plots_mcsp.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "RlDh7cN1B8w5"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sns.set_theme(\n",
        "    style = 'darkgrid',\n",
        "    #  notebook, paper, talk, poster\n",
        "    context = 'paper',\n",
        "    # deep, muted, bright, pastel, dark, colorblind\n",
        "    palette = 'muted',\n",
        "    color_codes = True,\n",
        "    font = 'serif',\n",
        "    rc = {\n",
        "        'axes.labelpad': 10,\n",
        "        'savefig.bbox': 'tight',\n",
        "        'figure.autolayout': True,\n",
        "        'figure.figsize': (4.5, 3.6),\n",
        "        'font.family': ['serif'],\n",
        "        'font.size': 11,\n",
        "        'axes.titlesize': 'large',\n",
        "        'axes.labelsize': 'medium',\n",
        "        'xtick.labelsize': 'small',\n",
        "        'ytick.labelsize': 'small',\n",
        "        # 'text.usetex': True,\n",
        "        # 'pgf.rcfonts': False,\n",
        "        'pgf.preamble':  r\"\"\"\n",
        "            \\usepackage[brazilian]{babel}\n",
        "            \\usepackage[T1]{fontenc}\n",
        "            \\usepackage[utf8]{inputenc}\n",
        "        \"\"\",\n",
        "    },\n",
        ")\n",
        "\n",
        "def read_df(filename):\n",
        "    mixed = pd.read_csv(filename, na_values=[' ' * n for n in range(50)], dtype={'psoIter': 'Int64'})\n",
        "    df_obj = mixed.select_dtypes(['object'])\n",
        "    mixed[df_obj.columns] = df_obj.apply(lambda x: x.str.strip())\n",
        "    mixed[\"score\"] = (mixed[\"size\"] - mixed[\"blocks\"]) / (mixed[\"size\"] - 1)\n",
        "    return mixed[mixed['heuristic'] != \"psoComb\"]\n",
        "\n",
        "def read_pso_df(filename):\n",
        "    df = read_df(filename)\n",
        "    return df[df[\"heuristic\"] == \"pso\"]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "id": "wm_2j7gJihME",
        "outputId": "dedd3f6b-0a13-4d6d-8709-db267cd2516a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.17759856630824372\n",
            "0.18544973544973545\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# BASIC SCORE COMPARISON PLOT FOR A SINGLE FILE\n",
        "\n",
        "df = read_df(\"r-5-10-s-5-30-n80-ke0.5-ki0.1-kg0.1-dist1-3-3-3-iter100-part200.csv\")\n",
        "# df = read_df(\"r50-s-5-10-n220-ke0.5-ki0.1-kg0.1-dist1-3-3-3-iter100-part200.csv\")\n",
        "df['logscore'] = np.log(df['score'] + 1)\n",
        "\n",
        "sns.barplot(data=df, x='heuristic', y='score', hue='heuristic')\n",
        "plt.ylim(0.16, 0.21)\n",
        "# plt.ylim(0.072, 0.078)\n",
        "plt.xlabel(\"Heurística\")\n",
        "plt.ylabel(\"Pontuação\")\n",
        "plt.xticks([0, 1, 2, 3], [\"Combinação\", \"Combinação-S\", \"Gulosa\", \"PSO\"], rotation=25)\n",
        "plt.savefig(\"r-5-10-s-5-30-n80-ke0.5-ki0.1-kg0.1-dist1-3-3-3-iter100-part200.pgf\")\n",
        "# plt.savefig(\"r50-s-5-10-n220-ke0.5-ki0.1-kg0.1-dist1-3-3-3-iter100-part200.pgf\")\n",
        "\n",
        "print(df[df['heuristic'] == 'greedy']['score'].median())\n",
        "print(df[df['heuristic'] == 'pso']['score'].median())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "45WceUVSZ1ps",
        "outputId": "90616365-4f4b-43e5-eb91-b941e088ca8b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# PSO EXECUTION TIME\n",
        "\n",
        "time_edge_pso_df = read_pso_df('r-5-10-s-5-30-n80-ke5.0-ki0.02-kg0.02-dist3-1-1-1-iter1000-part10.csv')\n",
        "\n",
        "sns.scatterplot(time_edge_pso_df, x=\"maxRepeat\", y=\"edges\")\n",
        "plt.xlabel(\"Ocorrência máxima\")\n",
        "plt.ylabel(\"Arestas\")\n",
        "plt.savefig(\"arestas-vs-occ.pgf\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "EAzQe7fqdTyz",
        "outputId": "c86785f5-2616-4d93-f3f5-d928e597b576"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.scatterplot(time_edge_pso_df, x=\"edges\", y=\"time\")\n",
        "plt.xlabel(\"Arestas\")\n",
        "plt.ylabel(\"Tempo de execução [s]\")\n",
        "plt.savefig(\"tempo-vs-arestas.pgf\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "pPdqx4KX2ujh"
      },
      "outputs": [],
      "source": [
        "# COMPARING PSO RESULTS WITH OTHER HEURISTICS\n",
        "\n",
        "# mixed = read_df('ke5.0-ki0.02-kg0.02-dist3-1-1-1-iter1000-part10.csv')\n",
        "\n",
        "# l = mixed['left'].str.strip('[]').str.split(',').str.join(' ')\n",
        "# r = mixed['right'].str.strip('[]').str.split(',').str.join(' ')\n",
        "# mixed['pair'] = l + r\n",
        "\n",
        "# mixed.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vt2zYxY5wCZt",
        "outputId": "97459123-dadb-435c-edf0-fe7748b72c88"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>size</th>\n",
              "      <th>repeats</th>\n",
              "      <th>singles</th>\n",
              "      <th>maxRepeat</th>\n",
              "      <th>edges</th>\n",
              "      <th>heuristic</th>\n",
              "      <th>blocks</th>\n",
              "      <th>score</th>\n",
