theanets-1.1.1-Copy2.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,(ncells,'logistic'))))\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 = 1000000\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=8\n",
      "ntrain=2\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",
      "        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"
       ]
      }
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "e"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "[0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " 0,\n",
        " (OrderedDict([('loss', 0.0067113517670117848), ('err', 0.0067113517670117848)]),\n",
        "  OrderedDict([('loss', 0.0069140551770558748), ('err', 0.0069140551770558748)])),\n",
        " (OrderedDict([('loss', 0.0066841583260369318), ('err', 0.0066841583260369318)]),\n",
        "  OrderedDict([('loss', 0.0069245791927467406), ('err', 0.0069245791927467406)])),\n",
        " 0]"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(trainer.network.predict(valid[0])[4])\n",
      "plt.xlabel('hit index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "<matplotlib.text.Text at 0x7f1e3b3a3110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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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 0x7f10d5499910>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Rn4jIT0XktS5vMA3cavBa4bvBpgw0tvZkp8FrmdjglccwIsPWZZKHBm8jsJWAp+C19k8C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\nMtPXOfYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f10d5499410>"
       ]
      }
     ],
     "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": 16,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f1e3b34b990>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7f1e3b34bf10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "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": [
        "\t49.0\n",
        "[ 81.  77.  36.   0.   6.]\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}