theanets-1.1.1-Copy0-Copy0-Copy0.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import theanets\n",
      "import logging\n",
      "from sklearn.datasets import make_classification\n",
      "from sklearn.metrics import confusion_matrix\n",
      "import matplotlib.pyplot as plt\n",
      "import time\n",
      "import numpy as np\n",
      "from theanets.trainer import SupervisedPretrainer\n",
      "import math\n",
      "from random import uniform\n",
      "from math import cos,tan,sqrt\n",
      "from numpy import float32\n",
      "from sklearn.cluster import KMeans\n",
      "import pickle"
     ],
     "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": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nlayers = 4\n",
      "module_size=20 #cm\n",
      "module_cell = 5 #per edge\n",
      "distance_between_layers = 5 #cm\n",
      "ncells=module_cell*module_cell*nlayers\n",
      "nhidden=ncells\n",
      "nhidden_layers = 10\n",
      "layers = (ncells,) + tuple([nhidden for i in range(nhidden_layers)])\n",
      "#net = theanets.Regressor((ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,(ncells,'logistic')))\n",
      "trainer = theanets.Experiment(theanets.feedforward.Regressor(layers=(ncells,ncells,ncells,ncells,(ncells,'logistic'))),'layerwise')\n",
      "maximum_hight = nlayers * distance_between_layers\n",
      "theta_max = math.atan(0.5 * module_size / maximum_hight)\n",
      "theta_min = -1 * theta_max\n",
      "y = []\n",
      "y.append(0)\n",
      "y_prime = []\n",
      "y_prime.append(0)\n",
      "z = []\n",
      "z.append(0)\n",
      "x = []\n",
      "x.append(0)\n",
      "for i in range (nlayers) :\n",
      "    y.append((i+1) * distance_between_layers)\n",
      "    y_prime.append((i+1) * distance_between_layers)\n",
      "    z.append(0)\n",
      "    x.append(0)\n",
      "spent = []\n",
      "list_error = []\n",
      "color_list = ['bo','yo','ro','co','mo','go','ko','wo']\n",
      "min_ntracks = 4\n",
      "max_ntracks = 4\n",
      "nsamples = 100000\n",
      "#nsamples = 2 * ncells * ncells * (nhidden_layers + 1)\n",
      "#print nsamples\n",
      "training_ratio = 0.8\n",
      "ds_input = np.zeros((nsamples,ncells))\n",
      "ds_output = np.zeros((nsamples,ncells))\n",
      "cut = int(nsamples * training_ratio)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def addSample (sample_number = 1,sampleInput = np.zeros (ncells),sampleOutput = np.zeros (ncells)) :\n",
      "    for i in range (ncells) :\n",
      "        ds_input[sample_number][i] = sampleInput[i]\n",
      "        ds_output[sample_number][i] = sampleOutput[i]\n",
      "    \n",
      "def sample_creater(sample_number = 1,display = False) :                  # creates a sample with a random number of tracks\n",
      "    sampleInput = np.zeros (ncells)\n",
      "    sampleOutput = np.zeros (ncells)\n",
      "    ntracks = int( uniform(min_ntracks, max_ntracks ) ) #should be a better model than flat\n",
      "    sample_matrix = [[0 for t in range(nlayers + 1)] for t in range(ntracks)]\n",
      "# first argument of each row is track's radius and the other \"nlayers\" ones are hit indices of that track\n",
      "# then we would be able to sort these hits by their radius\n",
      "    \n",
      "# theta is the angle between the circle plane and xy plane. we randomly choose two points in the second and 4th layer and then fit a circle through these two points and also the coordinate origin\n",
      "# as y is fixed for each layer and z can be derived easily by y*tan(theta) , to specify a point in a layer we only need x of it\n",
      "    track = 0\n",
      "    while track < ntracks :\n",
      "        theta = uniform (theta_min,theta_max)\n",
      "        x[2] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "        x[4] = uniform (-1*module_size/2.0,module_size/2.0)\n",
      "        for i in range (4) :\n",
      "            y_prime[i+1] = y[i+1] / cos(theta)\n",
      "            z[i+1] = y[i+1] * tan(theta)\n",
      "        # center point of our circle\n",
