PIN_recognition.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1VHMJd8VrEegRdNX8tXyexZpTzxEY6sF1",
      "authorship_tag": "ABX9TyNXYLUXAzFndN78Fj+0kCWC",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/matteonerini/pin-side-channel-attacks/blob/main/PIN_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N4Gh6Ukcrico"
      },
      "source": [
        "# Digit Classification with SVM, RF, k-NN, and MLP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWqZf1NYb87q"
      },
      "source": [
        "## Data Pre-processing\n",
        "\n",
        "The dataset is uploaded from a .txt file and pre-processed."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "amSPm9cerIFi"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "short = True\n",
        "url = 'https://raw.githubusercontent.com/matteonerini/pin-side-channel-attacks/main/dataset.txt'\n",
        "\n",
        "names = ['Digit','Acc_X','Acc_Y','Acc_Z','Gir_X','Gir_Y','Gir_Z','Gra_X','Gra_Y','Gra_Z',\n",
        "         'Lin_X','Lin_Y','Lin_Z','Rot_Z','Ori_X','Ori_Y','M']\n",
        "\n",
        "dataset = pd.read_csv(url, names=names).values\n",
        "\n",
        "n_class = 10\n",
        "n_entries, n_feat = dataset.shape\n",
        "n_feat = n_feat -1\n",
        "test_size = 0.1 if short else 0.05\n",
        "n_test_digit = int(n_entries*test_size/n_class)\n",
        "\n",
        "X = dataset[:,1:17]\n",
        "y = dataset[:,0]\n",
        "\n",
        "# Feature scaling\n",
        "scaler=StandardScaler()\n",
        "X_scaled = scaler.fit_transform(X)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPj8YwVmdFHg"
      },
      "source": [
        "## Model Generation, Training and Testing\n",
        "\n",
        "The dataset is randomly divided in train and test sets. The model is generated, trained and tested with different seeds."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h5QX5UVtlmTu"
      },
      "source": [
        "from datetime import datetime\n",
        "import csv\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import heapq\n",
        "from itertools import combinations\n",
        "import scipy.special\n",
        "\n",
        "model = 'rf' # 'svm', 'rf', 'knn', or 'mlp'\n",
        "divType = 'prod' # 'prod' for {'svm','rf','mlp'}, or 'sum' for 'knn'\n",
        "n_div = 10\n",
        "n_seeds = 20"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YWjR4wVnmmlm"
      },
      "source": [
        "In the following cell, the diversity is implemented as follows:\n",
        "\n",
        "1. The probabilities are calculated for each class.\n",
        "2. The $d$ probabilities obtained for each class are multiplied/summed up, depending on the variable `divType`.\n",
        "3. The class with the gratest product/sum is selected."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRRr_LotLdjK"
      },
      "source": [
        "csvFileName = \"./\" + model + '-' + divType + '.csv'\n",
        "\n",
        "# Precompute all the combinations with iDiv items and the combination indexes\n",
        "comb_list_all = []\n",
        "iComb_list_all = []\n",
        "for div in range(2,n_div+1):\n",
        "    comb_list_all.append(list(combinations(range(0,n_test_digit), div)))\n",
        "    iComb_list_all.append(np.array(range(int(scipy.special.binom(n_test_digit, div)))))\n",
        "\n",
        "for seed in range(n_seeds):\n",
        "\n",
        "    # Split into train test sets with seeds from 1 to n_seeds\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=test_size, random_state=seed+1, stratify=y)\n",
        "    \n",
        "    if model == 'svm':\n",
        "        # SVM\n",
        "        print('Model: ' + model + '. Computing seed: ' + str(seed+1))\n",
        "        clf = svm.SVC(C=3, decision_function_shape='ovr', probability=True)\n",
        "        clf.fit(X_train, y_train)\n",
        "    elif model == 'rf':\n",
        "        # RF\n",
        "        print('Model: ' + model + '. Computing seed: ' + str(seed+1))\n",
        "        clf = RandomForestClassifier(n_estimators=150, min_samples_split = 4, min_samples_leaf = 2)\n",
        "        clf.fit(X_train, y_train)\n",
        "    elif model == 'knn':\n",
        "        # k-NN\n",
        "        print('Model: ' + model + '. Computing seed: ' + str(seed+1))\n",
        "        clf = KNeighborsClassifier(n_neighbors=8)\n",
        "        clf.fit(X_train, y_train)\n",
        "    elif model == 'mlp':\n",
        "        # MLP\n",
        "        print('Model: ' + model + '. Computing seed: ' + str(seed+1))\n",
        "        clf = MLPClassifier(hidden_layer_sizes=(100,50), max_iter=1000, learning_rate_init=5e-3, early_stopping=True,\n",
        "                            n_iter_no_change=25)\n",
        "        clf.fit(X_train, y_train)\n",
        "    else:\n",
        "        print('Insert a correct model name')\n",
        "\n",
        "    # div==1 (no diversity)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    conf1 = confusion_matrix(y_test, y_pred)\n",
        "    with open(csvFileName, 'a', newline='') as file:\n",
        "        writer = csv.writer(file)\n",
        "        writer.writerow(conf1.flatten())\n",
        "    y_proba = clf.predict_proba(X_test)\n",
        "    #y_proba_max = []\n",
        "    #for i in range(len(y_test)):\n",
        "    #    index_i = np.argmax(y_proba[i,:])\n",
        "    #    y_proba_max.append(index_i+1)\n",
        "    y_proba_max2 = []\n",
        "    for i in range(len(y_test)):\n",
        "        index_temp2 = heapq.nlargest(2, range(len(y_proba[i,:])), key=y_proba[i,:].__getitem__)\n",
        "        index_i2 = index_temp2[1]\n",
        "        y_proba_max2.append(index_i2+1)\n",
        "    conf2 = confusion_matrix(y_test, y_proba_max2)\n",
        "    with open(csvFileName, 'a', newline='') as file:\n",
        "        writer = csv.writer(file)\n",
        "        writer.writerow(conf2.flatten())\n",
        "\n",
        "    # div!=1 (diversity)\n",
        "    for div in range(2,n_div+1):\n",
        "\n",
        "        # The testset is ordered\n",
        "        testset = np.column_stack((y_test, X_test))\n",
        "        testset_ord = testset[testset[:,0].argsort()]\n",
        "\n",
        "        comb_list = comb_list_all[div-2]\n",
        "        iComb_list = iComb_list_all[div-2]\n",
        "        \n",
        "        conf_temp1 = []\n",
        "        conf_temp2 = []\n",
        "        for j in range(n_class):\n",
        "    \n",
        "            testsetj = testset_ord[j*n_test_digit:(j+1)*n_test_digit,:]\n",
        "            y_proba_div_temp = []\n",
        "\n",
        "            np.random.seed(seed+1)\n",
        "            np.random.shuffle(testsetj)\n",
        "    \n",
        "            # List all combinations with div items\n",
        "            #comb_list = list(combinations(range(0,n_test_digit), div))\n",
        "            #iComb_list = np.array(range(int(scipy.special.binom(n_test_digit, div))))\n",
        "            #np.random.shuffle(iComb_list)\n",
        "            np.random.shuffle(iComb_list)\n",
        "\n",
        "            for iComb in range(min(len(iComb_list), 250)):\n",
        "                i = comb_list[iComb_list[iComb]]\n",
        "                if div == 2: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17])))\n",
        "                if div == 3: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17])))\n",
        "                if div == 4: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17])))\n",
        "                if div == 5: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17],\n",
        "                                                                             testsetj[i[4],1:17])))\n",
        "                if div == 6: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17],\n",
        "                                                                             testsetj[i[4],1:17], testsetj[i[5],1:17])))\n",
        "                if div == 7: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17],\n",
        "                                                                             testsetj[i[4],1:17], testsetj[i[5],1:17],\n",
        "                                                                             testsetj[i[6],1:17])))\n",
        "                if div == 8: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17],\n",
        "                                                                             testsetj[i[4],1:17], testsetj[i[5],1:17],\n",
        "                                                                             testsetj[i[6],1:17], testsetj[i[7],1:17])))\n",
        "                if div == 9: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17],\n",
        "                                                                             testsetj[i[4],1:17], testsetj[i[5],1:17],\n",
        "                                                                             testsetj[i[6],1:17], testsetj[i[7],1:17],\n",
        "                                                                             testsetj[i[8],1:17])))\n",
        "                if div == 10: y_proba_div_comb = clf.predict_proba(np.vstack((testsetj[i[0],1:17], testsetj[i[1],1:17],\n",
        "                                                                             testsetj[i[2],1:17], testsetj[i[3],1:17],\n",
        "                                                                             testsetj[i[4],1:17], testsetj[i[5],1:17],\n",
        "                                                                             testsetj[i[6],1:17], testsetj[i[7],1:17],\n",
        "                                                                             testsetj[i[8],1:17], testsetj[i[9],1:17])))\n",
        "                if divType == 'prod':\n",
        "                    y_proba_div_temp.append(np.prod(y_proba_div_comb, axis = 0))\n",
        "                elif divType == 'sum':\n",
        "                    y_proba_div_temp.append(np.sum(y_proba_div_comb, axis = 0))\n",
        "                else:\n",
        "                    print('Insert a correct divType name')\n",
        "            y_proba_div = np.array(y_proba_div_temp)\n",
        "    \n",
        "            y_proba_max_divj = []\n",
        "            y_proba_max_divj2 = []\n",
        "            for i in range(len(y_proba_div)):\n",
        "                index_i = np.argmax(y_proba_div[i,:])\n",
        "                y_proba_max_divj.append(index_i+1)\n",
