Hamiltonian Monte Carlo investigation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "os.chdir(\"../\")\n",
    "import pints\n",
    "import pints.toy as toy\n",
    "import pints.plot\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load a forward model\n",
    "model = toy.LogisticModel()\n",
    "\n",
    "# Create some toy data\n",
    "real_parameters = [0.015, 500]\n",
    "times = np.linspace(0, 1000, 10)\n",
    "org_values = model.simulate(real_parameters, times)\n",
    "\n",
    "# Add noise\n",
    "noise = 10\n",
    "values = org_values + np.random.normal(0, noise, org_values.shape)\n",
    "os.chdir(\"./studies/\")\n",
    "# np.savetxt(\"logistic_values.csv\", values, delimiter=\",\")\n",
    "values=np.loadtxt(\"logistic_values.csv\")\n",
    "real_parameters = np.array(real_parameters)\n",
    "\n",
    "# Get properties of the noise sample\n",
    "noise_sample_mean = np.mean(values - org_values)\n",
    "noise_sample_std = np.std(values - org_values)\n",
    "\n",
    "# Create an object with links to the model and time series\n",
    "problem = pints.SingleOutputProblem(model, times, values)\n",
    "\n",
    "# Create a log-likelihood function (adds an extra parameter!)\n",
    "log_likelihood = pints.KnownNoiseLogLikelihood(problem, noise)\n",
    "\n",
    "# Create a uniform prior over both the parameters and the new noise variable\n",
    "log_prior = pints.UniformLogPrior(\n",
    "    [0.01, 400],\n",
    "    [0.02, 600]\n",
    ")\n",
    "\n",
    "# Create a posterior log-likelihood (log(likelihood * prior))\n",
    "log_posterior = pints.LogPosterior(log_likelihood, log_prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-38.34757636521622, array([ -4.88845916e+03,   6.83545547e-02]))"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_posterior.evaluateS1(real_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.69314718055994529, array([ 0.,  0.]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_prior.evaluateS1(real_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-37.654429184656273, array([ -4.88845916e+03,   6.83545547e-02]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_likelihood.evaluateS1(real_parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[    0.        ,   111.11111111,   222.22222222,   333.33333333,\n",
       "          444.44444444,   555.55555556,   666.66666667,   777.77777778,\n",
       "          888.88888889,  1000.        ],\n",
       "       [    6.92386882,     8.78695784,    52.52906841,   161.11680282,\n",
       "          386.63268646,   483.58923317,   504.08240212,   499.22826941,\n",
       "          496.22513868,   489.61964976]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([times, values])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0.        ,   111.11111111,   222.22222222,   333.33333333,\n",
       "         444.44444444,   555.55555556,   666.66666667,   777.77777778,\n",
       "         888.88888889,  1000.        ])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "for step in np.array([1]):\n",
    "    print(step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.array([1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=np.array(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a[a<0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "len() of unsized object",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-af8c77e09569>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: len() of unsized object"
     ]
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.atleast_1d(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.asarray([], dtype='int'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}