Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import mf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import sudoku\n",
    "import scipy.signal\n",
    "import pickle\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "np.set_printoptions(precision=3, suppress=True)\n",
    "pd.set_option('precision', 1)\n",
    "\n",
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f16ba517bd0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16baca97d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N1t6VKQhI6ugOoQqtWNFp/e9XGwUBEalsTz0FAwbAhx82fW/69PD64INw6aWdmq1qoSAg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\nuIMbVNJh2+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f16ba7ec150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('experiment4/0_2_mul2_False.lrn','rb') as f:\n",
    "    lr = pickle.load(f)\n",
    "with open('experiment4/4_2_mul2_False.lrn','rb') as f:\n",
    "    lr2 = pickle.load(f)\n",
    "plt.plot(moving_average(lr[0],10000/50),color='red')\n",
    "plt.plot(moving_average(lr2[0],10000/50),color='blue')\n",
    "plt.plot(np.array(lr[0])-np.array(lr[2]))\n",
    "plt.figure()\n",
    "plt.plot(lr[1],color='red')\n",
    "plt.plot(lr2[1],color='blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f16babb66d0>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YVnnMMVlXpfUol77dJEmSpJz32WeRj8aMib/vvBOaNMm6qpV8/HF0YvnoI7jq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\nsfX7hymlPvhsqfZMAiblnJ8vvR9EhDufMdWG/YGJOedpOecFwH3Anvh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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f165b10be50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CUw2HlcCycHkZ8Hhc+dJwBlIlwcDzmrALqtHMFofjCVf12qf7u74APBOejYiIjDgHD8K7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f165ac44790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('experiment4/0_3_mul_False.lrn','rb') as f:\n",
    "    lr = pickle.load(f)\n",
    "with open('experiment4/2_3_mul_False.lrn','rb') as f:\n",
    "    lr2 = pickle.load(f)\n",
    "plt.figure(figsize=(15,10))\n",
    "plt.plot(moving_average(lr[0],10000/50),color='red')\n",
    "plt.plot(moving_average(lr2[0],10000/50),color='blue')\n",
    "plt.plot(np.array(lr[0])-np.array(lr[2]))\n",
    "plt.plot(np.array(lr2[0])-np.array(lr2[2]))\n",
    "plt.figure()\n",
    "plt.plot(lr[1],color='red',alpha=0.5)\n",
    "plt.plot(lr2[1],color='blue',alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(33026, 6974)\n"
     ]
    },
    {
     "data": {
      "image/png": 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v124BAIYwV5hV1R/PapT9Und/chp+fro8menxhWn8UJIzZzbfOY0dmpaPHH+d7r61u3d3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\nAGAQwgwAYBDCDABgEMIMAGAQ/w8HrW9rP9Gb+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4b09a5d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct,incorrect = pickle.load(open('energies.pkl','rb'))\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.hist(correct,4,alpha=0.8)\n",
    "plt.hist(incorrect,20,alpha=0.8)\n",
    "print(len(correct),len(incorrect))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n = 10\n",
    "p = 2\n",
    "\n",
    "def softmax(x):\n",
    "    e_x = np.exp(x - np.max(x))\n",
    "    return np.transpose(np.transpose(e_x,(2,0,1)) / e_x.sum(axis=-1),(1,2,0))\n",
    "\n",
    "def compute_circular_convolution(image, filtr):\n",
    "    image_1 = tf.concat(2*[image], axis=1)\n",
    "    image_2 = tf.concat(2*[image_1], axis=2)\n",
    "    image_3 = tf.slice(image_2, [0,0,0,0], [-1, 2*n-1, 2*n-1, -1])\n",
    "    strides = [1, 1, 1, 1]\n",
    "    padding = 'VALID'\n",
    "    return tf.nn.conv2d(image_3, filtr, strides, padding)\n",
    "\n",
    "image = tf.placeholder(tf.float32, [1,n,n,p])\n",
    "filtr = tf.placeholder(tf.float32, [n,n,p,p])\n",
    "res = compute_circular_convolution(image, filtr)\n",
    "sess = tf.Session()\n",
    "\n",
    "def circular_convolution(np_image, np_filtr):\n",
    "    global image, filtr, sess, res\n",
    "    return sess.run(res, feed_dict={image: np.expand_dims(np_image, axis=0), filtr: np_filtr})[0]\n",
    "def compute_theta_star(weights, unary, theta):\n",
    "    q = softmax(-theta)\n",
