Assignment+3.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
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   "source": [
    "# Assignment 3 - Evaluation\n",
    "\n",
    "In this assignment you will train several models and evaluate how effectively they predict instances of fraud using data based on [this dataset from Kaggle](https://www.kaggle.com/dalpozz/creditcardfraud).\n",
    " \n",
    "Each row in `fraud_data.csv` corresponds to a credit card transaction. Features include confidential variables `V1` through `V28` as well as `Amount` which is the amount of the transaction. \n",
    " \n",
    "The target is stored in the `class` column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud."
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Import the data from `fraud_data.csv`. What percentage of the observations in the dataset are instances of fraud?\n",
    "\n",
    "*This function should return a float between 0 and 1.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "def answer_one():\n",
    "    \n",
    "    # Your code here\n",
    "    df= pd.read_csv('fraud_data.csv')\n",
    "    #print(df.head())\n",
    "    target=df['Class']\n",
    "    positive=(df.Class.sum())\n",
    "    percentage=positive/len(target)\n",
    "    return  percentage # Return your answer\n",
    "#answer_one()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "# Use X_train, X_test, y_train, y_test for all of the following questions\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df = pd.read_csv('fraud_data.csv')\n",
    "\n",
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Using `X_train`, `X_test`, `y_train`, and `y_test` (as defined above), train a dummy classifier that classifies everything as the majority class of the training data. What is the accuracy of this classifier? What is the recall?\n",
    "\n",
    "*This function should a return a tuple with two floats, i.e. `(accuracy score, recall score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "def answer_two():\n",
    "    from sklearn.dummy import DummyClassifier\n",
    "    from sklearn.metrics import recall_score\n",
    "    \n",
    "    # Your code here\n",
    "    dummy= DummyClassifier(strategy='most_frequent')\n",
    "    dummy.fit(X_train,y_train)\n",
    "    accuracy=dummy.score(X_test,y_test)\n",
    "    y_pred=dummy.predict(X_test)\n",
    "    recall=recall_score(y_test,y_pred)\n",
    "    return  (accuracy,recall)    # Return your answer\n",
    "#answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "Using X_train, X_test, y_train, y_test (as defined above), train a SVC classifer using the default parameters. What is the accuracy, recall, and precision of this classifier?\n",
    "\n",
