gradient_descent.ipynb

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    "# Gradient descent\n",
    "\n",
    "Gradient descent is a method of finding a minimum of a function.\n",
    "It does so by \"moving\" in direction of descent specified by a derivative in the given point.\n",
    "\n",
    "Given a start point the method iteratively searches for the point that corresponds to the minimum of the function, hopefully global.\n",
    "On every iteration, we check for the gradient of the function on the current step and comupute what next point  should we check to be the minimum of the function.\n",
    "\n",
    "$$\n",
    "    x_{\\mathrm{next}} = x - \\alpha f'(x) \n",
    "$$\n",
    "\n",
    "**Sloppy explanation** Given a point $x$, we check the value of the derivative of the function $f$ in point $x$. This gives us a direction to choose next point $x_\\mathrm{next}$ such that we expect $f(x_\\mathrm{next})$ to be smaller. We choose $x_\\mathrm{next}$ based on where we are right now $x$, direction and magnitude of descent $f'(x)$ and a step $\\alpha$.\n",
    "\n",
    "$\\alpha$ parameter regulates the size of the step.\n",
    "We multiple alpha by the value of the derivative in the given point.\n",
    "The derivative in a point tells us how steep in the curve at the point.\n",
    "\n",
    "**Question**: Why do we need $\\alpha$?\n",
    "\n",
    "It seems that without $\\alpha$ the value of the gradient is too big and we overshoot the minimum and diverge :confused:\n",
    "\n",
    "**Question**: Why can't we just set alpha and only use the sign of the derivative? Since we just need to know in which direction to move?\n",
    "\n",
    "In this case, we succeded only if we get the step correctly. In the example below, the only value for $\\alpha$ that works in this case is $\\alpha = 0.5$, since we start at $-2.0$. In all other cases, how ever small they are we are most likely to never reach the minimum.\n",
    "\n",
    "**Question**: Why should $\\alpha \\in (0,1)$?\n",
    "\n",
    "If $\\alpha >= 1$ the process diverges (in case of symmetric quadratic function) and we can't reach minimum ever."
   ]
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    "### Simple 1D example\n",
    "\n",
    "In this example, we select $f = x^2$ and the respective jacobian is $J=f'(x) = 2x$.\n",
    "$$\n",
    "    x_{\\mathrm{next}} = x - \\alpha * 2x \n",
    "$$\n",
    "\n",
    "\n",
    "The piece of code below checks different setups for the computation of the step:\n",
    "* 1) Just using the sign of the derivative and skipping the magnitude + the parameter $\\alpha$, also called the **learning rate**.\n",
    "    * Works, if $\\alpha$ is chosen correctly. The step is not adaptable. Can stuck bumping from one side of  the curve to another \n",
    "* 2) Using the value of the derivative + $\\alpha$.\n",
    "    * Works.\n",
    "* 3) Using the value of the derivative only.\n",
    "    * Step is too big. Always overshoots. At least in this example.\n",
    "    \n"
   ]
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   "cell_type": "code",
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    "import numpy as np\n",
    "\n",
    "def f_1D(x):\n",
    "    return np.power(x,2);\n",
    "\n",
    "def jacobian(x):\n",
    "    ## jacobians for x^2\n",
    "    return 2*x;\n",
    "\n",
    "alpha = 0.1;\n",
    "error = 1000;\n",
    "x_prev = -2.0;\n",
    "opt_way = []\n",
    "numOfIter =0;\n",
    "while error >1e-05: \n",
    "    # 1) just using sign of the derivative\n",
    "#     x_opt = x_prev - alpha *np.sign(jacobian(x_prev)); \n",
    "    # 2)using value of the derivative and step\n",
    "    x_opt = x_prev - alpha *jacobian(x_prev); \n",
    "    # 3) just using derivatives have huge steps that overshoot minimum\n",
    "#     x_opt = x_prev - jacobian(x_prev);\n",
    "    print(\"Step is: \",jacobian(x_prev))\n",
    "    error  = abs(x_prev -x_opt);\n",
    "    x_prev = x_opt;\n",
    "    opt_way.append(x_opt)\n",
    "    numOfIter+=1;\n",
    "print(\"Converged with \", numOfIter, \"iterations\\n\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.arange(-2.0,2.0,0.1)\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(x,f_1D(x))\n",
