50K2L-relu-rprop.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:cbed01629456e9b213d68064f419699706285bb90cd2889ea9beb5c71b8c2411"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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,'relu'),(ncells,'relu'),(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 = 5000\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": 2
    },
    {
     "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": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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",
      "        col = np.reshape(length_array,(len(length_array),1))\n",
      "        algo = KMeans(n_clusters=2)\n",
      "        gap_candidates = algo.fit_predict(col)\n",
      "        if gap_candidates[0] == 0:\n",
      "            accepted = list(gap_candidates).count(1)\n",
      "        else :\n",
      "            accepted = list(gap_candidates).count(0)\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",
      "    if accepted == 0 :\n",
      "        out_matrix [0] = [0,n_gaps]\n",
      "        return accepted+1,out_matrix\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 = max(1,int((0.75*central_tracks)))\n",
      "    max_n = int(1.25*central_tracks)\n",
      "    if max_n <= min_n + 1 :\n",
      "        return clusterer (in_array,max_n)\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",
      "    col = np.reshape(d_goc,(len(d_goc),1))\n",
      "    algo = KMeans(n_clusters=2)\n",
      "    gap_candidates = algo.fit_predict(col)\n",
      "    if gap_candidates[0] == 0:\n",
      "        accepted = list(gap_candidates).count(1)\n",
      "    else :\n",
      "        accepted = list(gap_candidates).count(0)\n",
      "    revised_number = max_n-accepted\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,requested_efficiency = 1) : # Dear reader, 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",
      "    # doesn't work quite well for efficiencies less than 1,I know the reason,but don't have time to fix it !\n",
      "    remained_NNtracks = np.array([i for i in range (len(nnM))])\n",
      "    for exptrack in range (n_exp) :\n",
      "        for nntrack in remained_NNtracks :\n",
      "            if similarity(expM[exptrack],nnM[nntrack]) >= requested_efficiency * len(expM[1]) :\n",
      "                goodTrack +=1\n",
      "                remained_NNtracks = np.array([remained_NNtracks[i] for i in range (len(remained_NNtracks)) if i != nntrack]) #like !\n",
      "        #similarities[exptrack] = max([similarity(expM[exptrack],nnM[nntrack]) for nntrack in range (len(nnM))])\n",
      "    for exptrack in range (n_exp) :\n",
      "        if similarities[exptrack] >= requested_efficiency * len(expM[1]) :\n",
      "            goodTrack +=1\n",
      "    return float(goodTrack)/len(expM)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "new_samples()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    plt.figure(i)\n",
      "    plt.imshow(np.reshape(ds_input[9],(4,module_cell,module_cell))[i,:],interpolation='nearest')\n",
      "    plt.xlabel(i+1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f258097f510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2HxLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2580896dd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQIQNBCJsIBBhA4EIGwhE2EAgwgYC/QcUD9Ot2sNyJAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f25807c7690>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nImwgEGEDgQgbCPQf6ibLmpE4YTgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f258072dad0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    plt.figure(i)\n",
      "    plt.imshow(np.reshape(ds_output[9],(4,module_cell,module_cell))[i,:],interpolation='nearest')\n",
      "    plt.xlabel(i+1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nImwgEGEDgQgbCETYQCDCBgIRNhDo/9lEsKjojd6IAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb3a3ca3d10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAACYpJREFUeJzt3U+InPUdx/HPJ/+MYHuRUojZOl48FGsNRbS1TTcFYdHS\nk5UWpCD0ohRCD1UshWzAS0+5FLxUpSkkiFiEEtJAaXaT4CWQNW5c0EsHEg9bEJEICUHy7WGftLtm\nM88zM8/Mb+a77xc8MDt5mPmy7Du/+fs8jggByGVb6QEAtI+wgYQIG0iIsIGECBtIiLCBhHYMewO2\neb8MKCgi/NXrhg57zaF2buY2C5JmR3Tbo7Cg6ZpXYuZxWNDo5j286bU8FAcSImwgoQkPu1N6gD51\nSg8wgE7pAQbQKT1Anzpjv0fCblWn9AAD6JQeYACd0gP0qTP2e5zwsAEMgrCBhAgbSIiwgYQIG0iI\nsIGECBtIiLCBhAgbSIiwgYQIG0iIsIGEasO2PWd72faK7ZfHMRSA4fQM2/Zdkl6TNCfpYUnP2N43\njsEADK5uxX5M0ocR8UlEfCnpLUlPj34sAMOoC3uvpMvrfr5SXQdggtUdzLDhEUgX1l3uaPq+CA9M\ni2619VYX9hVJM+t+ntHGFbwy23AoAMPpaOPCubjpXnUPxc9Lesj2fbZ3SnpW0skWpgMwQj1X7Ii4\nbvsFSae09p/AXyPiwlgmAzCw2hMGRMRJsUoDU4VPngEJETaQEGEDCRE2kBBhAwkRNpAQYQMJETaQ\nEGEDCRE2kBBhAwkRNpAQYQMJETaQEGEDCRE2kBBhAwkRNpAQYQMJETaQEGEDCRE2kBBhAwkRNpAQ\nYQMJETaQEGEDCRE2kBBhAwkRNpAQYQMJETaQEGEDCRE2kBBhAwnVhm37DdurtpfHMRCA4TVZsd+U\nNDfqQQC0pzbsiDgr6bMxzAKgJTzHBhIibCChHe3czMK6y51qA9C+brX11lLYs+3cDIAaHW1cOBc3\n3avJ213HJb0n6UHbl20/38J0AEaodsWOiF+OYxAA7eHFMyAhwgYSImwgIcIGEiJsICHCBhIibCAh\nwgYSImwgIcIGEiJsICHCBhIibCAhwgYSImwgIcIGEiJsICHCBhJq52CG8/Ot3My4HJp36RH6dliH\nSo/Qt/jT4dIj9M2/mb7f82ZYsYGECBtIiLCBhAgbSIiwgYQIG0iIsIGECBtIiLCBhAgbSIiwgYQI\nG0iIsIGECBtIiLCBhAgbSIiwgYRqw7Y9Y/uM7WXbH9l+aRyDARhck0Mj3ZD0YkRcsn2PpAu2T0XE\nxRHPBmBAtSt2RKxGxKXq8heSPpC0Z9SDARhcX8+xbXckPSrp3CiGAdCOxkcprR6Gvy3pYERc3fCP\np+f/f7kzKz0w28ZsAG7TrbbeGoVte6ekdyQdi4h3b9vhwHw/kwEYWKfablncdK8mr4pb0uuSViLi\nSAuTARixJs+xn5D0nKQDtpeqbW7EcwEYQu1D8Yg4Jz7IAkwVggUSImwgIcIGEiJsICHCBhIibCAh\nwgYSImwgIcIGEiJsICHCBhIibCAhwgYSImwgIcIGEiJsICHCBhJyRAx3A3b8IV5paZzxeNW7So8A\ntOSwIsJfvZYVG0iIsIGECBtIiLCBhAgbSIiwgYQIG0iIsIGECBtIiLCBhAgbSIiwgYQIG0iIsIGE\nCBtIiLCBhAgbSKg2bNu7bZ+3vWT7Y9tHxjEYgMHtqNshIq7b3h8R12zvkHTO9oGIOD2G+QAMoNFD\n8Yi4Vl3cJWm7pNWRTQRgaI3Ctr3N9vtaC/p0RKyMdiwAw2i6Yt+MiEck7ZW03/bsSKcCMJTa59jr\nRcTntk9IelzSwq3rF+fP/m+f+2e/pc7s/W3NB2CDbrX1Vhu27Xsl3YiIq7bvlvSkpD+u3+fH8z8a\naEQA/epU2y2Lm+7VZMXeI+mobUvaLelYRJwYcjoAI9Tk7a5lSfvGMAuAlvDJMyAhwgYSImwgIcIG\nEiJsICHCBhIibCAhwgYSImwgIcIGEiJsICHCBhIibCAhwgYSImwgIcIGEiJsICHCBhLq6yild/Kq\nd7VxMwBawooNJETYQEKEDSRE2EBChA0kRNhAQoQNJETYQEKEDSRE2EBChA0kRNhAQoQNJETYQEKE\nDSRE2EBChA0k1Chs29ttL9n++6gHAjC8piv2QUkrkmKEswBoSW3YtvdKekrSnyV55BMBGFqTFfuI\npN9JujniWQC0pOdRSm3/VNJ/ImLJ9uyd91xYd7lTbQDa16223uoOP/wDST+z/ZSk3ZK+bvtoRPxq\n426zAwwIoH8dbVw4Fzfdq+dD8Yj4fUTMRMQDkn4h6V+3Rw1g0vT7PjavigNToPGZQCJiUXda9wFM\nFD55BiRE2EBChA0kRNhAQoQNJETYQEKEDSRE2EBChA0kRNhAQoQNJETYQEKEDSRE2EBCEx52t/QA\nfeqWHmAA3dIDDKBbeoA+dcd+j4Tdqm7pAQbQLT3AALqlB+hTd+z3OOFhAxgEYQMJOWK4w5jZ5jho\nQEERcduJPIYOG8Dk4aE4kBBhAwlNZNi252wv216x/XLpeerYfsP2qu3l0rM0ZXvG9pnq9/yR7ZdK\nz9SL7d22z1enc/7Y9pHSMzVV4jTUExe27bskvSZpTtLDkp6xva/sVLXe1Nq80+SGpBcj4juSvifp\n17a/W3imO4qI65L2R8Q+Sd+W9H3bBwqP1dTYT0M9cWFLekzShxHxSUR8KektSU8XnqmniDgr6bPS\nc/QjIlYj4lJ1+QtJH0jaU3aq3iLiWnVxl6TtklYLjtNIqdNQT2LYeyVdXvfzleo6jIjtjqRHJZ0r\nO0lvtrfZfl9rQZ+OiJXSMzVQ5DTUkxg277+Nke17JL0t6WBEXC09Ty8RcTMiHtHaf/T7e5/aubz1\np6HWGFdraTLDviJpZt3PM9q4gqMltndKekfSsYh4t/Q8TUXE55JOSHq89Cw1bp2G+t+Sjkv6ie2j\n47jjSQz7vKSHbN9X/eE9K+lk4ZnSsW1Jr0taiYiJf4XZ9r22v1ZdvlvSk5Im+l2Ikqehnriwq1c/\nX5B0StJFSX+LiAtlp+rN9nFJ70l60PZl28+XnqmBJyQ9J+lA9VbMku1JfmV/j6Qz1XPsJUn/jIgT\nhWfq19ieZvKRUiChiVuxAQyPsIGECBtIiLCBhAgbSIiwgYQIe4uatq9toj+8j71F2f6mpG9ExKXq\n8+IXJP08Ii4WHg0tYMXeoqbxa5tojrAxNV/bRHOEvcVN09c20Rxhb2HT+rVN1OPFsy2q+trmXyR9\nGhG/LT0P2kXYW5TtH0o6o7UXzW79EbwSEf8oNxXaQthAQjzHBhIibCAhwgYSImwgIcIGEiJsICHC\nBhIibCCh/wKyROTtYc5vgAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb3a3a56ad0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb3a3acf2d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nETYQiLCBQIQNBCJsINB/Afn4xPKnoAmOAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb3a3a13610>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    plt.figure(i)\n",
      "    plt.imshow(np.reshape(ds_input[1],(4,module_cell,module_cell))[i,:],interpolation='nearest')\n",
      "    plt.xlabel(i+1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLTVk2FJD/wcwVuxuhMtpGQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f480e6d4a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW2rof1z7IHcJDM2TAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f47f94c5990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQ4YtNfR3xnEPBRw4Oj8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f47f946b090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDRm21JBhSw0ZttSQYUsNGbbUkGFLDf0ftBP260xzNScAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f47f94f3850>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    plt.figure(i)\n",
      "    plt.imshow(np.reshape(ds_output[1],(4,module_cell,module_cell))[i,:],interpolation='nearest')\n",
      "    plt.xlabel(i+1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1JBhSw0ZttSQYUsNGbbUkGFLDf0TKeTt5UT6uLQAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f480e6d4a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npYYMW2rIsKWGDFtqyLClhgxbauj/Af+0IwKszHkyAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f47f91dcfd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNWTYUkOGLTVk2FJDhi019L+6HhH9Jgi5+gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f47f9169f50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqSHDlhoybKkhw5YaMmypIcOWGvo/j7P5UCDqyB4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f47f90df2d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    plt.figure(i)\n",
      "    plt.imshow(np.reshape(ds_input[1],(4,module_cell,module_cell))[i,:],interpolation='nearest')\n",
      "    plt.xlabel(i+1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7efff85ad610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkGFLDRm21ND/A3sJJPQo9dGSAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7effe30b1fd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW2rIsKWGDFtqyLClhgxbauhvfxAb/V6ReiwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7effe303ff50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npYb+HzhLIl1grosnAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7effe2fb42d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for i in range(4):\n",
      "    plt.figure(i)\n",
      "    plt.imshow(np.reshape(ds_output[1],(4,module_cell,module_cell))[i,:],interpolation='nearest')\n",
      "    plt.xlabel(i+1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7efff85ada90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIcOWGvp/vP8zmej4sicAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7effe339a990>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDRm21JBhSw39P9+QH37ozs41AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7effe3341090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAEKCAYAAAAhNageAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nr0KKAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7effe33c8850>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "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": 6
    },
    {
     "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",
      "        epoch_counter = 0\n",
