project_strangecues.ipynb

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      "name": "project-strangecues.ipynb",
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "15WnEHxQEA9N"
      },
      "source": [
        "#IMDb Data Analysis and Top 1% Movies and TV series"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2S0cb_peDdbi"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.pipeline import make_pipeline, make_union\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.feature_extraction import DictVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.metrics import roc_auc_score, f1_score, average_precision_score, precision_score, recall_score, fbeta_score, precision_recall_curve, auc\n",
        "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
        "from sklearn.model_selection import ParameterGrid\n",
        "from sklearn.base import BaseEstimator, TransformerMixin\n",
        "from tqdm.notebook import tqdm\n",
        "import csv\n",
        "from gensim.models.callbacks import CallbackAny2Vec\n",
        "from itertools import groupby\n",
        "from gensim.models import Word2Vec\n",
        "import gensim\n",
        "from pathlib import Path\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import random\n",
        "pd.options.display.max_columns = 999"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tfo58y-9Ddbl"
      },
      "source": [
        "PATH = Path('/content/drive/MyDrive/Colab_Notebooks/imdb-dataset')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bf5LzjLItnDf"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPY4w4S8Ddbm"
      },
      "source": [
        "def load_data(start_year, min_minutes, min_votes):\n",
        "    title_basics = pd.read_csv(PATH / 'title.basics.tsv' / 'title.basics.tsv', sep='\\t')\n",
        "    title_ratings = pd.read_csv(PATH / 'title.ratings.tsv'/ 'title.ratings.tsv', sep='\\t')\n",
        "    title_basics.genres = title_basics.genres.apply(\n",
        "                        lambda x: x.split(',') if ((type(x)!=float) & (x!=r'\\N')) else ['no_genre'])\n",
        "\n",
        "    title_basics.runtimeMinutes = (\n",
        "     title_basics.runtimeMinutes.apply(lambda x: np.nan if not x.isdigit() else x).astype(float)\n",
        "                                )\n",
        "    \n",
        "    title_basics = title_basics[\n",
        "        title_basics.titleType.isin(['movie'])\n",
        "        & ~title_basics.runtimeMinutes.isna()\n",
        "        & (title_basics.runtimeMinutes <= 3.5 * 60)\n",
        "        & title_basics.genres.apply(lambda x: 'Short' not in x)\n",
        "        ]\n",
        "    \n",
        "    movies = pd.merge(title_basics, title_ratings, on='tconst', how='left')\n",
        "    movies['startYear'] = movies['startYear'].apply(lambda x: np.nan if x == r'\\N' else int(x))\n",
        "    \n",
        "    # MY CONDITIONS:\n",
        "    movies = movies[movies.startYear > start_year].dropna(subset=['averageRating'])\n",
        "    movies = movies[movies.runtimeMinutes >= min_minutes]\n",
        "    movies = movies[movies.numVotes>=min_votes]\n",
        "    return movies"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ki7ujC5JDdbm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb0f539a-7216-4a80-8566-6623d44a385d"
      },
      "source": [
        "movies = load_data(start_year = 1960, min_minutes = 60, min_votes = 15)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2822: DtypeWarning: Columns (5) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  if self.run_code(code, result):\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztTJLIQJDdbn"
      },
      "source": [
        "principals = pd.read_csv(PATH /'title.principals.tsv'/'title.principals.tsv',delimiter=\"\\t\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JzOY0IXqDdbn"
      },
      "source": [
        "cast = principals[principals['category'].isin(['actor', 'actress'])]\n",
        "crew = principals[~principals['category'].isin(['actor', 'actress'])]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUQ7P0iBDdbo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "0613e964-4e15-4283-ad9b-a8ad426e2540"
      },
      "source": [
        "ordered_cast = pd.DataFrame(cast.groupby(by='tconst').apply(lambda x: [x for x in x['nconst']]))\n",
        "ordered_cast = ordered_cast.reset_index().rename(columns={0: 'cast'})\n",
        "ordered_cast.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>cast</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000005</td>\n",
              "      <td>[nm0443482, nm0653042]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000007</td>\n",
              "      <td>[nm0179163, nm0183947]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst                    cast\n",
              "0  tt0000005  [nm0443482, nm0653042]\n",
              "1  tt0000007  [nm0179163, nm0183947]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-rWxHNAaDdbo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9d57d58-a8d6-4f02-80ef-423c4bdda184"
      },
      "source": [
        "# If it went right, we should have no duplicated movies (tconst)\n",
        "ordered_cast.duplicated('tconst').any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwNMGoo-Ddbp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "cc9c9830-395e-48f9-ee2d-635942b96168"
      },
      "source": [
        "ordered_crew = pd.DataFrame(crew.groupby(by='tconst').apply(lambda x: [x for x in x['nconst']]))\n",
        "ordered_crew = ordered_crew.reset_index().rename(columns={0: 'crew'})\n",
        "ordered_crew.head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>tt0000001</td>\n",
              "      <td>[nm1588970, nm0005690, nm0374658]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>tt0000002</td>\n",
              "      <td>[nm0721526, nm1335271]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      tconst                               crew\n",
              "0  tt0000001  [nm1588970, nm0005690, nm0374658]\n",
              "1  tt0000002             [nm0721526, nm1335271]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFlgi9tlDdbp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5d9d297b-061d-44f3-ddca-c62efe0ecef1"
      },
      "source": [
        "# If it went right, we should have no duplicated movies (tconst)\n",
        "ordered_crew['tconst'].value_counts().max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zVyV0RYGDdbp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f83ba5f-b700-49d1-8258-d1742ecfcf8a"
      },
      "source": [
        "movies = pd.merge(movies, ordered_cast, on='tconst', how='left').merge(ordered_crew, on='tconst', how='left')\n",
        "\n",
        "# If it went right, we should have no duplicated movies (tconst)\n",
        "movies.duplicated('tconst').any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zo457f1-Ddbq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "outputId": "a013803b-3621-467f-8f35-a0a44b269b62"
      },
      "source": [
        "# CHECK: Avengers Endgame\n",
        "movies[movies['tconst']=='tt4154796']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>115780</th>\n",
              "      <td>tt4154796</td>\n",
              "      <td>movie</td>\n",
              "      <td>Avengers: Endgame</td>\n",
              "      <td>Avengers: Endgame</td>\n",
              "      <td>0</td>\n",
              "      <td>2019.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>181.0</td>\n",
              "      <td>[Action, Adventure, Drama]</td>\n",
              "      <td>8.5</td>\n",
              "      <td>602740.0</td>\n",
              "      <td>[nm0000375, nm0262635, nm0749263, nm1165110]</td>\n",
              "      <td>[nm0456158, nm0751577, nm0751648, nm1321655, n...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           tconst titleType       primaryTitle      originalTitle  isAdult  \\\n",
              "115780  tt4154796     movie  Avengers: Endgame  Avengers: Endgame        0   \n",
              "\n",
              "        startYear endYear  runtimeMinutes                      genres  \\\n",
              "115780     2019.0      \\N           181.0  [Action, Adventure, Drama]   \n",
              "\n",
              "        averageRating  numVotes                                          cast  \\\n",
              "115780            8.5  602740.0  [nm0000375, nm0262635, nm0749263, nm1165110]   \n",
              "\n",
              "                                                     crew  \n",
              "115780  [nm0456158, nm0751577, nm0751648, nm1321655, n...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MovsWd4FuRXY"
      },
      "source": [
        "We do however have some movies with no cast or crew."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PjJB0il4Ddbq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9aaacf3c-84ce-49c4-d9ed-bcfc8b2c3d51"
      },
      "source": [
        "movies['cast'].isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12260"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I5eOhlNyDdbr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "929cc224-3a89-4ce5-bb73-06fc86b0b07f"
      },
      "source": [
        "movies['crew'].isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "531"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OV7Sr5xXwDgN"
      },
      "source": [
        "Let's check a couple."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1giDm8TyDdbr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "9caf4757-0497-41fb-9b2f-9497d241f95f"
      },
      "source": [
        "movies[movies['cast'].isna()].head(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>tt0054050</td>\n",
              "      <td>movie</td>\n",
              "      <td>The Making of the President 1960</td>\n",
              "      <td>The Making of the President 1960</td>\n",
              "      <td>0</td>\n",
              "      <td>1963.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>90.0</td>\n",
              "      <td>[Documentary, History]</td>\n",
              "      <td>7.3</td>\n",
              "      <td>18.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[nm0142216, nm0300010, nm0448123, nm0633271, n...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>214</th>\n",
              "      <td>tt0054770</td>\n",
              "      <td>movie</td>\n",
              "      <td>The Crimebusters</td>\n",
              "      <td>The Crimebusters</td>\n",
              "      <td>0</td>\n",
              "      <td>1962.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>88.0</td>\n",
              "      <td>[Crime, Drama]</td>\n",
              "      <td>7.3</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>[nm0818157, nm0725052, nm0300010, nm0683984, n...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        tconst titleType                      primaryTitle  \\\n",
              "53   tt0054050     movie  The Making of the President 1960   \n",
              "214  tt0054770     movie                  The Crimebusters   \n",
              "\n",
              "                        originalTitle  isAdult  startYear endYear  \\\n",
              "53   The Making of the President 1960        0     1963.0      \\N   \n",
              "214                  The Crimebusters        0     1962.0      \\N   \n",
              "\n",
              "     runtimeMinutes                  genres  averageRating  numVotes cast  \\\n",
              "53             90.0  [Documentary, History]            7.3      18.0  NaN   \n",
              "214            88.0          [Crime, Drama]            7.3      17.0  NaN   \n",
              "\n",
              "                                                  crew  \n",
              "53   [nm0142216, nm0300010, nm0448123, nm0633271, n...  \n",
              "214  [nm0818157, nm0725052, nm0300010, nm0683984, n...  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2L6kOnE0Ddbr"
      },
      "source": [
        "movies['cast'].fillna('Unknown', inplace=True)\n",
        "movies['crew'].fillna('Unknown', inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9EOYR6cDdbr"
      },
      "source": [
        "## Word2Vec"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WoIqxco4Ddbr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce4cd76d-a9d8-4518-97a6-0f9239207260"
      },
      "source": [
        "to_wtv = pd.DataFrame(principals.groupby(by='tconst').apply(lambda x: [x for x in x['nconst']])).reset_index().iloc[:, 1]\n",
        "to_wtv.head(3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0               [nm1588970, nm0005690, nm0374658]\n",
              "1                          [nm0721526, nm1335271]\n",
              "2    [nm0721526, nm5442194, nm1335271, nm5442200]\n",
              "Name: 0, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XhoZv3MFDdbs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36413228-6b95-4509-ee6f-68c0edcaade5"
      },
      "source": [
        "wtv = Word2Vec(window=10, min_count=5)\n",
        "wtv.build_vocab(to_wtv)\n",
        "wtv.train(to_wtv, total_words=wtv.corpus_total_words, epochs=10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(319604950, 364688170)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "byQsKk_RDdbs"
      },
      "source": [
        "from IPython.display import HTML, display\n",
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "\n",
        "def get_name(id):\n",
        "    response = requests.get(f'https://www.imdb.com/name/{id}/')\n",
        "    soup = BeautifulSoup(response.content)\n",
        "    return soup.select('.header .itemprop')[0].text\n",
        "\n",
        "def get_image(id):\n",
        "    response = requests.get(f'https://www.imdb.com/name/{id}/')\n",
        "    soup = BeautifulSoup(response.content)\n",
        "    candidates = soup.select('#name-poster')\n",
        "    return candidates[0].attrs['src'] if candidates else 'https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png'\n",
        "\n",
        "def render_person(id):\n",
        "    name = get_name(id)\n",
        "    picture = get_image(id)\n",
        "    return f\"\"\"\n",
        "    <div style=\"width: 150px; text-align: center\">\n",
        "        <h4 style='margin-top: -5px'>{name}</h4>\n",
        "        <div style='font-size:75%; margin-bottom: 5px'>{id}</div>\n",
        "        <a href=\"https://www.imdb.com/name/{id}\" target=\"_blank\">\n",
        "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"{picture}\"/>\n",
        "        </a>\n",
        "    </div>\n",
        "    \"\"\"\n",
        "\n",
        "def show_similars(id, n=10):\n",
        "    if id in wtv.wv: \n",
        "        display(HTML(render_person(id)))\n",
        "    renders = []\n",
        "    for similar_id, score in wtv.wv.most_similar(id, topn=n):\n",
        "        renders.append(render_person(similar_id))\n",
        "        \n",
        "    carousel = ''.join(\n",
        "        [\n",
        "            f'<div style=\"margin-left: 10px; float: left\">{p}</div>' \n",
        "            for p in renders\n",
        "        ]\n",
        "        )\n",
        "    display(HTML(f'<div style=\"width: 1800px\">{carousel}</div>'))\n",
        "\n",
        "def show_similars_tovector(id, n=10):\n",
        "    renders = []\n",
        "    \n",
        "    for similar_id, score in wtv.wv.most_similar(id, topn=n):\n",
        "        renders.append(render_person(similar_id))\n",
        "        \n",
