runSOM2.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from minisom import MiniSom\n",
      "import random as r\n",
      "import copy as cop\n",
      "import os\n",
      "import numpy as np\n",
      "\n",
      "from glob import glob\n",
      "# import matplotlib as plt\n",
      "\n",
      "\n",
      "%pylab inline\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['f', 'mod']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "class SOM:\n",
      "    def pertWeights( self, scale=.2 ):     \n",
      "        print( 'Adding noise to SOM weights')\n",
      "        pertAmount = scale*(np.random.random_sample( self.som.weights.shape)-.5)\n",
      "        self.som.weights = self.som.weights + pertAmount\n",
      "    \n",
      "    def pertData( self, p=.2, verbose=False):\n",
      "        if verbose:\n",
      "            print( 'Making %.2f percent of inputs 0.0' %(p*100))\n",
      "        # randomly get proportion of indices to switch, then replace\n",
      "        noiseIndex = np.random.binomial(1,p, self.data.shape)  #ones at p proportion of samples\n",
      "        self.data[noiseIndex ==1 ] = 0\n",
      "        #return data\n",
      "    \n",
      "    # plot the thing\n",
      "    def show( self, maxIdx=None):\n",
      "        print( 'Training exemplar projection')\n",
      "        som = self.som\n",
      "        if maxIdx == None:\n",
      "            maxIdx = len(self.data)\n",
      "        data= self.data[0:maxIdx]\n",
      "        target = self.target\n",
      "        bone()\n",
      "        pcolor(som.distance_map().T) # plotting the distance map as background\n",
      "        colorbar()\n",
      "        t = zeros(len(target),dtype=int)\n",
      "        t[target == 'A'] = 0\n",
      "        t[target == 'B'] = 1\n",
      "        # use different colors and markers for each label\n",
      "        markers = ['o','s','D']\n",
      "        colors = ['r','g','b']\n",
      "        for cnt,xx in enumerate(data):\n",
      "            w = som.winner(xx) # getting the winner\n",
      "            # palce a marker on the winning position for the sample xx\n",
      "            plot(w[0]+.5,w[1]+.5,markers[t[cnt]],markerfacecolor='None',\n",
      "                markeredgecolor=colors[t[cnt]],markersize=12,markeredgewidth=2)\n",
      "        axis([0,som.weights.shape[0],0,som.weights.shape[1]])\n",
      "        show() # show the figure\n",
      "        \n",
      "    def showIdeal( self):\n",
      "        print( 'Ideal exemplar projection')\n",
      "        som = self.som\n",
      "        data= self.ideal\n",
      "        target = self.idealTarget\n",
      "        bone()\n",
      "        pcolor(som.distance_map().T) # plotting the distance map as background\n",
      "        colorbar()\n",
      "        t = zeros(len(target),dtype=int)\n",
      "        t[target == 'A'] = 0\n",
      "        t[target == 'B'] = 1\n",
      "        # use different colors and markers for each label\n",
      "        markers = ['o','s','D']\n",
      "        colors = ['r','g','b']\n",
      "        for cnt,xx in enumerate(data):\n",
      "            w = som.winner(xx) # getting the winner\n",
      "            # palce a marker on the winning position for the sample xx\n",
      "            plot(w[0]+.5,w[1]+.5,markers[t[cnt]],markerfacecolor='None',\n",
      "                markeredgecolor=colors[t[cnt]],markersize=12,markeredgewidth=2)\n",
      "        axis([0,som.weights.shape[0],0,som.weights.shape[1]])\n",
      "        show() # show the figure        \n",
      "        \n",
      "    def train(self, iters = 30):\n",
      "        self.som.train_random(self.data,iters)\n",
      "#         print( 'Post training quantization error: %f' % self.som.quantization_error(self.data))\n",
      "\n",
      "    \"\"\"def trainOneGliozzi(self):\n",
      "        # using gliozzi thing\n",
      "        # init learning params\n",
      "        self.som.sigma_gliozzi = linspace(1.2, 0.8, num=len(self.data))\n",
      "        self.som._init_T(len(self.data))   \n",
      "        \n",
      "        #  will use the data idx to slowly reduce sigma, and learning rate in update function\n",
      "        self.som.update_gliozzi(self.data[self.dataIdx],self.som.winner(self.data[self.som.dataIdx]), self.som.dataIdx)\n",
      "\n",
      "        # update training index (for next time)\n",
      "        self.dataIdx += 1\n",
      "        if self.dataIdx >= len(self.data):\n",
      "            self.dataIdx = 0 \n",