      "        for trn, val in trainer.itertrain(train,valid,hidden_dropout=0.6, algo='rprop',learning_rate = 0.0000001,iteration_size = 100,batch_size=10000,validate_every = 5,patience=10,min_improvement = 0.001) :\n",
      "            if trn['err'] < 0.2 and val['err'] < 0.2 :\n",
      "                training_error.append(trn['err'])\n",
      "                validation_error.append(val['err'])\n",
      "                if epoch_counter%1 == 0 :\n",
      "                    print \"training_error = \\t\",trn['err']\n",
      "                    print \"validation_error = \\t\",val['err']\n",
      "                epoch_counter += 1\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",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0223121567203\n",
        "validation_error = \t0.0293132223304\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0146823585756\n",
        "validation_error = \t0.0293132223304\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0128132380195\n",
        "validation_error = \t0.0293132223304\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0120485404455\n",
        "validation_error = \t0.0293132223304\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0112518640862\n",
        "validation_error = \t0.0293132223304\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0105105398573\n",
        "validation_error = \t0.0110097177292\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00987900568926\n",
        "validation_error = \t0.0110097177292\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00933873639749\n",
        "validation_error = \t0.0110097177292\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00888408575818\n",
        "validation_error = \t0.0110097177292\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00852723329803\n",
        "validation_error = \t0.0110097177292\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00822571074053\n",
        "validation_error = \t0.00854645798939\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00795419716603\n",
        "validation_error = \t0.00854645798939\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00771108347336\n",
        "validation_error = \t0.00854645798939\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00750679417738\n",
        "validation_error = \t0.00854645798939\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00733067617015\n",
        "validation_error = \t0.00854645798939\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00718104144318\n",
        "validation_error = \t0.00741699484538\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00705240689117\n",
        "validation_error = \t0.00741699484538\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00693681510172\n",
        "validation_error = \t0.00741699484538\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00683144856831\n",
        "validation_error = \t0.00741699484538\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00673621233345\n",
        "validation_error = \t0.00741699484538\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00665746322101\n",
        "validation_error = \t0.00685728456903\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00659176673418\n",
        "validation_error = \t0.00685728456903\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00653620088734\n",
        "validation_error = \t0.00685728456903\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00648909315574\n",
        "validation_error = \t0.00685728456903\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00644855285159\n",
        "validation_error = \t0.00685728456903\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00641259919104\n",
        "validation_error = \t0.00660095003994\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0063808834824\n",
        "validation_error = \t0.00660095003994\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00635159379763\n",
        "validation_error = \t0.00660095003994\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00632531644511\n",
        "validation_error = \t0.00660095003994\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00630187000104\n",
        "validation_error = \t0.00660095003994\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00628100965286\n",
        "validation_error = \t0.00646448149431\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00626200916398\n",
        "validation_error = \t0.00646448149431\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00624439059548\n",
        "validation_error = \t0.00646448149431\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00622852283567\n",
        "validation_error = \t0.00646448149431\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00621437881441\n",
        "validation_error = \t0.00646448149431\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0062013936372\n",
        "validation_error = \t0.00638746882563\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00618880114831\n",
        "validation_error = \t0.00638746882563\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00617710731216\n",
        "validation_error = \t0.00638746882563\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00616614382416\n",
        "validation_error = \t0.00638746882563\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00615542865348\n",
        "validation_error = \t0.00638746882563\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00614501243569\n",
