noLabel dp) :: NeighborList k dp - -instance - ( ProbabilityClassifier (KNearestNeighbor tree k dp) - , Ord (Scalar (tree dp)) - , Num (Scalar (tree dp)) - , Ord (Label dp) - ) => Classifier (KNearestNeighbor tree k dp) - where - classify model dp = {-trace "KNN-classify" $-} mean $ probabilityClassify model dp - -} - -------------------------------------------------------------------------------- --- test - -type DP = MaybeLabeled Char (L2 VU.Vector Double) - -zs = - [ MaybeLabeled (Just 'x') $ L2 $ VU.fromList [2,3] - , MaybeLabeled (Just 'x') $ L2 $ VU.fromList [2,5] - , MaybeLabeled (Just 'x') $ L2 $ VU.fromList [3,5] - , MaybeLabeled (Just 'y') $ L2 $ VU.fromList [3,4] - , MaybeLabeled (Just 'y') $ L2 $ VU.fromList [2,-2] - , MaybeLabeled (Just 'y') $ L2 $ VU.fromList [2,-1] - ] - :: [DP] - -q1 = L2 $ VU.fromList [2,1.1] :: L2 VU.Vector Double -q2 = L2 $ VU.fromList [5,5] :: L2 VU.Vector Double -q3 = L2 $ VU.fromList [2,3] :: L2 VU.Vector Double - --- ct i = train $ take i zs :: CoverTree DP --- ct' i = train $ take i $ map getAttributes zs :: CoverTree (Attributes DP) --- ct = train zs :: CoverTree DP --- ct' = train $ map getAttributes zs :: CoverTree (Attributes DP) - --- m = train zs :: KNearestNeighbor (AddUnit (CoverTree' (2/1) V.Vector) ()) 1 DP diff --git a/src/HLearn/Models/Classifiers/Perceptron.hs b/src/HLearn/Models/Classifiers/Perceptron.hs deleted file mode 100644 index a168c2a..0000000 --- a/src/HLearn/Models/Classifiers/Perceptron.hs +++ /dev/null @@ -1,74 +0,0 @@ -module HLearn.Models.Classifiers.Perceptron - where - -import qualified Data.Map as Map -import qualified Data.Vector.Unboxed as VU - -import HLearn.Algebra -import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common -import HLearn.Models.Classifiers.Centroid -import HLearn.Models.Classifiers.NaiveNN - -------------------------------------------------------------------------------- --- data structures - -data Perceptron label dp = Perceptron - { centroids :: Map.Map label (Centroid dp) - } --- deriving (Read,Show,Eq,Ord) - -deriving instance (Show (Centroid dp), Show label) => Show (Perceptron label dp) - -------------------------------------------------------------------------------- --- algebra - -instance (Ord label, Monoid (Centroid dp)) => Monoid (Perceptron label dp) where - mempty = Perceptron mempty - p1 `mappend` p2 = Perceptron - { centroids = Map.unionWith (<>) (centroids p1) (centroids p2) - } - -------------------------------------------------------------------------------- --- model - -instance - ( Monoid dp - , Num (Scalar dp) - , Ord label - ) => HomTrainer (Perceptron label dp) - where - type Datapoint (Perceptron label dp) = (label,dp) - - train1dp (label,dp) = Perceptron $ Map.singleton label $ train1dp dp - -------------------------------------------------------------------------------- --- classification - -instance Probabilistic (Perceptron label dp) where - type Probability (Perceptron label dp) = Scalar dp - --- instance --- ( Ord label --- , Ord (Scalar dp) --- , MetricSpace (Centroid dp) --- , Monoid dp --- , HasScalar dp --- , label ~ Scalar dp --- ) => ProbabilityClassifier (Perceptron label dp) --- where --- type ResultDistribution (Perceptron label dp) = (Categorical (Scalar dp) label) --- --- probabilityClassify model dp = probabilityClassify nn (train1dp (dp) :: Centroid dp) --- where --- nn = NaiveNN $ Map.toList $ centroids model --- --- instance --- ( ProbabilityClassifier (Perceptron label dp) --- , Ord dp --- , Ord (Scalar dp) --- , Ord label --- , Num (Scalar dp) --- ) => Classifier (Perceptron label dp) --- where --- classify model dp = mean $ probabilityClassify model dp diff --git a/src/HLearn/Models/Distributions.hs b/src/HLearn/Models/Distributions.hs index d156a37..59759fe 100644 --- a/src/HLearn/Models/Distributions.hs +++ b/src/HLearn/Models/Distributions.hs @@ -41,6 +41,8 @@ data Moments v = Moments , m2 :: !(v> Group (Moments v) where instance Hilbert v => Module (Moments v) where (Moments a b c).*r = Moments (r*a) (b.*r) (c.*r) +instance Hilbert v => FreeModule (Moments v) where -- TODO: what is the probabilistic interpretation of this? (Moments a1 b1 c1).*.(Moments a2 b2 c2) = Moments (a1*a2) (b1.*.b2) (c1.*.c2) @@ -89,20 +92,34 @@ instance Hilbert v => Constructible (Moments v) where -- | FIXME: I haven't properly tested this code newtype Normal v = Normal (Moments v) --- deriveHierarchy ''Normal [ ''Group , ''Constructible ] --- --- instance (FiniteModule v, Hilbert v) => Distribution (Normal v) where --- --- mean (Normal (Moments m0 m1 m2)) = m1 ./ m0 --- --- pdf (Normal (Moments m0 m1 m2)) v --- = (2*pi*size sigma)**(-dim v/2)*exp((-1/2)*(v' `vXm` reciprocal sigma)<>v') --- where --- v' = v - mu --- --- mu = m1 ./ m0 --- sigma = 1 + m2 ./ m0 - mu> Semigroup (Normal v) where + (Normal n1)+(Normal n2)=Normal $ n1+n2 + +instance Hilbert v => Monoid (Normal v) where + zero = Normal zero + +train1Normal :: Hilbert v => v -> Normal v +train1Normal v = Normal $ singleton v + +instance (FiniteModule v, Hilbert v) => Distribution (Normal v) where + + mean (Normal (Moments m0 m1 m2)) = m1 ./ m0 + + pdf (Normal (Moments m0 m1 m2)) v + = (2*pi*size sigma)**(-fromIntegral (dim v)/2)*exp((-1/2)*(v' `vXm` reciprocal sigma)<>v') + where + v' = v - mu + + mu = m1 ./ m0 + sigma = 1 + m2 ./ m0 - mu> Normal v -> v> NFData (MarkovChain order datatype prob) where - rnf mc = deepseq (transitions mc) - $ deepseq (startchain mc) - $ rnf (endchain mc) - -instance NFData LogFloat where - rnf lf = seq lf () - -order :: forall n datatype prob. (SingI n) => MarkovChain n datatype prob -> Int -order _ = fromInteger $ fromSing (sing :: Sing n) - --- newtype ChainGang (order::Nat) datatype prob = ChainGang (Categorical [datatype] prob) --- deriving (Show,Read,Eq,Ord,Monoid,Group) - -------------------------------------------------------------------------------- --- Algebra - -instance - ( SingI order - , Ord datatype - , Ord prob - , Num prob - ) => Monoid (MarkovChain order datatype prob) - where - mempty = MarkovChain - { transitions = V.replicate (order (undefined::MarkovChain order datatype prob)) mempty - , startchain = mempty - , endchain = mempty - } - mc1 `mappend` mc2 = MarkovChain - { transitions = V.zipWith (<>) transitionVec (V.zipWith (<>) (transitions mc1) (transitions mc2)) - , startchain = (startchain mc1)++(take (order' - (length $ startchain mc1)) $ startchain mc2) - , endchain = (takeLast (order' - (length $ endchain mc2)) $ endchain mc1)++(endchain mc2) - } - where - order' = order mc1 - transitionVec = V.fromList $ [mempty] ++ (map train1dp newTransitions) ++ (replicate (order mc1 - length newTransitions) mempty) - newTransitions = reverse . transL $ endchain mc1 - transL [] = [] - transL startL = - [ startL ++ end - | end <- take (order' - length startL) $ tail $ inits $ startchain mc2 - ] ++ (transL $ tail startL) - -takeLast :: Int -> [a] -> [a] -takeLast n xs = drop ((length xs)-n) xs - -------------------------------------------------------------------------------- --- training - -instance (SingI order, Ord datatype, Num prob) => HomTrainer (MarkovChain order datatype prob) where - type Datapoint (MarkovChain order datatype prob) = datatype - - train1dp dp = MarkovChain - { transitions = V.fromList $ [train1dp [dp]] ++ (replicate (fromIntegral $ fromSing (sing :: Sing order)) mempty) - , startchain = [dp] - , endchain = [dp] - } - ---------------------------------------- - -{-instance (SingI order,Ord datatype, Num prob) =>HomTrainer (ChainGang order datatype prob) where - type Datapoint (ChainGang order datatype prob) = [datatype] - - train1dp dp = ChainGang $ transition $ (train dp :: MarkovChain order datatype prob)-} - -------------------------------------------------------------------------------- --- Markov - -transitionProbability :: (Ord datatype, Ord prob, Fractional prob) => MarkovChain order datatype prob -> [datatype] -> prob -transitionProbability mc [] = 1 -transitionProbability mc dp = pdf (transitions mc V.! (length dp-1)) dp - -data TransitionProbabilities = TransitionProbabilities - -instance - ( SingI order - , Ord datatype - , Fractional prob - , Ord prob - ) => Function TransitionProbabilities (MarkovChain order datatype prob,[datatype]) [prob] - where - function _ (mc,dpL) = {-trace (show $ drop 1 $ reverse $ tails $ take order dpL) $-} prefix ++ remainder - where - order = fromIntegral $ fromSing (sing :: Sing order) - - prefix = map (transitionProbability mc) $ drop 1 $ reverse $ tails $ take order dpL - - remainder = go $ dpL - go xs = if length x < order - then [] - else (transitionProbability mc x):(go $ tail xs) - - where - x = take order xs - -data LogProbability = LogProbability -instance - ( SingI order - , Ord datatype - , Floating prob - , Ord prob - ) => Function LogProbability (MarkovChain order datatype prob,[datatype]) prob - where - function _ domain = sum $ map log $ function TransitionProbabilities domain - -data GeometricProbability = GeometricProbability -instance - ( SingI order - , Ord datatype - , Floating prob - , Ord prob - ) => Function GeometricProbability (MarkovChain order datatype prob,[datatype]) prob - where - function _ domain = exp $ (sum $ map log probL) / (fromIntegral $ length probL) - where - probL = function TransitionProbabilities domain - --- class (Probabilistic m) => DatasetModel m where --- type Dataset m --- datasetProb :: m -> Dataset m -> Probability m --- normProb :: m -> Dataset m -> Probability m --- --- instance Probabilistic (MarkovChain order datatype prob) where --- type Probability (MarkovChain order datatype prob) = prob --- --- instance --- ( SingI order --- , Ord datatype --- , Floating prob --- , Ord prob --- ) => DatasetModel (MarkovChain order datatype prob) --- where --- type Dataset (MarkovChain order datatype prob) = [datatype] --- --- datasetProb mc dp = prefixProbability*remainderProbability --- where --- order = fromIntegral $ fromSing (sing :: Sing order) --- --- prefixProbability = foldr (\a b -> if a>0 --- then a*b --- else b) 1 $ map (pdf $ transition mc) $ tails $ take order dp --- --- remainderProbability = go $ tail dp --- --- go xs = if length x < order --- then 1 --- else (pdf (transition mc) x)*(go $ tail xs) --- --- where --- x = take order xs --- --- normProb mc dp = (datasetProb mc dp)**(1/(fromIntegral $ length dp)) --- -- normProb mc dp = (datasetProb mc dp)/(0.5^(length dp)) --- --- -- pdf (ChainGang $ transition mc) dp --- --- --------------------------------------- --- --- instance Probabilistic (ChainGang order datatype prob) where --- type Probability (ChainGang order datatype prob) = prob --- --- -- instance (SingI order, Ord datatype, Num prob) => PDF (ChainGang order datatype prob) where --- --- --- -- instance (SingI order, Ord datatype, Num prob) => PDF (ChainGang order datatype prob) where --- -- pdf (ChainGang cat) (x:xs) = 0 --- -- where --- -- go orderlen rest = --- --- -- class (Sampler mc) => Probability mc where --- -- type Probability mc --- -- prob :: mc -> [Sample mc] -> Probability mc --- -- --- -- instance Probability (ChainGang order sampletype prob) where --- -- type Probability (ChainGang order sampletype prob) = prob --- ---------------------------------------- - -class Sampler t where - type Sample t - sampleRight :: (RandomGen g) => t -> [Sample t] -> Rand g (Sample t) - - replicateSamples :: (RandomGen g) => Int -> t -> [Sample t] -> Rand g [Sample t] - replicateSamples 0 mc dpL = return [] - replicateSamples n mc dpL = do - x <- sampleRight mc dpL - xs <- replicateSamples (n-1) mc (dpL++[x]) - return $ x:xs - -instance - ( SingI order - , Ord datatype, Show datatype - , Random prob, Num prob, Ord prob, Show prob - ) => Sampler (MarkovChain order datatype prob) - where - type Sample (MarkovChain order datatype prob) = datatype - sampleRight mc dpL = do - index <- getRandomR (1,totalcount) - return $ go index dist - where - go count ((dp,n):xs) - | count <= n = dp - | otherwise = go (count-n) xs - go count list = error $ "MarkovChain.sampleRight: count="++show count++"; list="++show list++"; matchingTransitions="++show matchingTransitions - - totalcount = sum $ map snd dist - dist = map (\(str,n)-> (last str,n)) matchingTransitions - matchingTransitions = filter (\(str,n) -> take (length dp) str == dp) $ dist2list $ transitions mc V.! (length dp) - dp = takeLast (order mc-1) dpL - ---------------------------------------- - --- instance --- ( SingI order --- , Ord datatype, Show datatype --- , Random prob, Num prob, Ord prob, Show prob --- ) => Sampler (ChainGang order datatype prob) --- where --- type Sample (ChainGang order datatype prob) = datatype --- --- sampleRight (ChainGang dist) dpL = sampleRight (MarkovChain dist [] [] :: MarkovChain order datatype prob) dpL - - -------------------------------------------------------------------------------- --- helpers - --- instance Random LogFloat where --- randomR (lo,hi) g = (logFloat a,g') --- where --- (a,g') = randomR (fromLogFloat lo :: Double,fromLogFloat hi) g --- random g = randomR (0::LogFloat,1) g - -------------------------------------------------------------------------------- --- testing - -x=train "ATTATTATATATGCGCATGCATGCT" :: MarkovChain 3 Char Double - --- y = train --- [ "AAAAAAAAAAAAAAAAAA" --- , "CCCCCCCCCCCCCCCCCC" --- , "TGTGTGTGTGTGTGGGTG" --- ] --- :: ChainGang 2 Char Double --- diff --git a/src/HLearn/Models/Regression.hs b/src/HLearn/Models/Regression.hs deleted file mode 100644 index 66afd27..0000000 --- a/src/HLearn/Models/Regression.hs +++ /dev/null @@ -1,267 +0,0 @@ -{-# LANGUAGE DataKinds,PolyKinds,QuasiQuotes,RankNTypes,MultiParamTypeClasses,FlexibleContexts,UndecidableInstances,TemplateHaskell #-} - -module HLearn.Models.Regression - where - -import Data.List -import qualified Data.Foldable as F -import qualified Data.Semigroup as SG -import Numeric - -import Foreign.Storable -import Numeric.LinearAlgebra hiding ((<>)) -import qualified Numeric.LinearAlgebra as LA - -import HLearn.Algebra -import HLearn.Models.Classifiers.Common hiding (Regression (..)) -import HLearn.Models.Regression.Parsing - -import HLearn.Algebra.Types.Reflection -import Data.Constraint -import Data.Reflection -import qualified Language.Haskell.TH as TH -import Debug.Trace - -------------------------------------------------------------------------------- --- data types - -data Method = Linear | Ridge -data instance Sing (s::Method) = SMethod Method -instance SingI 'Linear where sing = SMethod Linear -instance SingI 'Ridge where sing = SMethod Ridge -instance SingE (Kind::Method) Method where - fromSing (SMethod r) = r - ---------------------------------------- - -data Regression (method::Maybe Method) (expr:: Maybe [TermT]) dp = Regression - { beta :: Matrix (Ring dp) - , rawM :: !(Matrix (Ring dp)) - , rawV :: !(Vector (Ring dp)) - , _numdp :: !(Ring dp) - , modelparams :: ModelParams (Regression method expr dp) - } - --- mkParams ''Regression - -class Param_expr m where param_expr :: m -> [Term] - -instance ReifiableConstraint Param_expr where - data Def Param_expr m = Def_Param_expr { param_expr_ :: m -> [Term] } - reifiedIns = Sub Dict - -instance Reifies s (Def Param_expr a) => Param_expr (ConstraintLift Param_expr a s) where - param_expr a = param_expr_ (reflect a) (lower a) - -instance SingI expr => Param_expr (Regression method (Just expr) dp) where - param_expr _ = fromSing (sing :: Sing expr) - -instance SetParam Param_expr (Regression method Nothing dp) where - data DefParam Param_expr (Regression method Nothing dp) = - SetParam_expr { unSetParam_expr :: [Term] } --Def Param_expr (Regression method Nothing dp) } - setParam p a = using (Def_Param_expr $ \x -> unSetParam_expr p) a - --- setParam' :: Def p a -> ((p -setParam' :: - ( SetParam p m - , p ~ Param_expr - , m ~ Regression method Nothing dp - ) => DefParam p m -> ((p m) => m) -> (m -> a) -> a --- setParam' :: DefParam Param_expr (Regression method Nothing dp) -> ((Param_expr m) => m) -> (m -> a) -> a -setParam' p m f = using' (Def_Param_expr $ \x -> unSetParam_expr p) m f - --- instance Param_expr (Regression method Nothing dp) where --- param_expr m = mp_expr $ modelparams m - ---------------------------------------- - -data family ModelParams (m :: *) -data instance ModelParams (Regression Nothing Nothing dp) = ModelParams_Reg_Empty { mp_method :: Method, mp_expr :: [Term] } -data instance ModelParams (Regression (Just method) Nothing dp) = ModelParams_Reg_1 { mp_expr1 :: [Term] } -data instance ModelParams (Regression Nothing (Just expr) dp) = ModelParams_Reg_2 { mp_expr2 :: [Term] } -data instance ModelParams (Regression (Just method) (Just expr) dp) = ModelParams_Reg_Full () - -class CreateModelParams m where - createModelParams :: m -> ModelParams m - -instance Param_expr (Regression method expr dp) => CreateModelParams (Regression method expr dp) where - createModelParams _ = undefined -- ModelParams_Reg $ param_expr (undefined :: Regression method Nothing dp) - -type family Set_expr (r:: *) (expr:: [TermT]) :: * -type instance Set_expr (Regression method exprold dp) expr = Regression method (Just expr) dp - -type DefRegression dp = Regression Nothing Nothing dp - -data Regression' method expr dp = Regression' - { model :: !(Regression method expr dp) - , params :: !