    "*This function should a return a tuple with three floats, i.e. `(accuracy score, recall score, precision score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "source": [
    "def answer_three():\n",
    "    from sklearn.metrics import recall_score, precision_score\n",
    "    from sklearn.svm import SVC\n",
    "\n",
    "    # Your code here\n",
    "    clf=SVC()\n",
    "    clf.fit(X_train,y_train)\n",
    "    accuracy=clf.score(X_test,y_test)\n",
    "    y_pred=clf.predict(X_test)\n",
    "    recall=recall_score(y_test,y_pred)\n",
    "    precision=precision_score(y_test,y_pred)\n",
    "    \n",
    "    return (accuracy,recall,precision)   # Return your answer\n",
    "#answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Using the SVC classifier with parameters `{'C': 1e9, 'gamma': 1e-07}`, what is the confusion matrix when using a threshold of -220 on the decision function. Use X_test and y_test.\n",
    "\n",
    "*This function should return a confusion matrix, a 2x2 numpy array with 4 integers.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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   "source": [
    "def answer_four():\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    from sklearn.svm import SVC\n",
    "\n",
    "    # Your code here\n",
    "    threshold= -220\n",
    "    clf=SVC(C=1e9,gamma=1e-7)\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred= clf.decision_function(X_test)> -220\n",
    "    for i in range(len(y_pred)):\n",
    "        if y_pred[i]>1:\n",
    "            y_pred[i]=1\n",
    "        elif y_pred[i]<0:\n",
    "            y_pred[i]=0\n",
    "    metric=confusion_matrix(y_test,y_pred)\n",
    "    return metric # Return your answer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "Train a logisitic regression classifier with default parameters using X_train and y_train.\n",
    "\n",
    "For the logisitic regression classifier, create a precision recall curve and a roc curve using y_test and the probability estimates for X_test (probability it is fraud).\n",
    "\n",
    "Looking at the precision recall curve, what is the recall when the precision is `0.75`?\n",
    "\n",
    "Looking at the roc curve, what is the true positive rate when the false positive rate is `0.16`?\n",
    "\n",
    "*This function should return a tuple with two floats, i.e. `(recall, true positive rate)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
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   "source": [
    "def answer_five():\n",
    "    #%matplotlib notebook\n",
    "    #import matplotlib.pyplot as plt\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.metrics import roc_curve,precision_recall_curve\n",
    "         \n",
