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    "# <img src=\"https://img.icons8.com/dusk/64/000000/mind-map.png\" style=\"height:50px;display:inline\"> CS 236756 - Technion - Intro to Machine Learning\n",
    "---\n",
    "#### Tal Daniel\n",
    "\n",
    "## Tutorial 07 - Optimization\n",
    "---"
   ]
  },
  {
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   "source": [
    "### <img src=\"https://img.icons8.com/bubbles/50/000000/checklist.png\" style=\"height:50px;display:inline\"> Agenda\n",
    "---\n",
    "* [Optimization Problems](#-Optimization-Problems)\n",
    "* [1-Dimensional Optimization](#-1-D-Optimization)\n",
    "    * [Gradient Descent](#(Batch)-Gradient-Descent)\n",
    "    * [Least-Squares](#-Example---Linear-Least-Squares)\n",
    "    * [Stochastic Gradient Descent (SGD)](#Stochastic-Gradient-Descent-(Mini-Batch-Gradient-Descent))\n",
    "* [Mathematical Background](#-Mathematical-Background)\n",
    "    * [Gradient](#-Multivariate-Calculus)\n",
    "    * [Chain Rule](#-The-Chain-Rule)\n",
    "    * [Multi-dimensional Calculus](#--Multi-Dimensional-Optimization)"
   ]
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   },
   "source": [
    "* [Multi-dimensional Optimization](#--Multi-Dimensional-Optimization)\n",
    "* [Constrained Optimization](#-Constrained-Optimization)\n",
    "    * [Largrange Multipliers](#-Largrange-Multipliers)\n",
    "    * [Examples: Entropy](#-Exercise-2---Max-Entropy-Distribution)\n",
    "* [Recommended Videos](#-Recommended-Videos)\n",
    "* [Credits](#-Credits)"
   ]
  },
  {
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   "source": [
    "# imports for the tutorial\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib notebook"
   ]
  },
  {
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   "source": [
    "## <img src=\"https://img.icons8.com/dusk/64/000000/ask-question.png\" style=\"height:50px;display:inline\"> Optimization Problems\n",
    "---\n",
    "### <img src=\"https://img.icons8.com/dusk/64/000000/define-location.png\" style=\"height:50px;display:inline\"> Definitions\n",
    "\n",
    "* **Objective Function** - mathematical function which is optimized by changing the values of the design variables.\n",
    "* **Design Variables** - variables that we, as designers, can change.\n",
    "* **Constraints** - functions of the design variables which establish limits in individual variables or combinations of design.\n",
    "    * For example - \"Find $\\theta$ that minimizes $f_{\\theta}(x)$ s.t. (subject to) $\\theta \\leq 1$\"\n",
    "    \n",
    "**The main problem in optimization** is *how* to search for the values of decision variables that minimize the cost/objective function."
   ]
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   "source": [
    "#### Types of Objective Functions\n",
    "---\n",
    "* **Unimodal** - only **one** optimum, that is, the *local* optimum is also global.\n",
    "<img src=\"./assets/tut_06_unimodal.jpg\" style=\"height:200px\">"
   ]
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   "source": [
    "* **Multimodal** - more than one optimum\n",
    "<img src=\"./assets/tut_06_multimodal.jpg\" style=\"height:200px\">\n",
    "\n",
    "Most search schemes are based on the assumption of **unimodal** surface. The optimum determined in such cases is called **local optimum design**.\n",
    "\n",
    "The **global optimum** is the best of all *local optimum* designs."
   ]
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   "source": [
    "### <img src=\"https://img.icons8.com/office/80/000000/statistics.png\" style=\"height:50px;display:inline\"> Convexity\n",
    "---\n",
    "* **Definition**: $$\\forall x_1, x_2 \\in X, \\forall t \\in [0,1]: $$ $$ f(tx_1 + (1-t)x_2) \\leq tf(x_1) + (1-t)f(x_2) $$\n",
    "<img src=\"./assets/tut_06_convex_1.jpg\" style=\"height:200px\">"
   ]
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   "metadata": {
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    }
   },
   "source": [
    "<img src=\"./assets/tut_06_convex_concave.gif\" style=\"height:200px\">\n",
    "<a href=\"http://mathworld.wolfram.com/Convex.html\">Image Source</a>"
   ]
  },
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   "source": [
    "* Convex functions are **unimodal**\n",
    "<img src=\"./assets/tut_06_convex_2.jpg\" style=\"height:200px\">"
   ]
  },
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   "source": [
    "### <img src=\"https://img.icons8.com/color/96/000000/tenses.png\" style=\"height:50px;display:inline\"> Continuous Optimization\n",
    "---\n",
    "* Derivative based optimization - the search directions are determined based on deivative information\n",
    "* Methods include:\n",
    "    * **Gradient Descent**\n",
    "    * Newton method\n",
    "    * Conjugate gradient\n",
    "* We will focus on *Gradient Descent*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## <img src=\"https://img.icons8.com/ultraviolet/80/000000/sine.png\" style=\"height:50px;display:inline\"> 1-D Optimization\n",
    "---\n",
    "### <img src=\"https://img.icons8.com/dusk/64/000000/treasure-map.png\" style=\"height:50px;display:inline\">(Batch) Gradient Descent\n",
    "---\n",
    "* Generic optimization algorithm capable of finding optimal solutions to a wide range of problems.\n",
    "* The general idea is to tweak parameters **iteratively** to minimize a cost function.\n",
    "* It measures the local gradient of the error function with regards to the parameter vector ($\\theta$ or $w$), and it goes down in the direction of the descending gradient. Once the gradient is zero - the minimum is reached (=convergence)."
   ]
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    "* **Learning Rate** hyperparameter - it is the size of step to be taken in each iteration.\n",
    "    * Too *small* $\\rightarrow$ the algorithm will have to go through many iterations to converge, which will take a long time\n",
    "    * Too *high* $\\rightarrow$ might make the algorithm diverge as it may miss the minimum\n",
    "    * <img src=\"./assets/tut_06_lr.png\" style=\"height:250px\"> <a href=\"https://www.jeremyjordan.me/nn-learning-rate/\">Image Source</a>"
   ]
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   "source": [
    "* **Pseudocode**:\n",
    "    * **Require**: Learning rate $\\alpha_k$\n",
    "    * **Require**: Initial parameter $w$\n",
    "    * **While** stopping criterion not met **do**\n",
    "        * Compute gradient: $g \\leftarrow f'(x,w)$ (more specifically, for $M$ samples: $g \\leftarrow \\frac{1}{M}\\sum_{i=1}^M f'(x_i,w)$, where $f'$ is w.r.t $\\theta$ or $w$)\n",
    "        * Apply update: $w \\leftarrow w - \\alpha_k g$\n",
    "        * $k \\leftarrow k + 1$\n",
    "    * **end while**"
   ]
  },
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   },
   "source": [
    "* **Visualization**:\n",
    "<img src=\"./assets/tut_06_gd.gif\">"
   ]
  },
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   "metadata": {
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    }
   },
   "source": [
    "<img src=\"./assets/tut_06_minimum.jpg\" style=\"height:200px\">\n",
    "\n",
    "* **Convergene**: When the cost function is *convex* and its slope does not change abruptly, (Batch) GD with a *fixed* learning rate will eventually converge to the optimal solution (but the time is depndent on the rate)."