        "                index_temp2 = heapq.nlargest(2, range(len(y_proba_div[i,:])), key=y_proba_div[i,:].__getitem__)\n",
        "                index_i2 = index_temp2[1]\n",
        "                y_proba_max_divj2.append(index_i2+1)\n",
        "\n",
        "            y_proba_max_divj_conf = []\n",
        "            for i in range(n_class):\n",
        "                y_proba_max_divj_conf.append(np.count_nonzero(np.array(y_proba_max_divj) == i+1))\n",
        "            conf_temp1.append(y_proba_max_divj_conf)\n",
        "\n",
        "            y_proba_max_divj_conf2 = []\n",
        "            for i in range(n_class):\n",
        "                y_proba_max_divj_conf2.append(np.count_nonzero(np.array(y_proba_max_divj2) == i+1))\n",
        "            conf_temp2.append(y_proba_max_divj_conf2)\n",
        "\n",
        "        conf1 = np.array(conf_temp1)\n",
        "        with open(csvFileName, 'a', newline='') as file:\n",
        "            writer = csv.writer(file)\n",
        "            writer.writerow(conf1.flatten())\n",
        "        conf2 = np.array(conf_temp2)\n",
        "        with open(csvFileName, 'a', newline='') as file:\n",
        "            writer = csv.writer(file)\n",
        "            writer.writerow(conf2.flatten())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1HCSyH2xVlok"
      },
      "source": [
        "## Plot Performance\n",
        "\n",
        "These cells read the .csv file and plot the data. The .csv file is formatted in the following way:\n",
        "\n",
        "```\n",
        "1        xx ... x <- div = 1, conf1, seed = 1\n",
        "2        xx ... x <- div = 1, conf2\n",
        "3        xx ... x <- div = 2, conf1\n",
        "4        xx ... x <- div = 2, conf2\n",
        "...\n",
        "2n_div-1 xx ... x <- div = 8, conf1\n",
        "2n_div   xx ... x <- div = 8, conf2\n",
        "2n_div+1 xx ... x <- div = 1, conf1, seed = 2\n",
        "2n_div+2 xx ... x <- div = 1, conf2\n",
        "```\n",
        "\n",
        "where `xx ... x` is a 100 elements confusion matrix, `conf1` means at the first attempt, and `conf2` means at the second attempt."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the confusion matrix of one predictor for div=1 and div=10\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sn\n",
        "from google.colab import widgets\n",
        "\n",
        "n_div = 10\n",
        "n_seeds = 20\n",
        "iDiv = [1, 10]\n",
        "\n",
        "dataset = pd.read_csv('rf-prod.csv', header=None).values\n",
        "#dataset = pd.read_csv('svm-prod.csv', header=None).values\n",
        "#dataset = pd.read_csv('knn-sum.csv', header=None).values\n",
        "#dataset = pd.read_csv('mlp-prod.csv', header=None).values\n",
        "\n",
        "perf_all1 = []\n",
        "perf_all2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    mat = 100*mat / (mat[0].sum())\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all1.append(mat)\n",
        "    else:\n",
        "        perf_all2.append(mat)\n",
        "\n",
        "perf1 = [0] * n_div\n",
        "for i in range(len(perf_all1)):\n",
        "    perf1[i%n_div] = perf1[i%n_div] + perf_all1[i]    \n",
        "perf1 = np.array(perf1) / n_seeds\n",
        "\n",
        "tb = widgets.TabBar(['Div '+str(i) for i in iDiv])\n",
        "\n",
        "for i in iDiv:\n",
        "    with tb.output_to('Div '+str(i)):\n",
        "        mat_cm = pd.DataFrame(np.round(perf1[i-1]), (1,2,3,4,5,6,7,8,9,0), (1,2,3,4,5,6,7,8,9,0))\n",
        "        fig, ax = plt.subplots()\n",
        "        sn.heatmap(mat_cm, annot=True, cbar=False, cmap='Blues')\n",
        "        ax.set_ylabel('True Class')\n",
        "        ax.set_xlabel('Predicted Class')\n",
        "        plt.yticks(rotation=0)\n",
        "        plt.savefig('knn-conf'+str(i)+'.pdf', bbox_inches='tight')"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/tabbar.css": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "text/css"
                ]
              ],
              "status": 200,
              "status_text": ""
            },
            "http://localhost:8080/nbextensions/google.colab/tabbar_main.min.js": {
              "data": "// clang-format off
(function(){var h,aa=function(a){var b=0;return function(){return b<a.length?{done:!1,value:a[b++]}:{done:!0}}},ba=function(a){var b="undefined"!=typeof Symbol&&Symbol.iterator&&a[Symbol.iterator];return b?b.call(a):{next:aa(a)}},l=this||self,ca=function(){},da=function(a){a.Ba=void 0;a.R=function(){return a.Ba?a.Ba:a.Ba=new a}},ea=function(a){var b=typeof a;if("object"==b)if(a){if(a instanceof Array)return"array";if(a instanceof Object)return b;var c=Object.prototype.toString.call(a);if("[object Window]"==c)return"object";
if("[object Array]"==c||"number"==typeof a.length&&"undefined"!=typeof a.splice&&"undefined"!=typeof a.propertyIsEnumerable&&!a.propertyIsEnumerable("splice"))return"array";if("[object Function]"==c||"undefined"!=typeof a.call&&"undefined"!=typeof a.propertyIsEnumerable&&!a.propertyIsEnumerable("call"))return"function"}else return"null";else if("function"==b&&"undefined"==typeof a.call)return"object";return b},fa=function(a){return"array"==ea(a)},ha=function(a){var b=ea(a);return"array"==b||"object"==
b&&"number"==typeof a.length},ia=function(a){return"function"==ea(a)},m=function(a){var b=typeof a;return"object"==b&&null!=a||"function"==b},ja="closure_uid_"+(1E9*Math.random()>>>0),ka=0,la=function(a,b){var c=Array.prototype.slice.call(arguments,1);return function(){var d=c.slice();d.push.apply(d,arguments);return a.apply(this,d)}},n=function(a,b){function c(){}c.prototype=b.prototype;a.i=b.prototype;a.prototype=new c;a.prototype.constructor=a};var ma,na={eb:"activedescendant",jb:"atomic",kb:"autocomplete",mb:"busy",pb:"checked",qb:"colindex",vb:"controls",xb:"describedby",Ab:"disabled",Cb:"dropeffect",Db:"expanded",Eb:"flowto",Gb:"grabbed",Kb:"haspopup",Mb:"hidden",Ob:"invalid",Pb:"label",Qb:"labelledby",Rb:"level",Wb:"live",kc:"multiline",lc:"multiselectable",pc:"orientation",qc:"owns",rc:"posinset",tc:"pressed",xc:"readonly",zc:"relevant",Ac:"required",Ec:"rowindex",Hc:"selected",Jc:"setsize",Lc:"sort",Zc:"valuemax",$c:"valuemin",ad:"valuenow",
bd:"valuetext"};var oa=function(a,b,c){for(var d in a)b.call(c,a[d],d,a)},pa=function(a,b){for(var c in a)if(a[c]==b)return!0;return!1},qa=function(a,b,c){if(null!==a&&b in a)throw Error('The object already contains the key "'+b+'"');a[b]=c},sa=function(a){var b={},c;for(c in a)b[a[c]]=c;return b},ta="constructor hasOwnProperty isPrototypeOf propertyIsEnumerable toLocaleString toString valueOf".split(" "),ua=function(a,b){for(var c,d,e=1;e<arguments.length;e++){d=arguments[e];for(c in d)a[c]=d[c];for(var f=0;f<ta.length;f++)c=
ta[f],Object.prototype.hasOwnProperty.call(d,c)&&(a[c]=d[c])}};var va={fb:"alert",gb:"alertdialog",hb:"application",ib:"article",lb:"banner",nb:"button",ob:"checkbox",rb:"columnheader",sb:"combobox",tb:"complementary",ub:"contentinfo",wb:"definition",yb:"dialog",zb:"directory",Bb:"document",Fb:"form",Hb:"grid",Ib:"gridcell",Jb:"group",Lb:"heading",Nb:"img",Sb:"link",Tb:"list",Ub:"listbox",Vb:"listitem",Xb:"log",Yb:"main",Zb:"marquee",$b:"math",ac:"menu",bc:"menubar",cc:"menuitem",dc:"menuitemcheckbox",ec:"menuitemradio",mc:"navigation",nc:"note",oc:"option",
sc:"presentation",uc:"progressbar",vc:"radio",wc:"radiogroup",yc:"region",Bc:"row",Cc:"rowgroup",Dc:"rowheader",Fc:"scrollbar",Gc:"search",Ic:"separator",Kc:"slider",Mc:"spinbutton",Nc:"status",Oc:"tab",Pc:"tablist",Qc:"tabpanel",Rc:"textbox",Sc:"textinfo",Tc:"timer",Uc:"toolbar",Vc:"tooltip",Wc:"tree",Xc:"treegrid",Yc:"treeitem"};var wa=function(a){if(Error.captureStackTrace)Error.captureStackTrace(this,wa);else{var b=Error().stack;b&&(this.stack=b)}a&&(this.message=String(a))};n(wa,Error);wa.prototype.name="CustomError";var xa;var ya=function(a,b){a=a.split("%s");for(var c="",d=a.length-1,e=0;e<d;e++)c+=a[e]+(e<b.length?b[e]:"%s");wa.call(this,c+a[d])};n(ya,wa);ya.prototype.name="AssertionError";
var za=function(a,b,c,d){var e="Assertion failed";if(c){e+=": "+c;var f=d}else a&&(e+=": "+a,f=b);throw new ya(""+e,f||[]);},q=function(a,b,c){a||za("",null,b,Array.prototype.slice.call(arguments,2));return a},Aa=function(a,b,c){m(a)&&1==a.nodeType||za("Expected Element but got %s: %s.",[ea(a),a],b,Array.prototype.slice.call(arguments,2))},Ca=function(a,b,c,d){a instanceof b||za("Expected instanceof %s but got %s.",[Ba(b),Ba(a)],c,Array.prototype.slice.call(arguments,3))},Ba=function(a){return a instanceof
Function?a.displayName||a.name||"unknown type name":a instanceof Object?a.constructor.displayName||a.constructor.name||Object.prototype.toString.call(a):null===a?"null":typeof a};var Da=Array.prototype.indexOf?function(a,b){q(null!=a.length);return Array.prototype.indexOf.call(a,b,void 0)}:function(a,b){if("string"===typeof a)return"string"!==typeof b||1!=b.length?-1:a.indexOf(b,0);for(var c=0;c<a.length;c++)if(c in a&&a[c]===b)return c;return-1},r=Array.prototype.forEach?function(a,b,c){q(null!=a.length);Array.prototype.forEach.call(a,b,c)}:function(a,b,c){for(var d=a.length,e="string"===typeof a?a.split(""):a,f=0;f<d;f++)f in e&&b.call(c,e[f],f,a)},Ea=Array.prototype.filter?
function(a,b){q(null!=a.length);return Array.prototype.filter.call(a,b,void 0)}:function(a,b){for(var c=a.length,d=[],e=0,f="string"===typeof a?a.split(""):a,g=0;g<c;g++)if(g in f){var k=f[g];b.call(void 0,k,g,a)&&(d[e++]=k)}return d},Fa=Array.prototype.every?function(a,b){q(null!=a.length);return Array.prototype.every.call(a,b,void 0)}:function(a,b){for(var c=a.length,d="string"===typeof a?a.split(""):a,e=0;e<c;e++)if(e in d&&!b.call(void 0,d[e],e,a))return!1;return!0},Ga=function(a,b){return 0<=
Da(a,b)},Ha=function(a,b){b=Da(a,b);var c;if(c=0<=b)q(null!=a.length),Array.prototype.splice.call(a,b,1);return c},Ia=function(a){return Array.prototype.concat.apply([],arguments)},Ja=function(a){var b=a.length;if(0<b){for(var c=Array(b),d=0;d<b;d++)c[d]=a[d];return c}return[]},La=function(a,b,c,d){q(null!=a.length);Array.prototype.splice.apply(a,Ka(arguments,1))},Ka=function(a,b,c){q(null!=a.length);return 2>=arguments.length?Array.prototype.slice.call(a,b):Array.prototype.slice.call(a,b,c)};var Ma=String.prototype.trim?function(a){return a.trim()}:function(a){return/^[\s\xa0]*([\s\S]*?)[\s\xa0]*$/.exec(a)[1]},Na=/&/g,Oa=/</g,Pa=/>/g,Qa=/"/g,Ra=/'/g,Sa=/\x00/g,Ta=/[\x00&<>"']/,t=function(a,b){return-1!=a.indexOf(b)},Ua=function(a,b){return a<b?-1:a>b?1:0};var u;a:{var Va=l.navigator;if(Va){var Wa=Va.userAgent;if(Wa){u=Wa;break a}}u=""};/*

 Copyright The Closure Library Authors.