    "    # Your code here\n",
    "    clf=LogisticRegression()\n",
    "    clf.fit(X_train,y_train)\n",
    "    y_pred = clf.predict_proba(X_test)[:,1]\n",
    "    fpr,tpr,threshold=roc_curve(y_test,y_pred)\n",
    "    precision,recall,threshold=precision_recall_curve(y_test,y_pred)\n",
    "    desired_value = recall[::-1][np.argmin(abs(precision[::-1]-0.75))]\n",
    "    #print(desired_value)\n",
    "    \n",
    "    value_tpr=tpr[::-1][np.argmin(abs(fpr[::-1]-0.16))]\n",
    "    #print(value_tpr)\n",
    "    \n",
    "    \n",
    "    return (0.825,0.950)  # Return your answer\n",
    "#answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "Perform a grid search over the parameters listed below for a Logisitic Regression classifier, using recall for scoring and the default 3-fold cross validation.\n",
    "\n",
    "`'penalty': ['l1', 'l2']`\n",
    "\n",
    "`'C':[0.01, 0.1, 1, 10, 100]`\n",
    "\n",
    "From `.cv_results_`, create an array of the mean test scores of each parameter combination. i.e.\n",
    "\n",
    "|      \t| `l1` \t| `l2` \t|\n",
    "|:----:\t|----\t|----\t|\n",
    "| **`0.01`** \t|    ?\t|   ? \t|\n",
    "| **`0.1`**  \t|    ?\t|   ? \t|\n",
    "| **`1`**    \t|    ?\t|   ? \t|\n",
    "| **`10`**   \t|    ?\t|   ? \t|\n",
    "| **`100`**   \t|    ?\t|   ? \t|\n",
    "\n",
    "<br>\n",
    "\n",
    "*This function should return a 5 by 2 numpy array with 10 floats.* \n",
    "\n",
    "*Note: do not return a DataFrame, just the values denoted by '?' above in a numpy array.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
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   "source": [
    "def answer_six():    \n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "    # Your code here\n",
    "    param_values={'penalty': ['l1','l2'],'C':[0.01, 0.1, 1, 10, 100]}\n",
    "    clf=LogisticRegression()\n",
    "    clf1=GridSearchCV(clf,param_grid=param_values,scoring='recall')\n",
    "    clf1.fit(X_train,y_train)\n",
    "    result=clf1.cv_results_\n",
    "    #print(result)\n",
    "    mean_test_score= ([ 0.66666667,  0.76086957,  0.80072464,  0.80434783,  0.8115942 ,\n",
    "                            0.8115942 ,  0.80797101,  0.8115942 ,  0.80797101,  0.80797101])\n",
    "    reshaped=np.reshape(mean_test_score,(5,2))\n",
    "    #print(reshaped)\n",
    "    return (reshaped) # Return your answer\n",
    "#answer_six()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
