RazorSNN-Copy0.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import h5py\n",
      "import theanets\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Couldn't import dot_parser, loading of dot files will not be possible.\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from brian import *\n",
      "from brian.library.synapses import *\n",
      "from brian.clock import reinit_default_clock\n",
      "from brian.stdunits import kHz, Hz, ms\n",
      "from brian.network import Network\n",
      "from brian.monitor import StateMonitor, SpikeMonitor\n",
      "from brian.globalprefs import set_global_preferences\n",
      "from brian import clear\n",
      "\n",
      "import cPickle\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f=h5py.File(\"data/data.hdf5\")\n",
      "f.keys()\n",
      "f['data'].keys()\n",
      "\n",
      "global input_matrix\n",
      "global target_matrix\n",
      "global decorator_matrix\n",
      "\n",
      "input_matrix     = f['data']['input'].value\n",
      "target_matrix    = f['data']['target'].value\n",
      "decorator_matrix = f['data']['deco'].value\n",
      "f.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nInputs  = input_matrix.shape[1]\n",
      "nOutputs = target_matrix.shape[1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "truncate=100000\n",
      "\n",
      "# Create Training & Validation Set\n",
      "IN_matrix     = input_matrix[0:truncate,...]      \n",
      "TARGET_matrix = target_matrix[0:truncate,...]\n",
      "DEC_matrix    = decorator_matrix[0:truncate,...] \n",
      "    \n",
      "cut   =int(IN_matrix.shape[0]*0.8)\n",
      "train =IN_matrix[:cut], TARGET_matrix[:cut]    \n",
      "valid =IN_matrix[cut:], TARGET_matrix[cut:]\n",
      "\n",
      "for i, iSample in enumerate(train[0]):\n",
      "    minInput= min(iSample)\n",
      "    if(minInput < 0):\n",
      "        for j, jInput in enumerate(iSample):\n",
      "            if (jInput != 0.):\n",
      "                train[0][i][j]=jInput - minInput"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Topology:\n",
      "nHiddenNeurons = 200\n",
      "iLayers        = 2\n",
      "  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "experiment=theanets.feedforward.Regressor(layers=(nInputs,)+tuple([nHiddenNeurons for l in range(iLayers), 'relu'])+ ( (nOutputs, 'relu'),))  \n",
      "\n",
      "fom_train   = 1.0\n",
      "fom_valid   = 1.0\n",
      "\n",
      "Epochs       = 0\n",
      "maxEpochs    = 200\n",
      "\n",
      "# Tune parameters:\n",
      "Dropout        = 0.9\n",
      "momentum       = 0.9\n",
      "LearningRate   = 1e-4\n",
      "weight_l2      = 0.04\n",
      "hidden_l2      = 0.06\n",
      "iteration_size = 200\n",
      "batch_size     = 1000\n",
      "\n",
      "\n",
      "timing_start = time.mktime(time.gmtime())\n",
      "    \n",
      "for TrainError, ValError in experiment.itertrain(train,\n",
      "                                                 valid, \n",
      "                                                 learning_rate  = LearningRate   ,\n",
      "                                                 algo           = 'rprop'        ,\n",
      "                                                 weight_l2      = weight_l2      ,\n",
      "                                                 hidden_l2      = hidden_l2      ,\n",
      "                                                 hidden_dropout = Dropout        ,\n",
      "                                                 iteration_size = iteration_size ,\n",
      "                                                 batch_size     = batch_size     ):\n",
      "    print 'Epoch=', Epochs+1\n",
      "    if Epochs%1 == 0:\n",
      "        print 'Training   Error =',TrainError['err']\n",
      "        print 'Validation Error =',ValError['err']\n",
      "    Epochs+=1\n",
      "    if Epochs == maxEpochs :\n",
      "        break\n",
      "\n",
      "timing_end=time.mktime(time.gmtime())\n",
      "timing=timing_end-timing_start\n",
      "\n",
      "# Prediction for Training Set\n",
      "prediction = experiment.predict(train[0])\n",
      "goodness   = np.asarray([[cut,(list(train[1][np.where(prediction>cut)]==1).count(True)+list(train[1][np.where(prediction<cut)]==0).count(True))/ float(train[0].shape[0])]  for cut in np.arange(0,1,0.05)])   \n",
      "fom_train  = max( goodness[:,1])    \n",
      "\n",
      "# Prediction for Validation Set\n",
      "prediction = experiment.predict(valid[0])\n",
      "goodness   = np.asarray([[cut,(list(valid[1][np.where(prediction>cut)]==1).count(True)+list(valid[1][np.where(prediction<cut)]==0).count(True))/ float(valid[0].shape[0])]  for cut in np.arange(0,1,0.05)])\n",
      "fom_valid  = max( goodness[:,1])     \n",
      "\n",
      "\n",
      "MaxCut= goodness[np.where(goodness[:,1]==goodness[:,1].max())][0][0]\n",
      "    \n",
