create_playlist_matrix.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n",
    "from numpy.linalg import norm\n",
    "\n",
    "\n",
    "# ADD PATH TO PLAYLIST FOLDER HERE\n",
    "folder_path = '/Users/john/john/projects/dealing_with_data/MusicRecommendation/playlists/turkish'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PLAYLIST VS ARTIST\n",
    "# where folder_path contains directories named by user, containing each user's playlists as csv files\n",
    "# used Exportify for Spotify tool.\n",
    "\n",
    "def make_matrix_by_playlist():\n",
    "    # Initialize a DataFrame for the final output\n",
    "    final_df = pd.DataFrame()\n",
    "\n",
    "    # Set for storing unique artist names\n",
    "    all_artists = set()\n",
    "\n",
    "    # Iterate over each subfolder in the folder_path\n",
    "    for user_folder in os.listdir(folder_path):\n",
    "        user_folder_path = os.path.join(folder_path, user_folder)\n",
    "        if os.path.isdir(user_folder_path):\n",
    "            # List all CSV files in the subfolder\n",
    "            for filename in os.listdir(user_folder_path):\n",
    "                if filename.endswith('.csv'):\n",
    "                    file_path = os.path.join(user_folder_path, filename)\n",
    "                    playlist_df = pd.read_csv(file_path)\n",
    "                    playlist_name = f\"{user_folder}/{os.path.splitext(filename)[0]}\"\n",
    "                    artist_count = {}\n",
    "\n",
    "                    for _, row in playlist_df.iterrows():\n",
    "                        # Check if 'Artist Name(s)' is a string and not NaN\n",
    "                        if isinstance(row['Artist Name(s)'], str):\n",
    "                            artists = [str(artist).strip() for artist in row['Artist Name(s)'].split(',')]\n",
    "                            for artist in artists:\n",
    "                                all_artists.add(artist)\n",
    "                                artist_count[artist] = artist_count.get(artist, 0) + 1\n",
    "                        else:\n",
    "                            # Handle non-string 'Artist Name(s)' values here if needed\n",
    "                            pass\n",
    "\n",
    "                    # Create a DataFrame for the current playlist\n",
    "                    playlist_artist_df = pd.DataFrame([{'Playlist Name': playlist_name, **artist_count}])\n",
    "                    final_df = pd.concat([final_df, playlist_artist_df], ignore_index=True)\n",
    "\n",
    "    # Add columns for any missing artists in each playlist\n",
    "    missing_artists = all_artists - set(final_df.columns)\n",
    "    for artist in missing_artists:\n",
    "        final_df[artist] = 0\n",
    "\n",
    "    return final_df\n",
    "\n",
    "playlist_df = make_matrix_by_playlist()\n",
    "playlist_original_df = playlist_df #saved for later use\n",
    "playlist_df.to_csv(\"raw_by_PLAYLISTS.csv\", index=False)\n",
    "playlist_df = playlist_df.apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MATRIX BY USER vs ARTIST\n",
    "\n",
    "def make_matrix_by_user():\n",
    "    # Initialize a DataFrame for the final output\n",
    "    final_df = pd.DataFrame()\n",
    "    all_artists = set()\n",
    "\n",
    "    # Iterate over each subfolder (user) in the folder_path\n",
    "    for user_folder in os.listdir(folder_path):\n",
    "        user_folder_path = os.path.join(folder_path, user_folder)\n",
    "        if os.path.isdir(user_folder_path):\n",
    "            # Initialize a dict to store artist counts for the user\n",
    "            user_artist_count = {}\n",
    "\n",
    "            # List all CSV files in the subfolder (user folder)\n",
    "            for filename in os.listdir(user_folder_path):\n",
    "                if filename.endswith('.csv'):\n",
    "                    file_path = os.path.join(user_folder_path, filename)\n",
    "                    playlist_df = pd.read_csv(file_path)\n",
    "\n",
    "                    for _, row in playlist_df.iterrows():\n",
    "                        # Check if 'Artist Name(s)' is a string and not NaN\n",
    "                        if isinstance(row['Artist Name(s)'], str):\n",
    "                            artists = [str(artist).strip() for artist in row['Artist Name(s)'].split(',')]\n",
    "                            for artist in artists:\n",
    "                                all_artists.add(artist)\n",
    "                                user_artist_count[artist] = user_artist_count.get(artist, 0) + 1\n",
    "                        else:\n",
    "                            # Handle non-string 'Artist Name(s)' values here if needed\n",
    "                            pass\n",
    "\n",
    "            # Create a DataFrame for the current user\n",
    "            user_artist_df = pd.DataFrame([{'User': user_folder, **user_artist_count}])\n",
    "            final_df = pd.concat([final_df, user_artist_df], ignore_index=True)\n",
    "\n",
    "    # Add columns for any missing artists in each user's data\n",
    "    missing_artists = all_artists - set(final_df.columns)\n",
    "    for artist in missing_artists:\n",
    "        final_df[artist] = 0\n",
    "\n",
    "    return final_df\n",
    "\n",
    "user_df = make_matrix_by_user()\n",
    "users_original_df = user_df #saved for columns later\n",
    "user_df.to_csv(\"raw_by_USER.csv\", index=False)\n",
    "user_df = user_df.apply(pd.to_numeric, errors='coerce')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NORMALIZATION -- want to normalize by row to find recs for each user\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# create a MinMaxScaler instance\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "# Transpose the matrix b/c MinMaxScaler works on columns by default. We want to work with rows -- normalize for each user\n",
    "# `fit_transform` finds min and max for each row and scales values accordingly\n",
    "# `.T` turns matrix back to original orientation\n",
    "\n",
    "# print(user_df) # this is to compare normalized vs denormalized values in the matrix, lowkey just ignore\n",
    "\n",
    "user_df_normalized = scaler.fit_transform(user_df.T).T\n",
    "columns = user_df.columns  \n",
    "user_df_normalized_df = pd.DataFrame(user_df_normalized, columns=columns)\n",
    "# print(user_df_normalized_df) # to compare, ignore this\n",
    "playlist_df_normalized = scaler.fit_transform(playlist_df.T).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "## ALTERNATING MINIMIZATION FUNCTIONS\n",
    "\n",
    "def solve_V(X, U):\n",
    "    \"\"\"\n",
    "    Given matrix X of shape (n, m),  U of shape (n, k), returns V of shape (m, k) such that X = U * transpose(V)\n",
    "    :param X:\n",
    "    :param U:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    n, m = X.shape\n",
    "    k = U.shape[1]\n",
    "    V = np.zeros((m, k))\n",
    "    for i in range(m):\n",
    "        column = X[:, i].flatten()\n",
    "        indexes = np.argwhere(~np.isnan(column)).flatten()\n",
    "        U_omega = U[indexes, :]\n",
    "        y_omega = X[indexes, i]\n",
    "        V[i, :] = np.linalg.lstsq(U_omega, y_omega)[0]\n",
    "    return V\n",
    "\n",
    "\n",
    "def alt_min(X, U_0, T):\n",
    "    \"\"\"\n",
    "    Given X, initial values of U and number of iterations T, returns U, V such that X = U * transpose(V)\n",
    "    :param X:\n",
    "    :param U_0:\n",
    "    :param T:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    U = U_0\n",
    "    for _ in range(T):\n",
    "        V = solve_V(X, U)\n",
    "        U = solve_V(np.transpose(X), V)\n",
    "    return U, V\n",
    "\n",
    "def alt_min2(X, U_0, T, tol=1e-3):\n",
    "    \"\"\"\n",
    "    Given X, initial values of U, number of iterations T, and a tolerance, returns U, V such that X = U * transpose(V)\n",
    "    Also stops if the change in U and V is below a tolerance.\n",
    "    :param X:\n",
    "    :param U_0:\n",
    "    :param T:\n",
    "    :param tol: tolerance for convergence\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    U = U_0\n",
    "    U_old = np.zeros_like(U)\n",
    "    V_old = np.zeros((X.shape[1], U.shape[1]))\n",
    "    iter = 0\n",
    "\n",
    "    for _ in range(T):\n",
    "        V = solve_V(X, U)\n",
    "        U = solve_V(np.transpose(X), V)\n",
    "\n",
    "        # Check for convergence\n",
    "        if norm(U - U_old, ord='fro') < tol and norm(V - V_old, ord='fro') < tol:\n",
    "            break\n",
    "        \n",
    "        U_old = U.copy()\n",
    "        V_old = V.copy()\n",
    "        iter += 1\n",
    "\n",
    "    print('Number of Alt Min Iterations:' + str(iter))\n",
    "    return U, V\n",
    "\n",
    "\n",
    "def matrix_completion(X, T, k):\n",
    "    \"\"\"\n",
    "    Given a matrix X with missing values, fills missing values in X\n",
    "    :param X:\n",
    "    :param T:\n",
    "    :param k:\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    X_hat = np.nan_to_num(X)\n",
    "    U, e, V_t = np.linalg.svd(X_hat, full_matrices=True)\n",
    "    U, V = alt_min(X, U[:, :k], T)\n",
    "    X_filled = np.matmul(U, np.transpose(V))\n",
    "    return X_filled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## PARAMETER OPTIMIZATION - chose k\n",
    "\n",
    "def split_data(data_matrix, test_ratio=0.2):\n",
    "    \"\"\"\n",
    "    Split the data into training and test sets.\n",
    "    \"\"\"\n",
    "    np.random.seed(0)  # For reproducibility\n",
    "    mask = np.random.rand(*data_matrix.shape) < test_ratio\n",
    "\n",
    "    # Create masks for non-NaN values\n",
    "    non_nan_mask = ~np.isnan(data_matrix)\n",
    "\n",
    "    # Split data into training and test sets\n",
    "    test_data = np.where(mask & non_nan_mask, data_matrix, np.nan)\n",
    "    training_data = np.where(~mask & non_nan_mask, data_matrix, np.nan)\n",
    "\n",
    "    # Create masks for training and test data\n",
    "    mask_train = ~np.isnan(training_data)\n",
    "    mask_test = ~np.isnan(test_data)\n",
    "\n",
    "    return training_data, test_data, mask_train, mask_test\n",
    "\n",
    "def compute_error(A_filled, A_true, mask):\n",
    "    \"\"\"\n",
    "    Compute the error between the filled matrix and the true matrix.\n",
    "    \"\"\"\n",
    "    # Ensure shapes are compatible\n",
    "    if A_filled.shape != A_true.shape or mask.shape != A_filled.shape:\n",
    "        raise ValueError(\"Shapes of A_filled, A_true, and mask must match.\")\n",
    "\n",
    "    return np.sum((A_filled[mask] - A_true[mask]) ** 2)\n",
    "\n",
    "\n",
    "def k_error(max_k, training_data, test_data, mask_train, mask_test, T, type, min_k=1):\n",
    "    training_errors = []\n",
    "    test_errors = []\n",
    "    k_values = range(min_k, max_k)\n",
    "\n",
    "    for k in range(min_k, max_k):  # max_k is the maximum rank you want to test\n",
    "        A_filled = matrix_completion(training_data, T, k)\n",
    "        training_error = compute_error(A_filled, training_data, mask_train)\n",
    "\n",
    "        B_filled = matrix_completion(training_data, T, k)\n",
    "        test_error = compute_error(B_filled, test_data, mask_test)\n",
    "\n",
    "        # Append errors to the lists\n",
    "        training_errors.append(training_error)\n",
    "        test_errors.append(test_error)\n",
    "\n",
    "        print(f\"k={k}, Training Error: {training_error}, Test Error: {test_error}\")\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(k_values, training_errors, label='Training Error')\n",
    "    plt.plot(k_values, test_errors, label='Test Error')\n",
    "    plt.xlabel('Rank Parameter k')\n",
    "    plt.ylabel('Error')\n",
    "    if type == 'user':\n",
    "        plt.title('USERS - Training and Test Errors vs Rank Parameter k')\n",
    "    else: \n",
    "        plt.title('PLAYLISTS - Training and Test Errors vs Rank Parameter k')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "    plt.plot(k_values, training_errors, label='Training Error')\n",
    "    plt.plot(k_values, test_errors, label='Test Error')\n",
    "\n",
    "# Labeling\n",