   ]
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   "source": [
    "### <img src=\"https://img.icons8.com/dusk/64/000000/classroom.png\" style=\"height:50px;display:inline\"> Example - Linear Least Squares\n",
    "---\n",
    "* **Problem Formulation**\n",
    "    * $y \\in \\mathbb{R}^N$ - vector of values\n",
    "    * $X \\in \\mathbb{R}^N$ - data matrix with *one feature* (= data *vector*)\n",
    "    * $w \\in \\mathbb{R}$ - the *parameter* to be learnt\n",
    "* **Goal**: find $w$ that best fits the measurement y\n",
    "* Mathematically:\n",
    "$$\\min_w f(w;x,y) = \\min_w \\sum_{i=1}^N(wx_i-y_i)^2 $$\n",
    "* In vector form:\n",
    "$$\\min_w f(w;x,y) = \\min_w ||wX - Y||_2^2 $$"
   ]
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   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\">   LLS - Analytical Solution\n",
    "---\n",
    "* Mathematically:\n",
    "$$\\min_w f(w;x,y) = \\min_w \\sum_{i=1}^N(wx_i-y_i)^2 = \\min_w \\sum_{i=1}^N w^2x_i^2 -2wx_iy_i + y_i^2$$\n",
    "* The derivative: \n",
    "$$\\sum_{i=1}^N 2wx_i^2 - 2x_iy_i = 0 \\rightarrow w = \\frac{\\sum_{i=1}^N y_i x_i}{\\sum_{i=1}^N x_i^2}$$\n",
    "* The second derivative, to ensure minimum:\n",
    "$$f''(w;x,y) =  \\sum_{i=1}^N 2x_i^2 > 0 \\rightarrow good! $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "outputs": [],
   "source": [
    "# generate some random data\n",
    "N = 20\n",
    "x = np.linspace(1, 20, N)\n",
    "y = 5 * x + np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best w: 4.973943975346493\n"
     ]
    }
   ],
   "source": [
    "# lls - analytical solution\n",
    "# we want to find w that minimizes (wx-y)^2\n",
    "# by the above derivation\n",
    "w = np.sum(y * x) / np.sum(np.square(x))\n",
    "print(\"best w:\", w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "outputs": [],
   "source": [
    "def plot_lls_sol(x, y, w, title=\"\"):\n",
    "    fig = plt.figure(figsize=(8,5))\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    ax.scatter(x, y, label=\"y\")\n",
    "    ax.plot(x, w * x, label=\"wx\", color='r')\n",
    "    ax.legend()\n",
    "    ax.grid()\n",
    "    ax.set_xlabel(\"x\")\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot\n",
    "plot_lls_sol(x, y, w, \"Analytical Solution for min(wx-y)^2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\">   LLS - Gradient Descent Solution\n",
    "* **Pseudocode**:\n",
    "    * **Require**: Learning rate $\\alpha_k$\n",
    "    * **Require**: Initial parameter $w$\n",
    "    * **While** stopping criterion not met **do**\n",
    "        * Compute gradient: $g \\leftarrow \\frac{1}{N}\\sum_{i=1}^N 2wx_i^2 -2x_iy_i$\n",
    "        * Apply update: $w \\leftarrow w - \\alpha_k g$\n",
    "        * $k \\leftarrow k + 1$\n",
    "    * **end while**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter: 0  w =  0\n",
      "iter: 1  w =  7.1376096046222175\n",
      "iter: 2  w =  4.032749426611554\n",
      "iter: 3  w =  5.383363604046192\n",
      "iter: 4  w =  4.795846436862124\n",
      "iter: 5  w =  5.051416404587194\n",
      "iter: 6  w =  4.940243468626789\n",
      "iter: 7  w =  4.988603695769565\n",
      "iter: 8  w =  4.967566996962457\n",
      "iter: 9  w =  4.9767179609435495\n",
      "iter: 10  w =  4.972737291611774\n",
      "iter: 11  w =  4.974468882771096\n",
      "iter: 12  w =  4.973715640616791\n",
      "iter: 13  w =  4.974043300953914\n",
      "iter: 14  w =  4.973900768707265\n",
      "iter: 15  w =  4.973962770234557\n",
      "iter: 16  w =  4.973935799570186\n",
      "iter: 17  w =  4.973947531809187\n",
      "iter: 18  w =  4.973942428285222\n",
      "iter: 19  w =  4.973944648318146\n",
      "best w: 4.973943682603824\n"
     ]
    }
   ],
   "source": [
    "# lls - gradient descent solution\n",
    "N = 20 # num samples\n",
    "num_iterations = 20\n",
    "alpha_k = 0.005\n",
    "# we want to find w that minimizes (wx-y)^2\n",
    "# initialize w\n",
    "w = 0\n",
    "for i in range(num_iterations):\n",
    "    print(\"iter:\", i, \" w = \", w)\n",
    "    gradient = np.sum(2 * w * np.square(x) - 2 * x * y) / N\n",
    "    w = w - alpha_k * gradient\n",
    "print(\"best w:\", w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_lls_sol(x, y, w, \"Gradient Descent Solution for min(wx-y)^2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Least Squares Visualization**:\n",
    "<img src=\"./assets/tut_06_lls_anim.gif\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/dusk/64/000000/waypoint-map.png\" style=\"height:50px;display:inline\">Stochastic Gradient Descent (Mini-Batch Gradient Descent)\n",
    "---\n",
    "* The main problem with (Batch) GD is that it uses the **whole** training set to compute the gradients. But what if that training set is huge? Computing the gradient can take a very long time.\n",
    "* *Stochastic* Gradient Descent on the other hand, samples just one instance randomly at every step and computes the gradients based on that single instance. This makes the algorithm much faster but due to its randomness, it is much less stable. Instead of steady decreasing untill reaching the minimum, the cost function will bounce up and down, **decreasing only on average**. With time, it will get *very close* to the minimum, but once it is there it will continue to bounce around!\n",
    "* The final parameters are good but **not optimal**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* When the cost function is very irregular, this bouncing can actually help the algorithm escape local minima, so SGD has better chance to find the *global* minimum.\n",
    "* How to find optimal parameters using SGD?\n",
    "    * **Reduce the learning rate gradually**: this is called *learning schedule*\n",
    "        * But don't reduce too quickly or you will get stuck at a local minimum or even frozen!\n",
    "* *Mini-Batch* Gradient Descent - same idea as SGD, but instead of one instance each step, $m$ samples.\n",
    "    * Get a little bit closer to the minimum than SGD but a little harder to escape local minima."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **Pseudocode**:\n",
    "    * **Require**: Learning rate $\\alpha_k$\n",
    "    * **Require**: Initial parameter $w$\n",
    "    * **While** stopping criterion not met **do**\n",