              "      <th>time</th>\n",
              "      <th>left</th>\n",
              "      <th>right</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>combine</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.590</td>\n",
              "      <td>[1,1,1,2,0 6,7,6,4,2,9,0,7,13 4,8 4,12,0,10,2 ...</td>\n",
              "      <td>[0,0,9,7,2,2 6,9,2,8 4,1,4,5,0 6,9,4,7,11 2,7,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>combineS</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.591</td>\n",
              "      <td>[1,1,1,2,0 6,7,6,4,2,9,0,7,13 4,8 4,12,0,10,2 ...</td>\n",
              "      <td>[0,0,9,7,2,2 6,9,2,8 4,1,4,5,0 6,9,4,7,11 2,7,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>34</td>\n",
              "      <td>9</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>greedy</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.588</td>\n",
              "      <td>[1,1,1,2,0 6,7,6,4,2,9,0,7,13 4,8 4,12,0,10,2 ...</td>\n",
              "      <td>[0,0,9,7,2,2 6,9,2,8 4,1,4,5,0 6,9,4,7,11 2,7,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>76</td>\n",
              "      <td>5</td>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>combine</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.013333</td>\n",
              "      <td>-4.527</td>\n",
              "      <td>[1,14,4,4,6,1 2,12,4 1,1 3,1,0,19,3 2,4,0,27,0...</td>\n",
              "      <td>[6,4,5,0,9,10,4,2 2,1,34,13,0,1,29,7,2,1 3,32,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>76</td>\n",
              "      <td>5</td>\n",
              "      <td>30</td>\n",
              "      <td>11</td>\n",
              "      <td>30</td>\n",
              "      <td>combineS</td>\n",
              "      <td>1</td>\n",
              "      <td>-0.013333</td>\n",
              "      <td>-4.526</td>\n",
              "      <td>[1,14,4,4,6,1 2,12,4 1,1 3,1,0,19,3 2,4,0,27,0...</td>\n",
              "      <td>[6,4,5,0,9,10,4,2 2,1,34,13,0,1,29,7,2,1 3,32,...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   size  repeats  singles  maxRepeat  edges heuristic  blocks     score  \\\n",
              "0    34        9        5          5      6   combine       0  0.000000   \n",
              "1    34        9        5          5      6  combineS       0  0.000000   \n",
              "2    34        9        5          5      6    greedy       0  0.000000   \n",
              "4    76        5       30         11     30   combine       1 -0.013333   \n",
              "5    76        5       30         11     30  combineS       1 -0.013333   \n",
              "\n",
              "    time                                               left  \\\n",
              "0 -0.590  [1,1,1,2,0 6,7,6,4,2,9,0,7,13 4,8 4,12,0,10,2 ...   \n",
              "1 -0.591  [1,1,1,2,0 6,7,6,4,2,9,0,7,13 4,8 4,12,0,10,2 ...   \n",
              "2 -0.588  [1,1,1,2,0 6,7,6,4,2,9,0,7,13 4,8 4,12,0,10,2 ...   \n",
              "4 -4.527  [1,14,4,4,6,1 2,12,4 1,1 3,1,0,19,3 2,4,0,27,0...   \n",
              "5 -4.526  [1,14,4,4,6,1 2,12,4 1,1 3,1,0,19,3 2,4,0,27,0...   \n",
              "\n",
              "                                               right  \n",
              "0  [0,0,9,7,2,2 6,9,2,8 4,1,4,5,0 6,9,4,7,11 2,7,...  \n",
              "1  [0,0,9,7,2,2 6,9,2,8 4,1,4,5,0 6,9,4,7,11 2,7,...  \n",
              "2  [0,0,9,7,2,2 6,9,2,8 4,1,4,5,0 6,9,4,7,11 2,7,...  \n",
              "4  [6,4,5,0,9,10,4,2 2,1,34,13,0,1,29,7,2,1 3,32,...  \n",
              "5  [6,4,5,0,9,10,4,2 2,1,34,13,0,1,29,7,2,1 3,32,...  "
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def diff_pso(column: pd.Series):\n",
        "    match column.name:\n",
        "        case 'heuristic':\n",
        "            column.iat[-1] = 'best'\n",
        "            return column\n",
        "        case 'blocks':\n",
        "            column = column - column.iat[-1]\n",
        "            column.iat[-1] = column.iloc[0:3].min()\n",
        "            return column\n",
        "        case 'score':\n",
        "            column = column - column.iat[-1]\n",
        "            column.iat[-1] = column.iloc[0:3].max()\n",
        "            return column\n",
        "        case 'time':\n",
        "            column = column - column.iat[-1]\n",
        "            column.iat[-1] = column.iloc[0:3].min()\n",
        "            return column\n",
        "        case _:\n",
        "            return column\n",
        "\n",
        "df = read_df(\"r-5-10-s-5-30-n80-ke5.0-ki0.1-kg0.1-dist1-3-3-3-iter100-part200.csv\")\n",
        "l = df['left'].str.strip('[]').str.split(',').str.join(' ')\n",
        "r = df['right'].str.strip('[]').str.split(',').str.join(' ')\n",
        "df['pair'] = l + r\n",
        "\n",
        "pso_diff = df.groupby('pair').transform(diff_pso)\n",
        "pso_diff = pso_diff[pso_diff[\"heuristic\"] != \"best\"]\n",
        "pso_diff.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "b9_KA6STyTWs",