    "    plt.xlabel('Rank Parameter k')\n",
    "    plt.ylabel('Error')\n",
    "    if type == 'user':\n",
    "        plt.title('USERS - Training and Test Errors vs Rank Parameter k')\n",
    "        plt.xlim(5.5, 9)  # Zoom in on the x-axis from 5 to 7\n",
    "        plt.ylim(0, 13) # Zoom in on the y-axis from 0 to 30\n",
    "\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    else: \n",
    "        plt.title('PLAYLISTS - Training and Test Errors vs Rank Parameter k')\n",
    "\n",
    "\n",
    "\n",
    "# USERS\n",
    "data_matrix = user_df\n",
    "training_data, test_data, mask_train, mask_test = split_data(data_matrix)\n",
    "max_k = 15\n",
    "T = 50\n",
    "k_error(max_k, training_data, test_data, mask_train, mask_test, T, 'user')\n",
    "\n",
    "# PLAYLISTS\n",
    "data_matrix = playlist_df\n",
    "training_data, test_data, mask_train, mask_test = split_data(data_matrix)\n",
    "min_k = 30\n",
    "max_k = 60\n",
    "T = 50\n",
    "k_error(max_k, training_data, test_data, mask_train, mask_test, T, 'playlist', min_k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lm/dmlc5y790cq1twzy4dtrk2rc0000gn/T/ipykernel_16682/3649065614.py:18: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  V[i, :] = np.linalg.lstsq(U_omega, y_omega)[0]\n",
      "/var/folders/lm/dmlc5y790cq1twzy4dtrk2rc0000gn/T/ipykernel_16682/3649065614.py:18: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  V[i, :] = np.linalg.lstsq(U_omega, y_omega)[0]\n"
     ]
    }
   ],
   "source": [
    "# run on USER matrix\n",
    "user_new = matrix_completion(user_df_normalized, 10, 10)\n",
    "user_new = pd.DataFrame(user_new)\n",
    "user_new.columns = users_original_df.columns\n",
    "user_new.index = users_original_df['User']\n",
    "user_new.to_csv(\"complete_users.csv\", index=True)\n",
    "\n",
    "\n",
    "# run on PLAYLIST matrix\n",
    "playlist_new = matrix_completion(playlist_df_normalized, 100, 58)\n",
    "playlist_new = pd.DataFrame(playlist_new)\n",
    "playlist_new.columns = playlist_original_df.columns\n",
    "playlist_new.index = playlist_original_df['Playlist Name']\n",
    "playlist_new.to_csv(\"complete_playlist.csv\", index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis of Completed Matrices by Playlist & User"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum values by row:\n",
      "User\n",
      "Sara       1.0\n",
      "Ebu        1.0\n",
      "Alperen    1.0\n",
      "dtype: float64\n",
      "\n",
      "Artist w greatest # of songs per user:\n",
      "User\n",
      "Sara          Taylor Swift\n",
      "Ebu        Frédéric Chopin\n",
      "Alperen       Saint Levant\n",
      "dtype: object\n",
      "\n",
      "Maximum values by row and their corresponding columns:\n",
      "Row 0: Max Value = 1.0000000000000004, Column = Taylor Swift\n",
      "Row 1: Max Value = 1.0000000000000004, Column = Frédéric Chopin\n",
      "Row 2: Max Value = 1.0000000000000009, Column = Saint Levant\n",
      "Minimum values by row:\n",
      "User\n",
      "Sara      -3.937448e-14\n",
      "Ebu       -2.426651e-14\n",
      "Alperen   -4.175493e-16\n",
      "dtype: float64\n",
      "\n",
      "Artist w least # of songs per user:\n",
      "User\n",
      "Sara       Frédéric Chopin\n",
      "Ebu           Taylor Swift\n",
      "Alperen    Frédéric Chopin\n",
      "dtype: object\n",
      "\\Minimum values by row and their corresponding columns:\n",
      "Row 0: Min Value = -3.9374480010551597e-14, Column = Frédéric Chopin\n",
      "Row 1: Min Value = -2.4266505014966586e-14, Column = Taylor Swift\n",
      "Row 2: Min Value = -4.175492621711707e-16, Column = Frédéric Chopin\n"
     ]
    }
   ],
   "source": [
    "## RECOMMEND\n",
    "max_values_by_row = user_new.max(axis=1)\n",
    "# should just be 1s if normalized min max\n",
    "print(\"Maximum values by row:\")\n",
    "print(max_values_by_row)\n",
    "\n",
    "max_values_columns = user_new.idxmax(axis=1)\n",
    "print(\"\\nArtist w greatest # of songs per user:\")\n",
    "print(max_values_columns)\n",
    "\n",
    "print(\"\\nMaximum values by row and their corresponding columns:\")\n",
    "for index, (value, column) in enumerate(zip(max_values_by_row, max_values_columns)):\n",
    "    print(f\"Row {index}: Max Value = {value}, Column = {column}\")\n",
    "\n",
    "# let's also think about min values by row\n",
    "min_values_by_row = user_new.min(axis=1)\n",
    "# should just be 1s if normalized min max\n",
    "print(\"Minimum values by row:\")\n",
    "print(min_values_by_row)\n",
    "\n",
    "min_values_columns = user_new.idxmin(axis=1)\n",
    "print(\"\\nArtist w least # of songs per user:\")\n",
    "print(min_values_columns)\n",
    "\n",
    "print(\"\\Minimum values by row and their corresponding columns:\")\n",
    "for index, (value, column) in enumerate(zip(min_values_by_row, min_values_columns)):\n",
    "    print(f\"Row {index}: Min Value = {value}, Column = {column}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sbHDt2jXcvn1bo3oLo6DbM3DgQKSnpysd3tu2bRuysrLyPX/nwIEDAKB0WAgA/vOf/wCAyiERDw8PBAYGFqj2gr6fWrVqhezsbJw+fRqANKLQqlUrtGrVCidPngQgfR8SEhKUTrQubrVq1YKDgwPc3d0xePBgVK9eHb/99pviL97IyEjcvn0b/fr1w7NnzxAfH4/4+HikpKSgTZs2OHHiBORyOeRyOfbs2YMuXbqoPS8vZzTxwIED8Pb2VhxiBQALCwt8/PHHiI6OxvXr15XmGzhwoNIogY+PD4QQKocKfHx88ODBA5VD0m3atFE60dbHxwcA0L17d1haWqpMz/k5Luh256bu98KzZ8+QlJSk2HYAGDNmjFK/sWPHvr67lH4mMjMz8ezZM1SvXh02NjYa/w7JceDAATg7O6Nv376KaYaGhhgzZgySk5Px+++/K/Xv3bu30ghefr+Lc6tXrx4iIyPx0UcfITo6Gt9++y26desGJycnrF69WtHv8OHDePnyJSZOnKhyfmPO++XixYt48uQJRo4cqdSnU6dOqF27tsrPLgClEXRAOqRlbW2Ntm3bKr6P8fHx8PLygoWFBY4dOwYAihGg/fv3IzMzM99t1AaGm2Lk7e2NgIAApVdur19JBUjHOhctWoQaNWrA2NgY9vb2cHBwUAzLv65KlSpKX+f8cL1+bPTevXv46KOP0L17dyxdulSj+5r07dsXJ0+exIsXL3Do0CH069cPly9fRpcuXZCWlpbvvCYmJnBwcFCp8fX69u/fj2bNmsHExAR2dnZwcHDAd999p3ab86Nunz5//hyfffYZnJycYGpqCgcHB0W/Ny3f3t4ebdq0wfbt2xXTtm3bBgMDA3z44YeKadOnT0dCQgJq1qyJBg0a4IsvvsBff/2lUe0FVdDtqV27Npo2bYrNmzcrpm3evBnNmjVD9erV81z+P//8Az09PZU+zs7OsLGxwT///KM0Xd0+z09B3k/vvPMOzMzMFEEmJ9y0bt0aFy9eRFpamqIt9wd/cfv5559x+PBhbNmyBc2aNcOTJ0+UPlhzwu2gQYPg4OCg9FqzZg3S09ORmJiIp0+fIikp6Y1XT/7zzz9qD/PlHEp5/Xvx+u8Da2trAICrq6vKdLlcrvL+12R+4N/fMwXd7vzW9frvrpz34euHntXtj1evXmHKlClwdXVV+r2ZkJCg8e+QHP/88w9q1Kih8kdgQfd9Xr+L1alZsyY2bdqE+Ph4/PXXX5g1axYMDAzw8ccf48iRIwD+Pa8rv/dMTk3q9lHt2rVVajYwMEDlypWVpt2+fRuJiYlwdHRU+V4mJycrTnL28/ND9+7dERISAnt7e7z//vtYv369yrlc2sJzbrRI3RUls2bNwjfffIPBgwdjxowZsLOzg56eHsaOHavylw+APM+XEf8/3ySHi4sLXFxccODAAVy8eLHAV3HlZmVlhbZt26Jt27YwNDTExo0bce7cOfj5+eU5T1715Xby5El07doVrVu3xooVK+Di4gJDQ0OsX79e5US5N1G3T3v16oXTp0/jiy++gKenJywsLCCXy9G+fXu1+/R1ffr0QXBwMCIjI+Hp6Ynt27ejTZs2SudPtW7dGlFRUdi7dy8OHTqENWvWYNGiRVi5ciWGDh2q0Ta8iSbbM3DgQHz22Wd4+PAh0tPTcfbsWcVJ0G9S0ACsbp8XRH7vJ0NDQ/j4+ODEiRO4c+cOYmNj0apVKzg5OSEzMxPnzp3DyZMnUbt2bZXwXJxat26t+L536dIFDRo0QP/+/REREQE9PT3F/p83bx48PT3VLsPCwgLPnz8vlvry+nkr6O+Jws5f0O0uTE0F8emnn2L9+vUYO3YsfH19YW1tDZlMhj59+hToZ7woFMX26Ovro0GDBmjQoAF8fX3x7rvvYvPmzSp/GBeV3EcKcsjlcjg6Oir9UZRbzs+bTCbDzp07cfbsWfzyyy84ePAgBg8ejAULFuDs2bNqz4MrSQw3pczOnTvx7rvvYu3atUrTExISlD5MNWViYoL9+/fjvffeQ/v27fH777+jXr16hV5ekyZNsHHjRsTExBR6GTl+/vlnmJiY4ODBgzA2NlZMX79+vUpfTe+k++LFC4SHhyMkJETp5ElNDh9169YNn3zyieLQ1N9//41Jkyap9LOzs0NwcDCCg4ORnJyM1q1bY9q0aUUabjTdnj59+mDcuHH46aefFPchyn04TR03NzfI5XLcvn1b6WTLuLg4JCQkqL2q422pez+1atUKc+bMwZEjR2Bvb4/atWtDJpOhXr16OHnyJE6ePInOnTsXan1FcUdmCwsLTJ06FcHBwdi+fTv69OmjGGWwsrLK9wPJwcEBVlZWuHr1ar7rcHNzw61bt1Sm37x5U9FeGhR0uzWR8z6MiopSGolQtz927tyJQYMGYcGCBYppaWlpKjdY1OT77ubmhr/++gtyuVwpAJTUvs/5AzTnZyJnH1+9ejXPkdecmm7duqVycvKtW7cKVHO1atVw5MgRtGjRokB/uDRr1gzNmjXDzJkzsWXLFvTv3x9bt24t8j/qNMXDUqWMvr6+StLfsWOHymV8hWFtbY2DBw/C0dERbdu2Vbl89XWpqal5Xi7822+/AVA//KkpfX19yGQypcvDo6Oj1d6J2NzcXKM7wub8NfX6Pl28eHGBl2FjY4PAwEBs374dW7duhZGRkcpNBF+/rNzCwgLVq1dXGqJNTEzEzZs3Cz1MDmi+Pfb29ujQoQN+/PFHbN68Ge3bt39jSO7YsaPaZS5cuBAA1F6tUhCavp9atWqF9PR0LF68GC1btlR8MLVq1QqbNm3C48ePC32+Tc69PN727sL9+/dH5cqVFVf4eHl5oVq1apg/f77i8vTcnj59CgCKx0D88ssvuHjxokq/nO9vx44dcf78eaX9lpKSgu+//x7u7u6oW7fuW9VfVAq63Zro0KEDAGDJkiVK09W919X93ly6dKnKLSc0+b537NgRsbGxSufbZWVlYenSpbCwsMh3xFoTJ0+eVHvOSs45Rzk/E+3atYOlpSVCQ0NVTgfI2fYmTZrA0dERK1euVPrd89tvv+HGjRsF+tnt1asXsrOzMWPGDJW2rKwsxb578eKFyj7PGbUrDYemOHJTynTu3BnTp09HcHAwmjdvjitXrmDz5s2oWrVqkSzf3t4ehw8fRsuWLREQEIA//vgDlSpVUts3NTUVzZs3R7NmzdC+fXu4uroiISEBe/bswcmTJ9GtWzc0btz4rWvq1KkTFi5ciPbt26Nfv3548uQJli9fjurVq6uct+Ll5YUjR45g4cKFqFixIjw8PBQnN6pjZWWF1q1bY+7cucjMzESlSpVw6NAh3Lt3T6Mae/fujY8++ggrVqxAYGCgyk3h6tatC39/f3h5ecHOzg4XL17Ezp07MXr0aEWf3bt3Izg4GOvXry/04xMKsz0DBw5Ejx49AEDtL6zXNWrUCIMGDcL333+PhIQE+Pn54fz589i4cSO6deuGd999t1C1a/p+8vX1hYGBAW7duqW49B+QDg199913AFDocOPp6Ql9fX3MmTMHiYmJMDY2xnvvvaf2viL5MTQ0xGeffYYvvvgCYWFhaN++PdasWYMOHTqgXr16CA4ORqVKlfDo0SMcO3YMVlZW+OWXXwBIh6APHToEPz8/fPzxx6hTpw5iYmKwY8cO/PHHH7CxscHEiRPx008/oUOHDhgzZgzs7OywceNG3Lt3Dz///HOpuemnnp5egbe7oDw9PdG3b1+sWLECiYmJaN68OcLDw9Xex6tz587YtGkTrK2tUbduXZw5cwZHjhxBhQoVVJZZ0O/7xx9/jFWrViEoKAgRERFwd3fHzp07cerUKSxevFjpBOu3MWfOHERERODDDz9Ew4YNAUgXBvzwww+ws7NTnEBtZWWFRYsWYejQoWjatKnifl5//vknUlNTsXHjRhgaGmLOnDkIDg6Gn58f+vbtq7gU3N3dHZ9//vkb6/Hz88Mnn3yC0NBQREZGol27djA0NMTt27exY8cOfPvtt4pHmaxYsQIffPABqlWrhpcvX2L16tWwsrJS/IGkVVq5RkvH5VwKntclcjmXbe7YsUOlLS0tTfznP/8RLi4uwtTUVLRo0UKcOXNG5TLFvJah7pJsdZfC3rlzR7i4uIg6deqIp0+fqq0zMzNTrF69WnTr1k24ubkJY2NjYWZmJho3bizmzZundAl6XpeCm5ubqyxX3eW6a9euFTVq1BDGxsaidu3aYv369Wr73bx5U7Ru3Vpx6WTOZeE5fdVty8OHD8UHH3wgbGxshLW1tejZs6d4/PixyuWk+UlKSlKsM/eloTn++9//Cm9vb2FjYyNMTU1F7dq1xcyZM5Uuy815X+TeR2+i7lJwTbcnPT1d2NraCmtra5VLSIVQ//3IzMwUISEhwsPDQxgaGgpXV1cxadIkkZaWptTPzc2twJfAa/J+ytG0aVMBQJw7d05p+wEIV1fXAm2Luve/EEKsXr1aVK1aVejr67/xsvD83l+JiYnC2tpa6efz8uXL4sMPPxQVKlQQxsbGws3NTfTq1UuEh4crzfvPP/+IgQMHCgcHB2FsbCyqVq0qRo0apbQvoqKiRI8ePYSNjY0wMTER3t7eYv/+/UrLyev3QV6/i9RtDwAxatQopX45P9fz5s0r0PoKst157cucWnNfiv7q1SsxZswYUaFCBWFubi66dOkiHjx4oPJef/HihQgODhb29vbCwsJCBAYGips3bwo3NzeVW0fk9X1//XesEELExcUplmtkZCQaNGig8vOb1z7K2adv+h1z6tQpMWrUKFG/fn1hbW0tDA0NRZUqVURQUJCIiopS6b9v3z7RvHlzYWpqKqysrIS3t7f46aeflPps27ZNNG7cWBgbGws7OzvRv39/8fDhQ6U+ef1+zvH9998LLy8vYWpqKiwtLUWDBg3El19+KR4/fiyEEOLSpUuib9++okqVKsLY2Fg4OjqKzp07i4sXL+a7vSVFJkQhzt4iojIjKysLFStWRJcuXVTO5SIi0kWlY0yTiIrNnj178PTpUwwcOFDbpRARlQiO3BDpqHPnzuGvv/7CjBkzYG9vX+ibmRERlTUcuSHSUd999x1GjBgBR0dH/PDDD9ouh4ioxHDkhoiIiHQKR26IiIhIpzDcEBERkU4pdzfxk8vlePz4MSwtLYvkFuxERERU/IQQePnyJSpWrPjGG1iWu3Dz+PFjlSfcEhERUdnw4MEDlaeZv67chZucW2Y/ePAAVlZWWq6GiIiICiIpKQmurq4FevRFuQs3OYeirKysGG6IiIjKmIKcUsITiovJ06dPMWLECFSpUgXGxsZwdnZGYGAgTp06pe3SiIiIdFq5G7kpKd27d0dGRgY2btyIqlWrIi4uDuHh4Xj27FmhlpednQ2ZTFZqngJMRERUWvGTshgkJCTg5MmTmDNnDt599124ubnB29sbkyZNQteuXQEACxcuRIMGDWBubg5XV1eMHDkSycnJimVs2LABNjY22LdvH+rWrQtjY2Pcv38fFy5cQNu2bWFvbw9ra2v4+fnxtvpERES5cOSmGFhYWMDCwgJ79uxBs2bNYGxsrNJHT08PS5YsgYeHB+7evYuRI0fiyy+/xIoVKxR9UlNTMWfOHKxZswYVKlSAo6Mj7t69i0GDBmHp0qUQQmDBggXo2LEjbt++XaCTrIiICis7OxuZmZnaLoN0mJGRUZEcoSh3j19ISkqCtbU1EhMTi/WE4p9//hnDhg3Dq1ev8M4778DPzw99+vRBw4YN1fbfuXMnhg8fjvj4eADSyE1wcDAiIyPRqFGjPNcjl8thY2ODLVu2oHPnzsWyLURUvgkhEBsbi4SEBG2XQjpOT08PHh4eMDIyUmnT5PObIzfFpHv37ujUqRNOnjyJs2fP4rfffsPcuXOxZs0aBAUF4ciRIwgNDcXNmzeRlJSErKwspKWlITU1FWZmZgCkBPt6GIqLi8PkyZNx/PhxPHnyBNnZ2UhNTcX9+/e1sZlEVA7kBBtHR0eYmZnxBqhULHJushsTE4MqVaq81fuM4aYYmZiYoG3btmjbti2++eYbDB06FFOnToW/vz86d+6MESNGYObMmbCzs8Mff/yBIUOGICMjQxFuTE1NVb65gwYNwrNnz/Dtt9/Czc0NxsbG8PX1RUZGhjY2kYh0XHZ2tiLYVKhQQdvlkI5zcHDA48ePkZWVBUNDw0IvhycUl6C6desiJSUFERERkMvlWLBgAZo1a4aaNWvi8ePHBVrGqVOnMGbMGHTs2BH16tWDsbGx4lAWEVFRyznHJuePLqLilHM4Kjs7+62Ww5GbYvDs2TP07NkTgwcPRsOGDWFpaYmLFy9i7ty5eP/991G9enVkZmZi6dKl6NKlC06dOoWVK1cWaNk1atTApk2b0KRJEyQlJeGLL76AqalpMW8REZV3PBRFJaGo3mccuSki2XKBM1HPsDfyEa7GpaOptzcWLVqE1q1bo379+vjmm28wbNgwLFu2DI0aNcLChQsxZ84c1K9fH5s3b0ZoaGiB1rN27Vq8ePEC77zzDgYMGIAxY8bA0dGxmLeOiIio7NDq1VInTpzAvHnzEBERgZiYGOzevRvdunXLd57jx49j3LhxuHbtGlxdXTF58mQEBQUVeJ3FcbVU2NUYhPxyHTGJaYppLtYmmNqlLtrXdymSdRARaUNaWhru3bsHDw8PmJiYaLsc0nH5vd80+fzW6shNSkoKGjVqhOXLlxeo/71799CpUye8++67iIyMxNixYzF06FAcPHiwmCvNW9jVGIz48ZJSsAGA2MQ0jPjxEsKuxmipMiIiKk5paWkYNWoUKlSoAAsLC3Tv3h1xcXH5zhMXF4egoCBUrFgRZmZmaN++PW7fvq1of/78OT799FPUqlULpqamqFKlCsaMGYPExMRi3Za//voLrVq1gomJCVxdXTF37tw3ziOTyVReW7duVepz/PhxvPPOOzA2Nkb16tWxYcOGYtoCZVoNNx06dMB///tffPDBBwXqv3LlSnh4eGDBggWoU6cORo8ejR49emDRokXFXKl62XKBkF+uQ93QV860kF+uI1term4lRERULnz++ef45ZdfsGPHDvz+++94/PgxPvzwwzz7CyHQrVs33L17F3v37sXly5fh5uaGgIAApKSkAAAeP36Mx48fY/78+bh69So2bNiAsLAwDBky5K1qlclkiI6OVtuWlJSEdu3awc3NDREREZg3bx6mTZuG77///o3LXb9+PWJiYhSv3EdftDogIUoJAGL37t359mnVqpX47LPPlKatW7dOWFlZ5TlPWlqaSExMVLwePHggAIjExMS3rvn0nXjhNmG/4mXl20tU6PS50jS3CfvF6Tvxb70uIiJtePXqlbh+/bp49eqVtkvRyMaNG4WdnZ1IS0tTmv7++++Ljz766K2Xn5CQIAwNDcWOHTsU027cuCEAiDNnzqid59atWwKAuHr1qmJadna2cHBwEKtXr85zXdu3bxdGRkYiMzNTCCFESEiIcHFxEfHx/362dOzYUfj7+4vs7Gy1ywAg7t27p7ZtxYoVwtbWVqSnpyumTZgwQdSqVSvPmnKWmd/n9pdffinq1aunNK13794iMDAwz3nye78lJiYW+PO7TJ1QHBsbCycnJ6VpTk5OSEpKwqtXr9TOExoaCmtra8XL1dW1yOp58lL5UJSRfRUY2lV+Yz8iIipePXv2RHZ2Nvbt26eY9uTJE/z6668YPHgwTp48qXhUTl6vzZs357n8iIgIZGZmIiAgQDGtdu3aqFKlCs6cOaN2nvT0dABQOpdET08PxsbG+OOPP/JcV845JgYG0gXOX3/9Ndzd3TF06FAAwPLly3H69Gls3LixUI8uOHPmDFq3bq10V+DAwEDcunULL168yHfeUaNGwd7eHt7e3li3bh1ErtN4z5w5o7R/cpab1/4pSjp/KfikSZMwbtw4xddJSUlFFnAcLZVPdjKv61+gfkREVLxMTU3Rr18/rF+/Hj179gQA/Pjjj6hSpQr8/f2RlpaGyMjIfJfx+h/TucXGxsLIyAg2NjYq88TGxqqdJyf8TJo0CatWrYK5uTkWLVqEhw8fIiZG/fmZ8fHxmDFjBj7++GPFNH19ffz444/w9PTExIkTsWTJEqxZswZVqlTJd3vy2xYPDw+V7chps7W1VTvf9OnT8d5778HMzAyHDh1SPAB6zJgxinnzG5AoztuYlKlw4+zsrHKyVlxcHKysrPLcScbGxmofXFkUvD3s4GJtgtjENLXn3cgAOFubwNvDrljWT0REeRs2bBiaNm2KR48eoVKlStiwYQOCgoIgk8lgamqK6tWrF2g5s2bNwqxZsxRfX79+vVD1GBoaYteuXRgyZAjs7Oygr6+PgIAAdOjQQWnEI0dSUhI6deqEunXrYtq0aUptVatWxfz58/HJJ5+gd+/e6Nevn1J7hw4dcPLkSaVp9erVU9xHxs3NDdeuXSvUduT45ptvFP9v3LgxUlJSMG/ePEW40aYyFW58fX1x4MABpWmHDx+Gr6+vVurR15Nhape6GPHjJcgApYCTcxuiqV3qQl+PN78iIippjRs3RqNGjfDDDz+gXbt2uHbtGn799VcAwMmTJ9GhQ4d851+1ahX69++P4cOHo1evXorpFStWhLOzMzIyMpCQkKA0ehMXFwdnZ+c8l+nl5YXIyEgkJiYiIyMDDg4O8PHxQZMmTZT6vXz5Eu3bt4elpSV2796t9lEEJ06cgL6+PqKjo5GVlaU4bAUAa9asUTpdo0aNGjhw4AAqVaoEAErLy2vgIKetoHx8fDBjxgykp6fD2Ni4UAMSRUWr4SY5ORl37txRfH3v3j1ERkbCzs5OMXT36NEj/PDDDwCA4cOHY9myZfjyyy8xePBgHD16FNu3b1e8WbWhfX0XfPfROyr3uXHmfW6IiLRu6NChWLx4MR49eoSAgADFaQlNmjQp8GEpOzs72Nkpj8B7eXnB0NAQ4eHh6N69OwDg1q1buH//foH+4La2tgYA3L59GxcvXsSMGTMUbUlJSQgMDISxsTH27dun9v5C27Ztw65du3D8+HH06tULM2bMQEhIiKI9J8Tk5ubmBnd3d5Xpvr6++Prrr5GZmakIPYcPH0atWrXyPCSlTmRkJGxtbRVHS7Q6IPHGU46L0bFjxwSkAQ+l16BBg4QQQgwaNEj4+fmpzOPp6SmMjIxE1apVxfr16zVapyZnW2siK1suTt+JF3suPxSn78SLrGx5kS6fiEgbyurVUjkSEhKEmZmZMDIyElu3bi3SZQ8fPlxUqVJFHD16VFy8eFH4+voKX19fpT61atUSu3btUny9fft2cezYMREVFSX27Nkj3NzcxIcffqhoT0xMFD4+PqJBgwbizp07IiYmRvHKysoSQgjx4MEDYWtrK5YsWSKEECIsLEwYGBjkeZWWEPlfLZWQkCCcnJzEgAEDxNWrV8XWrVuFmZmZWLVqlaLPrl27lK6e2rdvn1i9erW4cuWKuH37tlixYoUwMzMTU6ZMUfS5e/euMDMzE1988YW4ceOGWL58udDX1xdhYWF51llUV0uVmkvBS0pxhRsiIl1U1sONEEIMGDBA7WXhb+vVq1di5MiRwtbWVpiZmYkPPvhAxMTEKPUBoPRH+LfffisqV64sDA0NRZUqVcTkyZOVLsHO64/+nHAil8tFmzZtRGBgoJDL//0j+tNPPxXVqlUTL1++VFtrfuFGCCH+/PNP0bJlS2FsbCwqVaokZs+erdS+fv16kXs85LfffhOenp7CwsJCmJubi0aNGomVK1eqXIqu6YBEUYUbrT5+QRuK4/ELRES6Shcev9CmTRvUq1cPS5Ys0XYp9AZF9fiFMnVCMRERUUG9ePECx48fx/Hjx7FixQptl0MliOGGiIh0UuPGjfHixQvMmTMHtWrV0nY5VIIYboiISCfl9Swl0n1l6vELRERERG/CkRsiItKMEEBqqnbWbWYGyHhjVMofww0REWkmNRWwsNDOupOTAXNz7aybygweliIiIiKdwpEbIiLSjJmZNIKirXUXsQcPHmDAgAF48uQJDAwM8M033yieJE5lE8MNERFpRibTqUNDBgYGWLx4MTw9PREbGwsvLy907NgR5jq0jeUNww0REZVrLi4ucHGRHnLs7OwMe3t7PH/+nOGmDOM5N0REpNP8/Pwgk8kgk8lgZGSEOnXqYMuWLWr7RkREIDs7W/H08OKyfPlyuLu7w8TEBD4+Pjh//ny+/UNDQ9G0aVNYWlrC0dER3bp1w61btxTt06ZNU2xjzqt27drFug2lGcMNERHpLCEELl++jPnz5yMmJga3bt1C+/btMXDgQNy7d0+p7/PnzzFw4EB8//33xVrTtm3bMG7cOEydOhWXLl1Co0aNEBgYiCdPnuQ5z++//45Ro0bh7NmzOHz4MDIzM9GuXTukpKQo+tSrVw8xMTGK1x9//FGs21GaMdwQEZHOun37Nl6+fIn27dvD2dkZHh4eGDJkCLKzs5VGPtLT09GtWzdMnDgRzZs3L9aaFi5ciGHDhiE4OBh169bFypUrYWZmhnXr1uU5T1hYGIKCglCvXj00atQIGzZswP379xEREaHoY2BgAGdnZ8XL3t6+WLejNGO4ISIinRUREQFbW1vUrVsXAPDw4UN8/fXXMDY2RsOGDQFIoztBQUF47733MGDAgDcuc9asWbCwsMj3df/+fbXzZmRkICIiAgEBAYppenp6CAgIwJkzZwq8XYmJiQAAOzs7xbTbt2+jYsWKqFq1Kvr3759nDeUBTygmIiKddenSJSQmJsLS0hLZ2dlIS0uDqakpVq5ciYoVKwIATp06hW3btqFhw4bYs2cPAGDTpk1o0KCB2mUOHz4cvXr1yne9Oct+XXx8PLKzs+Hk5KQ03cnJCTdv3izQNsnlcowdOxYtWrRA/fr1AQA+Pj7YsGEDatWqhZiYGISEhKBVq1a4evUqLC0tC7RcXcJwQ0REOuvSpUsYNWoUxowZg4SEBIwfPx4tWrRAUFCQok/Lli0hl8sLvEw7OzulEZOSNmrUKFy9elXpnJoOHToo/t+wYUP4+PjAzc0N27dvx5AhQ7RRplbxsBQREemsS5cuoXnz5qhevTqaNGmCFStWYM6cOW/1xPC3OSxlb28PfX19xMXFKU2Pi4uDs7PzG9c9evRo7N+/H8eOHUPlypXz7GdjY4OaNWvizp07mm2cjmC4ISIinXT37l0kJCQoDt0AQN26dVGtWrU8LwUviOHDhyMyMjLfV16HpYyMjODl5YXw8HDFNLlcjvDwcPj6+ua5TiEERo8ejd27d+Po0aPw8PDIt8bk5GRERUUp7t9T3vCwFBER6aSIiAgYGhqiZs2aStPbtGmD3bt346uvvirUct/2sNS4ceMwaNAgNGnSBN7e3li8eDFSUlIQHBys6LNs2TLs3r1bEYJGjRqFLVu2YO/evbC0tERsbCwAwNraGqamphg/fjy6dOkCNzc3PH78GFOnToW+vj769u1b6DrLMoYbIiLSSZcuXUKNGjVgZGSkND0gIAArV67Ew4cP8z20U1x69+6Np0+fYsqUKYiNjYWnpyfCwsKUTjKOj49HVFSU4uvvvvsOAODv76+0rPXr1yMoKAgPHz5E37598ezZMzg4OKBly5Y4e/YsHBwcSmSbShuZEEJou4iSlJSUBGtrayQmJsLKykrb5RARlWppaWm4d+8ePDw8YGJiou1ySMfl937T5POb59wQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiInqjcnZhLWlJUb3PGG6IiChPhoaGAIDU1FQtV0LlQUZGBgBAX1//rZbDm/gREVGe9PX1YWNjgydPngAAzMzMIJPJtFwV6SK5XI6nT5/CzMwMBgZvF08YboiIKF85D3TMCThExUVPTw9VqlR56wDNcENERPmSyWRwcXGBo6MjMjMztV0O6TAjIyPo6b39GTMMN0REVCD6+vpvfS4EUUngCcVERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSth5vly5fD3d0dJiYm8PHxwfnz5/Ptv3jxYtSqVQumpqZwdXXF559/jrS0tBKqloiIiEo7rYabbdu2Ydy4cZg6dSouXbqERo0aITAwEE+ePFHbf8uWLZg4cSKmTp2KGzduYO3atdi2bRu++uqrEq6ciIiISiuthpuFCxdi2LBhCA4ORt26dbFy5UqYmZlh3bp1avufPn0aLVq0QL9+/eDu7o527dqhb9++bxztISIiovJDa+EmIyMDERERCAgI+LcYPT0EBATgzJkzaudp3rw5IiIiFGHm7t27OHDgADp27JjnetLT05GUlKT0IiIiIt1loK0Vx8fHIzs7G05OTkrTnZyccPPmTbXz9OvXD/Hx8WjZsiWEEMjKysLw4cPzPSwVGhqKkJCQIq2diIiISi+tn1CsiePHj2PWrFlYsWIFLl26hF27duHXX3/FjBkz8pxn0qRJSExMVLwePHhQghUTERFRSdPayI29vT309fURFxenND0uLg7Ozs5q5/nmm28wYMAADB06FADQoEEDpKSk4OOPP8bXX38NPT3VrGZsbAxjY+Oi3wAiIiIqlbQ2cmNkZAQvLy+Eh4crpsnlcoSHh8PX11ftPKmpqSoBRl9fHwAghCi+YomIiKjM0NrIDQCMGzcOgwYNQpMmTeDt7Y3FixcjJSUFwcHBAICBAweiUqVKCA0NBQB06dIFCxcuROPGjeHj44M7d+7gm2++QZcuXRQhh4iIiMo3rYab3r174+nTp5gyZQpiY2Ph6emJsLAwxUnG9+/fVxqpmTx5MmQyGSZPnoxHjx7BwcEBXbp0wcyZM7W1CURERFTKyEQ5O56TlJQEa2trJCYmwsrKStvlEBERUQFo8vldpq6WIiIiInoThhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6heGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6ReNwM336dKSmpqpMf/XqFaZPn14kRREREREVlsbhJiQkBMnJySrTU1NTERISonEBy5cvh7u7O0xMTODj44Pz58/n2z8hIQGjRo2Ci4sLjI2NUbNmTRw4cEDj9RIREZFuMtB0BiEEZDKZyvQ///wTdnZ2Gi1r27ZtGDduHFauXAkfHx8sXrwYgYGBuHXrFhwdHVX6Z2RkoG3btnB0dMTOnTtRqVIl/PPPP7CxsdF0M4iIiEhHFTjc2NraQiaTQSaToWbNmkoBJzs7G8nJyRg+fLhGK1+4cCGGDRuG4OBgAMDKlSvx66+/Yt26dZg4caJK/3Xr1uH58+c4ffo0DA0NAQDu7u4arZOIiIh0W4HDzeLFiyGEwODBgxESEgJra2tFm5GREdzd3eHr61vgFWdkZCAiIgKTJk1STNPT00NAQADOnDmjdp59+/bB19cXo0aNwt69e+Hg4IB+/fphwoQJ0NfXVztPeno60tPTFV8nJSUVuEYiIiIqewoUbt555x2Eh4fD1tYWGzduxODBg2FhYfFWK46Pj0d2djacnJyUpjs5OeHmzZtq57l79y6OHj2K/v3748CBA7hz5w5GjhyJzMxMTJ06Ve08oaGhhToXiIiIiMqmAp1QfOPGDaSkpAAATpw4gVevXhVrUXmRy+VwdHTE999/Dy8vL/Tu3Rtff/01Vq5cmec8kyZNQmJiouL14MGDEqyYiIiISlqBRm48PT0RHByMli1bQgiBefPm5TlyM2XKlAKt2N7eHvr6+oiLi1OaHhcXB2dnZ7XzuLi4wNDQUOkQVJ06dRAbG4uMjAwYGRmpzGNsbAxjY+MC1URERERlX4HCzYYNGzB16lTs378fMpkMv/32GwwMVGeVyWQFDjdGRkbw8vJCeHg4unXrBkAamQkPD8fo0aPVztOiRQts2bIFcrkcenrSoNPff/8NFxcXtcGGiIiIyh+ZEEJoMoOenh5iY2PVXqqtqW3btmHQoEFYtWoVvL29sXjxYmzfvh03b96Ek5MTBg4ciEqVKiE0NBQA8ODBA9SrVw+DBg3Cp59+itu3b2Pw4MEYM2YMvv766wKtMykpCdbW1khMTISVldVbbwMREREVP00+vzW+z41cLi90Ya/r3bs3nj59iilTpiA2Nhaenp4ICwtTnGR8//59xQgNALi6uuLgwYP4/PPP0bBhQ1SqVAmfffYZJkyYUGQ1ERERUdlWoJGbffv2oUOHDjA0NMS+ffvy7du1a9ciK644cOSGiIio7NHk87tA4Sb3oajcIykqC5PJkJ2drXnFJYjhhoiIqOwp8sNSuQ9FFeVhKSIiIqKipvGDM9PS0oqjDiIiIqIiofEJxTY2NvD29oafnx/8/f3RvHlzmJqaFkdtRERERBrTeOTmyJEjaN++Pc6dO4f3338ftra2aNmyJb7++mscPny4OGokIiIiKjCN73OTW1ZWFi5cuIBVq1Zh8+bNkMvlPKGYiIiIilyx3ucGkO4KfPz4ccUrPT0dnTt3hr+/f2EWR0RERFRkNA43lSpVwqtXr+Dv7w9/f39MmDABDRs2hEwmK476iIiIiDSi8Tk3Dg4OSE1NRWxsLGJjYxEXF6e1p4QTERERvU7jcBMZGYnY2FhMnDgR6enp+Oqrr2Bvb4/mzZsX+PlORERERMXlrU4ofvbsGY4fP469e/fip59+4gnFREREVCyK9YTiXbt2KU4kvn79Ouzs7NCyZUssWLAAfn5+hS6aiIiIqChoPHLj6OiI1q1bw9/fH35+fmjQoEFx1VYsOHJDRERU9hTryM2TJ08KXRgRERFRcStwuMnKykJ2djaMjY0V0+Li4rBy5UqkpKSgS5cuaNWqVbEUSURERFRQBQ43w4YNg5GREVatWgUAePnyJZo2bYq0tDS4uLhg0aJF2Lt3Lzp27FhsxRIRERG9SYEvBT916hS6d++u+PqHH35AdnY2bt++jT///BPjxo3DvHnziqVIIiIiooIqcLh59OgRatSoofg6PDwc3bt3h7W1NQBg0KBBuHbtWtFXSERERKSBAocbExMTpTsRnz17Fj4+PkrtycnJRVsdERERkYYKHG48PT2xadMmAMDJkycRFxeH9957T9EeFRWFihUrFn2FRERERBoo8AnFU6ZMQYcOHbB9+3bExMQgKCgILi4uivbdu3ejRYsWxVIkERERUUEVONz4+fkhIiIChw4dgrOzM3r27KnU7unpCW9v7yIvkIiIiEgTb/VsqbKIdygmIiIqezT5/Nb4qeBEREREpRnDDREREekUhpuiFBICpKRouwoiIqJyTaNwk52djRMnTiAhIaGYyinDDh4Epk0DLCwAmQxYuxYoX6czERERlQoahRt9fX20a9cOL168KK56yq7Dh5W/HjoU0NMDKlYE/vxTOzURERGVQxoflqpfvz7u3r1bHLWUbfPnA2lpwKefKk+PiQE8PaXRnN69gcRErZRHRERUXmgcbv773/9i/Pjx2L9/P2JiYpCUlKT0KteMjYElS6TDUXfvAo0bK7dv3w7Y2EhBZ+lSHrYiIiIqBhrf50ZP7988JJPJFP8XQkAmkyE7O7voqisGWrnPzb59wPvvq2+zsZHO1+ENEImIiPKkyed3ge9QnOPYsWOFLqzc6tpVGqXJzAS++QaYM+fftoQEIOcBpJ07Axs2ABUqaKNKIiIincA7FGvLw4fSOTinT6tvnz0b+OIL6aRkIiKick6Tz+9ChZuEhASsXbsWN27cAADUq1cPgwcPhrW1deEqLkGlJtzkdvgw0K6d+jYjI6m9deuSrYmIiKgUKdbHL1y8eBHVqlXDokWL8Pz5czx//hwLFy5EtWrVcOnSpUIXXa61bSsdtsrKku6Vk1tGBuDnJ52E3KYNEBenlRKJiIjKCo1Hblq1aoXq1atj9erVMDCQTtnJysrC0KFDcffuXZw4caJYCi0qpXLkRp0nT4CPPlK9f06OKVOkl75+ydZFRESkBcV6WMrU1BSXL19G7dq1laZfv34dTZo0QWpqquYVl6AyE25yO3lSOmyVlqa+PSwMCAws2ZqIiIhKULEelrKyssL9+/dVpj948ACWlpaaLo4KolUr4NUrIDsbmDdPtb19e+mwla8v8OBByddHRERUimgcbnr37o0hQ4Zg27ZtePDgAR48eICtW7di6NCh6Nu3b3HUSDn09IDx46Xzc549U713ztmzQJUqUtD54gvp0nMiIqJyRuPDUhkZGfjiiy+wcuVKZGVlAQAMDQ0xYsQIzJ49G8bGxsVSaFEpk4el3uTCBWn05vlz9e179uR9E0EiIqIyoNgvBQeA1NRUREVFAQCqVasGMzOzwiymxOlkuMkhBPDdd8CoUerbGzYEdu8GqlYt2bqIiIjeUrGec5PDzMwMDRo0QIMGDcpMsNF5MhkwcqQUchITgT59lNv/+guoVk3qN2pU3icoExERlWEFGrn58MMPC7zAXbt2vVVBxU2nR27y8tdfQKdO0l2R1dmyBeD5UkREVIoV+ciNtbV1gV9UCjVsKF1FJZcD69aptvfrJ43m1KgB3LxZ8vUREREVIT5bqrxKSQHGjFEfdgAgKAhYtgwwNy/RsoiIiNQp1nNupk6din/++afQxVEpYW4OrF0rnZ9z4wZQs6Zy+4YNgIWFNKKT04+IiKgM0Djc7N27F9WqVUObNm2wZcsWpKenF0ddVJJq1wZu3ZICzE8/qbYPHSrdY6dSJeDPP0u+PiIiIg1oHG4iIyNx4cIF1KtXD5999hmcnZ0xYsQIXLhwoTjqo5LWp48Ucl69AkaPVm57/Bjw9JRGc3r3lq7IIiIiKmUKdSl448aNsWTJEjx+/Bhr167Fw4cP0aJFCzRs2BDffvstEvmhV/aZmABLl0pBJypKCjW5bd8O2NhIQWfZMh62IiKiUqPQ97kBACEEMjMzkZGRASEEbG1tsWzZMri6umLbtm1FVSNpW9WqwOXLUoDZu1e1/dNPpcNWtrbA+fMlXx8REVEuhQo3ERERGD16NFxcXPD555+jcePGuHHjBn7//Xfcvn0bM2fOxJgxY4q6VioNunaVQk5GBvDll8ptCQmAj480mtO1a96PgyAiIipGGl8K3qBBA9y8eRPt2rXDsGHD0KVLF+jr6yv1iY+Ph6OjI+RyeZEWWxR4KXgxePhQOgfn9Gn17XPmSA/81HurgUIiIirHivVS8F69eiE6Ohq//vorunXrphJsAMDe3r5UBhsqJpUrA6dOSSM6Bw+qtk+YAOjrA8bGwIkTJV8fERGVKxqHm2+++QaVKlUqjlpIF7RrJ4WcrCxg2jTltowMwM9POmzVpg0QF6eVEomISLcV6g7FDx8+xL59+3D//n1kZGQotS1cuLDIiisOPCylBXFxwEcfAUeOqG+fMkV6qRkFJCIiAjT7/NY43ISHh6Nr166oWrUqbt68ifr16yM6OhpCCLzzzjs4evToWxVf3BhutOzkSWl0J68nkh88KLUTERHlUqzn3EyaNAnjx4/HlStXYGJigp9//hkPHjyAn58fevbsWaiCly9fDnd3d5iYmMDHxwfnC3g58datWyGTydCtW7dCrZe0oFUr6QaB2dnA3Lmq7YGB0mErX9+8n2JORESUD43DzY0bNzBw4EAAgIGBAV69egULCwtMnz4dc+bM0biAbdu2Ydy4cZg6dSouXbqERo0aITAwEE+ePMl3vujoaIwfPx6tWrXSeJ1UCujpAV98IZ2f8+yZdOl4bmfPAq6uUtD58ksgM1M7dRIRUZmjcbgxNzdXnGfj4uKCqKgoRVt8fLzGBSxcuBDDhg1DcHAw6tati5UrV8LMzAzr8npaNYDs7Gz0798fISEhqFq1qsbrpFLGzk66OaAQwLlz0te5zZsHGBlJQUfdTQSJiIhyKXC4mT59OlJSUtCsWTP88ccfAICOHTviP//5D2bOnInBgwejWbNmGq08IyMDERERCAgI+LcgPT0EBATgzJkz+dbi6OiIIUOGaLQ+KgO8vaWRHLlceqzD67p1k0KOpydw715JV0dERGVAgcNNSEgIUlJSsHDhQvj4+CimtWnTBtu2bYO7uzvWrl2r0crj4+ORnZ0NJycnpelOTk6IjY1VO88ff/yBtWvXYvXq1QVaR3p6OpKSkpReVAbIZMCoUdJoTmKi9EDP3P78U3oshEwmPeAzrxOUiYio3ClwuMm5qKpq1apo2LAhAOkQ1cqVK/HXX3/h559/hpubW/FU+X8vX77EgAEDsHr1atjb2xdontDQUFhbWyterq6uxVojFQMrK+Cnn6SgExkJvH6fpeXLAVNTKejwmWZEROWeRufcyGSyIl25vb099PX1Effazdzi4uLg7Oys0j8qKgrR0dHo0qULDAwMYGBggB9++AH79u2DgYGB0vk/OSZNmoTExETF68GDB0W6DVTCGjWSrqKSywF152X16SOFnBo1gJs3S74+IiLSOo3CTc2aNWFnZ5fvSxNGRkbw8vJCeHi4YppcLkd4eDh8fX1V+teuXRtXrlxBZGSk4tW1a1e8++67iIyMVDsqY2xsDCsrK6UX6QCZDAgOlkZzkpOl/+d25w5Qp47Ub/BgICVFO3USEVGJM9Ckc0hICKytrYu0gHHjxmHQoEFo0qQJvL29sXjxYqSkpCD4/x9WAwcORKVKlRAaGgoTExPUr19faX4bGxsAUJlO5Yi5uTSKs26dNFrTtStw+/a/7evXSy8AWLtWCkJFPApJRESlh0bhpk+fPnB0dCzSAnr37o2nT59iypQpiI2NhaenJ8LCwhQnGd+/fx96fJo0FVTt2sDff0v/37oV6NtXuX3IEOlVqRJw4ADw//PHiIhIdxT48Qv6+vqIiYkp8nBT0vj4hXIoLU26YaC6S8sBoHdv4PvvpROXiYioVCqWxy8U4vmaRKWDiQmwdKl0fk5UlHRScm7btgHW1tKhquXLpX5ERFRmFTjcyOXyMj9qQ4SqVaXLyYUA9uxRbR89Wno0hJ0dcOFCSVdHRERFgCezUPn1/vtSyMnIkA5b5fbihXS3ZJlMOkH5+XPt1EhERBpjuCEyNJSeUC4E8OCB9ETy3H75BahQQQo68+ZJ99ghIqJSi+GGKLfKlYHTp6Wgc/CgavuXXwL6+tJ5PCdPlnx9RET0Rgw3RHlp104KOVlZwNSpym3p6UDr1tJoTkAA8NpdtomISHsYbojeRF8fmDZNCjqxsUCbNsrt4eGAs7MUdKZNA7KztVElERH9H8MNkSacnIAjR6Sgc/w4YGSk3B4SAhgYSEHn8GGtlEhEVN4x3BAVlp+fdHgqOxuYM0e1vV07KeQ0by497JOIiEoEww3R29LTk040FgJ49gzo0kW5/cwZwNVVCjoTJgCZmdqpk4ionGC4ISpKdnbAvn1S0Dl3DrC1VW6fO1c6lCWTSf2IiKjIMdwQFRdvb+nmf3K59PiH173/vhRyGjcG7t0r+fqIiHQUww1RcZPJpMc6CAEkJkoP6swtMlJ6LIRMBnz6qXQeDxERFRrDDVFJsrICtm6Vgs7ly0DFisrty5ZJNwiUyaQHehIRkcYYboi0xdMTePRIOmy1dq1qe58+UsipVQu4davEyyMiKqsYboi0TSYDBg+WRnOSk4GgIOX2v/8GateW+g0ZAqSmaqVMIqKyguGGqDQxNwfWr5eCzvXrQPXqyu3r1kl9ZLJ/+xERkRKGG6LSqk4d4PZtKcBs3qzaPniwdI8dV1fgr79Kvj4iolKK4YaoLOjXTwo5r14BI0cqtz18CDRqJI3m9O0LJCVpp0YiolKC4YaoLDExAZYvl4LOnTtAw4bK7Vu3AtbWUtDJ6UdEVM4w3BCVVdWqAX/+KQWY3btV20ePlg5bVagAXLhQ8vUREWkJww2RLujWTQo5GRnAF18otz1/Lt0tWSaT7or8/LlWSiQiKikMN0S6xNBQen6VEMD9+0CzZsrt+/ZJIzkyGTBvnnSPHSIiHcNwQ6SrXF2lJ5ILAYSFqbZ/+SWgrw+YmgJ//FHy9RERFROGG6LyIDBQCjmZmcCUKcptaWlAq1bSaE7btsCTJ9qpkYioiDDcEJUnBgZASIgUdGJigPfeU24/cgRwcpKCTkgIkJ2tnTqJiN4Cww1ReeXsDISHS0Hn+HHAyEi5fdo0KQzJZMDhw9qokIioUBhuiAjw8wPS06WRmtmzVdvbtZNCTosW0sM+iYhKMYYbIvqXnh4wYYI0mhMfD3TurNx++jRQubIUdCZMkM7hISIqZRhuiEi9ChWAX36Rgs7Zs4CNjXL73LnSoSyZTOpHRFRKMNwQ0Zv5+AAvXkj3xVmyRLW9a1cp5DRuDNy7V/L1ERHlwnBDRAUnkwGffiqN5iQkAL16KbdHRgJVq0r9xoyRzuMhIiphDDdEVDjW1sC2bVLQuXwZqFhRuX3pUulBnzKZ1I+IqIQw3BDR2/P0lK6iksuBNWtU2/v0kUJO7drA33+XeHlEVL4w3BBR0ZHJgCFDpNGc5GQgKEi5/dYtoFatf/ulpmqlTCLSbQw3RFQ8zM2B9euloHP9OlC9unL7unVSH5kM2LBBKyUSkW5iuCGi4lenDnD7thR0Nm9WbQ8OlkKOqyvw118lXx8R6RSGGyIqWf36SSHn1Stg5EjltocPgUaNpKDTty+QlKSdGomoTGO4ISLtMDEBli+Xgs6dO0DDhsrtW7dKV2TJZMCKFVI/IqICYLghIu2rVg34808pwOzerdo+apT0aIgKFYALF0q+PiIqUxhuiKh06dZNCjnp6cD48cptz58D3t7SaE63btJdk4mIXsNwQ0Slk5ERMG+eFHTu3weaNVNu37sXsLOTgs78+dI9doiIwHBDRGWBqytw5owUdH77TbX9iy8AfX3AzAw4dark6yOiUoXhhojKlvbtpZCTmQlMmaLc9uoV0LKlNJrTrh3w5Il2aiQirWK4IaKyycAACAmRgk5MDPDuu8rthw8DTk5S0Jk+HcjO1k6dRFTiGG6IqOxzdgaOHpWCzvHjgKGhcvvUqVIYksmk0ENEOo3hhoh0i58fkJEhjdSEhqq2t2snhZyWLaWHfRKRzmG4ISLdpKcHTJwojebExwOdOim3nzoFVK4sBZ2JE6VzeIhIJzDcEJHuq1AB2L9fCjpnzgBWVsrtc+ZIl57LZFI/IirTGG6IqHxp1gxITJTui/Ptt6rtXbpIIeedd4Do6BIvj4jeHsMNEZVPMhkwZow0mpOQAPTsqdx++TLg4SH1++wz6Y7JRFQmMNwQEVlbA9u3S0Hn8mXAxUW5fckS6UGfMhmwY4d2aiSiAmO4ISLKzdMTePxYOmy1erVqe69eUsipXRv4++8SL4+I3ozhhohIHZkMGDpUGs1JTgYGDVJuv3ULqFXr336pqdqpk4hUMNwQEb2JuTmwYYMUdK5fB6pVU25fu1bqI5NJ/YhIqxhuiIg0UacOcOeOFHR+/FG1PThYCjlVqgBXrpR8fURUOsLN8uXL4e7uDhMTE/j4+OD8+fN59l29ejVatWoFW1tb2NraIiAgIN/+RETFpn9/KeS8egWMGKHc9uAB0LChFHT69QNevtROjUTlkNbDzbZt2zBu3DhMnToVly5dQqNGjRAYGIgneTzN9/jx4+jbty+OHTuGM2fOwNXVFe3atcMj3kadiLTFxARYsUIKOrdvA/XrK7f/9JN040CZDPjuO6kfERUbmRDa/Snz8fFB06ZNsWzZMgCAXC6Hq6srPv30U0ycOPGN82dnZ8PW1hbLli3DwIED39g/KSkJ1tbWSExMhNXrdyklIipKu3YB3burb6tQAQgLA5o0KdmaiMooTT6/tTpyk5GRgYiICAQEBCim6enpISAgAGfOnCnQMlJTU5GZmQk7O7viKpOIqHA+/FAapUlPB8aNU2579gxo2lQazfngA+DFC+3USKSDtBpu4uPjkZ2dDScnJ6XpTk5OiI2NLdAyJkyYgIoVKyoFpNzS09ORlJSk9CIiKlFGRsCCBVLQuX8f8PFRbt+zB7Czk4JOTj8iKjStn3PzNmbPno2tW7di9+7dMDExUdsnNDQU1tbWiperq2sJV0lElIurK3D2rBRgDhxQbR8/Xnqiubm59ORyItKYVsONvb099PX1ERcXpzQ9Li4Ozs7O+c47f/58zJ49G4cOHULDhg3z7Ddp0iQkJiYqXg8ePCiS2omI3lqHDlLIycwEvvlGuS01FWjZUhrNCQwEnj7VTo1EZZBWw42RkRG8vLwQHh6umCaXyxEeHg5fX98855s7dy5mzJiBsLAwNHnDyXjGxsawsrJSehERlSoGBsD06VLQefwY8PdXbj90CHB0lILOjBlAdrZWyiQqK7R+WGrcuHFYvXo1Nm7ciBs3bmDEiBFISUlBcHAwAGDgwIGYNGmSov+cOXPwzTffYN26dXB3d0dsbCxiY2ORnJysrU0gIio6Li7AsWNS0Dl2TAo+uU2ZIk2TyYBcfxgS0b+0Hm569+6N+fPnY8qUKfD09ERkZCTCwsIUJxnfv38fMTExiv7fffcdMjIy0KNHD7i4uChe8+fP19YmEBEVD39/6ZBVdjYwa5Zqe0CAFHJatZJGfIgIQCm4z01J431uiKhMi4+XHuKp7mRkAJg4UTrEZWhYsnURFbMyc58bIiLSkL098Ouv0mGr06elOx/nNnu2dOm5TAbs36+dGom0jOGGiKis8vUFEhMBuRxYvFi1vUsXKeR4eQHR0SVdHZHWMNwQEZV1Mhnw2WfSaM6LF0CPHsrtly4BHh7/9ktP106dRCWE4YaISJfY2AA7dkhB59Il4PV7hi1ZIj3oUyaT+hHpIIYbIiJd1bgxEBMjHbZavVq1vVcvKeTUqQP8/XfJ10dUTBhuiIh0nUwGDB0qjea8fAkMHKjcfvMmUKuW1G/YMOnuyERlGMMNEVF5YmEBbNwoBZ1r14CqVZXb16yRnmslk0n9iMoghhsiovKqbl0gKkoKOps2qbYHBUkhx80NuHq1xMsjKiyGGyIiAj76SAo5qanA8OHKbffvAw0aSEGnf3/p0BZRKcZwQ0RE/zI1Bb77Tgo6t28D9esrt2/ZIt04UCb7tx9RKcNwQ0RE6lWvDly5IgWYn39WbR85EtDTAxwcgIiIkq+PKA8MN0RE9GYffiiFnPR0YNw45bb4eKBJE2k054MPpBsJEmkRww0RERWckRGwYIEUdP75B/D2Vm7fswews5OCzsKFPGxFWsFwQ0REhVOlCnDunBRg1D2l/D//kQ5bmZsDp06VfH1UbjHcEBHR2+vQQQo5mZnA5MnKbampQMuW0mhO+/bA06faqZHKDYYbIiIqOgYGwIwZUtB5/Bjw91duP3gQcHSUgs6MGUB2tlbKJN3GcENERMXDxQU4dkwKOkePAvr6yu1TpkhhSCYDwsO1UyPpJIYbIiIqfu++C2RlSSM1s2aptgcESCGnVStpxIfoLTDcEBFRydHTAyZNkkZznj4FOnZUbv/jD6BSJSnoTJokBSIiDTHcEBGRdtjbA7/+KgWd06cBS0vl9tmzAUNDKejs36+dGqlMYrghIiLt8/UFkpIAuRxYvFi1vUsXKeR4eQHR0SVdHZUxDDdERFR6yGTAZ59JozkvXgDduyu3X7oEeHhI/caOBTIytFImlW4MN0REVDrZ2AA7d0pBJyICcHJSbv/2W8DYWAo6O3ZopUQqnRhuiIio9HvnHSA2Vjps9f33qu29ekkhp25d6WnmVK4x3BARUdkhkwHDhkmjOS9fAgMHKrffuAHUrCn1+/hj4NUr7dRJWsVwQ0REZZOFBbBxoxR0rl6VzsXJbfVqwMxMCjo//KCdGkkrGG6IiKjsq1cPuHtXCjqbNqm2DxokhRw3NykIkU5juCEiIt3y0UdSyElNBT75RLnt/n2gQQMp6Hz0kXRoi3QOww0REekmU1Ng5Uop6Ny+LY3u5LZ5M2BlJQWdnH6kExhuiIhI91WvLh2OEkL9ZeMjRkiPhnB0lO6lQ2Uaww0REZUvPXpIISc9Hfj8c+W2p0+luyDLZMCHHwIJCVopkd4Oww0REZVPRkbAwoVS0ImOBpo0UW7fvRuwtZWCzqJFPGxVhjDcEBERubkBFy5IAUbdQzrHjZMOW1lYSA/5pFKN4YaIiCi3Tp2kkJOZCXz9tXJbSgrQooU0mtOhg3QYi0odhhsiIiJ1DAyA//5XCjqPHwN+fsrtYWHSCcgyGTBzpvRoCCoVGG6IiIjexMUFOH5cCjpHjwL6+srtkydL0/T0pHbSKoYbIiIiTbz7LpCVBWRnSyM2uQkBtGkjjea0bi2N+FCJY7ghIiIqDD094KuvpEDz9Kl0Dk5uJ08ClSpJQeerr6RARCWC4YaIiOht2dsDBw5IQefUKemqqtxCQwFDQynoHDignRrLEYYbIiKiotS8ufTMKrlcuj/O6zp1kkJOkybAP/+UfH3lAMMNERFRcZDJgLFjpdGcFy+kOx7nFhEBuLtL/T7/HMjI0EaVOonhhoiIqLjZ2AA//ywFnYsXpUvIc1u8GDA2loLOzz9ro0KdwnBDRERUkry8gLg46bDVypWq7T16SCGnXj3paeakMYYbIiIibZDJgE8+kUZzkpKAjz5Sbr9+HahZ899+r15pp84yiOGGiIhI2ywtgU2bpKBz9ap0Lk5u338PmJlJQWfTJq2UWJYw3BAREZUm9eoB9+5JQeeHH1TbBw6UQo67O3DtWomXVxYw3GjJ8ePHIZPJkJCQoO1SiIiotBowQAo5qanAxx8rt/3zD1C/vhR0BgwAkpPfenUymQx79ux56+VoG8NNMTtz5gz09fXRqVMnbZdCRERllakpsGqVFHT+/huoW1e5/ccfpUNbMtm//dQICgqCTCZTebVv374ENqLkMNwUs7Vr1+LTTz/FiRMn8FgLzxjJzMws8XUSEVExqlFDOhwlBLBjh2r78OHSoyGcnIBLl1Sa27dvj5iYGKXXTz/9VAKFlxyGm2KUnJyMbdu2YcSIEejUqRM2bNiQZ98NGzbAxsYGe/bsQY0aNWBiYoLAwEA8ePBAqd/evXvxzjvvwMTEBFWrVkVISAiycj2vRCaT4bvvvkPXrl1hbm6Omf9/qFtB5luzZg0++OADmJmZoUaNGti3b1/R7hAiIipaPXpIISctTbphYG5PnkiXnctkQPfuwP9PgzA2Noazs7PSy9bWVjFbTEwMOnToAFNTU1StWhU7d+5UtKk7pSIyMhIymQzR0dHFt52aEuVMYmKiACASExOLfV1r164VTZo0EUII8csvv4hq1aoJuVwuhBDi2LFjAoB48eKFEEKI9evXC0NDQ9GkSRNx+vRpcfHiReHt7S2aN2+uWN6JEyeElZWV2LBhg4iKihKHDh0S7u7uYtq0aYo+AISjo6NYt26diIqKEv/880+B56tcubLYsmWLuH37thgzZoywsLAQz549K/b9RERERSg6WogmTYSQYo/SaxAg3q9fX4j/fxa9DoCoUKGCWL16tbh165aYPHmy0NfXF9evXxdCqH52CSHE5cuXBQBx7969Yt0sTT6/GW6KUfPmzcXixYuFEEJkZmYKe3t7cezYMSGE+nADQJw9e1Yx/40bNwQAce7cOSGEEG3atBGzZs1SWsemTZuEi4uL4msAYuzYsUp9Cjrf5MmTFV8nJycLAOK3334r5NYTEZHW7d+vEm70AWGe8zI1Febm5mLmzJlCCOmzYPjw4UqL8PHxESNGjBBClJ1ww8NSxeTWrVs4f/48+vbtCwAwMDBA7969sXbt2jznMTAwQNOmTRVf165dGzY2Nrhx4wYA4M8//8T06dNhYWGheA0bNgwxMTFITU1VzNekSROl5RZ0voYNGyr+b25uDisrKzx58uTtdgQREWlPp05SrMnMBL76CgDwLoDInNerV4hMScHw8HAgPh4A4Ovrq7QIX19fxedQWWGg7QJ01dq1a5GVlYWKFSsqpgkhYGxsjGXLlhVqmcnJyQgJCcGHrz98DYCJiYni/+bm5oWaz9DQUKlNJpNBLpcXqlYiIipFDAyAmTOBR49gHhuL6q9eASdO/Nt+9Cjg4CD9f98+6W7JeqrjH3r/nyZyXY1VGi9cYbgpBllZWfjhhx+wYMECtGvXTqmtW7du+Omnn1C7dm218128eBHe3t4ApNGfhIQE1KlTBwDwzjvv4NatW6hevbpG9RR2PiIi0kEmJkBYmPT/o0eBtm2l51z939mff8ZAfX0p3Bw+jLNnz6Jx48YAAIf/B6CYmBjFSciRkZElWn5BMNwUg/379+PFixcYMmQIrK2tldq6d++OtWvXYt68eSrzGRoa4tNPP8WSJUtgYGCA0aNHo1mzZoqwM2XKFHTu3BlVqlRBjx49oKenhz///BNXr17Ff//73zzrKex8RESke9LT0xEbGyt9Ubcu8OgRDGQy2K9ZA0yejB0AmgBoKZdjc5s2OA9gbWYmEBOD6tWrw9XVFdOmTcPMmTPx999/Y8GCBVrcGvV4zk0RyZYLnIl6hr2RjzB/yXdo0yZAJdgAUri5ePEi/vrrL5U2MzMzTJgwAf369UOLFi1gYWGBbdu2KdoDAwOxf/9+HDp0CE2bNkWzZs2waNEiuLm55VtbYecjIiLdExYWBhcXF6VXSz8/4OuvAQAhs2dja4UKaAjgBwA/AagbEQFUrAhDIyP85O+PmzduoGHDhpgzZ47SH8m5PwvPRD1Dtlz9zQSLm0yIPG5jWIKWL1+OefPmITY2Fo0aNcLSpUsVoxXq7NixA9988w2io6NRo0YNzJkzBx07dizQupKSkmBtbY3ExERYWVkVSf1hV2MQ8st1xCSmKaa5WJtgape6aF/fpUDL2LBhA8aOHcvHMRARUelx+jQQGJj3ox1+/RX4/+dvUXwW5keTz2+tj9xs27YN48aNw9SpU3Hp0iU0atQIgYGBeV6lc/r0afTt2xdDhgzB5cuX0a1bN3Tr1g1Xr14t4colYVdjMOLHS0rfTACITUzDiB8vIexqjFbqIiIiemvNmwMvX0rn5CxcqNreqRMgkyGxvif+uzys1HwWan3kxsfHB02bNlVcQSSXy+Hq6opPP/0UEydOVOnfu3dvpKSkYP/+/YppzZo1g6enJ1auXPnG9RXlyE22XKDlnKNK30x5Zhqynj+CoaMH9GR6cLY2wR8T3oO+nizfZXHkhoiIyoQXL4ChQ4FduxST0gC4AOgCwLNRIJa3G4lsPX0AgAwo8GdhfsrMyE1GRgYiIiIQEBCgmKanp4eAgACcOXNG7TxnzpxR6g9I55Tk1T89PR1JSUlKr6Jy/t5zpWDz4vcNeLikH2I2fo7sl88gAMQkpuH8vedvXFZQUBCDDRERlX62tsDPP0v3z7l4EZl29jABsBjAAQAhfx7EwP3/nmSsyWdhUdFquImPj0d2djacnJyUpjs5Of17JvdrYmNjNeofGhoKa2trxcvV1bVoigfw5KXy8JuhvRtsWn2EikO/g4GVQ579iIiIdIKXFw6ER8L9y19wK3AU/gbwJYBkayeVriX5Wajzl4JPmjQJ48aNU3ydlJRUZAHH0dJE6WuLeu8WqB8REZGucLQ0AWQybPHsgC2eHfLvV0K0Gm7s7e2hr6+PuLg4pelxcXFwdnZWO4+zs7NG/Y2NjWFsbFw0Bb/G28MOLtYmiE1Mg7oTl3KOM3p72BXL+omIiLStNH4WavWwlJGREby8vBAeHq6YJpfLER4ervJsixy+vr5K/QHg8OHDefYvTvp6MkztUheA9M3LLefrqV3qvtUJVERERKVZafws1Pql4OPGjcPq1auxceNG3LhxAyNGjEBKSgqCg4MBAAMHDsSkSZMU/T/77DOEhYVhwYIFuHnzJqZNm4aLFy9i9OjRWqm/fX0XfPfRO3C2Vh5uc7Y2wXcfvVMk1/YTERGVZqXts1Dr59z07t0bT58+xZQpUxAbGwtPT0+EhYUpThq+f/++4kFdANC8eXNs2bIFkydPxldffYUaNWpgz549qF+/vrY2Ae3ru6BtXWecv/ccT16mwdFSGn7jiA0REZUXpemzUOv3uSlpxXGHYiIiIipeZeY+N0RERERFjeGGiIiIdArDDREREekUhhsiIiLSKQw3REREpFMYboiIiEinMNwQERGRTmG4ISIiIp3CcENEREQ6ReuPXyhpOTdkTkpK0nIlREREVFA5n9sFebBCuQs3L1++BAC4urpquRIiIiLS1MuXL2FtbZ1vn3L3bCm5XI7Hjx/D0tISMlnRPswrKSkJrq6uePDgAZ9b9Za4L4sO92XR4b4sOtyXRae87EshBF6+fImKFSsqPVBbnXI3cqOnp4fKlSsX6zqsrKx0+g1Wkrgviw73ZdHhviw63JdFpzzsyzeN2OTgCcVERESkUxhuiIiISKcw3BQhY2NjTJ06FcbGxtoupczjviw63JdFh/uy6HBfFh3uS1Xl7oRiIiIi0m0cuSEiIiKdwnBDREREOoXhhoiIiHQKww0RERHpFIYbDS1fvhzu7u4wMTGBj48Pzp8/n2//HTt2oHbt2jAxMUGDBg1w4MCBEqq09NNkX167dg3du3eHu7s7ZDIZFi9eXHKFlgGa7MvVq1ejVatWsLW1ha2tLQICAt74Pi5PNNmXu3btQpMmTWBjYwNzc3N4enpi06ZNJVht6abp78scW7duhUwmQ7du3Yq3wDJEk325YcMGyGQypZeJiUkJVlsKCCqwrVu3CiMjI7Fu3Tpx7do1MWzYMGFjYyPi4uLU9j916pTQ19cXc+fOFdevXxeTJ08WhoaG4sqVKyVceemj6b48f/68GD9+vPjpp5+Es7OzWLRoUckWXIppui/79esnli9fLi5fvixu3LghgoKChLW1tXj48GEJV176aLovjx07Jnbt2iWuX78u7ty5IxYvXiz09fVFWFhYCVde+mi6L3Pcu3dPVKpUSbRq1Uq8//77JVNsKafpvly/fr2wsrISMTExildsbGwJV61dDDca8Pb2FqNGjVJ8nZ2dLSpWrChCQ0PV9u/Vq5fo1KmT0jQfHx/xySefFGudZYGm+zI3Nzc3hptc3mZfCiFEVlaWsLS0FBs3biyuEsuMt92XQgjRuHFjMXny5OIor0wpzL7MysoSzZs3F2vWrBGDBg1iuPk/Tffl+vXrhbW1dQlVVzrxsFQBZWRkICIiAgEBAYppenp6CAgIwJkzZ9TOc+bMGaX+ABAYGJhn//KiMPuS1CuKfZmamorMzEzY2dkVV5llwtvuSyEEwsPDcevWLbRu3bo4Sy31Crsvp0+fDkdHRwwZMqQkyiwTCrsvk5OT4ebmBldXV7z//vu4du1aSZRbajDcFFB8fDyys7Ph5OSkNN3JyQmxsbFq54mNjdWof3lRmH1J6hXFvpwwYQIqVqyoEsTLm8Luy8TERFhYWMDIyAidOnXC0qVL0bZt2+Iut1QrzL78448/sHbtWqxevbokSiwzCrMva9WqhXXr1mHv3r348ccfIZfL0bx5czx8+LAkSi4Vyt1TwYnoX7Nnz8bWrVtx/Pjx8nfCYRGxtLREZGQkkpOTER4ejnHjxqFq1arw9/fXdmllxsuXLzFgwACsXr0a9vb22i6nzPP19YWvr6/i6+bNm6NOnTpYtWoVZsyYocXKSg7DTQHZ29tDX18fcXFxStPj4uLg7Oysdh5nZ2eN+pcXhdmXpN7b7Mv58+dj9uzZOHLkCBo2bFicZZYJhd2Xenp6qF69OgDA09MTN27cQGhoaLkON5ruy6ioKERHR6NLly6KaXK5HABgYGCAW7duoVq1asVbdClVFL8vDQ0N0bhxY9y5c6c4SiyVeFiqgIyMjODl5YXw8HDFNLlcjvDwcKWEnJuvr69SfwA4fPhwnv3Li8LsS1KvsPty7ty5mDFjBsLCwtCkSZOSKLXUK6r3pVwuR3p6enGUWGZoui9r166NK1euIDIyUvHq2rUr3n33XURGRsLV1bUkyy9ViuJ9mZ2djStXrsDFxaW4yix9tH1Gc1mydetWYWxsLDZs2CCuX78uPv74Y2FjY6O4xG7AgAFi4sSJiv6nTp0SBgYGYv78+eLGjRti6tSpvBT8/zTdl+np6eLy5cvi8uXLwsXFRYwfP15cvnxZ3L59W1ubUGpoui9nz54tjIyMxM6dO5UuFX358qW2NqHU0HRfzpo1Sxw6dEhERUWJ69evi/nz5wsDAwOxevVqbW1CqaHpvnwdr5b6l6b7MiQkRBw8eFBERUWJiIgI0adPH2FiYiKuXbumrU0ocQw3Glq6dKmoUqWKMDIyEt7e3uLs2bOKNj8/PzFo0CCl/tu3bxc1a9YURkZGol69euLXX38t4YpLL0325b179wQAlZefn1/JF14KabIv3dzc1O7LqVOnlnzhpZAm+/Lrr78W1atXFyYmJsLW1lb4+vqKrVu3aqHq0knT35e5Mdwo02Rfjh07VtHXyclJdOzYUVy6dEkLVWuPTAghtDVqRERERFTUeM4NERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaISp2goCB069atSJe5YcMG2NjYFOkyiah0YrghojwFBQVBJpOpvErjA/h+//13vPfee7Czs4OZmRlq1KiBQYMGISMjAwDQu3dv/P3330W6zujoaMhkMkRGRhbpcono7TDcEFG+2rdvj5iYGKWXh4eHSr+cEKEN169fR/v27dGkSROcOHECV65cwdKlS2FkZITs7GwAgKmpKRwdHbVSnzb3DVF5xHBDRPkyNjaGs7Oz0ktfXx/+/v4YPXo0xo4dC3t7ewQGBgIAFi5ciAYNGsDc3Byurq4YOXIkkpOTFcvLOTx08OBB1KlTBxYWFooAlZcLFy7AwcEBc+bMUdt+6NAhODs7Y+7cuahfvz6qVauG9u3bY/Xq1TA1NVVab45p06bB09MTmzZtgru7O6ytrdGnTx+8fPlS0ScsLAwtW7aEjY0NKlSogM6dOyMqKkrRnhPyGjduDJlMBn9/fwD/HlabOXMmKlasiFq1agEANm3ahCZNmsDS0hLOzs7o168fnjx5osF3g4gKguGGiApt48aNMDIywqlTp7By5UoAgJ6eHpYsWYJr165h48aNOHr0KL788kul+VJTUzF//nxs2rQJJ06cwP379zF+/Hi16zh69Cjatm2LmTNnYsKECWr7ODs7IyYmBidOnNCo/qioKOzZswf79+/H/v378fvvv2P27NmK9pSUFIwbNw4XL15EeHg49PT08MEHH0AulwMAzp8/DwA4cuQIYmJisGvXLsW84eHhuHXrFg4fPoz9+/cDADIzMzFjxgz8+eef2LNnD6KjoxEUFKRRzURUANp+cicRlV6DBg0S+vr6wtzcXPHq0aOHEEJ6EnHjxo3fuIwdO3aIChUqKL5ev369ACDu3LmjmLZ8+XLh5OSktN73339f7Nq1S1hYWLzxSdtZWVkiKChIABDOzs6iW7duYunSpSIxMVFpvdbW1oqvp06dKszMzERSUpJi2hdffCF8fHzyXM/Tp08FAHHlyhUhxL9Pq798+bJSv0GDBgknJyeRnp6eb90XLlwQAMTLly/z7UdEmuHIDRHl691330VkZKTitWTJEkWbl5eXSv8jR46gTZs2qFSpEiwtLTFgwAA8e/YMqampij5mZmaoVq2a4msXFxeVwzPnzp1Dz549sWnTJvTu3TvfGvX19bF+/Xo8fPgQc+fORaVKlTBr1izUq1cv38Nd7u7usLS0zLOO27dvo2/fvqhatSqsrKzg7u4OALh//36+9QBAgwYNYGRkpDQtIiICXbp0QZUqVWBpaQk/P78CL4+ICo7hhojyZW5ujurVqyteLi4uSm25RUdHo3PnzmjYsCF+/vlnREREYPny5QCUT6o1NDRUmk8mk0EIoTStWrVqqF27NtatW4fMzMwC1VqpUiUMGDAAy5Ytw7Vr15CWlqY4XKaOujpyDjkBQJcuXfD8+XOsXr0a586dw7lz51S2JS+v75uUlBQEBgbCysoKmzdvxoULF7B79+4CL4+ICo7hhoiKTEREBORyORYsWIBmzZqhZs2aePz4caGWZW9vj6NHj+LOnTvo1atXgQNODltbW7i4uCAlJaVQ63/27Blu3bqFyZMno02bNqhTpw5evHih1CdnZCbniqz83Lx5E8+ePcPs2bPRqlUr1K5dmycTExUThhsiKjLVq1dHZmYmli5dirt372LTpk35jpy8iaOjI44ePYqbN2+ib9++yMrKUttv1apVGDFiBA4dOoSoqChcu3YNEyZMwLVr19ClS5dCrdvW1hYVKlTA999/jzt37uDo0aMYN26cSn2mpqYICwtDXFwcEhMT81xelSpVYGRkpNg3+/btw4wZMwpVGxHlj+GGiIpMo0aNsHDhQsyZMwf169fH5s2bERoa+lbLdHZ2xtGjR3HlyhX0799f7SiJt7c3kpOTMXz4cNSrVw9+fn44e/Ys9uzZozivRVN6enrYunUrIiIiUL9+fXz++eeYN2+eUh8DAwMsWbIEq1atQsWKFfH+++/nuTwHBwds2LABO3bsQN26dTF79mzMnz+/ULURUf5k4vUD3URERERlGEduiIiISKcw3BAREZFOYbghIiIincJwQ0RERDqF4YaIiIh0CsMNERER6RSGGyIiItIpDDdERESkUxhuiIiISKcw3BAREZFOYbghIiIincJwQ0RERDrlf9ZHLdaSu4nkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## CORRELATION BETWEEN ARTISTS ACROSS PLAYLISTS\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from adjustText import adjust_text \n",