      "        x_c = 0.5 * (((x[2]**2 + y_prime[2]**2) * y_prime[4]/y_prime[2] ) - x[4]**2 - y_prime[4]**2 )/ (x[2] * y_prime[4]/y_prime[2] - x[4])\n",
      "        y_c = 0.5 * (x[2]**2 + y_prime[2]**2 - 2 * x[2] * x_c) / y_prime[2]\n",
      "        R = sqrt(x_c**2 + y_c**2)\n",
      "# y_prime is the y component when we place our coordinate on the circle plane and as the layers are parallel, it would be y/cos(theta) for each layer\n",
      "        if x[2] > 0.5 * x[4] :\n",
      "            x[1] = x_c + sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "            x[3] = x_c + sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "        else :\n",
      "            x[1] = x_c - sqrt(R**2 - (y_prime[1] - y_c)**2)\n",
      "            x[3] = x_c - sqrt(R**2 - (y_prime[3] - y_c)**2)\n",
      "        sample_matrix [track][0] = R\n",
      "        x_edge = (-1) * module_size / 2.0\n",
      "        z_edge = (-1) * module_size / 2.0\n",
      "        \n",
      "        track_hits = np.zeros(nlayers)\n",
      "        no_good = False\n",
      "        for layer in range (1,nlayers+1) :\n",
      "            # local indices on the module\n",
      "            index_hit1 = int( (x[layer] - x_edge ) / module_size * module_cell )\n",
      "            index_hit2 = int( (z[layer] - z_edge ) / module_size * module_cell )\n",
      "            # transform into the global index\n",
      "            hit = module_cell**2 * (layer - 1) + index_hit2 * module_cell + index_hit1\n",
      "            track_hits[layer-1] = hit\n",
      "            if sampleInput[hit] == 1:\n",
      "                    #discard this track\n",
      "                    no_good = True\n",
      "        if no_good:\n",
      "            #print \"discarding an overlaping track\"\n",
      "            continue\n",
      "        else:\n",
      "            for (layer,hit) in enumerate(track_hits):\n",
      "                sampleInput[int(hit)] = 1\n",
      "                sample_matrix [track][layer+1] = int(hit)\n",
      "        if display:\n",
      "            for i in range (0,5) :\n",
      "                plt.figure(1)\n",
      "                plt.plot(x[i],y_prime[i],color_list[track])\n",
      "                axis([-module_size/2. + 1 , module_size/2. + 1, 0, maximum_hight/cos(theta) + 1])\n",
      "                plt.suptitle('x - y_prime (perpendicular view of the circle plane)')\n",
      "                plt.xlabel('x')\n",
      "                plt.ylabel('y_prime')\n",
      "                plt.figure(2)\n",
      "                plt.plot(x[i],z[i],color_list[track])\n",
      "                axis([-module_size/2. + 1, module_size/2. + 1,-module_size/2. + 1, module_size/2. + 1])\n",
      "                plt.suptitle('x - z')\n",
      "                plt.xlabel('x')\n",
      "                plt.ylabel('z')\n",
      "                plt.figure(3)\n",
      "                plt.plot(z[i],y[i],color_list[track])\n",
      "                axis([-module_size/2. + 1, module_size/2. + 1, 0, maximum_hight/cos(theta) + 1])\n",
      "                plt.suptitle('z - y')\n",
      "                plt.xlabel('z')\n",
      "                plt.ylabel('y')\n",
      "        track += 1\n",
      "    sample_matrix.sort(key=lambda x: x[0])\n",
      "    for i in range (ntracks) :\n",
      "        for j in range(1,nlayers+1) :\n",
      "            sampleOutput[sample_matrix[i][j]] = (i+1)/float(ntracks)\n",
      "    if display == False :\n",
      "        addSample(sample_number,sampleInput,sampleOutput)\n",
      "        \n",
      "def new_samples() :\n",
      "    ds_input = np.zeros((nsamples,ncells))\n",
      "    ds_output = np.zeros((nsamples,ncells))\n",
      "    for i in range (nsamples) :\n",
      "        sample_creater(i,False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def separating_point (array,x1,x2) :\n",
      "    for i in range (len(array)):\n",
      "        if abs(array[i] - x1) > abs(x2 - array[i]) :\n",
      "            return i\n",
      "def accepted_gaps (array) :\n",
      "    if len(array) == 1 :\n",
      "        return 0\n",