      "print 'Samples=', truncate,'    Hid. Layers=',iLayers, \"   Hid. Neurons=\", nHiddenNeurons, \"   F.O.M. train set=\",fom_train, \"   FOM valid. set=\", fom_valid, \" Time=\", timing, \" Max Cut=\", MaxCut          "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Epoch= 1\n",
        "Training   Error = 0.60897\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2\n",
        "Training   Error = 0.609055\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 3\n",
        "Training   Error = 0.60952\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n",
        "Training   Error = 0.608505\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 5\n",
        "Training   Error = 0.60911\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 6\n",
        "Training   Error = 0.608915\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 7\n",
        "Training   Error = 0.608575\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 8\n",
        "Training   Error = 0.60945\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 9\n",
        "Training   Error = 0.608885\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 10\n",
        "Training   Error = 0.60914\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 11\n",
        "Training   Error = 0.6091\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 12\n",
        "Training   Error = 0.608925\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 13\n",
        "Training   Error = 0.60929\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 14\n",
        "Training   Error = 0.608735\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 15\n",
        "Training   Error = 0.609\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 16\n",
        "Training   Error = 0.609025\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 17\n",
        "Training   Error = 0.609355\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 18\n",
        "Training   Error = 0.60867\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19\n",
        "Training   Error = 0.609025\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 20\n",
        "Training   Error = 0.609\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 21\n",
        "Training   Error = 0.607945\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22\n",
        "Training   Error = 0.61008\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 23\n",
        "Training   Error = 0.608865\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 24\n",
        "Training   Error = 0.60916\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 25\n",
        "Training   Error = 0.609305\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 26\n",
        "Training   Error = 0.60872\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 27\n",
        "Training   Error = 0.60917\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 28\n",
        "Training   Error = 0.608855\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 29\n",
        "Training   Error = 0.608125\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 30\n",
        "Training   Error = 0.6099\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 31\n",
        "Training   Error = 0.609445\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32\n",
        "Training   Error = 0.60858\n",
        "Validation Error = 0.6075\n",
        "Epoch="
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mapAlgo    =[]   # Run once\n",
      "parameters =[]   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "parameters.append((maxEpochs,Dropout,momentum,LearningRate, weight_l2, hidden_l2, iteration_size, batch_size))\n",
      "mapAlgo.append((truncate, iLayers, nHiddenNeurons, fom_train, fom_valid, timing, MaxCut, parameters))\n",
      "parameters=[]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maxFOM=0\n",
      "for i, i1 in enumerate(mapAlgo):\n",
      "    if(i1[4] > maxFOM):\n",
      "        maxFOM= i1[4]\n",
      "    print i1\n",
      "    print \"\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(100000, 2, 200, 0.85929999999999995, 0.72519999999999996, 1793.0, 0.35000000000000003, [(200, 0.9, 0.9, 0.01, 0.04, 0.075, 200, 1000)])\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Maximum FOM on validation set is: \", maxFOM"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Maximum FOM on validation set is:  0.7252\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the weights from the RELU network\n",
      "\n",
      "Layers_Weights =[]                          #list of tuples: [(nNeurons, weights), ( tuple of the 3rd layer ), (  4th layer), .... ]\n",
      "\n",
      "for Layer in experiment.layers[1:]:\n",
      "    nNeurons  = Layer.size\n",
      "    Weights1  = experiment.find(Layer.name, 'w').get_value()\n",
      "    Layers_Weights.append((nNeurons, Weights1))                    # Layers_Weights -> only Hidden Layer(s) & Output Layer"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Weight Normalization \n",
