Moving Neurons.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:4da2b4d06f7d6a4f3f804d5202fa872f26b4673f826c2552bd2b8fb27ee6886a"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import scipy.linalg\n",
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.cm as cm\n",
      "\n",
      "\n",
      "matplotlib.rcParams['savefig.dpi'] = 4 * matplotlib.rcParams['savefig.dpi']\n",
      "size = 5\n",
      "precision = np.eye(2)\n",
      "mean = np.zeros(2) + (size-1)/2.\n",
      "print mean\n",
      "\n",
      "\n",
      "points = np.asarray(np.dstack(np.meshgrid(np.arange(size), np.arange(size))).reshape(-1,2),dtype=np.int32)\n",
      "\n",
      "g = np.exp(-np.sum(np.dot(points-mean,precision)**2,axis=1))\n",
      "print g.reshape((size,size))\n",
      "full = np.zeros((32,32))\n",
      "full[:size,:size] = g.reshape((size,size))\n",
      "circ =  scipy.linalg.circulant(np.arange(32)).T\n",
      "plt.imshow(full[circ[1]],interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[ 2.  2.]\n",
        "[[  3.35462628e-04   6.73794700e-03   1.83156389e-02   6.73794700e-03\n",
        "    3.35462628e-04]\n",
        " [  6.73794700e-03   1.35335283e-01   3.67879441e-01   1.35335283e-01\n",
        "    6.73794700e-03]\n",
        " [  1.83156389e-02   3.67879441e-01   1.00000000e+00   3.67879441e-01\n",
        "    1.83156389e-02]\n",
        " [  6.73794700e-03   1.35335283e-01   3.67879441e-01   1.35335283e-01\n",
        "    6.73794700e-03]\n",
        " [  3.35462628e-04   6.73794700e-03   1.83156389e-02   6.73794700e-03\n",
        "    3.35462628e-04]]\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
        "<matplotlib.image.AxesImage at 0x7ff0e1376350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AAA7mqIr555577rzsdWVlZevdu3e3e//9909YvHjxudOnT//ByJEjn73vvvtu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ff0e190ef50>"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "col = (np.arange(5).reshape(1,5) + np.arange(32).reshape(32,1))%32\n",
      "print col\n",
      "row = (np.arange(5) * 32).reshape(5,1)\n",
      "conv = col.reshape(32,1,5) + row\n",
      "print conv\n",
      "conv_img = full.reshape(1024)[conv]\n",
      "\n",
      "#plt.imshow(,interpolation='nearest')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[[ 0  1  2  3  4]\n",
        " [ 1  2  3  4  5]\n",
        " [ 2  3  4  5  6]\n",
        " [ 3  4  5  6  7]\n",
        " [ 4  5  6  7  8]\n",
        " [ 5  6  7  8  9]\n",
        " [ 6  7  8  9 10]\n",
        " [ 7  8  9 10 11]\n",
        " [ 8  9 10 11 12]\n",
        " [ 9 10 11 12 13]\n",
        " [10 11 12 13 14]\n",
        " [11 12 13 14 15]\n",
        " [12 13 14 15 16]\n",
        " [13 14 15 16 17]\n",
        " [14 15 16 17 18]\n",
        " [15 16 17 18 19]\n",
        " [16 17 18 19 20]\n",
        " [17 18 19 20 21]\n",
        " [18 19 20 21 22]\n",
        " [19 20 21 22 23]\n",
        " [20 21 22 23 24]\n",
        " [21 22 23 24 25]\n",
        " [22 23 24 25 26]\n",
        " [23 24 25 26 27]\n",
        " [24 25 26 27 28]\n",
        " [25 26 27 28 29]\n",
        " [26 27 28 29 30]\n",
        " [27 28 29 30 31]\n",
        " [28 29 30 31  0]\n",
        " [29 30 31  0  1]\n",
        " [30 31  0  1  2]\n",