    "y = np.zeros((len(opt_way),1))\n",
    "plt.plot(opt_way,y, 'r*')\n",
    "plt.plot(opt_way, f_1D(opt_way), 'g*-')\n",
    "\n",
    "plt.show()"
   ]
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   "source": [
    "## Two dimensional case\n",
    "\n",
    "Just checking here that the same thoughts apply to the 2D case. For the 2D case, the formulas stay the same, but now we are going to work with vectors\n",
    "\n",
    "$$\n",
    "    p_{\\mathrm{next}} = p - \\alpha f'(p) \n",
    "$$\n",
    "\n",
    "where $p$ is a 2D point consisting of $(x,y)$ coordinate. In the example below, we will look at the function $f = x^2 + y^2$, so the derivative will have a different form.\n",
    "$$\n",
    "    \\begin{bmatrix}\n",
    "    x \\\\y\n",
    "    \\end{bmatrix}_{\\mathrm{next}} =  \\begin{bmatrix} x \\\\y \\end{bmatrix} - \\alpha \\begin{bmatrix} 2x \\\\2y \\end{bmatrix}\n",
    "$$"
   ]
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   "cell_type": "code",
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   "source": [
    "# Function: x^2 + y^2\n",
    "def f(x,y):\n",
    "    return np.square(x) + np.square(y);\n",
    "\n",
    "def jacobian(x,y):\n",
    "    return np.array([2*x, 2*y])\n",
    "\n",
    "\n",
    "\n",
    "alpha = 0.1;\n",
    "error = 1000;\n",
    "x_prev = [-4.0, 4.0];\n",
    "opt_way = []\n",
    "opt_way.append(x_prev)\n",
    "numOfIter =0;\n",
    "while error >1e-05:\n",
    "#     print(\"Step is: \", alpha *jacobian(x_prev))\n",
    "#     x_opt = x_prev - alpha *np.sign(jacobian(x_prev)); # just using sign of the derivative\n",
    "    x_opt = x_prev - alpha *jacobian(x_prev[0], x_prev[1]); # using value of the derivative\n",
    "    error  = np.linalg.norm(x_prev -x_opt);\n",
    "    x_prev = x_opt;\n",
    "    opt_way.append(x_opt)\n",
    "    numOfIter+=1;\n",
    "print(\"Converged with \", numOfIter, \"iterations\\n\")\n",
    "opt_way = np.array(opt_way)\n",
    "# print(opt_way)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "%matplotlib notebook\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm \n",
    "\n",
    "fig = plt.figure(2,figsize=(8,6))\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "x, y = np.meshgrid(np.linspace(-4.0, 4.0, 20), np.linspace(-4.0, 4.0, 20))\n",
    "ax.plot_surface(x, y, f(x,y), cmap=cm.coolwarm, alpha=0.5)\n",
    "ax.plot(opt_way[:,0], opt_way[:,1], f(opt_way[:,0], opt_way[:,1]), '*-',color='#495165')\n",
    "\n",
    "plt.show()"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## Gradient descent for linear regression task\n",
    "\n",
    "Linear regression is a task of finding/learning the parameters of the line that fits the given set of points the best.\n",
    "\n",
    "\n",
    "The line is defined as $y = mx+b$. \n",
    "The parameters we want to find/learn are: $m$ and $b$.\n",
    "We are given a set of 2D points $(x_i, y_i)$. Here the natural question arises ...\n",
    "\n",
    "**What does this has to do with gradient descent???**\n",
    "\n",
    "In this task, what we want to optimize is the \"function\" ($f$) of **distances from the points to the line**, such that our line fits the points.\n",
    "So, in this case we don't have an explicit definition of $f$ through $(x,y)$ but rather through all pairs $(x_i, y_i)$. Also, we want to find the parameters of the line or such a 2D point $(m,b)$ that minimizes the error function $f$, which in this case is defined through bunch of points as\n",
    "\n",
    "$$f = \\sum (y_i - (mx_i+b))^2$$\n",
    "and the respective gradients are:\n",
    "$$f'_m = 2\\sum (y_i - (mx_i+b))* (-x_i)$$\n",
    "$$f'_b = 2\\sum (y_i - (mx_i+b))* (-1)$$\n",
    "\n",
    "So, applying the formulas for 2D gradient descent we can find the *next* values for $(m,b)$ that would lead us closer to the minimum of function $f$.\n",
    "\n",
    "$$\n",
    "    \\begin{bmatrix}\n",
    "    m \\\\b\n",
    "    \\end{bmatrix}_{\\mathrm{next}} =  \\begin{bmatrix} m \\\\b \\end{bmatrix} - \\alpha \\begin{bmatrix} 2\\sum (y_i - (mx_i+b))* (-x_i) \\\\ 2\\sum (y_i - (mx_i+b))* (-1) \\end{bmatrix}\n",
    "$$\n",