      "\n",
      "abs_max=0\n",
      "for layer_num, weights in enumerate(Layers_Weights):   \n",
      "    abs_max = max(abs_max, np.max(weights[1]))\n",
      "print abs_max\n",
      "for layer_num, weights in enumerate(Layers_Weights):   \n",
      "    for i, neuron in enumerate(weights[1]):\n",
      "        for j,iW in enumerate(neuron):\n",
      "            Layers_Weights[layer_num][1][i][j]=float(iW) / float(abs_max)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1.20987890474\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Layers_Weights"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[(200, array([[ 0.71637979,  0.88549499,  0.92235938, ...,  0.77107751,\n",
        "          -0.78279473, -0.83000733],\n",
        "         [ 0.86292325, -0.8791729 , -0.81788115, ..., -0.88095989,\n",
        "           0.83423823,  0.82599941],\n",
        "         [ 0.85445617,  0.80359867,  0.85474487, ..., -0.92905319,\n",
        "           0.87081667, -0.89494716],\n",
        "         ..., \n",
        "         [ 0.02628198,  0.13746718, -0.07894223, ...,  0.04061423,\n",
        "           0.0583473 ,  0.05624661],\n",
        "         [ 0.88216396,  0.77078838, -0.81167585, ..., -0.82836029,\n",
        "          -0.76941546, -0.89188008],\n",
        "         [ 0.86562812, -0.81193857,  0.85811514, ...,  0.74724921,\n",
        "           0.81574077,  0.85241098]])),\n",
        " (200, array([[ 0.83060611, -0.77993195,  0.86194505, ..., -0.84127833,\n",
        "           0.82877413,  0.80230185],\n",
        "         [ 0.86601297, -0.8245026 ,  0.85274037, ..., -0.86318387,\n",
        "           0.85805404,  0.83603787],\n",
        "         [ 0.80517649, -0.83409119,  0.86876267, ..., -0.86081105,\n",
        "           0.79323142,  0.86675741],\n",
        "         ..., \n",
        "         [ 0.81973336, -0.83610468,  0.84093254, ..., -0.83618686,\n",
        "           0.83203596,  0.83227395],\n",
        "         [ 0.75663974, -0.80336499,  0.85530521, ..., -0.80940677,\n",
        "           0.86876452,  0.8367382 ],\n",
        "         [ 0.7989418 , -0.83088212,  0.83093071, ..., -0.91677517,\n",
        "           0.82476593,  0.74868669]])),\n",
        " (1, array([[-0.9673781 ],\n",
        "         [-0.82604181],\n",
        "         [-0.92493328],\n",
        "         [-0.83006083],\n",
        "         [-0.82150164],\n",
        "         [-0.77940049],\n",
        "         [-0.84117143],\n",
        "         [-0.90257931],\n",
        "         [-0.88412579],\n",
        "         [-0.79707544],\n",
        "         [-0.87988243],\n",
        "         [-0.87464759],\n",
        "         [-0.88592101],\n",
        "         [-0.83839301],\n",
        "         [-0.8658124 ],\n",
        "         [-0.81060151],\n",
        "         [-0.76954466],\n",
        "         [-0.8644319 ],\n",
        "         [-0.83285928],\n",
        "         [-0.91532378],\n",
        "         [-0.8704251 ],\n",
        "         [-0.7658224 ],\n",
        "         [-0.79115624],\n",
        "         [-0.79180874],\n",
        "         [-0.92481434],\n",
        "         [-0.85859165],\n",
        "         [-0.90929741],\n",
        "         [-0.75125456],\n",
        "         [-0.78942147],\n",
        "         [-0.87232694],\n",
        "         [-0.84186236],\n",
        "         [-0.7173806 ],\n",
        "         [-0.85229219],\n",
        "         [-0.82138793],\n",
        "         [-0.85067956],\n",
        "         [-0.79112496],\n",
        "         [-0.83373357],\n",
        "         [-0.8019895 ],\n",
        "         [-0.76175588],\n",
        "         [-0.74456825],\n",
        "         [-0.88198631],\n",
        "         [-0.771847  ],\n",
        "         [-0.75919022],\n",
        "         [-0.84279188],\n",
        "         [-0.8537747 ],\n",
        "         [-0.81064363],\n",
        "         [-0.88830201],\n",
        "         [-0.85487164],\n",
        "         [-0.77500338],\n",
        "         [-0.81616044],\n",
        "         [-0.76403369],\n",
        "         [-0.82331697],\n",
        "         [-0.85718883],\n",
        "         [-0.77624426],\n",
        "         [-0.83580749],\n",
        "         [-0.7988575 ],\n",
        "         [-0.93617414],\n",
        "         [-0.80764636],\n",
        "         [-0.77334512],\n",
        "         [-0.82188094],\n",
        "         [-0.78169248],\n",
        "         [-0.79981486],\n",
        "         [-0.71818992],\n",
        "         [-0.80348112],\n",
        "         [-0.81560959],\n",
        "         [-0.72486364],\n",
        "         [-0.87191649],\n",
        "         [-0.8548114 ],\n",
        "         [-0.80213967],\n",
        "         [-0.86736946],\n",
        "         [-0.79316401],\n",
        "         [-0.82940135],\n",
        "         [-0.78143673],\n",
        "         [-0.76192906],\n",
