20K1L-relu-rprop.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,'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 = 50000\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": [
      "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=1000,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": [
        "0.0226252772093\n",
        "validation_error = \t0.033404454156\n",
        "training_error = \t"
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      {
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        "validation_error = \t0.00580484317023\n",
        "training_error = \t"
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        "validation_error = \t0.00580484317023\n",
        "training_error = \t"
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        "validation_error = \t0.0058048182141\n",
        "training_error = \t"
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       "output_type": "stream",
       "stream": "stdout",
       "text": [
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        "validation_error = \t0.0058048182141\n",
        "training_error = \t"
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      {
       "output_type": "stream",
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       "text": [
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        "validation_error = \t0.0058048182141\n",
        "training_error = \t"
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       "output_type": "stream",
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        "validation_error = \t0.0058048182141\n",
        "training_error = \t"
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      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00575295225013\n",
        "validation_error = \t0.0058048182141\n",
        "\t training ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t493.0\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": [
        "0.00575295225013\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f9cbb0ba610>"
       ]
      }
     ],
     "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": [
        "0.0058048182141\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f9cbb0ba6d0>"
       ]
      }
     ],
     "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/20K1L-relu-rprop\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.load(\"network/20K1L-relu-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": 12,
       "text": [
        "<matplotlib.text.Text at 0x7f2bc4f5e8d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qL/BFIvhQLJq8SdZOyiRDEvg8vWgigUuW86ZdRbYSQ7P+jOJbXZVE1MGiqXJd\nadRe4ItG8KFbNN1qVVAngc+TZG0m2smo1bn20W6/CnwvPfhuJVlBAl8qRZKsIVk0aUnWLK0KspZJ\ntpvoVKcqmla9aJp5r1kFPrraGmhx5NZV4Kua6ATd8+CjdcmiKQlZNO3pdZI1hAh+YMA/ZmbS39PM\nokiKWhYhrKvA16EOvsp1pVF7gZdF054iE53KqIOvYxVNp9uzmcAlryIl8MXXJQ++RhQtk1QEn/4e\n59KtnTzNxuoWwaed+KD19mxWGZMWwbdLRtZV4Kuc6CSB71OKRPCyaJoL0tSUv41asupDFk2+pHVW\nDz7LjM9+7CjZ6wheSdY+pWiSNQSLptVM1mYHVLMoVQLfetu0smjkwbdeJm2ORR4WQpJVAl8Ssmja\nk+eGH2k18NF7Qr+jU5kevCyaWcq6J6uSrDVCFk178iRZ00okQRE85LsFYjPrJRlkZLFo6irwSrJ2\nTu0FXhF8e/JEnK1siLRWBa2+gzpW0eSxaLJG8FkEvt86