 SPDX-License-Identifier: Apache-2.0
*/
var Xa=function(a){Ta.test(a)&&(-1!=a.indexOf("&")&&(a=a.replace(Na,"&amp;")),-1!=a.indexOf("<")&&(a=a.replace(Oa,"&lt;")),-1!=a.indexOf(">")&&(a=a.replace(Pa,"&gt;")),-1!=a.indexOf('"')&&(a=a.replace(Qa,"&quot;")),-1!=a.indexOf("'")&&(a=a.replace(Ra,"&#39;")),-1!=a.indexOf("\x00")&&(a=a.replace(Sa,"&#0;")));return a};var Ya=function(a){Ya[" "](a);return a};Ya[" "]=ca;var $a=function(a,b){var c=Za;return Object.prototype.hasOwnProperty.call(c,a)?c[a]:c[a]=b(a)};var ab=t(u,"Opera"),v=t(u,"Trident")||t(u,"MSIE"),bb=t(u,"Edge"),x=t(u,"Gecko")&&!(t(u.toLowerCase(),"webkit")&&!t(u,"Edge"))&&!(t(u,"Trident")||t(u,"MSIE"))&&!t(u,"Edge"),y=t(u.toLowerCase(),"webkit")&&!t(u,"Edge"),z=t(u,"Macintosh"),cb=function(){var a=l.document;return a?a.documentMode:void 0},db;
a:{var eb="",fb=function(){var a=u;if(x)return/rv:([^\);]+)(\)|;)/.exec(a);if(bb)return/Edge\/([\d\.]+)/.exec(a);if(v)return/\b(?:MSIE|rv)[: ]([^\);]+)(\)|;)/.exec(a);if(y)return/WebKit\/(\S+)/.exec(a);if(ab)return/(?:Version)[ \/]?(\S+)/.exec(a)}();fb&&(eb=fb?fb[1]:"");if(v){var gb=cb();if(null!=gb&&gb>parseFloat(eb)){db=String(gb);break a}}db=eb}
var hb=db,Za={},A=function(a){return $a(a,function(){for(var b=0,c=Ma(String(hb)).split("."),d=Ma(String(a)).split("."),e=Math.max(c.length,d.length),f=0;0==b&&f<e;f++){var g=c[f]||"",k=d[f]||"";do{g=/(\d*)(\D*)(.*)/.exec(g)||["","","",""];k=/(\d*)(\D*)(.*)/.exec(k)||["","","",""];if(0==g[0].length&&0==k[0].length)break;b=Ua(0==g[1].length?0:parseInt(g[1],10),0==k[1].length?0:parseInt(k[1],10))||Ua(0==g[2].length,0==k[2].length)||Ua(g[2],k[2]);g=g[3];k=k[3]}while(0==b)}return 0<=b})},ib;
ib=l.document&&v?cb():void 0;var jb=!v||9<=Number(ib);var lb=function(a,b){oa(b,function(c,d){c&&"object"==typeof c&&c.dd&&(c=c.cd());"style"==d?a.style.cssText=c:"class"==d?a.className=c:"for"==d?a.htmlFor=c:kb.hasOwnProperty(d)?a.setAttribute(kb[d],c):0==d.lastIndexOf("aria-",0)||0==d.lastIndexOf("data-",0)?a.setAttribute(d,c):a[d]=c})},kb={cellpadding:"cellPadding",cellspacing:"cellSpacing",colspan:"colSpan",frameborder:"frameBorder",height:"height",maxlength:"maxLength",nonce:"nonce",role:"role",rowspan:"rowSpan",type:"type",usemap:"useMap",valign:"vAlign",
width:"width"},mb=function(a,b,c){function d(k){k&&b.appendChild("string"===typeof k?a.createTextNode(k):k)}for(var e=2;e<c.length;e++){var f=c[e];if(!ha(f)||m(f)&&0<f.nodeType)d(f);else{a:{if(f&&"number"==typeof f.length){if(m(f)){var g="function"==typeof f.item||"string"==typeof f.item;break a}if(ia(f)){g="function"==typeof f.item;break a}}g=!1}r(g?Ja(f):f,d)}}},nb=function(a,b){b=String(b);"application/xhtml+xml"===a.contentType&&(b=b.toLowerCase());return a.createElement(b)},ob=function(a){a&&
a.parentNode&&a.parentNode.removeChild(a)},pb=function(a,b){if(!a||!b)return!1;if(a.contains&&1==b.nodeType)return a==b||a.contains(b);if("undefined"!=typeof a.compareDocumentPosition)return a==b||!!(a.compareDocumentPosition(b)&16);for(;b&&a!=b;)b=b.parentNode;return b==a},qb=function(a){q(a,"Node cannot be null or undefined.");return 9==a.nodeType?a:a.ownerDocument||a.document},rb=function(a,b){b?a.tabIndex=0:(a.tabIndex=-1,a.removeAttribute("tabIndex"))},sb=function(a){return v&&!A("9")?(a=a.getAttributeNode("tabindex"),
null!=a&&a.specified):a.hasAttribute("tabindex")},tb=function(a){a=a.tabIndex;return"number"===typeof a&&0<=a&&32768>a},ub=function(a){this.a=a||l.document||document};ub.prototype.f=function(a){return"string"===typeof a?this.a.getElementById(a):a};
ub.prototype.b=function(a,b,c){var d=this.a,e=arguments,f=String(e[0]),g=e[1];if(!jb&&g&&(g.name||g.type)){f=["<",f];g.name&&f.push(' name="',Xa(g.name),'"');if(g.type){f.push(' type="',Xa(g.type),'"');var k={};ua(k,g);delete k.type;g=k}f.push(">");f=f.join("")}f=nb(d,f);g&&("string"===typeof g?f.className=g:fa(g)?f.className=g.join(" "):lb(f,g));2<e.length&&mb(d,f,e);return f};var vb=function(a,b){b?(q(pa(va,b),"No such ARIA role "+b),a.setAttribute("role",b)):a.removeAttribute("role")},xb=function(a,b,c){fa(c)&&(c=c.join(" "));var d=wb(b);""===c||void 0==c?(ma||(ma={atomic:!1,autocomplete:"none",dropeffect:"none",haspopup:!1,live:"off",multiline:!1,multiselectable:!1,orientation:"vertical",readonly:!1,relevant:"additions text",required:!1,sort:"none",busy:!1,disabled:!1,hidden:!1,invalid:"false"}),c=ma,b in c?a.setAttribute(d,c[b]):a.removeAttribute(d)):a.setAttribute(d,
c)},wb=function(a){q(a,"ARIA attribute cannot be empty.");q(pa(na,a),"No such ARIA attribute "+a);return"aria-"+a};var yb=Object.freeze||function(a){return a};var C=function(){this.U=this.U;this.J=this.J};C.prototype.U=!1;C.prototype.N=function(){this.U||(this.U=!0,this.u())};var zb=function(a,b){a.U?b():(a.J||(a.J=[]),a.J.push(b))};C.prototype.u=function(){if(this.J)for(;this.J.length;)this.J.shift()()};var Ab=function(a){a&&"function"==typeof a.N&&a.N()};var Bb=function(a){return"string"==typeof a.className?a.className:a.getAttribute&&a.getAttribute("class")||""},D=function(a){return a.classList?a.classList:Bb(a).match(/\S+/g)||[]},Cb=function(a,b){"string"==typeof a.className?a.className=b:a.setAttribute&&a.setAttribute("class",b)},Db=function(a,b){return a.classList?a.classList.contains(b):Ga(D(a),b)},Eb=function(a,b){if(a.classList)a.classList.add(b);else if(!Db(a,b)){var c=Bb(a);Cb(a,c+(0<c.length?" "+b:b))}},Fb=function(a,b){if(a.classList)r(b,
function(e){Eb(a,e)});else{var c={};r(D(a),function(e){c[e]=!0});r(b,function(e){c[e]=!0});b="";for(var d in c)b+=0<b.length?" "+d:d;Cb(a,b)}},Gb=function(a,b){a.classList?a.classList.remove(b):Db(a,b)&&Cb(a,Ea(D(a),function(c){return c!=b}).join(" "))},Hb=function(a,b){a.classList?r(b,function(c){Gb(a,c)}):Cb(a,Ea(D(a),function(c){return!Ga(b,c)}).join(" "))};var Ib=!v||9<=Number(ib),Jb=!v||9<=Number(ib),Kb=v&&!A("9"),Lb=function(){if(!l.addEventListener||!Object.defineProperty)return!1;var a=!1,b=Object.defineProperty({},"passive",{get:function(){a=!0}});try{l.addEventListener("test",ca,b),l.removeEventListener("test",ca,b)}catch(c){}return a}();var E=function(a,b){this.type=a;this.a=this.target=b;this.h=!1;this.Ka=!0};E.prototype.j=function(){this.h=!0};E.prototype.g=function(){this.Ka=!1};var F={L:"mousedown",M:"mouseup",aa:"mousecancel",hc:"mousemove",jc:"mouseover",ic:"mouseout",fc:"mouseenter",gc:"mouseleave"};var G=function(a,b){E.call(this,a?a.type:"");this.relatedTarget=this.a=this.target=null;this.button=this.screenY=this.screenX=this.clientY=this.clientX=0;this.key="";this.c=0;this.A=this.metaKey=this.shiftKey=this.altKey=this.ctrlKey=!1;this.pointerId=0;this.pointerType="";this.b=null;if(a){var c=this.type=a.type,d=a.changedTouches&&a.changedTouches.length?a.changedTouches[0]:null;this.target=a.target||a.srcElement;this.a=b;if(b=a.relatedTarget){if(x){a:{try{Ya(b.nodeName);var e=!0;break a}catch(f){}e=
!1}e||(b=null)}}else"mouseover"==c?b=a.fromElement:"mouseout"==c&&(b=a.toElement);this.relatedTarget=b;d?(this.clientX=void 0!==d.clientX?d.clientX:d.pageX,this.clientY=void 0!==d.clientY?d.clientY:d.pageY,this.screenX=d.screenX||0,this.screenY=d.screenY||0):(this.clientX=void 0!==a.clientX?a.clientX:a.pageX,this.clientY=void 0!==a.clientY?a.clientY:a.pageY,this.screenX=a.screenX||0,this.screenY=a.screenY||0);this.button=a.button;this.c=a.keyCode||0;this.key=a.key||"";this.ctrlKey=a.ctrlKey;this.altKey=
a.altKey;this.shiftKey=a.shiftKey;this.metaKey=a.metaKey;this.A=z?a.metaKey:a.ctrlKey;this.pointerId=a.pointerId||0;this.pointerType="string"===typeof a.pointerType?a.pointerType:Mb[a.pointerType]||"";this.b=a;a.defaultPrevented&&this.g()}};n(G,E);var Nb=yb([1,4,2]),Mb=yb({2:"touch",3:"pen",4:"mouse"}),Ob=function(a){return Ib?0==a.b.button:"click"==a.type?!0:!!(a.b.button&Nb[0])};G.prototype.j=function(){G.i.j.call(this);this.b.stopPropagation?this.b.stopPropagation():this.b.cancelBubble=!0};
G.prototype.g=function(){G.i.g.call(this);var a=this.b;if(a.preventDefault)a.preventDefault();else if(a.returnValue=!1,Kb)try{if(a.ctrlKey||112<=a.keyCode&&123>=a.keyCode)a.keyCode=-1}catch(b){}};var Pb="closure_listenable_"+(1E6*Math.random()|0),Qb=function(a){return!(!a||!a[Pb])},Rb=0;var Sb=function(a,b,c,d,e){this.listener=a;this.a=null;this.src=b;this.type=c;this.capture=!!d;this.ma=e;this.key=++Rb;this.X=this.ha=!1},Tb=function(a){a.X=!0;a.listener=null;a.a=null;a.src=null;a.ma=null};var Ub=function(a){this.src=a;this.a={};this.b=0};Ub.prototype.add=function(a,b,c,d,e){var f=a.toString();a=this.a[f];a||(a=this.a[f]=[],this.b++);var g=Vb(a,b,d,e);-1<g?(b=a[g],c||(b.ha=!1)):(b=new Sb(b,this.src,f,!!d,e),b.ha=c,a.push(b));return b};
var Wb=function(a,b){var c=b.type;c in a.a&&Ha(a.a[c],b)&&(Tb(b),0==a.a[c].length&&(delete a.a[c],a.b--))},Xb=function(a,b,c,d,e){a=a.a[b.toString()];b=-1;a&&(b=Vb(a,c,d,e));return-1<b?a[b]:null},Vb=function(a,b,c,d){for(var e=0;e<a.length;++e){var f=a[e];if(!f.X&&f.listener==b&&f.capture==!!c&&f.ma==d)return e}return-1};var Yb="closure_lm_"+(1E6*Math.random()|0),Zb={},$b=0,bc=function(a,b,c,d,e){if(d&&d.once)return ac(a,b,c,d,e);if(fa(b)){for(var f=0;f<b.length;f++)bc(a,b[f],c,d,e);return null}c=cc(c);return Qb(a)?dc(a,b,c,m(d)?!!d.capture:!!d,e):ec(a,b,c,!1,d,e)},ec=function(a,b,c,d,e,f){if(!b)throw Error("Invalid event type");var g=m(e)?!!e.capture:!!e,k=fc(a);k||(a[Yb]=k=new Ub(a));c=k.add(b,c,d,g,f);if(c.a)return c;d=gc();c.a=d;d.src=a;d.listener=c;if(a.addEventListener)Lb||(e=g),void 0===e&&(e=!1),a.addEventListener(b.toString(),
d,e);else if(a.attachEvent)a.attachEvent(hc(b.toString()),d);else if(a.addListener&&a.removeListener)q("change"===b,"MediaQueryList only has a change event"),a.addListener(d);else throw Error("addEventListener and attachEvent are unavailable.");$b++;return c},gc=function(){var a=ic,b=Jb?function(c){return a.call(b.src,b.listener,c)}:function(c){c=a.call(b.src,b.listener,c);if(!c)return c};return b},ac=function(a,b,c,d,e){if(fa(b)){for(var f=0;f<b.length;f++)ac(a,b[f],c,d,e);return null}c=cc(c);return Qb(a)?