      "    mean1 = array[0]\n",
      "    mean2 = array[len(array)-1]\n",
      "    not_yet = True\n",
      "    while not_yet :\n",
      "        sum1 = 0\n",
      "        sum2 = 0\n",
      "        s = separating_point(array,mean1,mean2)\n",
      "        for i in range (s) :\n",
      "            sum1 += array[i]\n",
      "        x1 = sum1 / float(s)\n",
      "        for i in range (s,len(array)) :\n",
      "            sum2 += array[i]\n",
      "        x2 = sum2 / float(len(array)-s)\n",
      "        if mean1 != x1 or mean2 != x2 :\n",
      "            mean1 = x1\n",
      "            mean2 = x2\n",
      "        else :\n",
      "            not_yet = False\n",
      "    return len(array)-separating_point(array,mean1,mean2)\n",
      "\n",
      "def clusterer (in_array,revised_number = 0) :\n",
      "    n_lines = len(in_array)\n",
      "    n_gaps = n_lines - 1\n",
      "    gap_matrix = np.zeros((n_gaps,3),dtype=float32)\n",
      "    length_array = np.zeros(n_gaps,dtype=float32)\n",
      "    for i in range (n_gaps) :\n",
      "        gap_matrix[i][2] = i\n",
      "        gap_matrix[i][0] = in_array[i+1] - in_array[i]\n",
      "        length_array[i] = in_array[i+1] - in_array[i]\n",
      "        gap_matrix[i][1] = (in_array[i+1] + in_array[i])/2.0\n",
      "    length_array = sorted(length_array)\n",
      "    gap_matrix.view('f32,f32,f32').sort(order=['f0'], axis=0)\n",
      "    out_matrix = np.zeros((int(n_lines),2),dtype=int32)\n",
      "    if revised_number == 0 :\n",
      "        accepted = accepted_gaps(length_array)\n",
      "    else :\n",
      "        accepted = revised_number-1\n",
      "    for i in range (n_gaps-accepted) :\n",
      "        gap_matrix[i][1] = in_array[n_gaps]\n",
      "    gap_matrix.view('f32,f32,f32').sort(order=['f1'], axis=0)\n",
      "    out_matrix [0] = [0,gap_matrix[0][2]]\n",
      "    out_matrix [accepted] = [gap_matrix[accepted-1][2]+1,n_gaps]\n",
      "    for bunch in range (1,accepted) :\n",
      "        out_matrix [bunch] = [int(gap_matrix[bunch-1][2]+1),int(gap_matrix[bunch][2])]\n",
      "    return accepted+1,out_matrix\n",
      "\n",
      "def clustering (in_array) :\n",
      "    central_tracks ,_ = clusterer (in_array)\n",
      "    col = np.reshape(in_array,(len(in_array),1))\n",
      "    min_n = int((0.75*central_tracks))\n",
      "    max_n = int(1.25*central_tracks)\n",
      "    goc = np.zeros(1+max_n-min_n)\n",
      "    for nclu in range(min_n ,max_n +1):\n",
      "        algo = KMeans(n_clusters=nclu)\n",
      "        track_candidates = algo.fit_predict(col)\n",
      "        track_candidate_values = [np.array(in_array)[np.where(track_candidates==n)] for n in range(nclu)]\n",
      "        goc[nclu - min_n] = algo.score(col)\n",
      "    d_goc = []\n",
      "    for i in range (max_n-min_n):\n",
      "        d_goc.append(goc[i+1] - goc[i])\n",
      "    revised_number = max_n-accepted_gaps(d_goc)\n",
      "    return clusterer (in_array,revised_number)\n",
      "def similarity (exptrack,nntrack) :\n",
      "    sim_counter = 0\n",
      "    for exphit in range (len(exptrack)) :\n",
      "        for nnhit in range (len(nntrack)) :\n",
      "            if exptrack[exphit] == nntrack[nnhit] :\n",
      "                sim_counter += 1\n",
      "    return sim_counter\n",
      "def got (exp,nn,cut = 0.1) : # get ready for awful variable names and no comment !\n",
      "    goodTrack = 0\n",
      "    # creating MidPut\n",
      "    for i in range (len(nn)) :\n",
      "        if nn[i] < cut :\n",
      "            nn[i] = 0\n",
      "    exp_Output = np.array([[i,exp[i]] for i in range (len(exp)) if exp[i] != 0],dtype = float32)\n",
      "    nn_Output = np.array([[i,nn[i]] for i in range (len(nn)) if nn[i] != 0],dtype = float32)\n",
      "    exp_Output.view('f32,f32').sort(order=['f1'], axis=0)\n",
      "    nn_Output.view('f32,f32').sort(order=['f1'], axis=0)\n",
      "    n_exp,exp_matrix = clustering (exp_Output[:,1])\n",
      "    n_nn,nn_matrix = clustering (nn_Output[:,1])\n",