    "\n",
    "artist1 = 'Frank Sinatra'\n",
    "artist2 = 'Taylor Swift'\n",
    "\n",
    "# chose USER OR PLAYLIST HERE -- playlist is how much steve lacy and frank ocean is on each playlist. user steve lacy and frank ocean in all user's playlists\n",
    "new_df = user_new\n",
    "x = new_df[artist1]\n",
    "y = new_df[artist2]\n",
    "\n",
    "slope, intercept = np.polyfit(x, y, 1)\n",
    "trendline = slope * x + intercept\n",
    "\n",
    "r_squared = np.corrcoef(x, y)[0, 1]**2\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.plot(x, trendline, color='red', label=f'y={slope:.2f}x+{intercept:.2f}\\n$R^2$ = {r_squared:.2f}')\n",
    "\n",
    "# Annotate points with artist names\n",
    "texts = [plt.text(x[i], y[i], new_df.index[i]) for i in range(len(new_df.index))]\n",
    "\n",
    "# Adjust text positions to avoid overlapping\n",
    "adjust_text(texts, arrowprops=dict(arrowstyle='-', color='black'), force_points=0.2)\n",
    "\n",
    "plt.title(str(artist1) + \" vs. \" + str(artist2) + \" Recommendation Scores\")\n",
    "plt.xlabel(artist1)\n",
    "plt.ylabel(artist2)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Post Malone is most similar to Sabrina Carpenter\n",
      "21 Savage is most similar to Nick Jonas\n",
      "ZAYN is most similar to Gdaal\n",
      "Taylor Swift is most similar to Selena Gomez\n",
      "Madison Beer is most similar to Olivia Rodrigo\n",
      "Sabrina Carpenter is most similar to Post Malone\n",
      "French Montana is most similar to Swae Lee\n",
      "Swae Lee is most similar to French Montana\n",
      "Nick Jonas is most similar to 21 Savage\n",
      "Robin Schulz is most similar to 21 Savage\n",
      "Selena Gomez & The Scene is most similar to Billie Eilish\n",
      "Selena Gomez is most similar to Dua Lipa\n",
      "Dua Lipa is most similar to One Direction\n",
      "Olivia Rodrigo is most similar to Madison Beer\n",
      "Travis Scott is most similar to 21 Savage\n",
      "Nancy Sinatra is most similar to Red Garland\n",
      "John Denver is most similar to Lord Huron\n",
      "Gdaal is most similar to ZAYN\n",
      "Sami Beigi is most similar to ZAYN\n",
      "Madgal is most similar to ZAYN\n",
      "Erfan is most similar to ZAYN\n",
      "Dynatonic is most similar to ZAYN\n",
      "DJ Tupic is most similar to ZAYN\n",
      "Drake is most similar to ZAYN\n",
      "Carly Simon is most similar to 21 Savage\n",
      "Dolly Parton is most similar to 21 Savage\n",
      "Rema is most similar to 21 Savage\n",
      "RAYE is most similar to ZAYN\n",
      "070 Shake is most similar to ZAYN\n",
      "Miley Cyrus is most similar to Post Malone\n",
      "Charlie Puth is most similar to Post Malone\n",
      "Cody Simpson is most similar to Post Malone\n",
      "Ehaam is most similar to ZAYN\n",
      "Faramarz Aslani is most similar to 21 Savage\n",
      "Lord Huron is most similar to John Denver\n",
      "Billie Eilish is most similar to Pitbull\n",
      "One Direction is most similar to Ariana Grande\n",
      "Lana Del Rey is most similar to John Denver\n",
      "Harry Styles is most similar to Ariana Grande\n",
      "Mohsen Yeganeh is most similar to ZAYN\n",
      "Roozbeh Nematollahi is most similar to ZAYN\n",
      "Rauw Alejandro is most similar to ZAYN\n",
      "Shakira is most similar to Post Malone\n",
      "Gym Class Heroes is most similar to ZAYN\n",
      "The Weeknd is most similar to Calvin Harris\n",
      "ABBA is most similar to 21 Savage\n",
      "Daft Punk is most similar to ZAYN\n",
      "Pharrell Williams is most similar to ZAYN\n",
      "Nile Rodgers is most similar to ZAYN\n",
      "X Ambassadors is most similar to ZAYN\n",
      "The Killers is most similar to ZAYN\n",
      "Zedd is most similar to 21 Savage\n",
      "Kid Cudi is most similar to 21 Savage\n",
      "Kygo is most similar to ZAYN\n",
      "Bruno Mars is most similar to ZAYN\n",
      "Angèle is most similar to ZAYN\n",
      "WILLOW is most similar to ZAYN\n",
      "Katy Perry is most similar to The Weeknd\n",
      "BANNERS is most similar to ZAYN\n",
      "Calvin Harris is most similar to The Weeknd\n",
      "Randy Newman is most similar to ZAYN\n",
      "Ed Sheeran is most similar to ZAYN\n",
      "Ariana Grande is most similar to Harry Styles\n",
      "Nicki Minaj is most similar to ZAYN\n",
      "Britney Spears is most similar to Post Malone\n",
      "Elton John is most similar to ZAYN\n",
      "PNAU is most similar to ZAYN\n",
      "Doja Cat is most similar to 21 Savage\n",
      "Bon Iver is most similar to ZAYN\n",
      "HAIM is most similar to ZAYN\n",
      "Videoclub is most similar to ZAYN\n",
      "Adèle Castillon is most similar to ZAYN\n",
      "Mattyeux is most similar to ZAYN\n",
      "Tech N9ne is most similar to ZAYN\n",
      "Joey Cool is most similar to ZAYN\n",
      "King Iso is most similar to ZAYN\n",
      "Dwayne Johnson is most similar to ZAYN\n",
      "Shawn Mendes is most similar to Post Malone\n",
      "Tainy is most similar to Post Malone\n",
      "Céline Dion is most similar to 21 Savage\n",
      "Chris Stapleton is most similar to ZAYN\n",
      "Adele is most similar to Madison Beer\n",
      "Maroon 5 is most similar to ZAYN\n",
      "Rae Sremmurd is most similar to ZAYN\n",
      "JONY is most similar to ZAYN\n",
      "Fleetwood Mac is most similar to ZAYN\n",
      "Rex Orange County is most similar to ZAYN\n",
      "Alexander Rybak is most similar to ZAYN\n",
      "DNCE is most similar to ZAYN\n",
      "Jaymes Young is most similar to ZAYN\n",
      "GROUPLOVE is most similar to ZAYN\n",
      "Rihanna is most similar to Adele\n",
      "Anna Margaret is most similar to ZAYN\n",
      "Wyclef Jean is most similar to ZAYN\n",
      "Megan Thee Stallion is most similar to 21 Savage\n",
      "David Guetta is most similar to 21 Savage\n",
      "Beyoncé is most similar to Madison Beer\n",
      "J Balvin is most similar to The Weeknd\n",
      "Willy William is most similar to ZAYN\n",
      "JAY-Z is most similar to French Montana\n",
      "Kelly Rowland is most similar to ZAYN\n",
      "Michelle Williams is most similar to ZAYN\n",
      "Coldplay is most similar to ZAYN\n",
      "Bad Bunny is most similar to ZAYN\n",
      "Adam Hicks is most similar to ZAYN\n",
      "Bridgit Mendler is most similar to ZAYN\n",
      "Naomi Scott is most similar to 21 Savage\n",
      "Hayley Kiyoko is most similar to ZAYN\n",
      "Grace Phipps is most similar to ZAYN\n",
      "Dominic Cooper is most similar to ZAYN\n",
      "Amanda Seyfried is most similar to Post Malone\n",
      "Ashley Lilley is most similar to 21 Savage\n",
      "Rachel McDowall is most similar to 21 Savage\n",
      "Meryl Streep is most similar to Post Malone\n",
      "Julie Walters is most similar to 21 Savage\n",
      "Christine Baranski is most similar to 21 Savage\n",
      "Guster is most similar to ZAYN\n",
      "Nelly Furtado is most similar to ZAYN\n",
      "Timbaland is most similar to ZAYN\n",
      "James Arthur is most similar to ZAYN\n",
      "Anne-Marie is most similar to ZAYN\n",
      "benny blanco is most similar to ZAYN\n",
      "Marshmello is most similar to ZAYN\n",
      "Lizzo is most similar to ZAYN\n",
      "OneRepublic is most similar to ZAYN\n",
      "Lil Nas X is most similar to ZAYN\n",
      "Jennifer Lopez is most similar to Post Malone\n",
      "Pitbull is most similar to Billie Eilish\n",
      "Alexandra Stan is most similar to ZAYN\n",
      "Christina Aguilera is most similar to John Denver\n",
      "Kesha is most similar to Madison Beer\n",
      "Lady Gaga is most similar to ZAYN\n",
      "Carly Rae Jepsen is most similar to ZAYN\n",
      "Jessie J is most similar to French Montana\n",
      "Glass Animals is most similar to Post Malone\n",
      "Madcon is most similar to ZAYN\n",
      "Jonas Brothers is most similar to Post Malone\n",
      "Steve Lacy is most similar to ZAYN\n",
      "Demi Lovato is most similar to 21 Savage\n",
      "Kanye West is most similar to 21 Savage\n",
      "Tory Lanez is most similar to ZAYN\n",
      "Ryan Gosling is most similar to ZAYN\n",
      "Music Factory is most similar to ZAYN\n",
      "Iggy Azalea is most similar to ZAYN\n",
      "Rita Ora is most similar to ZAYN\n",
      "Major Lazer is most similar to ZAYN\n",
      "MØ is most similar to ZAYN\n",
      "DJ Snake is most similar to ZAYN\n",
      "Tate McRae is most similar to ZAYN\n",
      "Bebe Rexha is most similar to ZAYN\n",
      "Keane is most similar to ZAYN\n",
      "5 Seconds of Summer is most similar to ZAYN\n",
      "Kendrick Lamar is most similar to ZAYN\n",
      "SZA is most similar to ZAYN\n",
      "Kenya Grace is most similar to ZAYN\n",
      "Bill Evans is most similar to Frédéric Chopin\n",
      "Keith Jarrett is most similar to Louis Armstrong\n",
      "Gary Peacock is most similar to Jack DeJohnette\n",
      "Jack DeJohnette is most similar to Gary Peacock\n",
      "John Coltrane Quartet is most similar to Sonny Clark\n",
      "Gerry Mulligan Sextet is most similar to Erroll Garner\n",
      "Charlie Haden is most similar to ZAYN\n",
      "Chet Baker is most similar to Sezen Aksu\n",
      "Miles Davis Quintet is most similar to ZAYN\n",
      "Sonny Clark is most similar to John Coltrane Quartet\n",
      "Rahsaan Roland Kirk is most similar to ZAYN\n",
      "Beegie Adair is most similar to John Coltrane Quartet\n",
      "Erroll Garner is most similar to Gerry Mulligan Sextet\n",
      "Oscar Peterson Trio is most similar to Herbie Hancock\n",
      "Scott LaFaro is most similar to Paul Motian\n",
      "Paul Motian is most similar to Scott LaFaro\n",
      "Red Garland is most similar to Paul Chambers\n",
      "Paul Chambers is most similar to Red Garland\n",
      "Art Taylor is most similar to Red Garland\n",
      "Shelly Manne is most similar to ZAYN\n",
      "Oscar Peterson is most similar to Robert Schumann\n",
      "Cannonball Adderley is most similar to John Coltrane Quartet\n",
      "Stan Getz is most similar to John Coltrane Quartet\n",
      "Miles Davis is most similar to Scott LaFaro\n",
      "John Coltrane is most similar to Turan Saka\n",
      "Jim Hall is most similar to Red Garland\n",
      "Bill Evans Trio is most similar to Gerry Mulligan Sextet\n",
      "Paul Chambers Quintet is most similar to ZAYN\n",
      "Barry Harris is most similar to Gerry Mulligan Sextet\n",
      "Gene Taylor is most similar to ZAYN\n",
      "Leroy Williams is most similar to ZAYN\n",
      "Lester Young is most similar to John Coltrane Quartet\n",
      "Egidijus Buožis is most similar to ZAYN\n",
      "Igor Stravinsky is most similar to Gerry Mulligan Sextet\n",
      "Teodor Currentzis is most similar to ZAYN\n",
      "musicAeterna is most similar to ZAYN\n",
      "Orchestre de l'Opéra National de Paris is most similar to Red Garland\n",
      "Myung-Whun Chung is most similar to Red Garland\n",
      "Arnold Schoenberg is most similar to Gary Peacock\n",
      "Malin Hartelius is most similar to Red Garland\n",
      "Gringolts Quartet is most similar to Red Garland\n",
      "Daniel Barenboim is most similar to Red Garland\n",
      "Chicago Symphony Orchestra is most similar to John Coltrane Quartet\n",
      "Charles Pikler is most similar to ZAYN\n",
      "John Sharp is most similar to ZAYN\n",
      "Joseph Golan is most similar to ZAYN\n",
      "Li-Kuo Chang is most similar to ZAYN\n",