    "Let's check a synthetic example to see if the above 2D gradient definition gives us what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def errorFunction_F(x, y, m, b):\n",
    "    # Error function. This function will be used for plotting only   \n",
    "    y_exp = m * x + b;\n",
    "    value = np.sum(np.square(y - y_exp))\n",
    "    return value\n",
    "\n",
    "def Jacobian(x, y, m, b):\n",
    "    y_exp = m * x + b;\n",
    "    J_m = 2 * np.sum(np.multiply(y - y_exp, -x));\n",
    "    J_b = -2 * np.sum(y - y_exp);\n",
    "    return np.array([J_m, J_b])\n",
    "\n",
    "\n",
    "def optimizeGradientDescent(p_prev, p_x, p_y, alpha=0.1):\n",
    "    error_delta = 1000; # difference between the values of the error function\n",
    "    opt_way = [];\n",
    "    opt_way.append(p_prev)\n",
    "    numOfIter = 0;\n",
    "    while (error_delta >1e-04):\n",
    "        step =  -alpha *Jacobian(p_x, p_y, p_prev[0], p_prev[1])\n",
    "        # alternative checking with just considering the sign of the derivative. Works, but requires \n",
    "        # tooo many iterations\n",
    "        # step = - alpha *np.sign(Jacobian(p_x, p_y, p_prev[0], p_prev[1])); \n",
    "        p_opt = p_prev + step;\n",
    "        \n",
    "        error_before = errorFunction_F(p_x, p_y, p_prev[0], p_prev[1])\n",
    "        error_after = errorFunction_F(p_x, p_y, p_opt[0], p_opt[1])\n",
    "        error_delta = np.fabs(error_after - error_before)\n",
    "\n",
    "        p_prev = p_opt;\n",
    "        opt_way.append(p_opt)\n",
    "        \n",
    "#         print(\"Params delta\", params_delta, \" Error delta \", error_delta)\n",
    "        numOfIter+=1;\n",
    "\n",
    "    print(\"Gradient descent converged with \", numOfIter, \"iterations\\n\")\n",
    "    opt_way = np.array(opt_way)\n",
    "    return opt_way\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected F value is 2, actual value is  2\n",
      "Expected J_m value is 6, actual value is  6\n",
      "Expected J_b value is 4, actual value is  4\n"
     ]
    }
   ],
   "source": [
    "# Small unit tests to check the Jacobians\n",
    "x = np.array([1,2])\n",
    "y = np.array([1,2])\n",
    "m = 1\n",
    "b = 1\n",
    "\n",
    "v = errorFunction_F(x, y, m, b)\n",
    "print(\"Expected F value is 2, actual value is \", v)\n",
    "[J_m, J_b] = Jacobian(x, y, m, b)\n",
    "print(\"Expected J_m value is 6, actual value is \", J_m)\n",
    "print(\"Expected J_b value is 4, actual value is \", J_b)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "## function for plotting the results\n",
    "%matplotlib notebook\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plotErrorConvergence(axis, x, y, opt_way):\n",
    "    ## plotting whole error function F\n",
    "    border = 5\n",
    "    m, b = np.meshgrid(np.linspace(np.min(-border), np.max(border), 20), np.linspace(np.min(-border),np.max(border), 20))\n",
    "    F_values  = np.zeros(m.shape)\n",
    "    for i in range(m.shape[0]):\n",
    "        for j in range(m.shape[1]):   \n",
    "            F_values[i][j] = errorFunction_F(x, y, m[i][j], b[i][j])\n",
    "    \n",
    "    axis.plot_surface(m, b, F_values, cmap=cm.coolwarm, alpha=0.5)\n",
    "\n",
    "    ## plotting optimization steps\n",
    "    opt_F_values = np.zeros(opt_way.shape[0])\n",
    "    for i in range(opt_way.shape[0]):\n",
    "        opt_F_values[i] = errorFunction_F(x, y, opt_way[i,0], opt_way[i,1])\n",
    "\n",
    "    axis.plot(opt_way[:,0], opt_way[:,1], opt_F_values, '*-',color='#495165')\n",
    "    plt.xlabel('m')\n",
    "    plt.ylabel('b')\n",
    "    return\n",
    "\n",
    "def plotFitting(axis, m, b, x, y):\n",
    "    axis.plot(x, y, '.', color='#495165') # plotting data points\n",
    "    x_plot = np.linspace(np.min(p_x), np.max(p_x), 20)\n",
    "    y_plot = m * x_plot + b;\n",
    "    axis.plot(x_plot, y_plot, '-', color='#ff336b')\n",
    "    return\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient descent converged with  1239 iterations\n",
      "\n",
      "[[-4.         -4.        ]\n",
      " [-0.48119277 -3.33350152]\n",
      " [ 0.8046048  -3.08414302]\n",
      " [ 1.27378159 -2.98735005]\n",
      " [ 1.44431861 -2.94636627]]\n"