        "    carousel = ''.join(\n",
        "        [\n",
        "            f'<div style=\"margin-left: 10px; float: left\">{p}</div>' \n",
        "            for p in renders\n",
        "        ]\n",
        "    )\n",
        "    display(HTML(f'<div style=\"width: 1800px\">{carousel}</div>'))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "WqkUGMauyi61",
        "outputId": "34975cdc-3b58-44b2-a594-30ce1740c161"
      },
      "source": [
        "show_similars('nm0000138')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Leonardo DiCaprio</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000138</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000138\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjI0MTg3MzI0M15BMl5BanBnXkFtZTcwMzQyODU2Mw@@._V1_UY317_CR10,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"width: 1800px\"><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Angelina Jolie</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0001401</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0001401\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BODg3MzYwMjE4N15BMl5BanBnXkFtZTcwMjU5NzAzNw@@._V1_UY317_CR22,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Brad Pitt</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000093</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000093\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjA1MjE2MTQ2MV5BMl5BanBnXkFtZTcwMjE5MDY0Nw@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Matt Damon</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000354</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000354\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTM0NzYzNDgxMl5BMl5BanBnXkFtZTcwMDg2MTMyMw@@._V1_UY317_CR11,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Tom Cruise</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000129</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000129\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTk1MjM3NTU5M15BMl5BanBnXkFtZTcwMTMyMjAyMg@@._V1_UY317_CR14,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Johnny Depp</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000136</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000136\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTM0ODU5Nzk2OV5BMl5BanBnXkFtZTcwMzI2ODgyNQ@@._V1_UY317_CR4,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Alexis Bensa</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1697532</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1697532\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Casey Affleck</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000729</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000729\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTY3Nzc0MDg1OF5BMl5BanBnXkFtZTgwMzk5OTk2OTE@._V1_UY317_CR129,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Simon Crane</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0186426</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0186426\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQ3MDUwNjY4OF5BMl5BanBnXkFtZTgwNDM5NDYxMTE@._V1_UY317_CR30,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Lee Clow</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1676090</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1676090\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTNmYTFhNDEtZmQwOC00ZTZhLWE4ZDEtOTIyZDk1NGNkMDMzXkEyXkFqcGdeQXVyMjE5MzM3MjA@._V1_UY317_CR409,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Joaquin Phoenix</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0001618</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0001618\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BZGMyY2Q4NTEtMWVkZS00NzcwLTkzNmQtYzBlMWZhZGNhMDhkXkEyXkFqcGdeQXVyNjk1MjYyNTA@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zbs3UE3ADdbs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "97599393-2a35-44a9-f11e-481f69f3b129"
      },
      "source": [
        "show_similars('nm0000206')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Keanu Reeves</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000206</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000206\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BYTkzODI4MDMtNDNmZC00NDZlLWFmNTktNDRhOWE2YzhlZTQ2XkEyXkFqcGdeQXVyMTE1MTYxNDAw._V1_UY317_CR19,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"width: 1800px\"><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>John J. Moers</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0595518</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0595518\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Joel Silver</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0005428</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0005428\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQ3Njc2MjMwNl5BMl5BanBnXkFtZTgwNzY5NTY5ODE@._V1_UY317_CR130,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Karina Friend Buck</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0118383</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0118383\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Nicolas Cage</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000115</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000115\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjUxMjE4MTQxMF5BMl5BanBnXkFtZTcwNzc2MDM1NA@@._V1_UY317_CR9,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Lawrence Bender</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0004744</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0004744\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjA3OTQzNjMwN15BMl5BanBnXkFtZTcwMTE3NjQxMw@@._V1_UY317_CR8,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Samuel L. Jackson</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000168</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000168\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQ1NTQwMTYxNl5BMl5BanBnXkFtZTYwMjA1MzY1._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Sigourney Weaver</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000244</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000244\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTk1MTcyNTE3OV5BMl5BanBnXkFtZTcwMTA0MTMyMw@@._V1_UY317_CR12,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Danny Elfman</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000384</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000384\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTU4MTU0ODA0Nl5BMl5BanBnXkFtZTgwNTk1Njk3MjE@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Mark Rance</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0709411</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0709411\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Thomas C. Grane</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0994529</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0994529\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psG-TY5-Ddbs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "4368f2a7-f9fe-4484-d06c-5dffaf437595"
      },
      "source": [
        "show_similars('nm1089991')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Tom Hiddleston</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1089991</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1089991\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BNWYwODAyZjAtOTQ1My00MDY2LTg0NDQtZGFiMDRiYzY4ZmM2XkEyXkFqcGdeQXVyNjg2NjQwMDQ@._V1_UY317_CR9,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"width: 1800px\"><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>David Heyman</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0382268</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0382268\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTYwNzE2MDgxM15BMl5BanBnXkFtZTcwMTIwMDY4NQ@@._V1_UY317_CR104,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Eric Fellner</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0271479</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0271479\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Nicholas Hoult</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0396558</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0396558\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTUxMTYxNzMzNF5BMl5BanBnXkFtZTcwMTU0NTcxOQ@@._V1_UY317_CR3,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Guy Ritchie</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0005363</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0005363\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTM2NDkxMTcxMl5BMl5BanBnXkFtZTcwNTMyNjI5MQ@@._V1_UY317_CR8,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Sam Claflin</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm3510471</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm3510471\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjI3MDQxNDUwOF5BMl5BanBnXkFtZTgwNjY4NjI3NjE@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Tim Bevan</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0079677</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0079677\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BNzc0MjU5NTkzMl5BMl5BanBnXkFtZTcwNDgyNDk5Nw@@._V1_UY317_CR27,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Tom Hardy</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0362766</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0362766\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQ3ODEyNjA4Nl5BMl5BanBnXkFtZTgwMTE4ODMyMjE@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Stuart Craig</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0186023</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0186023\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjExMjk4NTYwOF5BMl5BanBnXkFtZTcwNTE1OTYzNw@@._V1_UY317_CR8,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>James McAvoy</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0564215</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0564215\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQzNzIxOTYzMl5BMl5BanBnXkFtZTcwNjYxNTk1Nw@@._V1_UY317_CR14,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Andrew Garfield</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1940449</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1940449\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjE2MjI2OTk1OV5BMl5BanBnXkFtZTgwNTY1NzM4MDI@._V1_UY317_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C29jVLeaDdbs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "outputId": "04010015-9cba-4d1b-ee89-e8c775c52ca3"
      },
      "source": [
        "show_similars('nm1297015')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Emma Stone</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1297015</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1297015\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjI4NjM1NDkyN15BMl5BanBnXkFtZTgwODgyNTY1MjE@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"width: 1800px\"><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Channing Tatum</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1475594</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1475594\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTUyOTc2NTEwNV5BMl5BanBnXkFtZTgwNDgxMjMxNjE@._V1_UY317_CR59,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Tom Holland</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm4043618</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm4043618\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BNzZiNTEyNTItYjNhMS00YjI2LWIwMWQtZmYwYTRlNjMyZTJjXkEyXkFqcGdeQXVyMTExNzQzMDE0._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Jonah Hill</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1706767</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1706767\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTUyNDU0NzAwNl5BMl5BanBnXkFtZTcwMzQxMzIzNw@@._V1_UY317_CR28,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Lynn Hirschberg</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm4632914</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm4632914\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Evan T. Cohen</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0169372</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0169372\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Amanda Seyfried</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1086543</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1086543\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTYwMDYyMDY5Nl5BMl5BanBnXkFtZTcwODkyMjY5Ng@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Owen Wilson</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0005562</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0005562\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTgwMzQ4ODYxMV5BMl5BanBnXkFtZTcwNDAwMTc2NQ@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Jenno Topping</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0867768</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0867768\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BNjYwMDkxN2UtNTA2Ny00MTE3LWJmOTYtNzBlMTk2NTkzMzZkXkEyXkFqcGdeQXVyMDM2NDM2MQ@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Liam Hemsworth</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm2955013</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm2955013\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQ5ODI0MDc4M15BMl5BanBnXkFtZTgwNTM5MDk3MTE@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Anna Kendrick</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0447695</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0447695\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMjIzOTA0OTQyN15BMl5BanBnXkFtZTcwMjE1OTIwMw@@._V1_UY317_CR5,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ovyR59dzefA"
      },
      "source": [
        "Nice to see that results seem to make a lot of sense in most cases :) Let's now look for a random actor/actress. If our model is coherent, we should get other random not well-known people in return."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSzTN7e0Ddbt"
      },
      "source": [
        "random_actor = random.sample(list(principals.nconst.unique()), 1)[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "Tb2-HxwB0MWS",
        "outputId": "5a5ab8e3-3e67-4b96-a7fb-ceada7226e1e"
      },
      "source": [