        "validation_error = \t0.00633103248297\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00613580017755\n",
        "validation_error = \t0.00633103248297\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00612629000069\n",
        "validation_error = \t0.00633103248297\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00611764532219\n",
        "validation_error = \t0.00633103248297\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00610982574795\n",
        "validation_error = \t0.00633103248297\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00610200163419\n",
        "validation_error = \t0.00629073197306\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00609568278723\n",
        "validation_error = \t0.00629073197306\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0060894412968\n",
        "validation_error = \t0.00629073197306\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00608266448693\n",
        "validation_error = \t0.00629073197306\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00607594378638\n",
        "validation_error = \t0.00629073197306\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00606962226539\n",
        "validation_error = \t0.00625721123178\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00606379872874\n",
        "validation_error = \t0.00625721123178\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00605846702361\n",
        "validation_error = \t0.00625721123178\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00605378501456\n",
        "validation_error = \t0.00625721123178\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0060490377614\n",
        "validation_error = \t0.00625721123178\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00604414766388\n",
        "validation_error = \t0.00622829517453\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00604025584519\n",
        "validation_error = \t0.00622829517453\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00603584565692\n",
        "validation_error = \t0.00622829517453\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00603153660603\n",
        "validation_error = \t0.00622829517453\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00602763555162\n",
        "validation_error = \t0.00622829517453\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00602367146253\n",
        "validation_error = \t0.00620772200514\n",
        "\t training ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t2563.0\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "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": [
        "0.00602367146253\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEACAYAAAC+gnFaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f715ccaeb10>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "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": [
        "0.00620772200514\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYEAAAEACAYAAABVtcpZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPwYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7150afa790>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "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/50K2L-rprop\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.load(\"network/50K2L-rprop\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<theanets.feedforward.Regressor at 0x7f7d2045c810>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[0])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.text.Text at 0x7f7d18698b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEPCAYAAABIut/fAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXuYJVV57n9fzwwzzIAgEoXAEDAqekAE1AAehUbRZ7yA\nSUAUjRoSjZJIMPEomkSYSYzXnOiDJJhjMBpyokQuKgKHSKSj3Aw3EQYhBokwA85wlYHp6es6f6xd\ndHVNVe29a6+1alXV93uefrr37t17r67LW2+931erxBiDoiiK0j7G6h6AoiiK4gcVeEVRlJaiAq8o\nitJSVOAVRVFaigq8oihKS1GBVxRFaSl9BV5EviQim0TktpLXnCUi60XkZhE5xO0QFUVRlCoM4uD/\nAVhT9EsROR7YxxhzAPC7vdcriqIoNdNX4I0x3wceLXnJ64Dzeq+9BVgqInu7GZ6iKIpSFRcZ/N7A\nfanHG3rPKYqiKDXiqsgqmcc6/4GiKErNLHXwHhuA1cAPeo/37j23CBFR0VcURamAMSZrogfChYO/\nDHgbgIgcCswZYzbmvdAYo1/GcOaZZ9Y+hli+dFnostBlUf41Cn0dvIh8FTgK2F1E7gPOBJb1BPvv\njDEXisjRIrIemAJOHmlEiqIoihP6Crwx5qQBXvM+N8NRFEVRXKFXstbA+Ph43UOIBl0WC+iyWECX\nhRtk1Ixn4A8SMaE+S1EUpS2ICKbGIquiKIoSISrwiqIoLUUFXlEUpaWowCuKorQUFXhFUZSWogKv\nKIrSUlTgFUVRWooKvKIoSktRgVcURWkpKvCKoigtRQVeURSlpajAK0rL2boV7r+/7lEodaACrygt\n5+KL4cMfrnsUSh2owCtKy5masl9K91CBV5SWMztrv5TuoQKvKC1nZkYFvquowCtKy5mdtSKvdA8V\neEVpORrRdBcVeODGG+2XorQRFfjusrTuAcTAN78J8/PwkpfUPRJFcY8KfHdRgcfmk9PTdY9CUfyg\nRdbuohENduPfurXuUShpbrqp7hG0By2ydhcVeOwOMDlZ9yiUhMceg1e+su5RtAeNaLqLCjzW3ajA\nx8P0tEZmLlGB7y4q8KiDjw0VJLdoBt9dVOBRBx8biSAZU/dI2oEeMLuLCjzq4GMjEaO5uXrH0Ra0\nyOqWBx9szuRtKvCowMdGIvDqOt2gDt4tp50Gl1xS9ygGQwUe6260TTIeEreprtMNKvBu2bq1OYZQ\nBR518LGhDt4tWmR1S5MOmCrwaJE1NtTBu6VJgtQEZmaas22qwKMOPjbUwbtFi6xuadIBUwUeFfjY\nSMSoKTtR7DRJkJqAOviGMTNjW/KastLaTiJGuj7coBm8W1ol8CKyRkRuE5E7ROT0nN8/X0R+ICK3\n917zRj9D9Uey8auLjwONaNwyO2unw56fr3sk7aBJZ0SlAi8iy4FzgDXAQcAJInJI5mV/BnzJGHMg\ncDxwto+B+iRZWdoqGQdaZHWLXjjmljY5+MOA9caYjcaYWeB84PWZ19wH7NL7eVfgZ26H6J9kZamD\njwN18G7RyMstTSpa97vhx95YAU/YAIxnXvMJ4DoRORVYBbzK2egCoRFNXGiR1S16wHRLk2oa/QR+\nkOme/hr4e2PMZ0XkcOCfgAPyXrh27dqnfh4fH2d8fHywUXpmZgZWrFCBjwV1nG7RA6ZbfEc0ExMT\nTExMOHmvfgK/AVideryaxY4e4OXAmQDGmOtFZIWIPNMYszn7ZmmBj4nZWdh5ZxX4WFBBcos6eLf4\njmiy5nfdunWV36tfBn8DcKCI7CUiy4ATgcszr7kbOAZARF6AjWkerjyiGlCBjwt18G5RgXdLkyKa\nUoE3xmwDTgGuAG4FLjLG3Cwi60Tk2N7L/hh4r4isBy4E3mWMaVS9fmYGnvY0FfhYUEFyix4w3dKm\nIivGmMvJuHZjzJmpn+8CjnA/tHDMzlqB1zbJONCIxi26PN3SGgffFdTBx4U6TrfMzsIOOzRHlGKn\nTX3wnUAz+LhQx+mW2VnYcUddnq5oUkSjAo8KfGyog3eLCrxbNKJpGBrRxIUWWd0yO2uv89AD5ujM\nz9ubwTdlWarAow4+NjSicUtyIZ8uz9Fp2jxJnRf4ZAKmVatU4GNBIxq3JA5eBX50mmY+Oi/ws7Ow\ndCmsXKltkrHQtJ0odjSDd0fTzIcK/CwsW2Z3AHXwcdC0nSh2VODd0TTz0XmBn5mxDl4FPh5mZmBs\nrDk7UcwkEeQOO+gB0wWawTcMdfDxoY7THUkEuXSpLk8XNO3sUgV+Vh18bCQC35SdKGZU4N2iEU3D\n0IgmPrStzx3JGaoKvBvUwTcMjWjiQx28O9TBuyUxhE3ZNjsv8MkK0zbJeJiZ0QzeFcn2vWxZc0Qp\nZpq2bXZe4NXBx4cWWd2hDt4tTTu7VIHXImt0JC6pKTtRzKjAu0UdfMPQImt8qIN3hxZZ3TI7a+Pc\nppiPzgu8RjTx0bTT4JhJDIwKvBuadnbZeYFPF6GSx0q9aJukO5KIRousbtCIpmEkDh7UxceCRjTu\nCJ3BT0zA7bf7/5y6aNrZpQp8bwcAbZWMhaadBsdM6Az+/PPhyiv9f05dpLdNY+oeTX86L/BJRAPq\n4GNBHbw7Qjv46Wn71VaSG5iPjdm7O8VO5wVeIxq/PPAA3HPPcH+jDt4doYusMzPtXm8zM1YvmlLT\n6LzAq4P3y3nnwVlnDfc3egcid4QusrbdwTetK6nzAq8O3i9VdniNaNwROqJpu4NP9EIdfENIF1lV\n4N1TReA1onFH6CJrFxy8CnyD0IjGL+rg6yV0pNB2gW/a1A+dF/h0RKNtku6pcsquDt4dGtG4RR18\nw9CIxi9VHbwWWd2gRVa3NG365c4LfHJEBhV4H2hEUy+hM/i2O/imTd7WeYFXB+8XLbLWi17o5BaN\naBqGFln9og6+XrTI6hYtsjYM7YP3izr4etEiq1vUwTcMjWj8MqzAGwNzc1pkdYUWWd2iRdaGkY5o\ntE3SPcPu8LOzsGRJc3ag2NEiq1taV2QVkTUicpuI3CEipxe85kQRuUVEfiQi/+x+mP7QiMYv09PD\n7fBpx9mEHSh2NIN3S9MimqVlvxSR5cA5wMuBTcB1IvKvxphbUq95EfAB4OXGmCdFZDefA3aNFln9\nMjMzvINvkkOKHe2icUvbIprDgPXGmI3GmFngfOD1mdecDJxtjHkSwBjziPth+kMdvF+G3eGbtgPF\njhZZ3dI0A9JP4PcG7ks93tB7Ls3+wMEicqOI3CQix7kcoG+0yOqXKhl8k3ag2Ak9+6E6+LgojWiA\nQW5KNQbsi3X7q4FrReTqpjh5jWj8UtXBL13ajB0odmZnbfNAiAPm/LztgGrzeksbkCb8n/0EfgNW\ntBNWs9jR03t8tTFmDvhvEbkDeB5wffbN1q5d+9TP4+PjjI+PDz9ix2hE45cqDj4dKRgDIv7G13ZC\nFlkTwWu7g0/OiHwtz4mJCSYmJpy8Vz+BvwE4UET2AjYDJwLvybzmUuCNwJdFZHfgBcDdeW+WFvhY\nyDp4bZN0SxUHv2yZvedlct/LJUv8ja/thMzgk/XcBGdblRARTdb8rlu3rvJ7lWbwxphtwCnAFcCt\nwEXGmJtFZJ2IHNt7zcXAwyKyHrga+LAx5sHKIwpMdrpgdfBuSQR+0DvQp2siTck5Q3LVVbBly+Cv\nD1nT6IKDb1qNqJ+DxxhzOXB55rkzM48/gG2VbBxaZPVLsrOnD6RlpF/XlJ0oJB/9KKxbB6961WCv\nD3kl6/Q0LF/eboFvWpFVr2TVIqtXkp1g0J0hvT6aUsgKydSU/RqU0Bn8qlXtXmd6T9aGoUVWv0xP\nww47DO7q0utDr2bdnunp4QQ+dAa/alX7HXyTIhoV+EzmC804MjeF6WnYaafBd/qsg2/CThSSqanR\nupJ8Mj1tTdLcnC2OtxGNaBpG+o5OoC7eJXNz9vuOOw7n4LXIWkwVBx+yyLp8uT1ja+t6G6YP/kMf\ngosuCjOuIjov8GlBAW2VdEkSz1SNaNTBb8+wGXzoImuST7c1phmmD37jRvj5z8OMq4jOC3w6EgBt\nlXRJFYHXIms5sRdZk/Xd1vU2zAFz2HXlg84LfLZ9TyMad4zq4LXIuj2xF1m74uAHWZ4q8BGQF9Go\nwLuhyg6vRdZyqhRZQ2bwbXfwwxRZVeAjQIus/qiyw2uRtRhjqjv4ZNoHn90tVc7YmsYwRdZhD8Y+\n6LzAq4P3R9UMXous+SSCUiWDF/EfeXUpohlkWQ57MPZB5wU+W2RVgXdH1Qxei6z5JMuwioMH/wfM\nLkQ0WmRtGHlFVm2TdIMWWd2SiEWVC53Av8B3ycEPGtGowNdMNqLRNkl3uGiTVIFfIBGLKhc6gTp4\nF6SLrNpF0wC6WGS98044+WT/n+PCwbdVKKoQe0TTpSKrRjQNoYtF1g0b4Pbb/X+OOni3VHHw6eXp\n+4DZ9ojGmOGuK9AumgjoYpE1lLPQIqtbkmUYawbf9ohmbm7hTmPq4BtCF69k3bbNfvmm6oVOWmTN\nJ/YMvu0OPtvC20/gtU0yAroY0YRyFqNe6KQRzWKqCrw6eDdk466yZWmMOvgoyIto2t4mOTUVzsFr\nkdUd09P2BuRVM/hQDr6tRdZhts3Z2QWRr5NOC3xy6faSJQvPdaFNMmREo0VWd0xNwc47V3fwIYqs\nO+zQ3gPzMBFNlbMtH3Ra4OfmFi7jTtCIxh0u5oNvo1BUZXraCnwTiqxtdfCDRjRVLkrzQacFPtsD\nD90SeGP8fs6oDl6LrIuZmoKnPa3+IuuTT8KNN27/fJeKrP3OUtTBR0C2wArdEPgknvG9E7pok1SB\nXyBx8HUXWb/3PXs7uixdKLIOerCcmhq+XuKDTgt8tsAKbgT+qqvg3HNHew+fJBud7xx+1Nkk25rl\nVqWKg/dR0yiq4dRZZL37bvjhD/1+xjD1jCrrygedFvhsDzy4Efhbb4Vrrx3tPXyS7Jy+N74qp+zq\n4ItJiqyDLktjFupM4O6AWSbwdRVZL7zQv6kapsg6Pa0CXztFEc2obZKhulSqEsrBV+2DVwefz7AR\nzdycjQmSJgLfDr7OImuIfW6Y+pA6+AgoKrKOuqFs2xZ3jh+qAKRtkm4Ztk0ya2BCRTR1FFlDCPww\n5iNZVzMz/psZyui0wBc5+FE3lMnJuB18MrYYM3iNaIpJHPzMzGC33svWmEI6+NBnXqEc/DB98CtW\n1N9R1GmBzyuyrlgxuvtuSkQTq4Mvc0kXXwy///tux9kU0qIxiICqg3fLsH3wy5fbrzpjmk4LfFGR\n1UVEE7PAN9nBb95sv7rIsKKRJ/Ahiqx1OPgQZ83D9sEvX26XhQp8ACYn4dvfXvxcXkSTbJxzc9U/\nK3aBj9nB98s5Y1+2Pkkvz0EFPm1gXF04lqyDbLZcd5HVd90rWx/qJ/A77KAOPhg/+hF85COLn8sr\nsorY0+BRVkrsIjQ1ZefcidHB9yuyTk7GXcD2iQsH70rgjdle4LoQ0Qw6lbVGNIHJO4XLc/Aweg4f\nu8Bv2wa77BK/g88TpNiXrU+S5bl8+WDL02eRNf09/XldKbKOjS1MVpjH9PSCwGuRNQB5zi+vyAqj\n5/Cxi1DSoxvCwQ96/8qEfreYUwcfh4NPf0/ogoNPlqdIuYtXBx+YPGHIK7KCGwcfswhNTYVx8FUy\n2X5F1tgPnj4ZNtfNbt8ui6zp7wldKrJCuXFRgQ9MnugWRTRtd/BJRBNrBl9WZI3RwV9zDdxyi//P\nSU77hymyprdvl0XW9PeEuousIa9khfIDZmO6aERkjYjcJiJ3iMjpJa87XkTmReRQt0N0QyIM6cp/\nXpEV3GXwdV7BVsbUFOy6a7wZfNMc/AUXwKWX+v+cYV1h6Ay+7ojG94F/mK6kRjh4EVkOnAOsAQ4C\nThCRQ3JetzNwGnC9j0G6IFn56YVdVmQd1cFDvHNix+zg+xVZY7xKeOvWMLd5HLbIGjqDj6HI6tNU\ntTGiOQxYb4zZaIyZBc4HXp/zur8APglMAZLz+9pJBD59lC+LaEZxA8nfxiZECSGLrKPe8COvDz62\niCZUbBRTkXXZsvgc/Py836kthrlwrCl98HsD96Ueb+g99xS9SGYvY8xlvaeiDCaSjTG9I5ZFNKM6\neJG4Bb6pbZKTk2HuRjUMoQR+1AudXBZZn/70uIqsIa7OznPwg0Q0MbdJlu5GIjIG/DXwv9JPjzoo\nH4R08KEikCokd3vfaafmOvj09xiYnAwT0Yzq4F0WWXfdNT+iqeuGH5OT/rfpYYqs6T74Oh18jrwt\nYgOwOvV4NYsd/c7AAcCE2Emn9wC+JSLHGmNuzr7Z2rVrn/p5fHyc8fHxSoOuQp7AF/XBu3Dwe+0V\nlwglhMwGfRVZk+877uhurKOwdWuYsQzrCn0WWffYY/H2PTdnjcOSJeHn8Z+ft5+3++5+97kqRdYq\nXTQTExNMTExUHmeafgJ/A3CgiOwFbAZOBN6T/NIY8wvgl5LHInIV8IE8cYfFAh+aIgefF9GM0iY5\nN2ffd+ed48uKYWFGwlEPYoOQOLolS+xymZ+3VwAO8jdQHNEk35/+dPdjrkLoiCaGDD7r4JMCq0h4\nB5+IqYuJAssIVWTNmt9169YNP9gepbubMWYbcApwBXArcJEx5mYRWScix1b+1BrIy+B9TFWQCKjv\nja0q27aFd/DJTj/sFLdNimhiLbJmM3hfAp8UWCF8kTU5m3Nxu80yhi2yNiGiwRhzOXB55rkzC157\ntKNxOacoonHt4LdtC+eQqxDSwScCDwuubvny8r8ZpMi6yy5xnR2FyuCrFFl9TRdc5OAhfJE11D43\nM2O7zxIa3wffJiYnrZP07eBjF/g6HDwMfto+SJE1r4OjTrZuDe/gq2TwLoqsSd6dbbNNO/jQEU1I\ngR8mohnmmgVfdErg81yH66kKko0t1ogmEQnfO0MyneywO/0gDn7XXeNz8KEEvu4MvijvTh/MQxdZ\nJyfDCHwTI5rOCHzi/AYpsrbZwScRje8NLxF36TXNDiLwxthi7JIl9nFWkIxZmGYhpmUbQuCTA2bd\nAl+0fWcjmq44+KLlGUubZGcEfnISdttt8D74tmbwSUTje3xpRweD7fSzs1bck4NC1glOTdnnVq2K\nZ9kaEyaDzxas6yqyFm3f6YhmyZKFg3UIQu1zVRx89JONtYU8gfcx2djkpD1AuLh5tw9CnTpWEfjs\n+sgKUnpHjmXZJlfV+h5PennWeU/WQRw8hC