    "    E = circular_convolution(q, weights)\n",
    "    return E + unary\n",
    "\n",
    "def new_theta(weights,unary,theta,d,clipping=None):\n",
    "    theta_star = compute_theta_star(weights,unary,theta)\n",
    "    if clipping is None:\n",
    "        return d*theta_star + (1-d)*theta\n",
    "    else:\n",
    "        return np.clip(d*theta_star + (1-d)*theta,clipping[0],clipping[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "w = np.zeros((n,n,p,p))\n",
    "u = np.zeros((n,n,p))\n",
    "\n",
    "for (x,y) in [(-1,0),(1,0),(0,1),(0,-1)]:\n",
    "    for a in [0,1]:\n",
    "        for b in [0,1]:\n",
    "            w[x,y,a,b] = -1 if a == b else 1\n",
    "\n",
    "dataset = []\n",
    "for T in np.linspace(0.4,4,num=30):\n",
    "    for _ in tqdm(range(25)):\n",
    "        theta = np.random.random([n,n,p])\n",
    "        for i in range(100):\n",
    "            q = softmax(-theta)\n",
    "            E = circular_convolution(q, w/T)\n",
    "            energy = np.sum((E+u/T)*q)\n",
    "            theta = new_theta(w/T,u/T,theta,0.5)\n",
    "\n",
    "    dataset.append((T,q[0,0,0],energy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(75000, 3)\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"693\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f3d050dda90>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "dataset = np.array(dataset)\n",
    "print(dataset.shape)\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "ax.scatter(dataset[:,0], dataset[:,1], -dataset[:,2]/dataset[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]\n",
      " [1 1 1 1 1 1 1 1 1 1]]\n",
      "[[ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]\n",
      " [ 0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502  0.502]]\n"
     ]
    }
   ],
   "source": [
    "print(sudoku.infer_grid(softmax(-theta)))\n",
    "print(sudoku.infer_grid_probabilities(softmax(-theta)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "example= np.array([[0,8,0, 0,0,9, 7,4,3],\n",
    "                   [0,5,0, 0,0,8, 0,1,0],\n",
    "                   [0,1,0, 0,0,0, 0,0,0],\n",
    "\n",
    "                   [8,0,0, 0,0,5, 0,0,0],\n",
    "                   [0,0,0, 8,0,4, 0,0,0],\n",
    "                   [0,0,0, 3,0,0, 0,0,6],\n",
    "\n",
    "                   [0,0,0, 0,0,0, 0,7,0],\n",
    "                   [0,3,0, 5,0,0, 0,8,0],\n",
    "                   [9,7,2, 4,0,0, 0,5,0]])\n",
    "\n",
    "example= np.array([[0,8,0, 0,0,9, 7,4,3],\n",
    "                   [0,5,0, 0,0,8, 0,1,9],\n",
    "                   [0,1,0, 0,0,0, 0,0,0],\n",
    "\n",
    "                   [8,0,0, 0,0,5, 0,0,0],\n",
    "                   [0,0,0, 8,0,4, 0,0,0],\n",
    "                   [0,0,0, 3,0,0, 0,0,6],\n",
    "\n",
    "                   [0,0,0, 0,0,0, 0,7,0],\n",
    "                   [0,3,0, 5,0,0, 0,8,0],\n",
    "                   [9,7,2, 4,0,0, 0,5,0]])\n",
    "\n",
    "example= np.array([[0,8,0, 0,0,9, 7,4,3],\n",
    "                   [0,5,0, 0,0,8, 0,1,9],\n",
    "                   [0,1,0, 0,0,0, 0,0,0],\n",
    "\n",
    "                   [8,0,0, 0,0,5, 0,0,0],\n",
    "                   [0,0,0, 8,0,4, 0,0,0],\n",
    "                   [7,0,0, 3,0,0, 0,0,6],\n",
    "\n",
    "                   [0,0,0, 0,0,0, 0,7,0],\n",
    "                   [0,3,0, 5,0,0, 0,8,0],\n",
    "                   [9,7,2, 4,0,0, 0,5,0]])\n",
    "\n",
    "example= np.array([[0,8,0, 0,0,9, 7,4,3],\n",
    "                   [0,5,0, 6,0,8, 0,1,9],\n",
    "                   [0,1,0, 0,0,0, 0,0,0],\n",
    "\n",
    "                   [8,0,0, 0,0,5, 0,0,0],\n",
    "                   [0,0,0, 8,0,4, 1,0,0],\n",
    "                   [7,0,0, 3,0,0, 0,0,6],\n",
    "\n",
    "                   [0,0,0, 2,0,0, 0,7,0],\n",
    "                   [0,3,0, 5,0,0, 0,8,0],\n",
    "                   [9,7,2, 4,0,0, 0,5,0]])\n",
    "oexample= np.array([[1,2, 3,4],\n",
    "                   [3,4, 2,1],\n",
    "                  \n",
    "                   [4,3, 0,0],\n",
    "                   [2,1, 0,0]])\n",
    "\n",
    "example = sudoku.to_prob(example,p)\n",
    "\n",
    "# = open('_failure_mul.pkl','rb')\n",