        " [31  0  1  2  3]]\n",
        "[[[  0   1   2   3   4]\n",
        "  [ 32  33  34  35  36]\n",
        "  [ 64  65  66  67  68]\n",
        "  [ 96  97  98  99 100]\n",
        "  [128 129 130 131 132]]\n",
        "\n",
        " [[  1   2   3   4   5]\n",
        "  [ 33  34  35  36  37]\n",
        "  [ 65  66  67  68  69]\n",
        "  [ 97  98  99 100 101]\n",
        "  [129 130 131 132 133]]\n",
        "\n",
        " [[  2   3   4   5   6]\n",
        "  [ 34  35  36  37  38]\n",
        "  [ 66  67  68  69  70]\n",
        "  [ 98  99 100 101 102]\n",
        "  [130 131 132 133 134]]\n",
        "\n",
        " [[  3   4   5   6   7]\n",
        "  [ 35  36  37  38  39]\n",
        "  [ 67  68  69  70  71]\n",
        "  [ 99 100 101 102 103]\n",
        "  [131 132 133 134 135]]\n",
        "\n",
        " [[  4   5   6   7   8]\n",
        "  [ 36  37  38  39  40]\n",
        "  [ 68  69  70  71  72]\n",
        "  [100 101 102 103 104]\n",
        "  [132 133 134 135 136]]\n",
        "\n",
        " [[  5   6   7   8   9]\n",
        "  [ 37  38  39  40  41]\n",
        "  [ 69  70  71  72  73]\n",
        "  [101 102 103 104 105]\n",
        "  [133 134 135 136 137]]\n",
        "\n",
        " [[  6   7   8   9  10]\n",
        "  [ 38  39  40  41  42]\n",
        "  [ 70  71  72  73  74]\n",
        "  [102 103 104 105 106]\n",
        "  [134 135 136 137 138]]\n",
        "\n",
        " [[  7   8   9  10  11]\n",
        "  [ 39  40  41  42  43]\n",
        "  [ 71  72  73  74  75]\n",
        "  [103 104 105 106 107]\n",
        "  [135 136 137 138 139]]\n",
        "\n",
        " [[  8   9  10  11  12]\n",
        "  [ 40  41  42  43  44]\n",
        "  [ 72  73  74  75  76]\n",
        "  [104 105 106 107 108]\n",
        "  [136 137 138 139 140]]\n",
        "\n",
        " [[  9  10  11  12  13]\n",
        "  [ 41  42  43  44  45]\n",
        "  [ 73  74  75  76  77]\n",
        "  [105 106 107 108 109]\n",
        "  [137 138 139 140 141]]\n",
        "\n",
        " [[ 10  11  12  13  14]\n",
        "  [ 42  43  44  45  46]\n",
        "  [ 74  75  76  77  78]\n",
        "  [106 107 108 109 110]\n",
        "  [138 139 140 141 142]]\n",
        "\n",
        " [[ 11  12  13  14  15]\n",
        "  [ 43  44  45  46  47]\n",
        "  [ 75  76  77  78  79]\n",
        "  [107 108 109 110 111]\n",
        "  [139 140 141 142 143]]\n",
        "\n",
        " [[ 12  13  14  15  16]\n",
        "  [ 44  45  46  47  48]\n",
        "  [ 76  77  78  79  80]\n",
        "  [108 109 110 111 112]\n",
        "  [140 141 142 143 144]]\n",
        "\n",
        " [[ 13  14  15  16  17]\n",
        "  [ 45  46  47  48  49]\n",
        "  [ 77  78  79  80  81]\n",
        "  [109 110 111 112 113]\n",
        "  [141 142 143 144 145]]\n",
        "\n",
        " [[ 14  15  16  17  18]\n",
        "  [ 46  47  48  49  50]\n",
        "  [ 78  79  80  81  82]\n",
        "  [110 111 112 113 114]\n",
        "  [142 143 144 145 146]]\n",
        "\n",
        " [[ 15  16  17  18  19]\n",
        "  [ 47  48  49  50  51]\n",
        "  [ 79  80  81  82  83]\n",
        "  [111 112 113 114 115]\n",
        "  [143 144 145 146 147]]\n",
        "\n",