20prtoYnHwGtEERh28JVQXTZ7A99vh8668TP9NcvCM\nqb6RXEGZnanUNYlgQL1F1kEcPISNaUId+Ns42VhryMvgy65kbWsGH6qLJm9nGCSiaZqDTwR+6VK/\ngubKwfsU+PQBPWShNfYiq3bRBCC58tH3layxC3yoLpqqEU3ZDhSrg99xR3sTc585fNbBx5bB50U0\ndTh4jWgW0ymBHzSiabuDT8YXWwbfb3KsGB381q1W3H1fRZm0SMJoFzqFimhCXs0aUuDLIsQ0TZku\nuBXMz9uNLds/7fNK1phcZpq0s4jRwZc5zpgdvG+BT18FPMpkY20usoaIaAadflm7aAKSLOyVK9XB\np/vgp6f9FQZdOPixMdsWmLTbxejgQwl81YimS0XWWLpoku116VJ18EFIijDZnbCLXTRJkXVszG9h\nsKrAZ8+o0i4+ZgfvO4PXIuvwY3LNoEXWKgdjX3RG4PNclo87OsXoMtOkNz6fO4SLIiss3oliXLaT\nk+Ey+GS9xXihU51F1nQXTeg2ybzlWaWl1RedFvguRzTg1124cvDpnShU1joMyc0+QhZZ67zpthZZ\nFx4XnaWogw9M0XzRPoqsoXpyq5JENOB3jFlHN8gpe5GDT0c0sRWw0+bBd0QzrGh0qcgaKqIZpMiq\nAh+YImEocvDJSqlSgIzRZaYJtfFVKbrlrY/0gSG9bGOKaJIMXousC49DF1lD7HNVIxoVeM8MG9GM\njVVvb4q9TTIZH8SZwfcrssa2bENl8FWLrCEnG+takbUsohn2mgVfdFrgiyIaqO4Sm5DBh3Lwo7ZJ\nQvxF1pAZfLrIWncGn1xHkZzlxnAla+g2SY1oIiG9AQzi4KH6xtIEgY/Vwfcrsna5TTJbZK2awbsS\n+CVLFgtcXpG1jrloYuiiyauX+JyIroxOCHxdDj4Wl5kmXWSNzcH3K7LGuGzrupK17umCYbFBiMHB\nx9gHv3Tp4ov1QtMpgV++3K6QZGF31cHH2gdf5OCTnShWBx96LprEOc7Pl/+NzyIrLN5+utImOWyR\nFeqNaTol8CKL52ApE/g2Z/Cx9sE30cGHyuDTDn7Quzr5LLLC9gLfhblohu2DBxV476Q3yvSOWBbR\ntNXB19UHX+WGH7D4NDh9t6xYlm0d0wXDYBc7+SyywvYRTd1z0YR28INENFBvJ00nBD7ZCWGxwPt0\n8MuW2Sho1B3KNXX1wQ9yyp53wM1eyZpXLK+TkBl8+oA5yLrzWWSFcgdfR5E1lsnG1MEHpkjgi+7o\nlLxuFAcvYt+jzhapPGLugy9y8NkummTcdXUmpKljLhoYTDTyZuecn++f3ZcRc5E1OUsc5f8rY5gi\n67AHY190WuCL7ugE1R184iaS94glSkiIvQ++rMiabdGrcxKnhDrmooFqGbzI6IXWmIus2RqbS5ID\n45IlC88Nk8HXta12QuCLMnhfXTTJwSR2gY/NwQ8yF01syzbkdMGjZvAwekwTc5E1OyaXJMtSZOG5\nQfrgQR28d8oimjIHP0pEk7xHLFlxQnZ8sTn4fnPRpA/UMQl8aAdfJYMHfwJfV5HVmDCNA0XTaGgX\nTQRUKbJW2WFDbWyjkI1oYnPwg9zwA+I5eNZxoRNUy+AhnIMPVWRNtpmxse3H5ONz0mibZCRkBX7Q\nPvhhN5TZ2YU7JVV9D58Ys31EE7uDz+uDh3gcfMibbrsS+FGEN7Yia7rmBf62i6Kzy0G6aLRN0jNV\n++CH3WHTnwPxCfzMjC0SJYWi0A5+kD74fkXWWB18yLlowM3cPlWIrcgaap/TiCZiqvbBD7uhxC7w\n2Q0v9IVOLoqsMTn4+Xn7P4XozY89oqnrhh+h9rlRHLx20XgmT+CNcZ/BZze2GEQoTXZ8sV3oVObg\nk/pGTAXspN6SXPPQ9iLr/PxiIS9z8KEimvRZXTImH+thWAevffAByRP4uTkbVaTbntKogx8N122S\nMzPx1TeSi5yS8UxN+Zs1MAYHn8xjlOwzMRRZ64xoiv5HbZMMTF4GX+beoZoTaJrAx3ihU5Eg5Z0d\n1e3gk4ucYMHF+1rfVQp3eWdEoxRZy7bvuoqsTYloVOA9ktdeV1ZghWo7a97GVrcIpQl5AHLVJpm4\npPQ6hDgOntkx+Tzo5J32V72uoKqDL9t+6iqyhuqiGaXIGn0XjYisEZHbROQOETk95/cfFJH1InK7\niHxPRPZzP9Tq5EU0Phx8dmOLQYTSqIN3S0iBr3La7zqDH9bBtymi6dfhlaZRDl5ElgPnAGuAg4AT\nROSQzMuuBw41xhwI/DPw164HOgp5Au/LwcfmMtPE7uDLdqJYHXySwYPfVskqhTvXGfwwDr5tEU3e\nwbItXTSHAeuNMRuNMbPA+cDr0y8wxnzfGJNsbtcAe7kdZnWy3ReawS88js3BlznOGDuU0hk8xOfg\nQwt8XUXWGLtoGuPggb2B+1KPN/SeK+I9wDdHGZRLpqftikgu7hlU4F1k8DGIUJq6++CrXOiUCFKe\ng+9SROPqQictslajqUXWEol7ioFn3RaRtwGHAkfl/X7t2rVP/Tw+Ps74+Pigb12Zop2wX0TjysE/\n8shw7+GT0H3wWUc3PW3PqIpaU4tOgycn8w+ePq8cHYS6M/hHHy1+fTK97VjGwrW9yBrLlayj9MFP\nTEwwMTFRaZxZBhH4DcDq1OPVLHb0AIjIMcCfAkcaY3I9QlrgQ5FXZQ/l4GOPaHxn8OkdYmzMfs3N\nFS/3skghz8E//LD7cQ9DqAw+ucAovTz7iUbe9LbgJ6IxJn82yTqKrDvuCL/4hfvPCdkHnzW/69at\nG26wKQaJaG4ADhSRvURkGXAicHn6Bb2i6xeAY40xD1UejQeyGd0wDr5tbZJ1ZvDQ/7S9bCeKMf4K\nlcEnyzIt1oMKfBYfAj83Z8eWvhlGFyKapUvt/569s1ij2iSNMduAU4ArgFuBi4wxN4vIOhF5Q+9l\nnwZWAReIyC0i8g1vIx6SvNPobdsGc/BtK7LW2UUD/Xf6YR183QfPUBFN1hHCYMsyz8D4EPi8dV1n\nkTVURJMc1LLLM6YumkEiGowxl5Nx7caYM1M/v9rxuJxRtBMO0kXT9ogmcYFluXhVqgp8UZE1xmUb\nSuCzmS6M5uBdF1nzxC+kg3/GM7Yfk2uKlmdyIEv//zEVWVt/JWtRBj9okXWYGzvHKEJpksmxEsbG\n/BTDkvlY0qfsMFhEU3RHp6KorU5CZfB5Dr6faBTdUN5HkbVuBx8iFi3Si7zlqQIfkCJh6Ofgkxs7\nD7ORNuFK1vT4YGGSLJfk7fAwmoOPcdmGyuBdO3jXAp9tkYT6bvgRMqKBfI1QgQ9IWURT5uDTrx2U\nGAuBabIbHvi56UeZwJcdMPs5+K5OVVC03oatZ4C/DL7OiCbEPlcW0aSX5/z89p1iKvAeKSrOFZ3C\nphnWDTRtqgLw4+DzHB24L7LWvWzrLrL2c/AxFFmHiThdjsk1gzr45GwrXdOKuoum6WRP4ZYtsxvd\n5GR/gR/VwccgQmnqdvD98v6ym27HeHaUFXhfGXysRdZk28lz8OnrHnwSylT1K7ImVKmX+KT1Ap/d\nAJJ5u7ds6R/RVHHwTeqDBz87RN4OD9UcfNlkY3Uv22yRNaSDr1pkdengkzrVk08WH9B9F1rrdvDZ\niKZKnOaT1gt8VhjAPn788e45+LyIxoe78FFkjdHBN7HI6rKLBuzjLVuqrW8X1C3weRGNOviAFAn8\nli1+MviYBT6kg6+yw5cVWWN18CEimlgvdAL7+PHHq52xuSCvi8bHdjFoRKMCH5jsBgCDRzRtdPAh\nNr5RHHyTpguuu4um7jZJWBD4WCKakHd0gsEjGhV4T2QzeBg8onHh4JMrRWOgqA8+JgfftOmCQ2Tw\nVSKasgztmC4cAAASL0lEQVTeVZEVFiKauhx8nXPRgEY0tVMW0fh28L6uFK1KqI2vah98WZE1u2yX\nLrUHzqpu1AUhJxtTB188pvQ6SDp7XJuqYRx83kVfKvCeGKXIOqqDr/IePgk1vlH64Ad18Ek3VJ0u\nvu42ySoXOrW9yFrlCvRBGLTIWlQvmZ21F0GFphMCn5fBP/GEe4HP+6wYooSEGBz8KHPRxHbw7PqF\nThBfkTUZk+vtYpQiq0i4K3uztF7gizL4EBEN1C9Caerug+8XVw3j4CEOB19XBr906cJl8Xl0KaIJ\ndVZatcgK9eXwrRf40BFN9rNiEvjY++CHuek21LtsZ2etuKZ3+pBdNCLl664LRdZEWLP/p4/1UHZG\n1M/Bgwq8N0IVWY3JP5WOSeBj7oNP7iGanWK4qA8e6m2VTMaTnnMkZB88lOfwXXDweYYqGZMPB181\nogEVeG8UZXSuHXzeJEPDvodvYu6D73cP0SJxqSuiyTvgJIbBtajlRTRQnsMXOc42FVnzxpOMKbaI\npq5OmtYLfFkG73KqgqKNLYYLchJi7oPvV8TKW751O/h0/p7gIx4oc/BlAh/SwVepuYxKSIEfpQ8e\n6puPpvUCX3Rqn81P8xhmQwm5sVWlKKKJxcEXZZzbttn4Jvv7Oh18tgc+wYfAFzn4qhl8SAfvM6LJ\nOztPxlSngx92XfmkswIPYRx8LAI/P58vvLHc8KNMkLZsscsxG9/U7eDzBN5HDl8l13U9XfD8fL7I\n1dkmGdrBV+2DBxV4bxT1wUM4Bx9DH3wiumOZNe7DwVe50KksM56fLy6mxZTBg7+IpsgVhiqyJvFe\nXo1p69b6iqyh9jktskZKUQYP3XLwZRteDBl8mYOH+JZtyAy+SuHOdZG1bPtOxpI3Pt8OvuggG1uR\nVQXeE6NENG3K4MvG5yODH7boVlZkhXBueVCKMngfEU0MRdZ+Al+0vuty8DEWWVXgHZPcEzLvQghw\n2wcfsuBThSY4+Lz1MTZmY4HYlm3IiCaGImusDj6GIusgAq9TFXgg72IU6K6Dz9vwYm+TBPt8bA4+\ndAbv8kKnKq66zMBAPUXWWLto1MEHoiyjA7cOPmQeWIW8HniI60KnovWxbFl8B88YHHyVDD6kg9eI\nZgEVeA+U7YTQLQdftOHF4uCLIgUod/CxFVljb5P0VWRte5vkMBGN9sEHougUbpgumrYIfFFEE8sN\nP8oimjIHH1uRNZYrWWPI4Ouai8bXZGODLE/tgw9IPwc/SB+8izbJGPrgiyIaX27HVZEV4nXwdUc0\nIfvgtcg6WpFVBd4DZbPNgTp4iCuDL4toYjt4xlJkrZLBV3HVMUY0TSqy6mRjHnCRwbfpQqdQ4/NR\nZI3RwYfM4KsUWet28FpkXUAnG/NAvwy+X0STTF87yA7RBIEP6eCHvdCpX5E1NgcferIxLbIONyaN\naCytF/hRHDwMLiKxTxfchD54bZPMp2wuGi2y5o8p1GRj2gdfI0UbwJIldsX0c/Aw+MbShCtZy/rg\njXH3WSHbJLuQwVc57Y8hoqnLwfuai0b74COjaCcE+/wgDn7QHbapEc3YmNsbM0xNwUMP5e/wK1fC\nww/Dxo3b/65qm2RTM/ipKbjmmsE+q6jIWvVCp7YUWV10rv3sZ3D77XDLLXDTTcUHv9b2wYvIGhG5\nTUTuEJHTc36/XETO773mGhH5FdeDnJ6GM86Au+8e7u+KXDUMLvBFIvLQQ/DpTy9sELG3SRZFNDDY\nxnf33fDud8PERPFrNmyAI4+EAw6AF71o+9/vsQd8+MNwxBHwwx8u/l3VNslRl+3UFHznO/Dtb8PF\nF9vvc3P9/26UDP7xx+F1r4NXvxr+9E/Lz57m5+148rbVXXeFa6+FzZu3/10MDt53RDPKWfP8PPzZ\nn8Ghh8Jb3gK/8ztw4on25+zyMaZ4ee6xB1x/vT1IQLWOp/l5t2fQaUoFXkSWA+cAa4CDgBNE5JDM\ny94HPGCMeSHwGeAslwM0Bn7v9+Cyy+Dww+GLXxx8YfRz8INENCtX2qN8mscfh9e+Fj7/eXjve+14\nhskDJ8pU0hNFEQ2U7xDT0/Dxj8Nhh8Euu8BJJ9mDbXYnuOoqeOlL4Td/Ey68MH+HB/jIR+Azn7Hi\ndumlC8uiapvkKA5+ZgaOPx4+9CH4whfgK1+Bj34UjjvOruMyqkY0DzwARx0Fz38+/PSn8G//Bm9/\nu13OedtF0b1+Ad76VrscDz7YLsvs/zZskXXTJrjhhvzfFQl8ImauHfwg+0jVs+Ynn4Q3vcmalR//\neMHB33GH/d1v//bCQX7bNjjtNHje87a/lwLAK14Bn/wkHH00XHLJYJONXXcdvOpV8JznwG672WX3\nghfY/WLTpr7/9lD0c/CHAeuNMRuNMbPA+cDrM695HXBe7+dvAS8Tydscq/GJT9gV8O//blfIOefA\nscfaFdNP6ItEF+Av/xL226//5//Jn9gd6WtfW3jPN74RXvISO4bbbrOvGWZjq0Pgyxz8r/wKvP/9\ncN99C8/NzsLXv24dztVXw403wl/9Fdx8s91Ajz7aiuK7323d+kknwXnnwemn54tRmje/Gb71LXjX\nu+AP/3CC664rd/BlbZJVHfzcHLzznXas//Ef1rl/4xv25332sWcZP/1p8d8Pe0enJ5+EK6+E//k/\n4YQT4Oyzrfv77nft69esga9/fWK7vyuqZ4B9/uMft9vmH/yB/X8+9zn4x3+0Z1ODOvi5Ofibv4ED\nD4Tf+A27ff/kJ4tfU7R9J/UsVw7+3nvtmfG55070PZMaVuAffdQu7yOPhJ12sgfXZz5z4ffLl1tz\nsnEjnHIK3HWX3Q42brQuvYjf+i0r7u99r40gixz85s3wnvdYU/HOd8Lll8N//Zddx1/6ktWT/fe3\n7+OKfiHF3kBqt2cDMF70GmPMvIg8DDwTGPlYdP758Hd/ZwVl1Sp76n/99Vb0X/MauyKPPdaK7YMP\n2hWxaZPdsHbaye6sxx6b/95vfetgYzjxRHv0Pv54+MEP7E6/xx52B12yxDqnV7zCiuP73rf9369Y\nYd3grbfa8Q8SC/lgasq6hTyuusruVAcfbDeunXe2O/x++9kD4XHHLYj2nnvCFVfAZz9r18VLX7og\n8kUHkDyOOALWr7cHhne8w0Zeb3pT/mtdO3hj7Lp64AF7Zpg+sCxbBn/7t/b/f9nL7PLYe2/45V+2\nv7v1Vuv2/vu/4WlP2/6999zTCsPuu9vtZt994T//0+68Bx0Ef/7nVhASVq60B9KPf9yugxtvhJNP\nhl/7NfjFL6xQ91uuRx5pI69zzoF77rFZ8p572rOELEuXWkG55hq77B57zDrQHXe0BupXfxXOOsuu\nnze/2f4PO+xg/+8DD8z//BUriousGzfaCGzffe1yfOQRu6/ce689sOy2Gzz96fb5L3wBvv99e5C5\n8kr7N+94Bzz3uXaZ3nXXwvJYudKOqeisefNmG3/9/Odw//3WnT/6qF0H73wnnHpqvhFZudKaj9e8\nxurKpz9tt4F+puXww60+fOpTVqvy3vfqq+GFL7Rj2XXXxb9/2cvs1+c+B3feWf5ZwyCmxAaLyEnA\nkcaYU3qP3wKMG2Pem3rNXcArjDGbe4/vBI4yxmzKvJd5wxsGD5qMsQL9ne/k57nG2BV8ySXWRe+x\nh90Jn/Us61CeeMJ+nXCCPf0ZlUcftRsGwAUXLN6g773XnnZffLEVyTSzs/aofe21dsM+6CB46KG1\n7L//2tEHNQS33w4f+ED+QShhwwZYt87ueKeeCodkwzgPrF27ljPOWMuVV8IzngEvfvH2r/n1X7fj\nPuaYxc9PTdmd6bWvHe4zH3vMitt3v2sPZkVcfbU9mN1/vxWqbdvstnjIIfbAdsAB+X9njDUaP/mJ\nNQTPfa49EyqKyBLOOGMtRxyxli9/2YrZLrvYr4MOgo99bLj/sYj5eSteSX69YoU9sCZnMwmbNtkD\nxqOP2gPC9DT80R/li/wnP2m3rewZ2EMP2Ujupz+1B8T77rOCvs8+sHq1ff0jj9ivZcvsge1tb7Pr\ndO3atRx//Fq+8hUr0vvvb7/22ceOZetWe3awZs32nzs9bfP1nXe2upAc7J797PyYJY8tW6xpfPaz\nh1q8hczM2Kj3Oc8Z/m9FBGNMpVSkn8C/AjjdGPOG3uMPAjsYY/4y9Zp/673mRhEZwzr3Zxlj5jPv\n5amMoCiK0m6qCny/wOAG4EAR2QvYDJwIvCfzmsuA3wJuBN4IXJcV91EGqCiKolSjVOCNMdtE5BTg\nCmxB9jxjzM0isg640RhzCXA2cJ6I3AZsAQZMtxVFURSflEY0iqIoSnPxfiVrvwul2oyIrBaR7/X+\n/7tE5EO953cTke+IyI9E5AoR2bXfe7UFEVkiIreIyCW9x/uJyHW9ZfQ1ERng6oTmIyK7isjXReRW\nEfmxiBze1e1CRNaJyH+KyJ0icoGIrOzKdiEiXxKRTb0EJHmucDsQkbNEZL2I3JxzTdJ2eBX4AS+U\najPTwO/3LgJ7MfAuEXkRsA641BhzEHB573FXOA24A0hOHc8CPtVbRj/HXjjXBb4IXGSMeRFwAHaZ\ndG67EJHnAG8HDjTGPB+YA06iO9vFP2D1MU3udiAixwP7GGMOAH6397el+Hbwg1wo1VqMMZuMMbf3\nfn4C+BGwF4svDvsnOrJMRGRv7P/+9/ahLAEON8Z8o/eSTiwLEXkGcLAx5qtgrx8xxjxON7eLR4AZ\nYJWILAVWAvfSke3CGPN94NHM00XbweuT540xtwBLe/tUIb4FPu9CqdIBtRUR2Rd4KXA18EvGmIcB\njDEPYS8M6wKfBT4IJF1WzwQeSv1+I93YPp4LPCgi/yIit4vIP4rIznRwuzDGPAL8b6yo3w88BtxO\nN7eLhKLtYC+G1FPfAq8VXEBEdgIuAE7rObXOISJvADb3nEfSMtvV1tkx7MH+M8aYA7Eu9qP1Dqke\nRORXgfcD+wK/DOwEvLrOMUVOdp8p1VjfAr8BWJ16vJrFR6DW0ysOXQj839Qp54Misnvv97+Evcag\n7bwMOE5E7gG+CrwS+BSwe+o1e2O3mbZzH7DRGJNM7XUBcDCwuYPbxa8B1xpjHu7FuBcBR9LN7SKh\nSB+yetp3ufgW+KculOoJ3YnYokEn6E26di5whzHms6lfJReH0ft+WeixhcYY8yfGmNXGmP2AtwDf\nNca8HbheRH6997KuLIv7gIdE5Hm9p44BfozdNzq1XQD/BRwuIjv29pdjgDvp4HaRokgfLgPeBiAi\nhwJzxpicuyss4L0PXkRei51GOLlQ6hNePzAiROTlwPewxdVkQX8E+A9swflZ2A6BE40xj9UyyBoQ\nkaOADxhjjhOR/YB/xp6arwfebozxOIt4HPS6qf4eW1T8GXbHFTq4XYjIWuz/Pw/cAvw2sCcd2C5E\n5KvAUdgzlk3AGcA3KdgORORs4GhgCniXMebm0vfXC50URVHaSatv2acoitJlVOAVRVFaigq8oihK\nS1GBVxRFaSkq8IqiKC1FBV5RFKWlqMArjUFE9k1Pq5r53ToReWXv5/eLSM7tmEFEvigiQ92lV0Se\nGH60ilI/2gevNIbehG2X9KaQLXvdPcBLkgmbHHzuFmNMye25FSVO1MErTWOJiHyhNwvjhIisAhCR\nL4vI8SJyKnbSqqvE3hB+Eb2/ObT38xMi8rHeDUhuEZE9e88/r/f4JhH5WObvz+jdiOHHIvKJ3nO/\nISJX9n7eU+zNXVo/E6QSPyrwStN4LnB2bxbGTcCbes8bwBhjPo+ddnbcGPOqnL9Pn7KuBK42xhwC\n/CsLN5T/PPBpY8yLsdPYAiAixwF79W7EcADwQhE5xhhzMfCAiLwP+D/AGcaYLkwUpkSOCrzSNO5J\nbqIC3MTi2fWGZdoY8/9y3usI4F96P38t9frXAK8RkVt6r38edppbgFOx8wxtM8acP8KYFMUZS+se\ngKIMyVTq5zlGMynpyavmM+9VVJz6C2PMl3KeX90bz7NERIwWt5QIUAevtJFJYNUIf38tdmprgDen\nnr8COFlEVgCIyLNEZPferebOxU6DfCfwxyN8tqI4Qx280jSyzjjPKZ+LLbL+rCCHz/tbk3p8KnC+\niHwQuDR53hhziYj8D+BmEZkGtgFvBU4BvmeMuVZEfgTcICLfNsbcNew/pygu0TZJRVGUlqIRjaIo\nSktRgVcURWkpKvCKoigtRQVeURSlpajAK4qitBQVeEVRlJaiAq8oitJSVOAVRVFayv8HymPjL62d\nqxUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f7d1f695c50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ds_input[cut+4],'yo')\n",
      "plt.plot(ds_output[cut+4],'b^')\n",
      "plt.axis([0, 100, 0, 1.1])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x7f6c2e778b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEKCAYAAAAYd05sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nof4/jm8Ry088z5QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f6c2e6d4f90>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "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": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAEACAYAAACnJV25AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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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(valid[0])[105],'b')\n",
