deep-maxout-1L1000N50K.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=1000\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,(nhidden,'maxout:2'),(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)\n",
      "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 = 4,m = 4,efficiency = 1) : # n is nlayers , m is ntracks\n",
      "    counter = 0\n",
      "    output = np.zeros(len(out) , dtype=float32)\n",
      "    Otracks = np.zeros((m,n),dtype = float32)\n",
      "    Ltracks = np.zeros((m,n),dtype = float32)\n",
      "    for i in range (len(out)) :\n",
      "        output[i] = out[i]\n",
      "    learned = learn\n",
      "    for track in range (m) :\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",
      "            Otracks[track][layer] = max_finder(subOutput)+start\n",
      "            Ltracks[track][layer] = max_finder(subLearned)+start\n",
      "            output[max_finder(subOutput)+start] = 0\n",
      "            learned[max_finder(subLearned)+start] = 0\n",
      "    for track in range (m) :\n",
      "        if max([similarity(Otracks[track],Ltracks[i]) for i in range(m)]) >= efficiency * n :\n",
      "            counter += 1\n",
      "    return counter"
     ],
     "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=0.7, algo='rprop',learning_rate = 1e-4,iteration_size = 5000,batch_size=10,validate_every = 1,patience=6,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": [
        "0.00738326392208\n",
        "validation_error = \t0.00712089960115\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00872016308854\n",
        "validation_error = \t0.00794144142358\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00986282143772\n",
        "validation_error = \t0.00931949788524\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0105243722615\n",
        "validation_error = \t0.0100989944789\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0106790265314\n",
        "validation_error = \t0.0105790343754\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0108060148191\n",
        "validation_error = \t0.0107947085709\n",
        "training_error = \t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0111448042286\n",
        "validation_error = \t0.0109573813129\n",
        "\t training ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t791.0\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": [
        "0.00548557852199\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f40b1d23850>"
       ]
      }
     ],
     "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": [
        "0.00558594443323\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f40a41c0c90>"
       ]
      }
     ],
     "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": 10,
       "text": [
        "[1065.0]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.save(\"network/deep-maxout-1L1000N50K\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trainer.load(\"network/deep-maxout-1L1000N50K\")"
     ],
     "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": 11,
       "text": [
        "<matplotlib.text.Text at 0x7f40a400c290>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2ljt6AETkxcB/A37XGJO420QFfpQ4B79xYy+D77eDQs+FNEXgo+4dwiuT7FcH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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f40a407a8d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ds_output[cut+0])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x7f40a4115590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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YqG1WPdP1g/YF3tJkY7Nsg12irkFWqH+bHqRFE19dOk3g57Em8nLBFkQoySwJ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\neooLvOM4Tk9xgXccx+kp/x9WryTG/3hXPAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f40a3b98110>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([val for val in trainer.network.predict(train[0])[4] if val > 0.1 ], bins = 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(array([ 1.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,\n",
        "         0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,\n",
        "         1.,  0.,  1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,\n",
        "         1.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  3.]),\n",
        " array([ 0.11243238,  0.13007094,  0.14770949,  0.16534804,  0.1829866 ,\n",
        "         0.20062515,  0.2182637 ,  0.23590226,  0.25354081,  0.27117936,\n",
        "         0.28881792,  0.30645647,  0.32409502,  0.34173358,  0.35937213,\n",
        "         0.37701068,  0.39464924,  0.41228779,  0.42992634,  0.4475649 ,\n",
        "         0.46520345,  0.482842  ,  0.50048056,  0.51811911,  0.53575766,\n",
        "         0.55339622,  0.57103477,  0.58867332,  0.60631188,  0.62395043,\n",
        "         0.64158898,  0.65922754,  0.67686609,  0.69450464,  0.7121432 ,\n",
        "         0.72978175,  0.7474203 ,  0.76505886,  0.78269741,  0.80033596,\n",
        "         0.81797452,  0.83561307,  0.85325162,  0.87089018,  0.88852873,\n",
        "         0.90616728,  0.92380584,  0.94144439,  0.95908294,  0.9767215 ,\n",
        "         0.99436005]),\n",
        " <a list of 50 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pyLQDOLRb/jXgx1OQ6RnAf3TLm4GXTfJNl1V1DdDvt6eH3gRaVVuBNUl630+0h3EX+zBv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\nxmKXpMZY7JLUGItdkhrz/1n1F+f8VmbNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f3de003e150>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[114],'b')\n",
      "plt.plot(ds_output[cut+114],'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": 19,
       "text": [
        "<matplotlib.text.Text at 0x7f40a7b66b50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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T+PZJE/gib+fjWWYedTxe77rVoimjHB0j8FkbaLJmSHn3MiiKeAaf5UTLGk8f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\n8IZhGBXFBN4wDKOimMAbhmFUlP8P20+p2bomH0wAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f40a4140810>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "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],4,4,1)\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": [
        "62\n",
        "72"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "74"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "114"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "136"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "139"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "173"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "151.0\n",
        "[ 76.  69.  47.   1.   7.]\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "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": [
        "5\n",
        "6"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "28"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "40"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "45"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "51"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "65"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "69"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "82"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "86"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "89"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "91"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "105"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "114"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "117"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "119"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "120"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "149"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "167"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "169"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "180"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "185"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "186"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "187"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "195"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "196"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\t"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "272.0\n",
        "gots :\t[  0.  38.  75.  60.  27.]\n"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "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": {}
  }
 ]
}