    "# = pickle.load(f)\n",
    "#xample = x[0]\n",
    "\n",
    "clip = sudoku.grid_to_clip(sudoku.expand_matrix([example],g,p))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "w_true = np.zeros_like(w)\n",
    "u_true = np.zeros_like(u)\n",
    "\n",
    "for x in range(3*3*2):\n",
    "    for i in range(10):\n",
    "        for j in range(10):\n",
    "            if i != 0 and j != 0:\n",
    "                if i != j:\n",
    "                    w_true[0,x,i,j] = -1\n",
    "                    w_true[x,0,i,j] = -1\n",
    "                else:\n",
    "                    w_true[0,x,i,j] = 1\n",
    "                    w_true[x,0,i,j] = 1\n",
    "for x in range(-2,3):\n",
    "    for y in range(-2,3):\n",
    "        if x != 0 and y != 0:\n",
    "            for i in range(10):\n",
    "                for j in range(10):\n",
    "                    if i != 0 and j != 0:\n",
    "                        if i != j:\n",
    "                            w_true[x,y,i,j] = -1\n",
    "                            w_true[x,y,i,j] = -1\n",
    "                        else:\n",
    "                            w_true[x,y,i,j] = 1\n",
    "                            w_true[x,y,i,j] = 1\n",
    "\n",
    "       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "theta = np.random.random([n,n,p])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 15000/15000 [00:42<00:00, 354.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18, 18, 10)\n",
      "[[2 8 8 4 4 9 7 4 3]\n",
      " [2 5 4 6 4 8 2 1 9]\n",
      " [1 1 9 2 4 2 2 1 9]\n",
      " [8 2 1 9 2 5 1 2 4]\n",
      " [5 2 1 8 2 4 1 9 5]\n",
      " [7 1 1 3 2 4 5 9 6]\n",
      " [5 3 8 2 8 2 4 7 1]\n",
      " [1 3 2 5 5 2 4 8 2]\n",
      " [9 7 2 4 8 4 4 5 2]]\n",
      "False\n",
      "[[ 0.938  1.     0.715  0.395  0.486  1.     1.     1.     1.   ]\n",
      " [ 0.491  1.     0.457  1.     0.834  1.     0.684  1.     1.   ]\n",
      " [ 0.655  1.     0.467  0.629  0.841  0.817  0.734  0.363  0.626]\n",
      " [ 1.     0.5    0.736  0.45   0.801  1.     0.395  0.888  0.602]\n",
      " [ 0.685  0.666  0.489  1.     0.679  1.     1.     0.513  0.448]\n",
      " [ 1.     0.374  0.78   1.     0.927  0.712  0.583  0.786  1.   ]\n",
      " [ 0.477  0.497  0.505  1.     0.602  0.467  0.738  1.     0.744]\n",
      " [ 0.815  1.     0.606  1.     0.425  0.57   0.745  1.     0.713]\n",
      " [ 1.     1.     1.     1.     0.713  0.372  0.556  1.     0.533]]\n"
     ]
    },
    {
     "data": {
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AAKAZLLcAAABoBsstAAAAmsFyCwAAgGYs6nKbeDnX4rm1ZJbKXcqZJXNrU8vX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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cc8187d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8PAZ7lK3/+cw8dqSNigR/tCJiVelz/MdDj/HwGOwxNup7qC9VyOBLFRorwR/EL/2Mhx7j\n4THYYwzUHxOv8SUN1ljZ40saIIMvVcjgSxUy+FKFDL5Uof8GHB5atXboAgQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1cc0f12110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = 1.\n",
    "n_it = 15000\n",
    "hist = np.zeros([n_it])\n",
    "\n",
    "for i in tqdm(range(n_it)):\n",
    "    q = softmax(-theta)\n",
    "    E = circular_convolution(q, w/T)\n",
    "    hist[i] = np.sum((E+u/T)*q)\n",
    "    theta = new_theta(w/T,u/T,theta,0.5,clip[0])\n",
    "print(theta.shape)\n",
    "q = sudoku.reduce_matrix(softmax(-theta),g,p)\n",
    "fig = plt.figure(figsize=(12,12))\n",
    "for i in range(1,10):\n",
    "    plot = fig.add_subplot(3,3,i)\n",
    "    plot.matshow(q[:,:,i], cmap=cm.Greys)\n",
    "plt.matshow(sudoku.infer_grid_probabilities(q), cmap=cm.Greys)\n",
    "print(sudoku.infer_grid(q))\n",
    "print(sudoku.is_correct(sudoku.infer_grid(q),g))\n",
    "print(sudoku.infer_grid_probabilities(q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f1c841ef090>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9h0o4VHKEhrVrpPV1o4nWH3G49Ah/emdFhfIq2iTO1EXrQzqLq+iv6Yvte7w57zihNY8U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\ndA7P97uUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1ca478ba50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(n_it), hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}