      "plt.plot(ds_output[cut+105],'yo')\n",
      "plt.xlabel('hit index')\n",
      "plt.suptitle('expexted(yellow dots) vs NN(blue lines) output')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "<matplotlib.text.Text at 0x7f714de3a150>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEhCAYAAAB1HLuZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsQJvsVgsbYoVeIvFYmlT/j/+0fa/+/ZpyAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f714df09d90>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "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 (out,learn,n = nlayers,m = 4) : # m is ntracks\n",
      "    counter = 0\n",
      "    output = np.zeros(len(out) , dtype=float32)\n",
      "    for i in range (len(out)) :\n",
      "        output[i] = out[i]\n",
      "    learned = learn\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)\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 subcounter >= 0.75*n :\n",
      "            counter += 1\n",
      "    return counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counter = np.zeros(5 , dtype=float32)\n",
      "start = time.mktime(time.gmtime())\n",
      "for i in range (200) :\n",
      "    n_good_tracks = compare(ds_output[cut+i],trainer.network.predict(valid[0])[i],4,4)\n",
      "    if n_good_tracks == 4 :\n",
      "        print i\n",
      "    counter[n_good_tracks] += 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": [
        "22\n",
        "28"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "32"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "40"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "42"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "44"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "57"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "72"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "76"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "79"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "92"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "93"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "94"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "95"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "96"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "104"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "107"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "111"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "113"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "115"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "121"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "124"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "127"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "136"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "139"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "147"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "152"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "153"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "163"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "166"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "175"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "181"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "189"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "192"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "193"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "194"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "195"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "30.0\n",
        "[ 25.  46.  82.  10.  37.]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gots = np.zeros(5 , dtype=float32)\n",
      "start = time.mktime(time.gmtime())\n",
      "for i in range (200) :\n",
      "    n_goodTracks = int(4*got(ds_output[cut+i],trainer.network.predict(valid[0])[i],0.1,0.75))\n",
      "    if n_goodTracks == 4 :\n",
      "        print i\n",
      "    gots[n_goodTracks] += 1\n",
      "stop = time.mktime (time.gmtime())\n",
      "spent.append (stop - start)\n",
      "print \"\\t\" , stop - start\n",
      "print \"gots :\\t\",gots"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "12\n",
        "13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "17"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "30"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "46"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "62"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "75"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "84"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "87"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "112"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "118"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "120"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "140"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "156"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "177"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "189"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "64.0\n",
        "gots :\t[  7.  45.  70.  62.  16.]\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 38.  33.   3.   1.   0.]\n"
       ]
      }
     ],
     "prompt_number": 75
    }
   ],
   "metadata": {}
  }
 ]
}