     ]
    },
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       "            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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"799.3333333333334\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# # ## first let's generate some data \n",
    "m_gt = 1 # m - ground truth\n",
    "b_gt = 0 # b - ground truth\n",
    "sigma = 0.3 # gaussian noise\n",
    "\n",
    "p_x = np.arange(2, 8, 0.5)\n",
    "p_y = m_gt* p_x + b_gt + sigma * np.random.randn(p_x.shape[0])\n",
    "\n",
    "# # some synthetic data\n",
    "# p_x = np.array([2, 2.3, 3.1, 5, 5, 6.8, 7.1])\n",
    "# p_y = np.array([2, 4.2, 3,  2, 4.5, 4., 6])\n",
    "\n",
    "\n",
    "# let's check that generated data\n",
    "# initial start point for (m_0, b_0)\n",
    "p_prev = np.array([-4,-4])\n",
    "opt_way = optimizeGradientDescent(p_prev, p_x, p_y, alpha = 0.001)\n",
    "print(opt_way[0:5, 0:5])\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax_left = plt.subplot(1, 2, 1)\n",
    "ax_right = plt.subplot(122, projection='3d')\n",
    "plotErrorConvergence(ax_right, p_x, p_y, opt_way);\n",
    "ax_right.title.set_text(\"Error function\")\n",
    "\n",
    "plotFitting(ax_left, opt_way[-1,0], opt_way[-1,1], p_x, p_y);\n",
    "ax_left.title.set_text(\"Fitting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line fitting using Newton's method\n",
    "Here I am going to search for the minimum using Newton's method. It is an \"upgrade\" for the Gradient Descent that focuses on faster convergence around minimum.\n",
    "In the example, above we have a lot of points in the value of the error function, we want to reduce the number of points there. Let's see if this is going to happen!\n",
    "\n",
    "Newton's method proposes to take into account the curvature, hence Hessian $H$:\n",
    "$$H = \n",
    "\\begin{pmatrix}\n",
    "f''_{mm}  & f''_{mb} \\\\\n",
    "f''_{bm} & f''_{bb}\n",
    "\\end{pmatrix} = \n",
    "\\begin{pmatrix}\n",
    "    \\sum 2x_i^2 & \\sum 2x_i \\\\\n",
    "    \\sum 2x_i & \\sum 2 \n",
    "\\end{pmatrix} = 2\n",
    "\\begin{pmatrix}\n",
    "    \\sum x_i^2 & \\sum x_i \\\\\n",
    "    \\sum x_i & n \n",
    "\\end{pmatrix}\n",
    "$$\n",
    "where $x_i$ are the given points.\n",
    "\n",
    "Now, the step amount is computed differently and in comtrast to Gradient descent that required only Jacobian, here we compute a step as solution of the following system:\n",
    "\n",
    "$$\n",
    "    Hx = -J\n",
    "$$\n",
    "where $x$ is the searched value of step on every iteration. Just by looking at the math, we can say that Newton's method require more advanced computations than gradient descent method.\n",
    "\n",
    "For a little bit more math, please check this notebook [Least squares](https://nbviewer.jupyter.org/github/niosus/notebooks/blob/master/least_squares.ipynb) from [niosus](https://github.com/niosus)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Newton's converged with  2 iterations\n",
      "\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",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"799.3333333333334\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Hessian(x):\n",
    "    n = x.shape[0];\n",
    "    H = np.zeros((2,2));\n",
    "    H[0][0] = np.sum(np.power(x,2));\n",
    "    H[0][1] = np.sum(x);\n",
    "    H[1][0] = H[0][1];\n",
    "    H[1][1] = n\n",
    "    return 2*H;\n",
    "\n",
    "\n",
    "def optimizeNewton(p_prev, p_x, p_y, alpha):\n",
    "    error_delta = 1000;\n",
    "    opt_way = [];\n",
    "    opt_way.append(p_prev)\n",
    "    numOfIter = 0;\n",
    "\n",
    "    while (error_delta >1e-04):\n",
    "        J = Jacobian(p_x, p_y, p_prev[0], p_prev[1]);\n",
    "        H = Hessian(p_x);\n",
    "        # finding step by solving Hh = -J\n",
    "        step = np.linalg.solve(H,-J)\n",
    "        p_opt = p_prev + step; \n",
    "       \n",
    "        error_before = errorFunction_F(p_x, p_y, p_prev[0], p_prev[1])\n",
    "        error_after = errorFunction_F(p_x, p_y, p_opt[0], p_opt[1])\n",
    "        error_delta = np.fabs(error_after - error_before)\n",
    "\n",
    "        p_prev = p_opt;\n",