      "    \"\"\"\n",
      "    \n",
      "    def trainOne(self ):\n",
      "        \"\"\" Trains the SOM picking samples from data one at a time \"\"\"\n",
      "        #self.running = True        \n",
      "        d = self.dataIdx  # data index to train\n",
      "        self.som._init_T(len(self.data))      \n",
      "        self.som.update( self.data[d], self.som.winner(self.data[d]), d)  # update\n",
      "        #self.running = False\n",
      "        \n",
      "        # update training index (for next time)\n",
      "        self.dataIdx += 1\n",
      "        if self.dataIdx >= len(self.data):\n",
      "            self.dataIdx = 0        \n",
      "\n",
      "    def predOne(self, verbose=False):\n",
      "        # Do the next one prediction\n",
      "        \n",
      "        # get BMU index and weight \n",
      "        bmuIdx = self.som.winner(self.data[self.dataIdx])\n",
      "        bmuW = self.som.weights[bmuIdx]\n",
      "        protDist = []\n",
      "        protLab = []\n",
      "        \n",
      "        # find distance to all prototypes                    \n",
      "        for prot,label in self.getProt():\n",
      "            protMapped = self.som.weights[self.som.winner(prot) ]\n",
      "            protDist.append( norm( bmuW- protMapped )) # self.ideal[ idx ])) \n",
      "            protLab.append( label )             #  self.idealTarget[idx] )     \n",
      "        \n",
      "        # SOM decision (random if no previous input)\n",
      "        if len( protDist ) >0:            \n",
      "            somLab = protLab[ argmin( protDist ) ]  # nearest category                \n",
      "        else:\n",
      "            somLab = ( 'A' if r.random() > .5 else 'B' )\n",
      "        trueLab = self.target[self.dataIdx]\n",
      "        \n",
      "        # update SOM accuracy\n",
      "        self.somAcc[self.dataIdx] = (1 if somLab==trueLab else 0 )\n",
      "        \n",
      "        # get child label\n",
      "        #  somAcc = (1 if predLab == trueLab else 0 )\n",
      "        if self.acc[self.dataIdx ] == 1: # if accurate\n",
      "            childLab = trueLab\n",
      "        elif np.isnan(self.acc[self.dataIdx ]): # if NaN\n",
      "            childLab = 'N'\n",
      "        else: # if wrong\n",
      "            childLab = 'AB'.strip(trueLab) \n",
      "    \n",
      "        if verbose:\n",
      "            print( 'SOM/Child/True Label: %s/%s/%s.' % (  somLab, childLab, trueLab)  )\n",
      "        return ( somLab, childLab ) \n",
      "\n",
      "    def predAll(self, reset = True, skip =0, verbose= True, reps=1 ):\n",
      "        \n",
      "        fullSomList = np.array([])\n",
      "        fullChildList = np.array([])\n",
      "        \n",
      "        for numRepetitions in range(reps): # repeat for random initialization of som            \n",
      "            # initialize som weights, and data index if reset=True\n",
      "            if reset:\n",
      "                self.initWeights()                \n",
      "                self.dataIdx = 0 # to index what data sample for one shot learning\n",
      "            \n",
      "            # init for storing results\n",
      "            somList = np.chararray(len(self.data)) #*np.nan\n",
      "            childList = np.chararray(len(self.data)) #*np.nan\n",
      "            # go through each trial and predict\n",
      "            for idx in range(len(self.data)):          \n",
      "                #predict\n",
      "                somLab, childLab = self.predOne()\n",
      "                somList[idx] = somLab\n",
      "                childList[idx] = childLab\n",
      "                # train\n",
      "                self.trainOne()  #  don't use mod.trainOneGliozzi()\n",
      "\n",
      "            # deal with missing values and skip first if requested\n",
      "            somList = somList[skip:]  # kick out first skip samples\n",
      "            childList = childList[skip:] # kick out first skip samples        \n",
      "            somList = somList[ childList != 'N'] # N for nan\n",
      "            childList = childList[ childList != 'N']\n",
      "            \n",
      "            # store in the big list\n",
      "            fullSomList = np.concatenate( (fullSomList, somList), axis=1)\n",
      "            fullChildList = np.concatenate( (fullChildList, childList), axis=1)\n",
      "        \n",
      "        # get how well model does\n",
      "        if len(fullChildList) >0:\n",