        "outputId": "a1b8fea7-98a2-4d9e-8e23-1c485fae7a5c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(pso_diff, x=\"blocks\", hue=\"heuristic\", multiple=\"dodge\", binwidth=1, shrink=0.8)\n",
        "# plt.xticks(range(0, 8));\n",
        "plt.xlabel(\"Diferença de blocos\")\n",
        "plt.ylabel(\"Quantidade de instâncias\")\n",
        "plt.legend(title='Heurística', labels=[\"Gulosa\", \"Combinação\\ncom singletons\", \"Combinação\"])\n",
        "plt.savefig(\"pso-diff.pgf\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "30eHvSMCwbcF",
        "outputId": "da68890f-559c-4a7e-b318-96f589f8683a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Axes: xlabel='score', ylabel='Count'>"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.histplot(pso_diff, x=\"score\", hue=\"heuristic\", multiple=\"dodge\", binwidth=0.02)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "27tSCDdoZ2fr",
        "outputId": "26bdce42-735b-41b1-e0dc-49cd6a83d013"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.19347948538267387\n",
            "0.1936936689357678\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# COMPARING PSO ALONE, WITH RANDOM STARTING POSITIONS\n",
        "\n",
        "df = read_df(\"r-5-10-s-5-30-n80-ke2-ki0.1-kg0.1-dist1-3-3-3-iter100-part200-+pure.csv\")\n",
        "\n",
        "sns.barplot(df, x='heuristic', y='score', hue='heuristic')\n",
        "plt.ylim(0.17, 0.20)\n",
        "plt.xlabel(\"Heurística\")\n",
        "plt.ylabel(\"Pontuação\")\n",
        "plt.xticks([0, 1, 2, 3, 4], [\"Combinação\", \"Combinação-S\", \"Gulosa\", \"PSO\", \"PSO\\npuro\"], rotation=25)\n",
        "plt.savefig(\"pso-puro.pgf\")\n",
        "\n",
        "print(df[df['heuristic'] == 'psoPure']['score'].mean())\n",
        "print(df[df['heuristic'] == 'pso']['score'].mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "gkxn7MaktrD7",
        "outputId": "3dfab3b2-c46a-4fa8-a92a-26bd86bc0604"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.16819015671377338\n",
            "0.15444885707352868\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# COMPARING WITH FPT\n",
        "\n",
        "df = read_df(\"FPT3-exact.csv\")\n",
        "\n",
        "sns.barplot(df, x='heuristic', y='score', hue='heuristic')\n",
        "plt.ylim(0.12, 0.19)\n",
        "plt.xlabel(\"Heurística\")\n",
        "plt.ylabel(\"Pontuação\")\n",
        "plt.xticks([0, 1, 2, 3, 4, 5], [\"Combinação\", \"Combinação-S\", \"Gulosa\", \"PSO\", \"PSO\\npuro\", \"FPT\"], rotation=25)\n",
        "plt.savefig(\"fpt.pgf\")\n",
        "\n",
        "print(df[df['heuristic'] == 'FPT']['score'].mean())\n",
        "print(df[df['heuristic'] == 'pso']['score'].mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "zE2Q0zStWN5c"
      },
      "outputs": [],
      "source": [
        "# ANALYZING FIRST BEST PSO ITERATION\n",
        "\n",
        "# sns.kdeplot(mixed[mixed[\"heuristic\"] == \"pso\"], x=\"psoIter\", fill=True, clip=(0,None))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "KTBl98NM9uaT"
      },
      "outputs": [],
      "source": [
        "# COMPARISON WITH OTHER ALGORITHMS USING A SEPARATE OUTPUT FILE\n",
        "\n",
        "# def add_results(df, heuristics, interval):\n",
        "#     for name in heuristics:\n",
        "#         heuristics[name]['results'] = []\n",
        "#         with open(heuristics[name]['filename'], 'r') as file:\n",
        "#             lines = file.readlines()[:-1]\n",
        "#             for idx in range(0, len(lines), 2):\n",
        "#                 size = len(lines[idx].strip().split())\n",
        "#                 heuristics[name]['results'].append(size)\n",
        "\n",
        "#     new_df = pd.DataFrame()\n",
        "#     for name in heuristics:\n",
        "#         input_columns = ['size', 'repeats', 'singles', 'maxRepeat', 'edges', 'pair']\n",
        "#         new_data = {col: df.iloc[::interval][col] for col in input_columns}\n",
        "#         new_data['psoIter'] = ''\n",
        "#         new_data['time'] = 0\n",
        "#         new_data['heuristic'] = name\n",
        "#         new_data['blocks'] = heuristics[name]['results']\n",
        "\n",
        "#         current = pd.DataFrame(new_data)\n",
        "#         current['score'] = (current['size'] - current['blocks']) / (current['size'] - 1)\n",
        "#         new_df = pd.concat([new_df, current])\n",
        "\n",
        "#     return pd.concat([new_df, df])\n",
        "\n",
        "# with_other_algs = add_results(\n",
        "#     mixed,\n",
        "#     {\n",
        "#         'g-greedy': {'filename': '31-10-2023-g-greedy.txt'},\n",
        "#         'g-combine': {'filename': '31-10-2023-g-combine.txt'},\n",
        "#         'g-soar': {'filename': '31-10-2023-g-soar.txt'},\n",
        "#         'g-hs': {'filename': '31-10-2023-g-hs.txt'}\n",
        "#     },\n",
        "#     4\n",
        "# )\n",
        "\n",
        "# with_other_algs.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "menVAaakb1ar"
      },
      "outputs": [],
      "source": [
        "# sns.barplot(with_other_algs, x=\"heuristic\", y=\"blocks\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        },