    "        * Sample a minibatch of $m$ examples from the training set ($m=1$ for SGD)\n",
    "        * Set $\\{x_1,...,x_m,\\}$ with corresponding targets $\\{y_1,...,y_m\\}$\n",
    "        * Compute gradient: $g \\leftarrow \\frac{1}{m} \\sum_{i=1}^m f'(x_i,w)$\n",
    "        * Apply update: $w \\leftarrow w - \\alpha_k g$\n",
    "        * $k \\leftarrow k + 1$\n",
    "    * **end while**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "<img src=\"./assets/tut_06_sgd.png\" style=\"height:250px\">\n",
    "\n",
    "<a href=\"https://towardsdatascience.com/gradient-descent-algorithm-and-its-variants-10f652806a3\">Image Source</a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def batch_generator(x, y, batch_size, shuffle=True):\n",
    "    \"\"\"\n",
    "    This function generates batches for a given dataset x.\n",
    "    \"\"\"\n",
    "    N = len(x)\n",
    "    num_batches = N // batch_size\n",
    "    batch_x = []\n",
    "    batch_y = []\n",
    "    if shuffle:\n",
    "        # shuffle\n",
    "        rand_gen = np.random.RandomState(0)\n",
    "        shuffled_indices = rand_gen.permutation(np.arange(len(x)))\n",
    "        x = x[shuffled_indices]\n",
    "        y = y[shuffled_indices]\n",
    "    for i in range(N):\n",
    "        batch_x.append(x[i])\n",
    "        batch_y.append(y[i])\n",
    "        if len(batch_x) == batch_size:\n",
    "            yield np.array(batch_x), np.array(batch_y)\n",
    "            batch_x = []\n",
    "            batch_y = []\n",
    "    if batch_x:\n",
    "        yield np.array(batch_x), np.array(batch_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total batches: 4\n",
      "iter: 0 batch: 0  w =  0\n",
      "iter: 1 batch: 0  w =  3.714921791540526\n",
      "iter: 2 batch: 0  w =  4.660336847464463\n",
      "iter: 3 batch: 0  w =  4.900936698046245\n",
      "iter: 4 batch: 0  w =  4.962167252538953\n",
      "iter: 5 batch: 0  w =  4.9777498923220564\n",
      "iter: 6 batch: 0  w =  4.981715537654223\n",
      "iter: 7 batch: 0  w =  4.982724759655079\n",
      "iter: 8 batch: 0  w =  4.982981597814244\n",
      "iter: 9 batch: 0  w =  4.983046960876035\n",
      "iter: 10 batch: 0  w =  4.983063595202728\n",
      "iter: 11 batch: 0  w =  4.983067828493167\n",
      "iter: 12 batch: 0  w =  4.983068905828502\n",
      "iter: 13 batch: 0  w =  4.983069180000908\n",
      "iter: 14 batch: 0  w =  4.983069249775384\n",
      "iter: 15 batch: 0  w =  4.983069267532377\n",
      "iter: 16 batch: 0  w =  4.9830692720513765\n",
      "iter: 17 batch: 0  w =  4.983069273201423\n",
      "iter: 18 batch: 0  w =  4.983069273494099\n",
      "iter: 19 batch: 0  w =  4.983069273568582\n",
      "best w: 4.983069273587538\n"
     ]
    }
   ],
   "source": [
    "# mini-batch gradient descent\n",
    "batch_size = 5\n",
    "num_batches = N // batch_size\n",
    "print(\"total batches:\", num_batches)\n",
    "num_iterations = 20\n",
    "alpha_k = 0.001\n",
    "batch_gen = batch_generator(x, y, batch_size, shuffle=True)\n",
    "# we want to find w that minimizes (wx-y)^2\n",
    "# initialize w\n",
    "w = 0\n",
    "for i in range(num_iterations):\n",
    "    for batch_i, batch in enumerate(batch_gen):\n",
    "        batch_x, batch_y = batch\n",
    "        if batch_i % 5 == 0:\n",
    "            print(\"iter:\", i, \"batch:\", batch_i, \" w = \", w)\n",
    "        gradient = np.sum(2 * w * np.square(batch_x) - 2 * batch_x * batch_y) / len(batch_x)\n",
    "        w = w - alpha_k * gradient\n",
    "    batch_gen = batch_generator(x, y, batch_size, shuffle=True)\n",
    "print(\"best w:\", w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot\n",
    "plot_lls_sol(x, y, w, \"Mini-Batch Gradient Descent Solution for min(wx-y)^2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### GD Comparison Summary\n",
    "---\n",
    "\n",
    "| Method|Accuracy | Update Speed | Memory Usage |Online Learning |\n",
    "|---|---|---|---|---|\n",
    "| **Batch** Gradient Descent | Good | Slow |  High | No |\n",
    "| **Stochastic** Gradient Descent |  Good (with softening) | Fast |  Low |  Yes |\n",
    "| **Mini-Batch** Gradient Descent | Good | Medium | Medium | Yes (depends on the MB size) |\n",
    "\n",
    "\n",
    "* **\"Online\"** - samples arrive while the algorithm runs (that is, when the algorithm starts running, not all samples exist)\n",
    "* Note: All of the Gradient Descent algorithms require **scaling** if the feaures are not within the same range!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### Challenges\n",
    "---\n",
    "* Choosing a **learning rate**\n",
    "    * Defining **learning schedule**\n",
    "* Working with features of different scales (e.g. heights (cm), weights (kg) and age (scalar))\n",
    "* Avoiding **local minima** (or *suboptimal* minima)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <img src=\"https://img.icons8.com/dusk/64/000000/math.png\" style=\"height:50px;display:inline\"> Mathematical Background\n",
    "---\n",
    "### <img src=\"https://img.icons8.com/dusk/64/000000/point-objects.png\" style=\"height:50px;display:inline\"> Multivariate Calculus\n",
    "---\n",
    "* **The Derivative** - the derivative of $f: \\mathbb{R} \\rightarrow \\mathbb{R}$ is a *function* $f': \\mathbb{R} \\rightarrow \\mathbb{R}$ given by:\n",
    "$$ f'(x) = \\frac{df(x)}{dx} = \\lim_{h \\to 0} \\frac{f(x+h) - f(x)}{h}$$\n",
    "    * Illustration: <img src=\"./assets/tut_06_deriv.jpg\" style=\"height:200px\">\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Rewrite the above: $$ \\lim_{h \\to 0} \\frac{f(x+h) - f(x) -f'(x) \\cdot h}{h}=0$$\n",
    "    * <img src=\"./assets/tut_06_deriv_2.jpg\" style=\"height:200px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **The Gradient** - the gradient of  $f: \\mathbb{R}^N \\rightarrow \\mathbb{R}$ is a *function* $\\nabla f: \\mathbb{R}^N \\rightarrow \\mathbb{R}^N$ given by: $$ \\lim_{h \\to 0} \\frac{||f(\\overline{x}+\\overline{h}) - f(\\overline{x}) -\\nabla f(\\overline{x}) \\cdot \\overline{h}||}{||\\overline{h}||} = 0$$\n",
    "    * <br><img src=\"./assets/tut_06_gradient.gif\" style=\"height:200px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* The gradient can be expressed in terms of the function's **partial derivatives**: $$\\nabla f(x) = \\begin{bmatrix} \\frac{\\partial f}{\\partial x_1} \\\\ \\frac{\\partial f}{\\partial x_2} \\\\ \\vdots \\\\ \\frac{\\partial f}{\\partial x_n} \\end{bmatrix} $$\n",