      "            scAgreement = float(np.sum( fullSomList==fullChildList))/len(fullSomList)\n",
      "        else:\n",
      "            scAgreement = np.nan\n",
      "            \n",
      "        if verbose:\n",
      "            print 'SOM pred: ', fullSomList\n",
      "            print 'Inf pred: ', fullChildList            \n",
      "            print('SOM and infant agree %.2f of time' % scAgreement )\n",
      "        return fullSomList == fullChildList\n",
      "\n",
      "    \n",
      "    \n",
      "    def separation( self):\n",
      "        \"\"\"return the norm of the difference vector between the mean of projected idealized class\n",
      "            A exemplars, and class B exemplars.  Essentially, the separation between those classes \n",
      "            May need to include a variance normalization in future. \"\"\"\n",
      "        A =  self.ideal[ self.idealTarget=='A']\n",
      "        B =  self.ideal[ self.idealTarget=='B']\n",
      "        projA = numpy.empty(A.shape) * numpy.nan\n",
      "        projB = numpy.empty(B.shape) * numpy.nan\n",
      "        # go through all catA\n",
      "        for idx,x in enumerate(A):\n",
      "            projA[idx] = mod.som.weights[mod.som.winner(x)]\n",
      "        for idx,x in enumerate(B):\n",
      "            projB[idx] = mod.som.weights[mod.som.winner(x)]\n",
      "        diff = linalg.norm(projA.mean(axis=0)-projB.mean(axis=0) ) \n",
      "        \n",
      "        # pooled variance estimate (not standard, since using average variance across dimensions for a class)\n",
      "        pVar = pVar = (projA.var(axis=0).mean() + projB.var(axis=0).mean() )/2  \n",
      "        \n",
      "        return diff/np.sqrt( pVar)\n",
      "    \n",
      "    def getProt( self, idealProt = False ):\n",
      "        prot = []\n",
      "        label = []    \n",
      "        \n",
      "        # use true prototypes from ideal data\n",
      "        if idealProt:\n",
      "            for idx in self.protIdx:\n",
      "                prot.append(self.ideal[idx,:])\n",
      "                label.append(self.idealTarget[idx])        \n",
      "        # estimate prototypes based in training data\n",
      "        else:\n",
      "            # get data/labels just until the current iteration\n",
      "            targTemp = self.target[:self.dataIdx]\n",
      "            dataTemp = self.data[:self.dataIdx,:]\n",
      "            # loop through all labels, calc prototypes by average\n",
      "            for t in np.unique(targTemp):    \n",
      "                prot.append(np.mean( dataTemp[ targTemp==t, :], axis=0 ))  # mean of category\n",
      "                label.append(t) # corresponding label                \n",
      "        return zip(prot,label )   \n",
      "        \n",
      "    def initWeights(self, scale=1 ):\n",
      "        \"\"\" Initializes the weights of the SOM randomly around 0 \"\"\"        \n",
      "        self.som.weights =scale*(np.random.random_sample( self.som.weights.shape)-.5)\n",
      "\n",
      "\n",
      "    def showLearnCuves(self, window=6):\n",
      "        curveSOM = slideAcc( self.somAcc, window )\n",
      "        curveSubj = slideAcc( self.acc, window )\n",
      "        plot( np.arange(len(curveSOM)) ,curveSOM, 'k.-', linewidth=3, label='SOM')\n",
      "        plot( np.arange(len(curveSubj)), curveSubj, 'g--', linewidth=3, label='prt')\n",
      "        plot( np.arange(0,50), np.ones(50)*.5,'r-', linewidth=2, label='chance')\n",
      "        xlabel('trial')\n",
      "        legend()\n",
      "        self.curveSOM = curveSOM\n",
      "        self.curveSubj = curveSubj\n",
      "        \n",
      "    \n",
      "    def meanLrnCurves( self, iters = 10, removeProp = 0  ):\n",
      "        # backup ori data, init acc vector\n",
      "        dataOri = cop.copy(self.data)\n",
      "        accSum = np.zeros(len(self.acc))\n",
      "        \n",
      "        for i1 in range(iters):\n",
      "            # train/predict SOM several times            \n",
      "            self.data = cop.copy(dataOri)\n",
      "            self.pertData(removeProp)  #not perfect data to train                      \n",
      "            agreement = self.predAll( verbose=False, skip=0, reps=1)     \n",
      "            accSum =accSum + self.somAcc\n",
      "        self.somAcc = accSum/iters\n",
      "        # average accuracy\n",
      "\n",
      "\n",
      "    \n",
      "    #-------------------------------------------------\n",