        "id": "LajTtJ-5ZJCF",
        "outputId": "137d8ace-c79f-4750-8258-e8b6e64f5bed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "FPT: 117.16515950920247\n",
            "pso: 0.9107484662576687\n",
            "greedy: 0.00245398773006135\n",
            "combineS: 0.00012883435582822084\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>size</th>\n",
              "      <th>repeats</th>\n",
              "      <th>singles</th>\n",
              "      <th>maxRepeat</th>\n",
              "      <th>edges</th>\n",
              "      <th>psoIter</th>\n",
              "      <th>heuristic</th>\n",
              "      <th>blocks</th>\n",
              "      <th>score</th>\n",
              "      <th>time</th>\n",
              "      <th>left</th>\n",
              "      <th>right</th>\n",
              "      <th>pair</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>combine</td>\n",
              "      <td>12</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.000</td>\n",
              "      <td>[0,3 0,8,0,0,1,7,3,6,1 4,4,5]</td>\n",
              "      <td>[3 0,1,0,6,5,0,7,0,4,3,1 4,8]</td>\n",
              "      <td>0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>combineS</td>\n",
              "      <td>12</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.000</td>\n",
              "      <td>[0,3 0,8,0,0,1,7,3,6,1 4,4,5]</td>\n",
              "      <td>[3 0,1,0,6,5,0,7,0,4,3,1 4,8]</td>\n",
              "      <td>0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>greedy</td>\n",
              "      <td>12</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.000</td>\n",
              "      <td>[0,3 0,8,0,0,1,7,3,6,1 4,4,5]</td>\n",
              "      <td>[3 0,1,0,6,5,0,7,0,4,3,1 4,8]</td>\n",
              "      <td>0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>pso</td>\n",
              "      <td>12</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.276</td>\n",
              "      <td>[0,3 0,8,0,0,1,7,3,6,1 4,4,5]</td>\n",
              "      <td>[3 0,1,0,6,5,0,7,0,4,3,1 4,8]</td>\n",
              "      <td>0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>psoPure</td>\n",
              "      <td>12</td>\n",
              "      <td>0.153846</td>\n",
              "      <td>0.290</td>\n",
              "      <td>[0,3,0,8,0,0 1,7,3,6,1 4,4,5]</td>\n",
              "      <td>[3,0 1,0,6,5,0,7,0,4,3,1 4,8]</td>\n",
              "      <td>0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>FPT</td>\n",
              "      <td>11</td>\n",
              "      <td>0.230769</td>\n",
              "      <td>0.170</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>combine</td>\n",
              "      <td>13</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000</td>\n",
              "      <td>[3,0,5,2 3,2,3,7,6,3,4,0,8,9 0]</td>\n",
              "      <td>[0,0,9 0,2 3,4,3,3,8,7,3,6,2,5]</td>\n",
              "      <td>3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>combineS</td>\n",
              "      <td>13</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.000</td>\n",
              "      <td>[3,0,5,2,3,2,3,7,6,3 4,0,8,9 0]</td>\n",
              "      <td>[0,0,9 0,2,3 4,3,3,8,7,3,6,2,5]</td>\n",
              "      <td>3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>&lt;NA&gt;</td>\n",
              "      <td>greedy</td>\n",
              "      <td>13</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.001</td>\n",
              "      <td>[3,0,5,2,3,2,3,7,6,3 4,0,8,9 0]</td>\n",
              "      <td>[0,0,9 0,2,3 4,3,3,8,7,3,6,2,5]</td>\n",
              "      <td>3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>15</td>\n",
              "      <td>3</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>pso</td>\n",
              "      <td>13</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>0.360</td>\n",
              "      <td>[3,0,5,2 3,2,3,7,6,3,4,0,8,9 0]</td>\n",
              "      <td>[0,0,9 0,2 3,4,3,3,8,7,3,6,2,5]</td>\n",
              "      <td>3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    size  repeats  singles  maxRepeat  edges  psoIter heuristic  blocks  \\\n",
              "0     14        4        4          4      3     <NA>   combine      12   \n",
              "1     14        4        4          4      3     <NA>  combineS      12   \n",
              "2     14        4        4          4      3     <NA>    greedy      12   \n",
              "3     14        4        4          4      3        0       pso      12   \n",
              "4     14        4        4          4      3        0   psoPure      12   \n",
              "6     14        4        4          4      3     <NA>       FPT      11   \n",
              "7     15        3        6          4      4     <NA>   combine      13   \n",
              "8     15        3        6          4      4     <NA>  combineS      13   \n",
              "9     15        3        6          4      4     <NA>    greedy      13   \n",