      "Ruben Gonzalez is most similar to ZAYN\n",
      "Stephen Balderston is most similar to ZAYN\n",
      "Saint Paul Chamber Orchestra is most similar to ZAYN\n",
      "Christopher Hogwood is most similar to ZAYN\n",
      "Berliner Philharmoniker is most similar to Gary Peacock\n",
      "Pierre Boulez is most similar to Gary Peacock\n",
      "Rundfunkchor Berlin is most similar to ZAYN\n",
      "Sir Simon Rattle is most similar to ZAYN\n",
      "Sergei Prokofiev is most similar to ZAYN\n",
      "Herbert von Karajan is most similar to ZAYN\n",
      "Paul Hindemith is most similar to ZAYN\n",
      "Omega Ensemble is most similar to ZAYN\n",
      "Carl Orff is most similar to ZAYN\n",
      "Cleveland Orchestra is most similar to ZAYN\n",
      "Michael Tilson Thomas is most similar to ZAYN\n",
      "Alexander Mosolov is most similar to ZAYN\n",
      "Royal Concertgebouw Orchestra is most similar to ZAYN\n",
      "Riccardo Chailly is most similar to ZAYN\n",
      "Dmitri Shostakovich is most similar to Gary Peacock\n",
      "Maria Ewing is most similar to ZAYN\n",
      "Sergej Larin is most similar to ZAYN\n",
      "Aage Haugland is most similar to ZAYN\n",
      "Leonard Bernstein is most similar to ZAYN\n",
      "New York Philharmonic is most similar to ZAYN\n",
      "Royal Liverpool Philharmonic Orchestra is most similar to ZAYN\n",
      "Vasily Petrenko is most similar to ZAYN\n",
      "Edgard Varèse is most similar to Gerry Mulligan Sextet\n",
      "Polish National Radio Symphony Orchestra is most similar to Red Garland\n",
      "Christopher Lyndon-Gee is most similar to Red Garland\n",
      "Pierre Schaeffer is most similar to ZAYN\n",
      "Olivier Messiaen is most similar to ZAYN\n",
      "Robert Plane is most similar to ZAYN\n",
      "Gould Piano Trio is most similar to ZAYN\n",
      "Hilary Summers is most similar to ZAYN\n",
      "Ensemble Intercontemporain is most similar to ZAYN\n",
      "Karlheinz Stockhausen is most similar to ZAYN\n",
      "WDR Sinfonieorchester Köln is most similar to ZAYN\n",
      "Peter Eötvös is most similar to ZAYN\n",
      "Arturo Tamayo is most similar to Red Garland\n",
      "Jacques Mercier is most similar to ZAYN\n",
      "John Cage is most similar to Scott LaFaro\n",
      "JACK Quartet is most similar to ZAYN\n",
      "Gidon Kremer is most similar to ZAYN\n",
      "Naoko Yoshino is most similar to ZAYN\n",
      "Boris Berman is most similar to Red Garland\n",
      "Grete Sultan is most similar to ZAYN\n",
      "La Monte Young is most similar to John Coltrane Quartet\n",
      "Noël Akchoté is most similar to Red Garland\n",
      "Jeroen van Veen is most similar to ZAYN\n",
      "Terry Riley is most similar to ZAYN\n",
      "Steve Reich is most similar to Red Garland\n",
      "Russell Hartenberger is most similar to ZAYN\n",
      "Double Edge is most similar to ZAYN\n",
      "Krzysztof Penderecki is most similar to ZAYN\n",
      "Jonny Greenwood is most similar to ZAYN\n",
      "AUKSO Orchestra is most similar to ZAYN\n",
      "Iannis Xenakis is most similar to ZAYN\n",
      "Luxembourg Philharmonic Orchestra is most similar to ZAYN\n",
      "György Ligeti is most similar to ZAYN\n",
      "Jonathan Nott is most similar to ZAYN\n",
      "Gérard Grisey is most similar to ZAYN\n",
      "Asko Ensemble is most similar to ZAYN\n",
      "John Adams is most similar to ZAYN\n",
      "Edo de Waart is most similar to ZAYN\n",
      "San Francisco Symphony is most similar to ZAYN\n",
      "Kamran Ince is most similar to ZAYN\n",
      "Present Music is most similar to ZAYN\n",
      "Kevin Stalheim is most similar to ZAYN\n",
      "Fahir Atakoğlu is most similar to Gerry Mulligan Sextet\n",
      "Herbie Hancock is most similar to Oscar Peterson Trio\n",
      "Richard \"Groove\" Holmes is most similar to ZAYN\n",
      "Grover Washington\\ is most similar to Red Garland\n",
      "Jr. is most similar to Red Garland\n",
      "Chick Corea is most similar to Arthur Rubinstein\n",
      "Return To Forever is most similar to Red Garland\n",
      "Niño Josele is most similar to ZAYN\n",
      "Tony Williams is most similar to ZAYN\n",
      "Jaco Pastorius is most similar to Red Garland\n",
      "Mandrill is most similar to ZAYN\n",
      "Brian Culbertson is most similar to ZAYN\n",
      "Jeff Lorber Fusion is most similar to ZAYN\n",
      "George Duke is most similar to ZAYN\n",
      "Billy Cobham is most similar to ZAYN\n",
      "The Brecker Brothers is most similar to ZAYN\n",
      "George Benson is most similar to Red Garland\n",
      "Frank Sinatra is most similar to Zeki Müren\n",
      "Tony Bennett is most similar to Red Garland\n",
      "Ella Fitzgerald is most similar to Keith Jarrett\n",
      "Leonard Cohen is most similar to Red Garland\n",
      "Madeleine Peyroux is most similar to ZAYN\n",
      "Cyrille Aimee is most similar to ZAYN\n",
      "Nat King Cole is most similar to Oscar Peterson Trio\n",
      "Paul Anka is most similar to ZAYN\n",
      "Louis Armstrong is most similar to Keith Jarrett\n",
      "George Shearing is most similar to ZAYN\n",
      "Dean Martin is most similar to Scott LaFaro\n",
      "Eric Clapton is most similar to French Montana\n",
      "Elvis Presley is most similar to Logic1000\n",
      "Bud Powell is most similar to Red Garland\n",
      "Sweet Nothings Band is most similar to ZAYN\n",
      "Billie Holiday is most similar to ZAYN\n",
      "Justin Hurwitz is most similar to Red Garland\n",
      "Norah Jones is most similar to ZAYN\n",
      "Rachel Younger is most similar to ZAYN\n",
      "Brad Mehldau is most similar to Gary Peacock\n",
      "Ray Charles is most similar to ZAYN\n",
      "Jo Stafford is most similar to ZAYN\n",
      "Paul Weston And His Orchestra is most similar to ZAYN\n",
      "CMC 3 is most similar to ZAYN\n",
      "Emma Stone is most similar to ZAYN\n",
      "Sarah Vaughan is most similar to ZAYN\n",
      "Red Brick Lane is most similar to ZAYN\n",
      "Diana Krall is most similar to ZAYN\n",
      "Johnny Hartman is most similar to Red Garland\n",
      "Etta James is most similar to Red Garland\n",
      "Julie London is most similar to ZAYN\n",
      "Roberta Flack is most similar to ZAYN\n",
      "Michael Bublé is most similar to ZAYN\n",
      "Ricky Nelson is most similar to ZAYN\n",
      "Frazey Ford is most similar to ZAYN\n",
      "Irene Kral is most similar to Red Garland\n",
      "John Surman is most similar to ZAYN\n",
      "Nelson Ayres is most similar to ZAYN\n",
      "Rob Waring is most similar to ZAYN\n",
      "The Don Rendell / Ian Carr Quintet is most similar to ZAYN\n",
      "Samuel Joseph is most similar to ZAYN\n",
      "Count Basie is most similar to ZAYN\n",
      "Flora Martínez is most similar to ZAYN\n",
      "Paolo Fresu is most similar to ZAYN\n",
      "Richard Galliano is most similar to ZAYN\n",
      "Jan Lundgren is most similar to ZAYN\n",
      "The Dave Brubeck Quartet is most similar to ZAYN\n",
      "Duke Ellington is most similar to Red Garland\n",
      "Johnny Mathis is most similar to ZAYN\n",
      "Ray Brown is most similar to Red Garland\n",
      "Herb Ellis is most similar to ZAYN\n",
      "Ben Webster is most similar to Red Garland\n",
      "Ed Thigpen is most similar to ZAYN\n",
      "Fred Astaire is most similar to ZAYN\n",
      "Joe Pass is most similar to Gary Peacock\n",
      "Bud Shank is most similar to ZAYN\n",
      "Clare Fisher is most similar to ZAYN\n",
      "Django Reinhardt is most similar to John Coltrane Quartet\n",
      "Stéphane Grappelli is most similar to ZAYN\n",
      "Quintette du Hot Club de France is most similar to ZAYN\n",
      "McCoy Tyner is most similar to ZAYN\n",
      "Dizzy Gillespie is most similar to ZAYN\n",
      "Özdemir Erdoğan is most similar to ZAYN\n",
      "Andy Derrick Jazz Quartet is most similar to ZAYN\n",
      "Itzhak Perlman is most similar to ZAYN\n",
      "Teddy Wilson Trio is most similar to ZAYN\n",
      "Bernard Herrmann is most similar to ZAYN\n",
      "Red Garland Trio is most similar to Red Garland\n",
      "Lucky Millinder is most similar to ZAYN\n",
      "King Oliver is most similar to Red Garland\n",
      "King Oliver Orchestra is most similar to ZAYN\n",
      "Bill Withers is most similar to ZAYN\n",
      "Sonny Stitt is most similar to ZAYN\n",
      "Chet Atkins is most similar to ZAYN\n",
      "Benny Goodman Septet is most similar to ZAYN\n",
      "Grant Green is most similar to ZAYN\n",
      "Maurice Meunier is most similar to ZAYN\n",
      "Eddie Bernard is most similar to ZAYN\n",
      "Eugène Vées is most similar to ZAYN\n",
      "Emmanuel Soudieux is most similar to ZAYN\n",
      "Jacques Martinon is most similar to ZAYN\n",
      "B. Swanson Quartet is most similar to ZAYN\n",
      "Edward Simon is most similar to ZAYN\n",
      "Buddy Rich is most similar to ZAYN\n",
      "Dexter Gordon is most similar to ZAYN\n",
      "Joshua Redman is most similar to ZAYN\n",
      "Coleman Hawkins is most similar to ZAYN\n",
      "Art Blakey & The Jazz Messengers is most similar to ZAYN\n",
      "Roy Hargrove is most similar to ZAYN\n",
      "Neil Diamond is most similar to ZAYN\n",
      "London Music Works is most similar to ZAYN\n",
      "Bud Freeman is most similar to ZAYN\n",
      "Alex Welsh & His Band is most similar to ZAYN\n",
      "Alex Welsh is most similar to ZAYN\n",
      "His Band is most similar to ZAYN\n",
      "Thelonious Monk is most similar to ZAYN\n",
      "Dr. Lonnie Smith is most similar to ZAYN\n",
      "Ron Carter is most similar to ZAYN\n",
      "Joe Lovano is most similar to ZAYN\n",
      "Benny Golson is most similar to ZAYN\n",
      "Nina Simone is most similar to ZAYN\n",
      "Chet Baker Quartet is most similar to ZAYN\n",
      "Russ Freeman is most similar to ZAYN\n",
      "Charlie Parker is most similar to ZAYN\n",
      "Bulut Gülen is most similar to ZAYN\n",
      "Ozan Musluoğlu is most similar to ZAYN\n",
      "Ferit Odman is most similar to ZAYN\n",
      "Can Çankaya is most similar to ZAYN\n",
      "Önder Focan is most similar to ZAYN\n",
      "Rousseau is most similar to ZAYN\n",
      "Ludwig van Beethoven is most similar to John Coltrane Quartet\n",
      "Fazıl Say is most similar to Red Garland\n",
      "Erik Satie is most similar to Gary Peacock\n",
      "Philippe Entremont is most similar to Red Garland\n",
      "ibi is most similar to ZAYN\n",
      "Claude Debussy is most similar to John Coltrane Quartet\n",
      "Alain Planès is most similar to ZAYN\n",
      "Jean-Yves Thibaudet is most similar to Red Garland\n",
      "Alexandre Tharaud is most similar to John Coltrane Quartet\n",
      "Erkan Oğur is most similar to TAXI KEBAB\n",
      "Frédéric Chopin is most similar to Bill Evans\n",
      "Eleni Karaindrou is most similar to Red Garland\n",
      "Maria Bildea is most similar to ZAYN\n",
      "Sergiu Nastasa is most similar to ZAYN\n",
      "Renato Ripo is most similar to ZAYN\n",
      "Vangelis Christopoulos is most similar to ZAYN\n",
      "Spyros Kazianis is most similar to ZAYN\n",
      "Antonis Lagos is most similar to ZAYN\n",
      "Dinos Hadjiiordanou is most similar to ZAYN\n",
      "Camerata\\ is most similar to ZAYN\n",
      "Friends Of Music Orchestra is most similar to ZAYN\n",
      "Jean-Efflam Bavouzet is most similar to ZAYN\n",
      "Anna Markland is most similar to ZAYN\n",
      "Arthur Rubinstein is most similar to Chick Corea\n",
      "François Dumont is most similar to Gary Peacock\n",
      "Robert Schumann is most similar to Oscar Peterson\n",
      "Vladimir Horowitz is most similar to John Coltrane Quartet\n",
      "Zoltán Fejérvári is most similar to ZAYN\n",
      "Jan Lisiecki is most similar to John Coltrane Quartet\n",
      "Carl Maria von Weber is most similar to ZAYN\n",
      "Michael Endres is most similar to ZAYN\n",
      "Piotr Anderszewski is most similar to Red Garland\n",
      "Vladimir Feltsman is most similar to Chick Corea\n",
      "Isabelle Faust is most similar to ZAYN\n",
      "Alexander Melnikov is most similar to ZAYN\n",
      "Jean-Guihen Queyras is most similar to ZAYN\n",
      "Patrick Messina is most similar to ZAYN\n",