    "    * Illustration: <img src=\"./assets/tut_06_gradient_2.jpg\" style=\"height:200px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* **The Hessian Matrix**\n",
    "    * Definition: $H(f)(x)_{i,j} = \\frac{\\partial^2}{\\partial x_i \\partial x_j} f(x)$\n",
    "    * $$ \\begin{bmatrix} \\frac{\\partial^2 f}{\\partial x_1^2} & \\frac{\\partial^2 f}{\\partial x_1 \\partial x_2} & \\cdots & \\frac{\\partial^2 f}{\\partial x_1 \\partial x_n}  \\\\ \\frac{\\partial^2 f}{\\partial x_2 x_1} & \\frac{\\partial^2 f}{\\partial x_2^2} & \\cdots & \\frac{\\partial^2 f}{\\partial x_2 \\partial x_n}  \\\\ \\vdots  & \\vdots  & \\ddots & \\vdots  \\\\ \\frac{\\partial^2 f}{\\partial x_n x_1} & \\frac{\\partial^2 f}{\\partial x_n \\partial x_2} & \\cdots & \\frac{\\partial^2 f}{\\partial x_n^2}  \\end{bmatrix} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <img src=\"https://img.icons8.com/dusk/64/000000/scissors.png\" style=\"height:50px;display:inline\"> Matrix Calculus -  Vector & Matrix Derivatives\n",
    "---\n",
    "* We will use most of the derivations \"as is\" without derivation.\n",
    "* A good reference: **<a href=\"http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/3274/pdf/imm3274.pdf\">The Matrix Cookbook</a>**\n",
    "* <img src=\"https://img.icons8.com/plasticine/100/000000/error.png\" style=\"height:20px;display:inline\"> **REMEMBER** - ALWAYS write the dimensions of each component and identify whether the expression is a **matrix, vector or scalar**!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/bubbles/100/000000/multiply.png\" style=\"height:50px;display:inline\"> Derivative of Vector Multiplication\n",
    "---\n",
    "* Let $x, a \\in \\mathbb{R}^N \\rightarrow x, a$ are vectors\n",
    "* $\\frac{\\partial x^Ta}{\\partial x} = \\frac{\\partial a^Tx}{\\partial x} = a$\n",
    "    * $x^Ta = a^Tx$ are **scalars**\n",
    "    * $a$ is a **vector**\n",
    "    * Derivation: $$ f = x^Ta = [x_1, x_2, ..., x_n] \\begin{bmatrix} a_{1} \\\\a_{2} \\\\ \\vdots \\\\a_{n}\\end{bmatrix}  = a_1 x_1 + a_2 x_2 + ... + a_n x_n = \\sum_{i=1}^n a_i x_i$$ $$\\frac{\\partial x^Ta}{\\partial x} = \\begin{bmatrix}\\frac{\\partial f}{\\partial x_1} \\\\ \\frac{\\partial f}{\\partial x_2} \\\\ \\vdots \\\\ \\frac{\\partial f}{\\partial x_n} \\end{bmatrix} = \\begin{bmatrix} a_{1} \\\\ a_{2} \\\\ \\vdots \\\\ a_{n} \\end{bmatrix} = a $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/cotton/64/000000/bunch-of-keys-1.png\" style=\"height:30px;display:inline\"> Common Derivations\n",
    "---\n",
    "* $\\nabla_x Ax = A^{T}$\n",
    "* $\\nabla_x x^{T} A x = (A + A^{T}) x$ \n",
    "    * If $W$ is **symetric**:\n",
    "        * $\\frac{\\partial}{\\partial s} (x-As)^T W (x-As) = -2A^TW(x-As)$\n",
    "        * $\\frac{\\partial}{\\partial x} (x-As)^T W (x-As) = 2W(x-As)$\n",
    "* $\\frac{\\partial}{\\partial A} \\ln |A| = A^{-T}$\n",
    "* $\\frac{\\partial}{\\partial A} Tr[AB] = B^{T}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/dusk/64/000000/link.png\" style=\"height:50px;display:inline\"> The Chain Rule\n",
    "---\n",
    "* Let $$ f(x) = h(g(x))$$ $$x \\in \\mathbb{R}^n $$ $$ f, g : \\mathbb{R}^n \\rightarrow \\mathbb{R}$$ $$ h: \\mathbb{R} \\rightarrow \\mathbb{R}$$\n",
    "* $ \\nabla f = h' \\cdot \\nabla g$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/task.png\" style=\"height:30px;display:inline\"> Exercise 1 - The Chain Rule\n",
    "---\n",
    "Find the gradient of $f(x) = \\sqrt{x^TQx}$ ($Q$ is positive definite)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\"> Solution 1\n",
    "---\n",
    "* $g(x) = x^TQx \\rightarrow \\nabla g = (Q + Q^T) x = 2Qx$\n",
    "* $h(z) = \\sqrt{z} \\rightarrow h'(z) = \\frac{1}{2\\sqrt{z}}$\n",
    "* $\\nabla f =\\frac{1}{2\\sqrt{x^TQx}}2Qx = \\frac{Qx}{\\sqrt{x^TQx}} $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <img src=\"https://img.icons8.com/nolan/64/000000/3d-scale.png\" style=\"height:50px;display:inline\">  Multi-Dimensional Optimization\n",
    "---\n",
    "### <img src=\"https://img.icons8.com/dusk/64/000000/copyleft.png\" style=\"height:50px;display:inline\"> Optimality Conditions\n",
    "---\n",
    "* If $f$ has *local* optimum at $x_0$ then $\\nabla f(x_0) = 0$\n",
    "* If the **Hessian** is:\n",
    "    * **Positive Definite** (all eigenvalues *positive*) at $x_0 \\rightarrow$ *local minimum*\n",
    "    * **Negative Definite** (all eigenvalues *negative*) at $x_0 \\rightarrow$ *local maximum*\n",
    "    * Both **positive and negative** eigenvalues at $x_0 \\rightarrow$ *saddle* point\n",
    "    * <img src=\"./assets/tut_06_saddle.jpg\" style=\"height:100px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/dusk/64/000000/classroom.png\" style=\"height:50px;display:inline\"> Example - (Multivariate) Linear Least Squares\n",
    "---\n",
    "* **Problem Formulation**\n",
    "    * $y \\in \\mathbb{R}^N$ - vector of values\n",
    "    * $X \\in \\mathbb{R}^{N \\times L}$ - data matrix with $N$ examples and *$L$ features*\n",
    "    * $w \\in \\mathbb{R}^L$ - the *parameters* to be learnt, a **weight for each feature**\n",
    "* **Goal**: find $w$ that best fits the measurement y, that is, find a *weighted linear combination* of the feature vector to best fit the measurment $y$\n",
    "* Mathematiacally, the problem is:\n",
    "$$\\min_w f(w;x,y) = \\min_w \\sum_{i=1}^N||x_i w-y_i||^2 $$\n",
    "* In vector form:\n",
    "$$\\min_w f(w;x,y) = \\min_w ||Xw - Y||^2 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\">   (Multivariate) LLS - Analytical Solution\n",
    "---\n",
    "* Mathematically:\n",
    "$$\\min_w f(w;x,y) = \\min_w ||Xw - Y||^2 = \\min_w (Xw-Y)^T(Xw-Y)= \\min_w (w^TX^TXw -2w^TX^TY + Y^TY)$$\n",
    "* The derivative: \n",
    "$$\\nabla_w f(w;x,y) = (X^TX + X^TX)w -2X^TY = 0 \\rightarrow w=(X^TX)^{-1}X^TY $$ $$X^TX \\in \\mathbb{R}^{L \\times L} $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>187</td>\n",
       "      <td>874373</td>\n",
       "      <td>B</td>\n",
       "      <td>11.71</td>\n",
       "      <td>17.19</td>\n",
       "      <td>74.68</td>\n",
       "      <td>420.3</td>\n",
       "      <td>0.09774</td>\n",
       "      <td>0.06141</td>\n",