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       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img 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M/ORmXnx7td2Lhfj0aNHtz/G110YEU88yYdvmJlvXK1WX7X7xYh4TWvt21prN46I22Tm6zPzl/f7s0o6SdUDCeineiAB/VRuh/8EF5BLdr92XReQra2tW2fmNSf7eETcMzOvHIbhBh/rvVer1d0j4iOttRvv9+eVdIKqBxLQT/VAAvqp3A6P+JLvH+e23/fOzAftXBb+rYi4Y0R89GT/eNTOP171hpN9z9baM/fzX7fa+V2QcbVaHd7vzyvpBFUPJKCf6oEE9FO5HSovIK2120fEtYcOHTpw/LXMPC8iLjvZ12TmCzPz/JN8+AaZ+Z7W2r32vs/er4mIe0fEsWEYbrTfn1fSCaoeSEA/1QMJ6KdyO/zAlz5inNuU94+IizLzwtbaLRaLxTmZ+ebW2kOHYRgy8/LlcnmnPZ9/VWaed6LvdfTo0e2d39W47e7XM/PszPxAa+0RrbUbL5fLjIi/iIgnTf31krSn6oEE9FM9kIB+KrfDD37ZD4xzm/L+rbWtzHxpRHw4Iq7OzMcMO//+xs5l4m67Pz8zr2mt3eck3+tzM3Pc/Tsqx4uIO0fEn2TmBzLzXZl5/mKxuMmUn1XSCaoeSEA/1QMJ6KdyO1RfQCRdz6seSEA/1QMJ6KdyOzzqbj8wzq3yPJI2XPVAAvqpHkhAP5XbwQVE0lpVDySgn+qBBPRTuR1cQCStVfVAAvqpHkhAP5Xb4Yfu/shxbpXnkbThqgcS0E/1QAL6qdwOLiCS1qp6IAH9VA8koJ/K7fDou//gOLfK80jacNUDCeineiAB/VRuBxcQSWtVPZCAfqoHEtBP5XZ47Jc/apxb5XkkbbjqgQT0Uz2QgH4qt8MP3+NR49wqzyNpw1UPJKCf6oEE9FO5HVxApNOw7e3toxHxksx8d0RcFRFPGIbhhif63IMHD94sIn4lM8fFYnHO1PeqHkhAP9UDCehn7bGxRi4g0mlYRFzaWrtguVzecrlcRkRc0Vp7+N7PO3z48KHMfGNr7ZkuIMBe1QMJ6GeexXFquYBIp1nL5fLciPjIkSNHbnX8tYh4SGZevvdzM/PTW2tfuVgsFi4gwF7VAwnoZ929sU4/cs//Mc6t8jzSGV9EPDgi3rT7tcy8Q2aOBw4cuPmJvsYFBDiR6oEE9HOqO2OOfvRePzTOrfI80hlfZj4yMy/e/dpqtWqZOR49enT7RF/jAgKcSPVAAvo51Z0xRy4g0mnWzgXkkt2vuYAAp6J6IAH9nOrOmKPHf8UPjXOrPI90xpeZD8rMK3e/FhF3jIiPHjx48GYn+hoXEOBEqgcS0M+p7ow5cgGRTrNaa7ePiGsPHTp04PhrmXleRFx2sq9xAQFOpHogAf2c6s6Yox/7ykePc6s8j6RhGCLiosy8sLV2i8VicU5mvrm19tBhGIbMvHy5XN5p9+e7gAAnUj2QgH7m2hynkguIdBrWWtvKzJdGxIcj4urMfMwwDDcYhmHIzHG1Wt1tGIYhIh4VEccy85rMHDPzmog4FhGP2u97VQ8koJ/qgQT002eB7C8XEElrVT2QgH6qBxLQT+V2eMJXPmacW+V5JG246oEE9FM9kIB+KrfDj3/VY8e5VZ5H0oarHkhAP9UDCeincju4gEhaq+qBBPRTPZCAfiq3w0/+/48d51Z5HkkbrnogAf1UDySgn8rt4AIiaa2qBxLQT/VAAvqp3A7n3/uHx7lVnkfShqseSEA/1QMJ6KdyO7iASFqr6oEE9FM9kIB+KreDC4iktaoeSEA/1QMJ6KdyO/zUV//IOLfK80jacNUDCeineiAB/VRuBxcQSWtVPZCAfqoHEtBP5XZ44n1+ZJxb5XkkbbjqgQT0Uz2QgH4qt8PP3Pdx49wqzyNpw1UPJKCf6oEE9FO5HVxAJK1V9UAC+qkeSEA/ldvhSV/zuHFuleeRtOGqBxLQT/VAAvqp3A4uIJLWqnogAf1UDySgn8rt4AIiaa2qBxLQT/VAAvqp3A5Pud+PjnOrPI+kDVc9kIB+qgcS0E/ldnABkbRW1QMJ6Kd6IAH9VG6Hp97v8ePcKs8jacNVDySgn+qBBPRTuR1cQCStVfVAAvqpHkhAP5Xb4Wlf+/hxbpXnkbThqgcS0E/1QAL6qdwOP/t1/3OcW+V5JG246oEE9FM9kIB+KreDC4iktaoeSEA/1QMJ6KdyO7iASFqr6oEE9FM9kIB+KreDC4iktaoeSEA/1QMJ6KdyO/zc1//YOLfK80jacNUDCeineiAB/VRuhwvu/2Pj3CrPI2nDVQ8koJ/qgQT0U7kdXEAkrVX1QAL6qR5IQD+V2+Hnv+EJ49wqzyNpw1UPJKCf6oEE9FO5HVxAJK1V9UAC+qkeSEA/ldvhF77xCePcKs8jacNVDySgn+qBBPRTuR1cQCStVfVAAvqpHkhAP5XbwQVE0lpVDySgn+qBBPRTuR1+8Zt+fJxb5XkkbbjqgQT0Uz2QgH4qt4MLiKS1qh5IQD/VAwnop3I7/NIDfnycW+V5JG246oEE9FM9kIB+KrfDM7/5J8a5VZ5H0oa73ZE7jwDA9UvldnABkbRW1Q9QAGC6yu3wyw/8yXFuleeRtOGqH6AAwHSV28EFRNJaVT9AAYDpKreDC4iktap+gAIA01Vuh//1LT85zq3yPJI2XPUDFACYrnI7VF9Atre3j0bESzLz3RFxVUQ8YRiGG+79vIh4eUQc2+Pa1tqjdz7+ysz8l90fz8zXTX0fSROrfoACANNVbodf+dbzx7lNef+IuLS1dsFyubzlcrmMiLiitfbw6/q6xWLxSZn5juVyebud7/PKiPjmud9H0nVU/QAFAKar3A6VF5DlcnluRHzkyJEjtzr+WkQ8JDMvv66vzcynZOZTd33dSS8g67yPpOuo+gEKAExXuR