Exercise 2/Exercise 2 scriptToyData.ipynb

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   "source": [
    "%matplotlib nbagg\n",
    "import os\n",
    "import numpy as np\n",
    "import scipy.io as sio\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import FancyArrowPatch\n",
    "from mpl_toolkits.mplot3d import proj3d\n",
    "path = os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class Arrow3D(FancyArrowPatch):\n",
    "    def __init__(self, xs, ys, zs, *args, **kwargs):\n",
    "        FancyArrowPatch.__init__(self, (0,0), (0,0), *args, **kwargs)\n",
    "        self._verts3d = xs, ys, zs\n",
    "    def draw(self, renderer):\n",
    "        xs3d, ys3d, zs3d = self._verts3d\n",
    "        xs, ys, zs = proj3d.proj_transform(xs3d, ys3d, zs3d, renderer.M)\n",
    "        self.set_positions((xs[0],ys[0]),(xs[1],ys[1]))\n",
    "        FancyArrowPatch.draw(self, renderer) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "def usingSVD(dataMatrix, desiredVariancePercentage=1.0):\n",
    "    # This function should implement the PCA using the Singular Value\n",
    "    # Decomposition (SVD) of the given dataMatrix\n",
    "    \n",
    "    # De-Meaning the feature space\n",
    "    meanDataMatrix = dataMatrix.mean(1)\n",
    "    demeanedDataMatrix = (dataMatrix.T - meanDataMatrix)\n",
    "    \n",
    "    # SVD Decomposition\n",
    "    # You need to transpose the data matrix\n",
    "    U, s, V = np.linalg.svd(demeanedDataMatrix / np.sqrt(dataMatrix.shape[1]-1)) # Divide by sqrt(N-1) to get consistent results\n",
    "    V = V.T     # Numpy gives V transposed\n",
    "    \n",
    "    # Enforce a sign convention on the coefficients -- the largest element (absolute) in each\n",
    "    # column will have a positive sign.\n",
    "    for i in range(V.shape[0]):\n",
    "        if V[:,i][np.argmax(np.fabs(V[:,i]))] < 0:\n",
    "            V[:,i] = -V[:,i]\n",
    "            \n",
    "    # Compute the accumelative Eigenvalues to finde the desired\n",
    "    # Variance\n",
    "    s = s**2\n",
    "    acc_eigenvals = np.sum(s)\n",
    "    \n",
    "    # Keep the eigenvectors and eigenvalues of the desired\n",
    "    # variance, i.e. keep the first two eigenvectors and\n",
    "    # eigenvalues if they have 90% of variance.\n",
    "    for i in range(s.shape[0]):\n",
    "        if np.sum(s[:i+1])/acc_eigenvals >= desiredVariancePercentage:\n",
    "            eigenvals = s[:i+1]\n",
    "            eigenvecs = V[:,:i+1]\n",
    "            break\n",
    "    \n",
    "    # Project the data\n",
    "    projectedData = np.dot(demeanedDataMatrix, eigenvecs)\n",
    "    \n",
    "    # Return Data\n",
    "    return eigenvecs, eigenvals, meanDataMatrix, demeanedDataMatrix.T, projectedData.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "def usingCOV(dataMatrix, desiredVariancePercentage=1.0):\n",
    "    # This function should implement the PCA using the\n",
    "    # EigenValue Decomposition of a given Covariance Matrix \n",
    "    \n",
    "    # De-Meaning the feature space \n",
    "    meanDataMatrix = dataMatrix.mean(1)\n",
    "    demeanedDataMatrix = (dataMatrix.T - meanDataMatrix)\n",
    "            \n",
    "    # Computing the Covariance \n",
    "    covMatrix = np.dot(demeanedDataMatrix.T, demeanedDataMatrix)\n",
    "            \n",
    "    # Eigen Value Decomposition\n",
    "    # In COV, you need to order the eigevectors according to largest eigenvalues\n",
    "    eigenValues, eigenVectors = np.linalg.eig(covMatrix / (dataMatrix.shape[1] - 1))\n",
    "    idx = eigenValues.argsort()[::-1]\n",
    "    eigenValues = eigenValues[idx]\n",
    "    eigenVectors = eigenVectors[:,idx]\n",
    "    \n",
    "    # Enforce a sign convention on the coefficients -- the largest element (absolute) in each\n",
    "    # column will have a positive sign.\n",
    "    for i in range(eigenVectors.shape[0]):\n",
    "        if eigenVectors[:,i][np.argmax(np.fabs(eigenVectors[:,i]))] < 0:\n",