       "      <td>0.03809</td>\n",
       "      <td>0.03239</td>\n",
       "      <td>...</td>\n",
       "      <td>21.39</td>\n",
       "      <td>84.42</td>\n",
       "      <td>521.5</td>\n",
       "      <td>0.1323</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>0.1521</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.07097</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>323</td>\n",
       "      <td>895100</td>\n",
       "      <td>M</td>\n",
       "      <td>20.34</td>\n",
       "      <td>21.51</td>\n",
       "      <td>135.90</td>\n",
       "      <td>1264.0</td>\n",
       "      <td>0.11700</td>\n",
       "      <td>0.18750</td>\n",
       "      <td>0.25650</td>\n",
       "      <td>0.15040</td>\n",
       "      <td>...</td>\n",
       "      <td>31.86</td>\n",
       "      <td>171.10</td>\n",
       "      <td>1938.0</td>\n",
       "      <td>0.1592</td>\n",
       "      <td>0.4492</td>\n",
       "      <td>0.5344</td>\n",
       "      <td>0.26850</td>\n",
       "      <td>0.5558</td>\n",
       "      <td>0.10240</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>355</td>\n",
       "      <td>9010258</td>\n",
       "      <td>B</td>\n",
       "      <td>12.56</td>\n",
       "      <td>19.07</td>\n",
       "      <td>81.92</td>\n",
       "      <td>485.8</td>\n",
       "      <td>0.08760</td>\n",
       "      <td>0.10380</td>\n",
       "      <td>0.10300</td>\n",
       "      <td>0.04391</td>\n",
       "      <td>...</td>\n",
       "      <td>22.43</td>\n",
       "      <td>89.02</td>\n",
       "      <td>547.4</td>\n",
       "      <td>0.1096</td>\n",
       "      <td>0.2002</td>\n",
       "      <td>0.2388</td>\n",
       "      <td>0.09265</td>\n",
       "      <td>0.2121</td>\n",
       "      <td>0.07188</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>8611555</td>\n",
       "      <td>M</td>\n",
       "      <td>25.22</td>\n",
       "      <td>24.91</td>\n",
       "      <td>171.50</td>\n",
       "      <td>1878.0</td>\n",
       "      <td>0.10630</td>\n",
       "      <td>0.26650</td>\n",
       "      <td>0.33390</td>\n",
       "      <td>0.18450</td>\n",
       "      <td>...</td>\n",
       "      <td>33.62</td>\n",
       "      <td>211.70</td>\n",
       "      <td>2562.0</td>\n",
       "      <td>0.1573</td>\n",
       "      <td>0.6076</td>\n",
       "      <td>0.6476</td>\n",
       "      <td>0.28670</td>\n",
       "      <td>0.2355</td>\n",
       "      <td>0.10510</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>329</td>\n",
       "      <td>895633</td>\n",
       "      <td>M</td>\n",
       "      <td>16.26</td>\n",
       "      <td>21.88</td>\n",
       "      <td>107.50</td>\n",
       "      <td>826.8</td>\n",
       "      <td>0.11650</td>\n",
       "      <td>0.12830</td>\n",
       "      <td>0.17990</td>\n",
       "      <td>0.07981</td>\n",
       "      <td>...</td>\n",
       "      <td>25.21</td>\n",
       "      <td>113.70</td>\n",
       "      <td>975.2</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>0.2116</td>\n",
       "      <td>0.3344</td>\n",
       "      <td>0.10470</td>\n",
       "      <td>0.2736</td>\n",
       "      <td>0.07953</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>80</td>\n",
       "      <td>861103</td>\n",
       "      <td>B</td>\n",
       "      <td>11.45</td>\n",
       "      <td>20.97</td>\n",
       "      <td>73.81</td>\n",
       "      <td>401.5</td>\n",
       "      <td>0.11020</td>\n",
       "      <td>0.09362</td>\n",
       "      <td>0.04591</td>\n",
       "      <td>0.02233</td>\n",
       "      <td>...</td>\n",
       "      <td>32.16</td>\n",
       "      <td>84.53</td>\n",
       "      <td>525.1</td>\n",
       "      <td>0.1557</td>\n",
       "      <td>0.1676</td>\n",
       "      <td>0.1755</td>\n",
       "      <td>0.06127</td>\n",
       "      <td>0.2762</td>\n",
       "      <td>0.08851</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>530</td>\n",
       "      <td>91858</td>\n",
       "      <td>B</td>\n",
       "      <td>11.75</td>\n",
       "      <td>17.56</td>\n",
       "      <td>75.89</td>\n",
       "      <td>422.9</td>\n",
       "      <td>0.10730</td>\n",
       "      <td>0.09713</td>\n",
       "      <td>0.05282</td>\n",
       "      <td>0.04440</td>\n",
       "      <td>...</td>\n",
       "      <td>27.98</td>\n",
       "      <td>88.52</td>\n",
       "      <td>552.3</td>\n",
       "      <td>0.1349</td>\n",
       "      <td>0.1854</td>\n",
       "      <td>0.1366</td>\n",
       "      <td>0.10100</td>\n",
       "      <td>0.2478</td>\n",
       "      <td>0.07757</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>486</td>\n",
       "      <td>913102</td>\n",
       "      <td>B</td>\n",
       "      <td>14.64</td>\n",
       "      <td>16.85</td>\n",
       "      <td>94.21</td>\n",
       "      <td>666.0</td>\n",
       "      <td>0.08641</td>\n",
       "      <td>0.06698</td>\n",
       "      <td>0.05192</td>\n",
       "      <td>0.02791</td>\n",
       "      <td>...</td>\n",
       "      <td>25.44</td>\n",
       "      <td>106.00</td>\n",
       "      <td>831.0</td>\n",
       "      <td>0.1142</td>\n",
       "      <td>0.2070</td>\n",
       "      <td>0.2437</td>\n",
       "      <td>0.07828</td>\n",
       "      <td>0.2455</td>\n",
       "      <td>0.06596</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>84501001</td>\n",
       "      <td>M</td>\n",
       "      <td>12.46</td>\n",
       "      <td>24.04</td>\n",
       "      <td>83.97</td>\n",
       "      <td>475.9</td>\n",
       "      <td>0.11860</td>\n",
       "      <td>0.23960</td>\n",
       "      <td>0.22730</td>\n",
       "      <td>0.08543</td>\n",
       "      <td>...</td>\n",
       "      <td>40.68</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.4</td>\n",
       "      <td>0.1853</td>\n",
       "      <td>1.0580</td>\n",
       "      <td>1.1050</td>\n",
       "      <td>0.22100</td>\n",
       "      <td>0.4366</td>\n",
       "      <td>0.20750</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>48</td>\n",
       "      <td>857155</td>\n",
       "      <td>B</td>\n",
       "      <td>12.05</td>\n",
       "      <td>14.63</td>\n",
       "      <td>78.04</td>\n",
       "      <td>449.3</td>\n",