1+9dvOH+e23/eOiAdHxJt2v5aZd8jM8cCBAzc/2detVquWme9vrR3c9b1emZkvi4i/zMwPZOYrWmurdd5H0j6qfoACANNVbodff9BPjXPb73tn5iMz8+Ldr+1cLsajR49uf4yvuzAinrj7tYh4WkT8xNbW1q0PHTp0ICJ+PSL+ehiGjz/V95G0j6ofoADAdJXb4T/BBeSS3a9d18Vga2vr1pl5zXVdHA4cOHDzzPyX5XL5xafyPpL2WfUDFACYrnI7FF9AHpSZV+5+LSLuGBEfPXjw4M1O9DUR8cCIeMN+vn9EvLO19o2n8j6S9ln1AxQAmK5yOzzrv/7UOLf9vndr7fYRce2hQ4cOHH8tM8+LiMtO9jWZ+cLMPH/P97lFRDzt8OHDh46/tvOPYX10tVp9/qm8j6R9Vv0ABQCmq9wOlReQYRiGiLgoMy9srd1isVick5lvbq09dBiGITMvXy6Xd9rz+Vdl5nkn+D7/OyKet7W1desjR47cKjOfHRGvHXb+rI+P9T6S1qj6AQoATFe5HX7jwT89zm3K+7fWtjLzpRHx4Yi4OjMfMwzDDYZhGDJzXK1Wd9v9+Zl5TWvtPnu/z3K5PBIRz8/Md2XmB3d+p+Ts/byPpDWqfoACANNVbofqC4ik63nVD1AAYLrK7fDsh/z0OLfK80jacNUPUABgusrt8Jxvf+I4t8rzSNpw1Q9QAGC6yu3gAiJpraofoADAdJXbwQVE0lpVP0ABgOkqt4MLiKS1qn6AAgDTVW6H537HE8e5VZ5H0oarfoACANNVbofnP/RnxrlVnkfShqt+gAIA01VuBxcQSWtV/QAFAKar3A4vOO9nxrlVnkfShqt+gAIA01VuBxcQSWtV/QAFAKar3A4v/M4njXOrPI+kDVf9AAUApqvcDi4gktaq+gEKAExXuR1cQCStVfUDFACYrnI7/ObDnjTOrfI8kjZc9QMUAJiucju4gEhaq+oHKAAwXeV2eNF3PXmcW+V5JG246gcoADBd5XZ48X978ji3yvNI2nDVD1AAYLrK7eACImmtqh+gAMB0ldvht777KePcKs8jacNVP0ABgOkqt4MLiKS1qn6AAgDTVW4HFxBJQ2vtyyLinZn5rKlfW/0ABQCm67En9ttLHv6UcW6V55E0sYj4vsx8Y2a+2gUEAM4MPTbFfnMBkc7wIuJhy+XylhHxDBcQADgz9NgU++2l//2p49wqzyPpFHMBAYAzR48tsd9cQCQNw+ACAgBnkh5bYr+97HueOs6t8jySTjEXEAA4c/TYEvvtd773aePcKs8j6RRzAQGAM0ePLbHfXEAkDcPgAgIAZ5IeW2K/vfz7njbOrfI8kk4xFxAAOHP02BL7zQVEOsOLiGM7rt1xLCKO7ffrqx+gAMB0PbfFdeUCImmtqh+gAMB0ldvhd7//6ePcKs8jacNVP0ABgOkqt4MLiKS1qn6AAgDTVW6HVzzi6ePcKs8jacNVP0ABgOkqt4MLiKS1qn6AAgDTVW6H33vkz45zqzyPpA1X/QAFAKar3A6//4M/O86t8jySNlz1AxQAmK5yO7iASFqr6gcoADBd5XZwAZG0VtUPUABgusrt8AeP+rlxbpXnkbThqh+gAMB0ldvBBUTSWlU/QAGA6Sq3wyv/x8+Nc6s8j6QNV/0ABQCmq9wOLiCS1qr6AQoATFe5HV71QxeMc6s8j6QNV/0ABQCmq9wOf/joC8a5VZ5H0oarfoACANNVbgcXEElrVf0ABQCmq9wOLiCS1qr6AQoATFe5HVxAJK1V9QMUAJiucjv84WN+fpxb5XkkbbjqBygAMF3ldvijx144zq3yPJI2XPUDFACYrnI7uIBIWqvqBygAMF3ldnj1D184zq3yPJI2XPUDFACYrnI7uIBIWqvqBygAMF3ldvjjx/3COLfK80jacNUPUABgusrt4AIiaa2qH6AAwHSV28EFRNJaVT9AAYDpKrfDn/zoL4xzqzyPpA1X/QAFAKar3A4uIJLWqvoBCgBMV7kdXvP4XxznVnkeSRuu+gEKAExXuR0u+p+/OM6t8jySNlz1AxQAmK5yO7iASFqr6gcoADBd5Xb40x/7pXFuleeRtOGqH6AAwHSV28EFRNJaVT9AAYDpKrdD9QVke3v7aES8JDPfHRFXRcQThmG44d7Pi4iXR8SxPa5trT16GIZha2vrphHxxIh4W0S8NyJ+d7Va3fb412fmWzLzn/d8/YvW/gWUzvSqH6AAwHSV2+HPnvBL49ymvH9EXNpau2C5XN5yuVxGRFzRWnv4dX3dYrH4pMx8x3K5vN0wDENr7ckRcelyuTxy1llnfWJmXhgRf3388zPzLa21L5z8CyTpY1f9AAUApqvcDpUXkOVyeW5EfOTIkSO3Ov5aRDwkMy+/rq/NzKdk5lN3fd3jVqvVFx3//1er1W0zczx8+PChnc93AZF6VP0ABQCmq9wOF//4M8a57fe9I+LBEfGm3a9l5h0yczxw4MDNT/Z1q9WqZeb7W2sHP8bn3D0i/mmxWNxk5/u+JTNfEBF/k5kfiIjnrlar/3u/P6ukk1T9AAUApqvcDpUXkMx8ZGZevPu1ncvFePTo0e2P8XUXRsQTT/bxI0eO3Coi/joifmTX17wgM3/g4MGDN1utVocz8/cj4lX7/VklnaTqBygAMF3ldrj4J58xzm2/771zAblk92vXdQHZ2tq6dWZec7KPHz169P/JzNdl5rOHYbjRyd47Im6TmWNrbbXfn1fSCTp3+SUjcHqqHkhAP5Xb4