        "show_similars(random_actor)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Trent Nakamura</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm5052477</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm5052477\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div style=\"width: 1800px\"><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Jacob Michael Keller</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1782639</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1782639\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Liisa Evastina</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm1436451</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm1436451\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BYWQyNzczNjAtNjYyMC00OGVkLWIwZDAtZjRhYTRlZWYzYWI5XkEyXkFqcGdeQXVyMjQwMDg0Ng@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Lauren Poole</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm5311923</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm5311923\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Jessica Jones</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm5281097</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm5281097\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BOWRkMDlkNzYtMjQxNi00YzUwLTlkOTItOWE4NTA2NTI4NTA5XkEyXkFqcGdeQXVyMjQwMDg0Ng@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>John O.S. Houston</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm6003838</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm6003838\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMGU1Zjk0ODgtNGUzYi00ODA4LWJhYzAtMDYzZGM2YmRiODIzXkEyXkFqcGdeQXVyMTEwMjI2ODc3._V1_UY317_CR51,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Saxon Kincy</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm8280807</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm8280807\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Joseph Anzalone</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm5401729</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm5401729\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Cristina Guzmán</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm4976230</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm4976230\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Alex Hitchens</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm6140871</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm6140871\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div><div style=\"margin-left: 10px; float: left\">\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Elyté Salna</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm2980169</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm2980169\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/G/01/imdb/images/nopicture/medium/name-2135195744._CB466677935_.png\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    </div></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBaHYkfZ01zg"
      },
      "source": [
        "Indeed, we get a bunch of random unknown people, just as we hoped."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r7tcYDlJ1IDa"
      },
      "source": [
        "Looking at the vectors themselves make little sense to humans. But they do appear to make sense to a computer. Here's for example how Di Caprio's vector looks like:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9P_a_-3Ddbt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "295c5c3f-c64b-4e00-b9e7-a28148f82eae"
      },
      "source": [
        "wtv.wv['nm0000138']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.2580304 , -0.66913676, -1.2732809 , -0.8057905 ,  0.44032094,\n",
              "       -0.48130265, -1.1735309 , -0.30094367, -2.1865425 ,  0.539852  ,\n",
              "       -1.8611125 , -0.4508392 ,  0.41736978,  0.641328  , -1.1822482 ,\n",
              "       -1.1135494 , -1.4869791 ,  1.238677  , -0.545832  ,  0.69036996,\n",
              "        2.282117  ,  0.12582017,  0.4888337 , -1.2242796 , -0.24009012,\n",
              "        0.7273605 , -0.32881492,  1.055785  ,  2.1439772 , -0.9506904 ,\n",
              "        0.3051441 ,  1.2152051 , -1.9529264 ,  0.4441434 , -0.93215567,\n",
              "        2.293564  ,  1.2437452 , -0.9667633 , -1.5158325 ,  0.5195707 ,\n",
              "        0.22579722,  0.32820278,  1.4134562 ,  0.3232375 ,  1.4170094 ,\n",
              "       -0.39107582, -1.1989847 ,  0.8313034 , -1.2659988 , -0.0818631 ,\n",
              "        1.9499799 ,  0.89772964, -0.5979264 ,  1.2365289 , -0.9876877 ,\n",
              "        0.37904605,  0.12605576, -0.27065802,  0.29705545, -1.5633304 ,\n",
              "       -0.87470645, -0.18089887, -1.3299501 , -0.78592   , -1.3334956 ,\n",
              "        0.6081005 ,  0.85708207, -1.3292736 , -0.87474406,  0.51196843,\n",
              "        0.6663763 ,  0.71622443,  0.28703567,  0.5008991 ,  1.5802574 ,\n",
              "        0.5561004 ,  1.356243  ,  0.04715981, -0.3515332 ,  1.4852083 ,\n",
              "        0.2934122 ,  1.115532  , -0.00314867,  0.5141518 ,  1.3929895 ,\n",
              "       -0.02218026,  0.79087824, -1.0179503 ,  0.9825294 , -0.592306  ,\n",
              "       -0.78344613, -0.48697883, -0.5952073 , -0.07430459,  0.29141176,\n",
              "        1.1430728 , -0.18454608, -0.9703676 ,  1.3439994 , -1.8491925 ],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNa8bYyl1fdU"
      },
      "source": [
        "doesn't match returns which element from a list is the one that does not belong in the group. It computes the mean of all vectors and then returns the one who is furthest away from this mean."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XdsvSuWpDdbt"
      },
      "source": [
        "def who_doesnt_match(person1, person2, person3, person4):\n",
        "    \n",
        "    p1 = str(person1); p2=str(person2); p3=str(person3); p4=str(person4); \n",
        "    result = wtv.wv.doesnt_match([p1, p2, p3, p4])\n",
        "    \n",
        "    if result in wtv.wv: \n",
        "        display(HTML(render_person(result)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPzMxiV21yGd"
      },
      "source": [
        "### Fame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lgCEtkcK175h"
      },
      "source": [
        "Here we try three random actors/actresses and a famous one, who should be the one that does not belong if our model is capturing \"fame\" correctly hidden in those mysterious numbers of the vectors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k0JfKGzMDdbt"
      },
      "source": [
        "angelina_jolie= 'nm0001401'\n",
        "sean_penn = 'nm0000576'\n",
        "ryan_gosling = 'nm0331516'\n",
        "random1 = random.sample(list(principals.nconst.unique()), 1)[0]\n",
        "random2 = random.sample(list(principals.nconst.unique()), 1)[0]\n",
        "random3 = random.sample(list(principals.nconst.unique()), 1)[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JM-fdOaBDdbt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "09e5496e-4c39-42f3-e595-13a7b62b8d8d"
      },
      "source": [
        "print(random1, random2, random3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nm8924108 nm0772925 nm0005870\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NqCwk0knDdbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "71343823-c4be-4284-c265-e7ddf2c8ff6d"
      },
      "source": [
        "who_doesnt_match(angelina_jolie, random1, random2, random3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py:895: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Angelina Jolie</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0001401</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0001401\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BODg3MzYwMjE4N15BMl5BanBnXkFtZTcwMjU5NzAzNw@@._V1_UY317_CR22,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJjH5kqBDdbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "71d6bf81-6f19-4a90-e234-14a20e331bbb"
      },
      "source": [
        "who_doesnt_match(sean_penn, random1, random2, random3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py:895: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Sean Penn</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000576</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000576\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTc1NjMzMjY3NF5BMl5BanBnXkFtZTcwMzkxNjQzMg@@._V1_UY317_CR1,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FM_6FAgKDdbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "6655b326-8047-45c0-e125-cdfdc3938103"
      },
      "source": [
        "who_doesnt_match(ryan_gosling, random1, random2, random3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py:895: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Ryan Gosling</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0331516</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0331516\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTQzMjkwNTQ2OF5BMl5BanBnXkFtZTgwNTQ4MTQ4MTE@._V1_UY317_CR18,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LACzTLRD2XCQ"
      },
      "source": [
        "This model is pretty good at differentiating famous actors from the rest."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gxXeiq2b2bLe"
      },
      "source": [
        "### Age"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcQXZygS23fV"
      },
      "source": [
        "Now we take three new young stars and one star from the past. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IE9DfRfmDdbu"
      },
      "source": [
        "paul_newman = 'nm0000056'\n",
        "joseph_gordon_levitt = 'nm0330687'\n",
        "jennifer_lawrence = 'nm2225369'\n",
        "rooney_mara = 'nm1913734'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G6wywWk_Ddbu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "e816d7a8-6c65-40a1-8706-444fe74c536e"
      },
      "source": [
        "who_doesnt_match(paul_newman, joseph_gordon_levitt, jennifer_lawrence, rooney_mara)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py:895: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Paul Newman</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000056</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000056\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BODUwMDYwNDg3N15BMl5BanBnXkFtZTcwODEzNTgxMw@@._V1_UY317_CR22,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxrVtrt63A9d"
      },
      "source": [
        "### Genere"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ISgMH7Ch3JSK"
      },
      "source": [
        "At last we take three (mostly) comedy actors and one more serious-type actor such as Christian Bale, who should not belong here."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GvmnCc2HDdbu"
      },
      "source": [
        "christian_bale = 'nm0000288'\n",
        "will_ferrell = 'nm0002071'\n",
        "seth_rogen = 'nm0736622'\n",
        "adam_sandler = 'nm0001191'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NehQEwObDdbv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        },
        "outputId": "0f7ed351-b4af-44e1-99ed-128523204748"
      },
      "source": [
        "who_doesnt_match(christian_bale, will_ferrell, seth_rogen, adam_sandler)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py:895: FutureWarning: arrays to stack must be passed as a \"sequence\" type such as list or tuple. Support for non-sequence iterables such as generators is deprecated as of NumPy 1.16 and will raise an error in the future.\n",
            "  vectors = vstack(self.word_vec(word, use_norm=True) for word in used_words).astype(REAL)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div style=\"width: 150px; text-align: center\">\n",
              "        <h4 style='margin-top: -5px'>Christian Bale</h4>\n",
              "        <div style='font-size:75%; margin-bottom: 5px'>nm0000288</div>\n",
              "        <a href=\"https://www.imdb.com/name/nm0000288\" target=\"_blank\">\n",
              "            <img style=\"width: 100px; display: block; margin-left: auto; margin-right: auto;\" src=\"https://m.media-amazon.com/images/M/MV5BMTkxMzk4MjQ4MF5BMl5BanBnXkFtZTcwMzExODQxOA@@._V1_UX214_CR0,0,214,317_AL_.jpg\"/>\n",
              "        </a>\n",
              "    </div>\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cw4faXY43_tb"
      },
      "source": [
        "We have seen that using word2vec to embedd actors into vectors seem to work pretty well. These numbers contain information such as how famous the actor/actress is, in which era he/she was a star, and the kind of movies he/she is usually in."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cm87fyR9Ddbv"
      },
      "source": [
        "## Choosing proxy for movies' popularity"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n1y4gf1S448z"
      },
      "source": [
        "We used the number of IMDB votes as a proxy to measure a movie's popularity. The goal is to predict which movies will be among the Top 1% in terms of IMDB votes based on its cast and crew. We named these kinds of movies a \"Classic\" in our dataset to easily differentiate them from the rest."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fc1yOd-0Ddbv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "016e1438-c999-43be-d0dd-6662929defc7"
      },
      "source": [
        "np.percentile(movies.numVotes, 99)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "123618.60000000102"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SXhQPWsNDdbv"
      },
      "source": [
        "movies_docs = movies.to_dict(orient='records')\n",
        "movies['Classic'] = [1 if x['numVotes']>np.percentile(movies.numVotes, 99) else 0 for x in movies_docs]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sVA6DtNDdbw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "399044f1-2de3-4f00-ff01-a1e5aca6d68f"
      },
      "source": [
        "len(movies[movies['Classic']==1]) / len(movies)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.010002687722917559"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMLSBYHU5OZu"
      },
      "source": [
        "Here are some of these classic movies we have."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7kAG5ULDdbw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "86b4c70b-e517-4238-dd75-fc08ed86a4df"
      },
      "source": [
        "movies[movies['Classic']==1].sample(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tconst</th>\n",
              "      <th>titleType</th>\n",
              "      <th>primaryTitle</th>\n",
              "      <th>originalTitle</th>\n",
              "      <th>isAdult</th>\n",
              "      <th>startYear</th>\n",
              "      <th>endYear</th>\n",
              "      <th>runtimeMinutes</th>\n",
              "      <th>genres</th>\n",
              "      <th>averageRating</th>\n",
              "      <th>numVotes</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "      <th>Classic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>99395</th>\n",
              "      <td>tt2239822</td>\n",
              "      <td>movie</td>\n",
              "      <td>Valerian and the City of a Thousand Planets</td>\n",
              "      <td>Valerian and the City of a Thousand Planets</td>\n",
              "      <td>0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>137.0</td>\n",
              "      <td>[Action, Adventure, Fantasy]</td>\n",
              "      <td>6.5</td>\n",
              "      <td>147191.0</td>\n",
              "      <td>[nm2851530, nm5353321, nm0654110, nm1982597]</td>\n",
              "      <td>[nm0005636, nm0000108, nm0160391, nm0617789, n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66621</th>\n",
              "      <td>tt0458525</td>\n",
              "      <td>movie</td>\n",
              "      <td>X-Men Origins: Wolverine</td>\n",
              "      <td>X-Men Origins: Wolverine</td>\n",
              "      <td>0</td>\n",
              "      <td>2009.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>107.0</td>\n",
              "      <td>[Action, Adventure, Sci-Fi]</td>\n",
              "      <td>6.6</td>\n",
              "      <td>445998.0</td>\n",
              "      <td>[nm0413168, nm0000630, nm0005351, nm0396812]</td>\n",
              "      <td>[nm0003515, nm0004303, nm1125275, nm0940790, n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92182</th>\n",
              "      <td>tt1800241</td>\n",
              "      <td>movie</td>\n",
              "      <td>American Hustle</td>\n",
              "      <td>American Hustle</td>\n",
              "      <td>0</td>\n",
              "      <td>2013.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>138.0</td>\n",
              "      <td>[Crime, Drama]</td>\n",
