init

.gitignore

@@ -1,4 +1,4 @@
-
+.DS_Store
 /wekiLib/
 
 # Created by https://www.gitignore.io/api/emacs

README.md

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-# RapidLib
+# RapidLibBela
+
+## Context
+
+### RapidLib
 
 [![GitHub license](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/mzed/RapidLib/blob/master/LICENSE)
 
@@ -14,4 +18,47 @@
 
 RapidLib is a lightweight library for interactive machine learning. It currently features classification (using kNN), regression (multilayer perceptron), and series classification (using dynamic time warping).
 
-## More at: [https://mzed.github.io/RapidLib/](https://mzed.github.io/RapidLib/)
+More at: https://mzed.github.io/RapidLib/
+
+### Bela
+
+Bela is a platform for ultra-low latency audio and sensor processing.
+
+http://bela.io
+
+## Install
+
+See Releases.
+
+## Build
+
+- Requires "experimental" Bela image with recent CMake: https://github.com/BelaPlatform/bela-image-builder/releases/tag/v0.5.0alpha2.
+- Highly recommended to install `distcc` ([guide](https://gist.github.com/jarmitage/2a5dffbcfdd5532371f097e9bb80e4b0)).
+- `git clone` RapidLib onto Bela ([connecting to network on Bela](https://learn.bela.io/using-bela/bela-techniques/connecting-to-wifi))
+- Comment out DTW multi-threading test (see https://github.com/mzed/RapidLib/issues/20).
+- Build commands from inside `RapidLib` dir:
+
+```sh
+mkdir build && cd build
+CC="distcc-clang" CXX="distcc-clang++" cmake -DCMAKE_CXX_FLAGS="-Werror" ..
+CC="distcc-clang" CXX="distcc-clang++" cmake
+ctest
+```
+- `make` parameters for RapidLib projects (see [Bela Blog](https://blog.bela.io/using-an-external-library-with-bela/)):
+
+```
+LDFLAGS=-L/root/RapidLib/build;LDLIBS=-lrapidLib;CPPFLAGS=-I/root/RapidLib/src;
+```
+
+## Examples
+
+Currently very simple, based on https://github.com/mzed/ofxRapidLib
+
+## Todo
+
+- [ ] Benchmark RapidLib performance on Bela
+- [ ] Add C++ examples using `AuxiliaryTask`
+- [ ] Add C++ examples using sensor inputs and audio outputs
+- [ ] Add C++ examples with Bela GUIs
+- [ ] Add `install` to CMake to simplify installation
+- [ ] Scope out SuperCollider / Pure Data support and examples

bela_examples/knn/render.cpp

@@ -0,0 +1,53 @@
+// Based on https://github.com/mzed/ofxRapidLib/tree/main/example-objectClassification
+
+#include <Bela.h>
+#include "rapidLib.h"
+
+std::vector<rapidLib::trainingExample> myData;
+std::vector<double> trainingInput;
+std::vector<double> trainingOutput;
+std::vector<double> inputVec;
+
+rapidLib::classification myKnn;
+rapidLib::trainingExample tempExample;
+
+int examples = 10;
+int recordingState = 2;
+int result;
+
+bool setup(BelaContext *context, void *userData)
+{
+    tempExample.input = { 0.5, 0.3, 0.4 };
+    tempExample.output = { double(1) };
+    myData.push_back(tempExample);
+
+    tempExample.input = { 1.6, 1.7, 1.8 };
+    tempExample.output = { double(2) };
+    myData.push_back(tempExample);
+
+    tempExample.input = { 2.3, 2.4, 2.5 };
+    tempExample.output = { double(3) };
+    myData.push_back(tempExample);
+
+    if (myData.size() > 0)
+        myKnn.train(myData);
+
+    inputVec = { 0.34, 0.45, 0.54 };
+    result = myKnn.run(inputVec)[0];
+    rt_printf("Input: { %2f, %2f, %2f }\n", inputVec[0], inputVec[1], inputVec[2]);
+    rt_printf("Output: %i\n", result);
+
+    inputVec = { 1.34, 1.45, 1.54 };
+    result = myKnn.run(inputVec)[0];
+    rt_printf("Input: { %2f, %2f, %2f }\n", inputVec[0], inputVec[1], inputVec[2]);
+    rt_printf("Output: %i\n", result);
+
+    inputVec = { 2.34, 2.45, 2.54 };
+    result = myKnn.run(inputVec)[0];
+    rt_printf("Input: { %2f, %2f, %2f }\n", inputVec[0], inputVec[1], inputVec[2]);
+    rt_printf("Output: %i\n", result);
+
+	return true;
+}
+void render(BelaContext *context, void *userData){}
+void cleanup(BelaContext *context, void *userData){}

bela_examples/regression/render.cpp

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+#include <Bela.h>
+#include "rapidLib.h"
+
+rapidLib::regression mtofRegression;
+std::vector<rapidLib::trainingExample> myData;
+rapidLib::trainingExample tempExample;
+int inputMIDINote = 60;
+
+bool setup(BelaContext *context, void *userData)
+{
+    tempExample.input = { 48 };
+    tempExample.output = { 130.81 };
+    myData.push_back(tempExample);
+
+    tempExample.input = { 54 };
+    tempExample.output = { 185.00 };
+    myData.push_back(tempExample);
+
+    tempExample.input = { 60 };
+    tempExample.output = { 261.63 };
+    myData.push_back(tempExample);
+
+    tempExample.input = { 66 };
+    tempExample.output = { 369.994 };
+    myData.push_back(tempExample);
+
+    tempExample.input = { 72 };
+    tempExample.output = { 523.25 };
+    myData.push_back(tempExample);
+
+    mtofRegression.train(myData);
+
+    std::vector<double> inputVec = { double(inputMIDINote) };
+    double freqHz = mtofRegression.run(inputVec)[0];
+    rt_printf("MIDI Note %i is %2fHz\n", inputMIDINote, freqHz);
+
+	return true;
+}
+void render(BelaContext *context, void *userData){}
+void cleanup(BelaContext *context, void *userData){}