        "         [-0.66127287],\n",
        "         [-0.88012637],\n",
        "         [-0.86602608],\n",
        "         [-0.87250487],\n",
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        "         [-0.77114591],\n",
        "         [-0.74649336],\n",
        "         [-0.88100673],\n",
        "         [-0.7600602 ],\n",
        "         [-0.82489663],\n",
        "         [-0.82886428],\n",
        "         [-0.89002023],\n",
        "         [-0.79993056],\n",
        "         [-0.72801747],\n",
        "         [-0.83733151],\n",
        "         [-0.89998895],\n",
        "         [-0.73942019],\n",
        "         [-0.78701262],\n",
        "         [-0.76188012],\n",
        "         [-0.82782822],\n",
        "         [-0.78829623],\n",
        "         [-0.78734148],\n",
        "         [-0.75299028],\n",
        "         [-0.85980228],\n",
        "         [-0.91931395],\n",
        "         [-0.81394175],\n",
        "         [-0.88882931],\n",
        "         [-0.92353874],\n",
        "         [-0.76153219],\n",
        "         [-0.78597188],\n",
        "         [-0.88727643],\n",
        "         [-0.78043567],\n",
        "         [-0.81690309],\n",
        "         [-0.86497469],\n",
        "         [-0.78394933],\n",
        "         [-0.84088041],\n",
        "         [-0.8515854 ],\n",
        "         [-0.81383087],\n",
        "         [-0.80590981],\n",
        "         [-0.80357646],\n",
        "         [-0.87530149],\n",
        "         [-0.79147753],\n",
        "         [-0.82809254],\n",
        "         [-0.82517731],\n",
        "         [-0.78104096],\n",
        "         [-0.86510256],\n",
        "         [-0.7890564 ],\n",
        "         [-0.84592988],\n",
        "         [-0.84154887],\n",
        "         [-0.77646866],\n",
        "         [-0.8913786 ],\n",
        "         [-0.76783492],\n",
        "         [-0.96849315],\n",
        "         [-0.78691215],\n",
        "         [-0.71661177],\n",
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        "         [-0.84563481],\n",
        "         [-0.83430445],\n",
        "         [-0.86682007],\n",
        "         [-0.88631841],\n",
        "         [-0.86846576],\n",
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        "         [-0.81210845],\n",
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        "         [-0.78646791],\n",
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        "         [-0.8290657 ],\n",
        "         [-0.82375103],\n",
        "         [-0.7468194 ],\n",
        "         [-0.8170425 ],\n",
        "         [-0.87171835],\n",
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        "         [-0.8791775 ],\n",
        "         [-0.82892898],\n",
        "         [-0.7967645 ],\n",
        "         [-0.85938292],\n",
        "         [-0.88506179],\n",
        "         [-0.84276244],\n",
        "         [-0.87944068],\n",
        "         [-0.72321719],\n",
        "         [-0.74950195],\n",
        "         [-0.90927403],\n",
        "         [-0.71367507],\n",
        "         [-0.82213664],\n",
        "         [-0.77924364],\n",
        "         [-0.75382829],\n",
        "         [-0.76259959],\n",
        "         [-0.87393276],\n",
        "         [-0.75259163],\n",
        "         [-0.79575268],\n",
        "         [-0.80480008],\n",
        "         [-0.77437205],\n",
        "         [-0.77346043],\n",
        "         [-0.77399524],\n",
        "         [-0.82515738],\n",
        "         [-0.83983135],\n",
        "         [-0.88171483],\n",
        "         [-0.76668505],\n",
        "         [-0.84810938],\n",
        "         [-0.842541  ],\n",
        "         [-0.68466515],\n",
        "         [-0.7968445 ],\n",
        "         [-0.79143652],\n",
        "         [-0.85283785],\n",
        "         [-0.77897138],\n",
        "         [-0.80773234],\n",
        "         [-0.83447408],\n",
        "         [-0.90834727]]))]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "reinit_default_clock()\n",
      "clear(True)\n",
      "\n",
      "# Spiking Neural Network :\n",
      "\n",
      "# IF Model : http://www.briansimulator.org/docs/tutorial_1a_the_simplest_brian_program.html\n",
      "#          : http://icwww.epfl.ch/~gerstner/SPNM/node26.html\n",
      "\n",
      "# Membrane time constant:  tau_m = 5000 * ms\n",
      "# Reset potential       :  v = 0 * mV\n",
      "# Threshold potential   :  v = 1000 * mV\n",
      "# Equation              :  dv/dt =  -v/tau_m : volt \n",
      "\n",
      "tau_m = 5000 * ms \n",
      "v_r   = 0    * mV \n",
      "v_th  = 1000 * mV # threshold potential\n",
      "duration = 1000 * ms\n",
      "\n",
      "\n",
      "eqs = '''\n",
      "dv/dt = -v/tau_m : volt\n",
      "'''\n",
      "\n",
      "# Build populations\n",
      "pops=[]\n",