a.h.add(String(b),c,!0,m(d)?!!d.capture:!!d,e):ec(a,b,c,!0,d,e)},jc=function(a,b,c,d,e){if(fa(b))for(var f=0;f<b.length;f++)jc(a,b[f],c,d,e);else d=m(d)?!!d.capture:!!d,c=cc(c),Qb(a)?(a=a.h,b=String(b).toString(),b in a.a&&(f=a.a[b],c=Vb(f,c,d,e),-1<c&&(Tb(f[c]),q(null!=f.length),Array.prototype.splice.call(f,c,1),0==f.length&&(delete a.a[b],a.b--)))):a&&(a=fc(a))&&(c=Xb(a,b,c,d,e))&&kc(c)},kc=function(a){if("number"!==typeof a&&a&&!a.X){var b=a.src;if(Qb(b))Wb(b.h,a);else{var c=a.type,d=a.a;b.removeEventListener?
b.removeEventListener(c,d,a.capture):b.detachEvent?b.detachEvent(hc(c),d):b.addListener&&b.removeListener&&b.removeListener(d);$b--;(c=fc(b))?(Wb(c,a),0==c.b&&(c.src=null,b[Yb]=null)):Tb(a)}}},hc=function(a){return a in Zb?Zb[a]:Zb[a]="on"+a},mc=function(a,b,c,d){var e=!0;if(a=fc(a))if(b=a.a[b.toString()])for(b=b.concat(),a=0;a<b.length;a++){var f=b[a];f&&f.capture==c&&!f.X&&(f=lc(f,d),e=e&&!1!==f)}return e},lc=function(a,b){var c=a.listener,d=a.ma||a.src;a.ha&&kc(a);return c.call(d,b)},ic=function(a,
b){if(a.X)return!0;if(!Jb){if(!b)a:{b=["window","event"];for(var c=l,d=0;d<b.length;d++)if(c=c[b[d]],null==c){b=null;break a}b=c}d=b;b=new G(d,this);c=!0;if(!(0>d.keyCode||void 0!=d.returnValue)){a:{var e=!1;if(0==d.keyCode)try{d.keyCode=-1;break a}catch(g){e=!0}if(e||void 0==d.returnValue)d.returnValue=!0}d=[];for(e=b.a;e;e=e.parentNode)d.push(e);a=a.type;for(e=d.length-1;!b.h&&0<=e;e--){b.a=d[e];var f=mc(d[e],a,!0,b);c=c&&f}for(e=0;!b.h&&e<d.length;e++)b.a=d[e],f=mc(d[e],a,!1,b),c=c&&f}return c}return lc(a,
new G(b,this))},fc=function(a){a=a[Yb];return a instanceof Ub?a:null},nc="__closure_events_fn_"+(1E9*Math.random()>>>0),cc=function(a){q(a,"Listener can not be null.");if(ia(a))return a;q(a.handleEvent,"An object listener must have handleEvent method.");a[nc]||(a[nc]=function(b){return a.handleEvent(b)});return a[nc]};var oc=function(a){C.call(this);this.b=a;this.a={}};n(oc,C);
var pc=[],H=function(a,b,c,d){fa(c)||(c&&(pc[0]=c.toString()),c=pc);for(var e=0;e<c.length;e++){var f=bc(b,c[e],d||a.handleEvent,!1,a.b||a);if(!f)break;a.a[f.key]=f}return a},qc=function(a,b,c,d,e,f){if(fa(c))for(var g=0;g<c.length;g++)qc(a,b,c[g],d,e,f);else d=d||a.handleEvent,e=m(e)?!!e.capture:!!e,f=f||a.b||a,d=cc(d),e=!!e,c=Qb(b)?Xb(b.h,String(c),d,e,f):b?(b=fc(b))?Xb(b,c,d,e,f):null:null,c&&(kc(c),delete a.a[c.key]);return a},rc=function(a){oa(a.a,function(b,c){this.a.hasOwnProperty(c)&&kc(b)},
a);a.a={}};oc.prototype.u=function(){oc.i.u.call(this);rc(this)};oc.prototype.handleEvent=function(){throw Error("EventHandler.handleEvent not implemented");};var I=function(){C.call(this);this.h=new Ub(this);this.Ma=this;this.pa=null};n(I,C);I.prototype[Pb]=!0;I.prototype.ra=function(a){this.pa=a};I.prototype.removeEventListener=function(a,b,c,d){jc(this,a,b,c,d)};
var uc=function(a,b){sc(a);var c=a.pa;if(c){var d=[];for(var e=1;c;c=c.pa)d.push(c),q(1E3>++e,"infinite loop")}a=a.Ma;c=b.type||b;"string"===typeof b?b=new E(b,a):b instanceof E?b.target=b.target||a:(e=b,b=new E(c,a),ua(b,e));e=!0;if(d)for(var f=d.length-1;!b.h&&0<=f;f--){var g=b.a=d[f];e=tc(g,c,!0,b)&&e}b.h||(g=b.a=a,e=tc(g,c,!0,b)&&e,b.h||(e=tc(g,c,!1,b)&&e));if(d)for(f=0;!b.h&&f<d.length;f++)g=b.a=d[f],e=tc(g,c,!1,b)&&e;return e};
I.prototype.u=function(){I.i.u.call(this);if(this.h){var a=this.h,b=0,c;for(c in a.a){for(var d=a.a[c],e=0;e<d.length;e++)++b,Tb(d[e]);delete a.a[c];a.b--}}this.pa=null};var dc=function(a,b,c,d,e){sc(a);return a.h.add(String(b),c,!1,d,e)},tc=function(a,b,c,d){b=a.h.a[String(b)];if(!b)return!0;b=b.concat();for(var e=!0,f=0;f<b.length;++f){var g=b[f];if(g&&!g.X&&g.capture==c){var k=g.listener,p=g.ma||g.src;g.ha&&Wb(a.h,g);e=!1!==k.call(p,d)&&e}}return e&&0!=d.Ka},sc=function(a){q(a.h,"Event target is not initialized. Did you call the superclass (goog.events.EventTarget) constructor?")};var xc=function(a,b,c,d,e,f){if(y&&!A("525"))return!0;if(z&&e)return vc(a);if(e&&!d)return!1;if(!x){"number"===typeof b&&(b=wc(b));var g=17==b||18==b||z&&91==b;if((!c||z)&&g||z&&16==b&&(d||f))return!1}if((y||bb)&&d&&c)switch(a){case 220:case 219:case 221:case 192:case 186:case 189:case 187:case 188:case 190:case 191:case 192:case 222:return!1}if(v&&d&&b==a)return!1;switch(a){case 13:return x?f||e?!1:!(c&&d):!0;case 27:return!(y||bb||x)}return x&&(d||e||f)?!1:vc(a)},vc=function(a){if(48<=a&&57>=a||
96<=a&&106>=a||65<=a&&90>=a||(y||bb)&&0==a)return!0;switch(a){case 32:case 43:case 63:case 64:case 107:case 109:case 110:case 111:case 186:case 59:case 189:case 187:case 61:case 188:case 190:case 191:case 192:case 222:case 219:case 220:case 221:case 163:case 58:return!0;case 173:return x;default:return!1}},wc=function(a){if(x)a=yc(a);else if(z&&y)switch(a){case 93:a=91}return a},yc=function(a){switch(a){case 61:return 187;case 59:return 186;case 173:return 189;case 224:return 91;case 0:return 224;
default:return a}};var J=function(a,b){I.call(this);a&&zc(this,a,b)};n(J,I);h=J.prototype;h.S=null;h.na=null;h.Ca=null;h.oa=null;h.w=-1;h.F=-1;h.ua=!1;
var Ac={3:13,12:144,63232:38,63233:40,63234:37,63235:39,63236:112,63237:113,63238:114,63239:115,63240:116,63241:117,63242:118,63243:119,63244:120,63245:121,63246:122,63247:123,63248:44,63272:46,63273:36,63275:35,63276:33,63277:34,63289:144,63302:45},Bc={Up:38,Down:40,Left:37,Right:39,Enter:13,F1:112,F2:113,F3:114,F4:115,F5:116,F6:117,F7:118,F8:119,F9:120,F10:121,F11:122,F12:123,"U+007F":46,Home:36,End:35,PageUp:33,PageDown:34,Insert:45},Cc=!y||A("525"),Dc=z&&x;
J.prototype.a=function(a){if(y||bb)if(17==this.w&&!a.ctrlKey||18==this.w&&!a.altKey||z&&91==this.w&&!a.metaKey)this.F=this.w=-1;-1==this.w&&(a.ctrlKey&&17!=a.c?this.w=17:a.altKey&&18!=a.c?this.w=18:a.metaKey&&91!=a.c&&(this.w=91));Cc&&!xc(a.c,this.w,a.shiftKey,a.ctrlKey,a.altKey,a.metaKey)?this.handleEvent(a):(this.F=wc(a.c),Dc&&(this.ua=a.altKey))};J.prototype.b=function(a){this.F=this.w=-1;this.ua=a.altKey};
J.prototype.handleEvent=function(a){var b=a.b,c=b.altKey;if(v&&"keypress"==a.type){var d=this.F;var e=13!=d&&27!=d?b.keyCode:0}else(y||bb)&&"keypress"==a.type?(d=this.F,e=0<=b.charCode&&63232>b.charCode&&vc(d)?b.charCode:0):ab&&!y?(d=this.F,e=vc(d)?b.keyCode:0):("keypress"==a.type?(Dc&&(c=this.ua),b.keyCode==b.charCode?32>b.keyCode?(d=b.keyCode,e=0):(d=this.F,e=b.charCode):(d=b.keyCode||this.F,e=b.charCode||0)):(d=b.keyCode||this.F,e=b.charCode||0),z&&63==e&&224==d&&(d=191));var f=d=wc(d);d?63232<=
d&&d in Ac?f=Ac[d]:25==d&&a.shiftKey&&(f=9):b.keyIdentifier&&b.keyIdentifier in Bc&&(f=Bc[b.keyIdentifier]);x&&Cc&&"keypress"==a.type&&!xc(f,this.w,a.shiftKey,a.ctrlKey,c,a.metaKey)||(a=f==this.w,this.w=f,b=new Ec(f,e,a,b),b.altKey=c,uc(this,b))};J.prototype.f=function(){return this.S};
var zc=function(a,b,c){a.oa&&Fc(a);a.S=b;a.na=bc(a.S,"keypress",a,c);a.Ca=bc(a.S,"keydown",a.a,c,a);a.oa=bc(a.S,"keyup",a.b,c,a)},Fc=function(a){a.na&&(kc(a.na),kc(a.Ca),kc(a.oa),a.na=null,a.Ca=null,a.oa=null);a.S=null;a.w=-1;a.F=-1};J.prototype.u=function(){J.i.u.call(this);Fc(this)};var Ec=function(a,b,c,d){G.call(this,d);this.type="key";this.c=a;this.repeat=c};n(Ec,G);var Gc=x?"MozUserSelect":y||bb?"WebkitUserSelect":null,Hc=function(a,b){b=b?null:a.getElementsByTagName("*");if(Gc){var c="none";a.style&&(a.style[Gc]=c);if(b){a=0;for(var d;d=b[a];a++)d.style&&(d.style[Gc]=c)}}else if(v||ab)if(c="on",a.setAttribute("unselectable",c),b)for(a=0;d=b[a];a++)d.setAttribute("unselectable",c)};var Ic=function(){};da(Ic);Ic.prototype.a=0;var K=function(a){I.call(this);this.j=a||xa||(xa=new ub);this.qa=Jc;this.Y=null;this.m=!1;this.a=null;this.A=void 0;this.g=this.c=this.b=null;this.Ga=!1};n(K,I);K.prototype.Oa=Ic.R();
var Jc=null,Kc=function(a,b){switch(a){case 1:return b?"disable":"enable";case 2:return b?"highlight":"unhighlight";case 4:return b?"activate":"deactivate";case 8:return b?"select":"unselect";case 16:return b?"check":"uncheck";case 32:return b?"focus":"blur";case 64:return b?"open":"close"}throw Error("Invalid component state");},Lc=function(a){return a.Y||(a.Y=":"+(a.Oa.a++).toString(36))},Mc=function(a,b){if(a.b&&a.b.g){var c=a.b.g,d=a.Y;d in c&&delete c[d];qa(a.b.g,b,a)}a.Y=b};K.prototype.f=function(){return this.a};
var Nc=function(a){a=a.a;q(a,"Can not call getElementStrict before rendering/decorating.");return a},Oc=function(a){a.A||(a.A=new oc(a));return q(a.A)};K.prototype.ra=function(a){if(this.b&&this.b!=a)throw Error("Method not supported");K.i.ra.call(this,a)};K.prototype.ia=function(){this.a=nb(this.j.a,"DIV")};