       "      <td>0.10310</td>\n",
       "      <td>0.09092</td>\n",
       "      <td>0.06592</td>\n",
       "      <td>0.02749</td>\n",
       "      <td>...</td>\n",
       "      <td>20.70</td>\n",
       "      <td>89.88</td>\n",
       "      <td>582.6</td>\n",
       "      <td>0.1494</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3050</td>\n",
       "      <td>0.06548</td>\n",
       "      <td>0.2747</td>\n",
       "      <td>0.08301</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "187    874373         B        11.71         17.19           74.68      420.3   \n",
       "323    895100         M        20.34         21.51          135.90     1264.0   \n",
       "355   9010258         B        12.56         19.07           81.92      485.8   \n",
       "82    8611555         M        25.22         24.91          171.50     1878.0   \n",
       "329    895633         M        16.26         21.88          107.50      826.8   \n",
       "80     861103         B        11.45         20.97           73.81      401.5   \n",
       "530     91858         B        11.75         17.56           75.89      422.9   \n",
       "486    913102         B        14.64         16.85           94.21      666.0   \n",
       "9    84501001         M        12.46         24.04           83.97      475.9   \n",
       "48     857155         B        12.05         14.63           78.04      449.3   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "187          0.09774           0.06141         0.03809              0.03239   \n",
       "323          0.11700           0.18750         0.25650              0.15040   \n",
       "355          0.08760           0.10380         0.10300              0.04391   \n",
       "82           0.10630           0.26650         0.33390              0.18450   \n",
       "329          0.11650           0.12830         0.17990              0.07981   \n",
       "80           0.11020           0.09362         0.04591              0.02233   \n",
       "530          0.10730           0.09713         0.05282              0.04440   \n",
       "486          0.08641           0.06698         0.05192              0.02791   \n",
       "9            0.11860           0.23960         0.22730              0.08543   \n",
       "48           0.10310           0.09092         0.06592              0.02749   \n",
       "\n",
       "     ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "187  ...          21.39            84.42       521.5            0.1323   \n",
       "323  ...          31.86           171.10      1938.0            0.1592   \n",
       "355  ...          22.43            89.02       547.4            0.1096   \n",
       "82   ...          33.62           211.70      2562.0            0.1573   \n",
       "329  ...          25.21           113.70       975.2            0.1426   \n",
       "80   ...          32.16            84.53       525.1            0.1557   \n",
       "530  ...          27.98            88.52       552.3            0.1349   \n",
       "486  ...          25.44           106.00       831.0            0.1142   \n",
       "9    ...          40.68            97.65       711.4            0.1853   \n",
       "48   ...          20.70            89.88       582.6            0.1494   \n",
       "\n",
       "     compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "187             0.1040           0.1521               0.10990          0.2572   \n",
       "323             0.4492           0.5344               0.26850          0.5558   \n",
       "355             0.2002           0.2388               0.09265          0.2121   \n",
       "82              0.6076           0.6476               0.28670          0.2355   \n",
       "329             0.2116           0.3344               0.10470          0.2736   \n",
       "80              0.1676           0.1755               0.06127          0.2762   \n",
       "530             0.1854           0.1366               0.10100          0.2478   \n",
       "486             0.2070           0.2437               0.07828          0.2455   \n",
       "9               1.0580           1.1050               0.22100          0.4366   \n",
       "48              0.2156           0.3050               0.06548          0.2747   \n",
       "\n",
       "     fractal_dimension_worst  Unnamed: 32  \n",
       "187                  0.07097          NaN  \n",
       "323                  0.10240          NaN  \n",
       "355                  0.07188          NaN  \n",
       "82                   0.10510          NaN  \n",
       "329                  0.07953          NaN  \n",
       "80                   0.08851          NaN  \n",
       "530                  0.07757          NaN  \n",
       "486                  0.06596          NaN  \n",
       "9                    0.20750          NaN  \n",
       "48                   0.08301          NaN  \n",
       "\n",
       "[10 rows x 33 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let's load the cancer dataset\n",
    "dataset = pd.read_csv('./datasets/cancer_dataset.csv')\n",
    "# print the number of rows in the data set\n",
    "number_of_rows = len(dataset)\n",
    "# reminder, the data looks like this\n",
    "dataset.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plot_3d(x, y, z):\n",
    "    %matplotlib notebook\n",
    "    fig = plt.figure(figsize=(5, 5))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.scatter(x, y, z)\n",
    "    ax.set_xlabel('Radius Mean')\n",
    "    ax.set_ylabel('Area Mean')\n",
    "    ax.set_zlabel('Perimeter Mean')\n",
    "    ax.set_title(\"Breast Cancer - Radius Mean vs. Area Mean vs. Perimeter Mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's plot X = [radius, area], y = perimeter\n",
    "xs = dataset[['radius_mean']].values\n",
    "ys = dataset[['area_mean']].values\n",
    "zs = dataset[['perimeter_mean']].values\n",
    "plot_3d(xs, ys, zs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def plot_3d_lls(x, y, z, lls_sol, title=\"\"):\n",
    "    # plot\n",
    "    %matplotlib notebook\n",
    "    fig = plt.figure(figsize=(5, 5))\n",
    "    ax = fig.add_subplot(111, projection='3d')\n",
    "    ax.scatter(x, y, z, label='Y')\n",
    "    ax.scatter(x, y, lls_sol, label='Xw')\n",
    "    ax.legend()\n",