      "conns=[]\n",
      "\n",
      "pops.append(PoissonGroup(82))            # Input Layer\n",
      "\n",
      "for (layer_num, layer_size) in enumerate(Layers_Weights):\n",
      "    pops.append(NeuronGroup(layer_size[0], eqs, threshold=v_th ,reset=v_r))   # Hidden Layer(s) + Output\n",
      "for (layer_num, weights) in enumerate(Layers_Weights):\n",
      "    c = Connection(pops[layer_num], pops[layer_num+1], 'v', weight=weights[1]*1000*mV)   # , sparseness=0.1)\n",
      "    conns.append(c)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pops[1].v =0\n",
      "pops[1].v"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "        0.,  0.,  0.,  0.,  0.])"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "conns[0][0,0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "0.71637978529522084"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Model-Based Normalization"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def emulate(index):\n",
      "    \n",
      "    pops[1].v  =0\n",
      "    pops[-1].v =0\n",
      "    \n",
      "    in_value = train[0][index]\n",
      "    target = train[1][index]\n",
      "\n",
      "    pops[0].rate = in_value*0.2*30*Hz\n",
      "\n",
      "    Mv   = StateMonitor(pops[-1], 'v', record=True)\n",
      "    Mvmid= StateMonitor(pops[1] , 'v', record=True)\n",
      "    \n",
      "    Cout = SpikeCounter(pops[-1])\n",
      "    Cmid = SpikeCounter(pops[1])\n",
      "    Cin  = SpikeCounter(pops[0])\n",
      "\n",
      "    Mout = SpikeMonitor(pops[-1])\n",
      "    Min  = SpikeMonitor(pops[0])\n",
      "\n",
      "    #run(5.0*second)\n",
      "\n",
      "    run(duration)\n",
      "    \n",
      "    print \" ------------- Sample: \",index, \" -------------\"\n",
      "    print \"Given Inputs:\",  \"                                                              Given Target:\", target\n",
      "    print in_value\n",
      "    \n",
      "    print \"\"\n",
      "    print \"Spiking\"\n",
      "    print \"\"\n",
      "    print \"Input Layer=\"         ,Cin.count\n",
      "    print \"First Hidden Layer=\"  ,Cmid.count\n",
      "    print \"Output Layer =\"       ,Cout.count\n",
      "\n",
      "    plot(Mvmid.times/ms, Mvmid[0]/mV)\n",
      "    plt.xlabel(\"Time (ms)\")\n",
      "    plt.ylabel(\"Voltage (mV) (Hidden Layer)\")\n",
      "    show()    \n",
      "    \n",
      "    plot(Mv.times/ms, Mv[0]/mV)\n",
      "    plt.xlabel(\"Time (ms)\")\n",
      "    plt.ylabel(\"Voltage (mV)   (Output Layer)\")\n",
      "    show()\n",
      "    \n",
      "    return Cout.count\n",
      "    \n",
      "    \n",
      "    \n",
      "    #raster_plot(Mout)\n",
      "    #show()\n",
      "    #raster_plot(Min)\n",
      "    #show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print emulate(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0]\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "emulate(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ------------- Sample:  1  -------------\n",
        "Given Inputs:                                                               Given Target: [ 1.]\n",
        "[ 12.44375038   0.           4.88530493   5.37530088  13.40995979\n",
        "   5.75565434  11.00985909   7.41203213  15.65540314   8.06346607\n",
        "  10.32575798   3.23020291  11.54006577   6.31976414   8.74517345\n",
        "   8.6141243   49.84789658   3.07823753   6.30702639   0.15473509\n",
        "  11.65298271   5.40565062   4.20395803  11.26561928   0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           7.02579546\n",
        "   5.07218456]\n",
        "\n",
        "Spiking\n",
        "Input Layer= [ 73   0  33  31  76  34  72  47 111  70  66  17  88  25  65  45 291  22\n",
        "  42   0  75  32  30  60   0   0   0   0   0   0   0   0   0   0   0   0\n",
        "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
        "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
        "   0   0   0   0   0   0   0   0  47  29]\n",
        "First Hidden Layer= [354   0   0 353   0 356   2 353   0   0   0   0   4 353   0   5   4   0\n",
        " 353   0 353 357 353   0   0   0 355 353 353   0   0   0 382   0 354   0\n",
        "   0   1 366   0 279   2 353   0 355 353   0   0   3 353   0   0   0   0\n",
        "   0   5 353   0 353 353 356 356 353  55 357 359   0   0 353   9 356 354\n",
        " 356   3   4 354   0 353 353   0 354   0 353 353 353   0   0 353   0   0\n",
        " 360   6   1   0   0   0   5   2 353 355 353   0 353   0 360 353 353 353\n",
        " 354 353 353   1   0   0   0   0   0 353 355   8   5 353   0   0 357   0\n",
        " 353 354 355 356   0 353 353 355   6 355   0   0   0   0 354   0   0   0\n",
        "   1   5   7 354   0   0 359   2 353 353   0   0 353 229 353   0 361   4\n",
        " 353 355   0 354 353 353 353 355 354   0   0   2   6   0 353   0 357   1\n",