(ModelParams (Regression method expr dp)) - } - --- instance Show (Regression' method Nothing dp) where --- show r = setParam' (SetParam_expr $ mp_expr $ params r) (model r) show --- show r = show $ setParam (SetParam_expr $ mp_expr $ params r) $ model r --- show r = show $ model r - --- instance Param_expr (Regression' method Nothing dp) where --- param_expr m = mp_expr $ params m - -instance SingI expr => Param_expr (Regression' method (Just expr) dp) where - param_expr _ = fromSing (sing :: Sing expr) - -instance - ( Param_expr (Regression method expr dp) - , Element (Ring dp) - , Container Vector (Ring dp) - , Field (Ring dp) - , Fractional (Ring dp) - ) => Monoid (Regression' method expr dp) where - mempty = undefined - mappend a b = Regression' - { model = model a <> model b - , params = params a - } - -instance - ( Param_expr (Regression method expr dp) - , CreateModelParams (Regression method expr dp) - , Element (Ring dp) - , Container Vector (Ring dp) - , Field (Ring dp) - , Fractional (Ring dp) - , HomTrainer (Regression method expr dp) - ) => HomTrainer (Regression' method expr dp) - where - type Datapoint (Regression' method expr dp) = Datapoint (Regression method expr dp) - train xs = Regression' - { model = train xs - , params = createModelParams (undefined :: Regression method expr dp) - } - -instance HasRing dp => HasRing (Regression method expr dp) where - type Ring (Regression method expr dp) = Ring dp - -instance HasRing dp => NumDP (Regression method expr dp) where - numdp = _numdp - -instance - ( Labeled dp - , Label dp ~ Attributes dp - , Ring dp ~ Attributes dp - , Param_expr (Regression method expr dp) - , LA.Product (Attributes dp) - , HasRing dp - , Floating (Attributes dp) - , HomTrainer (Regression method expr dp) - ) => Classifier (Regression method expr dp) - where - classify = flip reg - ---------------------------------------- - --- expr = SetParam_expr -str2expr = SetParam_expr . parseExprSimple - -test_Nothing = setParam (str2expr "1 + x^2") $ train [(1,0),(2,1),(3,2)] :: Regression (Just Linear) Nothing (Double,Double) -test_Just = train [(1,0),(2,1),(3,2),(4,4),(5,5)] :: Regression (Just Linear) (Just [expr| 1 + x + x^2 |]) (Double,Double) -test_Just' = train [(1,0),(2,1),(3,2),(4,4),(5,5)] :: Regression' (Just Linear) (Just [expr| 1 + x + x^2 |]) (Double,Double) --- test_Just2 = train [(1,0),(2,1),(3,2)] :: (DefRegression (Double,Double)) `Set_expr` [expr| 1 + x|] - - -instance - ( Show (Ring dp) - , RealFloat (Ring dp) - , Element (Ring dp) - , Param_expr (Regression method expr dp) - ) => Show (Regression method expr dp) where - show m = "y = " ++ (concat $ intersperse " + " $ map prettyPrint $ filter shouldPrint $ zip coefL termL) - where - shouldPrint (c,t) = True -- c /= c || abs c > 1e-12 - - prettyPrint (c,CCon t) = showFFloat (Just 4) (c * fromRational t) "" - prettyPrint (c,t) = showGFloat (Just 4) c "" ++ ppShowTerm t - - termL = param_expr m - coefL = concat . toLists $ beta m - - -mkReg :: forall method expr dp. - ( Field (Ring dp) - , Element (Ring dp) - , Num (Ring dp) - , Param_expr (Regression method expr dp) - ) => Ring dp -> Vector (Ring dp) -> Matrix (Ring dp) -> Regression method expr dp -mkReg numdp' rawV' rawM' = Regression - { beta = (inv $ scale (1/numdp') rawM') LA.<> (scale (1/numdp') $ asColumn rawV') - , rawM = rawM' - , rawV = rawV' - , _numdp = numdp' - , modelparams = createModelParams (undefined :: Regression method expr dp) - } - -------------------------------------------------------------------------------- --- algebra - -instance - ( Element (Ring dp) - , Container Vector (Ring dp) - , Field (Ring dp) - , Fractional (Ring dp) - , Param_expr (Regression method expr dp) - ) => Monoid (Regression method expr dp) - where - mempty = mkReg 0 (fromList $ replicate numdim 0) (numdim> HomTrainer (Regression method expr dp) - where - type Datapoint (Regression method expr dp) = dp - - train1dp dp = mkReg - (1) - (scale (getLabel dp) (dp2vec expr $ getAttributes dp)) - ((asColumn $ dp2vec expr $ getAttributes dp) LA.<> (asRow $ dp2vec expr $ getAttributes dp)) - where - expr = param_expr (error "poop2" :: Regression method expr dp) - -{-# INLINE dp2vec #-} -dp2vec :: forall dp. - ( Floating dp - , Storable dp - ) => [Term] -> dp -> Vector dp -dp2vec expr dp = LA.fromList $ map (flip evalTerm dp) expr - -------------------------------------------------------------------------------- --- model use - -reg :: forall method expr dp. - ( Attributes dp ~ Label dp - , Ring dp ~ Attributes dp - , HasRing dp - , Floating (Label dp) - , LA.Product (Label dp) - , Storable (Label dp) - , Param_expr (Regression method expr dp) - ) => Attributes dp -> Regression method expr dp -> Label dp -reg dp r = ((asRow $ dp2vec expr dp) LA.<> (beta r)) @@> (0,0) - where - expr = param_expr (error "poop3" :: Regression method expr dp) diff --git a/src/HLearn/Models/Regression/Common.hs b/src/HLearn/Models/Regression/Common.hs deleted file mode 100644 index 263de99..0000000 --- a/src/HLearn/Models/Regression/Common.hs +++ /dev/null @@ -1,17 +0,0 @@ -module HLearn.Models.Regression.Common - where - -import HLearn.Models.Classifiers.Common - -data Coord ring = Coord - { coordx::ring - , coordy::ring - } - deriving (Read,Show,Eq,Ord) - -instance Labeled (Coord ring) where - type Label (Coord ring) = ring - type Attributes (Coord ring) = ring - getLabel = coordy - getAttributes = coordx - diff --git a/src/HLearn/Models/Regression/Exponential.hs b/src/HLearn/Models/Regression/Exponential.hs deleted file mode 100644 index f48c595..0000000 --- a/src/HLearn/Models/Regression/Exponential.hs +++ /dev/null @@ -1,113 +0,0 @@ --- | Exponential regression weights all data points equally. The "ModifiedExponential" type implements a regression that weights larger data points more heavily. In some cases, this can result in a better fit. See for more details. -module HLearn.Models.Regression.Exponential - ( Exponential - ) - where - -import Control.DeepSeq - -import HLearn.Algebra -import HLearn.Models.Classifiers.Common -import HLearn.Evaluation.RSquared -import HLearn.Models.Regression.Common - -------------------------------------------------------------------------------- --- data types - --- collect the term of sum in least square formula -data Exponential ring = Exponential - { n :: !ring -- quantity of sample - , lny :: !ring - , x2 :: !ring - , x1 :: !ring - , xlny :: !ring - } - deriving (Read,Show,Eq,Ord) - --- exhaust data -instance (NFData ring) => NFData (Exponential ring) where - rnf pl = deepseq (n pl) - $ deepseq (lny pl) - $ deepseq (x2 pl) - $ deepseq (x1 pl) - $ rnf (xlny pl) - -------------------------------------------------------------------------------- --- algebra - -instance (Num ring) => Monoid (Exponential ring) where - mempty = Exponential 0 0 0 0 0 - a `mappend` b = Exponential - { n = n a + n b - , lny = lny a + lny b - , x2 = x2 a + x2 b - , x1 = x1 a + x1 b - , xlny = xlny a + xlny b - } - -instance (Num ring) => Abelian (Exponential ring) - -instance (Num ring) => Group (Exponential ring) where - inverse a = Exponential - { n = -(n a) - , lny = -(lny a) - , x2 = -(x2 a) - , x1 = -(x1 a) - , xlny = -(xlny a) - } - -instance (Num ring) => HasRing (Exponential ring) where - type Ring (Exponential ring) = ring - -instance (Num ring) => Module (Exponential ring) where - r .* pl = Exponential - { n = r * (n pl) - , lny = r * (lny pl) - , x2 = r * (x2 pl) - , x1 = r * (x1 pl) - , xlny = r * (xlny pl) - } - -------------------------------------------------------------------------------- --- training - -instance (Num ring) => NumDP (Exponential ring) where - numdp = n - -instance (Floating ring) => HomTrainer (Exponential ring) where - type Datapoint (Exponential ring) = Coord ring -- (ring,ring) - train1dp dp = Exponential - { n = 1 - , lny = log y - , x2 = x ** 2 - , x1 = x - , xlny = x * log y - } - where - x = getAttributes dp - y = getLabel dp - -------------------------------------------------------------------------------- --- classification - -instance (Floating ring) => Classifier (Exponential ring) where - classify m x = a * exp (b * x) - where - c = n m * x2 m - x1 m ** 2 - b = ( n m * xlny m - x1 m * lny m ) / c - a = exp ( ( lny m * x2 m - x1 m * xlny m ) / c ) - -------------------------------------------------------------------------------- --- examples - --- this example follows a perfect Exponential, so result1 == 1.0 -dataset1 = [Coord x (exp (3*x)) | x<-[1..100]] -model1 = train dataset1 :: Exponential Double -result1 = rsquared model1 dataset1 - --- mostly Exponential, but not exact; result2 == 0.943; done in parallel just for fun -dataset2 = [Coord 1 2.7, Coord 1.5 4.47, Coord 5.5 244.7, Coord 2 7.38, Coord 4 54.6] -model2 = parallel train dataset2 :: Exponential Double -result2 = rsquared model2 dataset2 - - diff --git a/src/HLearn/Models/Regression/Logarithmic.hs b/src/HLearn/Models/Regression/Logarithmic.hs deleted file mode 100644 index cb6d9cb..0000000 --- a/src/HLearn/Models/Regression/Logarithmic.hs +++ /dev/null @@ -1,109 +0,0 @@ -module HLearn.Models.Regression.Logarithmic - where - -import Control.DeepSeq - -import HLearn.Algebra -import HLearn.Models.Classifiers.Common -import HLearn.Evaluation.RSquared -import HLearn.Models.Regression.Common - -------------------------------------------------------------------------------- --- data types - --- collect the term of sum in least square formula -data Logarithmic ring = Logarithmic - { n :: !ring -- quantity of sample - , ylnx :: !ring - , y1 :: !ring - , lnx :: !ring - , lnx2 :: !ring -- [ln(x)]^2 - } - deriving (Read,Show,Eq,Ord) - --- exhaust data -instance (NFData ring) => NFData (Logarithmic ring) where - rnf pl = deepseq (n pl) - $ deepseq (ylnx pl) - $ deepseq (y1 pl) - $ deepseq (lnx pl) - $ rnf (lnx2 pl) - -------------------------------------------------------------------------------- --- algebra - -instance (Num ring) => Monoid (Logarithmic ring) where - mempty = Logarithmic 0 0 0 0 0 - a `mappend` b = Logarithmic - { n = n a + n b - , ylnx = ylnx a + ylnx b - , y1 = y1 a + y1 b - , lnx = lnx a + lnx b - , lnx2 = lnx2 a + lnx2 b - } - -instance (Num ring) => Abelian (Logarithmic ring) - -instance (Num ring) => Group (Logarithmic ring) where - inverse a = Logarithmic - { n = -(n a) - , ylnx = -(ylnx a) - , y1 = -(y1 a) - , lnx = -(lnx a) - , lnx2 = -(lnx2 a) - } - -instance (Num ring) => HasRing (Logarithmic ring) where - type Ring (Logarithmic ring) = ring - -instance (Num ring) => Module (Logarithmic ring) where - r .* pl = Logarithmic - { n = r * (n pl) - , ylnx = r * (ylnx pl) - , y1 = r * (y1 pl) - , lnx = r * (lnx pl) - , lnx2 = r * (lnx2 pl) - } - -------------------------------------------------------------------------------- --- training - -instance (Num ring) => NumDP (Logarithmic ring) where - numdp = n - -instance (Floating ring) => HomTrainer (Logarithmic ring) where - type Datapoint (Logarithmic ring) = Coord ring -- (ring,ring) - train1dp dp = Logarithmic - { n = 1 - , ylnx = y * log x - , y1 = y - , lnx = log x - , lnx2 = log x ** 2 - } - where - x = getAttributes dp - y = getLabel dp - -------------------------------------------------------------------------------- --- classification - -instance (Floating ring) => Classifier (Logarithmic ring) where - classify m x = a + b * log x - where - b = ( n m * ylnx m - y1 m * lnx m ) / (n m * lnx2 m - lnx m * lnx m ) - a = (y1 m - b * lnx m) / n m - -------------------------------------------------------------------------------- --- examples - --- this example follows a perfect Exponential, so result1 == 1.0 -dataset1 = [Coord x (1 + 2 * log x) | x<-[1..100]] -model1 = train dataset1 :: Logarithmic Double -result1 = rsquared model1 dataset1 - --- mostly Exponential, but not exact; done in parallel just for fun -dataset2 = [Coord x (1 + 2 * log (x + 0.1*(-1)**x) ) | x<-[1..100]] -model2 = parallel train dataset2 :: Logarithmic Double -result2 = rsquared model2 dataset2 - - diff --git a/src/HLearn/Models/Regression/ModifiedExponential.hs b/src/HLearn/Models/Regression/ModifiedExponential.hs deleted file mode 100644 index 9035f8d..0000000 --- a/src/HLearn/Models/Regression/ModifiedExponential.hs +++ /dev/null @@ -1,116 +0,0 @@ --- | Exponential regression weights all data points equally, whereas this modified version places more emphasis on the larger values. In some cases, this can result in a better fit. See for more details. -module HLearn.Models.Regression.ModifiedExponential - where - -import Control.DeepSeq - -import HLearn.Algebra -import HLearn.Models.Classifiers.Common -import HLearn.Evaluation.RSquared -import HLearn.Models.Regression.Common - --- ModifiedExponential is similar from Exponential --- Exponential ==> ModifiedExponential --- n ==> y1 --- lny ==> ylny --- x2 ==> yx2 --- x1 ==> yx1 --- xlny ==> xylny -------------------------------------------------------------------------------- --- data types - --- collect the term of sum in least square formula -data ModifiedExponential ring = ModifiedExponential - { y1 :: !ring -- quantity of sample - , ylny :: !ring - , yx2 :: !ring - , yx1 :: !ring - , xylny :: !ring - } - deriving (Read,Show,Eq,Ord) - --- exhaust data -instance (NFData ring) => NFData (ModifiedExponential ring) where - rnf pl = deepseq (y1) - $ deepseq (ylny pl) - $ deepseq (yx2 pl) - $ deepseq (yx1 pl) - $ rnf (xylny pl) - -------------------------------------------------------------------------------- --- algebra - -instance (Num ring) => Monoid (ModifiedExponential ring) where - mempty = ModifiedExponential 0 0 0 0 0 - a `mappend` b = ModifiedExponential - { y1 = y1a + y1b - , ylny = ylny a + ylny b - , yx2 = yx2 a + yx2 b - , yx1 = yx1 a + yx1 b - , xylny = xylny a + xylny b - } - -instance (Num ring) => Abelian (ModifiedExponential ring) - -instance (Num ring) => Group (ModifiedExponential ring) where - inverse a = ModifiedExponential - { y1 = -(y1a) - , ylny = -(ylny a) - , yx2 = -(yx2 a) - , yx1 = -(yx1 a) - , xylny = -(xylny a) - } - -instance (Num ring) => HasRing (ModifiedExponential ring) where - type Ring (ModifiedExponential ring) = ring - -instance (Num ring) => Module (ModifiedExponential ring) where - r .* pl = ModifiedExponential - { y1 = r * (y1) - , ylny = r * (ylny pl) - , yx2 = r * (yx2 pl) - , yx1 = r * (yx1 pl) - , xylny = r * (xylny pl) - } - -------------------------------------------------------------------------------- --- training - -instance (Num ring) => NumDP (ModifiedExponential ring) where - numdp = n - -instance (Floating ring) => HomTrainer (ModifiedExponential ring) where - type Datapoint (ModifiedExponential ring) = Coord ring -- (ring,ring) - train1dp dp = ModifiedExponential - { y1 = y - , ylny = y * log y - , yx2 = y * x ** 2 - , yx1 = y * x - , xylny = x * y * log y - } - where - x = getAttributes dp - y = getLabel dp - -------------------------------------------------------------------------------- --- classification - -instance (Floating ring) => Classifier (ModifiedExponential ring) where - classify m x = a * exp (b * x) - where - c = y1 m * yx2 m - yx1 m ** 2 - b = ( y1 m * xylny m - yx1 m * ylny m ) / c - a = exp ( ( ylny m * yx2 m - yx1 m * xylny m ) / c ) - -------------------------------------------------------------------------------- --- examples - --- this example follows a perfect Exponential -dataset1 = [Coord x (exp (3*x)) | x<-[1..100]] -model1 = train dataset1 :: Exponential Double -result1 = rsquared model1 dataset1 - --- mostly Exponential, but not exact; -dataset2 = [Coord 1 2.7, Coord 1.5 4.47, Coord 5.5 244.7, Coord 2 7.38, Coord 4 54.6] -model2 = parallel train dataset2 :: Exponential Double -result2 = rsquared model2 dataset2 diff --git a/src/HLearn/Models/Regression/Parsing.hs b/src/HLearn/Models/Regression/Parsing.hs deleted file mode 100644 index cb4670a..0000000 --- a/src/HLearn/Models/Regression/Parsing.hs +++ /dev/null @@ -1,262 +0,0 @@ -{-# LANGUAGE DataKinds, PolyKinds, TemplateHaskell, QuasiQuotes, TypeOperators #-} - -module HLearn.Models.Regression.Parsing - where - -import Data.List -import Text.ParserCombinators.Parsec -import Text.ParserCombinators.Parsec.Expr -import Text.ParserCombinators.Parsec.Language -import Text.ParserCombinators.Parsec.Token - -import qualified Language.Haskell.TH as TH --- import Language.Haskell.TH hiding (Kind) -import Language.Haskell.TH.Quote - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- template haskell - -expr :: QuasiQuoter -expr = QuasiQuoter --- { quoteType = \str -> [t| '[] |] - { quoteType = exprType - } - -exprType str = return $ go (parseExprSimple str) - where - go [] = TH.PromotedNilT - go (x:xs) = TH.AppT (TH.AppT TH.PromotedConsT $ termType x) $ go xs - -termType :: Term -> TH.Type -termType CVar = TH.PromotedT $ TH.mkName "VarT" -termType (CCon x) = - TH.AppT - (TH.PromotedT $ TH.mkName "ConT") - (TH.AppT - (TH.AppT - (TH.PromotedT $ TH.mkName "Frac") - (TH.LitT $ TH.NumTyLit $ floor x)) - (TH.LitT $ TH.NumTyLit 1)) - -termType (CMon op t) = - TH.AppT - (TH.AppT - (TH.PromotedT $ TH.mkName "MonT") - (TH.PromotedT $ TH.mkName $ show op)) - (termType t) - -termType (CBin op t1 t2) = - TH.AppT - (TH.AppT - (TH.AppT - (TH.PromotedT $ TH.mkName "BinT") - (TH.PromotedT $ TH.mkName $ show op)) - (termType t1)) - (termType t2) - - -data DivFrac = DivFrac Nat Nat - - -------------------------------------------------------------------------------- --- syntax types - -type Expr = [Term] - -data TermT - = VarT - | ConT Frac - | MonT MonOp TermT - | BinT BinOp TermT TermT - -data Term - = CVar - | CCon Rational - | CMon MonOp Term - | CBin BinOp Term Term - deriving (Read,Show,Eq,Ord) - --- instance Show Term where --- show CVar = "CVar" --- show (CCon _) = "CCon" --- show (CMon op t) = "CMon "++show op++" ("++show t++show ")" --- show (CBin op t1 t2) = "CBin "++show op++" ("++show t1++show ") ("++show t2++show ")" - -data MonOp = CLog | CNeg | CSin - deriving (Read,Show,Eq,Ord) - -data BinOp = Mult | Div | Pow | Add - deriving (Read,Show,Eq,Ord) - ---------------------------------------- - -isCommutative :: BinOp -> Bool -isCommutative Mult = True -isCommutative Div = False -isCommutative Pow = False -isCommutative Add = True - -canonicalize :: Term -> Term -canonicalize (CBin op t1 t2) = if isCommutative op - then CBin op (min t1' t2') (max t1' t2') - else CBin op t1' t2' - where - t1' = canonicalize t1 - t2' = canonicalize t2 -canonicalize (CMon op t) = CMon op $ canonicalize t -canonicalize t = t - - -term2expr :: Term -> Expr -term2expr t = sort $ go t - where - go :: Term -> [Term] - go (CBin Add t1 t2) = go t1++go t2 - go t = [t] - ---------------------------------------- - -parseExprSimple :: String -> Expr -parseExprSimple str = case parseExpr str of - Right expr -> expr - Left err -> error $ "parseExprSimple: "++show err - -parseExpr :: String -> Either ParseError Expr -parseExpr str = fmap (term2expr . canonicalize) $ parse exprParser "(unknown)" str - -lexer = makeTokenParser $ emptyDef - { reservedOpNames - = ["*","/","+","-"] - ++["log"] - } - -matchParens = parens lexer -matchWhiteSpace = whiteSpace lexer -matchReserved = reserved lexer -matchReservedOp = reservedOp lexer -matchIdentifier = identifier lexer -matchNumber = naturalOrFloat lexer - -exprParser :: Parser Term -exprParser = buildExpressionParser opList matchTerm - -opList = - [ [Prefix (matchReservedOp "-" >> return (CMon CNeg )) ] - , [Prefix (matchReservedOp "log" >> return (CMon CLog)) ] - , [Infix (matchReservedOp "^" >> return (CBin Pow)) AssocLeft] - , [Infix (matchReservedOp "*" >> return (CBin Mult)) AssocLeft] - , [Infix (matchReservedOp "/" >> return (CBin Div )) AssocLeft] - , [Infix (matchReservedOp "-" >> return (\a b -> CBin Add a (CMon CNeg b) )) AssocLeft] - , [Infix (matchReservedOp "+" >> return (CBin Add )) AssocLeft] - ] - -matchTerm :: Parser Term -matchTerm = matchWhiteSpace >> (matchParens exprParser <|> var <|> matchConst ) - -var = char 'x' >> matchWhiteSpace >> return CVar - -matchConst = do - x <- matchNumber - case x of - Left y -> return $ CCon $ toRational y - Right y -> return $ CCon $ toRational y - ---------------------------------------- - -data instance Sing (f::TermT) = STerm Term --- data instance Sing (f::Term) = STerm Term -data instance Sing (f::MonOp) = SMonOp MonOp -data instance Sing (f::BinOp) = SBinOp BinOp - --- instance SingI CVar where -instance SingI VarT where - sing = STerm CVar -instance SingI c => SingI (ConT c) where - sing = STerm $ CCon (fromSing (sing::Sing c)) --- instance (SingI m, SingI t) => SingI (CMon m t) where -instance (SingI m, SingI t) => SingI (MonT m t) where - sing = STerm (CMon (fromSing (sing::Sing m)) (fromSing (sing::Sing t))) --- instance (SingI m, SingI t1, SingI t2) => SingI (CBin m t1 t2) where -instance (SingI m, SingI t1, SingI t2) => SingI (BinT m t1 t2) where - sing = STerm (CBin (fromSing (sing::Sing m)) (fromSing (sing::Sing t1)) (fromSing (sing::Sing t2))) - -instance SingI CLog where sing = SMonOp CLog -instance SingI CSin where sing = SMonOp CSin -instance SingI CNeg where sing = SMonOp CNeg -instance SingI Mult where sing = SBinOp Mult -instance SingI Pow where sing = SBinOp Pow -instance SingI Div where sing = SBinOp Div - --- instance SingE (Kind :: Term) Term where fromSing (STerm f) = f -instance SingE (Kind :: TermT) Term where fromSing (STerm f) = f -instance SingE (Kind :: MonOp) MonOp where fromSing (SMonOp f) = f -instance SingE (Kind :: BinOp) BinOp where fromSing (SBinOp f) = f - -------------------- - -data instance Sing (xs :: [TermT]) = STermL { unSTermL :: [ Term ] } - -instance SingI ('[] :: [TermT]) where - sing = STermL [] - -instance - ( SingI t - , SingI ts - ) => SingI (t ': (ts :: [TermT])) - where - sing = STermL $ (fromSing (sing :: Sing t)) : (unSTermL ( sing :: Sing ts)) - -instance SingE (Kind :: [TermT]) [Term] where --- instance SingE (Kind :: [a]) [Term] where - fromSing (STermL xs) = xs - --- instance SingE (Kind :: [Nat]) [Int] where - ---------------------------------------- - -ppShowTerm :: Term -> String -ppShowTerm CVar = "x" -ppShowTerm (CCon r) = show (fromRational r :: Double) -ppShowTerm (CMon op t) = ppShowMonOp op ++ "(" ++ ppShowTerm t ++ ")" -ppShowTerm (CBin op t1 t2) = "(" ++ ppShowTerm t1 ++ ")"++ ppShowBinOp op ++ "(" ++ ppShowTerm t2 ++ ")" - -ppShowMonOp :: MonOp -> String -ppShowMonOp CLog = "log" -ppShowMonOp CNeg = "-" -ppShowMonOp CSin = "sin" - -ppShowBinOp :: BinOp -> String -ppShowBinOp Mult = "*" -ppShowBinOp Div = "/" -ppShowBinOp Pow = "^" - ---------------------------------------- - -evalStr :: Floating x => String -> x -> x -evalStr str x = case parseExpr str of - Right term -> evalExpr term x - Left str -> error $ "evalStr: " ++ show str - -evalExpr :: Floating x => Expr -> x -> x -evalExpr (ts) x = sum $ map (flip evalTerm x) ts - -evalTerm :: Floating x => Term -> x -> x -evalTerm CVar x = x -evalTerm (CCon c) _ = fromRational c -evalTerm (CMon f t) x = (evalMonOp f) (evalTerm t x) -evalTerm (CBin f t1 t2) x = (evalBinOp f) (evalTerm t1 x) (evalTerm t2 x) - -evalMonOp :: Floating x => MonOp -> x -> x -evalMonOp CLog = log -evalMonOp CNeg = negate -evalMonOp CSin = sin - -evalBinOp :: Floating x => BinOp -> x -> x -> x -evalBinOp Mult = (*) -evalBinOp Div = (/) -evalBinOp Pow = (**) -evalBinOp Add = (+) - --- train [] :: LinearRegression [expr| 1 + x + x^2 + log x ] Double diff --git a/src/HLearn/Models/Regression/PowerLaw.hs b/src/HLearn/Models/Regression/PowerLaw.hs deleted file mode 100644 index 5360fe4..0000000 --- a/src/HLearn/Models/Regression/PowerLaw.hs +++ /dev/null @@ -1,107 +0,0 @@ -module HLearn.Models.Regression.PowerLaw - ( PowerLaw - ) - where - -import Control.DeepSeq - -import HLearn.Algebra -import HLearn.Models.Classifiers.Common -import HLearn.Evaluation.RSquared -import HLearn.Models.Regression.Common - -------------------------------------------------------------------------------- --- data types - -data PowerLaw ring = PowerLaw - { n :: !ring - , lnxlny :: !ring - , lnx :: !ring - , lnx2 :: !ring - , lny :: !ring - } - deriving (Read,Show,Eq,Ord) - -instance (NFData ring) => NFData (PowerLaw ring) where - rnf pl = deepseq (n pl) - $ deepseq (lnxlny pl) - $ deepseq (lnx pl) - $ deepseq (lnx2 pl) - $ rnf (lny pl) - -------------------------------------------------------------------------------- --- algebra - -instance (Num ring) => Monoid (PowerLaw ring) where - mempty = PowerLaw 0 0 0 0 0 - a `mappend` b = PowerLaw - { n = n a + n b - , lnxlny = lnxlny a + lnxlny b - , lnx = lnx a + lnx b - , lnx2 = lnx2 a + lnx2 b - , lny = lny a + lny b - } - -instance (Num ring) => Abelian (PowerLaw ring) - -instance (Num ring) => Group (PowerLaw ring) where - inverse a = PowerLaw - { n = -(n a) - , lnxlny = -(lnxlny a) - , lnx = -(lnx a) - , lnx2 = -(lnx2 a) - , lny = -(lny a) - } - -instance (Num ring) => HasRing (PowerLaw ring) where - type Ring (PowerLaw ring) = ring - -instance (Num ring) => Module (PowerLaw ring) where - r .* pl = PowerLaw - { n = r*(n pl) - , lnxlny = r*(lnxlny pl) - , lnx = r*(lnx pl) - , lnx2 = r*(lnx2 pl) - , lny = r*(lny pl) - } - -------------------------------------------------------------------------------- --- training - -instance (Num ring) => NumDP (PowerLaw ring) where - numdp = n - -instance (Floating ring) => HomTrainer (PowerLaw ring) where - type Datapoint (PowerLaw ring) = Coord ring -- (ring,ring) - train1dp dp = PowerLaw - { n = 1 - , lnxlny = log x * log y - , lnx = log x - , lnx2 = (log x)^2 - , lny = log y - } - where - x = getAttributes dp - y = getLabel dp - -------------------------------------------------------------------------------- --- classification - -instance (Floating ring, Eq ring) => Classifier (PowerLaw ring) where - classify m x = (exp a)*(x**b) - where - b = ((n m)*(lnxlny m)-(lnx m)*(lny m))/((n m)*(lnx2 m)-(lnx m)^2) - a = ((lny m)-b*(lnx m))/(n m) - -------------------------------------------------------------------------------- --- examples - --- this example follows a perfect power law, so result1 == 1.0 -dataset1 = [Coord x (x^3) | x<-[1..100]] -model1 = train dataset1 :: PowerLaw Double -result1 = rsquared model1 dataset1 - --- mostly powerlaw, but not exact; result2 == 0.943; done in parallel just for fun -dataset2 = [Coord 1 2, Coord 1.5 3, Coord 5.5 15, Coord 2 3, Coord 4 13] -model2 = parallel train dataset2 :: PowerLaw Double -result2 = rsquared model2 dataset2 diff --git a/src/HLearn/NPHard/BinPacking.hs b/src/HLearn/NPHard/BinPacking.hs deleted file mode 100644 index 42b043f..0000000 --- a/src/HLearn/NPHard/BinPacking.hs +++ /dev/null @@ -1,88 +0,0 @@ -{-# LANGUAGE DataKinds #-} - --- | Bin packing is one of the most well studied NP-hard problems. See wikipedia for a detailed description - -module HLearn.NPHard.BinPacking - ( BinPacking (..) - ) - where - -import qualified Data.Foldable as F -import qualified Data.Heap as Heap -import Data.List -import Data.List.Extras -import Debug.Trace -import Data.Proxy -import qualified Data.Map as Map -import qualified Data.Sequence as Seq -import GHC.TypeLits -import qualified Control.ConstraintKinds as CK -import HLearn.Algebra -import HLearn.DataStructures.SortedVector - -------------------------------------------------------------------------------- --- data types - -data BinPacking (n::Nat) a = BinPacking - { vector :: !(SortedVector a) - , packing :: Map.Map Int [a] - } - deriving (Read,Show,Eq,Ord) - -bfd :: forall a n. (Norm a, Ord (Scalar a), Num (Scalar a), KnownNat n) => SortedVector a -> BinPacking n a -bfd vector = BinPacking - { vector = vector - , packing = vector2packing (fromIntegral $ natVal (Proxy :: Proxy n)) vector - } - -vector2packing :: (Norm a, Ord (Scalar a), Num (Scalar a)) => Scalar a -> SortedVector a -> Map.Map Int [a] -vector2packing binsize vector = snd $ CK.foldr cata (Map.empty,Map.empty) vector - where - cata x (weight2bin,packing) = case Map.lookupLE (binsize - magnitude x) weight2bin of - Nothing -> (weight2bin',packing') - where - newbin = Map.size packing + 1 - weight2bin' = Map.insert (magnitude x) newbin weight2bin - packing' = Map.insert newbin [x] packing - Just (weight,bin) -> (weight2bin', packing') - where - weight2bin' = Map.insert (weight+magnitude x) bin $ - Map.delete weight weight2bin - packing' = Map.insertWith (++) bin [x] packing - -------------------------------------------------------------------------------- --- Algebra - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n) => Abelian (BinPacking n a) -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n) => Monoid (BinPacking n a) where - mempty = bfd mempty - p1 `mappend` p2 = bfd $ (vector p1) <> (vector p2) - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n, Group (SortedVector a)) => Group (BinPacking n a) where - inverse p = BinPacking - { vector = inverse $ vector p - , packing = error "Scheduling.inverse: schedule does not exist for inverses" - } - -type instance Scalar (BinPacking n a) = Scalar (SortedVector a) - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n, Module (SortedVector a)) => Module (BinPacking n a) where - r .* p = p { vector = r .* vector p } - ---------------------------------------- - -instance CK.Functor (BinPacking n) where - type FunctorConstraint (BinPacking n) x = (Ord x, Norm x, Num (Scalar x), KnownNat n) - fmap f sched = bfd $ CK.fmap f $ vector sched - --- instance CK.Monad (BinPacking n) where --- return = train1dp --- join (BinPacking v _) = bfd $ CK.join $ CK.fmap vector v - -------------------------------------------------------------------------------- --- Training - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n) => HomTrainer (BinPacking n a) where - type Datapoint (BinPacking n a) = a - train1dp dp = bfd $ train1dp dp - diff --git a/src/HLearn/NPHard/KCenters.hs b/src/HLearn/NPHard/KCenters.hs deleted file mode 100644 index ce50ae3..0000000 --- a/src/HLearn/NPHard/KCenters.hs +++ /dev/null @@ -1,99 +0,0 @@ -module HLearn.NPHard.KCenters - where - -import Data.List.Extras -import qualified Data.Foldable as F -import qualified Data.Map.Strict as Map -import qualified Data.Sequence as Seq -import GHC.TypeLits - -import Debug.Trace - -import HLearn.Algebra - -------------------------------------------------------------------------------- --- data types - -data KCenters dp (k::Nat) = KCenters - { clusters :: Map.Map dp (Cluster dp) - } - deriving (Read,Show,Eq,Ord) - -type Cluster dp = Seq.Seq dp - -------------------------------------------------------------------------------- --- algebra - -instance (SingI k, Ord dp, MetricSpace dp r) => Semigroup (KCenters dp k) where - kc1 <> kc2 = KCenters - { clusters = merge $ Map.unionWith (<>) (clusters kc1) (clusters kc2) - } - where - k = fromIntegral $ fromSing (sing :: Sing k) - - merge clusters = if Map.size clusters <= k - then clusters - else let (dp1,dp2) = closest $ Map.keys clusters - (Just dp2data) = Map.lookup dp2 clusters - inserted = Map.insertWith (<>) dp1 dp2data clusters - in Map.delete dp2 inserted - -kc1 <<>> kc2 = KCenters - { clusters = merge $ Map.unionWith (<>) (clusters kc1) (clusters kc2) - } - where - k = 3 - - merge clusters = if Map.size clusters <= k - then clusters - else let (dp1,dp2) = closest $ Map.keys clusters - res = trace ("dp2: "++show dp2++" clusters: "++show clusters) $ Map.lookup dp2 clusters - (Just dp2data) = trace ("res: "++show res) res - inserted = Map.insertWith (<>) dp1 dp2data clusters - in Map.delete dp2 inserted - -x=train [1,2,3,100,101,102,1000,1001,1002::Double] :: KCenters Double 3 -y=train [10,11,12,100,101,102,1000,1001,1002::Double] :: KCenters Double 3 - - -closest :: (MetricSpace dp r) => [dp] -> (dp,dp) -closest dpL = argmin (uncurry dist) [(dp1,dp2) | dp1<- dpL, dp2 <- dpL, dp1/=dp2] - -instance (SingI k, Ord dp, MetricSpace dp r) => Monoid (KCenters dp k) where - mappend = (<>) - mempty = KCenters - { clusters = mempty - } - -------------------------------------------------------------------------------- --- model - -instance Model (NoParams (KCenters dp k)) (KCenters dp k) where - getparams _ = NoParams - -instance DefaultModel (NoParams (KCenters dp k)) (KCenters dp k) where - defparams = NoParams - -instance (SingI k, Ord dp, MetricSpace dp r) => HomTrainer (NoParams (KCenters dp k)) dp (KCenters dp k) where - train' _ dps = KCenters $ Map.fromList $ map (\x -> (x,mempty)) (go k []) - where - k = fromIntegral $ fromSing (sing :: Sing k) - - go :: Int -> [dp] -> [dp] - go 0 centerL = centerL - go i [] = go (i-1) $ [head $ F.toList dps] - go i centerL = go (i-1) $ (F.foldr1 gonext dps):centerL - where - gonext dp1 dp2 = - if clusterdistance centerL dp1 container dp -> dp -> r -clusterdistance centerL dp = dist dp $ F.foldr1 go centerL - where - go dp1 dp2 = - if dist dp dp1 - -module HLearn.NPHard.Scheduling - ( - - Scheduling (..) - - -- * Operations - , getSchedules - , maxpartition - , minpartition - , spread - ) where - -import Control.DeepSeq -import qualified Control.ConstraintKinds as CK -import qualified Data.Foldable as F -import qualified Data.Heap as Heap -import Data.List -import Data.List.Extras -import Data.Proxy -import Debug.Trace -import qualified Data.Map as Map -import qualified Data.Sequence as Seq -import GHC.TypeLits -import HLearn.Algebra -import HLearn.DataStructures.SortedVector - -------------------------------------------------------------------------------- --- data types - -type Bin = Int - -data Scheduling (n::Nat) a = Scheduling - { vector :: !(SortedVector a) - , schedule :: Map.Map Bin [a] - } - deriving (Read,Show,Eq,Ord) - -instance NFData a => NFData (Scheduling n a) where - rnf s = deepseq (vector s) - $ rnf (schedule s) - ---------------------------------------- - -lptf :: forall a n. (Norm a, Ord (Scalar a), Num (Scalar a), KnownNat n) => SortedVector a -> Scheduling n a -lptf vector = Scheduling - { vector = vector - , schedule = vector2schedule (fromIntegral $ natVal (Proxy :: Proxy n)) vector - } - -vector2schedule :: (Norm a, Ord (Scalar a), Num (Scalar a)) => Bin -> SortedVector a -> Map.Map Bin [a] -vector2schedule p vector = snd $ CK.foldr cata (emptyheap p,Map.empty) vector - where - -- maintain the invariant that size of our heap is always p - -- the processor with the smallest workload is at the top - cata x (heap,map) = - let Just top = Heap.viewHead heap - set = snd top - prio = (fst top)+magnitude x - heap' = Heap.insert (prio,set) (Heap.drop 1 heap) - map' = Map.insertWith (++) set [x] map - in (heap',map') - -emptyheap :: (Num ring, Ord ring) => Bin -> Heap.MinPrioHeap ring Bin -emptyheap p = Heap.fromAscList [(0,i) | i<-[1..p]] - ---------------------------------------- - --- | Returns a list of all schedules. The schedules are represented by a list of the elements within them. -getSchedules :: Scheduling n a -> [[a]] -getSchedules = Map.elems . schedule - --- | Returns the size of the largest bin -maxpartition :: (Ord (Scalar a), Num (Scalar a), Norm a) => Scheduling n a -> Scalar a -maxpartition p = maximum $ map (sum . map magnitude) $ Map.elems $ schedule p - --- | Returns the size of the smallest bin -minpartition :: (Ord (Scalar a), Num (Scalar a), Norm a) => Scheduling n a -> Scalar a -minpartition p = minimum $ map (sum . map magnitude) $ Map.elems $ schedule p - --- | A schedule's spread is a measure of it's \"goodness.