      "    expM = np.array([[exp_Output[i][0] for i in range (exp_matrix[track][0],exp_matrix[track][1]+1)] for track in range (n_exp)])\n",
      "    nnM = np.array([[nn_Output[i][0] for i in range (nn_matrix[track][0],nn_matrix[track][1]+1)] for track in range (n_nn)])\n",
      "    similarities = np.zeros(n_exp,dtype = float32)\n",
      "    for exptrack in range (n_exp) :\n",
      "        similarities[exptrack] = max([similarity(expM[exptrack],nnM[nntrack]) for nntrack in range (len(nnM))])\n",
      "        if similarities[exptrack] >= 0.75 * len(expM[1]) :\n",
      "            goodTrack +=1\n",
      "    return float(goodTrack)/len(expM)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_samples()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "training_error = []\n",
      "validation_error = []\n",
      "train = ds_input[:cut], ds_output[:cut]\n",
      "valid = ds_input[cut:], ds_output[cut:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# we try to create some training samples and train on them and then check the mean error in some testing samples\n",
      "# a function of iteration and train per iterations\n",
      "e = []\n",
      "for i in range(10) :\n",
      "    e.append(0)\n",
      "niter=1\n",
      "ntrain=1\n",
      "for i in range (1,niter+1) :\n",
      "    # training on training samples\n",
      "    for j in range (ntrain) :\n",
      "        start = time.mktime(time.gmtime())\n",
      "        print \"training started ... \"\n",
      "        for trn, val in trainer.itertrain(train,valid,hidden_dropout=0.6, algo='adadelta',rms_regularizer = 1e-1,rms_halflife = 0.2,iteration_size = 100,batch_size=1000,validate_every = 1,patience=100,min_improvement = 0.00001) :\n",
      "            if trn['err'] < 0.2 and val['err'] < 0.2 :\n",
      "                training_error.append(trn['err'])\n",
      "                validation_error.append(val['err'])\n",
      "        print \"\\t training ended\"\n",
      "        stop = time.mktime (time.gmtime())\n",
      "        spent.append (stop - start)\n",
      "        print \"training time (seconds) \\t\" , stop - start"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "training started ... \n",
        "\t training ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t16060.0\n"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(training_error,'b')\n",
      "print training_error[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00455000070757\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2881wzTWZ67ODwJIlYeiXbIcemn+bJGeckblsBvvvnz//JZek51NtLZtuGmpp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\nIlIxBREREamYgoiIiFTs/wNEJT1XyNFULwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f754f4f7f50>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(validation_error,'b')\n",
      "print validation_error[-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00866129235362\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9VZlpV+yhPBERkbgL3CIi0kLTpsFdd8FOO8EOO4Rm++2hqio0220Xmp49oUeP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\nAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f75501f4d90>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "[(OrderedDict([('loss', 0.0085986440083445168), ('err', 0.0085986440083445168)]),\n",
        "  OrderedDict([('loss', 0.0086501840236289668), ('err', 0.0086501840236289668)])),\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "spent"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "[18.0, 15.0, 38.0, 201.0, 210.0, 1113.0, 2674.0]"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.save(\"network/100K3L\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.load(\"network/100K3L\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "<theanets.feedforward.Regressor at 0x7f7549eb2c90>"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(train[0])[4])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.text.Text at 0x7f7551933050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4nIQyyNrJwe/b5+OZiDzT+drNY64jvUY0cdExyz8Ndb5Q1gU/wD8/cCDdweeJ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\nHgOuBL5ORjsws2uAVwCTwJu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       "text": [