              "      <td>7.2</td>\n",
              "      <td>425229.0</td>\n",
              "      <td>[nm0000288, nm0010736, nm0177896, nm2225369]</td>\n",
              "      <td>[nm0837112, nm0751102, nm2545235, nm2691892, n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107494</th>\n",
              "      <td>tt3065204</td>\n",
              "      <td>movie</td>\n",
              "      <td>The Conjuring 2</td>\n",
              "      <td>The Conjuring 2</td>\n",
              "      <td>0</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>134.0</td>\n",
              "      <td>[Horror, Mystery, Thriller]</td>\n",
              "      <td>7.3</td>\n",
              "      <td>204766.0</td>\n",
              "      <td>[nm0267812, nm0933940, nm4068901, nm0640323]</td>\n",
              "      <td>[nm0755911, nm1490123, nm0370937, nm0370928, n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31744</th>\n",
              "      <td>tt0118571</td>\n",
              "      <td>movie</td>\n",
              "      <td>Air Force One</td>\n",
              "      <td>Air Force One</td>\n",
              "      <td>0</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>\\N</td>\n",
              "      <td>124.0</td>\n",
              "      <td>[Action, Drama, Thriller]</td>\n",
              "      <td>6.5</td>\n",
              "      <td>170358.0</td>\n",
              "      <td>[nm0000148, nm0000198, nm0000335, nm0187724]</td>\n",
              "      <td>[nm0000025, nm0000583, nm0549256, nm0077000, n...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           tconst titleType                                 primaryTitle  \\\n",
              "99395   tt2239822     movie  Valerian and the City of a Thousand Planets   \n",
              "66621   tt0458525     movie                     X-Men Origins: Wolverine   \n",
              "92182   tt1800241     movie                              American Hustle   \n",
              "107494  tt3065204     movie                              The Conjuring 2   \n",
              "31744   tt0118571     movie                                Air Force One   \n",
              "\n",
              "                                      originalTitle  isAdult  startYear  \\\n",
              "99395   Valerian and the City of a Thousand Planets        0     2017.0   \n",
              "66621                      X-Men Origins: Wolverine        0     2009.0   \n",
              "92182                               American Hustle        0     2013.0   \n",
              "107494                              The Conjuring 2        0     2016.0   \n",
              "31744                                 Air Force One        0     1997.0   \n",
              "\n",
              "       endYear  runtimeMinutes                        genres  averageRating  \\\n",
              "99395       \\N           137.0  [Action, Adventure, Fantasy]            6.5   \n",
              "66621       \\N           107.0   [Action, Adventure, Sci-Fi]            6.6   \n",
              "92182       \\N           138.0                [Crime, Drama]            7.2   \n",
              "107494      \\N           134.0   [Horror, Mystery, Thriller]            7.3   \n",
              "31744       \\N           124.0     [Action, Drama, Thriller]            6.5   \n",
              "\n",
              "        numVotes                                          cast  \\\n",
              "99395   147191.0  [nm2851530, nm5353321, nm0654110, nm1982597]   \n",
              "66621   445998.0  [nm0413168, nm0000630, nm0005351, nm0396812]   \n",
              "92182   425229.0  [nm0000288, nm0010736, nm0177896, nm2225369]   \n",
              "107494  204766.0  [nm0267812, nm0933940, nm4068901, nm0640323]   \n",
              "31744   170358.0  [nm0000148, nm0000198, nm0000335, nm0187724]   \n",
              "\n",
              "                                                     crew  Classic  \n",
              "99395   [nm0005636, nm0000108, nm0160391, nm0617789, n...        1  \n",
              "66621   [nm0003515, nm0004303, nm1125275, nm0940790, n...        1  \n",
              "92182   [nm0837112, nm0751102, nm2545235, nm2691892, n...        1  \n",
              "107494  [nm0755911, nm1490123, nm0370937, nm0370928, n...        1  \n",
              "31744   [nm0000025, nm0000583, nm0549256, nm0077000, n...        1  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TKvJjB4eDdbw"
      },
      "source": [
        "## Feature Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RvjJzMlJDdbw"
      },
      "source": [
        "### Cast & Crew"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVdfzbZf5zAt"
      },
      "source": [
        "The class below takes, each movie, its entire cast or crew vectors, and then computes the mean of all of them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57-DiOyx59km"
      },
      "source": [
        "The result of this is a one 100-length vector that summarizes the cast or crew that was involved in the movie."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6g3Hm9jaDdbw"
      },
      "source": [
        "class W2VFeatures(BaseEstimator, TransformerMixin):\n",
        "    def __init__(self, wtv, category, min_cnt_movies=2):\n",
        "        self.category = category\n",
        "        self.min_cnt_movies = min_cnt_movies\n",
        "        self.wtv = wtv\n",
        "\n",
        "    def fit(self, X, y):\n",
        "        self.default_vector = np.zeros(100)\n",
        "        #self.default_vector = np.mean(wtv.wv.vectors, axis=0)\n",
        "        return self\n",
        "    \n",
        "    def _get_movie_vector(self, x_i):\n",
        "        vectors = []\n",
        "        for person in x_i[self.category]:\n",
        "            if person not in self.wtv.wv or self.wtv.wv.vocab[person].count < self.min_cnt_movies: continue\n",
        "            vectors.append(self.wtv.wv[person])\n",
        "            \n",
        "        if len(vectors) == 0:\n",
        "            return self.default_vector\n",
        "        else:\n",
        "            return np.mean(vectors, axis=0)\n",
        "            \n",
        "    def transform(self, X):\n",
        "        return np.asarray([self._get_movie_vector(x_i) for x_i in X])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cKqjPKtNDdbw"
      },
      "source": [
        "### Other features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zqm12P6d6PxI"
      },
      "source": [
        "There are some other things, like the Genre of a movie, its Runtime and the Year it was released, that affect the number of votes it gets."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3i4OQuZDdbx"
      },
      "source": [
        "#### RunTime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyTP3ndVDdbx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1501b658-fe05-4fbc-f27d-a5ace5f8b123"
      },
      "source": [
        "movies.runtimeMinutes.corr(np.log(movies.numVotes))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.23236357418167666"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NKtfs2xo7DWI"
      },
      "source": [
        "We see that there is a weak/moderate correlation between runtime and the number of votes. But let's add it to our model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oJweFeeMDdbx"
      },
      "source": [
        "#### Genre"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXs7PIxx7OPm"
      },
      "source": [
        "First let's see how balanced/imbalanced the genres are in our data according to whether they are \"classic\" movies:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mgfRcfn8Ddby"
      },
      "source": [
        "class GenreDummies(BaseEstimator, TransformerMixin):\n",
        "    def fit(self, X, y): return self\n",
        "    def transform(self, X):\n",
        "        res = []\n",
        "        for e in X:\n",
        "            res.append({g: 1 for g in e['genres']})\n",
        "        return res  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhfDHuyYDdby"
      },
      "source": [
        "v = DictVectorizer(sparse=False)\n",
        "dummies_genre = v.fit_transform(GenreDummies().transform(movies_docs))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HjoveJViDdby"
      },
      "source": [
        "df_genres = pd.DataFrame(dummies_genre, columns=v.feature_names_)\n",
        "df_genres = df_genres.astype(int)\n",
        "genres_analysis = pd.concat([movies, df_genres], axis=1)\n",
        "graph1 = pd.DataFrame(genres_analysis[genres_analysis['numVotes'] > np.percentile(genres_analysis.numVotes,97)].iloc[:,14:-1] \\\n",
        "                     .sum(axis=0) / len(genres_analysis[genres_analysis['numVotes'] > \\\n",
        "                    np.percentile(genres_analysis.numVotes,97)])).reset_index().rename(columns={'index': 'genre', 0: 'pct'})\n",
        "graph1['classic']=\"Classic\"\n",
        "graph2 = pd.DataFrame(genres_analysis.iloc[:,14:-1] \\\n",
        "                     .sum(axis=0) / len(genres_analysis)).reset_index().rename(columns={'index': 'genre', 0: 'pct'})\n",
        "graph2['classic'] = \"All\"\n",
        "graph = pd.concat([graph1, graph2], axis=0) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKzpHdiODdby",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5fe23d4a-af44-43df-a293-5549123a225c"
      },
      "source": [
        "import seaborn as sns\n",
        "sns.set_style(\"white\")\n",
        "fig, ax = plt.subplots(figsize=(14,20))\n",
        "ax = sns.barplot(y='genre', x='pct', hue='classic', data=graph, palette='viridis')\n",
        "ax.grid(color='grey', linestyle='-', linewidth=0.1, axis='x')\n",
        "ax.set_xticks([0.05, 0.10, .15, .20, .25, .30, .35, .40, .45, .50])\n",
        "ax.set_yticklabels(graph.genre[:23], size = 13, fontfamily='serif')\n",
        "ax.set_xlabel('% of total movies', fontsize=15)\n",
        "ax.set_ylabel('Genre', fontsize=15, fontfamily='serif')\n",
        "ax.tick_params(labelbottom=True,labeltop=True)\n",
        "plt.title('% of total movies per genre (1960-2020)', fontsize=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, '% of total movies per genre (1960-2020)')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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v+1AiIyMhk8kwbNgwlTscTp8+HaampoiIiFA7XVITW1tblZtHAMDIkSMBAHFxcSWKWW78+PGoWbOm4vdPPvkEzs7OePXqFYYMGaJ0PEZGRujduzdkMhnu3LmjaC/u8deqVQvt2rXD9evXcfv2baX1w8PDAQD9+/d/Z+ybN2+GgYEBFixYAGNjY6VlEyZMgIWFBfbv36+ynVgsVmmrXr36O/f3prp162Lo0KFKbd27d4ejoyPu3bunGNUrLCxEWFgYatSogdmzZyuNuujr68PPzw8ikUhtnFZWVsUa/QRe509PTw8zZsxQOn9q166tNEou9+LFC0RGRsLe3h5ff/210jKxWIxvv/0WgiCoja9Vq1bw9PRUahswYAAMDAw0PubC29sbTZo0KbUY1Hn48CEMDQ1VRvPeV5cuXQAAQUFBSlOzc3JylG6m9fLlSwBARkYG8vPzkZ6ejqVLl2L8+PE4fvw4zp49i8DAQADAnDlzFCOiABQjrppGkqtWraq03vtKSEjAr7/+ClNTU0ybNk1pWWnGsGTJEty6dQtdunRRGVHMyspS6u9t8v3L8/mmGjVqAAAePXr0zhiIKjpOTyWiCsPDw0MxBfRNixYtAvD6urrr169j6tSpcHV1xbx583D48GH4+/ujZs2aKndZfduNGzcAvJ6G9vat6wHAyckJkZGRuHHjhlZFyPuSx6Fu2pi5uTmaNGmCCxcu4O7du2jUqJFWfebk5GDz5s04cuQIkpOTkZ2drXQdz9OnT0sl9jevKZL717/+BQBo2rSpyjJ5gfn48WNF2/scv4eHB06dOoV9+/bhP//5D4DXd3yMioqCpaWl4oO6Jq9evcLNmzdRrVo1xdThtxkZGSkVt+7u7jh8+DC8vb3Rq1cvODk5oVWrViqPEdBG69at1T5KwNHREefPn8eNGzfg6OiIpKQkpKeno169eggKClLbl7GxMe7evavS3qhRI5XpeUXJysrC/fv3Ubt2bcW0ybdjftvVq1dRUFAAkUiElStXqizPz88HALXx2dvbq7QZGhrC0tJS7Qd+ACqPvyhpDOqkp6crpkKXhuHDh+PgwYO4fPky3Nzc0KVLFwiCgOPHjwN4XfxkZmYqzgf5NaoFBQUYNGiQUuHv5eWF3NxcBAYG4rfffkO7du3eO66QkBCVAq579+5K0+bflJSUhPHjxyM/Px9Lly7Fp59++t77LsrmzZuxceNGNGjQAIsXLy7VvuXXaL548aJU+yUqj1g0ElGFdvr0aezevRtr1qxB1apVsXHjRpiammLRokWoUqUKnJyccOrUKaxfv/6dRaP8A5P82+e3ydtL+u38u2gbh6YP0m+TyWQYMWIEEhISIJFI0Lt3b1SvXl1xjdCqVauKNWpZFHWjCm/evOht8pEy+Qd54P2O38XFBWZmZoiMjMTMmTOhr6+PY8eOIT09HSNGjFC53uptL1++hCAISEtL0/omNq6urli7di02btyIvXv3Kka0mzZtipkzZ6JDhw5a9QNA4zPu5O3y0RT54xySk5OLjDM7O1vrfWgi36elpaXa5era5fFdvXpV4018NMWnqTAzMDBQubmPnLpjKkkM6hgbG5fKzaLkTE1NsXXrVqxduxaHDh3Czp07YWpqii5dumDmzJno2bMnDAwMFAXNm/9vXFxcVPpzcXFBYGCg0misfBv53/Bt8v9jb/a9efNmPHjwQGk9GxsbtUVjUlIShg8fjoyMDCxduhTdunVTWed9YnhbWFgYfvrpJ3z22WcICQmBhYWFyjry1xxNr8vy/as7v+QjvW/PLCCqjFg0ElGFlZ2djTlz5sDd3V1xc5q7d++ifv36irsSikQiNG7cWOXGEurIP7zIb1TxtmfPngHQPN2qtLwZR8OGDTXGUdSHrTfFxMQgISEBnp6eWLhwodKyp0+fal0klZX3OX5jY2P06tULu3btwqlTp9C5c2fs27cPANSOTr9N/jdt0qSJYjttfPHFF/jiiy+Qk5ODK1eu4NixY9i2bRvGjRuH8PBwfPbZZ1r1o+mck7fL45Mfs4uLS7H/buqmJxdFvs/nz5+rXa6uXR7fyJEjMXv27GLt732oO6bSjsHS0hLJycmQyWRqZyC8D1NTU8yYMQMzZsxQak9JSUFOTg6aNm2q2JeJiQlq166NR48eqS185G1vTnWtX78+gNfFnTr37t0DAKXnKsbGxmoV+507dzBixAikp6fjl19+Qffu3dWuJ48hOTlZ6xjeFBISgoULF0IikSAkJETjlxf169fHtWvXkJycrDJanZ+fj9TUVBgYGKi92Y78C4biTicnqoh4TSMRVVhLliyBVCrF999/r9T+9qiZtqME8mujLl68qDTyJSe/pby6aZbFpaenh4KCArXL5N/sq3scwsuXL5GYmAixWKx0vZ98Kpu6Pu/fvw9A/SjFhQsXih/8B/Y+xw/8X3EYHh6OtLQ0xMXFwc7OTuP0ujeZmpqiYcOGuH379jsfzq5OlSpV0K5dO8yePRvjxo2DTCbDiRMntN7+0qVLakfT5I8XkJ+bDRo0UNzRVSaTFTvO4jAzM4OtrS2ePHmC1NRUleUXL15UaXNwcICenp7GO6uWhdKOwc7ODoDmAqw0ya/BdXd3V2qXTzu9deuWyjby63jfnEL86aefwtraGsnJyUqPtJCTn5vFvXPqX3/9BR8fH2RkZGDVqlUaC0bg9SMtjI2NcenSJZXRRvmdbDXFsG7dOixcuBCNGzfGpk2bNBaMb26v7rrsCxcu4NWrV2jZsqXaqdnyKcravEYQVXQsGomoQoqPj8fWrVsxd+5cpRtU/Pvf/8bff/+t+KCUmZmJixcvajXiU6tWLXTo0AEPHjxQua7typUrOHDgAMzNzYv8oKQtCwsLpKWlqX1GYd++fWFoaIiwsDDFt/Fyy5cvR1ZWFvr27av0IUg+bevN51bKyR+98fbzzVJSUvC///2vxMdS2t7n+IHX19jVq1cPMTEx2LZtG2QymcqNVYoycuRIyGQyfPfdd2qn/mZkZOD69euK3y9cuKD2ywX5CFxxprwlJyerPJsvOjoa58+fR926dRWP3DAwMMCwYcPw7NkzBAYGqj1/nj59ir///lvrfRelf//+KCwsxNKlS5WugX306JHaaz8tLS3h7u6Oa9eu4ddff9X4JYa6Qqa0lHYMjo6OAF6/BpQWdVM25dPoP/30UwwaNEhp2Zdffgk9PT2sX79e6XmkUqkUy5YtAwC4ubkp2kUikeK5hv/973+VvpCIjo5GfHw8PvvsM8WxaSMxMRHDhw9HdnY2Vq9e/c7p/qampujXrx9ycnJURsXDwsLw4MEDdOzYUWUE8Ndff8WSJUvQtGlThISEvHMUsGfPnqhWrRqioqKUpiNLpVIsX74cADBkyBC12165cgX6+vr4/PPPi9wHUWXA6alEVOHk5ubi+++/h6urK3r27Km0bPTo0YiKisKIESPg4uKC06dP4+XLlxg7dqxWff/4448YMmQIFi9ejFOnTsHe3l7xnEY9PT0sWLCgVKantmvXDlevXsWYMWPQpk0bGBkZoVGjRnB2dkadOnUwe/ZsBAQEwMPDA7169UL16tVx4cIFXL58GQ0aNMA333yj0t/BgwcxefJkdOnSBWKxGNbW1ujfvz+6du2KunXrIjg4GLdu3ULjxo3x6NEjHD16FF988YXaQlOX3uf45fr164fly5cjKCgIBgYGKiM2RRk4cCCuX7+OrVu3wsXFBR07dkTt2rWRkZGB1NRUXLhwAZ6enggICADw+rmgT548QatWrWBjYwNDQ0Ncv34dZ8+ehY2NjdKH+Hfp1KkTFi1ahBMnTqBRo0aK5zSKxWIsWLBA6SY5EyZMwM2bN7F9+3YcPXoUTk5OqFmzJp4/f4579+7h0qVLmD59utZTY4syZswYREdHIyoqCklJSejQoQMyMzNx8OBBtGnTBtHR0SpTRP39/XHv3j2sWLECkZGRaNWqFaysrPD06VPcuXMHV69exdKlS7V6Nt/7Ks0YunXrhgULFiAuLg5eXl4qy+Pj47F7924Ar284Bbyeeunn56dYR36zLrmePXvCzs4ODRo0gFgsxo0bN3D69GlYWVlh9erViun1cvb29pg4cSJWrlyJPn36wNnZGWKxGCdPnkRycjJatmypcqfYUaNG4ejRozh06BC8vLzQrl07xWuZiYmJynlVlIyMDIwcORLp6elo164d/vzzT/z5558q640YMUJpCu306dNx7tw5BAcHIzExEQ4ODrhz5w5iYmJgaWmJefPmKW2/b98+rFixAvr6+mjTpg1CQ0NV9mFjY6P0ZZCZmRkCAwMxZcoUDB8+HL1794a5uTliY2ORlJSEHj16oHfv3ir9ZGZmIiEhAe3atdN6qj9RRcaikYgqnOXLlyM9PR3+/v4qyxo3boyVK1fil19+wZYtW1CrVi0EBga+8+6Zcra2ttizZw9Wr16NEydO4Pz58zA1NUWnTp3g6+ur9m6N72P8+PF4+fIljh49ikuXLqGgoAAeHh5wdnYG8HpkoW7duti4cSMOHz6MV69eoXbt2hg9ejR8fX1Vrm2SPww9KioKv/32G/Lz8+Ho6Ij+/fujSpUq2LRpE/73v//h/PnziI+Ph62tLSZMmIBRo0bh999/L5VjKk3FPX65/v37Y+XKlZDJZOjatWuR09rUmTdvHjp37ozt27fj9OnTyMzMhLm5uWLfffv2Vaw7btw4REdH49q1azhz5gxEIhGsra3h6+uLESNGKG5koo3mzZtj4sSJWL58OcLCwiAIApycnDBt2jSVc87Q0BCrV69GREQE9u3bh2PHjiEnJwfVqlVDnTp1MHXq1GIVy0UxNjbG5s2bsWLFChw8eBAhISGoU6cOxo0bpyga3/4SxczMDKGhodi5cycOHDiAw4cPQyqVwsrKCnXr1sXs2bPRvn37UolPk9KMoXbt2ujatSuOHj2KjIwMlb/r/fv3Va6Dff78uVLb20Wju7s7Tp48icuXLyM/P1/xmJ8xY8aovdkLAEyaNAkSiQSbNm3C77//DplMhk8//RTTpk3D6NGjVUbejYyMEBwcjHXr1iEqKgohISEwMzNDt27dMGXKlGJ9qZCZmamYtn3mzBmlx3u8ycPDQ+n/ZrVq1bBjxw6sWrUKMTExuHjxIiwsLODp6YmpU6eq3GlYPg26oKBA412MHR0dVWYQdO/eHaGhoVizZo3iby3/O/v4+Ki99vX333+HVCrVOApJVNmIhDfnkxAREdFH49y5cxg+fDgmTZqEyZMn6zqcYtm5cyfmzp2LH3/8UTEVsqK6dOkShgwZgtmzZyuebUrlm6enJ3JychAVFaX0vFOiyorXNBIREdF7e/LkiUrbw4cPsXr1ahgYGCjuXFyRtWrVCj179sT69evx6tUrXYdDJRQdHY3r169j1qxZLBiJ/j9OTyUiIqL3NmXKFMhkMtjb26Nq1ap48OABjh07hlevXmHmzJmoWbOmrkMsE7NmzcKePXuQmpqq9lEwVH7k5uZi9uzZleILDyJtsWgkIiKi99a3b19ERkbi0KFDyMrKQpUqVeDg4IBhw4bB1dVV1+GVGWtr63I3hZjU69Onj65DIPro8JpGIiIiIiIi0ojXNBIREREREZFGLBqpUjhx4gR69OgBFxcXrFu3TmV5Xl4epk2bBhcXF3h5eSlu652amgoHBwf069cP/fr1U/sIh4rkXXm6cOECPDw80KRJExw8eFBp2b59++Dq6gpXV1eV28tXNCXJU+PGjRXnk6+vb1mFrBPvylNwcDB69+4Nd3d3jBgxAg8ePFAs4/n0f4rKE8+n/7Nt2za4u7ujX79+GDJkCP7++2/FsrVr18LFxQU9evRAXFxcWYZd5t43T5Xt/Q54d67kDh06BDs7O1y9elXRxnNK1dt5qmzn1LvytHfvXjg5OSnysWvXLsWycvGeJxBVcPn5+UK3bt2E+/fvC1KpVHB3dxdu376ttE5YWJgwd+5cQRAE4cCBA8LUqVMFQRCElJQUwc3Nrcxj1gVt8pSSkiIkJiYK3377rfDHH38o2l+8eCE4OzsLL168ENLT0wVnZ2chPT29rA+hTJQkT4IgCC1atCjLcHVGmzydOXNGyMnJEQRBELZs2aL4f9pftXwAACAASURBVMfzSbs8CQLPpzdlZmYqfo6Ojha++uorQRAE4fbt24K7u7sglUqF+/fvC926dRPy8/PLNP6yUpI8Vab3O0HQLleC8DpfQ4cOFby8vISEhARBEHhOaZunynROaZOnPXv2CD/++KPKtuXlPY8jjVThJSQkoG7durC1tYWRkRHc3NwQExOjtE5sbCw8PDwAAD169MCZM2cgVLLLfbXJU506ddCoUSPo6Sm/dJw8eRIdOnSAhYUFzM3N0aFDhwr7zWtJ8lSZaJMnJycnmJiYAABatGiBx48fA+D5pG2eKhNt8mRmZqb4+dWrV4oHtsfExMDNzQ1GRkawtbVF3bp1kZCQUKbxl5WS5Kmy0SZXALB8+XJ8/fXXEIvFijaeU9rlqTLRNk/qlJf3vMr7iYYqjSdPnqBWrVqK32vWrKnyXLEnT56gdu3aAAADAwNUrVoVL168APB6ekX//v0xbNgwxMfHl13gZUybPH2Ibcubkh6rVCqFp6cnvL29ER0d/SFC/CgUN0+7d+9G586d32vb8qwkeQJ4Pr1ty5Yt6N69O/773/9izpw5xdq2IihJnoDK834HaJer69ev4/Hjx/jiiy+KvW1FUZI8AZXnnNL2nDh8+DDc3d0xZcoUPHr0qFjb6hofuUFUhH/96184evQoqlWrhmvXrmHixImIiopS+qaWqDiOHj2KmjVrIiUlBSNGjIBEIsGnn36q67B0KiIiAteuXUNYWJiuQ/moqcsTzydlX375Jb788kvs378fQUFB+Pnnn3Ud0kdJXZ74fqessLAQixYtwsKFC3UdyketqDzxnFLWtWtX9OnTB0ZGRti+fTtmzZqFzZs36zosrXGkkSq8mjVrKk3nevLkicrDpmvWrKn4xic/Px+ZmZmoVq0ajIyMUK1aNQCAvb09Pv30UyQlJZVd8GVImzx9iG3Lm5Ieq3xdW1tbODo64saNG6Ue48dA2zydPn0aa9asQVBQEIyMjIq1bUVQkjzJtwd4Pr3Nzc1NMfLK80m7PFWm9zvg3bnKzs7GrVu3MHz4cDg7O+PPP//E+PHjcfXqVZ5TWuapMp1T2pwT8s+VAODl5YXr169rve3HgEUjVXjNmjVDcnIyUlJSkJeXh6ioKDg7Oyut4+zsrLhb1aFDh+Dk5ASRSIS0tDQUFBQAAFJSUpCcnAxbW9syP4ayoE2eNOnYsSNOnjyJjIwMZGRk4OTJk+jYseMHjlg3SpKnjIwM5OXlAQDS0tJw6dIlfPbZZx8yXJ3RJk83btyAv78/goKCYGlpqWjn+aRdnng+KecpOTlZ8fOxY8dQt25dAK9f36OiopCXl6d4HXdwcCjL8MtMSfJUmd7vgHfnqmrVqjh37hxiY2MRGxuLFi1aICgoCM2aNeM5pWWeKtM5pc3/vadPnyp+jo2Nxb///W8A5ec9j9NTqcIzMDCAv78/xowZg4KCAgwYMAANGzbE8uXLYW9vj27dumHgwIH49ttv4eLiAnNzcyxbtgzA60cnrFixAgYGBtDT08OPP/4ICwsLHR/Rh6FNnhISEjBp0iS8fPkSR48excqVKxEVFQULCwtMmDABAwcOBABMnDiReVKTpzt37mDevHkQiUQQBAFff/11hf2Qr02eFi9ejJycHEydOhUAULt2baxZs4bnk5Z54vmknKewsDCcOXMGBgYG+OSTTxRTUxs2bIhevXqhd+/e0NfXh7+/P/T19XV8RB9GSfJUmd7vAO1ypQnPKe3yVJnOKW3yFBoaitjYWOjr68Pc3Fwxpbe8vOeJhMp2i0giIiIiIiLSGqenEhERERERkUYsGomIiIiIiEgjFo1ERERERESkEYtGIiIiIiIi0ohFIxEREREREWnEopGIiIiIiIg0YtFIREREREREGrFoJCIiIiIiIo0MdB0AfXht27aFjY2NrsP4qAmCAAAQiUQ6juTjxjxph3nSDvOkPeZKO8yTdpgn7TBP2mGetFMe8vTgwQOcO3dO7TIWjZWAjY0N9u7dq+swPmpSqRQAIBaLdRzJx4150g7zpB3mSXvMlXaYJ+0wT9phnrTDPGmnPOTJ09NT4zJOTyUiIiIiIiKNWDQSERERERGRRpyeWgn8/ynUVISPearAx4R50g7zpB3mSXsVLVdSWT7EhvwIQkRUXvAVuxIQiYBO4+brOgwiIiIAQNzauboOgYgqAZlMhtTUVOTm5uo6lI/qRjjGxsaoU6cODA0Ntd6GRSMREREREVU4qampqFq1KurVq6fzYq2wsBAAoKen26sDBUHA8+fPkZqaivr162u9Ha9pJCIiIiKiCic3NxeWlpY6Lxg/JiKRCJaWlsUefWXRSEREREREFRILRlXvkxMWjURERERERMW0cuVKbNiwodT6Gzx4cKn1VdpYNBIREREREenY9u3bdR2CRrwRDhERERER0TuEh4djw4YNEIlEsLOzw6effqpYtnPnTuzYsQMymQx169bF4sWLYWJigj/++AO//vor9PT0YGZmhq1bt+L27duYPXs2ZDIZCgsLsXLlStSrVw8tW7bE5cuXAQDr1q3D/v37IRKJ0LlzZ3zzzTe6OmwALBqJiIiIiIiKdPv2bQQFBWHbtm2oXr060tPTERoaqlju4uICb29vAMCyZcuwe/du+Pj4YPXq1diwYQNq1KiBly9fAng9ojh8+HD07dsXeXl5ijuryh0/fhyxsbHYuXMnTExMkJ6eXnYHqgGLRiIiIiIioiKcPXsWPXv2RPXq1QEAFhYWSstv376NX375BZmZmcjOzkbHjh0BAC1btoSfnx969OgBFxcXAECLFi2wZs0aPH78GK6urqhXr55SX2fOnIGnpydMTEzU7ksXeE0jERERERFRCfj5+cHf3x/79+/HpEmTkJeXBwAICAjAtGnT8PjxYwwcOBAvXryAu7s7goKCYGxsjLFjx+LMmTM6jv7dWDQSEREREREVwcnJCQcPHsSLFy8AQGXKaHZ2NmrUqAGZTIb9+/cr2u/fv4/mzZtjypQpqF69Oh4/foyUlBTY2tpi+PDh6NatG/766y+lvtq3b4+9e/fi1atXavelC5yeSkREREREVISGDRvC19cXPj4+0NPTQ5MmTWBjY6NYPnXqVHh5eaF69epo3rw5srOzAQCLFy/GvXv3UFhYiHbt2qFRo0ZYv349IiIiYGBgACsrK4wbN05pX507d8bNmzcxYMAAGBoaokuXLpgxY0aZHu/bRIIgCDqNoJzJyMhAhw4d8MMPP2DgwIEl6mvs2LHw9PREz549Syk69Tw9PfGsRssPug8iIiJtxa2d+0H6lUqlAACxWPxB+q8omCftME/a+ZjzlJiYiMaNG+s6DABQ3OxGT+/jmOipLjeenp7Yu3ev2vU/jqjLkaioKJiamiIiIqJY29nZ2SE1NVWpbcmSJYoLYomIiIiIiD5GLBqLKSYmBvPmzcOFCxfw8OHDEvVVtWpV6Ovrl1JkREREREREpY/XNBZDcnIyrK2t4erqCisrK0RGRsLX1xfA6yHnDRs2IDo6GmKxGPn5+fD29kbv3r0xevRoAMCMGTMgFovh6+uLixcvIjw8HI6Ojli0aBEA4OnTpwgMDMTz589RUFCA1q1bY+rUqTAyMsKiRYuwf/9+9OrVC1lZWUhOTkZBQQGWLVuGOnXq6CwnRERERERUsbFoLIbw8HB4enrCwMAAffv2RUREhKJo3Lx5M8LDw7Fjxw6YmZkhISEB/v7+6N+/P0JDQ2FnZ4elS5cqCrwOHTpAJBLhwYMHAF4XnePHj0fXrl0xadIkyGQyjBkzBkuXLoWfnx/8/PyQnp6O06dPY+fOnTAzM8P333+PjRs3wt/fv8i4BQgf7PoRIl3IK5DBSN9Q12EQ0XuSyvIhNuRHECKi8oKv2FoSBAHXrl3DtGnTAAADBgzAhg0bkJCQAAcHB2zfvh0eHh4wMzMDADg4OOC7777Tuv+rV6/i2rVrWLNmDQDA0NAQXl5emDNnDmbNmgWRSATg9S145fuwt7dHdHT0O/sWQQTX7bOLdbxEH7PDgxfqOoQifcw3BfiYME/aq2i5YsFIRFS+8FVbS/Hx8UhKSoKPj4+irUqVKoiMjISDgwMePXoES0tLpW0cHR217l8+4vhmH5aWlnj16hVevHiB6tWrAwDMzc0Vy8ViseLBoURERERERB8Cb4SjpYiICISEhCA0NFTxb/z48YiKioJMJkPt2rXx/PlzpW2uXLmidf/W1tYAgH/++UfR9vz5c5iYmKBatWqlcxBERERERFSmnj17hhkzZsDV1RWenp74+uuvkZSUhD59+pTaPpYvX47Tp0+XWn9vY9GoBalUitTUVNja2iq19+rVC2lpaYiLi8PgwYMRGRmJrKwsAMD58+exbNkyxbqmpqbIzs7G5cuXsXjxYpV9ODg4oGnTpti2bRsAQCaTYdeuXfD29lZMTSUiIiIiovcjleWXeX+CIGDSpElwdHTE4cOHsXfvXsycOVNlsKmkpk6divbt25dqn2/i9NR3yMzMxIgRI/Ds2TMEBwdj1KhRimWBgYEwNDREQEAAJk6ciP79++Orr76CWCyGiYkJFi78v+uuRo0ahW+++QaGhob47rvvsGLFCoSHh0MqlWLRokXw8/NDUFAQfvrpJwwdOhT5+flo3bo1pk+fDgBYsWIF4uLiIBaLYWlpiZo1a2LdunX4559/4O/vj4CAgDLPDRERERFReSE2NECncfNLrT9tbjR59uxZGBgYYPDgwYq2Ro0aKT2/PTU1Ff/5z3/w6tUrAMDcuXPRqlUrPH36FNOnT0dWVhYKCgrwww8/oGXLlvj+++9x7do1iEQiDBgwACNHjoSfnx+++OIL9OzZEwkJCViwYAFycnJgZGSEkJAQxT1R3heLxneoWrUq9u7dq3bZ2rVrVdq+/vprtetOnjwZkydPVvzepk0bTJkyRWmdmjVrYsWKFWq3nzJlisr63bp1KzJ2IiIiIiLSndu3b6Np06ZFrmNpaYng4GCIxWIkJydjxowZ2Lt3Lw4cOICOHTti/PjxKCgowKtXr5CYmIgnT57gwIEDAICXL18q9ZWXl4fp06dj2bJlcHBwQFZWFoyNjUt8HCwaiYiIiIiIdCQ/Px8BAQG4efMm9PT0kJycDABo1qwZvvvuO+Tn56N79+5o3LgxbG1tkZKSgvnz56NLly7o2LGjUl9JSUmoUaMGHBwcAKDEI4xyvKaRiIiIiIjoA2jYsCGuX79e5DohISGwsrJCREQE9uzZA5lMBgD4/PPPERYWhpo1a8LPzw/h4eEwNzdHREQEHB0dsX37dnz//fdlcRgsGomIiIiIiD4EJycn5OXlYefOnYq2mzdv4vHjx4rfMzMzUaNGDejp6SEiIgIFBQUAXj+Sz8rKCt7e3vDy8sL169eRlpYGQRDQo0cPTJs2DTdu3FDaX/369fHs2TMkJCQAALKyspCfX/IbAHF6KhERERER0QcgEomwatUqLFiwAL/99hvEYjFsbGzw3XffKdYZOnQoJk+ejPDwcHTq1AlVqlQB8PppDBs2bICBgQGqVKmCn3/+GU+fPsXs2bNRWFgIAJgxY4bS/oyMjLBs2TIEBgYiNzcXxsbGCA4OhoFByco+kSAIQol6oI+ep6cnsrztdB0GUak5PHjhu1fSIalUCgAQi8U6juTjxjxpj7nSDvOkHeZJO8yTdj7mPCUmJqJx48aK36WyfIgNS2/MrDj9yYs8Pb2PY6Ln27kBXtcMmm4A+nFETURERERE9AGVZsH4Ifr7mLFoJCIiIiIiIo1YNBIREREREZFGLBqJiIiIiIhIIxaNREREREREpBGLRiIiIiIiItKo8tzypxITIHz0jyggKo68AhmM9A11HQYRERHRO0VHR2PixImIiorCZ599htTUVPj6+uLAgQM4d+4cNm7ciLVr1+o6zCJxpLESEEGk6xA+elKpVPGcIdLsY8kTC0YiIiIqrrwCmU76O3DgAFq3bo2oqKhS3X9Z4kgjERERERFVeEb6hnDdPrvU+tNmJl92djYuXryIkJAQTJgwAVOnTi21/ZcljjQSERERERF9ADExMejUqRPq168PCwsLXLt2TdchvRcWjURERERERB9AVFQU3NzcAAC9e/cut1NUOT2ViIiIiIiolKWnp+Ps2bO4desWAKCwsBAikQhDhw7VcWTFx6KRiIiIiIiolB06dAj9+vVDQEAACgsLAQDDhw/H48ePdRxZ8XF6KhERERERUSk7cOAAunfvrtTm6ur60T9eQx2ONBIRERERUYWXVyAr1WeXv+u50aGhoSptw4cPx/DhwxW/t23bFm3bti21mD4UjjQSEREREVGFV9rPea5Mz41m0UhEREREREQasWgkIiIiIiIijVg0EhERERFRhSQIgq5D+Oi8T05YNBIRERERUYVjbGyM58+fs3B8gyAIeP78OYyNjYu1He+eSkREREREFU6dOnWQmpqKZ8+e6ToUReEqEol0HMnrYrpOnTrF2oZFIxERERERVTiGhoaoX7++rsMAAEilUgCAWCzWcSTvh9NTiYiIiIiISCMWjURERERERKQRi0YiIiIiIiLSiEUjERERERERacSikYiIiIiIiDRi0VhKMjIyYG9vj927d2tcJz09HT4+PrCzs0NqaqpW/a5btw7Ozs7w8/MrrVCJiIiIiIi0xqKxlERFRcHU1BQREREa17GwsEBoaGix+h07diw8PDyU2lauXMkikoiIiIiIygSLxlISExODefPm4cKFC3j48KGuwyEiIiIiIioVLBpLQXJyMqytreHq6gorKytERkYqluXl5WH+/Plwc3PDmDFjlJbl5uaqTFf19/dHmzZtsHfvXrX72rFjB/bt24e4uDj4+Phg5syZH/bgiIiIiIioUjPQdQAVQXh4ODw9PWFgYIC+ffsiIiICvr6+AIC1a9ciPj4eu3fvhomJCUJCQhTbGRsbIzQ0FHZ2doq2gIAAJCUladzXoEGD8PTpUzx48ACLFi36YMdEREREREQEcKSxxARBwLVr19CyZUsAwIABA3D37l0kJCQAACIjI+Hm5gYTExMAQN++fcs+Rggl7iOvQFYKkRARERERUXnDkcYSio+PR1JSEnx8fBRtVapUQWRkJBwcHPDkyRNUr15dsezNn8uKCCK4bp9doj4OD15YStEQEREREVF5wqKxhCIiIhASEgJbW1tF27p16xAcHIxZs2ahZs2aSEtLUyx782c5Q0NDSKVSxe/p6ekfNmgiIiIiIiItcXpqCUilUqSmpioVjADQq1cvpKWlIS4uDv369cP+/fuRk5MDAGpvcFO3bl1cu3YNAHDz5k3cv3+/yP2amZkhOzsbADBjxgw8fvy4NA6HiIiIiIhIBYvG95SZmYkhQ4bgzp07CA4OVloWGBgIQ0NDBAQEwMrKCo6Ojhg4cCBGjRqluLZxxowZSExMBADMnj0ba9asgY+PDw4dOgQHBwesW7cO4eHhWLduneJuqfIb37i4uODevXsYMmQIRCIRatWqVbYHT0RERERElYZIEISS3yWFPmqenp7I8rZ794pFqOjXNMqnB4vFYh1H8nFjnrTDPGmHedIec6Ud5kk7zJN2mCftME/aKQ958vT01PjYP440EhERERERkUYsGomIiIiIiEgjFo1ERERERESkEYtGIiIiIiIi0ohFIxEREREREWnEopGIiIiIiIg0YtFIREREREREGrFoJCIiIiIiIo1YNBIREREREZFGLBqJiIiIiIhIIxaNREREREREpJGBrgOgD0+AgMODF5aoj7wCGYz0DUspIiIiIiIiKi840lgJiCAqcR8sGImIiIiIKicWjURERERERKQRi0YiIiIiIiLSiEUjERERERERacSikYiIiIiIiDRi0UhEREREREQasWgkIiIiIiIijVg0EhERERERkUYGug6APjxB0HUEHz+xWKzrEMoF5kk7zJN2dJEnqSwfYkO+9RERERUH3zkrAZEI6DRuvq7DICLSubi1c3UdAhERUbnD6alERERERESkEYtGIiIiIiIi0ohFIxEREREREWnEopGIiIiIiIg0YtFIREREREREGrFoJCIiIiIiIo1YNBIREREREZFGLBqJiIiIiIhIIxaNREREREREpBGLRiIiIiIiItKIRSMRERERERFpVCGKxoyMDNjb22P37t0a10lPT4ePjw/s7OyQmppahtERERERERGVXxWiaIyKioKpqSkiIiI0rmNhYYHQ0NAyjArw8/PDypUry3SfREREREREpalCFI0xMTGYN28eLly4gIcPH+o6HCIiIiIiogrDQNcBlFRycjKsra3h6uoKKysrREZGwtfXFwCQl5eHn3/+GWfPnkXt2rXRt29fxXZHjhxBQEAA8vPz4e3tjenTpyM4OBjr16+HnZ0dgoOD8eTJEwQGBuL58+coLCxEx44dMWHCBOjp6WHmzJk4fvw4Ro4cib///hv379+Hubk5fvnlF5ibm2PVqlWIi4uDWCzG+fPn0bRpU7Ru3RpLlixBjRo1EBoaipSUFMycORNXrlzBX3/9BQCKfkeNGoVbt24hKSkJWVlZiI2NxZ07d7BgwQJIpVLk5+ejb9++GDp0qE7yTkRERERElUO5LxrDw8Ph6ekJAwMD9O3bFxEREYqice3atYiPj8fu3bthYmKCkJAQxXYuLi6KonLKlCkAgBEjRiA2NhYbN25EYWEhxo8fj/bt22PlypXIy8vD8OHDYW5uDh8fHyxZsgQ+Pj44f/48NmzYAENDQ4waNQq7d+/G6NGjMWnSJKSmpsLGxgaTJ09W7DczMxP79u0DANja2mLp0qXo1q2bYrm831OnTiE4OBhGRkaYN28ecnJyMGrUKIwfPx5DhgxBZmYmPDw8UKtWLTg7OxeZIwEC4tbOLa2Uk5byCmQw0jfUdRhE9AapLB9iw3L/1kdERFSmyvU7pyAIuHbtGqZNmwYAGDBgADZs2ICEhAQ4ODggMjISXl5eMDExAQD07dsXCxcuVGzv4uKC+fPn49ixY+jWrRvi4uLQsWNHiEQiXLlyBdevX0dQUBAAwMjICL1798auXbvg4+Oj6MPZ2RmGhq8Lg6ZNm+LevXulcmyurq4Qi8UAgICAAERFRSEjIwNeXl4AgKpVq6Jbt27YtWvXO4tGEURw3T67VOIi7R0evPDdK5UzUqkUABTnJqnHPGlHF3liwUhERFR85frdMz4+HklJSUpFXJUqVRAZGQkHBwc8efIE1atXVyx782fgdSHYv39/7Ny5E926dcO+ffvw/fffA4Di2shvvvlGsX5OTg4EQVDq45NPPlH8LBaLkZeXVyrH9ma/8ngEQcCoUaMUbZmZmbCysiqV/REREREREalTrovGiIgIhISEwNbWVtG2bt06BAcHY9asWahZsybS0tIUy978Wc7b2xt9+vRBQkICAKBGjRoAAGtrawBAUFAQzMzMALwe2Xzx4kWJYjY0NFR8uw68flyINqytrWFsbKx0B9j8/HxkZWWVKB4iIiIiIqKilNu7p0qlUqSmpioVjADQq1cvpKWlIS4uDv369cP+/fuRk5MDANi7d69KPw0aNECrVq0wefJkeHp6KtodHBzQtGlTbN++XdEWHh6uNL31XczMzJCdnQ0A8PHxQUFBAerWrYu7d+8q2g8fPqxVX127doWxsTH++OMPRduaNWuUrtMkIiIiIiIqbeWyaMzMzMSQIUNw584dBAcHKy0LDAyEoaEhAgICYGVlBUdHRwwcOBCjRo1SXNs4Y8YMJCYmKrbx9vaGvr4+OnbsqGjT09NDUFAQEhISMGTIEPj4+ODkyZPw9/cHAPj7+yMxMRHr1q3DkSNHsGPHDuzbtw9xcXFYsWIFgNfXUMbFxcHb2xvNmjWDvr4+HBwc0K9fPwwcOBC+vr6oU6cOgNdFZVpamlK/P/30kyKeKlWqIDg4GHv27MGXX36JL7/8Ei9evMCECRM+TJKJiIiIiIgAiIS3L9KjCsfT0xNZ3na6DqPS4Y1wKi/mSTvMk/aYK+0wT9phnrTDPGmHedJOeciTp6en2pmZQDkdaSQiIiIiIqKywaKRiIiIiIiINGLRSERERERERBqxaCQiIiIiIiKNWDQSERERERGRRiwaiYiIiIiISCMWjURERERERKQRi0YiIiIiIiLSiEUjERERERERacSikYiIiIiIiDRi0UhEREREREQaGeg6APrwBAg4PHihrsOodPIKZDDSN9R1GEREREREJcKRxkpABJGuQ/joSaVSSKXSUu2TBSMRERERVQQsGomIiIiIiEgjFo1ERERERESkEYtGIiIiIiIi0ohFIxEREREREWnEopGIiIiIiIg0YtFIREREREREGvE5jZWAIOg6go+fWCx+5zpSWT7EhvwvQ0RERESVCz8BVwIiEdBp3Hxdh1Huxa2dq+sQiIiIiIjKHKenEhERERERkUYsGomIiIiIiEgjFo1ERERERESkEYtGIiIiIiIi0ohFIxEREREREWnEopGIiIiIiIg0YtFIREREREREGrFoJCIiIiIiIo1YNBIREREREZFGLBqJiIiIiIhIIxaNREREREREpFGFKRozMjJgb2+P3bt3F3tbd3d3XLly5QNEpdn69esxf/78Mt0nERERERFRcVWYojEqKgqmpqaIiIgo9rahoaFwcHD4AFG95uPjg7179yq1jRgxAjNnzvxg+yQiIiIiIioNFaZojImJwbx583DhwgU8fPiwWNtaWFhAJBJ9oMjUMzIyQpUqVcp0n0RERERERMVloOsASkNycjKsra3h6uoKKysrREZGwtfXF2lpaZg6dSrOnz+PwMBAxMbGIjk5Ge3bt8fcuXMBAP7+/oiJicHgwYMxefJk/Pbbb9i6dStatmwJc3Nz3LhxA/n5+fjll18QHh6Oc+fOIT09HYsXL0bjxo0BACdOnMCGDRugp6cHqVQKV1dXjBw5EgDwww8/IDExJa/6lwAAIABJREFUEc+ePcO+ffvQpUsX1K1bF6tWrUJmZiZiY2MBAHl5eVi+fDkuXrwIfX19WFpaYs6cOfjXv/6F8PBwrFmzBjVq1EDr1q1x8eJFPHv2DHPmzEHHjh11knMiIiIiIqocKsRIY3h4ODw9PWFgYIC+ffsqpqhWr14doaGhAIDU1FQEBQVh9+7diIiIwNWrVwEAAQEB6NSpk6KvMWPGwMPDA2fOnMGECROwfft2NGvWDKNHj0aPHj0QGhoKd3d3LF++XLFNdnY2AgMDERwcjC1btiAmJgbx8fEAXheNjRs3xtixYxEaGooxY8bAxcUF3333ndIxLFmyBDdu3EBYWBi2bNmCBg0aYMKECSgsLET//v0xduxYJCQkoGfPnggNDcX48eOxaNGiD5pXIiIiIiKicj/SKAgC/h979x4XZZ3///85DMOQgIpKeHa1FFHLTNbyY7jmKV3X0yREbqOpu6SluemGZEqaimiAK7Zlroc2zEMpqGWfPJWFddvQzUg3ty3xrKuAmueZEeb7hz/nJ5+8bDzggDzu/8Rch/f7NS+i2zx7X9c1O3fu1J/+9CdJ0uOPP64FCxbo22+/LXWf4mOPPSZJCgoKUpMmTbRv3z7dd999huM+8MADqlWrliSpRYsW+vrrr9W0aVNJUsuWLbVy5UrPsZGRkZo1a5YOHz4si8Wi/Px8ffPNN4qKivL6PSxfvlzTpk2Tv/+lX8nAgQP15ptvaseOHWrdurUkqVGjRmrevLkkqVWrVtq7d69348utnLcmenXsreQsdinAbLnt85YVh+uirJYK/ycDAAAAXJcK/wl427Zt2rNnj+x2u2dblSpVtGbNmlKhsWrVqp6frVarnE7nNce98niz2Vzqtb+/v1wul+f1M888o+joaKWlpclkMikxMVHnz5/3+j0cP35c58+fV40aNTzbatasKUk6fPiwJzRWq1at1Hu4soZrMcmk7ste8rqeW2V93PTbPueNcjgcki711QiBEQAAAJVRhf8UvHr1ar399ttq0KCBZ9u8efO0aNEijRs3ThZL2a50HT9+XHv37lVSUpLnYTrehrnLQkNDFRgYqKKiIs+2yz/XrVv31hULAAAAANepQt/T6HA4dPDgwVKBUZJ69uyp48ePKycnp8xrqF69ukJDQ7V161ZJ0unTpz33M14WFBSks2fPyul0eh6QcyU/Pz/Fxsbq/fff9wTOJUuWqGXLlte8hBYAAAAAypp50qRJk3xdxI04ffq0nnrqKR08eFAmk0lt2rTx7EtISNChQ4eUk5OjzMxMnTlzRnl5eXr44Yc1a9Ysffnll9q1a5fCw8P197//XZs3b9YPP/wgl8ulf//731qyZIny8/N19uxZnTp1Sq+//rr279+vgwcPqnr16poyZYoOHTqkvLw89enTRy1atNCCBQu0du1aff3117JYLMrNzZXFYtF9992noKAgzZ07V2vWrFHPnj117NgxpaWl6dChQ/rqq6/Uv39/Pfzww9q9e7def/11ZWdny+FwaMaMGQoJCdGGDRtK1RAREaEXXnhBR48eVW5urn7729967oW8muXLl8vZstbt+LWUYm/V9bbPeaOKi4sl6Zp9BH3yFn3yDn3yHr3yDn3yDn3yDn3yDn3yTkXo0/Lly/XEE09cdZ/J7Xa7b3M9uM1sNpvOxEbc9nnvtHsaQZ+8RZ+8Q5+8R6+8Q5+8Q5+8Q5+8Q5+8UxH6ZLPZlJWVddV9FfryVAAAAABA2SI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIb8fV0Ayp5bbq2Pm37b53UWuxRgttz2eQEAAADcOqw0VgImmXwyL4ERAAAAqPgIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAM+fu6AJQ9t9vXFZR/VqvV1yVIkhyui7Ja+LMEAABA+cGn00rAZJKin5ni6zLghZy3Jvq6BAAAAKAULk8FAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACGKnRozM3NVWxsrCIiItSvXz/Z7Xb17t1bvXr10vr16yVJhw4dUseOHeV0On1c7bWlpKSoQ4cOmjNnjq9LAQAAAAAPf18XcDPatWun9PR0denSRRMmTFBUVJQkKS0tTWPHjtVnn32mevXqafXq1QoICPBxtdeWmJiokydP+roMAAAAACilQq80GomKipLT6dShQ4ckSaGhoT6uCAAAAAAqpgq90ng1Fy9e1Lp161SvXj3dc889stvt+uabbzR//nw99NBDkqTPPvtMc+fOldlsVnFxsYYPH67f/OY3kiSn06kZM2boH//4h2rXrq0uXbpo8uTJat26tSZPnqz58+frs88+05AhQ/Sf//xHe/bs0ZkzZ/TJJ59o5syZ2rFjh8xms0wmk/785z+rZcuWKigo0JgxY5Sbm6uXXnpJOTk5OnbsmJo1a6bJkycrODjYU/9PP/2kxMRE7d27V8XFxZo1a5bq168vu92u3NxcPfDAA8rIyJAkjRgxQoWFhXr11VfVqVOn295rAAAAAHe+OyY0Tp06VSEhIdq/f78CAwP1l7/8RVWqVFFmZqY6d+7sOe67777T6NGjlZ2drcaNG2v37t2y2WxatmyZIiMj9dZbb2nbtm1asWKF7rrrLs2fP1+SlJ6ervr16ystLU12u11ffPGFFi1apICAAL3yyiuSpBo1auidd96RyWTSgQMHNHToUG3YsEFhYWHKzMxURESE9u/frwULFqi4uFh//OMfNX36dE2bNs1T35dffqn33ntPwcHBevnll7Vw4UIlJSXp73//u7p06aKBAwcqPDxcktSnTx/Vrl37FwOjW27lvDXxFnf89nIWuxRgtvi6jDLncF2U1XLH/FkCAADgDnDHfDq98p7GTZs2adCgQZo/f75n22XLly9X27Zt1bhxY0nSPffco6ioKC1btkyTJ0/WmjVrFBMTo7vuukuSZLPZ9Nprr/1svu7du8tqtUqSXn31VUlSnTp1NGjQIJWUlMjPz0/79+/X8ePHVaNGDc95NptNkmQ2m/X4448rMTFRU6ZMkZ/fpSuFO3To4Fl5bNWqlTZu3ChJ8vPz0+OPP67ly5erb9++nve5cOHCX+yNSSZ1X/aSN20st9bHTS/T8R0OhyR5fqe+QmAEAABAeXNHfkLt0qWLGjRooHffffdnofHw4cOqWbNmqW01a9bUkSNHJElHjx4tFfKu/PlKVatWLfX6n//8p1588UUtW7ZM999/vyQpIiJC586dKzVG9erVS43tdDp14sQJT01Xjmu1Wks99XXAgAF64403tHv3bp04cUL33XefLJY7f/UNAAAAgO/ckaFRkud+xf+rTp06ngfkXFZUVKT69etLksLDw3X8+HHPvit/vpbt27erZs2ansBo9BUfJ0+e9MxVVFSkgIAArx/UU7t2bUVHR2v58uU6deqUnnnmGa/OAwAAAIAbdUc+PfW7777TDz/8oA4dOvxs3xNPPKGtW7dq9+7dkqTdu3dr69ateuKJJyRJffv21QcffKBz585JktasWePVnE2aNFFRUZHy8/MlSZ9++ulVj1u9erWkSw/sWbFihfr06eO5NNUbsbGxWrVqlQoLCz2X2AIAAABAWanQK425ublKTU2V9P8/CKekpERnzpzRmDFj1LdvX9ntdhUUFCg5OVkJCQnq0KGDMjIy9PLLL8vf318XL15URkaGWrRoIUmKj4/XiRMnNGDAANWpU0e/+93vJEkmk0mSlJSUpF27dqmgoEC7du3Syy+/LEnq3LmzBg8erGHDhikiIkKRkZGSpDFjxigtLU0NGjSQdOkeyj/84Q86evSomjVrppdeunSvYUZGhnJycmS1WlWzZk2Fh4dr3rx5KiwsVFJSkue+yU6dOslqtap37963qcsAAAAAKjOT2+12+7qI8uTcuXPy8/NTYGCgpEuXkHbo0EHffPONZ9uNioiI0KZNmzyXp96ogQMHatGiRV4/tMVms+lMbMRNzelrleVBOOUdffIOffIOffIevfIOffIOffIOffIOffJOReiTzWZTVlbWVffdkZen3oxPP/1Ub775puf1+++/r/bt2990YLxZn332mf773//qn//8p1q0aFGu/4UDAAAAcOcgNP4fERERysvL08CBAxUXF6cdO3YoJSXlpsYsKCiQ3W6XdOly1e+///66xzhw4IAGDx6s1NRUHoADAAAA4Lap0Pc0loV7771Xb7/99i0dMywsTJmZmTc1xlNPPaWnnnrqFlUEAAAAAN5hpREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMCQv68LQNlzy631cdN9XcZNcRa7FGC2+LoMAAAAoNJhpbESMMnk6xJuGoERAAAA8A1CIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhviexkrA7fZ1BeWf1Wr1dQkVAn3yDn3yDn3yHr3yDn3yzs30yeG6KKuFj49AZcNffSVgMknRz0zxdRkAAKCCy3lroq9LAOADXJ4KAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAM+fu6AF86cuSIMjIylJ+fL4vFIqfTqXvuuUd9+vRR+/btfVrbzJkztXr1asXFxWnUqFE+rQUAAABA5VVpVxoPHjyomJgYtW3bVsuXL9fixYuVmZkps9lcLkJaQkKCoqOjfV0GAAAAgEqu0obGKVOmqHXr1howYIBnm9Vq1cSJExUcHOzDygAAAACg/KiUl6f+9NNP+vzzz/Xqq6/+bJ/VatWHH34oSTp27JimTp2qoqIiFRcXq23btho9erQCAgKUkpKiDz74QD179tTp06f1448/qmrVqnrttdc0b9487dixQy6XS7Nnz1a9evUkSadPn9b06dOVn58vPz8/RUZGKiEhQVarVZK0ceNGZWRkKDg4WM2aNZPT6fSc99RTT+n777/Xr3/9a7399tvatWuXEhMT5XA4NGvWLLVq1eo2dQ8AAABAZVIpVxr37t2rkpIS1alT56r7g4ODVVJSohEjRqhZs2Z69913lZmZqZ07dyo9PV2SlJiYqOjoaP3jH/9QUlKSVq5cKYvFovj4eMXHx2vp0qVq0aKFFi5c6Bl33LhxunDhgpYuXarFixeroKBAs2bNkiTt27dPo0eP1iuvvKIlS5Zo2LBhysnJkSSFhIRo8eLFCgwM1OjRo2U2m9WqVSu1b99e6enpBEYAAAAAZaZSrjSaTKZfPGbHjh3auXOn5s6dK0myWCyKiYnRhAkTNG7cOM8Y7du3V1BQkCQpMjJSP/zwg2rVqiVJatWqldatWydJKigo0KZNm7RkyRKZTCaZTCb17dtXCQkJSkxM1Nq1a9WwYUO1bdtWktSgQQM9+OCDnnpCQkLUs2dPLV++XFFRUXI4HPrPf/6j++677xffi1tu5bw18To6BABA2XMWuxRgtvi6DFwHh+uirJZK+fERqNQq5V99o0aN5OfnpyNHjhgec+jQIUlSzZo1Pdtq1qyp8+fP68SJE6pRo4YkqWrVqp79/v7+P3t9+RLTy3PNnDlTAQEBkiSn06lq1arp9OnTOnr0qGfMy0JDQ0u9jo2N1eDBgzVhwgRt3rxZ3bt39+r9mmRS92UveXUsAAC3y/q46bd0PIfDIUme2z5wdTfTJwIjUDlVyr/8atWqqVOnTtq8ebNiYmJK7Tt69KjGjRvneYJqYWGh7r77bklSUVGR7rrrrp+FOW9cvhR28uTJat68uWd7UVGRQkJCFB4erq+++qrUOSdOnPDcDylJbdq0UaNGjbRq1Srl5OR4Lm0FAAAAgLJSKe9plKSkpCTt2LFDK1eu9Gw7c+aMkpKS1Lp1a7Vp00YtW7bU0qVLJUkul0vvv/++YmNjvbq89f8KCwtTly5d9N5773m25ebm6vnnn5ck9erVSwcOHNC2bdskSQcOHFBubu7PxomJidH8+fNVq1YthYSEXHcdAAAAAHA9TG632+3rInzl6NGjSk9PV35+vgIDA+VyudS7d2/9/ve/9+yfNm2aCgsLdfHiRbVt21YvvPCCAgIClJGRoeXLl8tqteoPf/iDJGn+/PlyOBx64oknFBkZqbS0NBUWFuqxxx7TtGnTdPr0ac2YMUM//PCDrFargoKC9Morr6h27dqSLj09dfbs2QoODlb9+vXlcDi0c+dOxcXFKT4+XtKlJ79GR0dr4cKFioqK8up92mw2nYmNKIMOAgBw47g81Tfok3fok3fok3cqQp9sNpuysrKuuq9Sh8aKyOl0atCgQVq2bJnX5xAaAQDlEaHRN+iTd+iTd+iTdypCn64VGivt5akVzerVq3X+/Hl9/PHH6tGjh6/LAQAAAFBJVMoH4VRE//nPf/S3v/1N4eHhev31131dDgAAAIBKgtBYQbz44ot68cUXfV0GAAAAgEqGy1MBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAM+fu6AJQ9t9xaHzfd12UAAFCKs9ilALPF12UAAH4BK42VgEkmX5dQ7jkcDjkcDl+XUe7RJ+/QJ+/QJ+/dqb0iMAJAxUBoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGDI39cFoOy53b6uoPyzWq2+LqFCoE/eoU+/zOG66OsSAACAlwiNlYDJJEU/M8XXZQCAR85bE+VwFPu6DAAA4AUuTwUAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIb8fV1ARXPkyBFlZGQoPz9fFotFTqdT99xzj/r06aP27dsbnrd27Vp9+OGHevPNN29jtQAAAABwc1hpvA4HDx5UTEyM2rZtq+XLl2vx4sXKzMyU2WzWqFGjrnlujx49NHPmzNtUKQAAAADcGoTG6zBlyhS1bt1aAwYM8GyzWq2aOHGigoODr3mu2WxWSEhIWZcIAAAAALcUl6d66aefftLnn3+uV1999Wf7rFarPvzwQ61atUpz585VWFiYoqKitH37dm3btk2jRo3Spk2blJeXp++//17Hjx/X6NGjlZubqylTpuiTTz5Rfn6+BgwYoN/85jf661//qh9//FHt27fXxIkTPfN88803Sk1NlSRdvHhRQ4cOVffu3W9bDwAAAABUPoRGL+3du1clJSWqU6fOVfcHBwerX79+Kikp0eTJk/Xiiy9q9OjRWrJkiR544AH16tVLXbp0kSTVqFFDmZmZioiI0NGjRzV37lwdO3ZMnTt31pkzZ5SRkaFz587p0UcfVf/+/dWqVSsdPXpUQ4YM0ezZs9WxY0cdPXpUvXr1UsOGDdW8efNr1u6WWzlvTbzmMcCVnMUuBZgtvi4DdzCH66KvSwAAAF4iNHrJZDJ5fWyjRo10//33S5IGDhwo6dL9kFfTtWtXSdLdd9+tGjVqeM6rUqWKGjdurL1796pVq1ZavXq1wsLC1LFjR0lSeHi4Hn74Ya1YsUITJky4du0yqfuyl7yuH1gfN/2q2x0Oh6RLq+swRp9+mdXiL4ej2NdlAAAALxAavdSoUSP5+fnpyJEjv3hstWrVvB73yvsc/f39S72+/HRWSTp8+LBOnDghu93u2X/y5ElVr17d67kAAAAA4HoRGr1UrVo1derUSZs3b1ZMTEypfUePHtW4ceP0xhtvlNn8devWVf369ZWZmenZ5nQ6PSsaAAAAAFAWeHrqdUhKStKOHTu0cuVKz7YzZ84oKSlJrVu3VpUqVcps7r59+2r//v365ptvJElut1uTJk3Sxx9/XGZzAgAAAAArjdehTp06ev/995Wenq5ly5YpMDBQLpdLvXv31u9//3tt2LBB8+bNU2Fhoex2u9LT0xUWFqbt27dr+vRL94jZ7XbNnDlTCQkJkqQxY8YoLS1NycnJKigoUHJysiZNmqTs7Gzt2rVLBQUFqlKlinr06KEFCxYoLS1NbrdbxcXF6tChQ6mv/wAAAACAW83kdrvdvi4CZctms+lMbISvy0AFwoNwbg598g598h698g598g598g598g598k5F6JPNZlNWVtZV93F5KgAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMOTv6wJQ9txya33cdF+XgQrEWexSgNni6zIAAABQDrDSWAmYZPJ1CeWew+GQw+HwdRnlBoERAAAAlxEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGisBNzu6zve4bpYNoUAAAAAqHD8fV0Ayp7JJEU/M8Xr43PemliG1QAAAACoSFhpBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhvxv10S5ublKTU1VXl6eIiMjFRISorNnz6pevXoaOnSo2rRpc7tKAQAAAAB46batNLZr107p6emSpAkTJigzM1NZWVn63e9+p2HDhmnlypW3q5Tbas6cOUpMTPR1GQAAAABwQ27bSqORxx57TLt379akSZP0yCOPKDw83NclAQAAAAD+Pz4PjZIUFxen2bNn6+OPP9agQYO0cOFCrV+/Xv7+/qpSpYrGjx+vxo0bS5JOnz6tGTNm6Mcff5TFYtFdd92lESNGSJImTZqk06dP65NPPtGFCxf0xz/+Ubm5udq0aZPq16+vlJQUffDBB+rZs6dOnz6tH3/8UVWrVtVrr72mefPmaceOHXK5XJo9e7bq1avnmW/69OnKz8+Xn5+fIiMjlZCQIKvVWmq8M2fOaO/evSouLtasWbNUv359LV++XNnZ2XI4HLLb7br77ruVlpamb7/9VrNnz1ZJSYlcLpceeOABjR07ViaTSatWrdLcuXMVFhamqKgobd++Xdu2bdPdd9+tQ4cOKTIyUlOnTlVkZKSeeuop5efn689//rNiYmJ89vsDAAAAcOcqFw/CqVGjhkJCQrRnzx5lZmZq1apVWrhwod599111795dQ4YM0fnz5yVJ48aNk9Pp1NKlS5WZmal27drpo48+Ups2bTR+/HjPmIGBgcrMzCw1T2JioqKjo/WPf/xDSUlJWrlypSwWi+Lj4xUfH6+lS5eqRYsWWrhwoeeccePG6cKFC1q6dKkWL16sgoICzZo1q9R4X375pSZMmKBly5apWbNmnvOfeOIJ9e/fX9HR0crMzFRaWpok6ezZs3rhhRe0aNEiLV68WMeOHdPq1aslSf369VN8fLy+/fZbPfroo3r77bc1fvx4vf7663rwwQfVvXt3tWrVSmazWcOGDVN8fDyBEQAAAECZKRcrjZJUUlIik8mkpUuXqn///goKCpIk2Ww2TZ06VZ9++ql+/etfa9OmTVq2bJlMJpOkS6uU+/fvv6652rdv7xk/MjJSP/zwg2rVqiVJatWqldatWydJKigo0KZNm7RkyRKZTCaZTCb17dtXCQkJpe5T/J//+R8FBwd7zt+4ceM157/33ns1e/Zsz2rpoUOHFBQUpH79+nmOadSoke6//35J0sCBAyVJsbGxmjNnjoYPHy4/Pz9lZ2drypQpv/h+3XIr562J3ranUrJarb4uoUK4nX1yFrsUYLbctvkAAABwdeUiNBYVFens2bP61a9+paysLNWoUcOzz2w2KzQ0VIcPH9aRI0ckyRPwJCk4OFgtWrS4rvmqVq3q+dnf3/9nr51OpyR55ps5c6YCAgIkSU6nU9WqVdPp06cVEhIiSapWrZrnfKvV6jnfSEJCgoKCgpSZmSmLxaI5c+bo0KFDpY65cszLevbsqeTkZOXk5Khp06ayWq2lemXEJJO6L3vpF48DypP1cdN9XQIAAABUTkLjkiVLFBAQoB49emjp0qUqKiry7CsuLtaJEydUt25d1alTR5JUWFioBg0aSJLOnTungwcPqlmzZrJYLHI4HJ5zT548eVN1XZ5v8uTJat68uWd7UVGRJzDeiO3bt2v8+PGyWC6torhcLq/OCwwMVO/evfXee+8pIiJCNpvthmsAAAAAAG/4/J7Gjz/+WAsXLlRSUpLCw8MVFxenNWvW6MyZM5KkrKwsVa9eXZ06dVJYWJi6dOmipUuXyu12S5IWLlzouRy0QYMG+umnn3T48GFJ0vr162+qtsvzvffee55tubm5ev75570eIzg4WGfPnpUkjRkzRv/973/VpEkTbd26VdKllcstW7Z4PV5sbKw2b96sLVu2qEOHDl6fBwAAAAA34ratNObm5io1NVWSNHXqVIWEhOjs2bOqU6eO5s+fr7Zt20qSBg0aJKfTqSFDhiggIECBgYFauHChqlSpIkmaMWOGUlJS9OSTT8psNuuee+7RxImX7tcLCwvT888/ryFDhqhhw4aeewTHjBmjKVOmaN26dcrJyZHValXNmjUlyfN004yMDEVGRmrevHkqLCzUyy+/rGnTpmnGjBmaMWOGnnhwEOanAAAgAElEQVTiCVmtVgUFBXkeaJORkVFqvPDwcM/5SUlJevXVV9WtWzdlZ2frySefVN26dVW7dm1NnTpVEyZMkM1mU4MGDVS3bl3l5OT8rAa73a709HSFhYV5+ti8eXO1aNFCnTp18tzXCQAAAABlxeS+vGSHCuPZZ5/VK6+84vV3WtpsNp2JjSjjqoBbqyLf03j5MnkesHRt9Ml79Mo79Mk79Mk79Mk79Mk7FaFPNptNWVlZV91XLu5pxC/Ly8uT1WpVSEiIzGaz14ERAAAAAG4GobGCKCgo0PTp0xUaGqqUlBRflwMAAACgkiA0VhBdu3ZV165dfV0GAAAAgErG509PBQAAAACUX4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIb8fV0Ayp5bbq2Pm+7rMoDr4ix2KcBs8XUZAAAAlR4rjZWASSZfl1DuORwOORwOX5dR7t3OPhEYAQAAygdCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABD/r4uAGXP7fZ1BeWf1Wr1dQkVAn3yDn3yDn3yHr3yDn3yDn3yDn3yDn3yzv/tk8N1UVZLxYliFadS3DCTSYp+ZoqvywAAAAAgKeetib4u4bpweSoAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADDk7+sCyqvc3FylpqYqLy9PkZGRCgkJ0dmzZ1WvXj0NHTpUbdq08XWJAAAAAFDmWGk00K5dO6Wnp0uSJkyYoMzMTGVlZel3v/udhg0bppUrV/q4QgAAAAAoe6w0XqfHHntMu3fv1qRJk/TII48oPDzc1yUBAAAAQJkhNN6AuLg4zZ49W++//76++uor5ebmatq0adq4caN27dql9u3bKyEhQVOnTtXx48fldrsVGhqqpKQk1ahRQ99//70mTpyovLw8paWlKTs7WwcPHtSIESNUu3ZtvfPOO9q9e7cef/xxxcfHS5KOHz9uOB4AAAAAlBVC4w2oUaOGQkJCVFhYqMzMTEVERGj37t2aO3euDh48qA8//FAXLlxQly5d1KtXL0nSihUrlJqaquTkZEVERCg9PV1dunSR0+nUggUL9O2332rw4MEaPXq03njjDR09elRdu3aVzWZTrVq1rjneL3HLrZy3JpZpTwAAAIDyzlnsUoDZ4usy5HBdlNVScaJYxam0nCkpKZHJZPK87t27tySpfv36Gj58uFwul/bs2aMnn3xSZrNZp0+f1sWLF382TteuXSVJLVq00Llz5/TQQw9JksLDw1W9enUdOHBAtWrVUlhYmFfjXY1JJnVf9tLNvmUAAACgQlsfN90n8zocDkmS1Wq99M8KFBglQuMNKSoq0tmzZ/WrX/3Ks61q1aqljpk/f76ysrK0cuVKhYaG6quvvtJLL/08uF0+z9//0q8iJCTEs89iscjpdF7XeAAAAABwK/H01BuwZMkSBQQEqEePHobHbN++XW3atFFoaKgkyeVy3dSct3o8AAAAAPAGofE6ffzxx1q4cKGSkpKu+eTUJk2aaOfOnbpw4YIk6dNPP72peW/1eAAAAADgDUKjgdzcXI0ZM0aSNHXqVNntdtlsNn3wwQeaP3++YmJidOHCBdntdknSmDFjlJWV5Tl/+PDhaty4sfr06aPnnntOJSUlKigo0IgRI3TgwAHP2Ha7XSdPniw1zoEDBzRixAgVFBQoOTlZ27Ztu+Z4AAAAAFBWTG632+3rIlC2bDabzsRG+LoMAAAAwKfKy4NwyiObzVZqEexKrDQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABD/r4uAGXPLbfWx033dRkAAACATzmLXQowW3xdRoXDSmMlYJLJ1yWUew6HQw6Hw9dllHv0yTv0yTv0yXv0yjv0yTv0yTv0yTsVrU8ExhtDaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBDf01gJuN2+rqD8s1qt13W8w3VRVgt/PgAAALjz8am3EjCZpOhnpvi6jDtKzlsTfV0CAAAAcFtweSoAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADDk7+sCyrPc3FylpqYqLy9PkZGRCgkJ8ewrKChQfHy8bDbbLZtv0qRJatiwoYYOHaqsrCzNmzdPYWFhyszMvGVzAAAAAMD1IDReQ7t27ZSenq4uXbpowoQJioqK8uzLysq65fO9+OKLslgskuQJo9nZ2bd8HgAAAADwFqHxBnXo0OGWjxkUFHTLxwQAAACAm8E9jTcgMTFRLpdLH330kex2u55++mnZ7XZ98cUXnmPGjh2rqKgovf766xo9erR69+6tP/3pTyoqKtL48eM1YMAADRkyRKdOnZIkLVmyRD169JDdbjecMyIiQv369dPOnTtVXFysJ598Ug899JDef//92/K+AQAAAFQ+rDR6aerUqZ57GvPz8zVy5EiZTCYtWLBAAQEBOnXqlPr06aO1a9cqKChIaWlpstvt2rZtm+bPny+TyaR+/fopISFBGRkZCgoK0h/+8AetWLFCQ4cO1cCBAxUYGGh4OWpKSor27dun6OhotWrVSpI0bNgw7du3TzExMbetDwAAAAAqF0Kjl668pzExMVGS1LRpU40YMULnz5+X2WxWUVGRdu/erfvvv99zXqdOneTvf6nNERERCg0N9VyG2rJlS+3du9frGmJjYzVnzhwNHz5cfn5+ys7O1pQpU37xPLfcynlrotfz3GmcxS4FmC23dEyH66KsFv58AAAAcOfjU+8NSElJ0YEDBzR8+HClpqbqsccekyR17txZ58+fL3Vs1apVPT/7+/v/7LXT6fR63p49eyo5OVk5OTlq2rSprFaratSo8YvnmWRS92UveT3PnWZ93PRfPMbhcEiSrFarV2MSGAEAAFBZ8Mn3Bu3cuVNOp1OdO3f2bLueAHgjAgMD1bt3b7333nuKiIi4pV/3AQAAAABXw4NwblDjxo0lSdu2bZMk7dixQ4WFhWU+b2xsrDZv3qwtW7aUyRNcAQAAAOBKrDReQ25urlJTUyVdehBOdHS0xo4dK0lq3ry5xo0bp/Hjx6tJkyZq0qSJatWqpeTkZE2aNEnZ2dnatWuXCgoKFBISol27diknJ0dWq1U1a9aUdOk7GB0Oh2bNmqXw8HC98847Kiws1NixY9WhQwfNmzfP8zotLc0zb4sWLdSpUyeZTCbfNAYAAABApWFyu91uXxeB6/Pss8/qlVdeUXh4uFfH22w2nYmNKOOqyq+yuKexsqJP3qFP3qFP3qNX3qFP3qFP3qFP3qFP3qkIfbLZbMrKyrrqPlYaK4i8vDxZrVaFhITIbDZ7HRgBAAAA4GYQGiuIgoICTZ8+XaGhoUpJSfF1OQAAAAAqCUJjBdG1a1d17drV12UAAAAAqGR4eioAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhf18XgLLnllvr46b7ugyfcRa7FGC2+LoMAAAAoEJipbESMMnk6xJ8isAIAAAA3DhCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABD/r4uAGXP7fZ1BeWf1Wr1dQk+5XBdlNXCfw4AAADwc3xKrARMJin6mSm+LgPlWM5bE31dAgAAAMopLk8FAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACG/H1dgK/l5uYqNTVVeXl5ioyMVEhIiGdfQUGB4uPjZbPZfFghAAAAAPhOpQ+N7dq1U3p6urp06aIJEyYoKirKsy8rK+umx09MTFS9evU0atSomx4LAAAAAG63Sh8ar6VDhw6+LgEAAAAAfIrQaCAxMVEjR47Uhg0b9Mknn8hsNqu4uFjDhw/3hMmxY8fqs88+09NPP60ff/xR+/fvV7Vq1fSXv/xF1apV0+uvv66cnBxZrVbl5uaqZcuWSkxM1Oeff64FCxbIz89PDodD3bt319NPPy1JcrvdmjlzprZt26agoCD5+/tr5MiRysvL05w5c1S1alWNGDFCMTExmjJlilatWqV+/fpp4sSJPuwWAAAAgDsVofEKU6dO9dzTmJ+fr5EjR8pkMmnBggUKCAjQqVOn1KdPH61du1ZBQUFKS0uT3W5Xbm6uFixYIIvFoiFDhmjFihUaNmyYRo4cqYMHD/7s8tSzZ89q6tSpatCggdxutwYNGqRWrVopKipKOTk52rx5s9auXSs/Pz+tW7dOOTk5GjVqlI4fP668vDzFxMRIkp599lkdPXr0FwOjW27lvHXnhEpnsUsBZouvy7ijOFwXZbXwnwMAAAD8HJ8Sr3DlPY2JiYmSpKZNm2rEiBE6f/68zGazioqKtHv3bt1///2e8zp37iyL5VKIadmypfbt23fNeSIjIzVr1iwdPnxYFotF+fn5+uabbxQVFaVq1arpv//9rz766CN169ZN3bp1U6dOnSRJMTEx+tvf/qZ9+/apUaNGysrKUr9+/X7xfZlkUvdlL91IS8ql9XHTb/mYDodDkmS1Wm/52BUBgREAAABG+MoNAykpKXK73Ro+fLhiY2O1ZMkSZWZmKiwsTOfPny91bNWqVT0/W61WOZ3Oa479zDPPqEaNGlq6dKkyMzMVHR3tGbN169b661//qrVr16pjx45KTEzUiRMnJEn169dX+/bt9d5778ntduuzzz7zBEoAAAAAKAuExmvYuXOnnE6nOnfu7Nn2S4Hwlxw/flx79+7Vo48+KpPJJElyuVye/adPn1abNm305ptvat26dTp16pQSEhI8+2NiYpSdna0tW7bowQcflL8/K0QAAAAAyg6h8RoaN24sSdq2bZskaceOHSosLLyuMYKDg3X27FlJkt1uV7Vq1RQaGqqtW7dKuhQSL48vSRs2bNCSJUskSdWrV1dkZKSKi4s9+7t06SLp0qW0l+9tBAAAAICyUumXqXJzc5Wamirp0oNwoqOjNXbsWElS8+bNNW7cOI0fP15NmjRRkyZNVKtWLSUnJ2vSpEnKzs7Wrl27VFBQoJCQEB0/flzZ2dlyOBzKyMjQ888/rz59+uill17S119/raioKJnNZqWnp+vVV1/VV199pXr16qlRo0bKzs5WzZo19fDDDys1NVWff/65HA6HLBaLJk+e7KnXYrGof//++te//qUGDRr4pGcAAAAAKg+T2+12+7oIXJ8FCxaodu3a6tWrl1fH22w2nYmNKOOqbh8ehOM79Mk79Mk79Ml79Mo79Mk79Mk79Mk79Mk7FaFPNptNWVlZV93H5akVxMmTJ7Vu3ToVFxfrk08+Ubdu3XxdEgAAAIBKgNBYQTidTqWkpCgmJkZxcXEKCAjwdUkAAAAAKoFKf09jRXH33Xfr008/9XUZAAAAACoZVhoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAM+fu6AJQ9t9xaHzfd12XcMs5ilwLMFl+XAQAAAFQKrDRWAiaZfF3CLUVgBAAAAG4fQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADDk7+sCjBw5ckR//etf9eOPP8piscjhcCgsLEw9e/ZUr169ZDKZfF2itmzZojfeeENms1mS5HQ69etf/1ovvPCCzGazBg8erK+//lrz58/XQw895ONqAQAAAOD6lcvQeODAAcXFxemFF17Q1KlTJUnFxcV68803NXbsWHXr1k1Wq9WnNR48eFAjR47Uu+++q5YtW0qSdu3apbi4OI0aNUpms1l///vf1blzZ5/WCQAAAAA3o1xenjp58mQ9+OCDGjBggGeb2WzWc889p3vvvbdcrDLu3LlTgYGBnsAoSZGRkRo+fLj8/MplWwEAAADgupW7lcaTJ09qy5YtnhXGK5lMJq1du1aS9O2332r27NkqKSmRy+XSAw88oLFjx8pkMmnVqlWaO3euwsLC1LZtW23btk0//fSTUlJSlJeXpw0bNujIkSOaOHGiOnToIEkqKSnR/PnztXHjRlmtVgUFBWnSpEmqXbv2Vets2LChTpw4oUWLFumpp56SxWKRJI0YMeJnx+7atUuZmZnat2+fmjRpotTUVM/xe/bsUXJyss6dO6eLFy+qe/fuGjp0qA4ePKj4+HgdOXJEHTt2VEZGho4cOaIePXroyy+/VFBQkMaOHaucnBw999xzGjx48C3pPwAAAABcqdwtie3bt09ut9swrF129uxZvfDCC1q0aJEWL16sY8eOafXq1ZKkfv36KT4+Xjt27FCvXr20ePFi9e3bV88//7waNmyoRYsWadSoUUpJSfGM984772jlypVauHChMjMz1aFDBz377LOG87do0UKjRo1SWlqaoqOjNX78eOXk5Mjtdv/s2H/961+aM2eOsrOz9f3332vTpk2SpHPnzmnIkCH67W9/q3fffVcLFixQVlaWFi9erAYNGigjI0MXL17UtGnTJEkbN26Uy+VSTk6OJOlPf/qTevXqRWAEAAAAUGbKXWg0kpKSIrvdrs6dO+vjjz/Wvffeq2XLlikuLk52u13btm1TXl5eqXMaNWqkpk2bSroU8k6dOqVHHnlEktSyZUvt2bPHc+yyZcvUr18/BQcHS5L69++vf/3rX/ruu+8Maxo5cqQ2b96sESNGaP/+/frjH/8ou90uh8NR6rjHHntMJpNJ/v7+ioyM1L59+yRJmzdv1smTJ9WnTx9JUnBwsPr06aMlS5ZIkpo2bap69erp008/lSR99dVX6t+/vzZs2CBJWr9+vbp163ZjDQUAAAAAL5S7y1MbNmwok8mkI0eOlNqemJgo6VL4O3funBISEhQUFKTMzExZLBbNmTNHhw4dKnVO1apVPT+bzWaFhIR4Xvv7+8vlcnleHzlyRGvWrNGXX37p2VavXj0VFRXp888/19/+9jdJUq1atTRr1izPMbVq1dLgwYM1ePBgfffddxo4cKDWrl0rm83mOebKea1Wq5xOpyTp0KFDql69uufpq5JUs2bNUu+9e/fu2rBhg6KjoxUcHKxevXrp+eefl8vl0tatW1llBAAAAFCmyl1oDA0N1SOPPKKNGzcqJibG8Ljt27dr/PjxnnsDrwyAN6JOnTp68sknNWjQIM+2U6dOKTAwUAEBAerYsWOp4/Py8rRlyxY999xznm0tWrTQPffco1OnTnk1Z926dXXy5EkVFxd7gmNRUZHq1KnjOaZbt24aNGiQPvroIz366KNq166dzGaz1qxZo9DQUPn7l7tfIQAAAIA7SLm8PDUpKUk7duzQ4sWLS23/4osvVFJSIj8/PzVp0kRbt26VdOn7Ebds2XJTc8bFxWnNmjU6d+6cJKmwsFAxMTG6cOHCVY+/cOGCVqxYoWPHjnm2/fvf/9aePXv08MMPezXno48+qqpVqyo7O1uSdObMGa1Zs0ZxcXGeY+6//35Vr15d8+bNU8eOHeXv769HH31UM2fOVNeuXW/07QIAAACAV8rlMlXDhg21cuVKzZkzRwMGDFBAQIBOnz6tu+++WxkZGeratauaNWumCRMmyGazqUGDBqpbt65ycnKUkZGhyMhIzZs3T4WFhUpKSlL//v2VnJysgoICjRgxQuPHj9fYsWMlSXa7XXPnztWgQYPkcrn09NNPe74DcsqUKaUucb1S06ZN1bNnTz377LO66667VFJSouLiYr322mtq3ry5pEtPUi0oKFBycrImTZqkL774Qjk5ObJarapZs6YGDhyoRYsWKTk5WdnZ2XK5XOrbt6/sdnupubp27arDhw/rrrvuknTpktV169Z57s8EAAAAgLJicl/tcZ+4o9hsNmVlZfm6jHLt8sOLLv8PA1wdffIOffIOffIevfIOffIOffIOffIOffJORejTtTJDubw8FQAAAABQPhAaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADF1XaPzf//1fxcbGqn///pKkjIwMffDBB/+PvfuPirLO+z/+GoZh0MDfSLK6pm2LWJoWS7WWqahhraKzioAMkLoqpumamnhja2mi3kjZjzWxUtE1pELMu9qFWN3YtfumWjJtra38sa2WovgTE0aY7x+u843k0pEfDsrzcY5nmev6XJ95X+/2cHidz2euaZDCAAAAAACe53Zo3LJli1JSUvSTn/xEVVVVkqR+/fopOztbb775ZoMVCAAAAADwHLdD42uvvabNmzfrmWeeUYsWLSRJPXv21MqVK5WTk9NgBQIAAAAAPMft0Gg2m9WxY0dJkslkch1v3rx5/VeFeuV0erqC2it3nPN0CQAAAECT5u3uwJMnT+rs2bPy9fWtdvzIkSMqLS2t98JQf0wm6b6JCzxdRq0Urpzn6RIAAACAJs3t0HjvvfcqMTFRiYmJKisrU1FRkb788ktlZmYqIiKiIWsEAAAAAHiI26Fx+vTpmjNnjqZPny5JSkhIkNPp1LBhwzR16tQGKxAAAAAA4Dluh0aLxaJly5Zp2rRp+uyzzyRJt956q3760582WHEAAAAAAM9yOzQOHz5c/v7+WrduHUERAAAAAJoIt0Pj8ePHtWbNmgYsBQAAAADQ2Lj9lRvdunVTq1atajz33nvv1VtBAAAAAIDGw+3QOHToUK1du1bnzl38vXmZmZn1WhQAAAAAoHFwe3vqsmXLdOzYMaWlpaldu3by8vr/efPIkSMNUhwAAAAAwLPcDo2SNG7cuIuOOZ1O5ebm1ltBAAAAAIDGw+3QeP/992vKlCk1njt79my9FQQAAAAAaDzc/kzj7373O8Nzo0aNqpdiAAAAAACNi9uh8VKeeOKJ+pjGo4qKihQVFaXg4GANHz5cBQUFkqSDBw/KbrcrODhYERERWrt2rd5++20lJSV5uGIAAAAAaHhub0/9+uuvtWjRIu3atUsnT55syJo8IiwsTOnp6QoPD1dKSopCQ0MlSUFBQVq3bp26d++uCRMmyGazqbKyUn379nVr3jlz5ugnP/mJpk6d2pDlAwAAAECDcDs0Jicnq02bNpo8ebL8/f1lMpkknX8QzqpVqxqswMbIbDbL39/f02UAAAAAQINzOzQ6HA699NJLNZ6r6bsbr1fFxcVKTU3Vjh079MUXX0iSPv30Uy1evFhms1lVVVW6/fbbNXv2bL3wwgsqLCyU1WpVUVGRbr31Vs2ZM0cVFRVavny5Pv74Y5nNZrVt21YpKSlq3769cnNz9dJLLykgIEChoaEqLi7WRx99pPbt2+vAgQMKCQnRwoULFRISori4OO3Zs0czZ87kc6UAAAAAGoTbobFTp06G54KDg+ulmMZi4cKFF60kVlZWSpJ69+7t2sb6w/Hx8fH61a9+JYfDoaioKEnSlClT9O9///ui7anLli3TP//5T61fv17e3t569tlnNXnyZGVnZ2v48OGqqqrSk08+qVmzZmnatGnasGGDevXqpQULFui+++7TbbfdJun8V6Ds37//soHRKacKV86rl940lIpKh3zMlouOlzvOyWq5om+GAQAAAFCP3P5rfNiwYUpJSdHw4cMVGBgos9nsOpeamqqsrKwGKdATfviZxgu6d+9uOL5Fixb64x//qO7du6tr167asGGD4Vin06mNGzfq6aeflrf3+fbHxsZqxYoV2rlzp26//XZJUufOndWzZ0/XeUmKiorS888/r0mTJsnLy0ubNm3SggULLns/Jpk0OCv5suM8KS86tcbjBEYAAADAs9z+i/zCdzS++eab1Y47nU7X5xubqvT0dK1Zs0YTJkxQ8+bNlZiYKJvNVuPY0tJSff/992rTpo3rWNu2bSWdf1LrhdDYsmXLi64dMmSIFi1apMLCQt1yyy2yWq3V5gEAAACA+uZ2aOzWrZvmzp170XGn06nU1JpXiZqKs2fP6tFHH9Wjjz6qv/zlL3rkkUfUqVMn/eIXv7hobOvWreXr66ujR4+6jl34OSgo6JLv4+vrq6FDhyo7O1vBwcGGwRQAAAAA6ovb39MYGxursLCwi/7dddddGj9+fEPW2OhNnTpVp0+fliTdeeedslgsrocD+fn5qaysTJJkt9vldDoVFRWl119/XQ6HQ5K0YcMG3XrrrerRo8dl3ysqKkrbtm3TX//6V/Xp06eB7ggAAAAAznM7NF54uIsknTlzptq5X/3qV/VXkYcUFRVpxowZks4/2KagoEDS+S2jdrtdlZWVysjI0AsvvOAaZ7fbVVpaqoiICP3mN79RfHy8xowZo/Hjx+uee+6RdP6zoIWFhYqKilKPHlKkcDwAACAASURBVD1kNps1a9Ysde/eXWPGjFFsbKz27NmjFStWyMvLS/n5+crIyNDu3btlt9tVUlJSrc5u3bqpe/fu6tevX5PfFgwAAACg4ZmcTqfT3cEvv/yyVq9erebNmys/P1/z58/XjTfeqEmTJjVkjfiRyZMn63e/+50CAwPdGm+z2XQ6qnE/4dboQThXS3l5uSTJarV6tI7Gjj65hz65hz65j165hz65hz65hz65hz6551rok81mU05OTo3n3F5pzMzM1GuvvaYRI0a4vo5i/Pjx+vLLL5WRkVE/lcLQjh079Pnnn+vAgQMym81uB0YAAAAAqAu3Q+O7776rN998UzNnzpSfn58kqWPHjlq8eLG2bdvWUPXhP0pKSvTII49o2rRpmjZtmqfLAQAAANBEuP30VG9vb7Vq1eqi4xaLRVVVVfVaFC42cOBADRw40NNlAAAAAGhi3F5pPHXqlI4fP37R8b1797qeHAoAAAAAuL64vdL40EMPadSoUYqOjtaxY8e0adMmffnll9q0aZMmTpzYkDUCAAAAADzE7dD4m9/8RqdOndJzzz2n8vJyJScny2q1auzYsUpMTGzAEgEAAAAAnuJ2aJSkGTNmaNKkSfrqq68kSbfccouaNWvWIIUBAAAAADzvkqHx0KFD2rRpkySpT58+6tGjh5o3b64lS5bou+++kySZzWatWbNGQUFBDV8tAAAAAOCquuSDcN599109++yz+uKLL6p9EeXJkycVFhamsLAwtWzZUm+++WaDFwoAAAAAuPouudJYWFio//qv/5Ldbq92vFWrVkpNTZUkffXVV5o3b56mTp3acFUCAAAAADzikiuNJ06cUFxc3EXHe/fu7fr5Zz/7mc6cOVP/lQEAAAAAPO6SK43NmjWTyWS66PiMGTOqvb7hhhvqtyrUK6ecyotO9XQZl1RR6ZCP2eLpMgAAAAD8yCVXGt1VU7BE42FS4//vQ2AEAAAAGqdLhsZmzZrpm2++ueQEX3/9NV+7AQAAAADXqUuGxtGjR2vatGk6ePBgjef37dun6dOnKzY2tkGKAwAAAAB41iU/0xgeHq4PPvhAgwcP1l133aVbbrlFzZs3V1lZmT7//HN9/PHHiomJ0YABA65WvQAAAACAq+iSoVGSUlJS1KNHD61Zs0Zr166V0+mUyWRSSEiIUlNTNXTo0KtRJwAAAADAAy4bGiUpMjJSkZGROnv2rE6cOKGWLVvK19e3oWsDAAAAAHiYW6HxAl9fX8IiAAAAADQhVxQacW1yOj1dQeNntVo9XcI14Wr1qdxxTlYLv54AAAAaA/4qawJMJum+iQs8XQbgtsKV8zxdAgAAAP7jkl+5AQAAAABo2giNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCYw2KiooUFRWl4OBgDR8+XAUFBZKkgwcPym63Kzg4WBEREVq7dq2HKwUAAACAhkVorEFYWJjS09MlSSkpKQoPD5ckBQUFad26dTKbzZowYYISEhI8WSYAAAAANDhCIwAAAADAkLenC7jWVVRUaPny5fr4449lNpvVtm1bpaSkqH379srNzdVLL72kgIAAhYaGqri4WB999JEef/xxbdmyRTt27FB6erreeustffLJJ4qLi9PUqVOVm5ur1157TRaLRSaTSbNmzVLPnj31xRdfaN68eYbXAQAAAEB9IzRexsKFC+Xv71/tWGVlpevnZcuW6Z///KfWr18vb29vPfvss5o8ebKys7M1fPhwVVVV6cknn9SsWbM0bdo0bdiwQb169VL//v0VHh6uY8eOaeXKlfrkk0/01VdfqaCgQP/93/+tzZs3q127dvrggw+UmJiod999V8HBwUpPT6/xOgAAAABoCITGy0hJSVFoaGi1Y927d5ckOZ1Obdy4UU8//bS8vc+3MjY2VitWrNDOnTt1++23S5I6d+6snj17us5L0r///W9J0tChQyVJvXr1Uq9evTR+/HiFh4erXbt2kqR77rlHAQEB2rx5syZMmOCq4cfXXYpTThWunFf7JuC6V1HpkI/Z4ukyXMod52S18OsJAACgMeCvsjooLS3V999/rzZt2riOtW3bVtL5J61eCI0tW7Y0nOPH5w4ePKgePXpUO9auXTt9++23l7zuUkwyaXBWstvj0fTkRae6Na68vFySZLVaG7IcAiMAAEAjwoNw6qB169by9fXV0aNHXccu/BwUFFSrOTt06FBtPkk6cuSIOnToUPtCAQAAAKCWCI114OXlpaioKL3++utyOBySpA0bNujWW2+9aLXQXTExMcrPz1dJSYkk6YMPPtDhw4cVGRlZb3UDAAAAgLsIjTUoKirSjBkzJJ1/EE5BQYGk81tH7Xa7KisrlZGRobVr12rWrFnq3r27xowZo9jYWO3Zs0crVqyQl5eX8vPzlZGRod27d8tut7uC4DfffOOa32636/3333e998CBA/XYY4/pkUce0ZgxY/T73/9eq1evVmBg4CWvAwAAAICGYHI6nU5PF4GGZbPZdDoq2NNloBFrbJ9pvNbRJ/fQJ/fRK/fQJ/fQJ/fQJ/fQJ/dcC32y2WzKycmp8RwrjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwJC3pwtAw3PKqbzoVE+XgUasotIhH7PF02UAAACgEWKlsQkwyeTpEhq98vJylZeXe7oMjyEwAgAAwAihEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQmMT4HR6uoKalTvOeboEAAAAAJfh7ekC0PBMJum+iQs8XcZFClfO83QJAAAAAC6DlUYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0HgFCgoKFBUVpeDgYD399NM1jtm+fbuCg4MVERGhFStW1On93n77bSUlJdVpDgAAAACoC0LjFQgPD1d6erqaNWumN954Q8eOHbtoTGZmpkwmkyZMmFDnwBcREaGlS5fWaQ4AAAAAqAtCYy30799fzZs31/r166sd37Vrl4KCguTlVT9tNZvN8vf3r5e5AAAAAKA2CI21YLVaFR8fr/Xr1+vMmTOu46tXr9bYsWNdr/Pz8xURESG73S5J+uabb1zbWy84duyYkpKSFBsbq/j4eM2cOVOHDx9WcXHxRWOrqqq0atUqjR49WvHx8YqNjVVubu5VuGMAAAAATZW3pwu4VsXGxiojI0PZ2dlKTEzUvn37ZLFY1LFjR9eYQYMG6dSpU9q0aZMkqVOnTkpPT1d4eLhrzCuvvKKAgADX5x8XLFigvXv36q677rpobGZmpnJzc7Vx40b5+fnp008/1RNPPKHhw4dfslannCpcOa8+b79WKiod8jFbXK/LHedktfB/QQAAAKAx4y/2WvL391dMTIzWrFmjMWPG6NVXX622yuiuFi1aaOvWrfrwww8VGhqqxx9/3HB7a1ZWlkaMGCE/Pz9JUs+ePTV37tzLvodJJg3OSr7i2upbXnRqtdcERgAAAKDxY3tqHSQmJqq0tFSrV6/W8ePH9fOf//yK5xg/frwSEhKUlpamvn376sUXX1RlZWWNY7/99lu1bdu22rGwsLBa1Q4AAAAA7iA01kG7du00YsQIpaenKzExscYxFotF5eXlrtcnTpyodv7IkSOKiorSxo0btWbNGr3zzjtatWpVjXN16NBBR48erXZsx44ddbsJAAAAALgEQmMdTZo0SSkpKbrjjjtqPN+5c2ft2bNHZWVlkqS8vLxq59PT07Vz505J0s0336zAwEDDlcbo6Gi99dZbOn36tCSpqKhIzzzzTH3dCgAAAABchA+VXYGioiKlpaXpwIEDSk5OVmpqqjp06KC4uDhJ0v79+5WSkqLKykplZGToxIkTevjhhxUZGamRI0eqc+fOrgfb2O12LV++XEOGDNHSpUtlNpt18uRJdenSRePGjVNxcbFSU1OrjY2Pj5fD4dDYsWNltVrVrFkz1xgAAAAAaAgmp9Pp9HQRaFg2m02no4IvP7CB/fhBOI3JhS3EVqvVw5U0bvTJPfTJPfTJffTKPfTJPfTJPfTJPfTJPddCn2w2m3Jycmo8x/ZUAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgyNvTBaDhOeVUXnSqp8tQRaVDPmaLp8sAAAAAcAVYaWwCTDJ5ugRJIjACAAAA1yBCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNTYDTWbvryh3n6rcQAAAAANccb08XgIZnMkn3TVxwxdcVrpzXANUAAAAAuJaw0ggAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAw16dBYUFCgqKgoBQcH6+mnn65xzPbt2xUcHKyIiAitWLGiTu/39ttvKykpqU5zSNL777+vyMhIDRgwoM5zAQAAAMClNOnQGB4ervT0dDVr1kxvvPGGjh07dtGYzMxMmUwmTZgwoc6BLyIiQkuXLq3THJLUt29fzZ07t87zAAAAAMDlNOnQeEH//v3VvHlzrV+/vtrxXbt2KSgoSF5e9dMms9ksf3//epkLAAAAAK4GQqMkq9Wq+Ph4rV+/XmfOnHEdX716tcaOHet6nZ+fr4iICNntdknSN99849reesGxY8eUlJSk2NhYxcfHa+bMmTp8+LCKi4svGltVVaVVq1Zp9OjRio+PV2xsrHJzcyVJpaWlmjFjhhITE5WQkKDp06ertLS0oVsBAAAAANUQGv8jNjZW586dU3Z2tiRp3759slgs6tixo2vMoEGDNGHCBNfrTp06KT09vdo8r7zyigICArRhwwZlZmaqZcuW2rt3r3r37n3R2MzMTOXm5uqVV15RZmam5syZozVr1kiSzp49q/DwcK1Zs0Zr167Vvffeq7S0tAa6ewAAAACombenC2gs/P39FRMTozVr1mjMmDF69dVXq60yuqtFixbaunWrPvzwQ4WGhurxxx833N6alZWlESNGyM/PT5LUs2dP12cVAwICtHfvXsXExMhsNuvUqVM6d+5cre7NKacKV86r1bXuqKh0yMdsabD5AQAAAHgOofEHEhMTlZmZqdWrV+v48eP6+c9/fsVzjB8/Xq1atVJaWpoOHjwom82myZMny9v74lZ/++23atu2bbVjYWFhkqSXX35ZOTk5evPNN9W6dWv93//9n5KTk2t1XyaZNDirdte6Iy86tcHmBgAAAOBZbE/9gXbt2mnEiBFKT09XYmJijWMsFovKy8tdr0+cOFHt/JEjRxQVFaWNGzdqzZo1euedd7Rq1aoa5+rQoYOOHj1a7diOHTskScXFxerdu7dat24tSXI4HLW9LQAAAACoNULjj0yaNEkpKSm64447ajzfuXNn7dmzR2VlZZKkvLy8aufT09O1c+dOSdLNN9+swMBAVVZW1jhXdHS03nrrLZ0+fVqSVFRUpGeeeUaS1LVrV+3atUtnz56VJG3durXuNwcAAAAAV6hJb08tKipSWlqaDhw4oOTkZKWmpqpDhw6Ki4uTJO3fv18pKSmqrKxURkaGTpw4oYcffliRkZEaOXKkOnfurPDwcEmS3W7X8uXLNWTIEC1dulRms1knT55Uly5dNG7cOBUXFys1NbXa2Pj4eDkcDo0dO1ZWq1XNmjVzjZk0aZL27dunYcOG6ZZbblH79u1VUlKipKQkxcTEaNmyZSopKVFCQoLWrl3rmQYCAAAAuO6ZnE6n09NFoGHZbDadjgq+/MBauh4+03hhy7HVavVwJY0bfXIPfXIPfXIfvXIPfXIPfXIPfXIPfXLPtdAnm82mnJycGs+xPRUAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABjy9nQBaHhOOZUXndpg81dUOuRjtjTY/AAAAAA8h5XGJsAkU4POT2AEAAAArl+ERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACGvD1dABqe0+npCho/q9Xq6RKuCT/uU7njnKwWfo0AAABcz/hrrwkwmaT7Ji7wdBm4DhWunOfpEgAAANDA2J4KAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMNcnQWFBQoKioKAUHB+vpp5+uccz27dsVHBysiIgIrVix4ipXCAAAAACNQ5MMjeHh4UpPT1ezZs30xhtv6NixYxeNyczMlMlk0oQJE5SUlFTr98rJyZHdbq9LuQAAAADgMU0yNF7Qv39/NW/eXOvXr692fNeuXQoKCpKXV5NuDwAAAAA07dBotVoVHx+v9evX68yZM67jq1ev1tixY12v58yZo+DgYA0fPly7du1SZWWlYmJidNddd+n111/XsWPHlJSUpNjYWMXHx2vmzJk6fPiwCgoKlJGRod27d8tutyshIcE15xtvvKFRo0bJbrcrMTFRX375pSTpb3/7myIjIzVgwACtW7dO48aNU+/evXXvvfeqR48eGjJkiLZt2yZJevTRR3XnnXeyfRYAAABAg/H2dAGeFhsbq4yMDGVnZysxMVH79u2TxWJRx44dXWMWL16s/fv367777tNtt90mSRo3bpz279+vUaNGKS0tTQEBAa7wtmDBAu3du1fh4eE6ceKENm3apHXr1rnme++997R06VJt2bJFgYGB+vOf/6xx48YpPz9fffr00dy5czVu3DgFBgbqlVdeUX5+vnx8fLRlyxZZrVb169dPkpSUlKSAgIDLbp91yqnClfPquXPXhopKh3zMFk+Xcd0qd5yT1dLkf40AAABc15r8X3v+/v6KiYnRmjVrNGbMGL366qvVVhkviIqK0vPPP69JkybJy8tLmzZt0oIFCyRJLVq00NatW/Xhhx8qNDRUjz/++CW3tr722msaPHiwAgMDJUkDBgzQnDlz9P7772vQoEGSzq+CDh48WJJcx5o1a6aJEycqOTlZfn5+ys7O1ujRoy97jyaZNDgr+coac53Ii051a1x5ebmk832HsR/3icAIAABw/WvS21MvSExMVGlpqVavXq3jx4/r5z//+UVjhgwZolOnTqmwsFAHDx6U1WpVmzZtJEnjx49XQkKC0tLS1LdvX7344ouqrKw0fL9vv/1W27dvl91ud/1r1aqVjh8/7hrTsmXLi64LCwtTYGCg3nrrLX3//ffat2+funXrVg8dAAAAAICasUwgqV27dhoxYoTS09O1YcOGGsf4+vpq6NChys7OVnBwsGw2m+vckSNHFBUVpaioKH399deaNGmSLBaLpkyZUuNcHTp00M9+9jMlJ///1b/Tp0/L2/vy/zlGjRql7OxsWa1WRUREXOGdAgAAAMCVYaXxPyZNmqSUlBTdcccdhmOioqK0bds2/fWvf1WfPn1cx9PT07Vz505J0s0336zAwEDXSqOfn5/KysokSUuWLNGOHTsUExOjvLw8lZaWSpLKysoUFxengwcPXrbOESNG6KuvvlJGRoYeeuihWt8vAAAAALijSa40FhUVKS0tTQcOHFBycrJSU1PVoUMHxcXFSZL279+vlJQUVVZWKiMjQydOnNDDDz+sbt26qXv37urXr59MJpNrviFDhmjp0qUym806efKkunTponHjxkmSfvnLX2rVqlWKiYlRs2bN9Nvf/la33367Tp48qUmTJslqtaqqqkpJSUnq2rWriouLtWjRIpWUlMhut2vu3LkKCQlxvVebNm00cOBA3XDDDfLz87u6jQMAAADQ5DTJ0BgWFqbs7GzD8507d672tNMfCggI0K9//etqx+6//37df//9NY738/PT66+/ftFxm81WbYvrBb1799bmzZsvVb7atGmjYcOGXXIMAAAAANSHJhkar9SOHTtktVrl7+8vs9nseurp1bRv3z599913uu222/Tll1+qV69eV70GAAAAAE0PodENJSUlSk1NVevWrbV48WKP1HDq1CklJyerTZs2mjVrlkdqAAAAAND0EBrdMHDgQA0cONCjNfTo0UNbt271aA0AAAAAmh6engoAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGDI29MFoOE55VRedKqny/CIikqHfMwWT5cBAAAAXLNYaWwCTDJ5ugSPITACAAAAdUNoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKGxCXA6z/9vueOcZwsBAAAAcM0hNDYBJpN038QFslq8PV0KAAAAgGsMoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQCAAAAAAwRGgEAAAAABgiNNZSQUGBoqKiFBwcrNzc3GrnNm7cqMjISA0YMEDz58/3TIEAAAAAUA+8PV3AtSo8PFzBwcEaPHiwnnzySfXq1Us33XSTJGn06NG66aabVFRUpKlTp3q2UAAAAACoA1Ya6+iBBx5Qp06dNGPGDFVUVHi6HAAAAACoV4TGOrJarVq+fLn27t2rtLS0GsdUVVUpIyNDUVFRstvtmjRpkr777jtJ0hNPPKHg4GDZbDbt2rVLkpSYmKjXX39dkpSbm6v7779fcXFxrtcjR45UfHy87Hb7RVtjAQAAAKA+ERrrQZcuXfTUU08pMzNT27Ztu+h8Zmam3nzzTb366qtat26d+vTpo8mTJ0uSnnrqKd12220aOXKkbrvtNp04cUIff/yx3nvvPUnS8OHDFRISotWrV6usrExz587V73//e2VmZiotLU1btmy5mrcKAAAAoIkhNNaToUOHauTIkZozZ44OHTpU7VxWVpaGDx8uPz8/SdKIESP02Wef6R//+IckafDgwcrPz5ckbd26VWPGjNH27dt1+vRpHT16VC1btpTFYpHZbJafn582btyo0tJSBQYG6oUXXrhsbU45Vbhynut1RaWjvm4bAAAAwHWO0FiPUlJSFBAQoFmzZqmqqsp1/Ntvv9Vbb70lu90uu92upKQk/eQnP9HRo0clSYMGDVJRUZFOnDih999/X1OmTFGbNm30/vvv67333lN4eLgkydfXV1lZWTp8+LAeeughjR071hU8L8UkkwZnJbv++ZgtDdMAAAAAANcdnp5aj3x9fbV8+XL9+te/1qpVq9S7d29JUocOHRQTE6P4+HjX2JMnT8rX11eS1LVrV9100016++23VVVVJT8/Pw0aNEjvvfeeHA6Hli5dKklyOBxq1aqVFixYoHnz5ikjI0Pjx4/X9u3b1axZs6t/wwAAAACue6w01rOuXbtq/vz5+tvf/uY6Fh0drbfeektnzpyRJB05ckSjRo3S2bNnXWMGDRqk5cuX67777nO93rZtm7y9vV2B8NChQ5o7d66cTqd8fHx055136ty5czKZTFfxDgEAAAA0Jaw01lJBQYFWrlypAwcOKCkpSStWrHCdi4yMVFFRket1fHy8HA6HEhMTZbVaJUkLFixQixYtXGMGDx6sjIwM9e/fX5IUGhoqq9Wqfv36uca0adNGgYGBGjNmjMxms8rKyvTMM8+4ViwBAAAAoL6ZnE6n09NFoGHZbDadjgp2vc6LTvVgNY1TeXm5JLlCPWpGn9xDn9xDn9xHr9xDn9xDn9xDn9xDn9xzLfTJZrMpJyenxnNsTwUAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIa8PV0AGp5TTuVFp7peV1Q65GO2eLAiAAAAANcKVhqbAJNM1V4TGAEAAAC4i9AIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIa8PV1AXRQVFSktLU07duxQSEiI/P39derUKVVUVGjKlCl68MEH6zR/RkaGsrKyFBYWpsWLF0uSVq1ape+++07z5s2rj1tQaGioQkJCJEklJSU6cOCAevXq5Xpts9m0Zs0aff/994qIiFBqaqrr2mXLlik7O1s33XSTXn75Zfn7+9dLTQAAAABwwTUdGsPCwpSenq7w8HClpKQoNDRUkvTcc89p9uzZ6tGjhzp16lTr+SdMmKDy8nIdOHDAdSwhIUHnzp1zvc7JydGmTZu0bt26Wr1HSEiI69qcnBy98MIL1V5L0iOPPKLU1FTNnj272rXTp09XUVGRsrKyZDKZavX+aEgLrQAAIABJREFUAAAAAHAp1+X21AceeEAOh0OfffZZvc/t4+Oj5s2b19t8M2bMMDz3y1/+Ur/85S9dK6bvvPNOtfPbt2/X3XffTWAEAAAA0GCuy9BYWVkpSQoKCpIkffLJJ4qLi1NcXJyio6OVl5fnGvvpp59q3LhxevjhhxUXF6e0tDQ5nc4a583Pz1dkZKQGDBggSSooKFBGRoZ2794tu92uhIQErV27VqGhoRowYIBef/11SdKCBQt05513asGCBRfN2bt3b8P7uPHGG3XjjTeqdevWuv/++5Wbm1vt/ObNmzV8+PAr6AwAAAAAXJlrentqTZxOp/Ly8jRt2jT17NlThw4d0sMPP6zly5erb9++OnTokB566CH99Kc/Vbdu3VRWVqbf/va3uu222yRJs2fPNgxjgwYNUosWLZScnCxJCg8P14kTJy7anlpaWqodO3Zo1KhRkqTJkyfr0KFDdfocZGRkpKZOnao9e/aoa9euKisr0+HDh9WlS5dazwkAAAAAl3PdrDQuXLhQY8aM0d13361t27bp17/+taTzq3EBAQHq27evJCkwMFB333233njjDUnSz372M2VlZSk6Olp2u10fffSRduzYUadaRo0apaKiIu3fv1/S+c8m1nVFsF+/fmrZsqU2b94sScrLy9PgwYPrNCcAAAAAXM51ExpTUlL0hz/8Qa+99pr+9a9/6dlnn5UkHTx4UMeOHZPdbnf9279/v86ePSvp/Mri8ePHtW7dOq1bt04jRozQ999/X6daOnbsqHvuuUfZ2dlyOp36y1/+on79+kk6HyAv1PH000+7PaePj4+GDBmiLVu2yOl06u23367z02EBAAAA4HKuu+2pXbt21cSJE/X8889r2rRpCgoKUseOHattH62oqFB5ebkkqbi4WHPnzpXFYpEkORyOeqlj1KhReuqpp3T33XfrjjvukLf3+VbbbDbZbLZazRkZGamsrCxt2bJFvr6+atOmTb3UCgAAAABGrpuVxh+y2+3y9/fX6tWrFRkZqX/961/65JNPJJ3/zOP8+fP1xz/+UdL5kPnhhx9KOh8m//rXv17Re/n5+amsrEyStGTJEtfW1vDwcEnnV0AvfLaxru644w517txZTz75pIYOHVovcwIAAADApVzTobGoqMj1lRULFy7Uhg0bJEnNmzfXhAkTlJWVpZkzZ2rlypVatmyZ4uLiFBsbq6CgII0cOdJ13ddffy2bzaZZs2YpKChIhYWFeu6555SRkaFNmzapsLBQixcvVn5+vhYtWqSSkhIlJCRIOv+1GGazWTExMfriiy8UEhIiSbJYLBoxYoS6dOni1ndFZmRkKCMjQyUlJbLb7Tp16lSN44YNGyYvLy/179+/zv0DAAAAgMsxOY2+XwJ19sorr+jGG2/UQw895NE6bDabcnJyPFpDY3dhu7LVavVwJY0bfXIPfXIPfXIfvXIPfXIPfXIPfXIPfXLPtdCnS2WGa3qlsTE6fvy4/vSnP6myslJ//vOfNWjQIE+XBAAAAAC1RmisZxUVFVq8eLFGjRql6Oho+fj4eLokAAAAAKi16+7pqZ7Wvn17bd261dNlAAAAAEC9YKURAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAkLenC0DDczo9XUHjZ7VaPV3CNYE+nVfuOCerhV+fAACgaeCvnibAZJLum7jA02UA143ClfM8XQIAAMBVw/ZUAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwBChEQAAAABgyNvTBTSUoqIipaWlaceOHQoJCZG/v79OnTqliooKTZkyRQ8++KCnSwQAAACARu+6XWkMCwtTenq6JCklJUXr1q1Tbm6uIiIiNHv2bH3zzTcerhAAAAAAGr/rNjQaeeCBB+RwOPTZZ595uhQAAAAAaPSu2+2pRiorKyVJQUFBkqRPP/1UaWlpqqqqksPhUExMjIYPHy5JSkhI0N///nc99thj+vDDD/X111/r3nvv1bhx45SWlqYvv/xSXbp00bJly+Ttfb6VS5cu1c6dO2U2m2UymTRz5kzdeuutF8330Ucfaf/+/eratavS0tJksVgkSXv27NGiRYv0/fffq7KyUl26dNH06dMVGBioU6dOKTU1VXv27JGXl5dCQkI0e/ZsWa3Wq91GAAAAAE1EkwqNTqdTeXl5mjZtmnr27KlDhw4pMTFRL730ksLCwlRSUqJhw4apRYsWGjBggNauXasBAwbo888/14svvqizZ8+qX79+On36tJYsWSKTyaTIyEgVFBTogQcekCS1adNGmZmZMplM+uabbzR27Fjl5+dLkmu+zz77TM8//7wqKyv1q1/9SgUFBYqIiFBZWZkSExM1depUjRo1SlVVVZo8ebJ27typwMBAPf744/L19dVrr70mp9Op6dOn65lnntGcOXM82VYAAAAA17EmERoXLlyoG264QV999ZU6dOiglStXSpI2b96s9u3bKywsTJIUEBCgQYMGacOGDRowYIDr+oEDB0qSfH191aVLF3Xr1s21shgcHKx9+/a5xnbo0EHx8fGqqqqSl5eX/vWvf6m0tFRt2rRxjRk8eLBMJpO8vb0VEhKi/fv3S5K2bdumkydPymazSZK8vLyUnJwsX19flZSUqKCgQBs2bJDJZHIF1tmzZ182NDrlVOHKeXXsInB1VFQ65GO2eLqMSyp3nJPV0iR+fQIAADSN0JiSkqLQ0FDt2bNHI0eO1LPPPqvU1FQdPHhQbdu2rTa2bdu2+vjjj6sd8/f3d/3s7e2tFi1auF5bLBZVVFRIkj7++GPNmjVLWVlZ6tmzp6TzofLMmTPVQuMPr7dara7rDx48qFatWslsNrvOd+7cWdL5bbTS+e2vPj4+kqSKigq1bNlSp06dqlbjj5lk0uCs5Mu1CWgU8qJTPV3CZREYAQBAU9Kk/vLp2rWrJk6cqOeff17Tpk1TUFCQPvjgg2pjjh49qg4dOtRq/uLiYrVt29YVGC+EQXcFBQXp+PHjqqysdAXHQ4cOqaqqylXTk08+qW7dulWr91KBEQAAAADqosk9PdVut8vf31+rV69WZGSkDh8+7AqOJSUlys/PV3R0dK3m7tq1q44ePao9e/ZIkrZu3XpF1/fv318tWrRQTk6OpPMP7ZkzZ44OHDiggIAAhYeHKzs72zW+qKhIjz76aK1qBQAAAAB3XLcrjUVFRUpLS5N0/jONUVFRio2NVfPmzTVhwgQ9++yz+sc//qFVq1Zp+fLleuGFF+RwOPTYY4+5PsOYlJSkkpISLVq0SPPnz9emTZu0e/dulZSUyN/fX7t371ZhYaGsVqtat26tuLg4JSQkaNy4cQoODlZISIgkacaMGVq2bJkWLVpUbb6//e1vruvbtm2r2NhYrV69WqmpqcrNzVVlZaWGDh2q0NBQSdKSJUu0ZMkSjR49WlarVTfccIOWLVvmmQYDAAAAaBJMTqfT6eki0LBsNptORwV7ugzALdfCZxovp7y8XJL4OpzLoE/uo1fuoU/uoU/uoU/uoU/uuRb6ZLPZXDsef6zJbU8FAAAAALiP0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAY8vZ0AWh4TjmVF53q6TIAt1RUOuRjtni6DAAAAPwHK41NgEkmT5fQ6JWXl6u8vNzTZTR6V6NPBEYAAIDGhdAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwJC3pwtAw3M6PV1B42e1Wj1dgseVO87JauFXAgAAAKrjL8QmwGSS7pu4wNNloJErXDnP0yUAAACgEWJ7KgAAAADAEKERAAAAAGCI0AgAAAAAMERoBAAAAAAYIjQCAAAAAAwRGgEAAAAAhgiNAAAAAABDhEYAAAAAgCFCIwAAAADAEKERAAAAAGCI0AgAAAAAMOTt6QKuRWVlZUpPT9eOHTvUrFkznT17VkFBQYqLi9MvfvGLS1779ttv63/+53+0YsWKGs+/8847WrlypT7//HP16tVLPj4+rnMHDhxQamqq7rrrLg0dOlQLFy7U7bffXq/3BgAAAAA/RGishSVLluj48ePauHGjzGazzp07p+TkZP3lL3+5bGiMiIhQ3759Dc8/+OCDatu2reLj47V8+XLdeOONrnPPP/+86+d169apZcuWdb8ZAAAAALgEtqfWwt///nfdddddMpvNkiRvb29NmjRJ3bt3v+y1ZrNZ/v7+tXrfYcOGKTg4WJLUqlUrmUymWs0DAAAAAO4iNNZCp06dtHHjRu3du9d17Oabb9aDDz4oSTp06JAeffRRxcbGasyYMZo2bZq+/vprFRcXKyoqyhX8roTdblfnzp3VqlUrPfHEE+rTp0+1lUcAAAAAaAhsT62Fxx9/XFOmTNGQIUPUu3dvhYeHKzIyUgEBAaqqqlJSUpL69u2r5557TpL05JNPqrCwUImJiUpPT1d4eLhb7zNt2jTXZxp3797tOv7UU0+poqLC7Xqdcqpw5bwruMPGo6LSIR+zxdNlNAnljnOyWviVAAAAgOr4C7EWbrrpJm3ZskX/+7//qz/96U965ZVX9NxzzyktLU3t27fXZ599ppdeesk1PikpSWVlZVf8Pj/8TKPdbq91vSaZNDgrudbXe1JedOpVeZ/y8nJJktVqvSrv1xgRGAEAAFATtqfWkslk0j333KP58+fr/fff14MPPqjFixfr4MGDkqR27dq5xrZv315dunSpcR673e7698PVxB9bt25d/d4AAAAAALiBpYVaeOyxxzR//nzXA20sFosiIiL03nvvKSgoSJJ05MgRtW/fXpJ07NgxnTx5Up07d75oLsIgAAAAgMaMlcZaOHz4sP7whz+4Xp87d07vvvuu+vTpo549e+rWW2/Vhg0bXOeXLl2qTz75xBOlAgAAAECdEBprISEhQZ988olGjx4tu92u0aNHy9fXV0899ZS8vLy0YsUK7dmzRzExMYqOjlZgYKAiIyNVXFysGTNmSDq/LbW0tPSiud955x0tWrRI0vkH4axdu/aiMU888YQKCwu1adMmvfzyyw17swAAAACaNJPT6XR6ugg0LJvNptNRV/41H40BD8JpXOiTe+iTe+iT++iVe+iTe+iTe+iTe+iTe66FPtlsNuXk5NR4jpVGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAIAhQiMAAAAAwJC3pwtAw3PKqbzoVE+XUSsVlQ75mC2eLgMAAABoslhpbAJMMnm6hFojMAIAAACeRWgEAAAAABgiNAIAAAAADBEaAQAAAACGCI0AAAAAAEOERgAAAACAIUIjAAAAAMAQoREAAAAAYIjQ2AQ4nZ6uoLpyxzlPlwAAAADATd6eLgANz2SS7pu4wNNluBSunOfpEgAAAAC4iZVGAAAAAIAhQiMAAAAAwBChEQAAAABgiNAIAAAAADBEaAQAAAAAGCI0AgAAAAAMERoBAAAAAIYIjQAAAAAAQ4RGAAAAAICh/9fenYfHeO//H3+NTCb0IAmNPdIeldQSjVpqqxIJIs2ltLaS6kaVltLm8i0V1R5Fi0ajl4S6Wgclh9JTa9VS1VNLLZVWcWiPCrXva9bP7w8/8/3mcDNZR8zzcV29rsy9vu93PzLzyv2ZGUIjAAAAAMASoREAAAAAYMnu7gJKqkuXLmny5MnauXOnypQpo6tXr6patWrq06ePmjRp4u7yAAAAAKBQcKcxnyZMmKATJ04oJSVFs2fP1rx58+RwOLR+/foiPe+iRYsUGxtbpOcAAAAAgOsIjfm0fft2PfLII/Ly8pIk2e12DRgwQHXr1nVzZQAAAABQeJiemk+BgYFKSUlRixYtdP/990uSatWqpVq1amn69OmaP3++6tevr3vuuUd//PGHLl68qKFDhyo8PFySlJGRoSlTpmjbtm3y8vJSxYoV9dZbb6lSpUr68ssvlZSUpICAADVu3Fg7duzQ1q1bNWXKFE2fPl0nT55UbGysSpUqpVmzZrmzDQAAAADuctxpzKfhw4crJydHUVFR6tWrlz755BOdOHFCktS/f3916dJFGzZs0KBBgzRv3jzFx8dr8ODBSktLkyRNmjRJv/76q+bMmaO5c+fqr3/9qwYOHKicnBw98cQT6t+/v1JTU9W2bVt99tlnGjFihKpWrar+/furTp06mj17NoERAAAAQJHjTmM+3XfffVqyZIk2bdqkr7/+WjNnztRHH32kiRMnqn379pKkRx55RIGBgZKkJk2aqEaNGlq6dKkGDBiglJQUjR07Vnb7tf8FTz/9tKZNm6aff/5ZDz30kCQpKChIDRo0cK6XpD179uS5ViOjDcmjCnzNkpSRnSmHl3eBjpGemSUfb4YeAAAAUBLwyr0AbDabmjdvrubNm2vkyJEaNWqUxo8f7wyNfn5+ubavWLGijh07ptOnT+vKlSuqUKFCrnWS9OeffzpDo6+vb+HUKZvaz3+zUI61que4Ah+DwAgAAACUHExPzafXX39dFy5ccD729vZWx44ddf78eeeys2fP5trn5MmTqlKlivz9/VW6dGmdOnXKue76z9WqVSviygEAAADAdYTGfDp+/Ljmzp3rfJyVlaUVK1aoZcuWzmXbtm1zvodx8+bNOnz4sKKjo1WqVCl1795dCxYsUGZmpiTp888/V7169RQaGnrL85YtW1aXLl2SdO1rP3bu3FnYlwYAAAAATswTzKe+fftq4cKF6tGjhxwOhy5fvqz69etrxIgRzm0effRRzZgxQ/v379eFCxc0ZcoU53sc4+Li9OGHH6p3796y2+269957NW3aNJUqVUrffPNNrk9JnTx5sgICAiRJLVq00IwZM9SrVy+VKVNGQ4cOdcv1AwAAAPAMhMZ8ioiIUERExC23cTgceueddyzXDR8+/KbrIiMjFRkZedN1ZcuW1YIFC/JWLAAAAADkE9NTAQAAAACWCI1FYPr06Vq8eLE2bNig8ePHu7scAAAAAMg3pqcWgf79+6t///7uLgMAAAAACow7jQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJbs7i4ARc/IaFXPcYVyrIzsTDm8vAvlWAAAAADufNxp9AA22QrtWARGAAAAwLMQGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYsru7ABQ9Y9xdwZ3Px8fH3SWUCPTJNXdbn9Izs+TjzdMFAACeilcBHsBmkx596V13lwGghNqQPMrdJQAAADdieioAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBkd3cB7taxY0cFBARIks6fP689e/aoadOmzscPPPCA1q9frxEjRqhr1655OvayZcu0dOlSTZs2Tbt379bo0aO1c+dO7d27VydOnNCwYcO0ZcsWrVmzRjVq1Cj0awMAAACAgvL40BgQEKDZs2dLkjZv3qxnnnkm1+MtW7bo+PHj+Tp2x44d1bp1a0lSnTp1NHnyZLVr1y7XeUNCQgrhKgAAAACgaHj89NRhw4ZZrgsODtbjjz+e72N7eXmpXLly+d4fAAAAANzN4+80NmzY0HKdv7+//P39JUl//vmnXnvtNR08eFC+vr5KSEiQr6+vvvzySyUlJSkgIECNGzfWjh07tHXrVr366qtas2aNczqqq9atW6ekpCR5e3vLGKOhQ4eqcePG2rt3r0aNGqWdO3dq8uTJ+uqrr/TTTz+pT58+evXVVwvcBwAAAAC4GY8Pja7asmWLZs6cKW9vbz333HNauHChXnjhBT3xxBPKycnRmDFjFBcXpyFDhujzzz9XWFiYoqOjndNRXbFr1y4NHjxYCxcuVEhIiHbv3q3evXtr1apVCgkJcU5vPXPmjJKTk/XTTz9p//79tz2ukdGG5FEFuXwg3zKyM+Xw8nZ3GSiA9Mws+XjzdAEAgKfiVYCLwsPD5e197YVvvXr19Mcff+RaHxQUpAYNGkiSnn76aUnSoUOH8nSOlJQUNW7c2Pk+xzp16igoKEhLly7Vs88+69wuJiZGkhQWFqawsLDbHtcmm9rPfzNPtQCFZVXPce4uodilp6dLknx8fNxcSeEgMAIA4Nl4JeCi8uXLO3/28fFRRkZGrvW+vr4FPseRI0e0b98+xcbGOpddvXpVFy9eLPRzAQAAAIArCI13kKpVq8rHx0dTp051Lrty5YpycnLcWBUAAAAAT+bxn556J+nRo4c2bdqktLQ0SVJWVpYGDRqk1NRUN1cGAAAAwFMRGv+/RYsW6b333pMkxcbG6vfff5ckxcfHa/fu3Zo+fbq++eYbpaSkaPHixdqwYYM++ugjffPNN5o+fbp2796t2NhYnThxQpK0Y8cO59d5xMbGKjU1NdfjI0eOOKehDhs2THv37lW9evU0adIkxcXFKTY2VrGxsYqIiFDz5s2VlpaWa//vvvuuWPsDAAAAwDPZjDHG3UWgaHXt2lUXu4e4uwx4KD4IB1bok+volWvok2vok2vok2vok2tKQp+6du2qRYsW3XQddxoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS3Z3F4CiZ2S0quc4d5cBD5WRnSmHl7e7ywAAAEA+cafRA9hkc3cJd7z09HSlp6e7u4w7Xn76RGAEAAAo2QiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGj2AMUV/jvTMrKI/CQAAAIBiZ3d3ASh6Npv06EvvFuk5NiSPKtLjAwAAAHAP7jQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCIwAAAADAEqERAAAAAGCJ0AgAAAAAsERoBAAAAABYsru7ABQ9I6MNyaOK/bwZ2ZlyeHkX+3kBAAAAFB5Cowewyab2898s9vOu6jmu2M8JAAAAoHAxPRUAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCAAAAACwRGgEAAAAAFgiNAIAAAAALBEaAQAAAACWCI0AAAAAAEuERgAAAACAJbu7C0DRMzJa1XNcsZ83IztTDi/vYj8vAAAAgMLDnUYPYJPNLeclMAIAAAAlH6ERAAAAAGCJ0AgAAAAAsERoBAAAAABYIjQCAAAAACwRGgEAAAAAlgiNAAAAAABLhEYAAAAAgCVCowcwxrXt0jOzirYQAAAAACWO3d0FoOjZbNKjL7172+02JI8qhmoAAAAAlCTcaQQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgye7uAlD0jIw2JI/K174Z2ZlyeHkXckUAAAAASgpCowewyab289/M176reo4r5GoAAAAAlCRMTwUAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgye7uAlD0jIxW9RyXr30zsjPl8PIu5IoAAAAAlBTcafQANtnyvS+BEQAAAPBshEYAAAAAgCVCIwAAAADAEqERAAAAAGCJD8LxAIcPH1bXrl3dXQYAAACAO9Thw4ct19mMMaYYawEAAAAAlCBMTwUAAAAAWCI0AgAAAAAsERoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiM8wnfffacOHTooMjJS06dPv2F9RkaGXnvtNUVGRqpbt246dOiQJOnQoUNq0KCBOnfurM6dOys+Pr64Sy9Wt+vTjz/+qC5duqhu3bpauXJlrnWLFy9W+/bt1b59ey1evLi4SnaLgvSpTp06zvE0YMCA4irZLW7Xp08//VSdOnVSTEyM+vbtm+ujvhlP/+tWfWI8/a958+YpJiZGnTt3Vq9evbR//37nuuTkZEVGRqpDhw7asGFDcZZd7PLbJ097vpNu36vrvv76a4WEhOjnn392LmNM3ei/++RpY+p2fVq0aJGaNWvm7MeCBQuc60rEc54B7nJZWVmmXbt25uDBgyY9Pd3ExMSYffv25dpmzpw5ZtSoUcYYY5YuXWqGDBlijDEmLS3NREdHF3vN7uBKn9LS0szu3btNXFycWbFihXP5mTNnTHh4uDlz5ow5e/asCQ8PN2fPni3uSygWBemTMcaEhYUVZ7lu40qfNm7caC5WSdGuAAAPY0lEQVRfvmyMMWbu3LnOf3eMJ9f6ZAzj6f+6cOGC8+fVq1eb559/3hhjzL59+0xMTIxJT083Bw8eNO3atTNZWVnFWn9xKUifPOn5zhjXemXMtX49/fTTplu3biY1NdUYw5hytU+eNKZc6dMXX3xhxowZc8O+JeU5jzuNuOulpqYqKChIgYGBcjgcio6O1po1a3Jts3btWnXp0kWS1KFDB23cuFHGw77C1JU+1ahRQw8++KBKlcr9q+P7779Xy5Yt5efnJ19fX7Vs2fKu/ctrQfrkSVzpU7NmzVSmTBlJUlhYmI4ePSqJ8eRqnzyJK30qW7as8+crV67IZrNJktasWaPo6Gg5HA4FBgYqKChIqampxVp/cSlInzyNK72SpClTpqhfv37y8fFxLmNMudYnT+Jqn26mpDznee4rGniMY8eOqUqVKs7HlStX1rFjx27YpmrVqpIku92ucuXK6cyZM5KuTa944okn1KdPH23durX4Ci9mrvSpKPYtaQp6renp6eratau6d++u1atXF0WJd4S89mnhwoVq3bp1vvYtyQrSJ4nx9N/mzp2riIgIffDBB3rrrbfytO/doCB9kjzn+U5yrVe7du3S0aNH1aZNmzzve7coSJ8kzxlTro6JVatWKSYmRoMHD9aRI0fytK+72d1dAHAnq1SpktatWyd/f3/98ssvGjRokJYtW5brL7VAXqxbt06VK1dWWlqa+vbtq+DgYNWsWdPdZbnVP//5T/3yyy+aM2eOu0u5o92sT4yn3Hr37q3evXtryZIlmjZtmiZMmODuku5IN+sTz3e55eTkaPz48Ro3bpy7S7mj3apPjKnc2rZtq8cff1wOh0Pz58/X8OHD9fe//93dZbmMO42461WuXDnXdK5jx46pcuXKN2xz/S8+WVlZunDhgvz9/eVwOOTv7y9Jql+/vmrWrKn//Oc/xVd8MXKlT0Wxb0lT0Gu9vm1gYKCaNm2qX3/9tdBrvBO42qcffvhBSUlJmjZtmhwOR572vRsUpE/X95cYT/8tOjraeeeV8eRanzzp+U66fa8uXbqkf//733rmmWcUHh6un376SS+//LJ+/vlnxpSLffKkMeXKmLj+ulKSunXrpl27drm8752A0Ii7XmhoqA4cOKC0tDRlZGRo2bJlCg8Pz7VNeHi489Oqvv76azVr1kw2m02nT59Wdna2JCktLU0HDhxQYGBgsV9DcXClT1ZatWql77//XufOndO5c+f0/fffq1WrVkVcsXsUpE/nzp1TRkaGJOn06dPavn27HnjggaIs121c6dOvv/6q+Ph4TZs2TRUrVnQuZzy51ifGU+4+HThwwPnzt99+q6CgIEnXfr8vW7ZMGRkZzt/jDRo0KM7yi01B+uRJz3fS7XtVrlw5bd68WWvXrtXatWsVFhamadOmKTQ0lDHlYp88aUy58m/v+PHjzp/Xrl2rWrVqSSo5z3lMT8Vdz263Kz4+Xi+++KKys7P15JNPqnbt2poyZYrq16+vdu3a6amnnlJcXJwiIyPl6+urDz/8UNK1r0746KOPZLfbVapUKY0ZM0Z+fn5uvqKi4UqfUlNT9corr+j8+fNat26dEhMTtWzZMvn5+WngwIF66qmnJEmDBg2iTzfp02+//abRo0fLZrPJGKN+/frdtS/yXenT+++/r8uXL2vIkCGSpKpVqyopKYnx5GKfGE+5+zRnzhxt3LhRdrtd5cuXd05NrV27tqKiotSpUyd5eXkpPj5eXl5ebr6iolGQPnnS853kWq+sMKZc65MnjSlX+jR79mytXbtWXl5e8vX1dU7pLSnPeTbjaR8RCQAAAABwGdNTAQAAAACWCI0AAAAAAEuERgAAAACAJUIjAAAAAMASoREAAAAAYInQCADwKL/99pu6deumRo0aaejQobp06VKu9T/++KMeffTRG5YXxLZt29SlSxeFhoYqJCTkptucOnVKiYmJOnToUJ6Pn5GRocTERO3evTtf9YWHhzu/euFOdejQIYWEhGjdunXuLgUAPA6hEQDgUd58803VrFlTCQkJ2r9/v5KTk53rcnJyNHbsWL3++uv6y1/+UmjnHD16tMqVK6eZM2cqJSXlptucOnVKU6dO1eHDh/N8/MzMTE2dOjXfobEkqFSpklJSUtSoUSN3lwIAHsfu7gIAACguly5d0s6dO5WUlKQKFSrowoULmjlzpoYNGyZJ+uKLL2S329W5c+dCPe/vv/+u7t27q2nTpoV6XE/icDgUFhbm7jIAwCNxpxEA4DEyMzMlST4+PpKk0qVLO5ddvHhRCQkJGjlypGw2m8vH3Lhxo7p166bQ0FC1aNFCb7/9tnNq6+bNmxUSEqLs7GyNHTtWISEh+p//+Z8bjnHo0CHFxMRIkp555hmFhITkmsaalpamgQMH6uGHH1bDhg01YMAA/fHHH871Dz/8sKRrd1Gv73t9muvEiRMVExOjhg0bqnXr1nr99dd14sQJl6/vuutTWKdPn65WrVqpUaNGGj9+vIwxWr9+vaKjo9WwYUMNHDhQ586dy7Xv7eqPjY3V4MGDbzjnhAkT1KZNGxljLKenLliwQNHR0apfv77atm2rGTNm5Fq/b98+vfDCC2ratKnCwsIUFRWluXPn5vn6AcCTcacRAOAx/Pz8VL16dc2ZM0c9evTQP/7xD9WvX1+S9PHHH6t58+Zq2LChy8fbt2+f+vXrpxYtWigxMVFHjhzRpEmTlJaWppkzZ6pevXpKSUlRjx499Pzzz6tDhw6qUKHCDcepVKmSJk6cqDfeeEPx8fGqV6+ec11GRoaeffZZ2e12/e1vf5OXl5cSExPVp08fLVmyRH5+fpo1a5b69u2rl19+WW3atHEeU7o27fWll15SpUqVdPr0aX366afq27evli5dqlKl8va342XLlqlBgwZ67733tGvXLiUkJCgnJ0dbt27VkCFDdPXqVb377ruaNGmS3nnnHZfrj4qK0vvvv6/Lly/rnnvukSQZY7Ry5UpFRUVZhvhPPvlEH374oV588UU1bdpUu3bt0pQpU1SmTBn16dNHkjRgwADVqlVLH3zwgRwOh37//fdCfb8qAHgEAwCAB/n2229NWFiYCQ4ONu3btzeHDx82Bw4cMI0bNzZHjhzJ07Fee+01ExkZabKyspzLli1bZoKDg8327dudy4KDg83s2bNveay9e/ea4OBgs2nTplzLP//8c1OnTh1z8OBB57IjR46YevXqmaSkJGOMMRcvXjTBwcHmiy++uOU5srKyzNGjR01wcLDZsmWLc3nbtm3N+PHjb7lv27ZtTURERK5rffLJJ2+obcKECaZ58+Z5qv/UqVOmTp06ZunSpc5ttm/fboKDg01qaqoxxpi0tDQTHBxs1q5da4wx5sKFCyYsLMwkJibmqjMhIcG0aNHCZGVlmVOnTpng4GCzZ8+eW14bAODWmJ4KAPAojz32mH744QetXLlSy5cvV7Vq1TRu3Dj17dtXVapU0dy5c9WmTRu1adPmttMYU1NTFRERIS8vL+eyDh06yG63a9u2bYVSb2pqqurWravAwEDnsipVqqhhw4YunWP9+vXq2bOnGjVqpLp166p169aSpAMHDuS5lqZNm+a61qCgIFWvXj1XbUFBQTp9+rQyMjJcrr9ChQpq1qyZli9f7txm+fLlqlmzpkJDQ29ay44dO3T58mV17NhRWVlZzv+aNWumkydP6ujRo/Lz81PVqlU1evRoLV++XKdOncrzNQMAmJ4KAPBAZcqU0f333y9J+te//qW9e/cqISFBe/bs0ZQpUzRv3jxJcoatBx988KbHOXHihO69995cy7y8vOTn53fD+/ry62bnkKR7771Xf/755y33TU1N1cCBAxUREaF+/fqpYsWKstls6t69u9LT0/NcS/ny5XM99vb2Vrly5W5YZoxRZmamHA6Hy/V36tRJY8aM0cWLF3XPPfdo5cqV6tq1q2UtZ86ckSRFR0ffdP2RI0dUvXp1zZw5UwkJCRoxYoSuXr2qhx9+WG+99Zbq1q3r8nUDgKcjNAIAPFZ2drbGjRunuLg4lS5dWps3b1azZs1Uq1YtSVLz5s31448/WobGgICAG+5eZWdn6+zZs/L19S2UGgMCArR///4blp88efK251i9erX8/f2VkJDgfF9gfr7SoyBcrT8yMlJvv/22Vq9ererVq+v48eOKioqyPO71fZOTk1WxYsUb1l//o0CtWrWUmJiozMxMbd26VRMnTlT//v313Xff5fk9nQDgqfhtCQDwWPPmzVP58uXVqVMn57IrV644f758+bKMMZb7P/TQQ1q9erWys7Ody1atWqWsrKw8f5+gt7e3JN1wB/Chhx7Srl27lJaW5lx27Ngx7dixw3kOq32vXr0qb2/vXB8ks2TJkjzVVVCu1C9dC4GtWrXSihUrtHz5ctWqVcsyrEtSw4YNVbp0aR0/flyhoaE3/Fe2bNlc23t7e6t58+Z67rnndOLECZ0/f77wLxYA7lLcaQQAeKSzZ89q6tSpmjlzpnNZkyZN9MEHH2jhwoUyxmjTpk164403LI/x8ssvq0uXLho0aJB69eqlo0ePauLEiWrVqlWePoVVkqpVq6bSpUvryy+/VLly5WS32xUaGqquXbtqxowZ6tevnwYPHiwvLy9NnTpV/v7+6tGjh6Rr32FYo0YNrVixQrVr15aPj49CQkLUsmVLzZo1S2PHjlV4eLi2b9+ur776Kn8NyydX6r8uKipKI0eOVNmyZZ2ffmqlfPnyeuWVVzR27FgdPnxYTZo0UU5Ojg4cOKDNmzfr448/1p49e/T+++8rKipKgYGBOn/+vGbMmKEHH3xQfn5+RXnZAHBXITQCADzS1KlTFR4enuvrLerWrau4uDglJCRIkoYPH37Lu121a9fWjBkzNHnyZL3yyisqW7asoqOjFRcXl+d6fHx89O677+rjjz9WbGysMjMztXfvXjkcDn322WcaN26cRo4cKenaB9IkJibmCj5jxozRhAkT9NxzzykjI0Nr1qzRY489pjfeeENz5szRggULFBYWpuTkZHXo0CHP9eWXq/VLUrt27RQfH68zZ87kuvtrpV+/fqpUqZJmzZqlTz/9VD4+Prrvvvuc+wYEBKhixYpKSkrS8ePHVb58eT3yyCO3/EMAAOBGNnOreTcAAAAAAI/GexoBAAAAAJYIjQAAAAAAS4RGAAAAAIAlQiMAAAAAwBKhEQAAAABgidAIAAAAALBEaAQAAAAAWCI0AgAAAAAsERoBAAAAAJb+HxeofoL3i9NYAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmdgLZ5E7VRa"
      },
      "source": [
        "We find some very clear differences, mostly in the genres Action and Adventure. Action movies comprise around 12% of total movies, but almost 30% of these so-called Classic movies belong to the Action genre. <br>We find a similar situation with the Adventure genre movies. This suggests that we must control for a movie's genre in the model.\n",
        "\n",
        "Let's see how certain genres' popularity has evolved through time."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBT3eKMpDdby"
      },
      "source": [
        "genres_prog = genres_analysis.groupby(by='startYear').agg({'Horror': 'sum', 'Comedy': 'sum', \\\n",
        "                                                           'Drama':'sum', 'Sci-Fi':'sum', 'tconst': 'count'})\n",
        "genres_prog.reset_index(inplace=True)\n",
        "genres_prog['%_horror'] = genres_prog['Horror'] / genres_prog['tconst'] * 100\n",
        "genres_prog['%_comedy'] = genres_prog['Comedy'] / genres_prog['tconst'] * 100\n",
        "genres_prog['%_drama'] = genres_prog['Drama'] / genres_prog['tconst'] * 100\n",
        "genres_prog['%_scifi'] = genres_prog['Sci-Fi'] / genres_prog['tconst'] * 100\n",
        "genres_prog = genres_prog.iloc[:-1,:]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ud3gSha6Ddby",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "c1b28cc1-55a5-484d-ab3d-c3a2e65951da"
      },
      "source": [
        "ax = plt.subplots(figsize=(18,8))\n",
        "ax = sns.lineplot(x=genres_prog['startYear'], y=genres_prog['%_horror'], legend='brief')\n",
        "ax.set_xlabel('Year', fontsize=15)\n",
        "ax.set_ylabel('# of Horror films per 100 movies', fontsize=15)\n",
        "ax.grid(color='grey', linestyle='-', linewidth=0.1)\n",
        "ax.set_xticks([1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020])\n",
        "plt.title('Progression of Horror films through time', fontsize=16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Progression of Horror films through time')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-OWfouO_iP0"
      },
      "source": [
        "We see that the production of Horror films decreased heavily during the 90's after the golden age of the genre in the 80's. <br>\n",
        "It then started slowly increasing again after year 2000 and nowadays it seems to be quite popular again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n49ss41EDdbz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "outputId": "fe22943e-1ee2-448f-8ac4-7117e38f87d5"
      },
      "source": [
        "ax = plt.subplots(figsize=(18,8))\n",
        "ax = sns.lineplot(x=genres_prog['startYear'], y=genres_prog['%_scifi'], legend='brief')\n",
        "ax.set_xlabel('Year', fontsize=15)\n",
        "ax.set_ylabel('# of Sci-Fi films per 100 movies', fontsize=15)\n",
        "ax.grid(color='grey', linestyle='-', linewidth=0.1)\n",
        "ax.set_xticks([1965, 1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020])\n",
        "plt.title('Progression of Sci-Fi films through time', fontsize=16)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Progression of Sci-Fi films through time')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4YUOVnBlDdbz"
      },
      "source": [
        "#### Year of Release"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Prp7LvIDdbz"
      },
      "source": [
        "class ReleaseYear(BaseEstimator, TransformerMixin):\n",
        "    def fit(self, X, y): return self\n",
        "    def transform(self, X):\n",
        "        res = []\n",
        "        for e in X:\n",
        "            res.append({'release_year': int(e['startYear'])})\n",
        "        return res"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksmSiaiIDdbz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "outputId": "6e5782af-3523-44fe-d343-c1d673b70602"
      },
      "source": [
        "ax = plt.subplots(figsize=(18,8))\n",
        "ax = sns.lineplot(x=movies['startYear'], y=np.log(movies['numVotes']))\n",
        "ax.grid(color='grey', linestyle='-', linewidth=0.1)\n",
        "plt.title('Year vs. logVotes')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Year vs. logVotes')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5_G__Wu6_q_9"
      },
      "source": [
        "We see that the number of votes increases until the beginning of the 2000's and then slowly goes down."
      ]
    }
  ]
}