scores_workspace.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# interactive in the notebook\n",
    "# %matplotlib notebook\n",
    "\n",
    "# inlined but not interactive\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from kohonen import load_data, build_and_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your digits:  [2 3 5 7]\n",
      "Loading from binary...\n"
     ]
    }
   ],
   "source": [
    "data, labels, target_digits = load_data()  # default name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Kohonen algorithm hyper-parameters\n",
    "size_k = 6        # size of the Kohonen map (size_k, size_k)\n",
    "sigma  = 3        # width of the gaussian neighborhood\n",
    "eta    = 0.01      # learning rate\n",
    "# tmax   = 3*2000  # max iteration count; substitutes convergence criterion\n",
    "tmax   = 4000  # max iteration count; substitutes convergence criterion\n",
    "\n",
    "# initialise the centers randomly\n",
    "dim = data.shape[1]     # 28*28 = 784\n",
    "data_range = 255.0\n",
    "centers = np.random.rand(size_k**2, dim) * data_range\n",
    "\n",
    "# build a neighborhood matrix\n",
    "neighbor = np.arange(size_k**2).reshape((size_k, size_k))\n",
    "\n",
    "# set the random order in which the datapoints should be presented\n",
    "idxs_random = np.arange(tmax) % data.shape[0]\n",
    "np.random.shuffle(idxs_random)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0, 12.5, 25.0, 37.5, 50.0, 62.5, 75.0, 87.5, "
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "for step, idx in enumerate(idxs_random):\n",
    "    movement, winner = som_step(centers, data[idx,:],neighbor,eta,sigma)\n",
    "    if step %  50 == 0:\n",
    "        scores.append(score(centers, data))\n",
    "    if step % 500 == 0:\n",
    "        print(step/tmax*100, end=', ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0%, 10.0%, 20.0%, 30.0%, 40.0%, 50.0%, 60.0%, 70.0%, 80.0%, 90.0%, 100%\n",
      "0.0%, 10.0%, 20.0%, 30.0%, 40.0%, 50.0%, 60.0%, 70.0%, 80.0%, 90.0%, 100%\n",
      "0.0%, 10.0%, 20.0%, 30.0%, 40.0%, 50.0%, 60.0%, 70.0%, 80.0%, 90.0%, 100%\n",
      "0.0%, 10.0%, 20.0%, 30.0%, 40.0%, 50.0%, 60.0%, 70.0%, 80.0%, 90.0%, 100%\n",
      "0.0%, 10.0%, 20.0%, 30.0%, 40.0%, 50.0%, 60.0%, 70.0%, 80.0%, 90.0%, 100%\n"
     ]
    }
   ],
   "source": [
    "sigma = 3\n",
    "epoch=50\n",
    "etas = [0.9, 0.5, 0.3, 0.1, 0.05]\n",
    "all_centers, all_scores = [], []\n",
    "for eta in etas:\n",
    "    centers, scores = build_and_train(data, eta=eta, tmax=2500, sigma=sigma, epoch=epoch, seed=6)\n",
    "    all_centers.append(centers)\n",
    "    all_scores.append(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
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       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
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       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
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       "\n",
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       "\n",
       "\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",
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       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
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       "    // Updates the figure title.\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",
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       "}\n",
       "\n",
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       "    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",
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       "                    fig.imageObj.src);\n",
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       "\n",
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       "                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"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7fdc91ff6ba8>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib notebook\n",
    "f, (s1, s2) = plt.subplots(1, 2, sharey=True, figsize=(8,4))\n",
    "x = np.arange(0, len(all_scores[3]) * epoch, epoch)\n",
    "# f.ylim(3800,5500)\n",
    "# s = f.add_subplot(111, frameon=False)\n",
    "# plt.tick_params(labelcolor='none', top='off', bottom='off', left='off', right='off')\n",
    "\n",
    "s1.set_xlabel('Iterations')\n",
    "s2.set_xlabel('Iterations')\n",
    "s1.set_ylim(3800, 6000)\n",
    "# s2.set_ylim(0, 6000)\n",
    "\n",
    "s1.set_ylabel('Score')\n",
    "\n",
    "s1.set_title('sigma = 3, eta = %.1f' % etas[0])\n",
    "s2.set_title('sigma = 3, eta = %.1f' % etas[3])\n",
    "\n",
    "s1.plot(x, all_scores[0])\n",
    "s2.plot(x, all_scores[3])\n",
    "\n",
    "f.suptitle('Distance from data')\n",
    "\n",
    "# plt.savefig('dist_datax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0%, 10.0%, 20.0%, 30.0%, 40.0%, 50.0%, 60.0%, 70.0%, 80.0%, 90.0%, 100%\n"
     ]
    }
   ],
   "source": [
    "centers, _ = build_and_train(data, sigma=1, tmax=2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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igc93GPsSCvbLFGiYQsbHPefRzJUv8syO/vyTXjZWnrAFCxYsjBAjtYSfI0Zq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\nw4aNNuL/AxyyDM0PImW+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc917ceac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# show centers\n",
    "# f = plt.figure()\n",
    "plt.suptitle('sigma = 1, eta = 0.1')\n",
    "for j in range(6 ** 2):\n",
    "    plt.subplot(6, 6, j + 1)\n",
    "    plt.imshow(np.reshape(centers[j, :], [28, 28]), interpolation='nearest', cmap='Greys')\n",
    "    plt.axis('off')\n",
    "# plt.savefig('som3.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 234.49084314,  135.41656848,   79.08125652,   36.56581811,\n",
       "         20.65571828])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_devs = np.array(all_scores)\n",
    "np.std(std_devs[:, 10:], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "234.49084314251249"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_devs = np\n",
    "\n",
    "np.std(all_scores[0][10:])\n",
    "# len(all_scores[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def predictNeurons(centers, data, labels, target_digits):\n",
    "    accumul = np.zeros((len(centers),4))\n",
    "    for i in range(2000):\n",
    "        winner = np.argmin(np.sum(centers - data[i], axis=1)**2)\n",
    "        accumul[winner,target_digits==labels[i]] += 1\n",
    "    for j in range(len(centers)):\n",
    "        accumul[j] = target_digits[np.argmax(accumul[j])]\n",
    "    return accumul[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "predictNeurons(centers, data, labels, target_digits)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plt.imshow(np.reshape(data[1], [28, 28]))\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "labels[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "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": 0
}