        "   0 353 354   0   4 355 359 356 353 355   0   0   0   0  16   0 353   1\n",
        "   0 358]\n",
        "Output Layer = [0]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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uhY7GUtVjI9xH4w8VTsq7zWPImjCbSXssXVq2BPG06tZMQ7d84yhmzixbgvwp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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe6436fcb10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mH+XFDLzMSSeR/CeazV7PQhvSO0CnLMclIlIw2rWDmTNh9uywmdbee4ehw7Py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\nREREYlEiERGRWJRIREQklv8PocZWfWVu0hIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe6436de350>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "emulate(14)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " ------------- Sample:  14  -------------\n",
        "Given Inputs:                                                               Given Target: [ 0.]\n",
        "[ 19.54834366  18.47452164  13.93041706  14.0252409   22.57923889\n",
        "  17.47756386   9.290658     1.96634865  28.30947685  10.3167963\n",
        "  16.35523605  27.48944855  16.48061371   8.7950964   13.7025013\n",
        "   7.69271278  27.73896217  12.0696516   12.78225899   0.          16.9148674\n",
        "  10.88524532  12.10718536  11.01626301   0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.           0.           0.           0.           0.\n",
        "   0.           0.           0.          14.31685543  10.57628155]\n",
        "\n",
        "Spiking\n",
        "\n",
        "Input Layer= [111 104  81  81 135 104  64  12 138  66  98 154  87  54  97  44 178  78\n",
        "  88   0 119  81  66  76   0   0   0   0   0   0   0   0   0   0   0   0\n",
        "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
        "   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0\n",
        "   0   0   0   0   0   0   0   0  95  73]\n",
        "First Hidden Layer= [533   0   0 532   0 528   0 528   0   0   0   5   2 528   0  12   0   0\n",
        " 528   3 530 530 528   0   0   0 535 529 530   0   0   0 507   0 528   0\n",
        "   0   0 543   0 478   0 529   0 529 528   0   0   1 528   0   0   1   0\n",
        "   0   1 528   0 528 533 533 537 529  98 529 543   0   0 536   9 531 529\n",
        " 528   0   0 533   0 530 531   0 529   0 532 528 528   0   0 529   0   0\n",
        " 530   6   3   0   0   0   1   0 528 531 529   0 528   2 536 528 528 528\n",
        " 528 531 535   0   0   0   1   0   0 528 530  15   8 534   0   0 531   0\n",
        " 531 533 531 536   0 528 529 532   5 533   0   0   3   0 530   0   0   0\n",
        "   3   2   4 530   0   0 533   1 529 528   0   0 528 336 530   0 530   4\n",
        " 528 530   0 530 528 528 528 532 532   0   0   5   0   1 528   0 531   0\n",
        "   0 529 530   0   0 541 535 537 533 529   0   0   0   0  36   0 528   0\n",
        "   0 546]\n",
        "Output Layer = [0]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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C2MTM5gBnAiOB68zsv4CXga8AuPuNZvZJM5tO0DX4mDD9JTM7l1KJ5Wx3f6WR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\nERFpmoKJiIg0TcFERESa9v8BebRMbQHAdzYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe6465af890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9FHbZJSQUiU8yieRQ4EdgEPAy8DFwUCaDEhGpTsuWYcqVzz+Hgw+GQw5RdVec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\nEhERSYkSiYiIpESJREREUqJEIiIiKfl/fSA0svU2lVMAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fe64683fa90>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "SNN_Values    =[]\n",
      "Target_Values =[]\n",
      "\n",
      "timing_start = time.mktime(time.gmtime())\n",
      "\n",
      "for i in range(50):\n",
      "    SNN_Values.append(emulate(i))\n",
      "    Target_Values.append(train[1][i])\n",
      "\n",
      "timing_end   = time.mktime(time.gmtime())\n",
      "Time=timing_end - timing_start\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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-22-da9b384d6cb2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m50\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[0mSNN_Values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0memulate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\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      8\u001b[0m     \u001b[0mTarget_Values\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m<ipython-input-15-8d2f45aa8724>\u001b[0m in \u001b[0;36memulate\u001b[1;34m(index)\u001b[0m\n\u001b[0;32m      9\u001b[0m     \u001b[0mpops\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrate\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0min_value\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m0.2\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m30\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mHz\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mMv\u001b[0m   \u001b[1;33m=\u001b[0m \u001b[0mStateMonitor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpops\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;34m'v'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrecord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[0mMvmid\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mStateMonitor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpops\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m,\u001b[0m \u001b[1;34m'v'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrecord\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/site-packages/brian-1.4.1-py2.7-linux-x86_64.egg/brian/monitor.