        " [[ 16  17  18  19  20]\n",
        "  [ 48  49  50  51  52]\n",
        "  [ 80  81  82  83  84]\n",
        "  [112 113 114 115 116]\n",
        "  [144 145 146 147 148]]\n",
        "\n",
        " [[ 17  18  19  20  21]\n",
        "  [ 49  50  51  52  53]\n",
        "  [ 81  82  83  84  85]\n",
        "  [113 114 115 116 117]\n",
        "  [145 146 147 148 149]]\n",
        "\n",
        " [[ 18  19  20  21  22]\n",
        "  [ 50  51  52  53  54]\n",
        "  [ 82  83  84  85  86]\n",
        "  [114 115 116 117 118]\n",
        "  [146 147 148 149 150]]\n",
        "\n",
        " [[ 19  20  21  22  23]\n",
        "  [ 51  52  53  54  55]\n",
        "  [ 83  84  85  86  87]\n",
        "  [115 116 117 118 119]\n",
        "  [147 148 149 150 151]]\n",
        "\n",
        " [[ 20  21  22  23  24]\n",
        "  [ 52  53  54  55  56]\n",
        "  [ 84  85  86  87  88]\n",
        "  [116 117 118 119 120]\n",
        "  [148 149 150 151 152]]\n",
        "\n",
        " [[ 21  22  23  24  25]\n",
        "  [ 53  54  55  56  57]\n",
        "  [ 85  86  87  88  89]\n",
        "  [117 118 119 120 121]\n",
        "  [149 150 151 152 153]]\n",
        "\n",
        " [[ 22  23  24  25  26]\n",
        "  [ 54  55  56  57  58]\n",
        "  [ 86  87  88  89  90]\n",
        "  [118 119 120 121 122]\n",
        "  [150 151 152 153 154]]\n",
        "\n",
        " [[ 23  24  25  26  27]\n",
        "  [ 55  56  57  58  59]\n",
        "  [ 87  88  89  90  91]\n",
        "  [119 120 121 122 123]\n",
        "  [151 152 153 154 155]]\n",
        "\n",
        " [[ 24  25  26  27  28]\n",
        "  [ 56  57  58  59  60]\n",
        "  [ 88  89  90  91  92]\n",
        "  [120 121 122 123 124]\n",
        "  [152 153 154 155 156]]\n",
        "\n",
        " [[ 25  26  27  28  29]\n",
        "  [ 57  58  59  60  61]\n",
        "  [ 89  90  91  92  93]\n",
        "  [121 122 123 124 125]\n",
        "  [153 154 155 156 157]]\n",
        "\n",
        " [[ 26  27  28  29  30]\n",
        "  [ 58  59  60  61  62]\n",
        "  [ 90  91  92  93  94]\n",
        "  [122 123 124 125 126]\n",
        "  [154 155 156 157 158]]\n",
        "\n",
        " [[ 27  28  29  30  31]\n",
        "  [ 59  60  61  62  63]\n",
        "  [ 91  92  93  94  95]\n",
        "  [123 124 125 126 127]\n",
        "  [155 156 157 158 159]]\n",
        "\n",
        " [[ 28  29  30  31   0]\n",
        "  [ 60  61  62  63  32]\n",
        "  [ 92  93  94  95  64]\n",
        "  [124 125 126 127  96]\n",
        "  [156 157 158 159 128]]\n",
        "\n",
        " [[ 29  30  31   0   1]\n",
        "  [ 61  62  63  32  33]\n",
        "  [ 93  94  95  64  65]\n",
        "  [125 126 127  96  97]\n",
        "  [157 158 159 128 129]]\n",
        "\n",
        " [[ 30  31   0   1   2]\n",
        "  [ 62  63  32  33  34]\n",
        "  [ 94  95  64  65  66]\n",
        "  [126 127  96  97  98]\n",
        "  [158 159 128 129 130]]\n",
        "\n",
        " [[ 31   0   1   2   3]\n",
        "  [ 63  32  33  34  35]\n",
        "  [ 95  64  65  66  67]\n",
        "  [127  96  97  98  99]\n",
        "  [159 128 129 130 131]]]\n"
       ]
      }
     ],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}