        "<matplotlib.figure.Figure at 0x7f7553b5bb10>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ds_output[80004])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x7f10d6b30310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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McYxD1j7xEXifhU6g+EIWy7LdUO7TsRY7jbU9qmIWG93RaSB8Mnjw7xDFme88s1l2G6FI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\niAVFAi+EEAuKBF4IIRYUCbwQQiwo/x+/npQTl0T9/AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10dbb882d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([val for val in trainer.network.predict(train[0])[4] if val > 0.1 ], bins = 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(array([ 1.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,\n",
        "         0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,\n",
        "         1.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,\n",
        "         1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  3.]),\n",
        " array([ 0.11243238,  0.13007094,  0.14770949,  0.16534804,  0.1829866 ,\n",
        "         0.20062515,  0.2182637 ,  0.23590226,  0.25354081,  0.27117936,\n",
        "         0.28881792,  0.30645647,  0.32409502,  0.34173358,  0.35937213,\n",
        "         0.37701068,  0.39464924,  0.41228779,  0.42992634,  0.4475649 ,\n",
        "         0.46520345,  0.482842  ,  0.50048056,  0.51811911,  0.53575766,\n",
        "         0.55339622,  0.57103477,  0.58867332,  0.60631188,  0.62395043,\n",
        "         0.64158898,  0.65922754,  0.67686609,  0.69450464,  0.7121432 ,\n",
        "         0.72978175,  0.7474203 ,  0.76505886,  0.78269741,  0.80033596,\n",
        "         0.81797452,  0.83561307,  0.85325162,  0.87089018,  0.88852873,\n",
        "         0.90616728,  0.92380584,  0.94144439,  0.95908294,  0.9767215 ,\n",
        "         0.99436005]),\n",
        " <a list of 50 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pyLQDOLRb/jXgx1OQ6RnAf3TLm4GXTfJNl1V1DdDvt6eH3gRaVVuBNUl630+0h3EX+zBv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\nxmKXpMZY7JLUGItdkhrz/1n1F+f8VmbNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3de003e150>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(train[0])[4],'bo')\n",
      "plt.plot(ds_output[4],'y')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7fa6d0d602d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QH+C7gbcMPo9U6jCUywfb3r/qj6yiMYwcenqOrL6Wo8FriUbsMXj/zue3t964\nBar3926WC67P4J1vKm5L9tmT31WrVnV8Hk01eMZYDsAXAKwH8AiAmxljG4loFREt6vioXcLLAhXm\nI2xnNSvUto2JJogFP5pn8OrIASIIxiYpUqJRu7/zDL47C36YiUmzYwbRfi1nUDDG1gFYZ3tvhcu2\nZwo6Lyk4PQLbCzppm6R3nCQvGZpxvIqNyS+/HBebZKMPvrfDxK2TY+cqkyrdJ1ep4KKJFO1INDrA\ntyYoSSHOEo2ciU6H+daiGTMA1Gaw8p/Vuibhk2h0gPdFK5skYwYYKyKVmizMJqnaMmYiCUpSiGOx\nMUBe+WUREk3jNHv1ZLNu2iTNGdjNJRod4IVin/ZuD0hcckj7rEWjrqtANPbsWt6KTtGXaBhjDZqx\nyhOdGh1jgGquMWebZLASTbP6N3rJPsHYg4U9Q/IyMNKMeEo0cm19zWvRRKdtzeTCWjlSjg9elETj\nlsGrc01UKDbWbJERvWSfYFpJCl50s2ao7ioQTVCSgluAj5L85fR3qi3RNFa5VK2/d1eD95Is6iX7\nhNJKUvAy8t2MuGXwrSQvEcRHoml8UpHVniJcNOHM4FWUaHSAF0YrScHLwEgz4hbg7e0Z7IpO0Wpb\npxuZ6PY0F7xJJifGQqKxa/CdJm6dHVu7aAKnUVKoX9HJv0QTxwBvb0/xPvg4uGjcJBqRAb6WwPRU\njtl5+4UhwDNWQOOKTupINDrAC8ZZoilYPm/9WNUMZxdNdHRiO/b21BJN5zg9qYjW4K1t6XfA0V5H\nBUCl76vT37sv0fRpF02Q2GdFWte95J9riaYdtEQjDqcbmegbprUtm7k7vBCGDL7RJpkCwHw9uXg/\ntllsrN1aNNpF0zGNkkJt3Uvr5512fu2iCbqapDrZol+c/k7xGnyuLsD7y+DD4aKxBnggOJlGlVo0\nMQvwzZ0KdonGbSUWN+KXwTdKNDJsfXGoRePcN8UWbxMt0YQhwFttkoD/JxevmDdTXYsmQFo5Fcwv\nGVECnRTOilOA52UdCgHMvIy3RCNy0Nou0fjRo8MS4Bsz+GCcNLVkUbtoAqOVU8HvI2ycArxZSbJ+\n5qVe8KNTwifRqB/g7Ro8EKREk/Mg0egALxS3ySSmrGCVAzpZ/aVxhZboumhayV3ijuPmoomaTVJ+\nezb2b78BvtFFo1J/d9fg5Us0NUuqdtEEhrNEk4ZdouHv6wy+Gc6SQpA++Gi1rdNYgxybZHwyeLsP\nHuiWRONBxJ31AAAfZElEQVQtg+fF2rSLpmPcJZpCw+edBfj4uGiCkRT4zEt7IAHi4aKRbZP0l8mq\n76JRRaJxLzamXTRCaVXQyZqV8tHv9iWaOAd4OTMv63X+2rGi1bbBafDW/h31DF4Fm6R20QSGfUk0\noD4oWTV6LdE0xwy+VmRKCnai1rZu7akHWTvHySbZSeLWCZ1INDrA+6RVlqRdNN4JWlJoJAHAgLl0\nXNhxbs+00BtmYwITR5uk/AzeXBmutniQtkkGgjebpD8XTeMFi45ObCVoScEOl23Ucm34wb09Rfrg\ng5jopM716JYGX5tPQ12XaBpHryJMq4JOYlw0VutYtKx8VoKy9bln8NYvRI/rNmEh6PYUI9E42STV\n6e/2DD6TyWJ8/Als3ZrDE09sxvDwAgwNzRd+XK/t3Gir1gG+Y5xmXgJiJzo1rtDCq+sxxhwHCsOM\n+8zLoCQa9TJGPwTdnv6LjanvorFq8JlMFkuXrsf7338qtm49BaOjF2F8fBkACA/y9fMNmkk0zvFC\nJLGRaJxmXgLNNfh2vwCNj1zRkhGsBKPBO9ehqR1PrYDSKZlMFqOj9+Kqq9ZhcHA5MpksgDBk8OoH\neDODHxkZxfj4ahQKfUin+d89Pr4aa9feLvy4XpQAXueqLH2iU2wyePeaJrXJOVaXTScaZfNOH62m\ndmpPUzMW9cTiXaIJL2Zmef75b8Wjj34IY2OLq5nlvHniA3xPz5EAeP8ul1/veF9ufd0wDvg6R5EY\nRu2JPZ/n51oo9KGnp5a45XJJx9/1d9xav3Wzo/KkL1n3PdGDrB2SyWRx3nkrsWdPqS5DAuTWouH7\nD38QcsJ55mUS1vLL/o/hRaIJd9uamWU6nUOxyNvTzCxVnugUhr5ulWh6e/l5FYu91QweAPr6xD9d\n1w9mO7dzUO0X+QBvZkh3330x9u07EqOjV2Lp0vXVIN/cJulPogHU6/SicC8hIC4otc7g1RrU6wQz\ns0yncygUan9rLpfUpQp8YpVohocXoL9/WZ1E099/OZYsOUfCcb1INDrAC6GWIeWrXyCr9lafwefh\npxiTm7NApWXMROGWXYu0SjazSQLqBZROMDPLnp58XYDv6yvX1UkSgexiY6qNN1ltkkND87FmzSBm\nzfpfvPGNYxgcvAJr1iwMwEXjvIqUc6zQGnzbWDMk8xEYqGlvjaUKxNWi4fsPfxBywr3Ko7gA71T9\ns/5YKYT95jk8vADj48sqGTxvT55ZLqz0HVUlGvX7ut0mOTQ0H3PmfAD792/C1772dWnHbVzK0jRs\nWNvLqf3E3yAjH+DNDGnChH3I5SZW3ze1t2YafLH4WlvHCsNjqyjK5X1IpSY3vC9SVojDIKuZQR44\n8GPMnn0dZszIYMkSnlnu3fsAxC74ET+JpnFFp4kol/dLPa796dZs62SyFn+Car/IB3gzQ5o48d3Y\nt28KgFqGBDhp8J0XYwpDpxdFubwHfX0zGt4Xr8FHW6IBeJC/555e/Pznl6Ovb3r1fTk2yXgUGzMz\nYbsMkkpNQam0R+qx7YmJkxymA7wgzAzpjju+j4kTt2Nw8IpqhgTILTbG969OpxdJqbQbqdRhDe+L\n1eCj76IxcWpP0aUf4lSLxqlMAQCkUoehVNot+diNEo29rXWAFwjX3jZj376p+NKX6rU367qX2ibp\nnVJpD1KpKQ3vi1xHNA4uGoBnm+XyQSSTh9a9ryc6dQ6fte4U4LuRwTe2tXbRCIYHJKeM07qik4hi\nY2ovYyaKUmkPksnG9iQS5/yIi0RTKu1FKjUJRPVfRzk2SXEuGmsdFUAtxxjX39MN76dSh6Fclh3g\n7Rq8m0Rjb79E5TNxFVJjFOCdJQUzS2KM2Waytpfh8IvCYG/SqAQhO+pINGoEFD+USrtdbpZyJzpF\nuRZNtyUa6yRAZ4mmsf0A8U+lsQnw5XIzSaFYsVSlqnfV9gN8ufL79VP0Ver0Imkm0QQ30SkabevW\nljJ88PGRaJwDfDJ5GEql1yu1YOTQqUQDiE9aPAV4IlpIRI8R0eNEdJnD55cS0WYi2kREWSKaJewM\nBeEm0ZgBqXFgpLetDKf5BVOj04uE3zCd21OkTTIOxcaataUsF428evBqXA8niyTAJx4lEn0ol/dJ\nO7b9ydPJsRRUvGgZ4Il/w64BsBDAyQA+TESn2Da7F8BcxthsANcD+J6wMxSE22OwKSm4eVe9EqcA\nbxgllMsHGgYFAdEZfDxcNM3kQ5nlgqPsonHL4AH5Mo1zBt/aRQN0IcADmAdgM2NsJ+NHvhHAkHUD\nxtgGVkt3/wTgBGFnKIhWkoKXx6pmxCnAl8t7kUpNbhgUBGSUKohDgHeTaGSs6BQPicZNgwfkO2mc\nbZKKZvAApgHYbnm9o/KeG58H8Fs/JyWDZhKNYRQdBkbac9E4uQr4/qPnonFz0ABBT3SKhk2ylXwo\nQi+2L3gjoxaNSn29VQYv00ljL7Hh1UUDiA/wXnzwnnsXEZ0PYC6AM5w+X7lyZfXngYEBDAwMeN21\nb5q7aApaomkDt7YExPrgvdSiiULburtoEjDLLzv1rXbgtdFrC95wO2sJjBmOT2KtUD2DZ6zgqMED\n3ZFo7ON5zeLFnXduwN13Py7kXLz0mh0AplteT0d9Rl85MTobwDIA85lLCmcN8EHj5qIxnQr+JRo3\n25M6nV4UbpICINb54U2iUSNj9EOptAfp9LGOn9WeiPwG+Pq2rC0InUcyOaGDPYbBJtnogwdUkWjc\nbtpJnH7632PBgsXVd1atWtXxuXi5dT8AYDYRnUD8mWcxgHXWDSqDrj8EsIgx9krHZyOJVoOCpkRj\nvSh85Fu7aJxwc30AuhZNJzR7IhI1puG85mvnMo36Gby7RMOtkjIDvFeJRgENnjGWA/AFAOsBPALg\nZsbYRiJaRUTvq2z2LQATAdxERA8R0f8IO0MBlMt7kUw2zhQErDqnlmi80lqi0S6adgjihukkd/lx\n0oQhwKsk0XTLRePpuY8xtg62rJ0xtsLys/hlUQTSXFLQLpp2adaeeqJT+zRvz7SQG6ZTW/px0oQh\nwKvkorH77pXJ4KNAq4zTeaITb3z7SixuuI2K81Vu1Oj0omjmoglyRaeotG23JBo/Thp3F40a16O5\nTVKui8arROPsutMBvm1aZZxcg3d7hPX2BQhq6rEK8IAURAYfD4mm9RORf1eSW//utB6NewavRl9v\nncHHQ6KJRYD3onH6f4SNm4smCA0+Pi6aZvMK1JRo1HbRtNbg5Uo0jcXGvLloRFfkjEWA9/II7PcR\nNk4afOsbprh68HGoReNFQvSLHIlG3QDPff/dctHYLakKu2iiQGuJpnGiE9DeI2ycAnwziUaUZmzO\nvIy6TbJczgFgrk8qYjV4PcgKBCHRtHbk6QAvELeZgkBtgQq/X4B4BfhmGbwo10f9zEvnY4W/bc2n\nIbe/U1wGH0ebpNtEpyAy+PaX7AN0gO+IZgHJzSbJP9MB3okgatG0dtCo5drolGZ9E1BZg1c/wHfP\nReO3HrwO8G3hxbdtGPkGvbedgmPuxYPUWcZMFEFINK0GWAG1AkqnNOubgLjSD07jGaJtkty2qkZf\nb2aTTCYPRbl8wLMFuh0YYw3SYjeLjcUkwLeeeek/g9cuGkDuzMvGY6UQ9ptnM/kQUFmiUd9F4xbg\niRJIpSajXN4r4bh8cNc6a955LE8v2SeMIGyScZFo+KCg4Rp8ZUoKjccKf9t6kWj0IGv7NLNJAqaT\nRvxAq7NbSfEl+8KOl1IFPGN0KsakXTRWzKqc8gcFvWjw4W9btyqnJqLKL+tiY/XIKlfg/KSkbZJS\nae6i0Rl8O7TKOEWtQtRqFisQjbZtJh8CIm2nQbho+HiTzAWtvdLMBw/Ic9K4xxHtopFG65mXBR3g\nPeJFM9YSjXeiJdEQVBlobZ3BqyzR6ADfFl5dNM4z/fy7aFTo8KJo5frQNsn28NKe4m6YYl00bktU\nqjDw3UqDT6WmSLFKtifRaBeNb1oNCtZ88Ae1i8YDrSUaUbY+rxJN94OJH7rtoum82Jja/b3Zik6A\nChKNdtEIofWgYBJAAuXyAS3ReKCZBx6QKyk0Hiv8bRukD162RAOoc028DbLKkWjs8w3M2fKMGZbz\n0y4aIbQaxAJ4UCqXX3d8hNW1aOppZjkFxEoKzQqN8WOFv21b9c8wFRsD1Lkm3mySwUg0RFQp4VGL\nJVqDF0SzafUmiYQZ4HUG34rgBgXj4aIJ7oYp30UDqHNNvA2yBiPRAI1trdSSfWGm1SMwYGbw+xy/\nAMXiy56Oo3qHF0WptBsTJvS7fq5LFbRHa4lGlO003BJNJpPFyMgo8vkUentLGB5egKGh+a7bNytV\nAMiVaJzMAfa21gFeEF4lmmLxVZdaHd4lGjdXQdiDkJVWT0QyJ+Y0Hiv8bRukRCO/Fo2ca5LJZLF0\n6XqMj6+uvjc+vgwAXIO8WTLADVkFx9xKbNjbWrtoBNFKUgBQ6QhliS6acDs9rAQl0XitRRPmtmWM\noVTai2Rysus2qko0zVcwE3tNRkZG64I7AIyPr8batbe7/k5rm2TwEo11PC8oF1LkA3yrqeAAqh1B\na/Ct0S4acZTL+5FI9DUNRCpOdOIVE4PJQAEgn3cWGnI5p0XuOd2VaNw0+NYSjeiJYpEP8N4kGu6X\n9VOrw+mCZTJZXHnlr3HnnY9hcHA5MplsG2euJq0GBcXa+sLtoslkshgcXI6BgZWO199L3xQ7piGq\nFo0BIAFrxcTaPsVfk95e5/319bkHQj7I6u6Dl+eicdPgnSQarcH7plTag76+Nzbdht/pEw0N3s4j\nLGOluju3qRsee+zHMXXqzzA6emVL3TAMBLdARbhdNF5041Y3S6DmofaLyFo07tmnnGsyPLwA4+PL\n6tqyv/9yLFmysMk5dkuicdPgtYtGCl5cNIlEDxKJvobJUH4kGlM3PProDJJJfsG4bnhFyAO8lmi8\n4K4b166/V4eXais6BR3gzfZau/YK5HJJ9PWVsWTJwqbfo1Y2Sd4WDOVyDslk837WDn4lGh3g28Sr\ni8bLY1Uz7JqkqRuWy8lqgAea64aq42VQUKak0Ii6LhovunG3JZpOXTRu+jsgz9k0NDS/rcSolQZP\nRFUnjegA39NzZMP7ThKNs+tOB/i28Oqi8fJY1Zz6UXFTNyyXU0gkalphM91QdfigYG9Ag4LhdtF4\n0Y29TMJTsRaNmwMEUOeatMrggZpMk05P7egYTt78E0/0KtEE46KJQYBvLikArQJ8ZxKNqRuWy+dU\nM/hWuqHqeG1LXS7Ym27svT39zStgjHkuY+ttf8FKNJ3QSoMH/Dlp3MZYrrnmGZx00t82bO9dohH7\nBBT5AO9lIItr8CIkmlpzmo+Tv/nNT3HEEVsxOHhFS91QdbwNCoqbeRnmWjRedGOvT5d+M3iezSYb\nZBVzIh9jrGH8qfn+uh/gW81sbbXgB+DPSeM2xvLkkydj9uzGfmt3LDXT4EWWW458gPc6kOVlckIz\nnC7Y0NB8nHZaGk8/vQW33fZ17yetKF4HrHnlvPaChp2wu2iA1rqx1zkafgO8W1sSJSpt2NxSaKfb\nAd6LQ8mbRNP5sn1uYyxEzqXJu+WiibQPng8K7mk6KAjIkWhq+1Y7CLWDtwHrJADyrcOGXaLxglcD\ngP8A796Wncg03Q7wXma2etfgO5No3MZY0umiUi6a0AX4VpNHrJgFxFppcYlE2lEO8CPRmKgWhNpp\nPzteBgUBMd7tKEx0aoU3iSbte0yjmdzViZOm2wHei0OJa/DNn0r81KMZHl6A/v5lde/191+ON75x\nSscTnTKZLK666mb84Q8PC5sYGSqJpt2iQ14kBUCMi4aPiqu7hBnQWdEmK17bsyYrTOj4XL3VovHX\ntnYd913vOh733POc54qFfmlH8vJDM7mr3clOmUwW//mf1+ODH9yFf/mX5Q5tJG6qvZvO7sWh1Mom\nCfiTaNzGWI466h6Pcm99vDC/m9OmfRSHHfZfdRMj/RCqAO9l8ogVL4/AgHuAr63EUsatt/6p6aBO\nGDL4dtvPjpf2zGSySCRKWLToShhGuuMgKVuiabzZZfHHP16PUumH1W1kzzxutVwfoJZEY7aZYXwR\nZ5/9J8fZ2W7XpN1yv82SES8OJa8SzcGD4x7+cmecxlg2bmxs60wmi/vuG0Nv70FkswzDwwtwzDHO\nEyOPP/7mhomRfghVgG+36JAX1wfgHuBvvXUD0ukU5s79HLZs6cHBg+5f/jAE+E6KNllp1Z7ml/Jb\n35qM++67BLt2HdtxkJQd4BtvdqN1wR2QP/O4lUSTyWTx61//B84551l87WtO2bL771mD6cUXz8DR\nRzcP8F4CsNlmb3rTQyiXeV+yt5HTNWnnydE8jwceeBq7dv2q7jPzWKZhwcmhZP7+V79awNDQvzZ1\nrsmoR2OXFs2//eSTP4Bp056q3hR/9KO9dfGiNjGyft6M34mRLQM8ES0E8G3waVfXMcausn3eC+Dn\nAP4WwF4AH2WMPevrrFxofDTLAhjFo49ux+Bg4xegPUmhXjczL8y//dtEPP30FBw8+N26z+0dOwwB\n3vnRNotNm57AwMDKlplVqbQbvb3TXfdvBoBy+TqkUjzrtGYh7S3YkJeqwTfe7Pzd/DqhmYvG7H/A\nME47baPnWkZOwZToE7j00kYZJpPJYu/eV7BixQqMjR3TNIEBam2WTJaqAR6obyPzmlhvGJs2PYFX\nX72x7thON8/6c1/p+PeZx3LKns3ff+aZf8Vll30D69d/A08/7d5mbj74dp82rNjlMPM78da3/gjp\ndL76t+/efY3rxEjrzHe/EyObBvhK8L4GwGkAXgRwDxGNMsYesmz2JQDPM8bOI6IPABgB8H5fZ2XD\nbPCdO1/GhAkXVTpiFsB6AKuxaxcwOtrYKb08AmcyWTzxxIMADFx8ce0mYV6YQuGnSDeM1fAby733\n7sDg4HK8613HY/r0x3HvvTdi27b76zqEUxAaGxvDwMCAz1ZpjlMnbXy0zSKVuh6vvnoj7ryTv9Ms\ns/rrXzfgySdfxFNPPefY6c0AUCr1oKenALOdNmzYhGz25boA8uijn8Fxx/0KhnEAiUQKQBqTJx9T\nPddJk+Rm8I03O/fkYe7czzacn/m3dxIMzN+55JKX8JGPjOCiixZhaGh+Xb8w+9/MmZuRSvF5BV6e\nKJxkuJdfvgDbt29oOIelS9dj6dJ+bN8+qe7a8GMN4oILrsbs2X/E3r07AKTx17++BqAxwJtBKJPJ\nYnz8MTzyyA7ccMNbLPtc6XiuuVyyyY3AWTpxCnj2rD+dPohSqafyd7i3mVPBMb/jVPYnT/M7USj0\nIZ2uSWFEzGVi5BkNEyPXr7+y5XHdaJXBzwOwmTG2k58U3QhgCIA1wL8XwFcqP98C4MdERIwxZt+Z\n/YtiHdQyO9Hkycc0/Pz885Pxwgvfq+wliwkTzkMyWca+fTfV7d/eKf/hH57CzJn7ccklzo+35sVc\nsGA+kslSXZZUf2Gs2U/txrJnDzA6yrXbb3zjjXj44Qvw4IML6jqENQiZHXHLlrtwxBFvcvx7m7WL\nnzbbsOFq9Pcfh8mT92Hu3C9i0qSjHTKrLMbHCR//+E8xc+bPG/a3dOkb8OCDn8a9977PsdObQbNU\n6kEyeTeAJwGsxoEDywFcWXecF144Fi+8sBrApwBMBVC76WzY8AP8/vcHMTR0pesjdiaTxQ9/+Dt8\n6UsHqjfZdtqsUChbkgUAWIBU6qKKTGNNHrLYtWu95fxqN6dCoYytW8myD7Odb8bxxx/qeCPYufNl\nbN1KyOd/gK985ZvIZK7CE0/wJ5wHHqgFeOvN0nwaArK4//6/YGBgpeu1f+SR7baWyiKd/hFeeaVY\n95RbS2DORjpt/6ryv5/f+Gttwd9fhmRyYTXAm0HI/C598IPvxAsvPG+7YTjfPB988DF85CMvuNwI\nUgCW1bW700xwp6w/lSqiXK7p705PYZlMFjfe+Euce+4WfPWrtf7TKA3VvhPvfOdo04H4TCYLw3gZ\nixdfjUJhAoaHF1S/E/UBPotyOY/zzhtBPj+xbh+33PIjHHXUX8RNjOTF+53/AfgogGssr/8PgB/a\nttkC4BjL6ycBTHXYFwMuZwCr/LuTpVKfr/5c++xO23bLLD/X/h1++Cds7zXu46Mf/Qb73Oe+wgDG\n+vsvZ7///Z3MyoIFfN8XXngZ+/znL63ua3BwefWz6647kc2YcZ1l3/bz4a+/+90z2dy5f6jbB2OM\n5fMvsrvuOor9/vd3sv5+cx+fbPL3urVLp21m/6zWFmecsaJp+9n3t3btP7DZszc0/I0m5t947bV/\ny2bNutDy+yuYU5vxf6c3nEMqlWe3355yvW7mcXp797PbbuvrsM346wkTFrPZs5eywcHlbMWKq9ng\n4HJ2+OHnNel/1n14a+f6a89/Z9KkV9ktt0ypa8sVK1Y