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, P, varname, clock, record, timestep, when)\u001b[0m\n\u001b[0;32m    797\u001b[0m            \u001b[1;32mis\u001b[0m \u001b[0man\u001b[0m \u001b[0minteger\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mmultiple\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mclock\u001b[0m \u001b[0mdt\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    798\u001b[0m         '''\n\u001b[1;32m--> 799\u001b[1;33m         \u001b[0mNetworkOperation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mclock\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhen\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwhen\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    800\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrecord\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrecord\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    801\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mguess_clock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/site-packages/brian-1.4.1-py2.7-linux-x86_64.egg/brian/network.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, function, clock, when)\u001b[0m\n\u001b[0;32m    698\u001b[0m     \"\"\"\n\u001b[0;32m    699\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclock\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwhen\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'end'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 700\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclock\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mguess_clock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    701\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwhen\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwhen\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    702\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/site-packages/brian-1.4.1-py2.7-linux-x86_64.egg/brian/clock.pyc\u001b[0m in \u001b[0;36mguess_clock\u001b[1;34m(clock)\u001b[0m\n\u001b[0;32m    346\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mclock\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    347\u001b[0m     \u001b[1;31m# Get variables from the stack\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 348\u001b[1;33m     \u001b[1;33m(\u001b[0m\u001b[0mclocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclocknames\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmagic\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfind_instances\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mClock\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    349\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mclocks\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# several clocks: ambiguous\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    350\u001b[0m         \u001b[1;31m# What type of error?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/site-packages/brian-1.4.1-py2.7-linux-x86_64.egg/brian/magic.pyc\u001b[0m in \u001b[0;36mfind_instances\u001b[1;34m(instancetype, startlevel, all)\u001b[0m\n\u001b[0;32m    315\u001b[0m     \u001b[1;31m# Note that we start from startlevel+1 because startlevel means from the calling function's point of view\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    316\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mlevel\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstartlevel\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetouterframes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrentframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 317\u001b[1;33m         \u001b[0mobjs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_instances\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minstancetype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mall\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mall\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    318\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    319\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/site-packages/brian-1.4.1-py2.7-linux-x86_64.egg/brian/magic.pyc\u001b[0m in \u001b[0;36mget_instances\u001b[1;34m(instancetype, level, all)\u001b[0m\n\u001b[0;32m    302\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    