      "    \n",
      "    def __init__(self, file_name='4750.csv', columns = [0,1,2,3,4,5,6,7], width = 6, height = 5, sigma=1.2,  learning_rate=0.5 ):\n",
      "        \n",
      "        #       self.visual_only = [0,1,2,3,4,5,6,7]\n",
      "        #       self.visual_and_acoustic = [0,1,2,3,4,5,6,7,8]\n",
      "\n",
      "        # load data and normalization\n",
      "        self.data = np.genfromtxt( file_name, delimiter=',',usecols=columns, skip_header=1)        \n",
      "        self.data *= 2.0 \n",
      "        self.data += -1.0  # -0.5 \n",
      "        # get target labels\n",
      "        self.target = genfromtxt(file_name,delimiter=',',usecols=(9),dtype=str, skip_header=1) # loading the labels\n",
      "        #get infant accuracy\n",
      "        self.acc = genfromtxt(file_name,delimiter=',',usecols=(10),dtype=float, skip_header=1) \n",
      "        self.somAcc = self.acc*np.nan\n",
      "        \n",
      "        # init som\n",
      "        self.som = MiniSom(width, height, self.data.shape[1], sigma=sigma,learning_rate=learning_rate)\n",
      "        #self.som.random_weights_init(self.data)\n",
      "        self.initWeights()  \n",
      "        self.dataIdx = 0 # to index what data sample for one shot learning\n",
      "        \n",
      "        # get idealized data\n",
      "        self.ideal = np.genfromtxt( 'stimuli.csv', delimiter=',',usecols=columns, skip_header=1) \n",
      "        self.ideal *= 2.0 \n",
      "        self.ideal += -1.0  # -0.5 \n",
      "        self.idealTarget = genfromtxt('stimuli.csv',delimiter=',',usecols=(9),dtype=str, skip_header=1) # loading the labels\n",
      "               \n",
      "        # index of ideal samples that are category prototypes\n",
      "        self.protIdx = np.array([6,7])\n",
      "\n",
      "\n",
      "#------------------------------------------------------------------\n",
      "\n",
      "# def getIdeal( file_name='stimuli.csv', columns = [0,1,2,3,4,5,6,7]): \n",
      "#     # return the ideal data for checking quantization error\n",
      "#     data = np.genfromtxt( file_name, delimiter=',',usecols=columns, skip_header=1)     \n",
      "#     return ( data[:7,:], data[7:,:] )\n",
      "\n",
      "\n",
      "def slideAcc( acc, window=6 ):\n",
      "    # return a sliding window average of accuracy \n",
      "    N = len(acc)\n",
      "    wAcc = np.zeros((N))*np.nan\n",
      "    for i1 in range(window,N+1):      \n",
      "        a = acc[i1-window:i1]\n",
      "        #print i1, a\n",
      "        wAcc[i1-1] = nanmean(a) # get mean over window    \n",
      "    return wAcc\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
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     "input": [],
     "language": "python",
     "metadata": {},
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    {
     "cell_type": "code",
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     "input": [
      "# init som\n",
      "f = '4750.csv'\n",
      "\n",
      "\n",
      "fileDir = '../../KARS_reinf/Python/FeatIdealAdultBL/'\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubAdultBL/'\n",
      "\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubBaseline/'\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubLabel/'\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubSup/'\n",
      "files = glob( fileDir + '*.csv') #fileDir+'4760.csv'\n",
      "\n",
      "\n",
      "mod = SOM( files[1], learning_rate=0.4 ) \n",
      "mod.pertData(.8)\n",
      "mod.show()\n",
      "mod.showIdeal()\n",
      "\n",
      "# print mod.ideal[6:8,:]\n",
      "# print mod.idealTarget[6:8]\n",
      "# print mod.target\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CR/SympYkWCGE/kgLVgghfEO6CIQQwkd0kl8lwQoh9OdKJhq0J0mwQgjdkS4C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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x34fa510>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Ideal exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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37c1iL5mZmcjMzHR67UZiveHBBx/Egw8+6PTazJkzvZ6i6zHBnj17Fg8++CBsNhtsNhuW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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x34fd650>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# mod.data\n",
      "# mod.somAcc\n",
      "# mod.acc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "# train incrementally example\n",
      "\n",
      "\n",
      "somLab, childLab = mod.predOne( verbose=True)\n",