      "Fabrizio Chiovetta is most similar to ZAYN\n",
      "Varvara Tarasova is most similar to ZAYN\n",
      "Peter Jablonski is most similar to ZAYN\n",
      "Benjamin Grosvenor is most similar to ZAYN\n",
      "Felix Mendelssohn is most similar to John Coltrane Quartet\n",
      "Péter Nagy is most similar to ZAYN\n",
      "Martin Ivanov is most similar to Red Garland\n",
      "Khatia Buniatishvili is most similar to ZAYN\n",
      "Youri Egorov is most similar to ZAYN\n",
      "Martha Argerich is most similar to Red Garland\n",
      "Arcadi Volodos is most similar to ZAYN\n",
      "Maria João Pires is most similar to ZAYN\n",
      "Mitsuko Uchida is most similar to ZAYN\n",
      "Eugene Mursky is most similar to ZAYN\n",
      "Vittorio Forte is most similar to ZAYN\n",
      "Brigitte Engerer is most similar to ZAYN\n",
      "Claudio Arrau is most similar to ZAYN\n",
      "Franz Liszt is most similar to Scott LaFaro\n",
      "Jenő Jandó is most similar to ZAYN\n",
      "Donka Angatscheva is most similar to ZAYN\n",
      "John Field is most similar to ZAYN\n",
      "Elizabeth Joy Roe is most similar to ZAYN\n",
      "Max Richter is most similar to ZAYN\n",
      "Jacob's Piano is most similar to ZAYN\n",
      "Evgeny Grinko is most similar to ZAYN\n",
      "Sung Eun Choi is most similar to ZAYN\n",
      "Nikolai Lugansky is most similar to ZAYN\n",
      "Johann Sebastian Bach is most similar to ZAYN\n",
      "Yo-Yo Ma is most similar to ZAYN\n",
      "Ludovico Einaudi is most similar to Red Garland\n",
      "Paul Lewis is most similar to ZAYN\n",
      "Philip Thomson is most similar to ZAYN\n",
      "Pyotr Ilyich Tchaikovsky is most similar to ZAYN\n",
      "Yefim Bronfman is most similar to ZAYN\n",
      "Wolfgang Amadeus Mozart is most similar to ZAYN\n",
      "András Schiff is most similar to ZAYN\n",
      "Franz Schubert is most similar to Gary Peacock\n",
      "Krystian Zimerman is most similar to ZAYN\n",
      "Rudolf Buchbinder is most similar to ZAYN\n",
      "Copyright Control is most similar to ZAYN\n",
      "André Rieu is most similar to ZAYN\n",
      "Eugene Ormandy is most similar to ZAYN\n",
      "Philadelphia Orchestra is most similar to Red Garland\n",
      "Camille Saint-Saëns is most similar to ZAYN\n",
      "Stéphane Tétreault is most similar to ZAYN\n",
      "Fabien Gabel is most similar to ZAYN\n",
      "Orchestre Symphonique De Québec is most similar to ZAYN\n",
      "Girolamo Frescobaldi is most similar to ZAYN\n",
      "Bruno Cocset is most similar to ZAYN\n",
      "Les Basses Réunies is most similar to ZAYN\n",
      "Murray Perahia is most similar to ZAYN\n",
      "Sergei Rachmaninoff is most similar to John Coltrane Quartet\n",
      "Van Cliburn is most similar to ZAYN\n",
      "Ulvi Cemal Erkin is most similar to ZAYN\n",
      "Istanbul State Symphony Orchestra is most similar to ZAYN\n",
      "Theodore Kuchar is most similar to ZAYN\n",
      "Janet Baker is most similar to ZAYN\n",
      "Gerald Moore is most similar to ZAYN\n",
      "Johannes Brahms is most similar to ZAYN\n",
      "Philharmonisches Staatsorchester Hamburg is most similar to ZAYN\n",
      "Eugen Jochum is most similar to ZAYN\n",
      "YUNDI is most similar to ZAYN\n",
      "Leif Ove Andsnes is most similar to John Coltrane Quartet\n",
      "Lang Lang is most similar to ZAYN\n",
      "Daniil Trifonov is most similar to ZAYN\n",
      "Yannick Nézet-Séguin is most similar to ZAYN\n",
      "Valentina Lisitsa is most similar to ZAYN\n",
      "London Symphony Orchestra is most similar to ZAYN\n",
      "Michael Francis is most similar to ZAYN\n",
      "Claudio Monteverdi is most similar to ZAYN\n",
      "Carlo Gaifa is most similar to ZAYN\n",
      "Giovanni Faverio is most similar to ZAYN\n",
      "Mario Livraghi is most similar to ZAYN\n",
      "Cettina Cadelo is most similar to ZAYN\n",
      "Cristina Miatello is most similar to ZAYN\n",
      "Emanuele De Checchi is most similar to ZAYN\n",
      "Claudio Cavina is most similar to ZAYN\n",
      "Vincenzo Antonio Manno is most similar to ZAYN\n",
      "Cappella Mauriziana is most similar to ZAYN\n",
      "Marco Fodella is most similar to ZAYN\n",
      "Sabina Colonna Preti is most similar to ZAYN\n",
      "Caterina Dell'Agnelo is most similar to ZAYN\n",
      "Mara Galassi is most similar to ZAYN\n",
      "Roberto Gini is most similar to ZAYN\n",
      "Ensemble Concerto is most similar to ZAYN\n",
      "Mario Valsecchi is most similar to ZAYN\n",
      "Daniel Hope is most similar to ZAYN\n",
      "Zürcher Kammerorchester is most similar to ZAYN\n",
      "Jane Berthe is most similar to ZAYN\n",
      "Müzeyyen Senar is most similar to Ella Fitzgerald\n",
      "Çağdaş Suseven is most similar to Red Garland\n",
      "Cemal Çınarlı is most similar to ZAYN\n",
      "Neşet Ertaş is most similar to Scott LaFaro\n",
      "Zeki Müren is most similar to Frank Sinatra\n",
      "Hümeyra is most similar to ZAYN\n",
      "Aşık Mahzuni Şerif is most similar to ZAYN\n",
      "Emel Sayın is most similar to ZAYN\n",
      "Seçil Heper is most similar to ZAYN\n",
      "Kirpi Bülent is most similar to ZAYN\n",
      "Erkin Koray is most similar to Red Garland\n",
      "3 Hürel is most similar to ZAYN\n",
      "Mustafa Ertürk is most similar to Oscar Peterson\n",
      "Turan Saka is most similar to Chick Corea\n",
      "Nesrin Sipahi is most similar to John Coltrane Quartet\n",
      "Ergin Kızılay is most similar to ZAYN\n",
      "Sevim Deran is most similar to ZAYN\n",
      "Mediha Şen Sancakoğlu is most similar to ZAYN\n",
      "Behiye Aksoy is most similar to ZAYN\n",
      "Salim Dündar is most similar to ZAYN\n",
      "Semiha Yankı is most similar to Red Garland\n",
      "Belkıs Özener is most similar to ZAYN\n",
      "Tülay Özer is most similar to ZAYN\n",
      "Dario Moreno is most similar to Red Garland\n",
      "Sezen Aksu is most similar to Chet Baker\n",
      "Nilüfer is most similar to Gerry Mulligan Sextet\n",
      "Ayten Alpman is most similar to Gerry Mulligan Sextet\n",
      "Yeliz is most similar to ZAYN\n",
      "Ayla Dikmen is most similar to Gary Peacock\n",
      "Esmeray is most similar to ZAYN\n",
      "Tanju Okan is most similar to ZAYN\n",
      "Muazzez Abacı is most similar to ZAYN\n",
      "Suzan Kardeş is most similar to ZAYN\n",
      "Ajda Pekkan is most similar to ZAYN\n",
      "Haramiler is most similar to ZAYN\n",
      "Cihan Okan is most similar to ZAYN\n",
      "Melihat Gülses is most similar to ZAYN\n",
      "Selda Bağcan is most similar to ZAYN\n",
      "Tarkan is most similar to ZAYN\n",
      "Glass Domain is most similar to yunè pinku\n",
      "Logic1000 is most similar to Gaël Faye\n",
      "yunè pinku is most similar to Glass Domain\n",
      "Patrick Holland is most similar to Glass Domain\n",
      "Felipe Gordon is most similar to Glass Domain\n",
      "Blue Hawaii is most similar to Glass Domain\n",
      "Prequel is most similar to Beirut\n",
      "nand is most similar to Glass Domain\n",
      "Ceyolia is most similar to Glass Domain\n",
      "Saint Levant is most similar to Vindkaldr\n",
      "TAXI KEBAB is most similar to X-Alox\n",
      "Musique de Fête is most similar to Glass Domain\n",
      "Dilla is most similar to Glass Domain\n",
      "emi x is most similar to Glass Domain\n",
      "Dominik Hartz is most similar to Glass Domain\n",
      "Stanford Lin is most similar to Glass Domain\n",
      "El Alfa is most similar to ZAYN\n",
      "Farina is most similar to ZAYN\n",
      "Whitemary is most similar to ZAYN\n",
      "Raär is most similar to ZAYN\n",
      "X-Alox is most similar to TAXI KEBAB\n",
      "Gutter Freakz is most similar to ZAYN\n",
      "Collester is most similar to Glass Domain\n",
      "HCTM is most similar to TAXI KEBAB\n",
      "Dustmite is most similar to ZAYN\n",
      "Playyard is most similar to Glass Domain\n",
      "Zeina is most similar to TAXI KEBAB\n",
      "Yaya Bey is most similar to Glass Domain\n",
      "Karnaval Blues is most similar to Glass Domain\n",
      "LYNN is most similar to Arslanbek Sultanbekov\n",
      "Σtella is most similar to Glass Domain\n",
      "Redinho is most similar to Glass Domain\n",
      "MPL is most similar to Glass Domain\n",
      "Kolinga is most similar to Glass Domain\n",
      "Gaël Faye is most similar to Logic1000\n",
      "Balthazar is most similar to ZAYN\n",
      "illi is most similar to TAXI KEBAB\n",
      "Monolink is most similar to ZAYN\n",
      "Mauvaise Bouche is most similar to ZAYN\n",
      "Jadu Heart is most similar to ZAYN\n",
      "The Dø is most similar to ZAYN\n",
      "Goran Bregović is most similar to Glass Domain\n",
      "Vaska Jankovska is most similar to Glass Domain\n",
      "Rolando Alarcon is most similar to TAXI KEBAB\n",
      "Brothers of Metal is most similar to ZAYN\n",
      "Amira Medunjanin is most similar to Glass Domain\n",
      "Ayşenur Kolivar is most similar to Glass Domain\n",
      "Yuri Ryadchenko is most similar to ZAYN\n",
      "Sumru Ağıryürüyen is most similar to ZAYN\n",
      "Onok Bozkurt is most similar to ZAYN\n",
      "Kuan is most similar to Glass Domain\n",
      "Cem Karaca is most similar to Glass Domain\n",
      "Timuçin Esen is most similar to Glass Domain\n",
      "Sabaton is most similar to ZAYN\n",
      "Maria Faradouri is most similar to Glass Domain\n",
      "Zülfü Livaneli is most similar to TAXI KEBAB\n",
      "Gülay & The Ensemble Aras is most similar to ZAYN\n",
      "Hainides is most similar to Glass Domain\n",
      "Ensemble Galatia is most similar to Glass Domain\n",
      "Karolina Cicha is most similar to Logic1000\n",
      "Zulal is most similar to ZAYN\n",
      "Bart Palyga is most similar to Glass Domain\n",
      "Ender Akay is most similar to Glass Domain\n",
      "Beyazıt Öztürk is most similar to Glass Domain\n",
      "Haluk Bilginer is most similar to Glass Domain\n",
      "Stelios Kazantzidis is most similar to Glass Domain\n",
      "Kon Sira is most similar to Glass Domain\n",
      "Joan Baez is most similar to TAXI KEBAB\n",
      "Elina Duni is most similar to ZAYN\n",
      "Kâzım Koyuncu is most similar to Glass Domain\n",
      "Lizeta Kalimeri is most similar to Glass Domain\n",
      "Sokratis Malamas is most similar to ZAYN\n",
      "Ismail Hakki Demircioglu is most similar to TAXI KEBAB\n",
      "Yorgos Kazantzis is most similar to ZAYN\n",
      "Kardes Türküler is most similar to anna RF\n",
      "anna RF is most similar to Kardes Türküler\n",
      "Bedrije Misin & Ansambl Braće Koko is most similar to ZAYN\n",
      "Jorma Ikävalko is most similar to ZAYN\n",
      "Zohreh Jooya is most similar to ZAYN\n",
      "Saeed Farajpoury is most similar to ZAYN\n",
      "Rashid Beibutov is most similar to ZAYN\n",
      "Barcelona Gipsy Klezmer Orchestra is most similar to TAXI KEBAB\n",
      "Nihan Devecioglu is most similar to Glass Domain\n",
      "Alkistis Protopsalti is most similar to ZAYN\n",
      "Lavrentis Machairitsas is most similar to ZAYN\n",
      "Pix Lax is most similar to ZAYN\n",
      "Meri Lida is most similar to ZAYN\n",
      "Manolis Hiotis is most similar to ZAYN\n",
      "Imam Baildi is most similar to TAXI KEBAB\n",
      "Giannis Haroulis is most similar to ZAYN\n",
      "Miltos Pashalidis is most similar to Glass Domain\n",
      "Penny Baltatzi is most similar to ZAYN\n",
      "The Swingin' Cats is most similar to ZAYN\n",
      "Eleonora Zouganeli is most similar to ZAYN\n",
      "Marinella is most similar to ZAYN\n",
      "Haris Alexiou is most similar to Glass Domain\n",
      "Dimitra Galani is most similar to ZAYN\n",
      "Eleftheria Arvanitaki is most similar to ZAYN\n",
      "Melina Kana is most similar to ZAYN\n",
      "Glykeria is most similar to ZAYN\n",
      "Eleni Tsaligopoulou is most similar to ZAYN\n",
      "Tania Tsanaklidou is most similar to ZAYN\n",
      "Andriana Babali is most similar to ZAYN\n",
      "Melina Aslanidou is most similar to Glass Domain\n",
      "Christos Nikolopoulos is most similar to ZAYN\n",
      "Konstantinos Argiros is most similar to ZAYN\n",
      "Yanna Vasileiou is most similar to ZAYN\n",
      "Stelios Dionisiou is most similar to ZAYN\n",