\" The smaller the spread, the better the schedule. It is equal to `maxpartition` - `minpartition` -spread :: (Ord (Scalar a), Num (Scalar a), Norm a) => Scheduling n a -> Scalar a -spread p = (maxpartition p)-(minpartition p) - -------------------------------------------------------------------------------- --- Algebra - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n) => Abelian (Scheduling n a) -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n) => Monoid (Scheduling n a) where - mempty = lptf mempty - p1 `mappend` p2 = lptf $ (vector p1) <> (vector p2) - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n, Group (SortedVector a)) => Group (Scheduling n a) where - inverse p = Scheduling - { vector = inverse $ vector p - , schedule = error "Scheduling.inverse: schedule does not exist for inverses" - } - -type instance Scalar (Scheduling n a) = Scalar (SortedVector a) - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n, Module (SortedVector a)) => Module (Scheduling n a) where - r .* p = p { vector = r .* vector p } - ---------------------------------------- - -instance CK.Functor (Scheduling n) where - type FunctorConstraint (Scheduling n) x = (Ord x, Norm x, Num (Scalar x), KnownNat n) - fmap f sched = lptf $ CK.fmap f $ vector sched - --- instance CK.Monad (Scheduling n) where --- return = train1dp --- join (Scheduling v _) = lptf $ CK.join $ CK.fmap vector v - -------------------------------------------------------------------------------- --- Training - -instance (Ord a, Ord (Scalar a), Num (Scalar a), Norm a, KnownNat n) => HomTrainer (Scheduling n a) where - type Datapoint (Scheduling n a) = a - train1dp dp = lptf $ train1dp dp - train dp = lptf $ train dp - -------------------------------------------------------------------------------- --- Visualization - -class Labeled a where - label :: a -> String - -rmblankline :: String -> String -rmblankline [] = [] -rmblankline ('\n':'\n':xs) = rmblankline ('\n':xs) -rmblankline (x:xs) = x:(rmblankline xs) - -visualize :: (Norm a, Labeled a, Show (Scalar a), Fractional (Scalar a)) => Scalar a -> Map.Map Bin [a] -> String -visualize height m = rmblankline $ unlines - [ "\\begin{tikzpicture}" - , "\\definecolor{hlearn_bgbox}{RGB}{127,255,127}" - , unlines $ map mknodes $ Map.assocs m - , unlines $ map (mkproc height) $ Map.assocs m - , "\\end{tikzpicture}" - ] - --- mkproc :: (k,[a]) -> String -mkproc height (k,xs) = - "\\draw[line width=0.1cm] ("++show x++","++show height++") to ("++show x++",0) to node[below] {$s_{"++show k++"}$} ("++show x++"+2,0) to ("++show x++"+2,"++show height++");" - where - x = k*2 - --- mknodes :: (k,[a]) -> String -mknodes (k,xs) = unlines $ go 0 (reverse xs) - where - go i [] = [""] - go i (x:xs) = (node (k*2+1) (i+(magnitude x)/2) (magnitude x) (label x)):(go (i+magnitude x) xs) - - --- node :: Double->Double->Double->String->String -node x y height name = - "\\node[shape=rectangle,draw,fill=hlearn_bgbox,minimum width=2cm,minimum height="++show height++"cm] at ("++show x++","++show y++") { "++name++" };" diff --git a/src/HLearn/Optimization/Multivariate.hs b/src/HLearn/Optimization/Multivariate.hs index 0401f82..5c1f4d1 100644 --- a/src/HLearn/Optimization/Multivariate.hs +++ b/src/HLearn/Optimization/Multivariate.hs @@ -1,7 +1,15 @@ +-- | This module provides numerical routines for minimizing linear and nonlinear multidimensional functions. +-- Multidimensional optimization is significantly harder than one dimensional optimization. +-- In general, there are no black box routines that work well on every function, +-- even if the function has only a single local minimum. +-- +-- FIXME: better documentation module HLearn.Optimization.Multivariate ( - -- * Multivariate optimization methods + -- * Simple interface + + -- * Advanced interface -- ** Conjugate gradient descent -- | A great introduction is: @@ -9,9 +17,8 @@ module HLearn.Optimization.Multivariate -- -- NOTE: Does this paper has a mistake in the calculation of the optimal step size; -- it should be multiplied by 1/2? - fminunc_cgd - , fminunc_cgd_ - , Iterator_cgd (..) + fminunc_cgd_ + , Iterator_cgd -- *** Conjugation methods , ConjugateMethod @@ -21,20 +28,26 @@ module HLearn.Optimization.Multivariate , hestenesStiefel -- ** Newton-Raphson (quadratic gradient descent) + , Iterator_nr , fminunc_nr_ - , Iterator_nr (..) -- ** Quasi Newton methods + , Iterator_bfgs , fminunc_bfgs_ - , Iterator_bfgs (..) - -- * Multivariate line search - , MultivariateLineSearch + -- ** Multivariate line search + , LineSearch - -- ** Univariate wrappers - , multivariateBrent + , mkLineSearch + , lineSearch_brent + , lineSearch_gss - -- ** Unsafe line minimization + -- *** Backtracking + -- | Backtracking is much faster than univariate line search methods. + -- It typically requires 1-3 function evaluations, whereas the univariate optimizations typically require 5-50. + -- The downside is that backtracking is not guaranteed to converge to the minimum along the line search. + -- In many cases, however, this is okay. + -- We make up for the lack of convergence by being able to run many more iterations of the multivariate optimization. , Backtracking (..) , backtracking @@ -42,15 +55,37 @@ module HLearn.Optimization.Multivariate , amijo , weakCurvature , strongCurvature + + -- * Test functions + -- | See for more detail. + -- + -- FIXME: Include graphs in documentation. + -- + -- FIXME: Implement the rest of the functions. + + , sphere + , ackley + , beale + , rosenbrock ) where import SubHask +import SubHask.Algebra.Vector import SubHask.Category.Trans.Derivative import HLearn.History import HLearn.Optimization.Univariate +------------------------------------------------------------------------------- + +type MultivariateMinimizer (n::Nat) cxt opt v + = LineSearch cxt v + -> StopCondition (opt v) + -> v + -> Diff n v (Scalar v) + -> History cxt (opt v) + ------------------------------------------------------------------------------- -- generic transforms @@ -79,8 +114,21 @@ data Iterator_cgd a = Iterator_cgd } deriving (Typeable) -instance Show (Iterator_cgd a) where - show _ = "CGD" +type instance Scalar (Iterator_cgd a) = Scalar a +type instance Logic (Iterator_cgd a) = Bool + +instance (Eq (Scalar a), Eq a) => Eq_ (Iterator_cgd a) where + a1==a2 + = (_cgd_x1 a1 == _cgd_x1 a2) + && (_cgd_fx1 a1 == _cgd_fx1 a2) + && (_cgd_f'x1 a1 == _cgd_f'x1 a2) +-- && (_cgd_alpha a1 == _cgd_alpha a2) +-- && (_cgd_f'x0 a1 == _cgd_f'x0 a2) +-- && (_cgd_s0 a1 == _cgd_s0 a2) + +instance (Hilbert a, Show a, Show (Scalar a)) => Show (Iterator_cgd a) where +-- show cgd = "CGD; x="++show (_cgd_x1 cgd)++"; f'x="++show (_cgd_f'x1 cgd)++"; fx="++show (_cgd_fx1 cgd) + show cgd = "CGD; |f'x|="++show (size $ _cgd_f'x1 cgd)++"; fx="++show (_cgd_fx1 cgd) instance Has_x1 Iterator_cgd v where x1 = _cgd_x1 instance Has_fx1 Iterator_cgd v where fx1 = _cgd_fx1 @@ -109,38 +157,14 @@ hestenesStiefel f'x1 f'x0 s0 = -(f'x1 <> (f'x1 - f'x0)) / (s0 <> (f'x1 - f'x0)) ---------------------------------------- --- | Conjugate gradient descent with reasonable default parameters -fminunc_cgd :: - ( Hilbert v - , BoundedField (Scalar v) - , Optimizable v - , Optimizable (Scalar v) - ) => v - -> C1 (v -> Scalar v) - -> History (Iterator_cgd v) -fminunc_cgd x0 f = - fminunc_cgd_ - ( multivariateBrent ( stop_brent 1e-12 || maxIterations 20 )) - polakRibiere - x0 - f - ( maxIterations 20 || mulTolerance 1e-6 ) - - -- | A generic method for conjugate gradient descent that gives you more control over the optimization parameters +{-# INLINEABLE fminunc_cgd_ #-} fminunc_cgd_ :: - ( VectorSpace v + ( Hilbert v , ClassicalLogic v - , Optimizable v - ) => MultivariateLineSearch v - -> ConjugateMethod v - -> v - -> C1 (v -> Scalar v) - -> StopCondition_ (Iterator_cgd v) - -> History (Iterator_cgd v) -fminunc_cgd_ searchMethod conjugateMethod x0 f = {-# SCC fminunc_cgd #-} iterate - (step_cgd searchMethod conjugateMethod f) - $ Iterator_cgd + ) => ConjugateMethod v -> MultivariateMinimizer 1 cxt Iterator_cgd v +fminunc_cgd_ conj lineSearch stop x0 f = {-# SCC fminunc_cgd #-} + iterate (step_cgd lineSearch conj f) stop $ Iterator_cgd { _cgd_x1 = x0 , _cgd_fx1 = f $ x0 , _cgd_f'x1 = f' $ x0 @@ -152,29 +176,31 @@ fminunc_cgd_ searchMethod conjugateMethod x0 f = {-# SCC fminunc_cgd #-} iterate where f' = (derivative f $) --- | performs a single iteration of the conjugate gradient descent algorithm step_cgd :: - ( VectorSpace v + ( Hilbert v , ClassicalLogic v - ) => MultivariateLineSearch v + ) => LineSearch cxt v -> ConjugateMethod v -> C1 (v -> Scalar v) -> Iterator_cgd v - -> History (Iterator_cgd v) + -> History cxt (Iterator_cgd v) step_cgd stepMethod conjMethod f (Iterator_cgd x1 fx1 f'x1 alpha1 f'x0 s0 _) = {-# SCC step_cgd #-} do let f' = (derivative f $) - -- this test ensures that conjugacy will be reset when it is lost; the - -- formula is taken from equation 1.174 of the book "Nonlinear Programming" - --- let beta = if abs (f'x1 <> f'x0) > 0.2 * squaredInnerProductNorm f'x0 + -- This test on beta ensures that conjugacy will be reset when it is lost. + -- The formula is taken from equation 1.174 of the book "Nonlinear Programming". + -- + -- FIXME: + -- This test isn't needed in linear optimization, and the inner products are mildly expensive. + -- It also makes CGD work only in a Hilbert space. + -- Is the restriction worth it? +-- let beta = if abs (f'x1<>f'x0) > 0.2*(f'x0<>f'x0) -- then 0 -- else max 0 $ conjMethod f'x1 f'x0 s0 let beta = conjMethod f'x1 f'x0 s0 - let s1 = - f'x1 + beta *. f'x1 --- let s1 = - f'x1 + beta *. s0 + let s1 = -f'x1 + beta *. s0 alpha <- stepMethod (f $) f' x1 s1 alpha1 @@ -212,31 +238,25 @@ instance Has_fx1 Iterator_nr a where fx1 = _nr_fx1 ---------------------------------------- +{-# INLINEABLE fminunc_nr_ #-} fminunc_nr_ :: - ( Optimizable v - , Hilbert v + ( Hilbert v , BoundedField (Scalar v) - ) => v - -> C2 (v -> Scalar v) - -> StopCondition_ (Iterator_nr v) - -> History (Iterator_nr v) -fminunc_nr_ x0 f = iterate - (step_nr f) - $ Iterator_nr - { _nr_x1 = x0 - , _nr_fx1 = f $ x0 - , _nr_fx0 = infinity - , _nr_f'x1 = derivative f $ x0 - , _nr_alpha1 = 1 - } + ) => MultivariateMinimizer 2 cxt Iterator_nr v +fminunc_nr_ _ stop x0 f = iterate (step_nr f) stop $ Iterator_nr + { _nr_x1 = x0 + , _nr_fx1 = f $ x0 + , _nr_fx0 = infinity + , _nr_f'x1 = derivative f $ x0 + , _nr_alpha1 = 1 + } -step_nr :: forall v. - ( Optimizable v - , Hilbert v +step_nr :: forall v cxt. + ( Hilbert v , BoundedField (Scalar v) ) => C2 (v -> Scalar v) -> Iterator_nr v - -> History (Iterator_nr v) + -> History cxt (Iterator_nr v) step_nr f (Iterator_nr x0 fx0 _ f'x0 alpha0) = do let f' = (derivative f $) f'' = ((derivative . derivative $ f) $) @@ -271,6 +291,12 @@ step_nr f (Iterator_nr x0 fx0 _ f'x0 alpha0) = do ------------------------------------------------------------------------------- -- The BFGS quasi-newton method +-- | FIXME: +-- We currently build the full matrix, making things slower than they need to be. +-- Fixing this probably requires extending the linear algebra in subhask. +-- +-- FIXME: +-- Also, the code converges much slower than I would expect for some reason. data Iterator_bfgs v = Iterator_bfgs { _bfgs_x1 :: !v , _bfgs_fx1 :: !(Scalar v) @@ -281,43 +307,35 @@ data Iterator_bfgs v = Iterator_bfgs } deriving (Typeable) -instance Show (Iterator_bfgs v) where - show _ = "BFGS" +type instance Scalar (Iterator_bfgs v) = Scalar v + +instance (Show v, Show (Scalar v)) => Show (Iterator_bfgs v) where + show i = "BFGS; x="++show (_bfgs_x1 i)++"; f'x="++show (_bfgs_f'x1 i)++"; fx="++show (_bfgs_fx1 i) instance Has_x1 Iterator_bfgs a where x1 = _bfgs_x1 instance Has_fx1 Iterator_bfgs a where fx1 = _bfgs_fx1 +{-# INLINEABLE fminunc_bfgs_ #-} fminunc_bfgs_ :: - ( Optimizable v - , Optimizable (Scalar v) - , Hilbert v + ( Hilbert v , BoundedField (Scalar v) - ) => v - -> C1 ( v -> Scalar v) - -> MultivariateLineSearch v - -> StopCondition_ (Iterator_bfgs v) - -> History (Iterator_bfgs v) -fminunc_bfgs_ x0 f linesearch = iterate - ( step_bfgs f linesearch ) - ( Iterator_bfgs - { _bfgs_x1 = x0 - , _bfgs_fx1 = f $ x0 - , _bfgs_fx0 = infinity - , _bfgs_f'x1 = derivative f $ x0 - , _bfgs_f''x1 = 1 - , _bfgs_alpha1 = 1e-10 - } - ) + ) => MultivariateMinimizer 1 cxt Iterator_bfgs v +fminunc_bfgs_ linesearch stop x0 f = iterate (step_bfgs f linesearch) stop $ Iterator_bfgs + { _bfgs_x1 = x0 + , _bfgs_fx1 = f $ x0 + , _bfgs_fx0 = infinity + , _bfgs_f'x1 = derivative f $ x0 + , _bfgs_f''x1 = 1 + , _bfgs_alpha1 = 1e-10 + } -step_bfgs :: forall v. - ( Optimizable v - , Optimizable (Scalar v) - , Hilbert v +step_bfgs :: forall v cxt. + ( Hilbert v , BoundedField (Scalar v) ) => C1 ( v -> Scalar v) - -> MultivariateLineSearch v + -> LineSearch cxt v -> Iterator_bfgs v - -> History (Iterator_bfgs v) + -> History cxt (Iterator_bfgs v) step_bfgs f linesearch opt = do let f' = (derivative f $) @@ -336,7 +354,7 @@ step_bfgs f linesearch opt = do -- let alpha = x1 brent alpha <- linesearch (f $) f' x0 f'x0 alpha0 - let x1 = x0 + alpha *. d + let x1 = x0 - alpha *. d f'x1 = f' x1 let p = x1 - x0 @@ -365,6 +383,61 @@ step_bfgs f linesearch opt = do , _bfgs_alpha1 = alpha } + +------------------------------------------------------------------------------- +-- Line search + +-- | Functions of this type determine how far to go in a particular direction. +-- These functions perform the same task as "UnivariateMinimizer", +-- but the arguments are explicitly multidimensional. +type LineSearch cxt v + = (v -> Scalar v) -- ^ f = function + -> (v -> v) -- ^ f' = derivative of the function + -> v -- ^ x0 = starting position + -> v -- ^ f'(x0) = direction + -> Scalar v -- ^ initial guess at distance to minimum + -> History cxt (Scalar v) + +-- | Convert a "UnivariateMinimizer" into a "LineSearch". +{-# INLINEABLE mkLineSearch #-} +mkLineSearch :: + ( Has_x1 opt (Scalar v) + , VectorSpace v + , OrdField (Scalar v) + , cxt (LineBracket (Scalar v)) + , cxt (opt (Scalar v)) + , cxt (Scalar (opt (Scalar v))) + ) => proxy opt -- ^ required for type checking + -> UnivariateMinimizer cxt opt (Scalar v) + -> StopCondition (opt (Scalar v)) -- ^ controls the number of iterations + -> LineSearch cxt v +mkLineSearch _ fminuncM stops f _ x0 dir stepGuess = {-# SCC uni2multiLineSearch #-} do + let g y = f $ x0 + dir.*y + opt <- fminuncM stops stepGuess (return . g) + return $ x1 opt + +-- | Brent's method promoted to work on a one dimension subspace. +{-# INLINEABLE lineSearch_brent #-} +lineSearch_brent :: + ( VectorSpace v + , OrdField (Scalar v) + , cxt (LineBracket (Scalar v)) + , cxt (Iterator_brent (Scalar v)) + ) => StopCondition (Iterator_brent (Scalar v)) + -> LineSearch cxt v +lineSearch_brent stops = mkLineSearch (Proxy::Proxy Iterator_brent) fminuncM_brent stops + +-- | Golden section search promoted to work on a one dimension subspace. +{-# INLINEABLE lineSearch_gss #-} +lineSearch_gss :: + ( VectorSpace v + , OrdField (Scalar v) + , cxt (LineBracket (Scalar v)) + , cxt (Iterator_gss (Scalar v)) + ) => StopCondition (Iterator_gss (Scalar v)) + -> LineSearch cxt v +lineSearch_gss stops = mkLineSearch (Proxy::Proxy Iterator_gss) fminuncM_gss stops + ------------------------------------------------------------------------------- -- backtracking @@ -380,33 +453,37 @@ data Backtracking v = Backtracking } deriving (Typeable) +type instance Scalar (Backtracking v) = Scalar v + instance Show (Backtracking v) where show _ = "Backtracking" +instance Has_fx1 Backtracking v where + fx1 = _bt_fx + -- | Backtracking linesearch is NOT guaranteed to converge. -- It is frequently used as the linesearch for multidimensional problems. -- In this case, the overall minimization problem can converge significantly -- faster than if one of the safer methods is used. +{-# INLINABLE backtracking #-} backtracking :: ( Hilbert v - , Optimizable v - ) => StopCondition_ (Backtracking v) - -> MultivariateLineSearch v -backtracking stops f f' x0 f'x0 stepGuess = {-# SCC backtracking #-} do - let g y = {-# SCC backtracking_g #-} f $ x0 + y *. f'x0 - let grow=2.1 - - fmap _bt_x $ iterate (step_backtracking 0.5 f f') - (Backtracking - { _bt_x = (grow*stepGuess) - , _bt_fx = g (grow*stepGuess) - , _bt_f'x = grow *. (f' $ x0 + grow*stepGuess *. f'x0) - , _init_dir = f'x0 - , _init_x = x0 - , _init_fx = f x0 - , _init_f'x = f'x0 - }) - stops + , cxt (Backtracking v) + ) => StopCondition (Backtracking v) + -> LineSearch cxt v +backtracking stop f f' x0 f'x0 stepGuess = {-# SCC backtracking #-} beginFunction "backtracking" $ do + let g y = f $ x0 + y *. f'x0 + let grow=1.65 + + fmap _bt_x $ iterate (step_backtracking 0.85 f f') stop $ Backtracking + { _bt_x = (grow*stepGuess) + , _bt_fx = g (grow*stepGuess) + , _bt_f'x = grow *. (f' $ x0 + grow*stepGuess *. f'x0) + , _init_dir = f'x0 + , _init_x = x0 + , _init_fx = f x0 + , _init_f'x = f'x0 + } step_backtracking :: ( Module v @@ -414,7 +491,7 @@ step_backtracking :: -> (v -> Scalar v) -> (v -> v) -> Backtracking v - -> History (Backtracking v) + -> History cxt (Backtracking v) step_backtracking !tao !f !f' !bt = {-# SCC step_backtracking #-} do let x1 = tao * _bt_x bt return $ bt @@ -423,69 +500,90 @@ step_backtracking !tao !f !f' !bt = {-# SCC step_backtracking #-} do , _bt_f'x = g' x1 } where - g alpha = f $ _init_x bt + alpha *. _init_dir bt + g alpha = f (_init_x bt + alpha *. _init_dir bt) g' alpha = alpha *. f' (_init_x bt + alpha *. _init_dir bt) --------------------------------------- -- stop conditions {-# INLINABLE wolfe #-} -wolfe :: - ( Hilbert v - , Normed (Scalar v) - , Ord (Scalar v) - ) => Scalar v -> Scalar v -> StopCondition_ (Backtracking v) +wolfe :: Hilbert v => Scalar v -> Scalar v -> StopCondition (Backtracking v) wolfe !c1 !c2 !bt0 !bt1 = {-# SCC wolfe #-} do a <- amijo c1 bt0 bt1 b <- strongCurvature c2 bt0 bt1 return $ a && b {-# INLINABLE amijo #-} -amijo :: - ( Hilbert v - , Ord (Scalar v) - ) => Scalar v -> StopCondition_ (Backtracking v) +amijo :: Hilbert v => Scalar v -> StopCondition (Backtracking v) amijo !c1 _ !bt = {-# SCC amijo #-} return $ _bt_fx bt <= _init_fx bt + c1 * (_bt_x bt) * ((_init_f'x bt) <> (_init_dir bt)) {-# INLINABLE weakCurvature #-} -weakCurvature :: - ( Hilbert v - , Ord (Scalar v) - ) => Scalar v -> StopCondition_ (Backtracking v) +weakCurvature :: Hilbert v => Scalar v -> StopCondition (Backtracking v) weakCurvature !c2 _ !bt = {-# SCC weakCurvature #-} return $ _init_dir bt <> _bt_f'x bt >= c2 * (_init_dir bt <> _init_f'x bt) {-# INLINABLE strongCurvature #-} -strongCurvature :: - ( Hilbert v - , Ord (Scalar v) - , Normed (Scalar v) - ) => Scalar v -> StopCondition_ (Backtracking v) +strongCurvature :: Hilbert v => Scalar v -> StopCondition (Backtracking v) strongCurvature !c2 _ !bt = {-# SCC strongCurvature #-} return $ abs (_init_dir bt <> _bt_f'x bt) <= c2 * abs (_init_dir bt <> _init_f'x bt) +-------------------------------------------------------------------------------- -------------------------------------------------------------------------------- +-- | The simplest quadratic function +{-# INLINEABLE sphere #-} +sphere :: Hilbert v => C1 (v -> Scalar v) +sphere = unsafeProveC1 f f' -- f'' + where + f v = v<>v+3 + f' v = 2*.v +-- f'' _ = 2 --- | determine how far to go in a particular direction -type MultivariateLineSearch v - = (v -> Scalar v) -- ^ f = function - -> (v -> v) -- ^ f' = derivative of the function - -> v -- ^ x0 = starting position - -> v -- ^ f'(x0) = direction - -> Scalar v -- ^ initial guess at distance to minimum - -> History (Scalar v) +-- | +-- +-- > ackley [0,0] == 0 +-- +-- ackley :: (IsScalar r, Field r) => SVector 2 r -> r +{-# INLINEABLE ackley #-} +ackley :: SVector 2 Double -> Double +ackley v = -20 * exp (-0.2 * sqrt $ 0.5 * (x*x + y*y)) - exp(0.5*cos(2*pi*x)+0.5*cos(2*pi*y)) + exp 1 + 20 + where + x=v!0 + y=v!1 + +-- | +-- +-- > beale [3,0.5] = 0 +{-# INLINEABLE beale #-} +beale :: SVector 2 Double -> Double +beale v = (1.5-x+x*y)**2 + (2.25-x+x*y*y)**2 + (2.625-x+x*y*y*y)**2 + where + x=v!0 + y=v!1 + +-- | The Rosenbrock function is hard to optimize because it has a narrow quadratically shaped valley. +-- +-- See for more details. +-- +-- FIXME: +-- The derivatives of this function are rather nasty. +-- We need to expand "SubHask.Category.Trans.Derivative" to handle these harder cases automatically. +{-# INLINEABLE rosenbrock #-} +rosenbrock :: (ValidElem v (Scalar v), ExpRing (Scalar v), FiniteModule v) => C1 (v -> Scalar v) +rosenbrock = unsafeProveC1 f f' + where + f v = sum [ 100*(v!