    "    ax.set_xlabel('Radius Mean')\n",
    "    ax.set_ylabel('Area Mean')\n",
    "    ax.set_zlabel('Perimeter Mean')\n",
    "    ax.set_title(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w:\n",
      "[[6.19721311]\n",
      " [0.00676913]]\n"
     ]
    }
   ],
   "source": [
    "# multivariate lls - analytical solution\n",
    "X = dataset[['radius_mean', 'area_mean']].values\n",
    "Y = dataset[['perimeter_mean']].values\n",
    "xs = dataset[['radius_mean']].values\n",
    "ys = dataset[['area_mean']].values\n",
    "zs = dataset[['perimeter_mean']].values\n",
    "w = np.linalg.inv(X.T @ X) @ X.T @ Y\n",
    "lls_sol = X @ w\n",
    "print(\"w:\")\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plot_3d_lls(xs, ys, zs, lls_sol, \"Breast Cancer - Radius Mean vs. Area Mean vs. Perimeter Mean - LLS Analyitical\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## What If L is Very Large???\n",
    "If $L = 1000$, we would need to invert a $1000 \\times 1000$ matrix, which would take about $10^9$ operations!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/dusk/64/000000/treasure-map.png\" style=\"height:50px;display:inline\">(Batch) Gradient Descent\n",
    "---\n",
    "* **Pseudocode**:\n",
    "    * **Require**: Learning rate $\\alpha_k$\n",
    "    * **Require**: Initial parameter vector $w$\n",
    "    * **While** stopping criterion not met **do**\n",
    "        * Compute gradient: $g \\leftarrow \\nabla f(x,w)$ \n",
    "        * Apply update: $w \\leftarrow w - \\alpha_k g$\n",
    "        * $k \\leftarrow k + 1$\n",
    "    * **end while**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* For **Linear Least Squares**:\n",
    "    * **Require**: Learning rate $\\alpha_k$\n",
    "    * **Require**: Initial parameter vector $w$\n",
    "    * **While** stopping criterion not met **do**\n",
    "        * Compute gradient: $g \\leftarrow 2X^TXw - 2X^Ty$ \n",
    "        * Apply update: $w \\leftarrow w - \\alpha_k g$\n",
    "        * $k \\leftarrow k + 1$\n",
    "    * **end while**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "# multivariate lls - gradient descent solution\n",
    "X = dataset[['radius_mean', 'area_mean']].values\n",
    "Y = dataset[['perimeter_mean']].values\n",
    "# Scaling\n",
    "X = (X - X.mean(axis=0, keepdims=True)) / X.std(axis=0, keepdims=True)\n",
    "Y = (Y - Y.mean(axis=0, keepdims=True)) / Y.std(axis=0, keepdims=True)\n",
    "num_iterations = 20\n",
    "alpha_k = 0.0001\n",
    "L = X.shape[1]\n",
    "# initialize w\n",
    "w = np.zeros((L, 1))\n",
    "for i in range(num_iterations):\n",
    "    print(\"iter:\", i, \" w = \")\n",
    "    print(w)\n",
    "    gradient = 2 * X.T @ X @ w - 2 * X.T @ Y\n",
    "    w = w - alpha_k * gradient\n",
    "lls_sol = X @ w\n",
    "print(\"w:\")\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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>"
      ]
     },
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     "output_type": "display_data"
    },
    {
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\" width=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "plot_3d_lls(X[:,0], X[:, 1], Y, lls_sol, \"Breast Cancer - Radius Mean vs. Area Mean vs. Perimeter Mean - LLS GD\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/dusk/64/000000/waypoint-map.png\" style=\"height:50px;display:inline\">Stochastic Gradient Descent (Mini-Batch Gradient Descent)\n",
    "---\n",
    "* **Pseudocode**:\n",
    "    * **Require**: Learning rate $\\alpha_k$\n",
    "    * **Require**: Initial parameter $w$\n",
    "    * **While** stopping criterion not met **do**\n",
    "        * Sample a minibatch of $m$ examples from the training set ($m=1$ for SGD)\n",
    "        * Set $\\tilde{X} = [x_1,...,x_m] $ with corresponding targets $\\tilde{Y} = [y_1,...,y_m]$\n",
    "        * Compute gradient: $g \\leftarrow 2\\tilde{X}^T\\tilde{X} - 2\\tilde{X}^T \\tilde{Y}$\n",
    "        * Apply update: $w \\leftarrow w - \\alpha_k g$\n",
    "        * $k \\leftarrow k + 1$\n",
    "    * **end while**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def batch_generator(x, y, batch_size, shuffle=True):\n",
    "    \"\"\"\n",
    "    This function generates batches for a given dataset x.\n",
    "    \"\"\"\n",
    "    N, L = x.shape\n",
    "    num_batches = N // batch_size\n",
    "    batch_x = []\n",
    "    batch_y = []\n",
    "    if shuffle:\n",
    "        # shuffle\n",
    "        rand_gen = np.random.RandomState(0)\n",
    "        shuffled_indices = rand_gen.permutation(np.arange(N))\n",
    "        x = x[shuffled_indices, :]\n",
    "        y = y[shuffled_indices, :]\n",
    "    for i in range(N):\n",
    "        batch_x.append(x[i, :])\n",
    "        batch_y.append(y[i, :])\n",
    "        if len(batch_x) == batch_size:\n",
    "            yield np.array(batch_x).reshape(batch_size, L), np.array(batch_y).reshape(batch_size, 1)\n",
    "            batch_x = []\n",
    "            batch_y = []\n",
    "    if batch_x:\n",
    "        yield np.array(batch_x).reshape(-1, L), np.array(batch_y).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total batches: 56\n"
     ]
    }
   ],
   "source": [
    "# multivaraite mini-batch gradient descent\n",
    "X = dataset[['radius_mean', 'area_mean']].values\n",
    "Y = dataset[['perimeter_mean']].values\n",
    "# Scaling\n",
    "X = (X - X.mean(axis=0, keepdims=True)) / X.std(axis=0, keepdims=True)\n",
    "Y = (Y - Y.mean(axis=0, keepdims=True)) / Y.std(axis=0, keepdims=True)\n",
    "N = X.shape[0]\n",
    "batch_size = 10\n",
    "num_batches = N // batch_size\n",
    "print(\"total batches:\", num_batches)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "num_iterations = 10\n",
    "alpha_k = 0.001\n",
    "batch_gen = batch_generator(X, Y, batch_size, shuffle=True)\n",
    "# initialize w\n",
    "w = np.zeros((L, 1))\n",
    "for i in range(num_iterations):\n",
    "    for batch_i, batch in enumerate(batch_gen):\n",
    "        batch_x, batch_y = batch\n",
    "        if batch_i % 50 == 0:\n",
    "            print(\"iter:\", i, \"batch:\", batch_i, \" w = \")\n",
    "            print(w)\n",
    "        gradient = 2 * batch_x.T @ batch_x @ w - 2 * batch_x.T @ batch_y\n",
    "        w = w - alpha_k * gradient\n",