      "mod.trainOne()  #  don't use mod.trainOneGliozzi()\n",
      "print( 'Iter %d'% mod.dataIdx )\n",
      "\n",
      "mod.show(mod.dataIdx)\n",
      "mod.showIdeal()  # ideal exemplars on trained map\n",
      "\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SOM/Child/True Label: A/A/A.\n",
        "Iter 1\n",
        "Training exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ISKH/AzdtqX2hGMA2AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3b53050>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Ideal exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x34fdb50>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#####  Calculate learning curve (several initializations)\n",
      "\n",
      "f = files[2]\n",
      "removeProp = .5\n",
      "lrnRt = 0.3\n",
      "iters = 30\n",
      "\n",
      "\n",
      "mod = SOM( f, learning_rate= lrnRt )\n",
      "mod.meanLrnCurves( iters = iters, removeProp = removeProp  )\n",
      "\n",
      "# show curves\n",
      "mod.showLearnCuves(window=2)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8eflPAICvsy8mgII6IbaspsNbY8aMGUYdr3f4JSEhAdnZ2bh58yZkMhk2bNiA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\ndGRorLF++Pp61WEqf2X9hR8yfoDIQYT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       "text": [
        "<matplotlib.figure.Figure at 0x417ced0>"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# train all example\n",
      "\n",
      "\n",
      "removeProp = .6  #0.5\n",
      "lrnRt = 0.3\n",
      "N = 30\n",
      "\n",
      "\n",
      "f = files[1]\n",
      "#####  Calculate learning curve (several initializations)\n",
      "# init\n",
      "mod = SOM( f, learning_rate= lrnRt )\n",
      "accSum = np.zeros(len(mod.acc))\n",
      "for i1 in range(N):\n",
      "    # train/predict SOM several times \n",
      "    mod = SOM( f, learning_rate= lrnRt )\n",
      "    mod.pertData(removeProp)  #not perfect data to train\n",
      "    agreement = mod.predAll( verbose=False, skip=0, reps=1)     \n",
      "    accSum =accSum + mod.somAcc\n",
      "mod.somAcc = accSum/N   # average accuracy\n",
      "\n",
      "\n",
      "# show curves\n",
      "mod.showLearnCuves(window=2)\n",
      "\n",
      "\n",
      "# print mod.acc\n",
      "# print '\\n'\n",
      "# print mod.somAcc\n",
      "\n",
      "# print mod.getProt()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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++GM4OTnBxcXFqlJMm5KG7bzbQLStJk0dmIqvB30NoaD2O6dmn1KpVAqFQoHK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\nFIXwbKUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x3bd9490>"
       ]
      }
     ],
     "prompt_number": 278
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# mod.som.weights\n",
      "\n",
      "mod = SOM( files[1], learning_rate=0.4 ) \n",
      "# mod.initWeights()\n",
      "\n",
      "# mod.predAll()\n",
      "mod.predOne()\n",
      "mod.trainOne()\n",
      "mod.trainOne()\n",
      "mod.trainOne()\n",
      "\n",
      "\n",
      "\n",
      "potLabTup =   mod.getProt()\n",
      "\n",
      "# print mod.dataIdx\n",
      "for prot, label in potLabTup:\n",
      "    print prot\n",
      "    print label\n",
      "\n",
      "#prot,label  =  zip( *mod.getProt())\n",
      "# print label #[0]\n",
      "# print prot #[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 1.          1.          1.          0.33333333  1.         -0.33333333\n",
        "  1.          1.        ]\n",
        "A\n"
       ]
      }
     ],
     "prompt_number": 265
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# train incrementally and store predictions (just testing)\n",
      "\n",
      "# def allFilesPred(  ):\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubAdultBL/'\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubBaseline/'\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubLabel/'\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySubSup/'\n",
      "\n",
      "\n",
      "files = glob( fileDir + '*.csv') #fileDir+'4760.csv'\n",
      "agVec = np.array([])\n",
      "for idx,f in enumerate(files):  \n",
      "#     print nanmean(mod.acc)\n",
      "    mod = SOM( f, learning_rate=0.2 ) #'4750.csv'\n",
      "    agree =  mod.predAll( verbose=False, skip=0, reps=1) \n",
      "    \n",
      "    # compile how many times SOM and infant agree\n",
      "    agVec = np.append( agVec, agree)             \n",
      "print fileDir\n",