09M1jj93KbrjhDU2uT+trP3/+TWzVqg86\nXvtvfvO97NRTFze5Po1//5w5H2Nr105lg4PLq9fGPN9LLvkcW7RoqEmbubWf/fWKyrbL2eGHf6Lu\nWE7fYevvT578SkPbOvWf444bZ9dfP9PWR9y+E/a+5HyNf/vbw9nkya9UP1ux4mrW3385e/e7/4dd\neeWi6j5uuulYduSROxv6+K5dd7KNG0+vO18ept3jdLN/rTJ45u/2Uc/q1Y8CWFR59SSAt1Reu/0M\nAE8BeKRhXz09G1EsLrK807iPGTOexLp1nwbg/KhmzdKt5HJJXHrpezA+vgz5/CG45JKfYv/+IpLJ\n4wEkUC5bz4efX3//I9XHQnMfAHfilEq78dxzn8GnP83P7447/oSzznq3y9/brC06aTP7Z5znn/81\nzj+fYcGCP3s4Lt/fzJmbsG9fTTO2Z0Zm277++s/w5S//Fnv2PG/Z958tW9bO7447NuOss+r/vmTy\n/cjnucXS6bqZ2WcyWUQqVcDq1Z/poM1qHHPMrZg3728APIwPfQi4++6n8Oqrbv3Pug9v7cwYq157\n83d6ew82bUvzkX337i/iiCNeaPI32o/7KoA3V97jnx111Dps2/bWuvbs7WWV4x6CT37yESxaZP6O\n/W9u/P5NmfIyensn1s3ONr9LxWIaH/jA/Tj11Pp2AF5FOn0CGAOKxbkO7Vd/7nfcsQVnnfVnTJz4\nJE466WhMnfoagIfx2GPfrtvrP/3TUzjjjEfqfr+nZxaKRT5+45T1m/3n0EN34cgjn3NoW6fvhNNr\njvUaT5y4txpPxsdX4957r8CaNYP43e/+HXPm3IFvf/tBFArvwKRJrznKSKLH7MhBSal9SHQ6gMsY\nY++rvL4UQJoxttqyzR2VbR4kvsTLi+AZvGHbl9CbhUaj0cQFxlhHdT9aZfAPAJhNRCcAeAnAYgCf\nt21zK4CPAXgQfHD1Hntw93OCGo1Go+mMpgGeMZYjoi+Aj7IkAPyCMbaRiFYBeJAx9jsAPwDwCyJ6\nDMDr4Lq9RqPRaLpMU4lGo9FoNOFFerExIlpIRI8R0eNEdJns46kEEU0nomzl799CRF+pvH8EEd1O\nRI8S0Xoiaj0bKyIQUZKIHiKi31VezyKieypt9CtqNb88IhDRFCL6NRE9QkRPENGpce0XRLSKiJ4i\noieJ6CYiOiQu/YKIfkZEL1YUEPM9135ARCNEtJmINhLRKa32LzXAWyZKLQRwMoAPezmpCFEA8H8Z\nY28D8A4AnyWitwNYBSDDGDsZwLrK67iwFMDjqDm0RgBcVWmjF8AnzsWBHwO4mTH2dgAngbdJ7PoF\nEb0JwMcBzGaMvQW8etw/Iz794lrw+GjFsR8Q0YcAzGCMnQTgM5XfbYrsDL46UYpx7485USoWMMZe\nZIxtqvy8D8CjAE4Anxz2i8pmv0RM2oSIpoH/7T/hLykJ4FTG2P9UNolFWxDRkQDmMMZuAADGmMEY\n24t49ovXABQBTCQ+tfMQANsQk37BGNsAYJftbbd+MGS+z3g1gVTlO+WK7AA/DYB1at2Oynuxg4hm\nAngngLsAHM0YexUAGGOvADime2cWKN8HcCkA02V1DIBXLJ/vRDz6x5sBvExE/0VEm4jo50Q0CTHs\nF4yx1wB8FzyoPwdgN4BNiGe/MHHrByegzXgqO8DrEVwARHQogJsALK1karGDiN4H4KVK5mFaZuNq\nnU2A3+y/zRibDZ7F+qsLG1KIqB/AxQBmAjgewKEAzunmOSmO/TvTNMbKDvA7AFjLD05H/R0o8lQG\nh/4bwH9aHjlfJqKjKp8fDT7HIOr8A4B/JKJnANwA4D0ArgJwlGWbaeB9JupsB7CTMfZA5fVNAOYA\neCmG/eLvAdzNGHu1IuPeDGA+4tkvTNzigz2etmwX2QG+OlGqEugWgw8axALiq07/FMDjjLHvWz4y\nJ4eh8v+tQZ9b0DDGLmeMTWeMzQKvafRHxtjHAdxbqUIKxKcttgN4hYhOrLx1NoAnwL8bseoXAJ4G\ncCoRTah8X84GrwkQu35hwS0+3ArgfAAgorkAyqxSCNIN6T54IjoXvJ68OVHqm1IPqBBEdBp46bxH\nUXuU+hqA+8EHnKeCOwQWM8Y6W/03hBDRGQC+zBj7RyKaBeB68EfzzQA+zvwuYRQCKm6qn4APKj4L\n/sUlxLBfENFK8L/fAK9U+0kAxyEG/YKIbgBwBvgTy4sA/gXAb+HSD4joBwDOBJAH8FnG2Mam+9cT\nnTQajSaaSJ/opNFoNJruoAO8RqPRRBQd4DUajSai6ACv0Wg0EUUHeI1Go4koOsBrNBpNRNEBXqPR\naCKKDvAajUYTUf4/riwi5B3MQAQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fa6d0e74a90>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def max_finder (listt) :\n",
      "    m = 0\n",
      "    for i in range (len(listt)) :\n",
      "        if listt[i] > listt[m] :\n",
      "            m = i\n",
      "    return m\n",
      "def compare (output,learned,n = nlayers,m = 4) : # m is ntracks\n",
      "    counter = 0\n",
      "    for track in range (m) :\n",
      "        subcounter = 0\n",
      "        for layer in range (n) :\n",
      "            start = int(float(layer) * len(output) / n)\n",
      "            end = int(float(layer+1) * len(output) / n) + 1\n",
      "            subOutput = output[start:end]\n",
      "            subLearned = learned[start:end]\n",
      "            if max_finder(subOutput) == max_finder(subLearned) :\n",
      "                subcounter += 1\n",
      "                output[max_finder(subOutput)+start] = 0\n",
      "                learned[max_finder(subLearned)+start] = 0\n",
      "        if counter == n :\n",
      "            counter += 1\n",
      "    return counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counter = np.zeros(5 , dtype=float32)\n",
      "start = time.mktime(time.gmtime())\n",
      "for i in range (2000) :\n",
      "    counter[compare(ds_output[cut+i],trainer.network.predict(valid[0])[i],4,4)] += 1\n",
      "stop = time.mktime (time.gmtime())\n",
      "spent.append (stop - start)\n",
      "print \"\\t\" , stop - start\n",
      "print counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\t1113.0\n",
        "[ 2000.     0.     0.     0.     0.]\n"
       ]
      }
     ],
     "prompt_number": 29
    }
   ],
   "metadata": {}
  }
 ]
}