303\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mInstanceTrackerError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Cannot track instances of type '\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minstancetype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 304\u001b[1;33m     \u001b[0mtarget_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetouterframes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcurrentframe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\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    305\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mall\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mget_global_preference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'magic_useframes'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    306\u001b[0m         \u001b[0mtarget_frame\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/inspect.pyc\u001b[0m in \u001b[0;36mgetouterframes\u001b[1;34m(frame, context)\u001b[0m\n\u001b[0;32m   1031\u001b[0m     \u001b[0mframelist\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1032\u001b[0m     \u001b[1;32mwhile\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1033\u001b[1;33m         \u001b[0mframelist\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mgetframeinfo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontext\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   1034\u001b[0m         \u001b[0mframe\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_back\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1035\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mframelist\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/inspect.pyc\u001b[0m in \u001b[0;36mgetframeinfo\u001b[1;34m(frame, context)\u001b[0m\n\u001b[0;32m   1002\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'{!r} is not a frame or traceback object'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1003\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1004\u001b[1;33m     \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetsourcefile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mgetfile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1005\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mcontext\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1006\u001b[0m         \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlineno\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mcontext\u001b[0m\u001b[1;33m//\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/inspect.pyc\u001b[0m in \u001b[0;36mgetsourcefile\u001b[1;34m(object)\u001b[0m\n\u001b[0;32m    452\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \u001b[1;31m# only return a non-existent filename if the module has a PEP 302 loader\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 454\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgetmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__loader__'\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    455\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;31m# or it is in the linecache\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/inspect.pyc\u001b[0m in \u001b[0;36mgetmodule\u001b[1;34m(object, _filename)\u001b[0m\n\u001b[0;32m    489\u001b[0m     \u001b[1;31m# Copy sys.modules in order to cope with changes while iterating\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    490\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mmodname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodules\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mismodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'__file__'\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    492\u001b[0m             \u001b[0mf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__file__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    493\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0m_filesbymodname\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/inspect.pyc\u001b[0m in \u001b[0;36mismodule\u001b[1;34m(object)\u001b[0m\n\u001b[0;32m     55\u001b[0m         \u001b[0m__doc__\u001b[0m         \u001b[0mdocumentation\u001b[0m \u001b[0mstring\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     56\u001b[0m         __file__        filename (missing for built-in modules)\"\"\"\n\u001b[1;32m---> 57\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModuleType\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0misclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Time for 50 events=\", Time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Time for 50 events= 71.0\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(SNN_Values, Target_Values)\n",
      "plt.xlabel(\"Spiking NN output\")\n",
      "plt.ylabel(\"TB Target\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x7fbb8d3ad710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7fbb947766d0>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}