      "print( 'SOM and participants agee M+SD: %.2f +- %.2f '% (nanmean( agVec), nanstd(agVec))  )\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "../../KARS_reinf/Python/FeatIdealAdultBL/\n",
        "SOM and participants agee M+SD: 0.74 +- 0.44 \n"
       ]
      }
     ],
     "prompt_number": 268
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# mod.showLearnCuves(4)\n",
      "# print mod.curveSOM\n",
      "# print mod.curveSubj\n",
      "\n",
      "# mod.acc\n",
      "# "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Optimize learning rate to maximize SOM/infant prediction correlation\n",
      "\n",
      "def allFilesPred( x0 ): # learning rate\n",
      "    fileDir = '../../KARS_reinf/Python/FeatSeenBySubBaseline/'\n",
      "    files = glob( fileDir + '*.csv') #fileDir+'4760.csv'\n",
      "    agVec = np.array([])\n",
      "    for idx,f in enumerate(files):\n",
      "        #print f\n",
      "        mod = SOM( f, learning_rate=x0 ) # 0.3\n",
      "        agree =  mod.predAll( verbose=False, skip=8, reps=3) \n",
      "        # compile how many times SOM and infant agree\n",
      "        agVec = np.append( agVec, agree)             \n",
      "        \n",
      "    print(fileDir)\n",
      "    print( 'Learning rate: %.2f' % x0)        \n",
      "    print( 'SOM and participants agee M+SD: %.3f +- %.3f '% (nanmean( agVec), nanstd(agVec))  )\n",
      "#     return nanmean( agVec)\n",
      "\n",
      "\n",
      "# for lrnRt in np.arange(.1,2,.2):\n",
      "allFilesPred( lrnRt) \n",
      "    \n",
      "# x = fmin( "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "../../KARS_reinf/Python/FeatSeenBySubBaseline/\n",
        "Learning rate: 0.20\n",
        "SOM and participants agee M+SD: 0.546 +- 0.498 \n"
       ]
      }
     ],
     "prompt_number": 270
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 271,
       "text": [
        "'../../KARS_reinf/Python/FeatIdealAdultBL/6736_sra.csv'"
       ]
      }
     ],
     "prompt_number": 271
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = '4750.csv'\n",
      "# mod = SOM( f, learning_rate=.3 ) \n",
      "# mod.show()\n",
      "mod.trainOne()\n",
      "mod.show(mod.dataIdx)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x44d2050>"
       ]
      }
     ],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# how to use\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySub/'\n",
      "\n",
      "mod = SOM( '4750.csv', learning_rate=0.3 ) #fileDir+'4760.csv')\n",
      "mod.train(20)\n",
      "# mod.trainOneGliozzi()\n",
      "mod.show()\n",
      "\n",
      "# print( 'SOM with infant exemplars')\n",
      "mod.showIdeal()\n",
      "mod.separation()\n",
      "\n",
      "print mod.acc\n",
      "print nanmean( mod.acc)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xf/58lJWVuWzPREpEiuNs+VP4LdEIv+Xng1UuXHQc24yPj4fB8PNZuwaDAQkJ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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x45d4410>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Ideal exemplar projection\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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waordv9ORUiNHaysw1NiGFx4IQraP2mLbOS06fHinFhc9PPFwUJjdv9PRUiPn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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x44b3590>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  1.   1.   1.  nan   0.  nan   1.   1.   1.  nan   1.   0.   1.  nan   1.\n",
        "   0.   1.  nan]\n",
        "0.769230769231\n"
       ]
      }
     ],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# import sklearn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# run the model fitting N times\n",
      "N = 20\n",
      "sepList = np.arange(N,dtype=float)\n",
      "for i1 in range(N):\n",
      "    mod = SOM('4750.csv')\n",
      "    mod.train(20)\n",
      "    sepList[i1] = mod.separation()\n",
      "\n",
      "sepList.mean()\n",
      "# mod.show()\n",
      "# mod.showIdeal()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 188,
       "text": [
        "10.478541747141355"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 188
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# loop through files and train up SOMs, measure class separation\n",
      "# fileDir = '../../KARS_reinf/Python/FeatSeenBySub/'\n",
      "resultFile = 'catSeparation.txt'\n",
      "fOut = open(resultFile, 'w')\n",
      "fOut.write( 'subID\\tcatSep\\n')\n",
      "\n",
      "\n",
      "files= glob( fileDir+'*.csv')\n",
      "N = 50 # number models to generate per participant\n",