    "        opt_way.append(p_opt)\n",
    "        numOfIter+=1;\n",
    "\n",
    "    print(\"Newton's converged with \", numOfIter, \"iterations\\n\")\n",
    "    opt_way = np.array(opt_way)\n",
    "    return opt_way\n",
    "\n",
    "p_prev = np.array([-4,-4])\n",
    "alpha = 0.005\n",
    "opt_newton = optimizeNewton(p_prev, p_x, p_y, alpha)\n",
    "\n",
    "fig = plt.figure(figsize=(8,4))\n",
    "ax_left = plt.subplot(1, 2, 1)\n",
    "ax_right = plt.subplot(122, projection='3d')\n",
    "plotErrorConvergence(ax_right, p_x, p_y, opt_newton);\n",
    "ax_right.title.set_text(\"Error function\")\n",
    "\n",
    "plotFitting(ax_left, opt_newton[-1,0], opt_newton[-1,1], p_x, p_y);\n",
    "ax_left.title.set_text(\"Fitting\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient descent vs Newton\n",
    "Here we compare the results of Gradient Descent and Newton's method for the case of fitting a 2D line into the set of given points\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient descent converged with  1207 iterations\n",
      "\n",
      "Newton's converged with  2 iterations\n",
      "\n",
      "Gradient descent parameters for m and b [ 1.02828109 -0.05871209]\n",
      "Newton's parameters for m and b [1.00686901 0.05591663]\n"
     ]
    }
   ],
   "source": [
    "# different data to check \n",
    "m_gt = 1 # m - ground truth\n",
    "b_gt = 0 # b - ground truth\n",
    "sigma = 0.5 # gaussian noise\n",
    "alpha = 0.001\n",
    "\n",
    "p_x = np.arange(2, 8, 0.5)\n",
    "p_y = m_gt* p_x + b_gt + sigma * np.random.randn(p_x.shape[0])\n",
    "\n",
    "start_params = np.array([-4,-4]); # setting start values for m and b\n",
    "opt_gradient = optimizeGradientDescent(start_params, p_x, p_y, alpha);\n",
    "opt_newton = optimizeNewton(start_params, p_x, p_y, alpha);\n",
    "print(\"Gradient descent parameters for m and b\", opt_gradient[-1,:])\n",
    "print(\"Newton's parameters for m and b\", opt_newton[-1,:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "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",
       "        fig.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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
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\" width=\"999.1666666666667\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "ax_left = plt.subplot(131)\n",
    "ax_middle = plt.subplot(132, projection='3d')\n",
    "ax_right = plt.subplot(133, projection='3d')\n",
    "\n",
    "plotErrorConvergence(ax_middle, p_x, p_y, opt_gradient);\n",
    "ax_middle.title.set_text(\"Gradient descent\")\n",
    "\n",
    "plotErrorConvergence(ax_right, p_x, p_y, opt_newton);\n",
    "ax_right.title.set_text(\"Newton's\")\n",
    "\n",
    "plotFitting(ax_left, opt_gradient[-1,0], opt_gradient[-1,1], p_x, p_y);\n",
    "x_plot = np.linspace(np.min(p_x), np.max(p_x), 20)\n",
    "y_plot = opt_newton[-1,0] * x_plot + opt_newton[-1,1];\n",
    "ax_left.plot(x_plot, y_plot, '-', color='#336bff')\n",
    "ax_left.title.set_text(\"Fitting\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic gradient descent and mini-batch gradient descent\n",
    "\n",
    "My understanding of the stochastic gradient descent is that instead of using all the points for every iteration step, our gradient descent uses just one point to find the next estimate towards the minimum.\n",
    "Thus, the regression task can be written as\n",
    "\n",
    "$$\n",
    "    \\begin{bmatrix}\n",
    "    m \\\\b\n",
    "    \\end{bmatrix}_{\\mathrm{next}} =  \\begin{bmatrix} m \\\\b \\end{bmatrix} - \\alpha \\begin{bmatrix} 2(y_i - (mx_i+b))* (-x_i) \\\\ 2(y_i - (mx_i+b))* (-1) \\end{bmatrix}\n",
    "$$\n",
    "\n",
    "TODO: continue here\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary\n",
    "It seems that Newton's method converges faster for this case than gradient descent method.\n",
    "Newton's method, however, involves computing explicitly second partial derivatives, i.e. Hessian.\n",
    "This can be computationally demanding if we have a lot of parameters to minimize for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
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 "nbformat_minor": 2
}