theanets-1.1.1.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import climate\n",
      "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"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Couldn't import dot_parser, loading of dot files will not be possible.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nlayers = 4\n",
      "module_size=20 #cm\n",
      "module_cell = 5 #per edge\n",
      "distance_between_layers = 5 #cm\n",
      "ncells=module_cell*module_cell*nlayers\n",
      "nhidden=ncells\n",
      "nhidden_layers = 10\n",
      "layers = (ncells,) + tuple([nhidden for i in range(nhidden_layers)])\n",
      "#net = theanets.Regressor((ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,ncells,(ncells,'logistic')))\n",
      "trainer = theanets.Experiment(theanets.feedforward.Regressor(layers=(ncells,ncells,(ncells,'logistic'))),'layerwise')\n",
      "maximum_hight = nlayers * distance_between_layers\n",
      "theta_max = math.atan(0.5 * module_size / maximum_hight)\n",
      "theta_min = -1 * theta_max\n",
      "y = []\n",
      "y.append(0)\n",
      "y_prime = []\n",
      "y_prime.append(0)\n",
      "z = []\n",
      "z.append(0)\n",
      "x = []\n",
      "x.append(0)\n",
      "for i in range (nlayers) :\n",
      "    y.append((i+1) * distance_between_layers)\n",
      "    y_prime.append((i+1) * distance_between_layers)\n",
      "    z.append(0)\n",
      "    x.append(0)\n",
      "spent = []\n",
      "list_error = []\n",
      "color_list = ['bo','yo','ro','co','mo','go','ko','wo']\n",
      "min_ntracks = 4\n",
      "max_ntracks = 4\n",
      "nsamples = 100000\n",
      "#nsamples = 2 * ncells * ncells * (nhidden_layers + 1)\n",
      "#print nsamples\n",
      "training_ratio = 0.8\n",
      "ds_input = np.zeros((nsamples,ncells))\n",
      "ds_output = np.zeros((nsamples,ncells))\n",
      "cut = int(nsamples * training_ratio)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 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": [
      "new_samples()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "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",
      "train = ds_input[:cut], ds_output[:cut]\n",
      "valid = ds_input[cut:], ds_output[cut:]\n",
      "niter=6\n",
      "ntrain=2\n",
      "for i in range (8,niter+4) :\n",
      "    # training on training samples\n",
      "    for j in range (ntrain) :\n",
      "        start = time.mktime(time.gmtime())\n",
      "        print \"training started ... \"\n",
      "        e[i-1] = trainer.train(train,valid, algo='rprop',learning_rate= (1.0/(j+1)) * 10**(-1*i),batch_size=100)\n",
      "        print \"\\ttraining 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",
        "\ttraining ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t296.0\n",
        "training started ... \n",
        "\ttraining ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t296.0\n",
        "training started ... \n",
        "\ttraining ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t485.0\n",
        "training started ... \n",
        "\ttraining ended"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "training time (seconds) \t257.0\n"
       ]
      }
     ],
     "prompt_number": 35
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      "e"
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     ],
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    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[4])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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       "output_type": "pyout",
       "prompt_number": 37,
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vM2sCTcJZNAOQ7DTAYIL/3OeAu+6KnpcRfFPpgTZoKkXTha0KwjqFK3jLTpMM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\nZET5/3dI0pdrQ3IPAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1e3e449810>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(ds_output[80004])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.text.Text at 0x7f10d6b30310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3U8/gV1ayKqP77gt7PrOoi9kQHjxI4INT9ADzi7CpDl4CX09R1Hyz1hh18DB8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\niAVFAi+EEAuKBF4IIRYUCbwQQiwo/x+/npQTl0T9/AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10dbb882d0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist([val for val in trainer.network.predict(valid[0])[4] if val > 0.1 ], bins = 50)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "(array([ 2.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  1.,\n",
        "         0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
        "         1.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  1.,  0.,  1.,  0.,  0.,\n",
        "         0.,  0.,  0.,  0.,  1.,  0.,  0.,  1.,  0.,  0.,  2.]),\n",
        " array([ 0.25121408,  0.2661898 ,  0.28116552,  0.29614124,  0.31111696,\n",
        "         0.32609268,  0.34106839,  0.35604411,  0.37101983,  0.38599555,\n",
        "         0.40097127,  0.41594699,  0.4309227 ,  0.44589842,  0.46087414,\n",
        "         0.47584986,  0.49082558,  0.5058013 ,  0.52077701,  0.53575273,\n",
        "         0.55072845,  0.56570417,  0.58067989,  0.59565561,  0.61063132,\n",
        "         0.62560704,  0.64058276,  0.65555848,  0.6705342 ,  0.68550992,\n",
        "         0.70048563,  0.71546135,  0.73043707,  0.74541279,  0.76038851,\n",
        "         0.77536423,  0.79033994,  0.80531566,  0.82029138,  0.8352671 ,\n",
        "         0.85024282,  0.86521854,  0.88019425,  0.89516997,  0.91014569,\n",
        "         0.92512141,  0.94009713,  0.95507285,  0.97004856,  0.98502428,  1.        ]),\n",
        " <a list of 50 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pX3+8bGDMNc1x3Nbmc+4NSpJUjP8PoiQVY7FLUjEWuyQVY7FLUjEWuyQVY7FLUjEWuyQV\nY7FLUjH/D7LbfLgILxWCAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10d59cd390>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[4],'bo')\n",
      "plt.plot(ds_output[80004],'y')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f1e35c0d6d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Id0DtFbyehZndn/MuWPr8nhSRQeAK4M3AduB2EdmolLq35ZjjgXOBNyulXhSRfdM84SQw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\nePDRBvC9IqNht/uLi1+Kplb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FlcAWwL51XAd8utFGT2JNnCsCXwWuV0odDxyL1SaF6xci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\np9LgAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1e35c0d190>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[22],'bo')\n",
      "plt.plot(ds_output[80022],'yo')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f1e3b308090>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f1e3b308250>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "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))\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 == n :\n",
      "            counter += 1\n",
      "    return counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "counter = np.zeros(5)\n",
      "start = time.mktime(time.gmtime())\n",
      "for i in range (200) :\n",
      "    counter[compare(ds_output[80000+i],trainer.network.predict(valid[0])[i],4,4)] += 1\n",
      "stop = time.mktime (time.gmtime())\n",
      "spent.append (stop - start)\n",
      "print \"\\t\" , stop - start\n",
      "print counter"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\t51.0\n",
        "[ 81.  77.  36.   0.   6.]\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}