(i+1) - (v!i)**2)**2 + (v!i - 1)**2 | i <- [0..dim v-2] ] + + f' v = imap go v + where + go i x = if i==dim v-1 + then pt2 + else if i== 0 + then pt1 + else pt1+pt2 + where + pt1 = 400*x*x*x - 400*xp1*x + 2*x -2 + pt2 = 200*x - 200*xm1*xm1 + xp1 = v!(i+1) + xm1 = v!(i-1) -multivariateBrent :: - ( Hilbert v - , OrdField (Scalar v) - , Optimizable v - , Optimizable (Scalar v) - ) => StopCondition_ (Iterator_brent (Scalar v)) - -> MultivariateLineSearch v -multivariateBrent !stops !f _ !x0 !f'x0 !stepGuess = {-# SCC multivariateBrent #-} do - let g y = f $ x0 + y *. f'x0 - bracket <- lineBracket g (stepGuess/2) (stepGuess*2) - brent <- fminuncM_brent_ (return . g) bracket stops - return $ _brent_x brent diff --git a/src/HLearn/Optimization/Univariate.hs b/src/HLearn/Optimization/Univariate.hs index 324a181..7aa7f62 100644 --- a/src/HLearn/Optimization/Univariate.hs +++ b/src/HLearn/Optimization/Univariate.hs @@ -1,11 +1,14 @@ -- | This module provides numerical routines for minimizing/maximizing one dimensional functions. --- All function are translated from "Numerical Recipes in C". --- The module is divided into simple and advanced sections. +-- These methods are guaranteed to converge to the correct solution if your function has exactly one minimum. +-- +-- The module is divided into simple and advanced interface. +-- The simple interface is reminiscent of most optimization packages. +-- The advanced interface uses the "History" monad to provide complex debugging information. module HLearn.Optimization.Univariate ( - -- * Simple interface -- | These simple functions provide an interface similar to Matlab/Ocatave's optimization toolkit. + -- (See for example the [fminunc documentation](http://www.mathworks.com/help/optim/ug/fminunc.html).) -- They are recommended for quick prototypes and working from within GHCI. -- Here's an example: -- @@ -27,12 +30,10 @@ module HLearn.Optimization.Univariate -- We can use it to solve problems that can't be solved with the simple interface. fminunc , fmaxunc - , fminuncM - , fmaxuncM -- * Advanced interface -- | The advanced interface is more complex, but has two advantages. - -- First, it lets us specify a level of debugging information we want. + -- First, it lets us specify a detailed level of debugging information using the "History" monad. -- Second, it lets us specify in more detail the algorithms used in the optimization. -- -- Let's repeat the first example from the simple interface. @@ -42,33 +43,37 @@ module HLearn.Optimization.Univariate -- >>> :{ -- >>> traceHistory $ do -- >>> lb <- lineBracket f 0 1 - -- >>> fminuncM_brent_ (return . f) lb (maxIterations 10 || stop_brent 1e-10) + -- >>> fminuncM_bracketed_brent_ (return . f) lb (maxIterations 10 || stop_brent 1e-10) -- >>> }: -- Iterator_brent; x=5%1; f(x)=0%1 -- -- In this case, we get an exact answer. - -- The "traceHistory" command also gives us a record of how our optimization proceded. + -- The "traceHistory" command also gives us a record of how our optimization proceeded. + -- + -- FIXME: Add an example of nested optimization + + , UnivariateMinimizer + , UnivariateMinimizerLB -- ** Brent's Method -- | Brent's method uses parabolic interpolation to find the minimum. + -- This is a good black box method. -- -- See -- and Section 10.2 of "Numerical Recipes in C" for more details. - , fminunc_brent , fminuncM_brent - , fminuncM_brent_ + , fminuncM_bracketed_brent , stop_brent - , Iterator_brent (..) + , Iterator_brent -- ** Brent's method with derivatives -- | We can make Brent's method a bit faster sometimes by using the function's derivative. + -- This method can be slower than Brent's method for some functions. -- -- See section 10.3 of "Numerical Recipes in C" for more details. - , fminunc_dbrent - , fminuncM_dbrent - , fminuncM_dbrent_ + , fminuncM_bracketed_dbrent , stop_dbrent - , Iterator_dbrent (..) + , Iterator_dbrent -- ** Golden section search -- | Golden section search is used to find the minimum of "poorly behaved" functions. @@ -76,63 +81,89 @@ module HLearn.Optimization.Univariate -- -- See -- and Section 10.1 of "Numerical Recipes in C" for more details. - , fminunc_gss , fminuncM_gss - , fminuncM_gss_ + , fminuncM_bracketed_gss , stop_gss - , Iterator_gss (..) + , Iterator_gss -- ** Line bracketing -- | The univariate optimization methods above require an interval within which a minimum is guaranteed to exist. -- - -- FIXME: switch LineBracket to the Interval type provided by subhask - -- -- See Section 10.1 of "Numerical Recipes in C" for more details. + -- + -- FIXME: + -- Should we switch "LineBracket" to the "Interval" type provided by subhask? + -- This gives us a more consistent interface at the expensive of (sometimes) an extra function call. , lineBracket , lineBracketM - , LineBracket (..) + , LineBracket + + -- * Test Functions + , slopes + , slopesWithDiscontinuity + -- ** Lipshitz Univariate + -- | These problems come from a paper by Famularo, Sergeyev, and Pugliese (hence fsp). + -- See for more details. + , fsp_problem1 ) where import SubHask -import HLearn.History - -------------------------------------------------------------------------------- - -tester :: History Double -tester = do - lb <- lineBracketM f 0 1 - gss <- fminuncM_gss_ f lb (maxIterations 5) - return $ gss_x gss - where - f x = do - let f a = abs x + (a+5-x)*(a+5) +import SubHask.Category.Trans.Derivative - lb <- lineBracket f 0 1 - b <- fminuncM_brent_ - (return . f) - lb - (maxIterations 10) - return $ _brent_fx b +import HLearn.History ------------------------------------------------------------------------------- +-- simple interface -- | Finds the minimum of a function -fminunc :: (Optimizable a, OrdField a) => (a -> a) -> a -fminunc = fminunc_brent +{-# INLINABLE fminunc #-} +fminunc :: + ( OrdField a + ) => a -- ^ initial guess of the input with maximum value + -> (a -> a) -- ^ function to minimize + -> a -- ^ input with (approximately) the minimum value +fminunc x0 f = x1 $ evalHistory $ fminuncM_brent (maxIterations 50 || noProgress) x0 (return . f) -- | Finds the maximum of a function -fmaxunc :: (Optimizable a, OrdField a) => (a -> a) -> a -fmaxunc f = fminunc_brent (-f) - --- | Finds the minimum of a monadic function -fminuncM :: (Optimizable a, OrdField a) => (a -> History a) -> a -fminuncM = fminuncM_brent +{-# INLINABLE fmaxunc #-} +fmaxunc :: + ( OrdField a + ) => a -- ^ initial guess of the input with maximum value + -> (a -> a) -- ^ function to maximize + -> a -- ^ input with (approximately) the maximum value +fmaxunc x0 f = fminunc x0 (-f) --- | Finds the maximum of a monadic function -fmaxuncM :: (Optimizable a, OrdField a) => (a -> History a) -> a -fmaxuncM f = fminuncM_brent (-f) +------------------------------------------------------------------------------- +-- helpers + +-- | The type of univariate minimization functions that require a starting point +type UnivariateMinimizer cxt opt a + = StopCondition (opt a) -- ^ controls when to stop + -> a -- ^ initial guess of the input with minimum value + -> (a -> History cxt a) -- ^ monadic function we're minimizing + -> History cxt (opt a) -- ^ result of the optimization + +-- | The type of univariate minimization functions that require a "LineBracket" bounding the solution +type UnivariateMinimizerLB cxt opt a + = StopCondition (opt a) -- ^ controls when to stop + -> LineBracket a -- ^ search for the solution within this range + -> (a -> History cxt a) -- ^ monadic function we're minimizing + -> History cxt (opt a) -- ^ result of the optimization + +-- | Converts an optimization function that expects a "LineBracket" input into one that only requires an initial starting point. +unbracket :: + ( cxt (opt a) + , cxt (LineBracket a) + , cxt (Scalar a) + , cxt a + , OrdField a + ) => UnivariateMinimizerLB cxt opt a + -> UnivariateMinimizer cxt opt a +unbracket fminuncM_bracketed stop x0 f = do + lb <- lineBracketM f (x0/2) (x0*2) + fminuncM_bracketed stop lb f ------------------------------------------------------------------------------- -- line bracketing @@ -148,26 +179,30 @@ data LineBracket a = LineBracket } deriving (Typeable) +type instance Scalar (LineBracket a) = Scalar a + instance Show a => Show (LineBracket a) where show lb = "LineBracket; a="++show (_ax lb)++"; b="++show (_bx lb)++"; c="++show (_cx lb) -- | finds two points ax and cx between which a minimum is guaranteed to exist -- this is a transliteration of the 'lineBracket' function from the \"Numerical -- Recipes\" series +{-# INLINEABLE lineBracket #-} lineBracket :: ( OrdField a ) => (a -> a) -- ^ the function we're bracketing -> a -- ^ an initial guess for the lower bound -> a -- ^ an initial guess for the upper bound - -> Optimizable a => History (LineBracket a) + -> History cxt (LineBracket a) lineBracket f = lineBracketM (return . f) +{-# INLINEABLE lineBracketM #-} lineBracketM :: ( OrdField a - ) => (a -> History a) -- ^ the function we're bracketing + ) => (a -> History cxt a) -- ^ the function we're bracketing -> a -- ^ an initial guess for the lower bound -> a -- ^ an initial guess for the upper bound - -> Optimizable a => History (LineBracket a) + -> History cxt (LineBracket a) lineBracketM !f !pt1 !pt2 = beginFunction "lineBracketM" $ do let gold = 1.618034 @@ -189,20 +224,17 @@ lineBracketM !f !pt1 !pt2 = beginFunction "lineBracketM" $ do , _fc = fcx } - iterate - (step_LineBracket f) - lb0 - stop_LineBracket + iterate (step_LineBracket f) stop_LineBracket lb0 -stop_LineBracket :: OrdField a => LineBracket a -> LineBracket a -> History Bool +stop_LineBracket :: OrdField a => StopCondition (LineBracket a) stop_LineBracket _ lb = if _fb lb /= _fb lb then error "NaN in linebracket" else return $ _fb lb <= _fc lb step_LineBracket :: OrdField a - => (a -> History a) + => (a -> History cxt a) -> LineBracket a - -> History (LineBracket a) + -> History cxt (LineBracket a) step_LineBracket !f lb@(LineBracket ax bx cx fa fb fc) = do let sign a b = if b>0 then abs a else -(abs a) @@ -293,6 +325,14 @@ data Iterator_gss a = Iterator_gss } deriving (Typeable) +type instance Scalar (Iterator_gss a) = Scalar a + +instance (IsScalar a, POrd a) => Has_x1 Iterator_gss a where + x1 gss = if (_gss_fxb < _gss_fxc) gss then _gss_fxb gss else _gss_fxc gss + +instance (IsScalar a, POrd a) => Has_fx1 Iterator_gss a where + fx1 = inf _gss_fxb _gss_fxc + instance (ClassicalLogic a, Show a, OrdField a) => Show (Iterator_gss a) where show gss = "Iterator_gss; "++"x="++show x++"; f(x)="++show fx where @@ -308,24 +348,22 @@ gss_fx gss = min (_gss_fxb gss) (_gss_fxc gss) --------------------------------------- --- | A simple interface to golden section search -fminunc_gss :: ( Optimizable a , OrdField a ) => (a -> a) -> a -fminunc_gss f = fminuncM_gss (return . f) - --- | A simple monadic interface to golden section search -fminuncM_gss :: forall a. ( Optimizable a , OrdField a ) => (a -> History a) -> a -fminuncM_gss f = evalHistory $ do - lb <- lineBracketM f 0 1 - gss <- fminuncM_gss_ f lb (maxIterations 200 || stop_gss (1e-12 :: a)) - return $ gss_x gss - --- | The most generic interface to golden section search -fminuncM_gss_ :: ( Optimizable a , OrdField a ) - => (a -> History a) -- ^ the function we're minimizing - -> LineBracket a -- ^ bounds between which a minimum must exist - -> StopCondition_ (Iterator_gss a) -- ^ controls the number of iterations - -> History (Iterator_gss a) -fminuncM_gss_ f (LineBracket ax bx cx fa fb fc) stop = beginFunction "goldenSectionSearch_" $ do +-- | Golden section search initialized by a starting point +{-# INLINEABLE fminuncM_gss #-} +fminuncM_gss :: + ( cxt (LineBracket a) + , cxt a + , OrdField a + ) => UnivariateMinimizer cxt Iterator_gss a +fminuncM_gss = unbracket fminuncM_bracketed_gss + +-- | Golden section search that only searches within a specified "LineBracket" +{-# INLINEABLE fminuncM_bracketed_gss #-} +fminuncM_bracketed_gss :: + ( OrdField a + , cxt a + ) => UnivariateMinimizerLB cxt Iterator_gss a +fminuncM_bracketed_gss stop (LineBracket ax bx cx fa fb fc) f = beginFunction "goldenSectionSearch_" $ do let r = 0.61803399 c = 1-r @@ -344,13 +382,10 @@ fminuncM_gss_ f (LineBracket ax bx cx fa fb fc) stop = beginFunction "goldenSect , _gss_xd = cx } - iterate - (step_gss f) - gss0 - stop + iterate (step_gss f) stop gss0 where - step_gss :: OrdField a => (a -> History a) -> Iterator_gss a -> History (Iterator_gss a) + step_gss :: OrdField a => (a -> History cxt a) -> Iterator_gss a -> History cxt (Iterator_gss a) step_gss f (Iterator_gss f1 f2 x0 x1 x2 x3) = if f2 < f1 then do let x' = r*x2+c*x3 @@ -365,7 +400,8 @@ fminuncM_gss_ f (LineBracket ax bx cx fa fb fc) stop = beginFunction "goldenSect c = 1-r -- | Does not stop until the independent variable is accurate to within the tolerance passed in. -stop_gss :: OrdField a => a -> StopCondition_ (Iterator_gss a) +{-# INLINEABLE stop_gss #-} +stop_gss :: OrdField a => a -> StopCondition (Iterator_gss a) stop_gss tol _ (Iterator_gss _ _ !x0 !x1 !x2 !x3 ) = return $ abs (x3-x0) <= tol*(abs x1+abs x2) @@ -387,8 +423,24 @@ data Iterator_brent a = Iterator_brent } deriving Typeable -instance Show a => Show (Iterator_brent a) where - show b = "Iterator_brent; x="++show (_brent_x b)++"; f(x)="++show (_brent_fx b) +type instance Scalar (Iterator_brent a) = Scalar a +type instance Logic (Iterator_brent a) = Bool + +instance Eq a => Eq_ (Iterator_brent a) where + b1==b2 + = (_brent_a b1 == _brent_a b2) + && (_brent_b b1 == _brent_b b2) + && (_brent_d b1 == _brent_d b2) + && (_brent_e b1 == _brent_e b2) + && (_brent_fv b1 == _brent_fv b2) + && (_brent_fw b1 == _brent_fw b2) + && (_brent_fx b1 == _brent_fx b2) + && (_brent_v b1 == _brent_v b2) + && (_brent_w b1 == _brent_w b2) + && (_brent_x b1 == _brent_x b2) + +instance (Field a, IsScalar a, Show a) => Show (Iterator_brent a) where + show b = "Iterator_brent; x="++show (_brent_x b)++"; f(x)="++show (fx1 b) instance IsScalar a => Has_x1 Iterator_brent a where x1 = _brent_x @@ -396,117 +448,159 @@ instance IsScalar a => Has_x1 Iterator_brent a where instance IsScalar a => Has_fx1 Iterator_brent a where fx1 = _brent_fx --- | A simple interface to Brent's method -fminunc_brent :: ( Optimizable a, OrdField a ) => (a -> a) -> a -fminunc_brent f = fminuncM_brent (return . f) - --- | A simple monadic interface to Brent's method -fminuncM_brent :: ( Optimizable a, OrdField a ) => (a -> History a) -> a -fminuncM_brent f = evalHistory $ do - lb <- lineBracketM f 0 1 - b <- fminuncM_brent_ f lb ( maxIterations 200 || stop_brent 1e-12 ) - return $ _brent_x b - --- | The most generic interface to Brent's method -fminuncM_brent_ :: - ( Optimizable a , OrdField a - ) => (a -> History a) -- ^ the function we're minimizing - -> LineBracket a -- ^ bounds between which a minimum must exist - -> StopCondition_ (Iterator_brent a) -- ^ controls the number of iterations - -> History (Iterator_brent a) -fminuncM_brent_ f (LineBracket ax bx cx fa fb fc) stop = beginFunction "brent" $ do +-- | Brent's method initialized by a starting point +{-# INLINEABLE fminuncM_brent #-} +fminuncM_brent :: + ( cxt (LineBracket a) + , cxt a + , OrdField a + ) => UnivariateMinimizer cxt Iterator_brent a +fminuncM_brent = unbracket fminuncM_bracketed_brent + +-- | Brent's method that only searches within a specified "LineBracket" +{-# INLINEABLE fminuncM_bracketed_brent #-} +fminuncM_bracketed_brent :: + ( OrdField a + , cxt a + ) => UnivariateMinimizerLB cxt Iterator_brent a +fminuncM_bracketed_brent stop (LineBracket ax bx cx fa fb fc) f = beginFunction "brent" $ do fbx <- f bx - iterate - (step_brent f) - ( Iterator_brent - { _brent_a = min ax cx - , _brent_b = max ax cx - , _brent_d = zero - , _brent_e = zero - , _brent_v = bx - , _brent_w = bx - , _brent_x = bx - , _brent_fv = fbx - , _brent_fw = fbx - , _brent_fx = fbx - } - ) - stop - - where - step_brent :: - ( OrdField a - ) => (a -> History a) -> Iterator_brent a -> History (Iterator_brent a) - step_brent f brent@(Iterator_brent a b d e fv fw fx v w x) = do - let cgold = 0.3819660 - zeps = 1e-10 - sign a b = if b>0 then abs a else -(abs a) - tol = 1e-6 + iterate (step_brent_noTolerance f) stop $ Iterator_brent + { _brent_a = min ax cx + , _brent_b = max ax cx + , _brent_d = zero + , _brent_e = zero + , _brent_v = bx + , _brent_w = bx + , _brent_x = bx + , _brent_fv = fbx + , _brent_fw = fbx + , _brent_fx = fbx + } + +-- | This version of brent's method is slightly simpler than the one presented in "Numerical Recipes". +-- In particular, the "Numerical Recipes" version has a minor optimization where it does not reevaluate a function if the input changes below some tolerance. +-- Removing this check actually *improves* numeric stability. +-- We can use types the are more accurate than "Double"s (e.g. "Rational"s) where this check doesn't make sense. +step_brent_noTolerance :: + ( OrdField a + ) => (a -> History cxt a) -> Iterator_brent a -> History cxt (Iterator_brent a) +step_brent_noTolerance f brent@(Iterator_brent a b d e fv fw fx v w x) = do + let cgold = 0.3819660 + xm = 0.5*(a+b) - xm = 0.5*(a+b) - tol1' = tol*(abs x)+zeps - tol2' = 2*tol1' + r' = (x-w)*(fx-fv) + q' = (x-v)*(fx-fw) + p' = (x-v)*q'-(x-w)*r' + q'' = 2*(q'-r') + p'' = if q''>0 then -p' else p' + q''' = abs q'' + etemp' = e + + (d',e') = if abs p'' >= abs (0.5*q'''*etemp') || p'' <= q'''*(a-x) || p'' >= q'''*(b-x) + then let e'' = if x>=xm then a-x else b-x in (cgold*e'',e'') + else let d''=p''/q'''; u''=x+d'' in (d'',d) + + u' = x+d' + + fu' <- f u' + + return $ if fu' <= fx + then brent + { _brent_e = e' + , _brent_d = d' + , _brent_a = if u' >= x then x else a + , _brent_b = if u' >= x then b else x + , _brent_v = w + , _brent_w = x + , _brent_x = u' + , _brent_fv = fw + , _brent_fw = fx + , _brent_fx = fu' + } + else brent + { _brent_e = e' + , _brent_d = d' + , _brent_a = if u' < x then u' else a + , _brent_b = if u' < x then b else u' + , _brent_v = if fu' <= fw || w==x then w else if fu' <= fv || v==x || v==w then u' else v + , _brent_fv = if fu' <= fw || w==x then fw else if fu' <= fv || v==x || v==w then fu' else fv + , _brent_w = if fu' <= fw || w==x then u' else w + , _brent_fw = if fu' <= fw || w==x then fu' else fw + } - (d',e') = if abs e > tol1' - then let - r' = (x-w)*(fx-fv) - q' = (x-v)*(fx-fw) - p' = (x-v)*q'-(x-w)*r' - q'' = 2*(q'-r') - p'' = if q''>0 then -p' else p' - q''' = abs q'' - etemp' = e - in if abs p'' >= abs (0.5*q'''*etemp') || p'' <= q'''*(a-x) || p'' >= q'''*(b-x) - then let e'' = if x>=xm then a-x else b-x in (cgold*e'',e'') - else let d''=p''/q'''; u''=x+d'' in - if u''-a < tol2' || b-u'' < tol2' - then (sign tol1' (xm-x),d) - else (d'',d) - else let e'' = if x>=xm then a-x else b-x in (cgold*e'',e'') - - u' = if abs d' >= tol1' - then x+d' - else x+sign tol1' d' +-- | This version of Brent's method is a direct translation from "Numerical Recipes". +-- It is very slightly faster than "step_brent_noTolerance" but has worse accuracy. +step_brent_orig :: + ( OrdField a + ) => (a -> History cxt a) -> Iterator_brent a -> History cxt (Iterator_brent a) +step_brent_orig f brent@(Iterator_brent a b d e fv fw fx v w x) = do + let cgold = 0.3819660 + xm = 0.5*(a+b) - fu' <- f u' + sign a b = if b>0 then abs a else -(abs a) + zeps = 1e-10 + tol = 1e-12 + tol1' = tol*(abs x)+zeps + tol2' = 2*tol1' - return $ if fu' <= fx - then brent - { _brent_e = e' - , _brent_d = d' - , _brent_a = if u' >= x then x else a - , _brent_b = if u' >= x then b else x - , _brent_v = w - , _brent_w = x - , _brent_x = u' - , _brent_fv = fw - , _brent_fw = fx - , _brent_fx = fu' - } - else brent - { _brent_e = e' - , _brent_d = d' - , _brent_a = if u' < x then u' else a - , _brent_b = if u' < x then b else u' - , _brent_v = if fu' <= fw || w==x then w else if fu' <= fv || v==x || v==w then u' else v - , _brent_fv = if fu' <= fw || w==x then fw else if fu' <= fv || v==x || v==w then fu' else fv - , _brent_w = if fu' <= fw || w==x then u' else w - , _brent_fw = if fu' <= fw || w==x then fu' else fw - } + (d',e') = if abs e > tol1' + then let + r' = (x-w)*(fx-fv) + q' = (x-v)*(fx-fw) + p' = (x-v)*q'-(x-w)*r' + q'' = 2*(q'-r') + p'' = if q''>0 then -p' else p' + q''' = abs q'' + etemp' = e + in if abs p'' >= abs (0.5*q'''*etemp') || p'' <= q'''*(a-x) || p'' >= q'''*(b-x) + then let e'' = if x>=xm then a-x else b-x in (cgold*e'',e'') + else let d''=p''/q'''; u''=x+d'' in + if u''-a < tol2' || b-u'' < tol2' + then (sign tol1' (xm-x),d) + else (d'',d) + else let e'' = if x>=xm then a-x else b-x in (cgold*e'',e'') + + u' = if abs d' >= tol1' + then x+d' + else x+sign tol1' d' + + fu' <- f u' + + return $ if fu' <= fx + then brent + { _brent_e = e' + , _brent_d = d' + , _brent_a = if u' >= x then x else a + , _brent_b = if u' >= x then b else x + , _brent_v = w + , _brent_w = x + , _brent_x = u' + , _brent_fv = fw + , _brent_fw = fx + , _brent_fx = fu' + } + else brent + { _brent_e = e' + , _brent_d = d' + , _brent_a = if u' < x then u' else a + , _brent_b = if u' < x then b else u' + , _brent_v = if fu' <= fw || w==x then w else if fu' <= fv || v==x || v==w then u' else v + , _brent_fv = if fu' <= fw || w==x then fw else if fu' <= fv || v==x || v==w then fu' else fv + , _brent_w = if fu' <= fw || w==x then u' else w + , _brent_fw = if fu' <= fw || w==x then fu' else fw + } -- | Does not stop until the independent variable is accurate to within the tolerance passed in. --- --- FIXME: if we get an exact solution this doesn't stop the optimization -stop_brent :: OrdField a => a -> StopCondition_ (Iterator_brent a) -stop_brent tol _ opt = return $ abs (x-xm) < tol2'-0.5*(b-a) +{-# INLINEABLE stop_brent #-} +stop_brent :: OrdField a => a -> StopCondition (Iterator_brent a) +stop_brent tol _ opt = return $ abs (x-xm) < tol2'-0.5*(b-a) where (Iterator_brent a b d e fv fw fx v w x) = opt xm = 0.5*(a+b) tol1' = tol*(abs x)+zeps tol2' = 2*tol1' - zeps = 1e-10 - - + zeps = 0 -- 1e-10 ------------------------------------------------------------------------------- -- Brent's method with derivatives @@ -530,56 +624,49 @@ data Iterator_dbrent a = Iterator_dbrent } deriving Typeable +type instance Scalar (Iterator_dbrent a) = Scalar a + instance Show a => Show (Iterator_dbrent a) where show b = "Iterator_brent; x="++show (_dbrent_x b)++"; f(x)="++show (_dbrent_fx b) --- | A simple interface to Brent's method with derivatives -fminunc_dbrent :: ( Optimizable a, OrdField a ) => (a -> a) -> (a -> a) -> a -fminunc_dbrent f df = fminuncM_dbrent (return . f) (return . df) - --- | A simple monadic interface to Brent's method with derivatives -fminuncM_dbrent :: ( Optimizable a, OrdField a ) => (a -> History a) -> (a -> History a) -> a -fminuncM_dbrent f df = evalHistory $ do - lb <- lineBracketM f 0 1 - b <- fminuncM_dbrent_ f df lb ( maxIterations 200 || stop_dbrent 1e-12 ) - return $ _dbrent_x b - --- | The most generic interface to Brent's method with derivatives -fminuncM_dbrent_ :: - ( Optimizable a , OrdField a - ) => (a -> History a) -- ^ the function we're minimizing - -> (a -> History a) -- ^ the function's derivative - -> LineBracket a -- ^ bounds between which a minimum must exist - -> StopCondition_ (Iterator_dbrent a) -- ^ controls the number of iterations - -> History (Iterator_dbrent a) -fminuncM_dbrent_ f df (LineBracket ax bx cx fa fb fc) stop = beginFunction "dbrent" $ do +-- | Brent's method with derivatives that only searches within a specified "LineBracket" +-- +-- FIXME: +-- Update this function so it uses the derivative category. +-- This will require some careful thought about: +-- 1) how derivatives interact with monads, +-- 2) restructuring the "UnivariateMinimization" types to handle arbitrary categories. +-- +{-# INLINEABLE fminuncM_bracketed_dbrent #-} +fminuncM_bracketed_dbrent :: + ( OrdField a + , cxt a + ) => StopCondition (Iterator_dbrent a) -- ^ controls when to stop + -> LineBracket a -- ^ search for the solution within this range + -> (a -> History cxt a) -- ^ monadic function we're minimizing + -> (a -> History cxt a) -- ^ derivative of the function we're minimizing + -> History cxt (Iterator_dbrent a) -- ^ result of the optimization +fminuncM_bracketed_dbrent stop (LineBracket ax bx cx fa fb fc) f df = beginFunction "dbrent" $ do fbx <- f bx dfx <- df bx - iterate - (step_dbrent f df) - ( Iterator_dbrent - { _dbrent_a = min ax cx - , _dbrent_b = max ax cx - , _dbrent_d = zero - , _dbrent_e = zero - , _dbrent_v = bx - , _dbrent_w = bx - , _dbrent_x = bx - , _dbrent_fv = fbx - , _dbrent_fw = fbx - , _dbrent_fx = fbx - , _dbrent_dv = dfx - , _dbrent_dw = dfx - , _dbrent_dx = dfx - , _dbrent_break = False - } - ) - stop + iterate (step_dbrent f df) stop $ Iterator_dbrent + { _dbrent_a = min ax cx + , _dbrent_b = max ax cx + , _dbrent_d = zero + , _dbrent_e = zero + , _dbrent_v = bx + , _dbrent_w = bx + , _dbrent_x = bx + , _dbrent_fv = fbx + , _dbrent_fw = fbx + , _dbrent_fx = fbx + , _dbrent_dv = dfx + , _dbrent_dw = dfx + , _dbrent_dx = dfx + , _dbrent_break = False + } where - step_dbrent :: - ( OrdField a - ) => (a -> History a) -> (a -> History a) -> Iterator_dbrent a -> History (Iterator_dbrent a) step_dbrent f df dbrent@(Iterator_dbrent a b d e fv fw fx dv dw dx v w x _) = do let zeps = 1e-10 sign a b = if b>0 then abs a else -(abs a) @@ -659,9 +746,8 @@ fminuncM_dbrent_ f df (LineBracket ax bx cx fa fb fc) stop = beginFunction "dbre } -- | Does not stop until the independent variable is accurate to within the tolerance passed in. --- --- FIXME: if we get an exact solution this doesn't stop the optimization -stop_dbrent :: OrdField a => a -> StopCondition_ (Iterator_dbrent a) +{-# INLINEABLE stop_dbrent #-} +stop_dbrent :: OrdField a => a -> StopCondition (Iterator_dbrent a) stop_dbrent tol _ opt = return $ should_break || abs (x-xm) < tol2'-0.5*(b-a) where (Iterator_dbrent a b d e fv fw fx dv dw dx v w x should_break) = opt @@ -670,3 +756,48 @@ stop_dbrent tol _ opt = return $ should_break || abs (x-xm) < tol2'-0.5*(b-a) tol2' = 2*tol1' zeps = 1e-10 +-------------------------------------------------------------------------------- +-- test functions + +-- | A simple piecewise linear function. +{-# INLINEABLE slopes #-} +slopes :: (ClassicalLogic r, OrdField r) => r -> r +slopes x + | x < 0 = -2*x + | x < 30 = -x + | x >= 30 = x-60 + +-- | This problem is interesting because it has no minimum when the domain is the real numbers. +-- Notice the discontinuity at x==30. +{-# INLINEABLE slopesWithDiscontinuity #-} +slopesWithDiscontinuity :: (ClassicalLogic r, OrdField r) => r -> r +slopesWithDiscontinuity x + | x < 0 = -2*x + | x < 30 = -x + | x >= 30 = x-60+10 + +{-# INLINEABLE fsp_problem1 #-} +fsp_problem1 :: Real r => r -> r +fsp_problem1 x = -13/6*x + sin (13/4*(2*x+5)) - 53/12 + + +{- +tester :: History Double +tester = do + lb <- lineBracketM f 0 1 + gss <- fminuncM_gss_ f lb (maxIterations 5) + return $ gss_x gss + where + f x = do + let f a = abs x + (a+5-x)*(a+5) + + lb <- lineBracket f 0 1 + b <- fminuncM_brent_ + (return . f) + lb + (maxIterations 10) + return $ _brent_fx b + +------------------------------------------------------------------------------- + +-} diff --git a/src/exec/hlearn-embed.hs b/src/exec/hlearn-embed.hs deleted file mode 100644 index 24ab507..0000000 --- a/src/exec/hlearn-embed.hs +++ /dev/null @@ -1,423 +0,0 @@ -{-# LANGUAGE ScopedTypeVariables #-} -{-# LANGUAGE TemplateHaskell #-} -{-# LANGUAGE DeriveDataTypeable #-} -{-# LANGUAGE DataKinds #-} -{-# LANGUAGE FlexibleInstances #-} -{-# LANGUAGE TypeFamilies #-} -{-# LANGUAGE RankNTypes #-} -{-# LANGUAGE BangPatterns #-} -{-# LANGUAGE FlexibleContexts #-} -{-# LANGUAGE StandaloneDeriving #-} -{-# LANGUAGE GeneralizedNewtypeDeriving #-} -{-# LANGUAGE TypeOperators #-} -{-# LANGUAGE MultiParamTypeClasses #-} -{-# LANGUAGE UndecidableInstances #-} -{-# LANGUAGE ConstraintKinds #-} -{-# LANGUAGE RebindableSyntax #-} -{-# LANGUAGE ForeignFunctionInterface #-} -{-# LANGUAGE NoImplicitPrelude #-} - -import SubHask -import SubHask.Algebra.Parallel -import SubHask.Compatibility.Cassava -import SubHask.Compatibility.Containers -import SubHask.Compatibility.Vector -import SubHask.Compatibility.Vector.Lebesgue --- import SubHask.Monad as S -import Control.Monad - -import HLearn.Data.LoadData -import HLearn.History (HistoryMonad(..),DynamicHistory(..),runDynamicHistory) -import HLearn.History.DisplayMethods -import HLearn.History.Timing -import HLearn.Optimization.Common -import HLearn.Optimization.GradientDescent -import HLearn.Optimization.LineMinimization.Univariate -import HLearn.Optimization.LineMinimization.Multivariate - -import Data.List (replicate,tail,words,intersperse,zip) -import System.IO -import Debug.Trace - -import System.IO.Unsafe - -------------------------------------------------------------------------------- - -import qualified Control.Monad as M -import Control.Monad.Random hiding (fromList) -import Control.Monad.Identity (Identity) - -type UVector = UnboxedVector -type BVector = BoxedVector - --- _replicateM :: (Unfoldable as, Elem as~a, Monad Hask m) => Int -> m a -> m as -_replicateM n = fmap fromList . replicateM n - -{- --- type HistoryMonad_ m = (Monad Hask m, HistoryMonad m) ---------- - -instance Functor Hask (RandT g Identity) where - fmap = M.fmap - -instance Then (RandT g Identity) where - (>>) = (M.>>) - -instance Monad Hask (RandT g Identity) where - join = M.join - return_ = M.return - ---------- - -instance Functor Hask DynamicHistory where - fmap f (DynamicHistory h) = DynamicHistory $ M.fmap f h - -instance Then DynamicHistory where - (>>) (DynamicHistory a) (DynamicHistory b) = DynamicHistory $ (M.>>) a b - -instance Monad Hask DynamicHistory where - join = M.join - return_ a = DynamicHistory $ M.return a --} - -------------------------------------------------------------------------------- - -main = derivMain - -------------------------------------------------------------------------------- - -derivMain = do - let f :: Double -> Double - f x = x*x - f' x = 2*x - - print $ testDerivativeHilbert f f' 1000 (1e-6::Double) (1e-2::Double) - - putStrLn "done" - --- testDerivativeHilbert :: --- ( Hilbert a --- , Lattice_ a --- ) => (a -> Scalar a) -- ^ function to test --- -> (a -> a) -- ^ derivative to test --- -> a -- ^ test location --- -> a -- ^ direction to test --- -> Scalar a -- ^ scale of test --- -> Logic a -testDerivativeHilbert f f' x dir scale = --- trace ("hi="++show hi) $ --- trace ("lo="++show lo) $ - trace ("f'1="++show f'1) $ - trace ("nd="++show numderiv) $ - trace ("f'2="++show f'2) $ - - (f'1 >= numderiv && f'2 <= numderiv) || (f'1 <= numderiv && f'2 >= numderiv) - where - f'1 = normalize dir <> f' x - f'2 = normalize dir <> f' (x + dir) - numderiv = (size $ f x - f (x+dir)) / size dir - -------------------------------------------------------------------------------- - -embed = do - - -- load data - xls :: Array (Labeled' (UVector Double) Int) --- <- loadCSVLabeled "/home/user/proj/hlearn/datasets/csv/uci/haberman.data" 3 - <- loadCSVLabeled "/home/user/proj/hlearn/datasets/csv/uci/wine.csv" 0 - - let xs = ifor xls $ \_ (Labeled' v _) -> v - - ys :: Array (UVector Double) - <- _replicateM (size xs) - . fmap listToVector - . replicateM 2 - $ getRandomR (-1,1) - --- ys :: Array (UVector Double) --- <- fmap fromList --- . forM [0..size xs-1] $ \i -> --- fmap listToVector $ do --- a <- getRandomR (-1,1) --- return [fromIntegral i,a] - - -- run optimization - - let (f,f') = mksne xs - - cgd <- runDynamicHistory linearTrace $ fmap (jitter 40) $ conjugateGradientDescent_ - ( lineSearchBrent ( brentTollerance 1e-3 || maxIterations 10 )) - polakRibiere - (\ v -> f (ArrayT v) ) - (\ v -> unArrayT $ f' (ArrayT v) ) - (unArrayT ys) - ( maxIterations 500 ) - - cgd' <- runDynamicHistory linearTrace $ conjugateGradientDescent_ - ( lineSearchBrent ( brentTollerance 1e-3 || maxIterations 10 )) - polakRibiere - (\ v -> f (ArrayT v) ) - (\ v -> unArrayT $ f' (ArrayT v) ) - (__x1 cgd) - ( maxIterations 500 ) - - let ys' = ArrayT $ __x1 cgd' - - -- save output - let yls' = ifor ys' $ \i y -> Labeled' y (yLabeled' $ xls!i) - hout <- openFile "hlearn-embed.dat" WriteMode - forM_ (toList yls') $ \(Labeled' y l) -> do - hPutStr hout (show l) - hPutStr hout " " - hPutStr hout $ concat $ intersperse " " $ tail $ words $ replace ((=='[')||(==']')||(== ',')) ' ' $ show y - hPutStrLn hout "" - hClose hout - - putStrLn "done" - -------------------------------------------------------------------------------- - -replace :: (a -> Bool) -> a -> [a] -> [a] -replace f a xs = map (\b -> if f b then a else b) xs - -for :: [a] -> (a -> b) -> [b] -for xs f = map f xs - -ifor xs f = imap f xs -ifor_ xs f = ifor xs (\k v -> f k) - -isum xs f = sum $ ifor xs f -isum_ xs f = isum xs (\k v -> f k) - -sum_ = sumOver - -sumOver :: (Elem as~a, Foldable as, Monoid b) => as -> (a -> b) -> b -sumOver as f = sum $ for (toList as) f - -lg x = log x / log 2 - ----------------------------------------- - -jitter n a = a { __x1 = randShiftVector n $ __x1 a } - -randShiftVector :: Double -> BVector (UVector Double) -> BVector (UVector Double) -randShiftVector n v = unArrayT $ fromList $ map go $ toList $ ArrayT v - where - go v = unArrayT $ fromList $ map go2 $ zip [0..] $ toList $ ArrayT v - - go2 (i,v) = v+rand i - where - {-# NOINLINE rand #-} - rand i = (/n) $ unsafePerformIO $ randomRIO (-sumarr!i,sumarr!i) - - sumarr :: Array Double - sumarr = fromList . toList $ ArrayT $ maxarr-minarr - maxarr = foldl1' sup $ ArrayT v - minarr = foldl1' inf $ ArrayT v - ----------------------------------------- - -divergence x y = let z = distance x y in z*z - -mksne - :: Array (UVector Double) - -> ( Array (UVector Double) -> Double - , Array (UVector Double) -> Array (UVector Double) - ) -mksne xs = - trace ("max sigma="++show (maximum [sigma!i | i <- [0..size sigma-1]])) $ - trace ("min sigma="++show (minimum [sigma!i | i <- [0..size sigma-1]])) $ - (sne,sne') - where - sne :: Array (UVector Double) -> Double - sne ys = isum_ xs $ \i -> - isum_ xs $ \j -> - p i j * (logp i j - logq i j) - - where - logq i j = (- divergence (ys!i) (ys!j)) - (yarrlog!i) - - yarrlog = ifor_ ys $ \i -> - maximum $ - for (toList ys) $ \y -> -divergence (ys!i) y -- / (2 * sigma!i) - - sne' :: Array (UVector Double) -> Array (UVector Double) - sne' ys = ifor_ ys $ \i -> - isum_ ys $ \j -> - (ys!i - ys!j) .