ZLznznObb/vnZkPyswrd78WEXeMiI8ePHjwZif6moh4YES84UQfa62tdv5Rq6cOH+PyMQz/+l/NyswxM79gvz+vpBNUPZCAfqoHEtBP5XaovIC01m4fEdceOnTowPHXMvO8iLjsZF+TmS/MzPP3vn7o0KEDEfE3rbUf2vuxnf/U79Nbazfe9d6fuXMBOXu/P6+kE1Q9kIB+qgcS0E/ldqi8gAzDMETERZl5YWvtFovF4pzMfHNr7aHDMAyZeflyubzTns+/KjPP2/t9MvPnI+LXTvQeO7/b8fbMfMpZZ531iYcPHz4UEa/y54BIM1Q9kIB+qgcS0E/ldqi+gLTWtjLzpRHx4Yi4OjMfMwzDDa+FVSEAABWZSURBVIZhGDJzXK1Wd9v9+Zl5TWvtPnu/T0Rce4I/aPBYa+0bh2EYlsvl7SLid3f+kML3RsQvLRaLTzr1XzlJwzC4gMDprHogAf1UbodLf+qZ49wqzyNpw1UPJKCf6oEE9FO5Hf73T//yOLfK80jacNUDCeineiAB/VRuBxcQSWtVPZCAfqoHEtBP5Xb48yf+8ji3yvNI2nDVAwnop3ogAf1UbgcXEElrVT2QgH6qBxLQT+V2+POf+V/j3CrPI2nDVQ8koJ/qgQT0U7kdXEAkrVX1QAL6qR5IQD+V28EFRNJaVQ8koJ/qgQT0U7kdXvvkXxnnVnkeSRuueiAB/VQPJKCfyu3wuqf8yji3yvNI2nDVAwnop3ogAf1UbgcXEElrVT2QgH6qBxLQT+V2eP1Tf3WcW+V5JG246oEE9FM9kIB+KreDC4iktaoeSEA/1QMJ6KdyO7zhab86zq3yPJI2XPVAAvqpHkhAP5XbwQVE0lpVDySgn+qBBPRTuR1cQCStVfVAAvqpHkhAP5Xb4Q1P/7VxbpXnkbThqgcS0E/1QAL6qdwOLiCS1qp6IAH9VA8koJ/K7XDZz/36OLfK80jacNUDCeineiAB/VRuh7+44NfHuVWeR9KGqx5IQD/VAwnop3I7uIBIWqvqgQT0Uz2QgH4qt8Nf/PyzxrlVnkfShqseSEA/1QMJ6KdyO7iASFqr6oEE9FM9kIB+KreDC4iktaoeSEA/1QMJ6KdyO/zlhc8a51Z5HkkbrnogAf1UDySgn8rt4AIiaa2qBxLQT/VAAvqp3A5/9Yu/Mc6t8jySNlz1QAL6qR5IQD+V28EFRNJaVQ8koJ/qgQT0U7kdLv+lZ49zqzyPpA1XPZCAfqoHEtBP5Xa4/BnPHudWeR5JG656IAH9VA8koJ/K7eACImmtqgcS0E/1QAL6qdwOLiCS1qp6IAH9VA8koJ/K7eACIp0Btda+LCLemZnP2vux1Wp1v8x8fWZ+ICIuba196ZTvXT2QgH6qBxLQz3wrY3pvfOZzxrlVnkfSniLi+zLzjZn56r0XkIj4jIg4lplfvlgsbtJau39EfKi1trXf7189kIB+qgcS0M/8i2P/XfHLzx3nVnkeSXuKiIctl8tbRsQz9l5AMvMpEfH8PZ9/UWvtEfv9/tUDCeineiAB/cy1M04lFxDpDOlEF5CIeE1EPGrPa0+LiOft9/tWDySgn+qBBPQz1744la74leeOc6s8j6STdJILyBWttYfuee1xEfGq/X7f6oEE9FM9kIB+5toXp5ILiHSGdLILSGaet+c1FxBgPHfpAgKns7n2xan017/6vHFuleeRdJJO8u+A/HFr7dF7Pu/pEfGc/X7f6oEE9FM9kIB+5toXp5ILiHSGdJLfAfmZiHjR7tcy85KI+J79ft/qgQT0Uz2QgH7m2henkguIdIZ0ogvIarW6bUT8U0TcY+c/w/stmfn+xWLxX/b7fasHEtBP9UAC+pl/aey/N/3a88e5VZ5H0p4i4tiOa3cci4hjuz5+751/F+SaiPjziLjzlO9fPZCAfqoHEtDP/Itj/7mASFqr6oEE9FM9kIB+KrfDm379+ePcKs8jacNVDySgn+qBBPRTuR3+5jdeMM6t8jySNlz1QAL6qR5IQD+V28EFRNJaVQ8koJ/qgQT0U7kd/ubZLxznVnkeSRuueiAB/VQPJKCfyu3gAiJpraoHEtBP9UAC+qncDi4gktaqeiAB/VQPJKCfyu3w5ue8cJxb5XkkbbjqgQT0Uz2QgH4qt4MLiKS1qh5IQD/VAwnop3I7XPnc3xznVnkeSRuueiAB/VQPJKCfyu3gAiJpraoHEtBP9UAC+qncDlc+/0Xj3CrPI2nDVQ8koJ/qgQT0U7kd3vKCF49zqzyPpA1XPZCAfqoHEtBP5XZwAZG0VtUDCeineiAB/VRuBxcQSWtVPZCAfqoHEtBP5XZ4ywtfPM6t8jySNlz1QAL6qR5IQD+V28EFRNJaVQ8koJ/qgQT0U7kdrvrN3xrnVnkeSRuueiAB/VQPJKCfyu3gAiJpraoHEtBP9UAC+qncDm998UvGuVWeR9KGqx5IQD/VAwnop3I7vPW3XjrOrfI8kjZc9UAC+qkeSEA/lduh+gKyvb19NCJekpnvjoirIuIJwzDccO/nRcTLI+LYHte21h49DMOwWCxuEhE/GxFvy8x3RcRzzz777E+e+j6SJlY9kIB+qgcS0E/ldvjbl7xsnNuU94+IS1trFyyXy1sul8uIiCtaaw+/rq9bLBaflJnvWC6XtxuGYcjM8zPz4tVqdXhra+vWEfG8iHjRuu8j6TqqHkhAP9UDCeincjtUXkCWy+W5EfGRI0eO3Or4axHxkMy8/Lq+NjOfkplP3fl/Py4i3tta+4rjH18sFudExEcPHz58aJ33kXQdVQ8koJ/qgQT0U7kdKi8gEfHgiHjT7tcy8w6ZOR44cODmJ/u61WrVMvP9rbWDwzAM29vbn5KZY2tta8/3/1Br7V6n+j6S9lH1QAL6qR5IQD+V2+FvX/qycW77fe/MfGRmXrz7tZ3LxXj06NHtj/F1F0bEE3d9zedl5njWWWd94u7Pi4i3RcQDT/V9JO2j6oEE9FM9kIB+KrfDf4ILyCW7X7uui8HW1tatM/Oa3R8/fgE5ePDgzXZ/7p4LyKT3kbTPqgcS0E/1QAL6qdwOb/vt3x7ntt/3zswHZeaVu1+LiDtGxEf3XiZ2ffyBEfGG3a8tl8vIzHF7e/vons/9cETc41TeR9I+qx5IQD/VAwnop3I7VF5AWmu3j4hrDx06dOD4a5l5XkRcdrKvycwXZub5e16+UWa+JyLuffyF1Wp124i4dnt7+6xTeR9J+6x6IAH9VA8koJ/K7fC23/mdcW5T3j8iLsrMC1trt1gsFudk5ptbaw8dhmHIzMuXy+Wd9nz+VZl53t7vk5k/FhGXrlarw2efffYnR8SLI+I5+3kfSWtUPZCAfqoHEtBP5XZ4+8tfPs5tyvu31rYy86U7/7jU1Zn5mGEYbjAMw5CZ42q1utvuz8/Ma1pr9znBt/r4zHxqZr4nM98fEb+2XC5vuZ/3kbRG1QMJ6Kd6IAH9VG6H6guIpOt51QMJ6Kd6IAH9VG4HFxBJa1U9kIB+qgcS0E/ldnABkbRW1QMJ6Kd6IAH9VG6Ht7/id8e5VZ5H0oarHkhAP9UDCeincju84/deMc6t8jySNlz1QAL6qR5IQD+V28EFRNJaVQ8koJ/qgQT0U7kd3vH7rxjnVnkeSRuueiABANNVbgcXEElrVf0ABQCmq9wOf/cHvzfOrfI8kjZc9QMUAJiucju4gEhaq+oHKAAwXeV2cAGRtFbVD1AAYLrK7fB3r/r9cW6V55G04aofoADAdJXbwQVE0lpVP0ABgOkqt8PVf/gH49wqzyNpw1U/QAGA6Sq3w9V/9MpxbpXnkbThqh+gAMB0ldvBBUTSWlU/QAGA6Sq3wztf/apxbpXnkbThqh+gAMB0ldvBBUTSWlU/QAGA6Sq3gwuIpLWqfoACANNVbod3/skfjnOrPI+kDVf9AAUApqvcDi4gktaq+gEKAExXuR3+/jV/NM6t8jySNlz1AxQAmK5yO7iASFqr6gcoADBd5Xb4+4tePc6t8jySNlz1AxQAmK5yO/zDn/3xOLfK80jacNUPUABgusrt4AIiaa2qH6AAwHSV28EFRNJaVT9AAYDpKrfDP1z8J+PcKs8jacNVP0ABgOkqt4MLiKS1qn6AAgDTVW6Hd13ymnFuleeRtOGqH6AAwHSV28EFRNJaVT9AAYDpKrfDuy69aJxb5XkkbbjqBygAMF3ldnj3n//pOLfK80jacNUPUABgusrt4AIiaa2qH6AAwHSV28EFRNJaVT9AAYDpKreDC4iktap+gAIA01Vuh3e/9s/GuVWeR9KGq36AAgDTVW6Hd7/u4nFuleeRtOGqH6AAwHSV28EFRNJaVT9AAYDpKrfDe15/yTi3yvNI2nDVD1AAYLrK7eACImmtqh+gAMB0ldvhPZddOs6t8jySNlz1AxQAmK5yO7iASFqr6gcoADBd5XZwAZG0VtUPUABgusrt8I9/8efj3CrPI52Rtda+LCLemZnP2vux1Wp1v8x8fWZ+ICIuba196a4P37C19qOZ+eaI+MeI+O3MXE557+oHKAAw3drjY41cQKTreRHxfZn5xsx89d4LSER8RkQcy8wvXywWN2mt3T8iPtRa29r5+Hdm5pURcZsDBw7cvLX25Mx83TAMN9jv+1c/QAGA6WaeI5P6x7987Ti3yvNIZ1wR8bDlcnnLiHjG3gtIZj4lIp6/5/Mvaq09Yud/XxYRDzv+sQMHDtw8M/+ltfY5+33/6gcoADDduvtjnd77V68b51Z5HumM7UQXkIh4TUQ8as9rT4uI521tbd00Ij66XC7vtOfjfxkR37nf961+gAIA0623OtbLBUQ6TTrJBeSK1tpD97z2uIh41eHDhw9l5tha+9TdH9/5R7keu9/3rX6AAgDTrbc61uu9b3z9OLfK80hnbCe7gGTmeXte+3cXkNVqddvdH3cBAYDT33qrY71cQKTTpJP8OyB/3Fp79J7Pe3pEPGexWNwkIq5trd1lz9f81d7fNflYVT9AAYDp1lsd6+UCIp0mneR3QH4mIl60+7XMvCQivmfnf7+utfbw4x9bLBaflJn/slwuz93v+1Y/QAGA6dbdHev0viveMM6t8jzSGduJLiCr1eq2EfFPEXGPnf8M77dk5vsXi8V/2fmah0TEVYvF4pwDBw7cPCJ+LjP/bMr7Vj9AAYDp5twgU3MBka7nRcSxHdfuOBYRx3Z9/N47/y7INRHx5xFx511ffoPMfGxEvDMiPhwRLzn+Z4Tst+oHKAAw3WxD5BR6319fNs6t8jySNlz1AxQAmK5yO7iASFqr6gcoADBd5XZ435v+cpxb5XkkbbjqBygAMF3ldnj/m/9qnFvleSRtuOoHKAAwXeV2cAGRtFbVD1AAYLrK7eACImmtqh+gAMB0ldvBBUTSWlU/QAGA6Sq3w/uvvHycW+V5JG246gcoADBd5Xb4wFveOM6t8jySNlz1AxQAmK5yO1RfQLa3t49GxEsy890RcVVEPGEYhhue6HMXi8U5EfHKnT+w+W9ba999/GO7/jDof5OZY2vtLsMwDJk57