      "iters = 30 # number training iterations\n",
      "\n",
      "for f in files:\n",
      "    participant = f.split('/')[-1].split('.')[0]\n",
      "    sepList = np.arange(N,dtype=float) # init list\n",
      "    for i1 in range(N):            \n",
      "        mod = SOM( f)\n",
      "        mod.train(iters)\n",
      "        sepList[i1] = mod.separation()\n",
      "    # output result\n",
      "    fOut.write( '%s\\t%.4f\\n'%(participant, sepList.mean()) )\n",
      "    print( 'Participant %s, Category separation: %.3f' % (participant, sepList.mean()) )\n",
      "\n",
      "    \n",
      "fOut.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Participant 6375_sra, Category separation: 15.096\n",
        "Participant 6378_sra, Category separation: 15.339"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Participant 6474_sra, Category separation: 15.630"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Participant 6479_sra, Category separation: 14.831"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Participant 6519_sra, Category separation: 14.976"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-189-079e7dfb091f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi1\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mN\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m         \u001b[0mmod\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSOM\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m         \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m         \u001b[0msepList\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmod\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseparation\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m     \u001b[1;31m# output result\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-175-148ca3db575e>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, iters)\u001b[0m\n\u001b[0;32m     61\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miters\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m30\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_random\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miters\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;31m#         print( 'Post training quantization error: %f' % self.som.quantization_error(self.data))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/minisom.pyc\u001b[0m in \u001b[0;36mtrain_random\u001b[1;34m(self, data, num_iteration)\u001b[0m\n\u001b[0;32m    210\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0miteration\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum_iteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    211\u001b[0m             \u001b[0mrand_i\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mround\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom_generator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrand\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pick a random sample\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 212\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrand_i\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwinner\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mrand_i\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0miteration\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    213\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    214\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrunning\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/minisom.pyc\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, x, win, t)\u001b[0m\n\u001b[0;32m     96\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_index\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     97\u001b[0m             \u001b[1;31m# normalization\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 98\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_index\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_index\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mlinalg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmulti_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     99\u001b[0m             \u001b[0mit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miternext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    100\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r.random()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#  cat catSeparation.txt\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# x = arange(5)\n",
      "# x[:0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}