* (p i j - q i j + p j i - q j i) - - where - q i j = exp (- divergence (ys!i) (ys!j)) / (yarr!i) - - yarr = ifor_ ys $ \i -> - sum_ ys $ \y -> exp ( -divergence (ys!i) y ) -- / (2 * sigma!i)) - - -- common pij - p i j = exp (- divergence (xs!i) (xs!j) / (2 * sigma!i)) / (xarr!i) - - xarr = arr xs - arr zs = ifor_ zs $ \i -> - sum_ zs $ \z -> exp ( -divergence (zs!i) z / (2 * sigma!i)) - - -- log of above - logp i j = (- divergence (xs!i) (xs!j)) / (2 * sigma!i) - (xarrlog!i) - - xarrlog = arrlog xs - arrlog zs = ifor_ zs $ \i -> - maximum $ - for (toList zs) $ \z -> -divergence (zs!i) z / (2 * sigma!i) - - -- sigma - targetPerplexity = 15 - --- sigma :: UArray Double - sigma = ifor_ xs $ \i -> let s=go 1000000 1 10 i in s*s - where - go :: Double -> Double -> Double -> Int -> Double - go !hi !lo 0 _ = lo+(hi-lo)/10 - go !hi !lo !itr !i = if perplexity > targetPerplexity - then go ave lo (itr-1) i - else go hi ave (itr-1) i - - where - perplexity = 2 ** (-isum_ xs $ \j -> let pji = p j i in pji * lg pji) - - ave = lo+(hi-lo)/10 - - p i j = exp (- divergence (xs!i) (xs!j) / (2*ave*ave)) - / (xarr!i) - - xarr = arr xs - arr zs = ifor_ zs $ \i -> - sum_ zs $ \z -> exp ( -divergence (zs!i) z / (2*ave*ave)) - - ----------------------------------------- - -mktsne - :: Array (UVector Double) - -> ( Array (UVector Double) -> Double - , Array (UVector Double) -> Array (UVector Double) - ) -mktsne xs = (tsne,tsne') - where - tsne :: Array (UVector Double) -> Double - tsne ys = isum_ xs $ \i -> - isum_ xs $ \j -> - p i j * (logp i j - log (q i j)) - - where - q i j = (yarr!i)/(1 + divergence (ys!i) (ys!j)) - yarr = ifor_ ys $ \i -> - sum_ ys $ \y -> 1/(1+divergence (ys!i) y) - - - tsne' :: Array (UVector Double) -> Array (UVector Double) - tsne' ys = ifor_ ys $ \i -> - isum_ ys $ \j -> - (ys!i - ys!j) .* (p i j - q i j + p j i - q j i) - - where - q i j = (yarr!i)/(1 + divergence (ys!i) (ys!j)) - - yarr = ifor_ ys $ \i -> - sum_ ys $ \y -> 1/(1+divergence (ys!i) y) - - -- common pij - p i j = exp (- divergence (xs!i) (xs!j) / (2 * sigma!i)) / (xarr!i) - - xarr = arr xs - arr zs = ifor_ zs $ \i -> - sum_ zs $ \z -> exp ( -divergence (zs!i) z / (2 * sigma!i)) - - -- log of above - logp i j = (- divergence (xs!i) (xs!j)) - (xarrlog!i) - - xarrlog = arrlog xs - arrlog zs = ifor_ zs $ \i -> - maximum $ - for (toList zs) $ \z -> -divergence (zs!i) z - - -- sigma - targetPerplexity = 10 - - sigma = ifor_ xs $ \i -> go 5 50 5 i - where - go :: Double -> Double -> Double -> Int -> Double - go hi lo 0 _ = (hi+lo)/2 - go hi lo itr i = if perplexity < targetPerplexity - then go (hi-ave/2) lo (itr-1) i - else go hi (lo+ave/2) (itr-1) i - - where - perplexity = 2 ** entropy i - - ave = (hi+lo)/2 - - p i j = exp (- divergence (xs!i) (xs!j) / (2*ave*ave)) - / (xarr!i) - - xarr = arr xs - arr zs = ifor_ zs $ \i -> - sum_ zs $ \z -> exp ( -divergence (zs!i) z / (2*ave*ave)) - - entropy i = -isum_ xs $ \j -> let pji = p j i in pji * lg pji - - ----------------------------------------- ----------------------------------------- - -tsne :: Array (UVector Double) - -> Array (UVector Double) - -> Double -tsne xs ys --- = trace ("tsne ret="++show ret) $ ret - = ret - where - ret = isum_ xs $ \i -> - isum_ xs $ \j -> - p i j * (logp i j - log (q i j)) - - p i j = exp (- divergence (xs!i) (xs!j)) / (xarr!i) - q i j = (yarr!i)/(1 + divergence (ys!i) (ys!j)) - - logp i j = (- divergence (xs!i) (xs!j)) - (xarrlog!i) - - -- memoize the denominator - xarr = ifor_ xs $ \i -> - sum_ xs $ \x -> exp ( -divergence (xs!i) x ) - - yarr = ifor_ ys $ \i -> - sum_ ys $ \y -> 1/(1+divergence (ys!i) y) - - xarrlog = arrlog xs - arrlog zs = ifor_ zs $ \i -> - maximum $ - for (toList zs) $ \z -> -divergence (zs!i) z - -tsne' :: Array (UVector Double) - -> Array (UVector Double) - -> Array (UVector Double) -tsne' xs ys = ret - where - ret = ifor_ ys $ \i -> - isum_ ys $ \j -> - (ys!i - ys!j) .* (p i j - q i j + p j i - q j i) - - p i j = exp (- divergence (xs!i) (xs!j)) / (xarr!i) / (2*(fromIntegral $ size xs)) - q i j = (yarr!i)/(1 + divergence (ys!i) (ys!j)) - - logp i j = (- divergence (xs!i) (xs!j)) - (xarrlog!i) - - -- memoize the denominator - xarr = ifor_ xs $ \i -> - sum_ xs $ \x -> exp ( -divergence (xs!i) x ) - - yarr = ifor_ ys $ \i -> - sum_ ys $ \y -> 1/(1+divergence (ys!i) y) - - xarrlog = arrlog xs - arrlog zs = ifor_ zs $ \i -> - maximum $ - for (toList zs) $ \z -> -divergence (zs!i) z - diff --git a/src/exec/hlearn-knn.hs b/src/exec/hlearn-knn.hs deleted file mode 100644 index 7ef09bb..0000000 --- a/src/exec/hlearn-knn.hs +++ /dev/null @@ -1,416 +0,0 @@ -{-# LANGUAGE ScopedTypeVariables,TemplateHaskell,DeriveDataTypeable,DataKinds,FlexibleInstances,TypeFamilies,RankNTypes,BangPatterns,FlexibleContexts,StandaloneDeriving,GeneralizedNewtypeDeriving,TypeOperators, UndecidableInstances, ExistentialQuantification, GADTs #-} - -import Control.Applicative -import Control.DeepSeq -import Control.Monad -import Control.Monad.Random -import Data.Csv -import Data.List -import Data.Maybe -import qualified Data.ByteString.Lazy.Char8 as BS -import qualified Data.Foldable as F -import qualified Data.Set as Set -import qualified Data.Vector as V -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Storable as VS -import qualified Data.Vector.Unboxed as VU -import qualified Data.Vector.Unboxed.Mutable as VUM -import System.Console.CmdArgs.Implicit -import System.Environment -import System.IO -import Text.Read - -import Debug.Trace - -import qualified Control.ConstraintKinds as CK -import HLearn.Algebra -import HLearn.Models.Classifiers.Common -import HLearn.Models.Classifiers.NearestNeighbor -import HLearn.Models.Classifiers.NaiveNN -import HLearn.DataStructures.CoverTree -import HLearn.DataStructures.SpaceTree -import HLearn.DataStructures.SpaceTree.Algorithms.NearestNeighbor -import HLearn.DataStructures.SpaceTree.DualTreeMonoids -import HLearn.Evaluation.CrossValidation -import HLearn.Metrics.Lebesgue -import HLearn.Metrics.Mahalanobis -import HLearn.Metrics.Mahalanobis.Normal -import HLearn.Metrics.Mahalanobis.ITML -import HLearn.Metrics.Mahalanobis.Lego -import HLearn.Metrics.Mahalanobis.LegoPaper hiding (shuffle) -import HLearn.Metrics.Mahalanobis.Mega -import HLearn.Models.Distributions - -import Utils - -type DP = MaybeLabeled String (L2 VU.Vector Double) - -instance Num r => HasRing (V.Vector r) where type Ring (V.Vector r) = r -instance Num r => HasRing (VU.Vector r) where type Ring (VU.Vector r) = r - -instance (NFData label, NFData attr) => NFData (MaybeLabeled label attr) where - rnf (MaybeLabeled l a) = deepseq l $ rnf a - -------------------------------------------------------------------------------- --- command line args - -data Params = Params - { k :: Int - , folds :: Int - , trials :: Int - , param_nummetricsamples :: String - , regularization :: String - , regularization2 :: String - , splits :: String - , takes :: String - , metric :: String - , label_column :: Maybe Int - , data_file :: Maybe String - , verbose :: Bool - } - deriving (Show, Data, Typeable) - -sample = Params - { k = 1 &= help "number of nearest neighbors to find" - , folds = 2 &= help "number of folds for crossvalidation" - , trials = 10 &= help "number of trials to repeat crossvalidation" - , param_nummetricsamples = "10000" &= help "number of training samples for the metric learner" - , regularization = "[0.01]" &= help "regularization rate for metric learning" - , regularization2 = "[0]" &= help "regularization rate for metric learning (2nd param)" - , splits = "[1]" &= help "splits for monoid test" - , takes = "[1]" &= help "how many splits to actually use" - , metric = "[mega]" &= help "metric to use for nearest neighbor classificaiton" - , label_column = Nothing &= help "column of the label in data_file" - , data_file = def &= help "reference data set in CSV format" &= typFile - , verbose = True &= help "print tree statistics (takes some extra time)" - } - &= summary "HLearn k-nearest neighbor classifier, version 1.0" - -readParamList :: forall a. Read a => String -> [a] -readParamList str = case readMaybe str :: Maybe a of - Just x -> [x] - Nothing -> case readMaybe str :: Maybe [a] of - Just xs -> xs - Nothing -> error $ "readList failed to parse: "++str - -------------------------------------------------------------------------------- --- main - -main = do - - ----------------------------------- - -- validate command line args - - params <- cmdArgs sample - - let numMetricSamplesL = readParamList $ param_nummetricsamples params :: [Int] - print $ numMetricSamplesL - - let filename = case data_file params of - Just str -> str - Nothing -> error "must specify a data file" - - let label_index = case label_column params of - Just x -> x - Nothing -> error "must specify a label column" - - let metricLstr = words $ tail $ init $ map (\x -> if x==',' then ' ' else x) $ metric params :: [String] - regularizationL = readParamList $ regularization params :: [Double] - regularization2L = readParamList $ regularization2 params :: [Double] - splitsL = readParamList $ splits params :: [Int] - takesL = readParamList $ takes params :: [Int] - - print metricLstr - let metricL = join $ map go [ (a,b) | a <- metricLstr, b <- regularizationL ] - go (a,b) = case a of - "mega" -> [("Mega "++show b++" "++show c, MetricBox $ (mkMega b c - :: [(Double, VU.Vector Double)] -> Mega (1/1) (VU.Vector Double))) - | c<-regularization2L - ] - "lego" -> [("Lego "++show b, MetricBox $ train_LegoPaper 1 b)] - "lMon1" -> [("LegoMonoid1 "++show b++"; splits="++show c++"; takes="++show d, MetricBox $ train_LegoMonoid mappend1 c d b) - | c <- splitsL, d <- takesL ] - "lMon2" -> [("LegoMonoid2 "++show b++"; splits="++show c++"; takes="++show d, MetricBox $ train_LegoMonoid mappend1 c d b) - | c <- splitsL, d <- takesL ] - "itml" -> [("ITML "++show b, MetricBox $ train_ITML b)] - otherwise -> error $ a++" is not a known metric" - - ----------------------------------- - -- load data - - xse :: Either String (V.Vector (V.Vector String)) - <- timeIO "loading reference dataset" $ fmap (decode HasHeader) $ BS.readFile filename - xs <- case xse of - Right rs -> return rs - Left str -> error $ "failed to parse CSV file " ++ filename ++ ": " ++ take 1000 str - - let numdp = VG.length xs - let numdim = VG.length $ xs VG.! 0 - let numlabels = Set.size $ Set.fromList $ VG.toList $ VG.map (VG.! label_index) xs - - if verbose params - then do - hPutStrLn stderr " dataset info:" - hPutStrLn stderr $ " num dp: " ++ show numdp - hPutStrLn stderr $ " num dim: " ++ show numdim - hPutStrLn stderr $ " num labels: " ++ show numlabels - else return () - - ----------------------------------- - -- convert to right types - - let ys = VG.map (\x -> MaybeLabeled - { label = Just $ x VG.! label_index - , attr = VG.convert ( - VG.map read $ VG.fromList $ VG.toList $ VG.take label_index x <> VG.drop (label_index+1) x - :: V.Vector Double - ) - }) - xs - :: V.Vector DP - - ----------------------------------- - -- perform tests - - forM numMetricSamplesL $ \nummetricsamples -> do - forM metricL $ \(msg,trainmetric) -> do - - hPutStrLn stderr $ replicate 80 '-' - hPutStrLn stderr $ "-- Test: "++show msg++"; nummetricsamples="++show nummetricsamples - hPutStrLn stderr $ replicate 80 '-' - - cvraw <- forM [1..trials params] $ \i -> - timeIO ("iteration "++show i) $ evalRandIO $ cv_legit_MetricBox - (kfold $ folds params) - errorRate - ys - trainmetric - nummetricsamples - (train :: F.Foldable container => container DP -> - KNearestNeighbor (AddUnit (CoverTree' (13/10) V.Vector V.Vector) ()) 4 - (MaybeLabeled String (L2 VU.Vector Double))) - - let cv = reduce cvraw - putStrLn $ msg ++ " " ++ show nummetricsamples ++ " " ++ show (mean cv) - hFlush stdout - - hPutStrLn stderr $ "mean: "++show (mean cv) - hPutStrLn stderr $ "stddev: "++show (sqrt $ variance cv) - hPutStrLn stderr "" - hPutStrLn stderr $ "meanL: "++show (map mean cvraw) - hPutStrLn stderr $ "varL: "++show (map variance cvraw) - --- if verbose params --- then putStrLn $ "\nresults_raw: " ++ show cvraw --- else return () - -------------------------------------------------------------------------------- - -printTreeStats str t = do - putStrLn (str++" stats:") - putStr (str++" stNumDp..............") >> hFlush stdout >> putStrLn (show $ stNumDp t) - putStr (str++" stNumNodes...........") >> hFlush stdout >> putStrLn (show $ stNumNodes t) - putStr (str++" stNumLeaves..........") >> hFlush stdout >> putStrLn (show $ stNumLeaves t) - putStr (str++" stNumGhosts..........") >> hFlush stdout >> putStrLn (show $ stNumGhosts t) - putStr (str++" stNumGhostSingletons.") >> hFlush stdout >> putStrLn (show $ stNumGhostSingletons t) - putStr (str++" stNumGhostLeaves.....") >> hFlush stdout >> putStrLn (show $ stNumGhostLeaves t) - putStr (str++" stNumGhostSelfparent.") >> hFlush stdout >> putStrLn (show $ stNumGhostSelfparent t) - putStr (str++" stAveGhostChildren...") >> hFlush stdout >> putStrLn (show $ mean $ stAveGhostChildren t) - putStr (str++" stMaxChildren........") >> hFlush stdout >> putStrLn (show $ stMaxChildren t) - putStr (str++" stAveChildren........") >> hFlush stdout >> putStrLn (show $ mean $ stAveChildren t) - putStr (str++" stMaxDepth...........") >> hFlush stdout >> putStrLn (show $ stMaxDepth t) - putStr (str++" stNumSingletons......") >> hFlush stdout >> putStrLn (show $ stNumSingletons t) - putStr (str++" stExtraLeaves........") >> hFlush stdout >> putStrLn (show $ stExtraLeaves t) - - putStrLn (str++" properties:") - putStr (str++" covering...............") >> hFlush stdout >> putStrLn (show $ property_covering $ UnitLift t) - putStr (str++" leveled................") >> hFlush stdout >> putStrLn (show $ property_leveled $ UnitLift t) - putStr (str++" separating.............") >> hFlush stdout >> putStrLn (show $ property_separating $ UnitLift t) - putStr (str++" maxDescendentDistance..") >> hFlush stdout >> putStrLn (show $ property_maxDescendentDistance $ UnitLift t) - - putStrLn "" - - -------------------------------------------------------------------------------- --- - -data MetricBox = forall metric. (MahalanobisMetric metric, Double~Ring metric) => - MetricBox ([(Double,VU.Vector Double)] -> metric) - -cv_legit_MetricBox :: - ( HomTrainer model - , Classifier model - , RandomGen g - , Eq (Datapoint model) - , Eq (Label (Datapoint model)) - , F.Foldable container - , Functor container - , Datapoint model ~ DP - ) => SamplingMethod - -> LossFunction - -> container (Datapoint model) - -> MetricBox - -> Int - -> ([DP] -> model) - -> Rand g (Normal Double Double) -cv_legit_MetricBox genfolds loss xs (MetricBox trainmetric) nummetric trainmodel = do - xs' <- genfolds $ F.toList xs - return $ train $ do - testset <- xs' - let trainingset = concat $ filter (/=testset) xs' - metric = trainmetric $ genMetricConstraints (V.fromList $ F.toList trainingset) nummetric - trainingset' = fmap (\dp -> MaybeLabeled (label dp) (applyMahalanobis metric $ attr dp)) trainingset - testset' = fmap (\dp -> MaybeLabeled (label dp) (applyMahalanobis metric $ attr dp)) testset - let model = trainmodel trainingset' - return $ loss model testset' - -cvnormal :: - ( HomTrainer model - , Classifier model - , RandomGen g - , Eq (Datapoint model) - , Eq (Label (Datapoint model)) - , F.Foldable container - ) => SamplingMethod - -> LossFunction - -> container (Datapoint model) - -> ([Datapoint model] -> model) - -> Rand g (Normal Double Double) -cvnormal genfolds loss xs trainmodel = do - xs' <- genfolds $ F.toList xs - return $ train $ do - testset <- xs' - let trainingset = concat $ filter (/=testset) xs' - let model = trainmodel trainingset - return $ loss model testset - -cv_legit :: - ( HomTrainer model - , Classifier model - , MahalanobisMetric metric - , RandomGen g - , Eq (Datapoint model) - , Eq (Label (Datapoint model)) - , F.Foldable container - , Functor container - , Double ~ Ring metric - , Datapoint model ~ DP - ) => SamplingMethod - -> LossFunction - -> container (Datapoint model) - -> ([(Double,VU.Vector Double)] -> metric) - -> Int - -> ([DP] -> model) - -> Rand g (Normal Double Double) -cv_legit genfolds loss xs trainmetric nummetric trainmodel = do - xs' <- genfolds $ F.toList xs - return $ train $ do - testset <- xs' - let