var Pc=function(a,b){if(a.m)throw Error("Component already rendered");if(b&&a.xa(b)){a.Ga=!0;var c=qb(b);a.j&&a.j.a==c||(a.j=b?new ub(qb(b)):xa||(xa=new ub));a.va(b);a.D()}else throw Error("Invalid element to decorate");};h=K.prototype;h.xa=function(){return!0};h.va=function(a){this.a=a};h.D=function(){this.m=!0;Qc(this,function(a){!a.m&&a.f()&&a.D()})};h.P=function(){Qc(this,function(a){a.m&&a.P()});this.A&&rc(this.A);this.m=!1};
h.u=function(){this.m&&this.P();this.A&&(this.A.N(),delete this.A);Qc(this,function(a){a.N()});!this.Ga&&this.a&&ob(this.a);this.b=this.a=this.g=this.c=null;K.i.u.call(this)};h.sa=function(a,b){this.ta(a,Rc(this),b)};
h.ta=function(a,b,c){q(!!a,"Provided element must not be null.");if(a.m&&(c||!this.m))throw Error("Component already rendered");if(0>b||b>Rc(this))throw Error("Child component index out of bounds");this.g&&this.c||(this.g={},this.c=[]);if(a.b==this){var d=Lc(a);this.g[d]=a;Ha(this.c,a)}else qa(this.g,Lc(a),a);if(a==this)throw Error("Unable to set parent component");if(d=this&&a.b&&a.Y){var e=a.b;d=a.Y;e.g&&d?(e=e.g,d=(null!==e&&d in e?e[d]:void 0)||null):d=null}if(d&&a.b!=this)throw Error("Unable to set parent component");
a.b=this;K.i.ra.call(a,this);La(this.c,b,0,a);if(a.m&&this.m&&a.b==this)c=this.ja(),b=c.childNodes[b]||null,b!=a.f()&&c.insertBefore(a.f(),b);else if(c){this.a||this.ia();c=L(this,b+1);b=this.ja();c=c?c.a:null;if(a.m)throw Error("Component already rendered");a.a||a.ia();b?b.insertBefore(a.a,c||null):a.j.a.body.appendChild(a.a);a.b&&!a.b.m||a.D()}else this.m&&!a.m&&a.a&&a.a.parentNode&&1==a.a.parentNode.nodeType&&a.D()};h.ja=function(){return this.a};
var Sc=function(a){if(null==a.qa){var b=a.m?a.a:a.j.a.body;a:{var c=qb(b);if(c.defaultView&&c.defaultView.getComputedStyle&&(c=c.defaultView.getComputedStyle(b,null))){c=c.direction||c.getPropertyValue("direction")||"";break a}c=""}a.qa="rtl"==(c||(b.currentStyle?b.currentStyle.direction:null)||b.style&&b.style.direction)}return a.qa},Rc=function(a){return a.c?a.c.length:0},L=function(a,b){return a.c?a.c[b]||null:null},Qc=function(a,b,c){a.c&&r(a.c,b,c)},Tc=function(a,b){return a.c&&b?Da(a.c,b):-1};var Vc=function(a,b){if(!a)throw Error("Invalid class name "+a);if(!ia(b))throw Error("Invalid decorator function "+b);Uc[a]=b},Wc={},Uc={};var Xc=function(a){this.h=a};da(Xc);var Yc=function(a,b){a&&(a.tabIndex=b?0:-1)},$c=function(a,b,c){c.id&&Mc(b,c.id);var d=a.b(),e=!1,f=D(c);f&&r(f,function(g){g==d?e=!0:g&&this.g(b,g,d)},a);e||Eb(c,d);Zc(b,c);return c};Xc.prototype.g=function(a,b,c){b==c+"-disabled"?a.Z(!1):b==c+"-horizontal"?ad(a,"horizontal"):b==c+"-vertical"&&ad(a,"vertical")};
var Zc=function(a,b){if(b)for(var c=b.firstChild,d;c&&c.parentNode==b;){d=c.nextSibling;if(1==c.nodeType){a:{var e=c;q(e);e=D(e);for(var f=0,g=e.length;f<g;f++){var k=e[f];if(k=k in Uc?Uc[k]():null){e=k;break a}}e=null}e&&(e.a=c,a.isEnabled()||e.Z(!1),a.sa(e),Pc(e,c))}else c.nodeValue&&""!=Ma(c.nodeValue)||b.removeChild(c);c=d}},bd=function(a,b){b=b.f();q(b,"The container DOM element cannot be null.");Hc(b,x);v&&(b.hideFocus=!0);(a=a.h)&&vb(b,a)};Xc.prototype.b=function(){return"goog-container"};
Xc.prototype.c=function(a){var b=this.b(),c=[b,"horizontal"==a.H?b+"-horizontal":b+"-vertical"];a.isEnabled()||c.push(b+"-disabled");return c};var M=function(){},cd;da(M);var dd={button:"pressed",checkbox:"checked",menuitem:"selected",menuitemcheckbox:"checked",menuitemradio:"checked",radio:"checked",tab:"selected",treeitem:"selected"};M.prototype.h=function(){};M.prototype.c=function(a){return a.j.b("DIV",ed(this,a).join(" "),a.ba)};var gd=function(a,b,c){if(a=a.f?a.f():a){var d=[b];v&&!A("7")&&(d=fd(D(a),b),d.push(b));(c?Fb:Hb)(a,d)}};
M.prototype.g=function(a,b){b.id&&Mc(a,b.id);b&&b.firstChild?hd(a,b.firstChild.nextSibling?Ja(b.childNodes):b.firstChild):a.ba=null;var c=0,d=this.a(),e=this.a(),f=!1,g=!1,k=!1,p=Ja(D(b));r(p,function(B){f||B!=d?g||B!=e?c|=id(this,B):g=!0:(f=!0,e==d&&(g=!0));1==id(this,B)&&(Aa(b),sb(b)&&tb(b)&&rb(b,!1))},this);a.o=c;f||(p.push(d),e==d&&(g=!0));g||p.push(e);(a=a.wa)&&p.push.apply(p,a);if(v&&!A("7")){var w=fd(p);0<w.length&&(p.push.apply(p,w),k=!0)}f&&g&&!a&&!k||Cb(b,p.join(" "));return b};
var jd=function(a,b){if(a=a.h()){q(b,"The element passed as a first parameter cannot be null.");var c=b.getAttribute("role")||null;a!=c&&vb(b,a)}},kd=function(a,b){var c;if(a.v&32&&(c=a.f())){if(!b&&a.o&32){try{c.blur()}catch(d){}a.o&32&&a.Fa(null)}(sb(c)&&tb(c))!=b&&rb(c,b)}},ld=function(a,b,c){cd||(cd={1:"disabled",8:"selected",16:"checked",64:"expanded"});q(a,"The element passed as a first parameter cannot be null.");b=cd[b];var d=a.getAttribute("role")||null;d&&(d=dd[d]||b,b="checked"==b||"selected"==
b?d:b);b&&xb(a,b,c)};M.prototype.a=function(){return"goog-control"};
var ed=function(a,b){var c=a.a(),d=[c],e=a.a();e!=c&&d.push(e);c=b.o;for(e=[];c;){var f=c&-c;e.push(md(a,f));c&=~f}d.push.apply(d,e);(a=b.wa)&&d.push.apply(d,a);v&&!A("7")&&d.push.apply(d,fd(d));return d},fd=function(a,b){var c=[];b&&(a=Ia(a,[b]));r([],function(d){!Fa(d,la(Ga,a))||b&&!Ga(d,b)||c.push(d.join("_"))});return c},md=function(a,b){a.b||nd(a);return a.b[b]},id=function(a,b){a.j||(a.b||nd(a),a.j=sa(a.b));a=parseInt(a.j[b],10);return isNaN(a)?0:a},nd=function(a){var b=a.a(),c=!t(b.replace(/\xa0|\s/g,
" ")," ");q(c,"ControlRenderer has an invalid css class: '"+b+"'");a.b={1:b+"-disabled",2:b+"-hover",4:b+"-active",8:b+"-selected",16:b+"-checked",32:b+"-focused",64:b+"-open"}};var N=function(a,b,c){K.call(this,c);if(!b){b=this.constructor;for(var d;b;){d=b[ja]||(b[ja]=++ka);if(d=Wc[d])break;b=b.i?b.i.constructor:null}b=d?ia(d.R)?d.R():new d:null}this.B=b;this.ba=void 0!==a?a:null};n(N,K);h=N.prototype;h.ba=null;h.o=0;h.v=39;h.K=0;h.Ia=!0;h.wa=null;h.ya=!0;h.ia=function(){var a=this.B.c(this);this.a=a;jd(this.B,a);Hc(a,!v&&!ab);this.isVisible()||(a.style.display="none",a&&xb(a,"hidden",!0))};h.ja=function(){return this.f()};h.xa=function(){return!0};
h.va=function(a){this.a=a=this.B.g(this,a);jd(this.B,a);Hc(a,!v&&!ab);this.Ia="none"!=a.style.display};
h.D=function(){N.i.D.call(this);var a=Nc(this);q(this);q(a);this.isVisible()||xb(a,"hidden",!this.isVisible());this.isEnabled()||ld(a,1,!this.isEnabled());this.v&8&&ld(a,8,!!(this.o&8));this.v&16&&ld(a,16,!!(this.o&16));this.v&64&&ld(a,64,!!(this.o&64));a=this.B;Sc(this)&&gd(this.f(),a.a()+"-rtl",!0);this.isEnabled()&&kd(this,this.isVisible());if(this.v&-2&&(this.ya&&od(this,!0),this.v&32&&(a=this.f()))){var b=this.W||(this.W=new J);zc(b,a);H(H(H(Oc(this),b,"key",this.fa),a,"focus",this.Na),a,"blur",
this.Fa)}};var od=function(a,b){var c=Oc(a),d=a.f();b?(H(H(H(H(c,d,F.L,a.ka),d,[F.M,F.aa],a.la),d,"mouseover",a.Aa),d,"mouseout",a.za),a.ea!=ca&&H(c,d,"contextmenu",a.ea),v&&(A(9)||H(c,d,"dblclick",a.Ja),a.ga||(a.ga=new pd(a),zb(a,la(Ab,a.ga))))):(qc(qc(qc(qc(c,d,F.L,a.ka),d,[F.M,F.aa],a.la),d,"mouseover",a.Aa),d,"mouseout",a.za),a.ea!=ca&&qc(c,d,"contextmenu",a.ea),v&&(A(9)||qc(c,d,"dblclick",a.Ja),Ab(a.ga),a.ga=null))};
N.prototype.P=function(){N.i.P.call(this);this.W&&Fc(this.W);this.isVisible()&&this.isEnabled()&&kd(this,!1)};N.prototype.u=function(){N.i.u.call(this);this.W&&(this.W.N(),delete this.W);delete this.B;this.ga=this.wa=this.ba=null};var hd=function(a,b){a.ba=b};N.prototype.isVisible=function(){return this.Ia};N.prototype.isEnabled=function(){return!(this.o&1)};
N.prototype.Z=function(a){var b=this.b;b&&"function"==typeof b.isEnabled&&!b.isEnabled()||!O(this,1,!a)||(a||(qd(this,!1),P(this,!1)),this.isVisible()&&kd(this,a),Q(this,1,!a,!0))};
var P=function(a,b){O(a,2,b)&&Q(a,2,b)},qd=function(a,b){O(a,4,b)&&Q(a,4,b)},rd=function(a,b){O(a,8,b)&&Q(a,8,b)},sd=function(a,b){O(a,64,b)&&Q(a,64,b)},Q=function(a,b,c,d){if(!d&&1==b)a.Z(!c);else if(a.v&b&&c!=!!(a.o&b)){var e=a.B;if(d=a.f())(e=md(e,b))&&gd(a,e,c),ld(d,b,c);a.o=c?a.o|b:a.o&~b}},td=function(a,b,c){if(a.m&&a.o&b&&!c)throw Error("Component already rendered");!c&&a.o&b&&Q(a,b,!1);a.v=c?a.v|b:a.v&~b},R=function(a,b){return!!(255&b)&&!!(a.v&b)},O=function(a,b,c){return!!(a.v&b)&&!!(a.o&
b)!=c&&(!(a.K&b)||uc(a,Kc(b,c)))&&!a.U};h=N.prototype;h.Aa=function(a){(!a.relatedTarget||!pb(this.f(),a.relatedTarget))&&uc(this,"enter")&&this.isEnabled()&&R(this,2)&&P(this,!0)};h.za=function(a){a.relatedTarget&&pb(this.f(),a.relatedTarget)||!uc(this,"leave")||(R(this,4)&&qd(this,!1),R(this,2)&&P(this,!1))};h.ea=ca;