              "10    15        3        6          4      4        0       pso      13   \n",
              "\n",
              "       score   time                             left  \\\n",
              "0   0.153846  0.000    [0,3 0,8,0,0,1,7,3,6,1 4,4,5]   \n",
              "1   0.153846  0.000    [0,3 0,8,0,0,1,7,3,6,1 4,4,5]   \n",
              "2   0.153846  0.000    [0,3 0,8,0,0,1,7,3,6,1 4,4,5]   \n",
              "3   0.153846  0.276    [0,3 0,8,0,0,1,7,3,6,1 4,4,5]   \n",
              "4   0.153846  0.290    [0,3,0,8,0,0 1,7,3,6,1 4,4,5]   \n",
              "6   0.230769  0.170                              NaN   \n",
              "7   0.142857  0.000  [3,0,5,2 3,2,3,7,6,3,4,0,8,9 0]   \n",
              "8   0.142857  0.000  [3,0,5,2,3,2,3,7,6,3 4,0,8,9 0]   \n",
              "9   0.142857  0.001  [3,0,5,2,3,2,3,7,6,3 4,0,8,9 0]   \n",
              "10  0.142857  0.360  [3,0,5,2 3,2,3,7,6,3,4,0,8,9 0]   \n",
              "\n",
              "                              right  \\\n",
              "0     [3 0,1,0,6,5,0,7,0,4,3,1 4,8]   \n",
              "1     [3 0,1,0,6,5,0,7,0,4,3,1 4,8]   \n",
              "2     [3 0,1,0,6,5,0,7,0,4,3,1 4,8]   \n",
              "3     [3 0,1,0,6,5,0,7,0,4,3,1 4,8]   \n",
              "4     [3,0 1,0,6,5,0,7,0,4,3,1 4,8]   \n",
              "6                               NaN   \n",
              "7   [0,0,9 0,2 3,4,3,3,8,7,3,6,2,5]   \n",
              "8   [0,0,9 0,2,3 4,3,3,8,7,3,6,2,5]   \n",
              "9   [0,0,9 0,2,3 4,3,3,8,7,3,6,2,5]   \n",
              "10  [0,0,9 0,2 3,4,3,3,8,7,3,6,2,5]   \n",
              "\n",
              "                                                 pair  \n",
              "0   0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...  \n",
              "1   0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...  \n",
              "2   0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...  \n",
              "3   0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...  \n",
              "4   0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...  \n",
              "6   0 3 0 8 0 0 1 7 3 6 1 4 4 53 0 1 0 6 5 0 7 0 4...  \n",
              "7   3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...  \n",
              "8   3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...  \n",
              "9   3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...  \n",
              "10  3 0 5 2 3 2 3 7 6 3 4 0 8 9 00 0 9 0 2 3 4 3 3...  "
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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a2+3G4cOHUVJSEthWWloKp9OJurq6XsfX1tb2OFar1aKoqAi1tbWB/aWlpT0eU1JSEti/d+9eFBUV4Te/+Q0qKysxb9487Nq1K/QnFsO6PDIcTm9kAo03+iSiEIrpPjSz2QxJkpCenh7Y5v/eZDL1Ot5kMvU4FgD0ej2MRmNgf0FBQb/7T5w4gX/+85+4+eabUVNTg6amJlRUVCAtLS3oVloYFqsPK2vnuRGOAym7/5hgztO/WojFIcVdPQ3VUOotmbHegpMs9RbTgRZpbrcbCoUC8+fPBwAUFxfj1ltvxcaNG4MKNIMhNdRFDLvWTl9/YkFeKnJy9AN+XHb2wI/1KyqUAZjghnJQvyuRBFNvxHoLVqLXW0wHmsFggCiKsFqtgW3+77Ozs3sdn52d3eNYwDcQJCcn56L78/LyAPhafxkZGVCpVIH9w4YNw//93/8FVX6z2QFJiq/+oS9bHAAAFTwwmWyXPF4QfG+S1lYbBju2QyF1AQBOnLbDZFIPuqzxbCj1lsxYb8GJ93oTRcWAGggx3YemVqsxfvx4NDY2BrY1NDRAq9X2GGrvV15ejoaGhsDPLpcLx44dQ3l5eZ/7/c/n3z9hwgRYLBZ4POf6dFpbW5Gfnx/0OchyfP1rP28O2kAfE+x5+idut9mkqJ93NP7F4+sjFv6x3pKv3gYqpgMNABYsWIBt27YFRjXW1NSgoqICKSkp2LVrFx599NHAsZWVlThw4ACampoAAFu2bEFxcTEmTpwIAJg1axYsFgv27dsHAHjvvffg8XgwY8YMAL5Rj1lZWdi+fTsAX5/bu+++i4qKioidb7QFJlVHYFBIqlYBlSig3cFBIUQ0dDF9yREAZs6ciVOnTmHOnDlQKBQoLS3F4sWLAQBGoxFHjx4NHDty5Ei89NJLeOyxx6DRaKDRaLBy5UooFL7c1uv1WLVqFaqrq7Fy5UpIkoRVq1YFpgSkpKTglVdewc9//nNs3rwZAPDjH/8Yt9xyS4TPOnos3eGSGcaFif0EQYAhTeQCxUQUEoIsD6ZBR4NhNjvg8cRXH9qv3jiN2i868IeHx0AxgCFRggDk5OhhMgV3bf7Z107iVJsbv3mwaPAPjmNDrbdkxXoLTrzXm1KZAH1oFHkWh2+VkIGEWSgY0kTYOr3o8sThu4yIYgoDjXqwdERmlRA//6VNSwf70YhoaBhoFCDLMtodnogGWlYab/RJRKHBQKOADpcXHikyA0L8MnmjTyIKEQYaBbRHcMi+n4EtNCIKEQYaBUTixp4XMnS3BtvZQiOiIQoq0Hbv3h3qclAM8LfQMiM5KIQtNCIKkaACbdmyZfjkk09CXRaKMv9Iw4wI9qFpVAroNAqYuVoIEQ1RUIGmVquxceNGzJ49GzU1NT3u+EzxK5LLXp2Pq4UQUSgE9VH8e9/7Hu6++260tbVh06ZNuOeeezBp0iTcd999KC4uDnUZKUKicckR8N3o8/OTzoj+TiJKPEG10O6++24AQFZWFn7wgx/gzTffhF6vx+2334758+fjz3/+M7ze+FryiXwtNK1agEYV2bFChjQRzi4ZnS6+ZogoeEH95XrzzTcBAF1dXdixYwfuu+8+vPzyyxg5ciS+/vWv4x//+AfuvPPOwKr2FB8sDg8ydJFfr9o/7439aEQ0FEH99frDH/6ApqYmbNmyBWazGTfffDNWr16NG2+8MXBMe3s7vve972Hr1q0hKyyFl6VDQkFW5G+0ef5ctIKsiP96IkoQQQVaXV0dTp8+jbvvvhuVlZUoLCzsdYzL5YLZbB5yASkyPJIMW6c34gNCAF8fGsC5aEQ0NEEF2vjx47Fx40ao1f1/mn/nnXfw7W9/O+iCUWRZO6IzIOT839nGkY5ENARBBdr8+fN7hVlnZyeefPJJfP/738f48eMxZ86ckBSQIiMay175sYVGRKEQ1KCQvvrF1Go1pk2bhp///OdDLhRFnv9O1dEYFJKRKkIQuEAxEQ1NyMZni6KImTNnQqHg8pDxKFpz0ABAVAjI0IkwO3jJkYiCN+CP49u3b8f27dsB+AaFzJs3r9cxZrMZGo0mdKWjiLF0RO+SI+Bb05EtNCIaigEHml6vD4xm/OKLL3qNbBQEAVdffTXuueee0JaQIiKafWiArx+t2eiGV5ahEISolIGI4tuAA+3WW2/FrbfeCgB4/PHHsXTp0rAViiLP4vBAEID0lCgFWqoIyQvYOqSILo5MRIkjqA6vi4XZO++8E3RhKHosDgnpOhEKRXRaR/6RjuxHI6JgDfijsNPphFKphFKpxMmTJ/s9bvXq1YGWHMWPdocU0Rt7XujcfdE8KMpjPywRDd6AA+1b3/oWxowZgzVr1mDatGkQ+ujnkGW5z+0U22RZhsUhoSBLFbUynJuLxhYaEQVnwIE2b9485ObmAgDGjRuHJ554otcxsiyzby0OdbpldEly1AaEAIDBv0AxRzoSUZAGHGgLFiwIfF9RUYGJEyf2eVxFRcWQC0WR1d49qTozioMxzl+gmIgoGEENCulrWSur1drvPopt0bpT9