trainingset = concat $ filter (/=testset) xs' - metric = trainmetric $ genMetricConstraints (V.fromList $ F.toList xs) nummetric - trainingset' = fmap (\dp -> MaybeLabeled (label dp) (applyMahalanobis metric $ attr dp)) trainingset - testset' = fmap (\dp -> MaybeLabeled (label dp) (applyMahalanobis metric $ attr dp)) testset - let model = trainmodel trainingset' - return $ loss model testset' - -cv :: - ( HomTrainer model - , Classifier model - , MahalanobisMetric metric - , RandomGen g - , Eq (Datapoint model) - , Eq (Label (Datapoint model)) - , F.Foldable container - , Functor container - , Double ~ Ring metric - , Datapoint model ~ DP - ) => SamplingMethod - -> LossFunction - -> container (Datapoint model) - -> ([(Double,VU.Vector Double)] -> metric) - -> Int - -> ([DP] -> model) - -> Rand g (Normal Double Double) -cv genfolds loss xs trainmetric nummetric trainmodel = do - xs' <- genfolds $ F.toList xsmetric - return $ train $ do - testset <- xs' - let trainingset = concat $ filter (/=testset) xs' - let model = trainmodel trainingset - return $ loss model testset - where - xsmetric = fmap (\dp -> MaybeLabeled (label dp) (applyMahalanobis metric $ attr dp)) xs - metric = trainmetric $ genMetricConstraints (V.fromList $ F.toList xs) nummetric - -genClassifier :: - ( MahalanobisMetric metric - , Ring metric ~ Double - ) => ([(Double,VU.Vector Double)] -> metric) - -> Int - -> ([DP] -> classifier) - -> [DP] - -> classifier -genClassifier trainmetric n traintree dps = traintree dps' - where - dpsv = V.fromList dps - dps' = map (\dp -> MaybeLabeled (label dp) (applyMahalanobis metric $ attr dp)) dps - metric = trainmetric $ genMetricConstraints dpsv n - -genMetricConstraints :: VG.Vector vec DP => vec DP -> Int -> [(Double, VU.Vector Double)] -genMetricConstraints dps n = flip evalRand (mkStdGen seed) $ replicateM n genConstraint - where - seed=0 - numdp = VG.length dps - - distances :: MetricSpace dp => [dp] -> [Ring dp] - distances [] = [] - distances (x:xs) = map (distance x) xs ++ distances xs - --- distances (x1:x2:x3:xs) = distance x1 x2:distance x1 x3: distances (x2:x3:xs) --- distances _ = [] - - dps_shuffle = flip evalRand (mkStdGen $ seed+100000) $ shuffle $ VG.toList dps - ys2 = sort . distances $ map (L2 . attr) dps_shuffle - p05 = ys2 !! (floor $ 0.05 * fromIntegral (length ys2)) - p95 = ys2 !! (floor $ 0.95 * fromIntegral (length ys2)) - - genConstraint = do - i <- getRandomR (0,numdp-1) - j <- getRandomR (0,numdp-1) - let targetdist = if label (dps VG.! i) == label (dps VG.! j) - then p05 - else p95 - let dp = VG.zipWith (-) (attr $ dps VG.! i) (attr $ dps VG.! j) - return (targetdist,unL2 dp) - -data MetricNN metric k dp = MetricNN - { knnmetric :: metric - , knntree :: KNearestNeighbor (AddUnit (CoverTree' (13/10) V.Vector V.Vector) ()) k dp - } - -instance Monoid (MetricNN metric k dp) where - mempty = undefined - mappend = undefined - -instance - ( dp ~ vec Double - , VG.Vector vec Double - , Ring (vec Double) ~ Double - , Ord (vec Double) - , Double ~ Ring metric - , MahalanobisMetric metric - , HomTrainer metric - , MetricSpace dp - , Datapoint metric ~ dp - , Show (vec Double) - ) => HomTrainer (MetricNN metric k dp) - where - - type Datapoint (MetricNN metric k dp) = dp - train dps = MetricNN - { knnmetric = metric - , knntree = train dps' - } - where - metric = train dps - dps' = map (applyMahalanobis metric) $ F.toList dps diff --git a/src/exec/hlearn-linear.hs b/src/exec/hlearn-linear.hs deleted file mode 100644 index 0c3351a..0000000 --- a/src/exec/hlearn-linear.hs +++ /dev/null @@ -1,348 +0,0 @@ -{-# LANGUAGE ScopedTypeVariables #-} -{-# LANGUAGE DeriveDataTypeable #-} -{-# LANGUAGE RankNTypes #-} -{-# LANGUAGE FlexibleContexts #-} -{-# LANGUAGE NoImplicitPrelude #-} -{-# LANGUAGE RebindableSyntax #-} -{-# LANGUAGE ConstraintKinds #-} -{-# LANGUAGE GADTs #-} -{-# LANGUAGE TypeFamilies #-} - --- import Control.Applicative -import Control.DeepSeq --- import Control.Monad -import Control.Monad.Random -import Data.List.Extras -import Data.Maybe -import Data.Typeable -import qualified Data.ByteString.Lazy as BS -import qualified Data.Foldable as F -import qualified Data.Map.Strict as Map -import qualified Data.Set as Set -import qualified Data.Vector as V -import qualified Data.Vector.Generic as VG -import qualified Data.Vector.Storable as VS -import Foreign.Storable -import System.Exit -import System.IO - -import Data.Csv -import System.Console.CmdArgs.Implicit - -import Debug.Trace -import Prelude (head,words,replicate) - -import Data.Version -import Paths_HLearn - -import SubHask -import SubHask.Compatibility.HMatrix --- import HLearn.Algebra -import HLearn.Evaluation.CrossValidation -import HLearn.History -import HLearn.History.DisplayMethods --- import HLearn.Models.Distributions -import HLearn.Models.Classifiers.Common -import HLearn.Models.Classifiers.LinearClassifier -import HLearn.Models.Distributions.Common -import HLearn.Models.Distributions.Univariate.Normal -import HLearn.Optimization.Common --- import HLearn.Optimization.StochasticGradientDescent -import HLearn.Optimization.StepSize -import HLearn.Optimization.StepSize.Linear -import HLearn.Optimization.LineMinimization.Univariate -import HLearn.Optimization.GradientDescent --- import HLearn.Optimization.NewtonRaphson --- import qualified HLearn.Optimization.GradientDescent as Recipe --- import qualified HLearn.Optimization.Common as Recipe --- import HLearn.Optimization.Trace - -import HLearn.History.Timing -import HLearn.Data.LoadData - -instance - ( Logic (Scalar x)~Bool - , HasScalar x - ) => Labeled (Labeled' x y) where - type Label (Labeled' x y) = y - type Attributes (Labeled' x y) = x - - getLabel = yLabeled' - getAttributes = xLabeled' - -------------------------------------------------------------------------------- --- command line parameters - -data Params = Params - { data_file :: Maybe String - , label_col :: Int - - , maxdp :: Maybe Int - - , noCV :: Bool - , paramCVFolds :: Int - , paramCVReps :: Int - - , monoidType :: String - , monoidSplits :: Int - - , regType :: String - , regAmount :: Double - , step_eta :: Double - , maxitr :: Int - - , pca_data :: Bool - , varshift_data :: Bool - - , paramSeed :: Maybe Int - , quiet :: Bool - , verbose :: Bool - , debug :: Bool - } - deriving (Show, Data, Typeable) - -usage = Params - { data_file = Nothing - &= help "file to perform classification on" - - , label_col = 0 - &= help "label column" - - , maxdp = Nothing - &= help "maximum number of datapoints to use from data set" - - , noCV = False - &= help "do not cross validate; just build model" - &= groupname "Test Configuration" - - , paramCVFolds = 10 - &= help "number of folds for cross validation" - &= name "cvfolds" - &= explicit - - , paramCVReps = 1 - &= help "number of times to repeat cross validation" - &= name "cvreps" - &= explicit - - , monoidSplits = 1 - &= help "when training the classifier, first split the dataset into this many subsets; then combine using the specified monoidType" - - , monoidType = "MappendAverage" - &= help "see monoid splts" - - , regType = "L2" - &= help "valid values are: L1, L2, ElasticNet" - - , regAmount = 0.1 - &= help "regularization parameter lambda" - - , step_eta = 0.001 - &= help "eta parameter for step sizes" - - , maxitr = 10000 - &= help "maximum number of iterations" - - , pca_data = False - &= groupname "Data Preprocessing" - &= help "Rotate the data points using the PCA transform. Speeds up nearest neighbor searches, but computing the PCA can be expensive in many dimensions." - &= name "pca" - &= explicit - - , varshift_data = False - &= help "Sort the attributes according to their variance. Provides almost as much speed up as the PCA transform during neighbor searches, but much less expensive in higher dimensions." - &= name "varshift" - &= explicit - - , paramSeed = Nothing - &= help "specify the seed to the random number generator (default is seeded by current time)" - &= groupname "Debugging" - - , quiet = False - &= help "supress all output" - - , verbose = False - &= help "Print tree statistics (takes some extra time)" - - , debug = False - &= help "Test created trees for validity (takes lots of time)" - &= name "debug" - &= explicit - } - &= summary ("HLearn linear classifiers, version " ++ showVersion version) - -------------------------------------------------------------------------------- --- main - -main = do - - ----------------------------------- - -- process command line - - params <- cmdArgs usage - - when (data_file params == Nothing) $ error "must specify a data file" - - seed <- case paramSeed params of - Nothing -> randomIO - Just x -> return x - - let filename = fromJust $ data_file params - label_index = label_col params - - ----------------------------------- - -- load data - --- dps :: V.Vector (Labeled' (Vector Double) Int) --- <- loadLabeledNumericData $ DataParams --- { datafile = fromJust $ data_file params --- , labelcol = Just $ label_col params --- , pca = pca_data params --- , varshift = False --- } - dps :: Array (Labeled' (Vector Double) Int) - <- loadCSVLabeled (fromJust $ data_file params) (label_col params) - - ----------------------------------- - -- test parameters - - let traceEvents = - [] --- [ traceBFGS --- , traceNewtonRaphson --- , traceLinearClassifier (undefined::Labeled' (Vector Double) Int) --- ] - --- let withMonoid :: ( Monad m ) --- => ( model -> model -> model ) --- -> Int --- -> ( [dp] -> m model ) --- -> ( [dp] -> m model ) --- withMonoid f n _train dps = liftM (F.foldl1 f) $ mapM _train $ partition n dps - - let reg :: C2Function (Vector Double) - reg = case regType params of - "L1" -> l1reg - "L2" -> l2reg - "ElasticNet" -> elasticNet - - let monoidOperation = error "monoidOperation" - let readMonoidType = MappendAverage --- let readMonoidType = read $ monoidType params :: MonoidType --- let monoidOperation = case readMonoidType of --- MappendAverage -> mappendAverage --- MappendTaylor -> mappendQuadratic --- MappendUpperBound -> mappendQuadratic --- MixtureUpperTaylor _ -> mappendQuadratic --- MixtureAveTaylor _ -> mappendQuadratic --- MixtureAveUpper _ -> mappendQuadratic --- otherwise -> error $ "monoidtype ["++monoidType params++"] not supported" --- seq monoidOperation $ return () - - let maxdpselector :: SamplingMethod -> SamplingMethod - maxdpselector = case maxdp params of - Just x -> setMaxDatapoints x - Nothing -> \x -> x - --- let debugHistory = if debug params --- then runHistory compactTrace --- else runHistory compactTrace - --- let debugHistory = runHistory linearTrace - let debugHistory = runDynamicHistory - $ {- summaryStatistics - === -}removeLineMin --- ||| sampleSGD 10000 - ||| linearTrace --- === removeLineMin --- ||| (allowPasses (undefined::NewtonRaphson (Vector Double)) [2] --- ||| linearTrace) --- === allowFirstPass (undefined::ConjugateGradientDescent (Vector Double)) --- ||| mkOptimizationPlot --- (undefined::ConjugateGradientDescent (Vector Double)) --- "optplot.dat" - - let optMethod :: - ( HistoryMonad m - , Reportable m (LineBracket Double) - , Reportable m (Brent Double) - , Reportable m (ConjugateGradientDescent (Vector Double)) - ) => OptimizationMethod m (Labeled' (Vector Double) Int) - optMethod = cgd --- optMethod = sgd --- [ maxIterations $ maxitr params ] --- randomSample --- -- linearScan --- -- ( lrAlmeidaLanglois ) --- ( Hyperparams { eta = step_eta params, gamma = regAmount params } ) - - ----------------------------------- - -- single models - --- when (noCV params) $ do --- model <- runDynamicHistory --- ( summaryStatistics --- === removeLineMin --- ||| linearTrace --- === allowFirstPass (undefined::ConjugateGradientDescent (Vector Double)) --- ||| mkOptimizationPlot --- ( undefined::ConjugateGradientDescent (Vector Double)) --- "optplot.dat" --- ) --- ( trainLinearClassifier --- readMonoidType --- ( regAmount params ) --- reg --- logloss --- optMethod --- dps --- ) --- --- timeIO "training model" $ deepseq model $ return () --- exitSuccess - - ----------------------------------- - -- run cv tests - - res <- debugHistory $ flip evalRandT (mkStdGen seed) $ validateM - ( repeatExperiment - ( paramCVReps params ) - ( maxdpselector - ( kfold $ paramCVFolds params) - ) - ) - errorRate - dps - ( --withMonoid monoidOperation (monoidSplits params) $ - trainLinearClassifier - ( readMonoidType ) - ( 0 ) -- regAmount params ) - reg - logloss - optMethod - ) - - let showMaybe Nothing = "Nothing" - showMaybe (Just x) = show x - - putStrLn $ (showMaybe $ paramSeed params) - ++" "++ show (pca_data params) - ++" "++ show (varshift_data params) - ++" "++ show (paramCVFolds params) - ++" "++ show (paramCVReps params) - ++" "++ show (monoidSplits params) - ++" "++ (head $ words $ monoidType params) - ++" "++ (show (fromRational $ monoidMixRate readMonoidType::Double)) - ++" "++ (regType params) - ++" "++ show (regAmount params) - ++" "++ (showMaybe $ maxdp params) - ++ concat (replicate 19 " -- ") - ++" "++ show (mean res) - ++" "++ show (variance res) - - hPutStrLn stderr $ "mean = "++show (mean res) - hPutStrLn stderr $ "var = "++show (variance res) - - ----------------------------------- - hPutStrLn stderr "done." - diff --git a/subhask b/subhask index 3093a59..46fe7f1 160000 --- a/subhask +++ b/subhask @@ -1 +1 @@ -Subproject commit 3093a59a8d4c2d42d936ddeb80e0bb2a8a89786f +Subproject commit 46fe7f140bf2a3c338b87f34801ff505435f9731 diff --git a/test/BashTests.hs b/test/BashTests.hs new file mode 100644 index 0000000..acad4d1 --- /dev/null +++ b/test/BashTests.hs @@ -0,0 +1,45 @@ +{- + - This file is a hack so that we can run bash script tests using cabal. + - See: http://stackoverflow.com/questions/31213883/how-to-use-cabal-with-bash-tests + - + - Note that running `cabal test` will also run `cabal install`. + - + -} + +import Control.Monad +import System.Exit +import System.Process + +runBashTest :: String -> IO () +runBashTest cmd = do + putStr $ cmd ++ "..." + ExitSuccess <- system cmd + return () + +main = do + system "cabal install" + sequence_ + [ runBashTest $ "./test/allknn-verify/runtest.sh " + ++ " " ++ dataset + ++ " " ++ treetype + ++ " " ++ foldtype + ++ " " ++ rotate + + | dataset <- + [ "./test/allknn-verify/dataset-10000x2.csv" +-- , "./test/allknn-verify/dataset-10000x20.csv" +-- , "./test/allknn-verify/mnist-10000.csv" + ] + , treetype <- + [ "--treetype=simplified" + , "--treetype=ancestor" + ] + , foldtype <- + [ "--fold=foldsort" + , "--fold=fold" + ] + , rotate <- + [ "--rotate=norotate" + , "--rotate=variance" + ] + ] diff --git a/test/QuickCheck.hs b/test/QuickCheck.hs new file mode 100644 index 0000000..9e861bc --- /dev/null +++ b/test/QuickCheck.hs @@ -0,0 +1,37 @@ +{-# LANGUAGE NoImplicitPrelude #-} +{-# LANGUAGE TemplateHaskell #-} +{-# LANGUAGE DataKinds #-} + +module Main + where + +import HLearn.Data.SpaceTree.CoverTree + +import SubHask +import SubHask.Algebra.Container +import SubHask.Algebra.Vector +import SubHask.TemplateHaskell.Test + +import Test.Framework (defaultMain, testGroup) +import Test.Framework.Providers.QuickCheck2 (testProperty) +import Test.Framework.Runners.Console +import Test.Framework.Runners.Options + +-------------------------------------------------------------------------------- + +main = defaultMainWithOpts + [ testGroup "CoverTree_" + [ $( mkSpecializedClassTests [t| UCoverTree (UVector "dyn" Float) |] [ ''Constructible ] ) + , $( mkSpecializedClassTests [t| BCoverTree (UVector "dyn" Float) |] [ ''Constructible ] ) + ] + ] + $ RunnerOptions + { ropt_threads = Nothing + , ropt_test_options = Nothing + , ropt_test_patterns = Nothing + , ropt_xml_output = Nothing + , ropt_xml_nested = Nothing + , ropt_color_mode = Just ColorAlways + , ropt_hide_successes = Just True + , ropt_list_only = Just True + } diff --git a/test/allknn-mlpack/runtest.sh b/test/allknn-mlpack/runtest.sh new file mode 100755 index 0000000..73f463c --- /dev/null +++ b/test/allknn-mlpack/runtest.sh @@ -0,0 +1,20 @@ +#!/bin/bash + +K=1 + +tmpdir=$(mktemp --tmpdir -d) + +echo "--------------------------------------------------------------------------------" +echo "tempdir=$tmpdir" +echo "--------------------------------------------------------------------------------" +hlearn_neighbors="$tmpdir/neighbors_hlearn.csv" +hlearn_distances="$tmpdir/distances_hlearn.csv" +mlpack_neighbors="$tmpdir/neighbors_mlpack.csv" +mlpack_distances="$tmpdir/distances_mlpack.csv" + +hlearn-allknn -k $K -r $@ -n "$hlearn_neighbors" -d "$hlearn_distances" +RTS -K1000M -N1 +allknn -r $1 -n "$mlpack_neighbors" -d "$mlpack_distances" -k $K -v + +echo "-------------------------------------" +echo "num differences: " `diff $hlearn_neighbors $mlpack_neighbors | wc -l` " / " `cat $1 | wc -l` +diff $hlearn_neighbors $mlpack_neighbors > /dev/null diff --git a/test/allknn-verify/dataset-10000x2.csv b/test/allknn-verify/dataset-10000x2.csv new file mode 100644 index 0000000..8751845 --- /dev/null +++ b/test/allknn-verify/dataset-10000x2.csv @@ -0,0 +1,10000 @@ +947.2816332507371,861.9313411177409 +-890.7506684821334,-164.7983161725366 +364.11074324917786,577.4318942269047 +-399.65335951003715,-93.96649850311769 +-26.958518716353638,-174.05230730389553 +-955.2190206779683,255.04880386317063 +-317.26914828266456,255.3007072939281 +-771.1492399975555,-602.6363977383271 +26.38848173036945,895.2554913508056 +878.4585648189118,-521.2184138920717 +-260.3954534115145,-23.904124643637374 +-257.10106068545997,922.4980645612638 +-506.17969090976425,-407.3371342890706 +-421.8545666159397,-861.034877955275 +-605.9813766024715,13.393104794757733 +529.5904479890605,255.25839552971365 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