h.ka=function(a){if(this.isEnabled()&&(R(this,2)&&P(this,!0),Ob(a)&&!(y&&z&&a.ctrlKey))){R(this,4)&&qd(this,!0);var b;if(b=this.B){var c;b=this.v&32&&(c=this.f())?sb(c)&&tb(c):!1}b&&this.f().focus()}!Ob(a)||y&&z&&a.ctrlKey||a.g()};h.la=function(a){this.isEnabled()&&(R(this,2)&&P(this,!0),this.o&4&&ud(this,a)&&R(this,4)&&qd(this,!1))};h.Ja=function(a){this.isEnabled()&&ud(this,a)};
var ud=function(a,b){if(R(a,16)){var c=!(a.o&16);O(a,16,c)&&Q(a,16,c)}R(a,8)&&rd(a,!0);R(a,64)&&sd(a,!(a.o&64));c=new E("action",a);b&&(c.altKey=b.altKey,c.ctrlKey=b.ctrlKey,c.metaKey=b.metaKey,c.shiftKey=b.shiftKey,c.A=b.A);return uc(a,c)};N.prototype.Na=function(){R(this,32)&&O(this,32,!0)&&Q(this,32,!0)};N.prototype.Fa=function(){R(this,4)&&qd(this,!1);R(this,32)&&O(this,32,!1)&&Q(this,32,!1)};
N.prototype.fa=function(a){return this.isVisible()&&this.isEnabled()&&13==a.c&&ud(this,a)?(a.g(),a.j(),!0):!1};if(!ia(N))throw Error("Invalid component class "+N);if(!ia(M))throw Error("Invalid renderer class "+M);var vd=N[ja]||(N[ja]=++ka);Wc[vd]=M;Vc("goog-control",function(){return new N(null)});var pd=function(a){C.call(this);this.b=a;this.a=!1;this.c=new oc(this);zb(this,la(Ab,this.c));a=Nc(this.b);H(H(H(this.c,a,F.L,this.h),a,F.M,this.j),a,"click",this.g)};n(pd,C);var wd=!v||9<=Number(ib);
pd.prototype.h=function(){this.a=!1};pd.prototype.j=function(){this.a=!0};var xd=function(a,b){if(!wd)return a.button=0,a.type=b,a;var c=document.createEvent("MouseEvents");c.initMouseEvent(b,a.bubbles,a.cancelable,a.view||null,a.detail,a.screenX,a.screenY,a.clientX,a.clientY,a.ctrlKey,a.altKey,a.shiftKey,a.metaKey,0,a.relatedTarget||null);return c};
pd.prototype.g=function(a){if(this.a)this.a=!1;else{var b=a.b,c=b.button,d=b.type,e=xd(b,"mousedown");this.b.ka(new G(e,a.a));e=xd(b,"mouseup");this.b.la(new G(e,a.a));wd||(b.button=c,b.type=d)}};pd.prototype.u=function(){this.b=null;pd.i.u.call(this)};var S=function(a,b,c){K.call(this,c);this.da=b||Xc.R();this.H=a||"vertical"};n(S,K);h=S.prototype;h.Da=null;h.ca=null;h.da=null;h.H=null;h.T=!0;h.O=!0;h.l=-1;h.s=null;h.V=!1;h.I=null;var yd=function(a){return a.Da||a.f()};h=S.prototype;h.ia=function(){this.a=this.j.b("DIV",this.da.c(this).join(" "))};h.ja=function(){return this.f()};h.xa=function(a){return"DIV"==a.tagName};h.va=function(a){this.a=$c(this.da,this,a);"none"==a.style.display&&(this.T=!1)};
h.D=function(){S.i.D.call(this);Qc(this,function(b){b.m&&zd(this,b)},this);var a=this.f();bd(this.da,this);Ad(this,this.T);H(H(H(H(H(H(H(H(Oc(this),this,"enter",this.Wa),this,"highlight",this.Xa),this,"unhighlight",this.cb),this,"open",this.Ya),this,"close",this.Ua),a,F.L,this.Sa),qb(a),[F.M,F.aa],this.Va),a,[F.L,F.M,F.aa,"mouseover","mouseout","contextmenu"],this.Ta);Bd(this)};var Bd=function(a){var b=Oc(a),c=yd(a);H(H(H(b,c,"focus",a.Ha),c,"blur",a.Qa),a.ca||(a.ca=new J(yd(a))),"key",a.Ra)};h=S.prototype;
h.P=function(){Cd(this,-1);this.s&&sd(this.s,!1);this.V=!1;S.i.P.call(this)};h.u=function(){S.i.u.call(this);this.ca&&(this.ca.N(),this.ca=null);this.da=this.s=this.I=this.Da=null};h.Wa=function(){return!0};
h.Xa=function(a){var b=Tc(this,a.target);if(-1<b&&b!=this.l){var c=L(this,this.l);c&&P(c,!1);this.l=b;c=L(this,this.l);this.V&&qd(c,!0);this.s&&c!=this.s&&(c.v&64?sd(c,!0):sd(this.s,!1))}b=this.f();q(b,"The DOM element for the container cannot be null.");null!=a.target.f()&&xb(b,"activedescendant",a.target.f().id)};h.cb=function(a){a.target==L(this,this.l)&&(this.l=-1);a=this.f();q(a,"The DOM element for the container cannot be null.");a.removeAttribute(wb("activedescendant"))};
h.Ya=function(a){(a=a.target)&&a!=this.s&&a.b==this&&(this.s&&sd(this.s,!1),this.s=a)};h.Ua=function(a){a.target==this.s&&(this.s=null);var b=this.f(),c=a.target.f();b&&a.target.o&2&&c&&(a="",c&&(a=c.id,q(a,"The active element should have an id.")),xb(b,"activedescendant",a))};h.Sa=function(a){this.O&&(this.V=!0);var b=yd(this);b&&sb(b)&&tb(b)?b.focus():a.g()};h.Va=function(){this.V=!1};
h.Ta=function(a){a:{var b=a.target;if(this.I)for(var c=this.f();b&&b!==c;){var d=b.id;if(d in this.I){b=this.I[d];break a}b=b.parentNode}b=null}if(b)switch(a.type){case F.L:b.ka(a);break;case F.M:case F.aa:b.la(a);break;case "mouseover":b.Aa(a);break;case "mouseout":b.za(a);break;case "contextmenu":b.ea(a)}};h.Ha=function(){};h.Qa=function(){Cd(this,-1);this.V=!1;this.s&&sd(this.s,!1)};
h.Ra=function(a){return this.isEnabled()&&this.isVisible()&&(0!=Rc(this)||this.Da)&&Dd(this,a)?(a.g(),a.j(),!0):!1};
var Dd=function(a,b){var c=L(a,a.l);if(c&&"function"==typeof c.fa&&c.fa(b)||a.s&&a.s!=c&&"function"==typeof a.s.fa&&a.s.fa(b))return!0;if(b.shiftKey||b.ctrlKey||b.metaKey||b.altKey)return!1;switch(b.c){case 27:yd(a).blur();break;case 36:Ed(a);break;case 35:Fd(a);break;case 38:if("vertical"==a.H)Gd(a);else return!1;break;case 37:if("horizontal"==a.H)Sc(a)?Hd(a):Gd(a);else return!1;break;case 40:if("vertical"==a.H)Hd(a);else return!1;break;case 39:if("horizontal"==a.H)Sc(a)?Gd(a):Hd(a);else return!1;
break;default:return!1}return!0},zd=function(a,b){var c=b.f();c=c.id||(c.id=Lc(b));a.I||(a.I={});a.I[c]=b};S.prototype.sa=function(a,b){Ca(a,N,"The child of a container must be a control");S.i.sa.call(this,a,b)};S.prototype.ta=function(a,b,c){Ca(a,N);a.K|=2;a.K|=64;td(a,32,!1);a.m&&0!=a.ya&&od(a,!1);a.ya=!1;var d=a.b==this?Tc(this,a):-1;S.i.ta.call(this,a,b,c);a.m&&this.m&&zd(this,a);a=d;-1==a&&(a=Rc(this));a==this.l?this.l=Math.min(Rc(this)-1,b):a>this.l&&b<=this.l?this.l++:a<this.l&&b>this.l&&this.l--};
var ad=function(a,b){if(a.f())throw Error("Component already rendered");a.H=b};S.prototype.isVisible=function(){return this.T};var Ad=function(a,b){a.T=b;var c=a.f();c&&(c.style.display=b?"":"none",Yc(yd(a),a.O&&a.T))};S.prototype.isEnabled=function(){return this.O};S.prototype.Z=function(a){this.O!=a&&uc(this,a?"enable":"disable")&&(a?(this.O=!0,Qc(this,function(b){b.La?delete b.La:b.Z(!0)})):(Qc(this,function(b){b.isEnabled()?b.Z(!1):b.La=!0}),this.V=this.O=!1),Yc(yd(this),a&&this.T))};
var Cd=function(a,b){(b=L(a,b))?P(b,!0):-1<a.l&&P(L(a,a.l),!1)},Ed=function(a){Id(a,function(b,c){return(b+1)%c},Rc(a)-1)},Fd=function(a){Id(a,function(b,c){b--;return 0>b?c-1:b},0)},Hd=function(a){Id(a,function(b,c){return(b+1)%c},a.l)},Gd=function(a){Id(a,function(b,c){b--;return 0>b?c-1:b},a.l)},Id=function(a,b,c){c=0>c?Tc(a,a.s):c;var d=Rc(a);c=b.call(a,c,d);for(var e=0;e<=d;){var f=L(a,c),g;if(g=f)g=f.isVisible()&&f.isEnabled()&&!!(f.v&2);if(g){a.Ea(c);break}e++;c=b.call(a,c,d)}};
S.prototype.Ea=function(a){Cd(this,a)};var T=function(){};n(T,M);da(T);T.prototype.a=function(){return"goog-tab"};T.prototype.h=function(){return"tab"};T.prototype.c=function(a){var b=T.i.c.call(this,a);(a=a.Pa)&&b&&(b.title=a||"");return b};T.prototype.g=function(a,b){b=T.i.g.call(this,a,b);var c=b.title||"";c&&(a.Pa=c);a.o&8&&(c=a.b)&&ia(c.G)&&(Q(a,8,!1),c.G(a));return b};var Jd=function(a,b,c){N.call(this,a,b||T.R(),c);td(this,8,!0);this.K|=9};n(Jd,N);Vc("goog-tab",function(){return new Jd(null)});var U=function(){this.h="tablist"};n(U,Xc);da(U);U.prototype.b=function(){return"goog-tab-bar"};U.prototype.g=function(a,b,c){this.j||(this.a||Kd(this),this.j=sa(this.a));var d=this.j[b];d?(ad(a,Ld(d)),a.B=d):U.i.g.call(this,a,b,c)};U.prototype.c=function(a){var b=U.i.c.call(this,a);this.a||Kd(this);b.push(this.a[a.B]);return b};var Kd=function(a){var b=a.b();a.a={top:b+"-top",bottom:b+"-bottom",start:b+"-start",end:b+"-end"}};var V=function(a,b,c){a=a||"top";ad(this,Ld(a));this.B=a;S.call(this,this.H,b||U.R(),c);Md(this)};n(V,S);h=V.prototype;h.C=null;h.D=function(){V.i.D.call(this);Md(this)};h.u=function(){V.i.u.call(this);this.C=null};h.Ea=function(a){V.i.Ea.call(this,a);this.G(L(this,a))};h.G=function(a){a?rd(a,!0):this.C&&rd(this.C,!1)};
var Nd=function(a,b){if(b&&b==a.C){for(var c=Tc(a,b),d=c-1;b=L(a,d);d--){var e=b;if(e.isVisible()&&e.isEnabled()){a.G(b);return}}for(c+=1;b=L(a,c);c++)if(d=b,d.isVisible()&&d.isEnabled()){a.G(b);return}a.G(null)}};h=V.prototype;h.ab=function(a){this.C&&this.C!=a.target&&rd(this.C,!1);this.C=a.target};h.bb=function(a){a.target==this.C&&(this.C=null)};h.Za=function(a){Nd(this,a.target)};h.$a=function(a){Nd(this,a.target)};h.Ha=function(){L(this,this.l)||Cd(this,Tc(this,this.C||L(this,0)))};