    "    batch_gen = batch_generator(X, Y, batch_size, shuffle=True)\n",
    "\n",
    "lls_sol = X @ w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "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 / mpl.ratio, fig.canvas.height / mpl.ratio);\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=\"500\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w:\n",
      "[[0.55894282]\n",
      " [0.42792729]]\n"
     ]
    }
   ],
   "source": [
    "# plot\n",
    "plot_3d_lls(X[:,0], X[:, 1], Y, lls_sol, \"Breast Cancer - Radius Mean vs. Area Mean vs. Perimeter Mean - LLS Mini-Batch GD\")\n",
    "print(\"w:\")\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## <img src=\"https://img.icons8.com/office/80/000000/conflict.png\" style=\"height:50px;display:inline\"> Constrained Optimization\n",
    "---\n",
    "### <img src=\"https://img.icons8.com/dusk/64/000000/lambda.png\" style=\"height:30px;display:inline\"> Largrange Multipliers\n",
    "---\n",
    "* A method for optimization with **equality constraints**\n",
    "* The general case: $$ \\min f(x,y) $$ $$ \\textit{s.t. (subject to)}: g(x,y)=0 $$\n",
    "* The *Lagrange* function (*Lagrangian*) is defined by: $$ \\mathcal{L}(x,y,\\lambda) = f(x,y) -\\lambda \\cdot g(x,y) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Geometric Intuition: let's look at the following figure where we wish to **maximize** $f(x,y)$ s.t $g(x,y)=0$ - <br>\n",
    "<img src=\"./assets/tut_06_lagrange.jpg\" style=\"height:400px\"> <br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* To maximize $f(x,y)$ subject to $g(x,y)=0$ is to find the largest value $c \\in \\{7,8,9,10,11\\}$ such that the level curve (contour) $f(x,y) = c$ intersects with $g(x,y)=0$\n",
    "* It happens when the curves just touch each other\n",
    "    * When they have a common tangent line\n",
    "* Otherwise, the value of $c$ should be increased"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Since the gradient of a function is **perperndicular** to the contour lines:\n",
    "    * The *contour lines* of $f$ and $g$ are **parallel** iff the *gradients* of $f$ and $g$ are **parallel**\n",
    "    * Thus, we want points $(x,y)$ where $g(x,y) = 0$ and $$\\nabla_{x,y}f(x,y)=\\lambda \\nabla_{x,y} g(x,y) $$\n",
    "        * $\\lambda$ - \"The Lagrange Multiplier\" is required to adjust the **magnitudes** of the (parallel) gradient vectors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "###  <img src=\"https://img.icons8.com/dusk/64/000000/lambda.png\" style=\"height:30px;display:inline\"><img src=\"https://img.icons8.com/dusk/64/000000/lambda.png\" style=\"height:30px;display:inline\"> Multiple Constraints\n",
    "---\n",
    "* Extenstion of the above for problems with **multiple constraints** using a similar argument\n",
    "* The general case: minimize $f(x)$ s.t. $g_i(x)=0$, $i = 1,2,..., m$ \n",
    "* The **Lagrangian** is a weighted sum of objective and constraint functions: $$ \\mathcal{L}(x, \\lambda_1, ..., \\lambda_m) = f(x) - \\sum_{i=1}^m \\lambda_i g_i(x)$$\n",
    "    * $\\lambda_i$ is the Lagrange multipler associated with $g_i(x) = 0$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* The **solution** is obtained by solving the (unconstrained) optimization problem: $$\\nabla_{x, \\lambda_1, ..., \\lambda_m}\\mathcal{L}(x, \\lambda_1, ..., \\lambda_m) = 0 \\iff \\begin{cases}\n",
    "    \\nabla_x \\big[f(x) - \\sum_{i=1}^m \\lambda_ig_i(x) = 0 \\big]\\\\\n",
    "    g_1(x) = ... = g_m(x) = 0\n",
    "  \\end{cases}$$\n",
    "  * Amounts to solving $d + m$ equations in $d+m$ unknowns\n",
    "      * $d = |x|$ is the dimension of $x$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/task.png\" style=\"height:30px;display:inline\"> Exercise 2 - Max Entropy Distribution\n",
    "---\n",
    "Maximize $H(P) = -\\sum_{i=1}^d p_i \\log p_i$ subject to $\\sum_{i=1}^d p_i = 1$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "#### <img src=\"https://img.icons8.com/dusk/64/000000/idea.png\" style=\"height:30px;display:inline\"> Solution 2\n",
    "---\n",
    "* The Lagrangian is: $$L(P,\\lambda) = -\\sum_{i=1}^d p_i \\log p_i -\\lambda \\big(\\sum_{i=1}^dp_i -1 \\big) $$\n",
    "* Find stationary point for $L$:\n",
    "    * $\\forall i$, $\\frac{\\partial L(P,\\lambda)}{\\partial p_i} = -\\log p_i -1 -\\lambda =0 \\rightarrow p_i = e^{-\\lambda - 1}$\n",
    "    * $\\frac{\\partial L(P,\\lambda)}{\\partial \\lambda} = -\\sum_{i=1}^d p_i + 1 = 0 \\rightarrow \\sum_{i=1}^d e^{-\\lambda - 1} = 1 \\rightarrow e^{-\\lambda - 1} = \\frac{1}{d} = p_i$\n",
    "    * The Max Entropy distribution is the **uniform distribution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### <img src=\"https://img.icons8.com/bubbles/50/000000/video-playlist.png\" style=\"height:50px;display:inline\"> Recommended Videos\n",
    "---\n",
    "#### <img src=\"https://img.icons8.com/cute-clipart/64/000000/warning-shield.png\" style=\"height:30px;display:inline\"> Warning!\n",
    "* These videos do not replace the lectures and tutorials.\n",
    "* Please use these to get a better understanding of the material, and not as an alternative to the written material.\n",
    "\n",
    "#### Video By Subject\n",
    "\n",
    "* Gradient Descent - <a href=\"https://www.youtube.com/watch?v=sDv4f4s2SB8\">Gradient Descent, Step-by-Step</a>\n",
    "    * <a href=\"https://www.youtube.com/watch?v=jc2IthslyzM\">Mathematics of Gradient Descent - Intelligence and Learning</a>\n",
    "* Stochastic Gradient Descent - <a href=\"https://www.youtube.com/watch?v=vMh0zPT0tLI\">Stochastic Gradient Descent, Clearly Explained</a>\n",
    "* Constrained Optimization - <a href=\"https://www.youtube.com/watch?v=nUfYR5FBGZc\">Constrained Optimization with LaGrange Multipliers</a>\n",
    "* Lagrange Multipliers - <a href=\"https://www.youtube.com/watch?v=8mjcnxGMwFo\">Lagrange Multipliers | Geometric Meaning & Full Example</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    }
   },
   "source": [
    "## <img src=\"https://img.icons8.com/dusk/64/000000/prize.png\" style=\"height:50px;display:inline\"> Credits\n",
    "---\n",
    "* Icons from <a href=\"https://icons8.com/\">Icon8.com</a> - https://icons8.com\n",
    "* Datasets from <a href=\"https://www.kaggle.com/\">Kaggle</a> - https://www.kaggle.com/"
   ]
  }
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