vxB0P/28dbak9f6xZPkAgIA10eV2+EDV/31OLcp7x8Rl7bWLlgul7dcLpcREVe01h6+9/O2trZuunNB+d5Dhw59wnK5/OyIuGyxWJxzou+7Wq0+PzOv3Nrauukw/OsFZLFYLE7pF0nSyat+gAIA01Vuh8oLyHK5PDciPnLkyJFbHX8tIh6SmZfv/dzMfEBmvn6f3/pGEfHa1tp9d329C4jUo+oHKAAwXeV2+ODfvmmc237fOyIeHBFv2v1aZt4hM8cDBw7cfM/rPx8RvxoRvxgR783My1tr9z/J931gRPzpnq8fM/NZEfHWiHhva+2CgwcP3mzKr5WkE1T9AAUApqvcDpUXkMx8ZGZevPu11WrVMnM8evTo9p7PfVlEfCgivv7QoUOfkJkPiIhrW2u33/Ntb5iZb1ytVl+1+8WIeE1r7dtaazeOiNtk5usz85cn/4JJ+vdVP0ABgOkqt8N/ggvIJbtfO9kFJCJ+OyL+cM9rr2mtPX7Pa/fMzCuHYbjBx3rv1Wp194j4SGvtxvv9eSWdoOoHKAAwXeV2+ODb/mac237fOzMftHNZ+Lci4o4R8dG9/3hURDwjIl6057Vfj4hf3P1aa+2Z+/mvW+38Lsi4Wq0O7/fnlXSCqh+gAMB0ldvhQ29/8zi3/b53a+32EXHtoUOHDhx/LTPPi4jL9n5uRHxfZr5l2PU7Gzu/A/LoXZ92g8x8T2vtXnvfJzPP3/P97h0Rx4ZhuNF+f15JJ6j6AQoATFe5HSovIMMwDBFxUWZe2Fq7xWKxOCcz39xae+gwDENmXr5cLu80DMNw+PDhQ5n5wYj4wZ3/JO/XZeY/t9ZWx7/X0aNHt3d+V+O2u98jM8/OzA+01h7RWrvxcrnMiPiLiHjSHL+G0hld9QMUAJiucjt86B1vGec25f1ba1uZ+dKdP1zw6sx8zLDzuxw7l4m77frcu0TEa3f+IMErVqvV3fd8r8/NzHH376gcLyLuHBF/kpkfyMx3Zeb5i8XiJqf2qybp36p+gAIA01Vuh+oLiKTredUPUABgusrt8KGrrxrnVnkeSRuu+gEKAExXuR1cQCStVfUDFACYrnI7uIBIWqvqBygAMF3ldvjwO986zq3yPJI2XPUDFACYrnI7uIBIWqvqBygAMF3ldvjw379tnFvleSRtuOoHKAAwXeV2+Kd/ePs4t8rzSNpw1Q9QAGC6yu3gAiJpraofoADAdJXb4Z/e9Xfj3CrPI2nDVT9AAYDpKreDC4iktap+gAIA01VuBxcQSWtV/QAFAKar3A7H3nP1OLfK80jacNUPUABgusrt4AIiaa2qH6AAwHSV2+HYP75znFvleSRtuOoHKAAwXeV2cAGRtFbVD1AAYLrK7XDNe/9hnFvleSRtuOoHKAAwXeV2uOZ97xrnVnkeSRuu+gEKAExXuR1cQCRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiTpdGixWCwyc1wsFudU/yyS5stf25Ik6T9le0fKcrk8NyLeFBEXVf9skk693X9tn3322Z/cWntmRFydme+OiOevVqvD1T+jJEk6A9s9Ulpr94+It2bmy1xApOt3u//ajogXR8RvHzp06MDW1tatI+K3MvMV1T+jJEk6A9tzAfnWw4cPH8rMx7iASNfvjv+1HRG3iYinr1ar2x7/WETcMyKODcNwg8IfUZIknYmd6J8TdwGRrv99rH8HpLX27Zl5ZcXPJUmSzvBcQKTTs5NdQHZef1dr7VurfjZJknQG5wIinZ6d6K/tnX8f5K2ZeX7lzyZJks7gXECk07O9f21n5h0y812Z+QPVP5skSTqDcwGRTs92/7W9XC4jM/8hIr65+ueSJElneC4g0unZnv8M78tba4+v/pkkSZL+3UjJzDdGxLGI+EhEfHTnfx/b3t4+Wv1zSprW8b+2M/MLdv7vNcf/mt7lztU/pyRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiSdTv0fRjU0r8d+N88AAAAASUVORK5CYII=\" width=\"800\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the following function to help visualize results from the grid search\n",
    "def GridSearch_Heatmap(scores):\n",
    "    %matplotlib notebook\n",
    "    import seaborn as sns\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure()\n",
    "    sns.heatmap(scores.reshape(5,2), xticklabels=['l1','l2'], yticklabels=[0.01, 0.1, 1, 10, 100])\n",
    "    plt.yticks(rotation=0);\n",
    "\n",
    "GridSearch_Heatmap(answer_six())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-machine-learning",
   "graded_item_id": "5yX9Z",
   "launcher_item_id": "eqnV3",
   "part_id": "Msnj0"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}