var Md=function(a){H(H(H(H(Oc(a),a,"select",a.ab),a,"unselect",a.bb),a,"disable",a.Za),a,"hide",a.$a)},Ld=function(a){return"start"==a||"end"==a?"vertical":"horizontal"};Vc("goog-tab-bar",function(){return new V});function Od(a){var b={top:"bottom",bottom:"top",start:"right",end:"left"}[a.location],c=a.elementId,d=document.createElement("style");d.textContent="\n    fieldset {\n      padding: 10px;\n      border: 1px solid #369;\n    }\n\n    #"+c+" .goog-tab-content {\n      min-height: 3em;\n      margin: 0;\n      border: "+a.border+" solid "+a.borderColor+";\n      border-top: 0;\n      height: "+a.contentHeight+";\n      padding: 4px 8px;\n      margin-right: 4px;\n      background: var(--colab-primary-surface-color);\n      overflow: auto;\n    }\n\n    #"+
c+" .goog-tab-bar-"+a.location+" .goog-tab-selected {\n      background-color: var(--colab-primary-surface-color);\n      border: 1px solid "+a.borderColor+";\n      border-"+b+": 0px;\n    }\n\n    #"+c+" .goog-tab-bar-"+a.location+" {\n      padding-"+a.location+": 5px !important;\n      border-"+b+": 1px solid "+a.borderColor+" !important;\n      background: var(--colab-primary-surface-color);\n    }\n\n    #"+c+" .goog-tab-bar {\n       margin: 0;\n       border: 0;\n       padding: 0;\n       list-style: none;\n       cursor: default;\n       outline: none;\n       background: var(--colab-primary-surface-color);\n       margin-right: 4px;\n      }\n\n     #"+
c+" .goog-tab {\n       position: relative;\n       padding: 4px 8px;\n       color: var(--colab-primary-text-color);\n       text-decoration: initial;\n       cursor: default;\n      }\n\n      #"+c+" .goog-tab-hover {\n        background-color: var(--colab-highlighted-surface-color);\n      }\n      ";return d}
var Pd=function(a){var b=a.elementId,c=a.tabNames,d=a.selectedIndex;"contentBorder"in a||(a.contentBorder="0px");"contentHeight"in a||(a.contentHeight="initial");"borderColor"in a||(a.borderColor="var(--colab-border-color)");a.location||(a.location="top");var e=document.querySelector("#"+b),f=document.createElement("div");f.classList.add("goog-tab-bar");var g=a.location;f.classList.add("goog-tab-bar-"+g);for(var k=[],p=ba(c),w=p.next();!w.done;w=p.next()){w=w.value;var B=document.createElement("div");
B.classList.add("goog-tab");B.textContent=w;f.appendChild(B);k.push(B)}"bottom"!=g&&e.appendChild(f);p=null;if("top"==g||"bottom"==g)p=document.createElement("div"),p.classList.add("goog-tab-bar-clear");"top"==g&&p&&e.appendChild(p);B=document.createElement("div");B.classList.add("goog-tab-content");var ra=[];c=ba(c);for(w=c.next();!w.done;w=c.next())w=document.createElement("div"),w.id=e.id+"_content_"+ra.length,w.style.display="none",B.appendChild(w),ra.push(w);e.appendChild(B);"bottom"==g&&(p&&
e.appendChild(p),e.appendChild(f));var X=new V(g);Pc(X,f);var Y=-1;dc(X,"select",function(Z){Z=k.indexOf(Z.target.f());Z!=Y&&(0<=Y&&Y<ra.length&&(ra[Y].style.display="none",Y=-1),0<=Z&&Z<ra.length&&(Y=Z,ra[Y].style.display="inline",window.dispatchEvent(new Event("resize")),google.colab.output.resizeIframeToContent()),X.G(L(X,Y)))});X.G(L(X,d));window[b]={setSelectedTabIndex:function(Z){X.G(L(X,Z))}};document.head.appendChild(Od(a))},Qd=["colab_lib","createTabBar"],W=l;
Qd[0]in W||"undefined"==typeof W.execScript||W.execScript("var "+Qd[0]);for(var Rd;Qd.length&&(Rd=Qd.shift());)Qd.length||void 0===Pd?W[Rd]&&W[Rd]!==Object.prototype[Rd]?W=W[Rd]:W=W[Rd]={}:W[Rd]=Pd;}).call(this);
// clang-format on
",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "QFfuKDg2reSV",
        "outputId": "a5cefa28-618c-45a3-8170-6aa696f33c6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<link rel=stylesheet type=text/css href='/nbextensions/google.colab/tabbar.css'></link>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<script src='/nbextensions/google.colab/tabbar_main.min.js'></script>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div id=\"id2\"></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"ced4f9b2-c735-11ed-9e6b-0242ac1c000c\"] = colab_lib.createTabBar({\"location\": \"top\", \"elementId\": \"id2\", \"tabNames\": [\"Div 1\", \"Div 10\"], \"initialSelection\": 0, \"contentBorder\": [\"0px\"], \"contentHeight\": [\"initial\"], \"borderColor\": [\"#a7a7a7\"]});\n",
              "//# sourceURL=js_45defa8f78"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"ced5d922-c735-11ed-9e6b-0242ac1c000c\"] = window[\"id2\"].setSelectedTabIndex(0);\n",
              "//# sourceURL=js_3ea2cb7774"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"ceda82ce-c735-11ed-9e6b-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_49002a43ea"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cedb0c80-c735-11ed-9e6b-0242ac1c000c\"] = document.querySelector(\"#id2_content_0\");\n",
              "//# sourceURL=js_130508f879"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cedc61c0-c735-11ed-9e6b-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"cedb0c80-c735-11ed-9e6b-0242ac1c000c\"]);\n",
              "//# sourceURL=js_a45d3f0686"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cedcfc7a-c735-11ed-9e6b-0242ac1c000c\"] = window[\"id2\"].setSelectedTabIndex(0);\n",
              "//# sourceURL=js_49f55aea46"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cf93543e-c735-11ed-9e6b-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"ceda82ce-c735-11ed-9e6b-0242ac1c000c\"]);\n",
              "//# sourceURL=js_3f642579d2"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cf95bb7a-c735-11ed-9e6b-0242ac1c000c\"] = google.colab.output.getActiveOutputArea();\n",
              "//# sourceURL=js_e84127ee48"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cf95faf4-c735-11ed-9e6b-0242ac1c000c\"] = document.querySelector(\"#id2_content_1\");\n",
              "//# sourceURL=js_2cdf85f87b"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cf96738a-c735-11ed-9e6b-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"cf95faf4-c735-11ed-9e6b-0242ac1c000c\"]);\n",
              "//# sourceURL=js_13895e410c"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"cf96d398-c735-11ed-9e6b-0242ac1c000c\"] = window[\"id2\"].setSelectedTabIndex(1);\n",
              "//# sourceURL=js_5d9f1ab721"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "window[\"d0080374-c735-11ed-9e6b-0242ac1c000c\"] = google.colab.output.setActiveOutputArea(window[\"cf95bb7a-c735-11ed-9e6b-0242ac1c000c\"]);\n",
              "//# sourceURL=js_d7f477ba5f"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "K7M3BjA0I50g",
        "outputId": "43af5d2e-fec2-41f9-d759-ac424359b7a0"
      },
      "source": [
        "# Plot accuracy of all predictors (1st and 2nd) for different diversity\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n_div = 10\n",
        "n_seeds = 20\n",
        "\n",
        "fileNameMLP = 'mlp-prod.csv'\n",
        "fileNameRF  = 'rf-prod.csv'\n",
        "fileNameKNN = 'knn-sum.csv'\n",
        "fileNameSVM = 'svm-prod.csv'\n",
        "\n",
        "# RF\n",
        "dataset = pd.read_csv(fileNameRF, header=None).values\n",
        "\n",
        "perf_all_rf1 = []\n",
        "perf_all_rf2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_rf1.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "    else:\n",
        "        perf_all_rf2.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "\n",
        "perf_rf1 = [0] * n_div\n",
        "perf_rf2 = [0] * n_div\n",
        "for i in range(len(perf_all_rf1)):\n",
        "    perf_rf1[i%n_div] = perf_rf1[i%n_div] + perf_all_rf1[i]    \n",
        "    perf_rf2[i%n_div] = perf_rf2[i%n_div] + perf_all_rf2[i]\n",
        "perf_rf1 = np.array(perf_rf1) / n_seeds\n",
        "perf_rf2 = np.array(perf_rf2) / n_seeds\n",
        "\n",
        "# SVM\n",
        "dataset = pd.read_csv(fileNameSVM, header=None).values\n",
        "\n",
        "perf_all_svm1 = []\n",
        "perf_all_svm2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_svm1.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "    else:\n",
        "        perf_all_svm2.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "\n",
        "perf_svm1 = [0] * n_div\n",
        "perf_svm2 = [0] * n_div\n",
        "for i in range(len(perf_all_svm1)):\n",
        "    perf_svm1[i%n_div] = perf_svm1[i%n_div] + perf_all_svm1[i]    \n",
        "    perf_svm2[i%n_div] = perf_svm2[i%n_div] + perf_all_svm2[i]\n",
        "perf_svm1 = np.array(perf_svm1) / n_seeds\n",
        "perf_svm2 = np.array(perf_svm2) / n_seeds\n",
        "\n",
        "# MLP\n",
        "dataset = pd.read_csv(fileNameMLP, header=None).values\n",
        "\n",
        "perf_all_mlp1 = []\n",
        "perf_all_mlp2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_mlp1.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "    else:\n",