SnbTopHA1wiVSbanbIEPPYLPa9HIg2+9\nTFkWTZoHryRrn6J2we3J0y64E4HP0g++TgKfp0yykyqaUD34qic6Kcnah8iiaU+esr4qIvhutVCt\nmjzbs5M6+JAj+H6yaKan04MnCXyJyKJpT7OkYCvPuEyBHxzM1i+9X8hbJqmZrK2X6bcka3S1lZwr\nIoEvEUXw7cnrGZcl8FAfm2Zmxj/SmoGV4cHLoim+rm4JfLP9XgJfIiqTbE/eJGtZZZJQn0qa6KYd\nyagNyquDDzWCr7KbZBVJVgl8F9BM1vZUmWSN+pe3izrrEsE32y4gD74VdUyyNhP4oSEJfGnIomlP\nmUnWZClbMx8ySV0Evtm2hHLq4GXRFF+XLJqaEN1goVlE1Y5QLJoqk6xZ7Bmoj8C3i+A79eCVZJ2l\n36poJPAVMzbmhaNd9NiMkCyaqiY6tauBj6hLP5pWEXy3PHh1k2xO9P7kd1RVFY0EvkKKJFhBFk0Z\nrQpCi+DbCbw8+HS6JfDN2mZ0O8mqW/aVQJEEK4Rj0eRJsjYrkxwc9LbY9LR/nlXg61JF08qikQff\nnG5ZNK1Et1tJVkXwJVEkwQqzFk1dZlg2o8wySbO5kZYi+Fm66cH3U7Mx57J1ySwrgm9W2bJ/f/N7\n5pa5Lgl8SRS1aAYH/YSVVvclrQN52tu2i1RDFfgiSdayLJrIAihTrKpketoHBoODrZcro5tks/3R\nrHxLVgKOP3FDAAANIElEQVRfMUUjeAjDpslzww8JfDp791brwWexaAYG+svyyjJPAsrpJhkVXqRR\ntvBK4CumaAQPYSRay0yywnyBz3KS7ecqmslJ+Na34PWvh9NOgzPPTF+uW/3gob98+Kz/U5UWDZQf\nZCwEgW8Sa9SDoklWCKNUssyZrBBeBP+2t8GPfwxnnw0XXQRPf3r6ct3qBw/1Ffhdu7Ivn0ar/bGO\nEXytBb7V5VhWQrZoyhD4Turg+1Hgv/tduOUW2Lix/b6Wx4NPu2VfFjujjgL/q78K69bBi14EX/oS\nPPe5na8rNIFva9GY2Xozu9vM7jWz81L+PmRmlzeW+U8zO7qaoXZOWRF83QU+zz1ZJybKjeD7yTOO\nmJiA97wHPvvZbIFEt+rgoZ4Cv2QJXHklvOtd8LKXwQUXdH513SrgCE7gzWwIuAhYD6wDXmtmJyYW\new/wqHPuecCngM9WMdA8lJFkHRqCq66CCy+EP/sz+MIXilcnjI6OFvuAglx5JbzylfCNb8zelKBd\nFc3+/fCpT8GrXgXHHAMf+xj82q+lf34nAh9tiwMOgJ/8pL8qlv72b+E5z4Ezzsi2fNyD37QJ/vIv\n4dpr/bbdvx82bhyd9546CfzYGPzXf8HnPw/nnAMf+hB8//uwe/f8/6nZMWIG73ynv2q6+mo44gif\n+/jyl+Gpp9qPoVVOqFsCHzUb60b5dbsI/oXARufcVufcFHA5cHpimT8AvtT4/ZvAi83yNgcolzKS\nrKed5nfCBx+EVavg4oth/XrYsiX/Z/ZS4P/lX+CP/9j/Xx//OBx/PGzb1voGFfffD6ecAtdf7/3m\nq67yXugf/VH6OpYsgS9+ET7wAbjkktbfQbQtXv1q2LkTTj7ZryfJ9u1eGIaH4dRT4d57O/3P0xkf\n96Jzyy3+cccd2U4yDz3kI8gLL8y+ruiE+Y//CC95id+GH/mIF6kf/Qjuumt03nviAr9nDzz5ZDaB\nX77cnzz27En/+/S0F8V16/zJ/tZbs/8fWXAOHn4Y/vVf4X3vgxe8AA45xIvzzTf7E+OyZfAXf+H3\nraQQtjtGjjnG22P33w+veAV85SvwzGf6z//Rj/wyO3fCN78JH/4w/NVf+WXuuafzJKtz/uSRRZB3\n7ZpdrpnAd/MGN+08+NXA5tjzLcBws2WcczNm9gRwOLCtpDECfsPdcQfcdpt/rFgBL32pv1Q76igf\n/d19NzzwAKxZ43fcp54qLvB/93dzn7/vfT56PekkGBnxPuDKlfC0p/kvbPdu/zCDww+Hww7zSbe0\nG0B0mwsvhE9/Gn7wA3+AnXsu3Hij3/EPP3z+8osW+QPopS+Fj37UXxpnOXW/5S3+4D76aB+lDQ+3\nf8+qVf5E+rWvwZve5L+/FSv8975jB9x3H/z+7/urqEcf9d/7hz7kTyLt6qfT2L7dJ0T/4R/gyCNn\nfe3du+GJJ+B1r4M3vMGPa8sW2LwZHnvMC8DYmBeX97/fi0pWFi2CT37Sn1RvuMH/BPjFL/x3snHj\n/PcsXerXf/TR8Pjj8Oxney+6HRs2wN/8DaxeDX/4h/B7v+cFbMkS/zmf+ITfZz/5SX/svPrV8Lzn\n+Su0+++Hu+7yQc2znuW/i3Xr/P966KH+MTnpT4g33eSPyyVL4OCD/bHw6KP+hGEGv/mb8Fu/5fe7\n3/iN+dHzyIj/LnbsyL4d4xxxhE9un322344XX+yrmAYH/fd4yil+/52YgCuu8Prwpjelf9aBB8J5\n5/lxHnusH/9NN/ljZMcOfxy/6EX+sWaN/18OOMCv5wc/gO99z+8nhxziT+A//WnzPMHSpfDDH86W\nfjo3K/wrVsBxx+XbHknMtTgtmdkbgZc5585pPH8DMOyc+5PYMpuAlzrnHms8vx841Tm3LfFZ7owz\nOrsmGRuDRx6BrVt9VLVund/4J53kxfv66+G667wIHHWU//txx8HPfw533ukrGy64AP70TztabSZu\nv90L/eOP+7E8+eTsl7NihbdxHn/ci8Lu3f5LP+IIL6QPPzzC8cePlD+oFuzb53e+a6/1YpGFRx7x\nUfoFF/gTQhWMjIwwMjIy57W9e/3BuHix35YHHQQnnugPwIif/Qze8Q6/fTsRWfD70s03e1F7//vh\nhBPm/v3BB+GrX4XLL/ff7Zo1XiiPPNIHDEuX+oP9ne/MlvCMuOYaH5x88IPpV0xp28I5v5+vXu2/\nt05PZo8+Cpdd5sV4cnK2xcS73+1FPzphT0zApZf67bJ2rT+WjjnGb+e77vKPLVu8GG/f7t/zghd4\nAT35ZL+/79zpH4cd5oV99er8jf7StkVWoqvO447rrNpm+3b//fz4x/4xPe3/vxe/2J/oHnrIC/7N\nN/ur3rExf1ytWOGDmJe/3G+7n/3Mn8BvvNEHRScmTW28rffzn/vvYvFiv50iu3TtWn+lHWFmOOdy\nbcl2Av9S4Dzn3BmN5x8Eljjn/ia2zPcay/zQzAbwkfsRzrmZxGfVfMK/EEJUQ16Bb2fR3AqsNbNV\nwGPAWcC7EstcBbwF+CHwKuCmpLgXGaAQQoh8tBR459y4mZ0DXINPyH7JOXe7mW0AfuicuxL4HPAl\nM7sb2A00cbiEEEJ0k5YWjRBCiP6l8tqOdhOl6oyZrTGz6xr//yYz+1Dj9aeb2bVmdpeZXWNmK3s9\n1m5hZoNmdoeZXdl4/iwzu6mxjb5qZjlvsNhfmNlKM/tXM7vTzO4zs1NC3S/MbIOZPWBm95vZ181s\nWSj7hZldambbGg5I9FrT/cDMPmtmG83s9pQ5SfOoVOAzTpSqM/uBdzcmgZ0MnG1mvw5sAL7tnFsH\nXN14HgrvBe4FokvHzwKfaGyjX+AnzoXAF4ArnHO/DpyA3ybB7Rdm9mvAW4G1zrlnA9PAGwlnv/hn\nvD7GSd0PzOw1wK84504A3tF4b0uqjuCzTJSqLc65bc65exq/7wHuAlYxd3LYZQSyTcxsNf5/v9g/\ntUHgFOfcvzcWCWJbmNkhwPOdc18BP3/EObeLMPeLHcAksNzMFgHLgJ8TyH7hnLse2Jl4udl+cHr0\nunPuDmBR45hqStUCnzZRquWA6oqZPRP4TeAG4DDn3BMAzrnt+IlhIXAB8EEgqrI6HNge+/tWwtg/\njgUeN7Ovmdk9ZvYvZraCAPcL59wO4NN4UX8EeBK4hzD3i4hm+8EqOtTTqgVeGVzAzA4Evg68txGp\nBYeZnQE81og8opLZUEtnB/An+08559bio9j/3dsh9QYzOwZ4H/BM4CjgQOAVvRzTAid5zLTU2KoF\nfguwJvZ8DXPPQLWnkRz6N+D/xi45HzezQxt/Pww/x6DuvBh4pZk9BHwFeDnwCeDQ2DKr8ftM3dkM\nbHXORR1gvg48H3gswP3iBcCNzrknGjbuFcDLCHO/iGimD0k9bbtdqhb4X06UagjdWfikQRA0mq5d\nAtzrnLsg9qdochiNn1d1e2zdxjn3P51za5xzzwLeAHzfOfdW4GYz+2+NxULZFpuB7WYWdRw5DbgP\nf2wEtV8ADwKnmNkBjePlNOB+AtwvYjTTh6uANwOY2UnAtHNua6sPqrwO3sx+H99GOJoo9bFKV7iA\nMLOXANfhk6vRhv4L4L/wCecj8BUCZznnnuzJIHuAmZ0KfMA590ozexbwZfyl+Ubgrc65LvTZ6y2N\naqqL8UnFh/EHrhHgfmFmI/j/fwa4A/gfwDMIYL8ws68Ap+KvWLYBHwH+gyb7gZl9DvhtYAI42zl3\ne8vP10QnIYSoJwugia0QQogqkMALIURNkcALIURNkcALIURNkcALIURNkcALIURNkcCLvsHMnhlv\nq5r42wYze3nj9/eZ2QFNlvuCmXV0h1kz29P5aIXoPaqDF31Do2HblY0Wsq2Wewj4jahhUwnr3e2c\nW1HGZwnRTRTBi35j0Mz+qdGFcdTMlgOY2RfN7DVmdi6+adUPzN8Qfg6N95zU+H2Pmf114wYkd5jZ\nMxqvH9d4fpuZ/XXi/R9p3IjhPjP7WOO1PzSz/9f4/Rnmb+5S+06QYuEjgRf9xrHA5xpdGLcBr2u8\n7gDnnPt7fNvZYefc76S8P37Jugy4wTl3IvBdZm8o//fAJ51zJ+Pb2AJgZq8EVjVuxHAC8DwzO805\n9w3gUTN7D/B/gI8450JoFCYWOBJ40W88FN1EBbiNud31OmW/c+47KZ/1IuBrjd+/Glv+d4HfNbM7\nGssfh29zC3Auvs/QuHPu8gJjEqI0FvV6AEJ0yETs92mKBSnx5lUzic9qlpz6qHPu0pTX1zTGc4SZ\nmVNySywAFMGLOjIGLC/w/hvxra0BXh97/Rrg7Wa2FMDMjjCzQxu3mrsE3wb5fuD9BdYtRGkoghf9\nRjIyTouUL8EnWR9u4sOnvdfFnp8LXG5mHwS+Hb3unLvSzJ4L3G5m+4Fx4E3AOcB1zrkbzewu4FYz\n+5ZzblOn/5wQZaIySSGEqCmyaIQQoqZI4IUQoqZI4IUQoqZI4IUQoqZI4IUQoqZI4IUQoqZI4IUQ\noqZI4IUQoqb8f0l/YrC/yesqAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2bc56a0fd0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ds_output[cut+3])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x7f2bcaa64510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ENGX7lqsYYWYmeUyza8lFRNPlMeeqiwbM4HvBRZukVfAJbQZZIbwK3uUgKxTv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\nbxiGESj/H+fBBAYntbxcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f2bcaa4f7d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "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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       "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": 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5Bz7xiZHbbXcPPu0xBl1d3Xzta84JM2sWdHXV3vNbNOvWtbYdExRxJGtXVzdr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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": 14
    },
    {
     "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": [
        "5\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "7"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "23"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "28"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "31"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "37"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "47"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "48"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "57"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "64"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "65"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "72"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "83"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "88"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "97"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "99"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "108"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "111"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "117"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "129"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "130"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "137"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "142"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "153"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "161"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "165"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "171"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "172"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "173"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "184"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "189"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "190"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "193"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "22.0\n",
        "[ 20.  48.  89.   9.  34.]\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "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": [
        "8\n",
        "14"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "15"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "30"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "38"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "45"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "65"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "69"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "76"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "96"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "103"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "110"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "120"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "130"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "141"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "168"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "192"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "54.0\n",
        "gots :\t[  5.  42.  69.  67.  17.]\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 38.  33.   3.   1.   0.]\n"
       ]
      }
     ],
     "prompt_number": 75
    }
   ],
   "metadata": {}
  }
 ]
}