flStQqoRIEtNCIKWlCBtnPnTsybNw8HDx4EADzxxBOYNGkSpkyZgoaGhpAWkMIvmquE+AmCAEOaGCgLEdFgBRVomzZtwne/+11MmDABn376KbZt24bHH38cCxYswAsvvBDqMlKYBdZxjPL8r8w0JVtoRBS0oP6CCYKAqVOnAgB2796NG264IbAUFuehxR//JcfMKA7bB3z9aA0tXnR5ZKiUHC1LRIMTVAvN6/UC8I1qfOeddzBz5szAPpUqekO/KTjRXvbKzz8oxT9IhYhoMIJqoWm1WmzatAktLS2wWCz4xje+AQA4deoUOjo6QlpACj9LhwStSoBWHd07JWR1j3Rsd0jIzeAHIyIanKD+gv33f/83NmzYgD/96U9YsmQJ0tLSsGfPHtx+++2YMmVKqMtIYWZxSFFvnQG+PjSAc9GIKDhBtdCKi4vxxhtv9Nh222234bbbbgtJoSiy2h0eFGSpL31gmHEuGhENRcivMS1btizUT0lh5JFk2Dq9MdFC42ohRDQUQY/Trq2txcGDB2G1WiHLcmD77t278ZOf/CQkhaPws3ZEfw6an3+BYq7nSETBCCrQVq5ciZUrV0Kv10Ov1/fY19raGpKCUWTEyghHANCoFNBpFGyhEVFQggq0N998E5s2bcKVV17Za9/cuXOHXCiKHEtH96RqXWzcVNOQJvKeaEQUlKD60IYPH95nmAHA7373uyEViCLLf3kvFi45Ar7byJjtnh6XsYmIBiKoQBs/fny/N/l8+eWXh1QgiixLR+xccgR8LTRXlwynm4FGRIMT1HUmlUqF+++/H5MmTcLw4cOhUJzLRQ4KiS+xsDDx+fyrhbTZPSjURH8qARHFj6AC7Y9//CNycnLw8ccf99rHQSHxxeLwQBAAfUpsBNq5uWgeFGYz0Iho4IIKtKuuugrr16/vcx8HhcQXi0NChk6EQhEbiwFnp/tekq02jnQkosEJqg+turq6333Lly8PujAUee0xsuyVX7a++5KjjSMdiWhwggq0UaNGAQD27dsXWALr2LFjAICsrKwQFY3CTZblQAstVvgDrdXKFhoRDU5QgdbW1obZs2fju9/9Ln79618DAF599VXccccdMBqNIS0ghU+nW0aXJAcGYsQCnUYBrUpAGydXE9EgBRVoy5YtQ0lJCTZv3oz8/HwAwJNPPokHHngAzz//fEgLSOHTHrhTdey00ARBQJZeyRYaEQ1aUIF27NgxLF26FFdccQWUynOf7mfOnMkWWhyxxNiQfb9svRKtNk6uJqLBCSrQJEmCIPQ9Ks5qtQ6pQH1ZvXo1/uM//gN33XUXnnrqKbhcrn6P/eCDD3DXXXehsrISCxYsQEtLS4/9Bw8exL333os5c+bg3nvvxZEjR/p8nr1796K0tLTPqQmJIpbWcTxfll4Jt0eG3emNdlGIKI4EffuYTz/9tNe2N998ExqNZkgFutDOnTuxefNmbNiwAVu3boXVau33FjXNzc145JFH8Pzzz6Ompga33HILFi5cGPikb7PZUFVVhUWLFmHDhg2oqqpCVVVVr7tsS5KEFStWhPQ8YpElcMkxdvrQAA7dJ6LgBBVoDz74IObPn48FCxbg6NGjeOSRR3D77bfj8ccfx6JFi0JawLVr12L27NnQ6XQAgMrKSmzatAmdnZ29jt24cSPKy8sxduxYAEBFRQUaGxuxf/9+AMCOHTuQnp6OyZMnAwCmTZsGpVKJPXv29HqeW2+9NaTnEYti+ZIjwJGORDQ4QQXazTffjN///vfweDyw2Wz429/+hoyMDKxevToQFqHgdrtx+PBhlJSUBLaVlpbC6XSirq6u1/G1tbU9jtVqtSgqKkJtbW1gf2lpaY/HlJSUBPYDgMViweuvv4558+aF7DxiVawte+V3bi4aA42IBi6oa03+m3pWVlYiIyMDV111Va/7ooWC2WyGJElIT08PbPN/bzKZeh1vMpl6HAsAer0+MFDFZDKhoKCg3/0AsGLFClRVVUGtDs2yS/10NcYES4cErVqAVh38jcv95xfK88w575JjLNffUISj3pIB6y04yVJvgwo0t9uNpUuXYvPmzZCkcys5iKKI73znO/jpT38asiCIhqamJjQ1NeGpp54KyfMZDKkheZ5wsTtPIluvQk7O0D+MZGeH7gNNeoYXQDMcbiEkZYtloay3ZMJ6C06i19ugAu2//uu/sG/fPkybNg3l5eXQ6/WwWq3417/+hddeew3Nzc1YtWpVyApnMBggimKPkZP+77Ozs3sdn52d3WuUpc1mQ05OzkX35+XlAQBeeOEFLF68OGTlN5sdkKTYHalntLgxKlcNk8kW9HMIgu9N0tpqQyhH2afrRJw0dQ6pbLEsXPWW6FhvwYn3ehNFxYAaCAMOtF27dqG+vh7bt2/H5Zdf3mt/fX09qqqqsHv3bsyYMWNwpe2HWq3G+PHj0djYiKlTpwIAGhoaoNVqUVZW1uv48vJy1NfXB352uVw4duwYysvLA/svXFS5oaEB06dPh91ux9GjR7F06dIe+5977jmkp6djzZo1QbU+Y/XF4+7ywuH0wpCqDEkZZTm055rdPbk6VusvVEJdb8mC9RacRK+3AXeevPnmm3juuef6DDPAN1jj2Wefxeuvvx6qsgEAFixYgG3btgVGNdbU1KCiogIpKSnYtWsXHn300cCxlZWVOHDgAJqamgAAW7ZsQXFxMSZOnAgAmDVrFiwWC/bt2wcAeO+99+DxeDBjxgykpaXh7bffxvr16wP/AOCJJ57A+vXr4/pSal/M/jtVp8XWgBC/LL0Is0OC5E3gdx8RhdSAW2inT5/GjTfeeNFjbrrpJrz44otDLtT5Zs6ciVOnTmHOnDlQKBQoLS0NXBY0Go04evRo4NiRI0fipZdewmOPPQaNRgONRoOVK1cGbkCq1+uxatUqVFdXY+XKlZAkCatWrQpMCfDbtm0btm/fDsDXQps4cSKefPLJkJ5XtJm710o0pMXWHDS/bL2v5Wi2S4FBIkREFzPgvxRpaWkhPW4wHnjgATzwwAO9ts+fPx/z58/vsW3KlCmYMmVKv891xRVXYOPGjRf9fbNnz8bs2bODK2ycaOtuoRlitIUWmItm8zDQiGhABnzJ0d/KCdVxFF0x30JL51w0IhqcAf81O3LkyIAmG/c14ZliT8wHGlcLIaJBGtRfM65+njjMdgkCYm+VEL8srhZCRIM04EAbN25cryHvfZk7d+6QCkSR0e7wIF0nQinG5tIBGakiRAUXKCaigRtwh9cLL7wQ0uMousx2KWYHhACAQhBgSFMy0IhowAYcaMOHDw/pcRQ9XlmG2e6J2f4zP/+NPomIBoJDEpOQvdMLyRu7Q/b9stOVcDi9cHXF7vJhRBQ7GGhJqC3GRzj6nT8XjYjoUhhoScg/ZD9Wl73y49B9IhoMBloSMgdWCYmPFpqJgUZEA8BAS0Lm7kt4WTEeaDkZvvIZLQw0Iro0BloSMsf4Oo5+ud3LXxmtXVEuCRHFAwZaEjLbPVArBeg0sf3fr1YpkJkqsoVGRAMS23/RKCx8c9BECEJsrhJyvtwMJYwWttCI6NIYaEnIt0pIbPef+eVmqGDr9MLp5lw0Iro4BlqScXd54XB54yfQ0jkwhIgGhoGWZOJlQIhfXqYKAAeGENGlMdCSTKzfB+1C/hbaWbbQiOgSGGhJxuyIrxZaToavhWbiwBAiugQGWpKJtxZaVprvvmjsQyOiS2GgJZlzgRYfLTSFQkBOOofuE9GlMdCSjH9QSGZqfLTQAN/Q/bMWD2RZjnZRiCiGMdCSjNnuQbpOhFKM/UnVfsMyVXB75EAYExH1hYGWZHyTquPjcqNfvsE3MORMOy87ElH/GGhJxCvLMNs9Mb/K/oXyu+einTYz0Iiofwy0JNJulyB5gez0OAs0Q/dcNLbQiOgiGGhJpLX7Pmj+G2fGi9x0FRQCcJqBRkQXwUBLIoFAi7MWmlL0Dd0/w0uORHQRDLQk0mqNzxYa4OtHO9PugZdD94moHwy0JOIPtJw4a6EBvpGOXRKH7hNR/xhoSaTV5oGoADJS42vYPuCbiwaAlx2JqF8MtCTSavMgS6+EIg7uVH0h/9B9zkUjov4w0JJIq9WDnDjsPwOAYd2Tq0+2MdCIqG8MtCTR6fbdqTorDvvPACA3QwmVKOBUmzvaRSGiGMVASxLxPMIR8K26P8ygYguNiPrFQEsS8Tqp+nzDs1QwWT1wdXmjXRQiikEMtCThD7R4HLLvV5Dl60c7xVYaEfWBgZYkTJb4XCXkfAXZagDASfajEVEfGGhJwmj1tWoSoYXGfjQi6gsDLUkY2z0wpIpQK+P3v3yYQQVBAE62soVGRL3F7183GhSjtQu53ZOT45VaqUBuupItNCLqEwMtCXS6vbB1epEbx5cb/Qqz1TjT3gWPxEWKiagnBloSMFp8LZrcjPgPtBE5akhejnQkot4YaEnA2D3CMTcjvi85AsDIXN9Ix2aTK8olIaJYw0BLAv4WWl4CtNBG5vgC7biRA0OIqCcGWhJIpBbaMIMKKlFAMwONiC7AQEsCZy0eKEUgMy3+7oN2IVEhoDBHheMmBhoR9cRASwImaxdy0lVxeR+0vozK0cDikGDt4N2rieicuOhUWb16Nd566y0oFAqUlZVhyZIl0Gg0fR77wQcfYPny5VCr1dBoNKiurkZhYWFg/8GDB1FdXQ1RFCFJEp5++mmUlZUBABobG/Hyyy/DaDTC5XIhMzMTjz/+OIqKiiJxmmEhyzKMFg/GjdBGuyghMyrX34/mwoTRuiiXhohiRcy30Hbu3InNmzdjw4YN2Lp1K6xWK5YtW9bnsc3NzXjkkUfw/PPPo6amBrfccgsWLlwIWfbNWbLZbKiqqsKiRYuwYcMGVFVVoaqqCh0dHQCANWvWoKioCOvWrcNrr72Gyy67DFVVVZCk+G0JtDskuD1yQvSf+Z0b6cjLjkR0TswH2tq1azF79mzodL5P4pWVldi0aRM6Ozt7Hbtx40aUl5dj7NixAICKigo0NjZi//79AIAdO3YgPT0dkydPBgBMmzYNSqUSe/bsAQBcd911qKysDDzfnXfeiWPHjuHLL78M5ymG1Wmzb4Sj/47PicDfQjt2hoFGROfEdKC53W4cPnwYJSUlgW2lpaVwOp2oq6vrdXxtbW2PY7VaLYqKilBbWxvYX1pa2uMxJSUlgf133XUXcnJyAvtcLhdEUYTBYAjhWUVWINDifNmr86VqReRlKPHFGc5FI6JzYroPzWw2Q5IkpKenB7b5vzeZTL2ON5lMPY4FAL1eD6PRGNhfUFDQ7/4Lvf3225g1axaysrKCPodoj8M40+4LtOFZqrCUxf+ckT7P4uEa7KtzoMMlIVUbf6M3o1Vv8Y71FpxkqbeYDrRoqqurw/vvv49XX3016OcwGFJDWKLgtDlMEBXAuMsMEMXwvZqzs/Vhe+6+XFHsxL46B9qcIkaPiOzvDqVI11uiYL0FJ9HrLaYDzWAwQBRFWK3WwDb/99nZ2b2Oz87O7nEs4BsI4r+M2N/+vLy8HttOnjyJn/3sZ/jtb3+LjIyMoMtvNjsgSd6gHx8Kx890IjdDBbPZHpbnFwTfm6S11QY5gusF56X5vv6r3oyRmZH7vaESrXqLd6y34MR7vYmiYkANhJgONLVajfHjx6OxsRFTp04FADQ0NECr1QaG2p+vvLwc9fX1gZ9dLheOHTuG8vLywP7169f3eExDQwOmT58e+Pns2bP48Y9/jOrqaowcORKtra0A+g7QgYjmi8frlXGmvQsTRuvCXg5Zjuy5js5TQyEAX5x2xeUb1C/S9ZYoWG/BSfR6i+lBIQCwYMECbNu2LTCqsaamBhUVFUhJScGuXbvw6KOPBo6trKzEgQMH0NTUBADYsmULiouLMXHiRADArFmzYLFYsG/fPgDAe++9B4/HgxkzZgAA2tra8MMf/hA/+clPMGzYMDgcDvzlL3/B+++/H8lTDhmT1QPJm1gjHP00KgUKs9U4epoDQ4jIJ6ZbaAAwc+ZMnDp1CnPmzIFCoUBpaSkWL14MADAajTh69Gjg2JEjR+Kll17CY489Bo1GA41Gg5UrV0Kh8OW2Xq/HqlWrUF1djZUrV0KSJKxatSowJaC6uhoHDx7EnDlzepRh6dKlETrb0PKPcByegIEGAJcN0+D9QzaY7R4Y0mL+pUxEYSbIciI3QKPLbHbA44leH9rb/7Lg1b+14qd3D8dXRqWE5XcIApCTo4fJFPlr8x8csmH120YsnJmHiSVpkf3lQxTNeotnrLfgxHu9KZUD60OL+UuOFLzT3TfBzE+gOWjnK+1ezqvuhDPKJSGiWMBAS2AtbW5oVAKy9PE3T2sg8jKUMKSKqGegEREYaAntZGsXCrPUCbPK/oUEQUDJCC1OmNywd8bveptEFBoMtARl65Rg6ZBQmJOYlxv9Sgu1kAE0nmQrjSjZMdASVEurb+Hewmx1lEsSXuNG+Aa7sB+NiBhoCaql1TcgJNEDrSBbBX2KAoeP9777AhElFwZagmoxJUcLTSEImDBah+NGN9rtnmgXh4iiiIGWoFpa3dCqBWQn6AjH811Z5Lvs+NmXbKURJTMGWoI60epGYZYaQoKOcDzfFUU6CAA++7Ij2kUhoihioCUga4cEW6c34S83+qXrRIzJ1+DQsU5I3jhcBoGIQoKBloCau/vPRuQmR6ABwJVjUtDh8uJzDt8nSloMtAT05RnfCvRFeckTaNeO9a3ztr/BEeWSEFG0MNAS0LGzvkAbnauJckkiZ1SuGvmZKvyj0QEvLzsSJSUGWgI6dtaN/EwlUjTJ898rCAImlaai3SGhgZcdiZJS8vzFSxKdbi9Om7swOi95Wmd+E0u6LzvW87IjUTJioCWY40Y3ZABFSRhoI3PUKMhS4aN6O9xRvA8dEUUHAy3B+PvPivKTZ0CInyAIuPkKPexOLz5p5Jw0omTDQEswgQEhSdhCA4Abx+uhEgX87TNrtItCRBHGQEswX5x2ISddCX1K4i951Rd9iojrLk9FfYszcMcBIkoODLQEYu+U0NLahbHDk7N15ndLeToAYPcnliiXhIgiiYGWQJpO+S43lhRqo1yS6Lq8QIvSQi32HrHBZO2KdnGIKEIYaAmk8ZRv/tXYguQONAC44/pMSF7grX+wlUaULBhoCaSxxQmNSsDInOQb4Xihr4xKwWXDNHjvoBWnzWylESUDBlqC8Egymk67UDxMA1GR+LeMuRRBEFB5czYkL7DhvdZoF4eIIoCBliCOG91we2RczsuNAaWFWnxtXBoOHO3Av77g6iFEiY6BliD+fdx3t+bSEQy0833npiykqAX84S8m2DqlaBeHiMKIgZYgDh3vhEoUkn6E44Wy9ErMm5YDi0PCH942Qpa5Ej9RomKgJQBXlxcNLZ0oHaGFWsn/0gtNLkvD9aWp+LSpA2983B7t4hBRmPCvXwKob3HCIwETRqdEuygxSRAE/H/fyMWoXDW2fWjGh0ds0S4SEYUBAy0BHDrm6z9joPVPq1bgx3cOgyFNxO/3GPFRvT3aRSKiEGOgJYCDX3YgQydy/tklZOmVeLyiAJmpIn636yzeqeWka6JEwkCLcyfb3Ghp7cLVxToIAuefXcowgwpP3FOAvAwV1r3birV/NcHdxXunESUCBlqc+6TRN7/qustTo1yS+JGfqcL/VBagbIQWfz1gxc/+1IJmI1fmJ4p3DLQ4t7/BgVSNAmUj2X82GPoUET+9ezjuvsGAk21d+NmfWrD703ZIXg7rJ4pXDLQ4dsbcheNGN64Zq4NS5OXGwVIoBMyaZMCSewtgSBNR834bnt7Qgs9POqNdNCIKAgMtjn1Y5xupN/HytCiXJL4VD9eiet4I3D4xEy2tbjyz8STWvG2ExeGJdtGIaBCU0S4ABUfyynj/oBWGNBETini5cag0KgUqbszC5LI0rHvXhPcP2fBxvR3fvi4Tt12bAY2Kn/2IYh3fpXHqs6MdaLNLuHmCnqvrh1Bhthr/ffdw/GhWPjJSldj6oRk//f+b8fd/2+DlsllEMY0ttDj17mc2CALw9SvSo12UhCMIAq4dm4qrxujw7mdWvL7PjFV7jNj9iQWzJxtwTRxPkXC6vbB1SrB1SrA7vfCeN2NBqxKQliIiXSciTauAgh+UKM4w0OLQCZMbnx3twNXFOmTp+V8YLkpRwPSrM3BDWRp27m/HX2qtWP7mGRTlq3HX5CxcWZQSs8HW4fLi2FkXvjzrwnGjG2fMXTjT3gVb58Dm3KlEAcMMKgzPUmFUrhpjh2tRPFzDS68U0/jXMA698bEZMoBZkzKjXZSkkKoV8Z0p2fjmtRnYub8d735mxYvbT2PscA1un5iJqy7TQRHlYLN1Sqg74URdcyfqTjjRbOo5ry5dJ2KYQYUrilTI0IlIS/G1wvyjY2UZcHZ5Yevwtd5MVg9OtnXhHw0O7G/wzXVUCEBRvgZXjdHhqjE6FOWro37eROdjoMWZllY39tc7cNWYFFw2jLeKiaTMVCXun5qDb301E29+bMb7h2z43zfOYLhBhW9em4HJ49KgVUemBWPrlFB/wom6E5040twzwDJTRUwqTcVl+RqMztNgdJ4aqVoxqN/j6vLiuNGNhhYnGk86UXfCiS9Om7F9nxmZqSImlqTi+tI0FA/XxGxrlZKHIPMGUWFjNjvg8YRuWSVZlvHLbadx6Fgnnq4sQPHw6AeaIAA5OXqYTDYk2yupzebBO7VWvPuZFR0uLzQqAdddnoqbvqJHaaH2on1Qg623drsH9S1O378TvQNs3AgtykamYNxILYZlqsIWLpJXRuNJJw580YFPPu/AmfYuAEBuhhLXl6bha+PSMCKMa4om8+ttKOK93pRKBQyGS6+GxEALo1AH2odH7Hh591nc9JU0PPDNvJA971DE+xslFJxuL/YeseP/DtvwxWkXACBNq8CE0Sm4okiHsQUa5GeoegTcxeqtw+VFs9GFY0Y3vjzjQkOLE2ct5+bEZaSKKItQgF2MLMv48qwbH9XZ8XG9HW123x3BR+epcUOZL9wyUkN7EYivt+DEe70x0GJAKAPNbPfgqfUnAAC/WDAS+pTgLiGFWry/UUKtpdWND4/Y8dmXHTh29lwrSiUKKMhWIVuvRKpWAZ1GAX2aBharC26PDIdTQqtNQqvNA2uH1OM58zOVKCnUorQwBSWFWuRnKmPu8p5XltHQ4sSHR+zY3+BAh8sLhQBcUZSCG8r0uKZYB3UIBpTw9RaceK83BloMCFWgdXlkPLf5JJpOubBwZh4mlsTOyiDx/kYJJ4vDg8PHnThudOGEyY0TJjfaHRL6Wi5SEABDqojsdCVy01UYmavGqO5/oW7lhJvb40XtFx34+7/t+OxoB7wykKIWMLEkDTeUpaFkhDbowSR8vQUn3uuNgRYDQhFoklfG73efxUf1Dnz7qxn4zpTsEJUuNOL9jRJpsizD1SWjw+VFRqYOdlsHlAoBWrUiISfIWzsk7KuzY+8RG74842ux5qQrMbnMF27DswbX38bXW3Divd4YaDFgqIHm6vLi97vP4pPPO3DtWB0empkfc5Nd4/2NEi3JWG8nTG7sPWLDh0fsMHf3t102TINrx/qmAYzMUV/yUmos1pssy3C6ZVg6JFgcHlg7vXC6u/91yXC6vfBIcnfLXIYs+6ZJKEUBKqUAdeCfAiqlgBS1AmlaBVK1IlK1vu+Herk2FuttMBIq0FavXo233noLCoUCZWVlWLJkCTQaTZ/HfvDBB1i+fDnUajU0Gg2qq6tRWFgY2H/w4EFUV1dDFEVIkoSnn34aZWVlgf3Hjx/HU089BY/HA5fLhZ/85Cf42te+FlS5hxJox40u/G7XWbS0duH60lRU3ZYXkyvqx/sbJVqSud68XhlHTjjx93/b8GmjA84uXwVk6UWUj9HhiiIdLi/QIl3Xu584WvXmdHtx1tKFM+0enG3vgtHShbMW3/ftDgluT3gLoxIF6FMUyExTwpAqwpCmRGaa76shTURmqu+rTqPo80NBvL/eEibQdu7ciRUrVmD79u3Q6XRYtGgR8vPz8eSTT/Y6trm5GXfeeSdee+01jB07FuvXr8e2bduwbds2CIIAm82G6dOn48UXX8TkyZPx7rvv4umnn8af//xn6HQ6SJKEWbNmYcGCBaioqMCRI0dw//3346233sKwYcMGXfZgAs0jyVj/NxPeO2iDAOCuG7Lw7esyYnYCa7y/UaKF9ebT5ZFR39KJA0c7UPtFB860nxvNmZ+pxNjhWlxeoMWoXDWGZ6mQliKGpd68sox2uxQIKqOlC2fbPTjb/fOFA3UAQK0UkJehRJZeiYxUERk6JdJTRaSniEjRCNCqFNCqFdCqBChFAYIgwH+BRRB873W3R0aXx/fV7fGiyyOj0+2F3emFw+mFwyl1fy/B2uFFu8MDSz/9sIBv+bIsva9M2d1fs/QisvVKjB2VAUFyQaOMv9VeEibQKioqcOutt+I///M/AQD79u3DD37wA3z00UdISem5yvwvf/lL1NXVYc2aNQAAp9OJr371q1izZg0mTZqEP/3pT1i7di3+/Oc/Bx4zdepUPPTQQ5g9ezbef/99PPzww9i/fz9UKhUAYM6cObj++uvx0EMPDbrswQTacaMLS9a3oKRQi/u+no0x+X23RGMF/zAHh/XWt1NmN+qafZO4G0+6AvPc/PQpCozKT0F2mgIZOhF6nYj0FAXSdSJS1AqIogBRECCKgCgI6JJkuLq8cHX5vna6vbB2SGh3SLA4JFg6JLTZPDBaPOiSev9HpOtE5GUokZehQl6mEnmZqsD3GToxKqNNvV4Z1g4JZoeEdrsHZruENrsHZrsHbTbf+bTaPP22GlM1ivNCT7wg/JTISlNCpYytD9ADDbSYHj7ldrtx+PBh/PCHPwxsKy0thdPpRF1dHa6++uoex9fW1uLKK68M/KzValFUVITa2lpMmjQJtbW1KC0t7fGYkpIS1NbWYvbs2aitrcWYMWMCYeb/fbW1teE5wT6MytVg5YOjkabt+9IBUSIbblBjuEGNqVf6Ft22dkhoOuXEidYunDa7caqtC8fOOHH4y94tpmBoVQIy05QoG6lFXoYKuZm+8MrNUCI3Q4WUCK38MhgKha/MmWlKoJ8PvLIsw+H0os3uC7c2mwSnpMCJMx3dP3tw+HgHPP1Uo1YtIFXju4Sp0yig0yqQqlFAq/L186mUAlRi768Kha8VKgiAQhB8XxW+73UaBYqHa8J6tSmmA81sNkOSJKSnn1tR3v+9yWTqdbzJZOpxLADo9XoYjcbA/oKCgovuz8jI6Hf/YIlicG8Ggz723kT98b82lUoFWxqDwHobmKx0BbLSVbiu+2dBADIydDh91g57pwSHy3c5zuHyDb6QZRmSDMhewOMFlCICgy5USgU0SiBVq4Re1z3YIg4vvw1UpkpEpl6Fy4b76i0zMxXt7Y7A602GL/QsDinQarV2+Fqund2DWvxfHS4Z1k4JwNA+SHx/ei5G5w1+JZmB/i2N6UCLd+npyXPjzczMS18OoN5Yb8EZnq+PdhHi0oWvtywAI6NTlLCI6Y8nBoMBoijCarUGtvm/z87uPR8rOzu7x7EAYLPZkJOTM+D9Foul3/1ERBS7YjrQ1Go1xo8fj8bGxsC2hoYGaLXaHkPt/crLy9HQ0BD42eVy4dixYygvL+9zv//5/PuvvvpqHD16FB6Pp8/9REQUu2I60ABgwYIF2LZtGzo7OwEANTU1qKioQEpKCnbt2oVHH300cGxlZSUOHDiApqYmAMCWLVtQXFyMiRMnAgBmzZoFi8WCffv2AQDee+89eDwezJgxAwBwww03oKCgAK+//joAoK6uDnV1dbjnnnsidbpERBSkmB+2DwCvvPIKdu/eDYVCgdLSUvzP//wPNBoN1q5dizfeeAPbtm0LHOufWK3RaKDRaPDMM89gxIgRgf2RnFhNRESRExeBRkREdCkxf8mRiIhoIBhoRESUEBhoRESUEBhoRESUEBhoRESUELj0FQVtMPepSwYejwcbN24M3M3BZrPhzjvvxIIFCwLHvP7661i3bh1UKhXy8vLwzDPPIDMzM7D/UvfzSwaVlZVQKpVYv359YBvr7eI++ugj/OY3v4HX64XZbMbkyZPx1FNPAUiyupOJgrBjxw55+vTpssPhkGVZlh966CH52WefjXKpoqu5uVmePHmybDQaZVmW5ZMnT8rXXXedvH37dlmWZfmTTz6RJ02aFNi/dOlS+cEHHww8/vjx4/I111wjNzY2yrIsy+vWrZPvvPNO2ev1RvZEomjnzp3yNddcI99///2Bbay3izt8+LB8++23B+qntbVVXrhwoSzLyVd3vORIQVm7di1mz54NnU4HwPepetOmTYEVXZJRamoqfvSjHwXW/hw+fDgmTZqE999/HwCwfv16TJ8+PbB/zpw5+Otf/4rm5mYAwMaNG1FeXo6xY8cC8N0LsLGxEfv374/C2USe0+nEH//4R1RUVPTYznq7uN/+9re47777AvWTlZWFFStWAEi+umOg0aD571NXUlIS2Hb+feqSlcFg6LVMmtPpDCykXVtb26PORo4cCZ1OhwMHDvS5//z7+SWD1atX45577kFqas8V4Vlv/ZNlGR9++CE8Hg+qqqpw77334sknn0RbWxuA5Ks7BhoN2mDvU5eszGYzDhw4gPvuuw9A//fr89fZpe7nl8hOnz6NvXv34q677uq1j/XWP7PZDIfDgfXr1+O5555DTU0NFAoFHnzwQQDJV3cMNKIwefbZZ7Fo0SJcdtll0S5KzFu2bBkefvhhKBT8kzQYbrcbAHD77bcjJycHgiCgqqoKtbW1OHLkSJRLF3l89dCgDfY+dcloxYoVKCwsxP333x/Y1t/9+Px1dqn79SWq2tpauFwuTJo0qc/9rLf+6fW+G53m5uYGtuXn5wMATp48mXR1x2H7NGjn36du6tSpAC5+n7pks3r1algslsCw6fr6epSWlva6H19zczM6Ojp63K+vvr4+sP/C+/klqr179+LkyZOYO3cuAKClpQVWqxVz587FzJkzWW8XkZqaijFjxvS41O/vPxs2bFjy1V20h1lSfNqxY4f8zW9+U+7o6JBlWZYXLVokP/PMM1EuVfStX79e/tGPfiTbbDbZbrfLdrtdnjNnjizLviHU119/vWwymWRZluVf/OIX8g9+8IPAY48fPy5fe+218ueffy7Lsiy/+uqr8h133CFLkhT5E4miX//6172G7bPe+rdu3Tr5tttuk+12uyzLsvyrX/1Knj17tixJUtLVHW8fQ0Hr7z51yero0aOYMWMGLnxLFRYW4t133wUw8Emu/d3PL9HNnTs30EIrKyvDE088gbKyMtbbRciyjBUrVuDtt99GRkYGsrKy8NRTTwUuPSZT3THQiIgoIXBQCBERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGhERJQQGGlGcO3LkCFasWNFj24EDBzBx4kR89tlnUSoVUeQx0Iji3JEjR7By5coe21JTU1FUVASdThelUhFFnjLaBSCi0Bs7diw2bdoU7WIQRZQgy7Ic7UIQUXDWrl2LmpoaHD16FBMnTgQAfO1rX8O+ffuwf/9+LF26FLNnz8aePXvwhz/8AQcOHMDSpUuxd+9eHD16FADwi1/8AlarFevWrUN9fT1KS0vx3HPPIS0tLfB7Dhw4gBdffBGdnZ1Qq9XIzs7G4sWLMXLkyKicN1GfZCKKa1u3bpVLSkp6bS8pKZG3bt0a+Lm5uVkuKSmRq6qqZJfLJcuyLD/00EPyHXfcIa9Zs0aWZVnu6OiQp0yZIv/ud78LPK6+vl6+8sor5TfeeCOw7aWXXpJvuukm2W63h+u0iAaNfWhESWbGjBlQq9UAgEmTJuHIkSO47bbbAAApKSm46qqrcPDgwcDxq1evRk5ODmbNmhXYNn/+fJw5cwZvvfVWZAtPdBHsQyNKMvn5+YHvU1JSem3T6XRoaWkJ/Hzo0CFYrVbMnTu3x/MUFhaira0tzKUlGjgGGlGSUSh6X5gRRfGijxk9ejTWr18friIRhQQvORLFuQsDymazhfT5J0yYgOPHj8PtdvfY/tvf/haffPJJSH8X0VAw0IjiXG5uLgCgra0NZ8+exTe/+c2QPv8DDzwAt9uNV155JbBt//792LJlC0pLS0P6u4iGgsP2ieKcx+PBww8/jOPHj0OpVGLatGn4+OOPsX//fowZMwbf+ta3cO2112L58uU4cOAAxo0bh4ULF8JoNGLdunWBIf/PPfccXnnlFfz1r3+Fy+XCV77yFaxduxYAcPDgQbz44oswGo3IyclBSkoKFi9ejOLi4iifPdE5DDQiIkoIvORIREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJgYFGREQJ4f8B3fjqvJOEE5sAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 450x360 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# CALCULATING FPT TIME\n",
        "\n",
        "df = read_df(\"FPT3-exact.csv\")\n",
        "fpt_df = df[df[\"heuristic\"] == \"FPT\"]\n",
        "\n",
        "def print_time(name):\n",
        "    print(f'{name}: {df[df[\"heuristic\"] == name][\"time\"].mean()}')\n",
        "\n",
        "print_time(\"FPT\")\n",
        "print_time(\"pso\")\n",
        "print_time(\"greedy\")\n",
        "print_time(\"combineS\")\n",
        "\n",
        "# sns.boxplot(df[df[\"size\"] > 0], x=\"heuristic\", y=\"time\")\n",
        "# plt.ylim(0, 30)\n",
        "\n",
        "sns.kdeplot(fpt_df[fpt_df[\"time\"] < 600], x='time')\n",
        "df.head(10)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default",
      "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": "3.11.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}