    "            eigenVectors[:,i] = -eigenVectors[:,i]\n",
    "\n",
    "\n",
    "    # Compute the accumelative Eigenvalues to finde the desired\n",
    "    # Variance \n",
    "    acc_eigenvals = np.sum(eigenValues)\n",
    "            \n",
    "    # Keep the eigenvectors and eigenvalues of the desired\n",
    "    # variance, i.e. keep the first two eigenvectors and\n",
    "    # eigenvalues if they have 90% of variance. \n",
    "    for i in range(eigenValues.shape[0]):\n",
    "        if np.sum(eigenValues[:i+1])/acc_eigenvals >= desiredVariancePercentage:\n",
    "            eigenvals = eigenValues[:i+1]\n",
    "            eigenvecs = eigenVectors[:,:i+1]\n",
    "            break\n",
    "            \n",
    "    # Project the data \n",
    "    projectedData = np.dot(demeanedDataMatrix, eigenvecs)\n",
    "    \n",
    "    # Return data\n",
    "    return eigenvecs, eigenvals, meanDataMatrix, demeanedDataMatrix.T, projectedData.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = sio.loadmat(os.path.join(path,'data','toydata.mat'))['D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eigenvecs,eigenvals,meanDataMatrix,demeanedDataMatrix,projectedData = usingSVD(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "\n",
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       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
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       "    fig.ondownload(fig, format);\n",
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       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
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       "\n",
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       "\n",
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       "\n",
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       "}\n",
       "\n",
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       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
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       "    var cursor = msg['cursor'];\n",
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       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        break;\n",
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       "        break;\n",
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       "    }\n",
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       "\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;\n",
       "    var y = canvas_pos.y;\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",
       "    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 + '\">');\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 dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\">';\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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\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(data[0], data[1], data[2], c='r', marker='o')\n",
    "ax.scatter(projectedData[0], projectedData[1], projectedData[2], c='b', marker='o')\n",
    "for i in range(eigenvecs.shape[1]):\n",
    "    v = eigenvecs.T[i]\n",
    "    e = eigenvals[i]\n",
    "    a = Arrow3D([meanDataMatrix[0], (e*v[0])+meanDataMatrix[0]],\n",
    "                [meanDataMatrix[1], (e*v[1])+meanDataMatrix[1]], \n",
    "                [meanDataMatrix[2], (e*v[2])+meanDataMatrix[2]], \n",
    "                mutation_scale=20, \n",
    "                lw=2, arrowstyle=\"-|>\", color=\"r\")\n",
    "    ax.add_artist(a)\n",
    "b = Arrow3D([0, eigenvals[0]], [0,0], [0,0], mutation_scale=20, lw=2, arrowstyle=\"-|>\", color=\"b\")\n",
    "c = Arrow3D([0, 0], [0,eigenvals[1]], [0,0], mutation_scale=20, lw=2, arrowstyle=\"-|>\", color=\"b\")\n",
    "d = Arrow3D([0, 0], [0,0], [0,eigenvals[2]], mutation_scale=20, lw=2, arrowstyle=\"-|>\", color=\"b\")\n",
    "ax.add_artist(b)\n",
    "ax.add_artist(c)\n",
    "ax.add_artist(d)\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('Z')\n",
    "plt.draw()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}