        "        perf_all_mlp2.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "\n",
        "perf_mlp1 = [0] * n_div\n",
        "perf_mlp2 = [0] * n_div\n",
        "for i in range(len(perf_all_mlp1)):\n",
        "    perf_mlp1[i%n_div] = perf_mlp1[i%n_div] + perf_all_mlp1[i]    \n",
        "    perf_mlp2[i%n_div] = perf_mlp2[i%n_div] + perf_all_mlp2[i]\n",
        "perf_mlp1 = np.array(perf_mlp1) / n_seeds\n",
        "perf_mlp2 = np.array(perf_mlp2) / n_seeds\n",
        "\n",
        "# k-NN\n",
        "dataset = pd.read_csv(fileNameKNN, header=None).values\n",
        "\n",
        "perf_all_knn1 = []\n",
        "perf_all_knn2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_knn1.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "    else:\n",
        "        perf_all_knn2.append(100*np.trace(mat)/(mat[0].sum()*10))\n",
        "\n",
        "perf_knn1 = [0] * n_div\n",
        "perf_knn2 = [0] * n_div\n",
        "for i in range(len(perf_all_knn1)):\n",
        "    perf_knn1[i%n_div] = perf_knn1[i%n_div] + perf_all_knn1[i]    \n",
        "    perf_knn2[i%n_div] = perf_knn2[i%n_div] + perf_all_knn2[i]\n",
        "perf_knn1 = np.array(perf_knn1) / n_seeds\n",
        "perf_knn2 = np.array(perf_knn2) / n_seeds\n",
        "\n",
        "# Single digit plot\n",
        "labels = list(range(1,n_div+1))\n",
        "x = np.arange(len(labels))  # the label locations\n",
        "width = 0.2  # the width of the bars\n",
        "fig, ax = plt.subplots()\n",
        "ax.grid(axis='y', zorder=0)\n",
        "rects1 = ax.bar(x - 3*width/2, perf_rf1 , width, label='$A_1$ RF', zorder=3, color = '#E74C3C')\n",
        "rects3 = ax.bar(x - width/2  , perf_svm1, width, label='$A_1$ SVM', zorder=3, color = '#3498DB')\n",
        "rects5 = ax.bar(x + width/2  , perf_mlp1, width, label='$A_1$ NN', zorder=3, color = '#F39C12')\n",
        "rects7 = ax.bar(x + 3*width/2, perf_knn1, width, label='$A_1$ K-NN', zorder=3, color = '#27AE60')\n",
        "rects2 = ax.bar(x - 3*width/2, perf_rf2 , width, bottom=perf_rf1, label='$A_2$ RF', zorder=3, color = '#F5B7B1')\n",
        "rects4 = ax.bar(x - width/2  , perf_svm2, width, bottom=perf_svm1, label='$A_2$ SVM', zorder=3, color = '#AED6F1')\n",
        "rects6 = ax.bar(x + width/2  , perf_mlp2, width, bottom=perf_mlp1, label='$A_2$ NN', zorder=3, color = '#FAD7A0')\n",
        "rects8 = ax.bar(x + 3*width/2, perf_knn2, width, bottom=perf_knn1, label='$A_2$ K-NN', zorder=3, color = '#A9DFBF')\n",
        "\n",
        "ax.set_xlabel('Diversity')\n",
        "ax.set_ylabel('Accuracy [%]')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.set_ylim((0, 100))\n",
        "ax.legend(labelspacing=0.25, ncol=2, loc=4, framealpha=0.95)\n",
        "fig.tight_layout()\n",
        "plt.savefig('all-digit-acc.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "iDsyGabOLVPw",
        "outputId": "57f30cd0-064e-47b1-d678-7b6943f75446"
      },
      "source": [
        "# Plot F1-score of all predictors (1st and 2nd) for different diversity\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "n_div = 10\n",
        "n_seeds = 20\n",
        "\n",
        "fileNameMLP = 'mlp-prod.csv'\n",
        "fileNameRF  = 'rf-prod.csv'\n",
        "fileNameKNN = 'knn-sum.csv'\n",
        "fileNameSVM = 'svm-prod.csv'\n",
        "\n",
        "# RF\n",
        "dataset = pd.read_csv(fileNameRF, header=None).values\n",
        "\n",
        "perf_all_rf1 = []\n",
        "perf_all_rf2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    F1_k = []\n",
        "    for ik in range(10):\n",
        "        F1_k.append(2*mat[ik,ik] / (2*mat[ik,ik] + (np.sum(mat[:,ik])-mat[ik,ik]) + (np.sum(mat[ik,:])-mat[ik,ik])))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_rf1.append(100*np.mean(F1_k))\n",
        "    else:\n",
        "        perf_all_rf2.append(100*np.mean(F1_k))\n",
        "\n",
        "perf_rf1 = [0] * n_div\n",
        "perf_rf2 = [0] * n_div\n",
        "for i in range(len(perf_all_rf1)):\n",
        "    perf_rf1[i%n_div] = perf_rf1[i%n_div] + perf_all_rf1[i]    \n",
        "    perf_rf2[i%n_div] = perf_rf2[i%n_div] + perf_all_rf2[i]\n",
        "perf_rf1 = np.array(perf_rf1) / n_seeds\n",
        "perf_rf2 = np.array(perf_rf2) / n_seeds\n",
        "\n",
        "# SVM\n",
        "dataset = pd.read_csv(fileNameSVM, header=None).values\n",
        "\n",
        "perf_all_svm1 = []\n",
        "perf_all_svm2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    F1_k = []\n",
        "    for ik in range(10):\n",
        "        F1_k.append(2*mat[ik,ik] / (2*mat[ik,ik] + (np.sum(mat[:,ik])-mat[ik,ik]) + (np.sum(mat[ik,:])-mat[ik,ik])))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_svm1.append(100*np.mean(F1_k))\n",
        "    else:\n",
        "        perf_all_svm2.append(100*np.mean(F1_k))\n",
        "\n",
        "perf_svm1 = [0] * n_div\n",
        "perf_svm2 = [0] * n_div\n",
        "for i in range(len(perf_all_svm1)):\n",
        "    perf_svm1[i%n_div] = perf_svm1[i%n_div] + perf_all_svm1[i]    \n",
        "    perf_svm2[i%n_div] = perf_svm2[i%n_div] + perf_all_svm2[i]\n",
        "perf_svm1 = np.array(perf_svm1) / n_seeds\n",
        "perf_svm2 = np.array(perf_svm2) / n_seeds\n",
        "\n",
        "# MLP\n",
        "dataset = pd.read_csv(fileNameMLP, header=None).values\n",
        "\n",
        "perf_all_mlp1 = []\n",
        "perf_all_mlp2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    F1_k = []\n",
        "    for ik in range(10):\n",
        "        F1_k.append(2*mat[ik,ik] / (2*mat[ik,ik] + (np.sum(mat[:,ik])-mat[ik,ik]) + (np.sum(mat[ik,:])-mat[ik,ik])))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_mlp1.append(100*np.mean(F1_k))\n",
        "    else:\n",
        "        perf_all_mlp2.append(100*np.mean(F1_k))\n",
        "\n",
        "perf_mlp1 = [0] * n_div\n",
        "perf_mlp2 = [0] * n_div\n",
        "for i in range(len(perf_all_mlp1)):\n",
        "    perf_mlp1[i%n_div] = perf_mlp1[i%n_div] + perf_all_mlp1[i]    \n",
        "    perf_mlp2[i%n_div] = perf_mlp2[i%n_div] + perf_all_mlp2[i]\n",
        "perf_mlp1 = np.array(perf_mlp1) / n_seeds\n",
        "perf_mlp2 = np.array(perf_mlp2) / n_seeds\n",
        "\n",
        "# k-NN\n",
        "dataset = pd.read_csv(fileNameKNN, header=None).values\n",
        "\n",
        "perf_all_knn1 = []\n",
        "perf_all_knn2 = []\n",
        "for iRow in range(len(dataset)):\n",
        "    mat = np.reshape(dataset[iRow,:], (10, 10))\n",
        "    F1_k = []\n",
        "    for ik in range(10):\n",
        "        F1_k.append(2*mat[ik,ik] / (2*mat[ik,ik] + (np.sum(mat[:,ik])-mat[ik,ik]) + (np.sum(mat[ik,:])-mat[ik,ik])))\n",
        "    if iRow % 2 == 0:\n",
        "        perf_all_knn1.append(100*np.mean(F1_k))\n",
        "    else:\n",
        "        perf_all_knn2.append(100*np.mean(F1_k))\n",
        "\n",
        "perf_knn1 = [0] * n_div\n",
        "perf_knn2 = [0] * n_div\n",
        "for i in range(len(perf_all_knn1)):\n",
        "    perf_knn1[i%n_div] = perf_knn1[i%n_div] + perf_all_knn1[i]    \n",
        "    perf_knn2[i%n_div] = perf_knn2[i%n_div] + perf_all_knn2[i]\n",
        "perf_knn1 = np.array(perf_knn1) / n_seeds\n",
        "perf_knn2 = np.array(perf_knn2) / n_seeds\n",
        "\n",
        "# Single digit plot\n",
        "labels = list(range(1,n_div+1))\n",
        "x = np.arange(len(labels))  # the label locations\n",
        "width = 0.2  # the width of the bars\n",
        "fig, ax = plt.subplots()\n",
        "ax.grid(axis='y', zorder=0)\n",
        "rects1 = ax.bar(x - 3*width/2, perf_rf1 , width, label='RF', zorder=3, color = '#E74C3C')\n",
        "rects3 = ax.bar(x - width/2  , perf_svm1, width, label='SVM', zorder=3, color = '#3498DB')\n",
        "rects5 = ax.bar(x + width/2  , perf_mlp1, width, label='NN', zorder=3, color = '#F39C12')\n",
        "rects7 = ax.bar(x + 3*width/2, perf_knn1, width, label='K-NN', zorder=3, color = '#27AE60')\n",
        "\n",
        "ax.set_xlabel('Diversity')\n",
        "ax.set_ylabel('F-score [%]')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(labels)\n",
        "ax.set_ylim((0, 100))\n",
        "ax.legend(labelspacing=0.25, ncol=1, loc=4, framealpha=0.95)\n",
        "fig.tight_layout()\n",
        "plt.savefig('all-digit-F.pdf', bbox_inches='tight')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H8mBvc274pJD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}