1 Ejercicio Red Neuronal suma resta multi divi.ipynb

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   "source": [
    "## Ejericio para crear una red nueronal que aprenda a sumar, restar, dividir y multiplicar\n",
    "Es un ejercicio extraido desde youtube, el link es el siguiente\n",
    "https://www.youtube.com/watch?v=eZpSGS7vF5Y\n",
    "\n",
    "- difundir\n",
    "- compatir\n",
    "- aprender\n",
    "- hacer\n",
    "- volver aprender\n",
    "\n",
    "-> simpre busca la solución mas sencilla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_random_ops(rows=100):\n",
    "    data = []\n",
    "    for i in range(0, rows):\n",
    "        a = random.randint(1, 100)\n",
    "        b = random.randint(1, 100)\n",
    "        suma, resta, multi, divi = random.choice([\n",
    "            [1, 0, 0, 0],\n",
    "            [0, 1, 0, 0],\n",
    "            [0, 0, 1, 0],\n",
    "            [0, 0, 0, 1],\n",
    "        ])\n",
    "        if suma == 1:\n",
    "            y = a+b\n",
    "        if resta == 1:\n",
    "            y = a-b\n",
    "        if multi == 1:\n",
    "            y = a*b\n",
    "        if divi == 1:\n",
    "            y = a/b\n",
    "\n",
    "        data.append({\n",
    "            \"a\": a,\n",
    "            \"b\": b,\n",
    "            \"suma\": suma,\n",
    "            \"resta\": resta,\n",
    "            \"multi\": multi,\n",
    "            \"divi\": divi,\n",
    "            \"y\": round(y, 2)\n",
    "        })\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
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       "      <th>suma</th>\n",
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       "      <th>divi</th>\n",
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       "      <td>11</td>\n",
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       "      <td>0</td>\n",
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       "      <td>880.00</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.14</td>\n",
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      "text/plain": [
       "    a   b  suma  resta  multi  divi       y\n",
       "0  11  80     0      0      1     0  880.00\n",
       "1  19  86     0      1      0     0  -67.00\n",
       "2  20  41     0      0      1     0  820.00\n",
       "3  58  39     1      0      0     0   97.00\n",
       "4   9  63     0      0      0     1    0.14"
      ]
     },
     "execution_count": 56,
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   ],
   "source": [
    "# paso 1\n",
    "# data = pd.DataFrame(get_random_ops(rows=25000))\n",
    "# paso 2\n",
    "data = pd.DataFrame(get_random_ops(rows=1000000))\n",
    "data[[\"a\", \"b\", \"suma\", \"resta\", \"multi\", \"divi\", \"y\"]].head()"
   ]
  },
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       "\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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x0000023DDB81FB70>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023DF6E8E048>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023DF6E46198>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000023DF6E7F2E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023DF6E3B2E8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023DF6E3B320>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x0000023D86B69D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023D86BA0D68>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x0000023D86BDADD8>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data[[\"a\", \"b\", \"suma\", \"resta\", \"multi\", \"divi\"]], data[\"y\"],\n",
    "    test_size=0.30, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPRegressor(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,\n",
       "       beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(100, 100, 100), learning_rate='constant',\n",
       "       learning_rate_init=0.0001, max_iter=800, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=None,\n",
       "       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,\n",
       "       verbose=False, warm_start=False)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = MLPRegressor(\n",
    "    # paso 1\n",
    "    max_iter=800,\n",
    "    # paso 2\n",
    "    hidden_layer_sizes=(100, 100, 100),\n",
    "    learning_rate_init=0.0001,\n",
    ")\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a        66\n",
      "b        82\n",
      "suma      0\n",
      "resta     0\n",
      "multi     1\n",
      "divi      0\n",
      "Name: 878902, dtype: int64\n",
      "5412.0\n",
      "[5408.49216864]\n"
     ]
    }
   ],
   "source": [
    "print(X_test.iloc[50])\n",
    "print(y_test.iloc[50])\n",
    "print(model.predict([X_test.iloc[50]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predict: [-0.6201254846352788, -3.880607851182473, -0.73221913467327, -46.6045716290092, 52.59924718909204]\n"
     ]
    }
   ],
   "source": [
    "predict = model.predict(X_test)\n",
    "print(\"Predict: %s\" % list(predict[:5]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_check = pd.DataFrame(predict, columns=[\"predict\"])\n",
    "data_check[\"y\"]=list(y_test)\n",
    "data_check.set_index([\"y\"], drop=False, inplace=True)\n",
    "data_check.sort_values(by=[\"y\"], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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     "output_type": "display_data"
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\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23d872a04e0>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# paso con los 100.000 ejemplos y las tres neuronas.\n",
    "data_check.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paso 2"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}