      "Natasa Theodoridou is most similar to ZAYN\n",
      "Manolis Lidakis is most similar to ZAYN\n",
      "Kostas Makedonas is most similar to ZAYN\n",
      "Giorgos Margaritis is most similar to ZAYN\n",
      "Paola is most similar to ZAYN\n",
      "Pitsa Papadopoulou is most similar to ZAYN\n",
      "Giannis Ploutarhos is most similar to ZAYN\n",
      "Antonis Remos is most similar to ZAYN\n",
      "Stelios Rokkos is most similar to ZAYN\n",
      "Panagiotis Tsafaras is most similar to ZAYN\n",
      "Eleni Vitali is most similar to ZAYN\n",
      "Gothart is most similar to Glass Domain\n",
      "Tülay German is most similar to Glass Domain\n",
      "Muslimgauze is most similar to ZAYN\n",
      "King Raam is most similar to ZAYN\n",
      "Rana Mansour is most similar to ZAYN\n",
      "Hamed Nikpay is most similar to ZAYN\n",
      "Fatima Spar Und Die Freedom Fries is most similar to ZAYN\n",
      "Georgi Vinogradov is most similar to TAXI KEBAB\n",
      "Orange Blossom is most similar to ZAYN\n",
      "Beirut is most similar to Prequel\n",
      "Arany Zoltán is most similar to TAXI KEBAB\n",
      "Sahin Gültekin is most similar to ZAYN\n",
      "Yat-Kha is most similar to Glass Domain\n",
      "Myrkur is most similar to ZAYN\n",
      "Ethnic Band is most similar to ZAYN\n",
      "Divna Ljubojevic is most similar to ZAYN\n",
      "Ali Zand Vakili is most similar to ZAYN\n",
      "Laboratorium Pieśni is most similar to Glass Domain\n",
      "Şehir Işıkları is most similar to Glass Domain\n",
      "Ofdrykkja is most similar to ZAYN\n",
      "Anna German is most similar to ZAYN\n",
      "Golden Ring Ensemble is most similar to Glass Domain\n",
      "SAVAE (San Antonio Vocal Arts Ensemble) is most similar to ZAYN\n",
      "Jitka Šuranská is most similar to ZAYN\n",
      "Michal Elia Kamal is most similar to ZAYN\n",
      "Irén Lovász is most similar to ZAYN\n",
      "Brenna MacCrimmon is most similar to ZAYN\n",
      "Arto Tunçboyacıyan is most similar to Glass Domain\n",
      "Vasiliy Agapkin is most similar to ZAYN\n",
      "The Red Army Choir is most similar to Logic1000\n",
      "Victor Fedorov is most similar to ZAYN\n",
      "Josh Lippi & The Overtimers is most similar to ZAYN\n",
      "Dolapdere Big Gang is most similar to ZAYN\n",
      "Samovar Russian Folk Music Ensemble is most similar to ZAYN\n",
      "Elena Frolova is most similar to ZAYN\n",
      "Modena City Ramblers is most similar to ZAYN\n",
      "SesVerSus A Capella is most similar to Glass Domain\n",
      "Gülce Duru is most similar to ZAYN\n",
      "Altai Kai is most similar to Prequel\n",
      "Andreyev Balalaika Ensemble is most similar to ZAYN\n",
      "No Blues is most similar to ZAYN\n",
      "Burzum is most similar to ZAYN\n",
      "Nicu Alifantis is most similar to Glass Domain\n",
      "Zupfgeigenhansel is most similar to ZAYN\n",
      "Lela Tataraidze is most similar to ZAYN\n",
      "Äl Jawala is most similar to ZAYN\n",
      "Bayan Faroun is most similar to ZAYN\n",
      "Azam Ali is most similar to ZAYN\n",
      "HasSak ethno is most similar to ZAYN\n",
      "Die Streuner is most similar to Glass Domain\n",
      "Destan is most similar to ZAYN\n",
      "Hrdza is most similar to ZAYN\n",
      "Ayşe Önder is most similar to ZAYN\n",
      "Aytekin Ataş is most similar to ZAYN\n",
      "Gary Shearston is most similar to ZAYN\n",
      "Faraualla is most similar to ZAYN\n",
      "Polad Bülbüloğlu is most similar to ZAYN\n",
      "Фолк-группа \"Забава\" is most similar to ZAYN\n",
      "Bandiera Rossa is most similar to ZAYN\n",
      "Ural Cossacks Choir is most similar to ZAYN\n",
      "Uzory is most similar to ZAYN\n",
      "Light in Babylon is most similar to ZAYN\n",
      "Melike Şahin is most similar to ZAYN\n",
      "Cem Koklukaya is most similar to ZAYN\n",
      "Eldar Mansurov is most similar to ZAYN\n",
      "Brilliant Dadaşova is most similar to ZAYN\n",
      "Arslanbek Sultanbekov is most similar to LYNN\n",
      "Kemal Sahir Gürel is most similar to ZAYN\n",
      "Hüseyin Yıldız is most similar to ZAYN\n",
      "Erdal Güney is most similar to ZAYN\n",
      "Faun is most similar to TAXI KEBAB\n",
      "Elektro Hafiz is most similar to TAXI KEBAB\n",
      "Ayla Millepied-Şen is most similar to ZAYN\n",
      "Sami Yusuf is most similar to Glass Domain\n",
      "Özlem Özdil is most similar to ZAYN\n",
      "Soner Akalın is most similar to ZAYN\n",
      "Erkan Bektaş is most similar to ZAYN\n",
      "Bajar is most similar to ZAYN\n",
      "Kara Han is most similar to Glass Domain\n",
      "Pietra Montecorvino is most similar to ZAYN\n",
      "Bremen Dayanışma Korosu is most similar to ZAYN\n",
      "Modern Folk Üçlüsü is most similar to Glass Domain\n",
      "DakhaBrakha is most similar to Glass Domain\n",
      "Françoise Atlan is most similar to ZAYN\n",
      "Nena Venetsanou is most similar to ZAYN\n",
      "Trio Mandili is most similar to ZAYN\n",
      "Maria Dimitriadi is most similar to ZAYN\n",
      "Afroditi Manou is most similar to ZAYN\n",
      "Canan Uzerli is most similar to ZAYN\n",
      "Dolunay Obruk is most similar to ZAYN\n",
      "Tuğçe Kurtiş is most similar to ZAYN\n",
      "Alizarina is most similar to ZAYN\n",
      "Kıraç is most similar to Logic1000\n",
      "Efkan Şeşen is most similar to ZAYN\n",
      "АлконостЪ (ансамбль этнической и духовной музыки) is most similar to ZAYN\n",
      "Warsaw Village Band is most similar to ZAYN\n",
      "Tartalo Music is most similar to ZAYN\n",
      "Amma is most similar to ZAYN\n",
      "Nitin Sawhney is most similar to ZAYN\n",
      "Vika Tsyganova is most similar to ZAYN\n",
      "RIMON is most similar to ZAYN\n",
      "Tiggs Da Author is most similar to ZAYN\n",
      "Michaël Brun is most similar to ZAYN\n",
      "Lolo Zouaï is most similar to ZAYN\n",
      "Rayana Jay is most similar to ZAYN\n",
      "JVLY is most similar to ZAYN\n",
      "Myra is most similar to ZAYN\n",
      "Drugdealer is most similar to ZAYN\n",
      "Kate Bollinger is most similar to ZAYN\n",
      "La Femme is most similar to ZAYN\n",
      "Synapson is most similar to ZAYN\n",
      "Bonga is most similar to ZAYN\n",
      "Deluxe is most similar to ZAYN\n",
      "La Rue Ketanou is most similar to ZAYN\n",
      "Haris Hamza is most similar to ZAYN\n",
      "CHAAMA is most similar to ZAYN\n",
      "Oliver Koletzki is most similar to ZAYN\n",
      "Ever Mihigo is most similar to ZAYN\n",
      "Sabrina Bellaouel is most similar to ZAYN\n",
      "Eddy Woogy is most similar to ZAYN\n",
      "Weinzed is most similar to Glass Domain\n",
      "Femberi Bensen is most similar to Glass Domain\n",
      "Eluveitie is most similar to Glass Domain\n",
      "Kadebostany is most similar to Glass Domain\n",
      "Peter Nalitch is most similar to Glass Domain\n",
      "Ezginin Günlügü is most similar to Glass Domain\n",
      "Hakan Yılmaz is most similar to ZAYN\n",
      "Sansar Salvo is most similar to TAXI KEBAB\n",
      "Flo Rida is most similar to ZAYN\n",
      "Sia is most similar to Kardes Türküler\n",
      "Sagopa Kajmer is most similar to TAXI KEBAB\n",
      "Laura Branigan is most similar to ZAYN\n",
      "Hidra is most similar to Logic1000\n",
      "FerzanBeats is most similar to Glass Domain\n",
      "Şehinşah is most similar to Prequel\n",
      "Sean Paul is most similar to ZAYN\n",
      "Lyph3r is most similar to Glass Domain\n",
      "Wizkid is most similar to ZAYN\n",
      "Kyla is most similar to ZAYN\n",
      "Stromae is most similar to ZAYN\n",
      "AnnenMayKantereit is most similar to Glass Domain\n",
      "ALIZADE is most similar to ZAYN\n",
      "BEGE is most similar to ZAYN\n",
      "DJ Artz is most similar to Glass Domain\n",
      "Aşkın Nur Yengi is most similar to ZAYN\n",
      "Omega is most similar to TAXI KEBAB\n",
      "Tankurt Manas is most similar to Glass Domain\n",
      "Morcheeba is most similar to Glass Domain\n",
      "Kutanin is most similar to ZAYN\n",
      "Blue Swede is most similar to ZAYN\n",
      "Björn Skifs is most similar to ZAYN\n",
      "Amy Macdonald is most similar to ZAYN\n",
      "Amy Winehouse is most similar to ZAYN\n",
      "Françoise Hardy is most similar to Glass Domain\n",
      "Znous is most similar to Glass Domain\n",
      "Mehter is most similar to Logic1000\n",
      "Trio Aksak is most similar to ZAYN\n",
      "Kerim Sezer is most similar to Glass Domain\n",
      "Fikret Kızılok is most similar to ZAYN\n",
      "Mustafa Demirci is most similar to ZAYN\n",
      "Yolcular is most similar to ZAYN\n",
      "Mehmet Karakuş is most similar to ZAYN\n",
      "Mehmet Emin Ay is most similar to ZAYN\n",
      "İlahi Nidalar is most similar to ZAYN\n",
      "Sami Savni Ozer is most similar to ZAYN\n",
      "P!nk is most similar to ZAYN\n",
      "Vance Joy is most similar to ZAYN\n",
      "Alicia Keys is most similar to ZAYN\n",
      "Juicy J is most similar to ZAYN\n",
      "Eminem is most similar to ZAYN\n",
      "B.o.B is most similar to ZAYN\n",
      "Massaka is most similar to ZAYN\n",
      "Yener Çevik is most similar to ZAYN\n",
      "Anıl Piyancı is most similar to ZAYN\n",
      "Velet is most similar to ZAYN\n",
      "Monstar361 is most similar to ZAYN\n",
      "Defkhan is most similar to ZAYN\n",
      "Contra is most similar to Glass Domain\n",
      "Gekko G is most similar to ZAYN\n",
      "Farazi is most similar to ZAYN\n",
      "Kodes Kahra is most similar to ZAYN\n",
      "Ezhel is most similar to ZAYN\n",
      "Emel is most similar to ZAYN\n",
      "ibrahim1337 is most similar to ZAYN\n",
      "Christian Lisi is most similar to ZAYN\n",
      "SKÁLD is most similar to ZAYN\n",
      "Danheim is most similar to ZAYN\n",
      "Hedningarna is most similar to ZAYN\n",
      "Trevor Morris is most similar to ZAYN\n",
      "Haggard is most similar to Prequel\n",
      "Los Golden Boys is most similar to ZAYN\n",
      "Afrosound is most similar to ZAYN\n",
      "Garmarna is most similar to Glass Domain\n",
      "OMNIA is most similar to ZAYN\n",
      "Slayer is most similar to TAXI KEBAB\n",
      "Three Days Grace is most similar to TAXI KEBAB\n",
      "Kreator is most similar to Glass Domain\n",
      "Testament is most similar to Glass Domain\n",
      "Annihilator is most similar to ZAYN\n",
      "Overkill is most similar to Glass Domain\n",
      "Hard Bass School is most similar to TAXI KEBAB\n",
      "Rammstein is most similar to Logic1000\n",
      "Luciano Pavarotti is most similar to ZAYN\n",
      "Rotting Christ is most similar to ZAYN\n",
      "Måneskin is most similar to Glass Domain\n",
      "Domenico Sarro is most similar to ZAYN\n",
      "Academy of St. Martin in the Fields is most similar to ZAYN\n",
      "Sir Neville Marriner is most similar to ZAYN\n",
      "Powerwolf is most similar to Logic1000\n",
      "Slipknot is most similar to ZAYN\n",
      "Korpiklaani is most similar to Prequel\n",
      "Ghostemane is most similar to ZAYN\n",
      "Waterflame is most similar to ZAYN\n",
      "Pickpocket is most similar to ZAYN\n",
      "Rodrigo Amarante is most similar to ZAYN\n",
      "Poolside is most similar to ZAYN\n",
      "Fatnotronic is most similar to ZAYN\n",
      "Eleine is most similar to ZAYN\n",
      "Epica is most similar to TAXI KEBAB\n",
      "Piotr Musiał is most similar to ZAYN\n",
      "Nytt Land is most similar to ZAYN\n",
      "Marcin Przybyłowicz is most similar to ZAYN\n",
      "Daniel Lavoie is most similar to ZAYN\n",
      "Luck Mervil is most similar to ZAYN\n",
      "Bruno Pelletier is most similar to ZAYN\n",
      "Nightwish is most similar to ZAYN\n",
      "Percival Schuttenbach is most similar to Glass Domain\n",
      "Turisas is most similar to Glass Domain\n",
      "Lindemann is most similar to ZAYN\n",
      "Blind Guardian is most similar to Kardes Türküler\n",
      "Betzefer is most similar to ZAYN\n",
      "NanowaR of Steel is most similar to Logic1000\n",
      "Charly Glamour is most similar to ZAYN\n",
      "Gigatron is most similar to ZAYN\n",
      "Michael Starr is most similar to ZAYN\n",
      "Apocalypse Orchestra is most similar to ZAYN\n",
      "Folknery is most similar to ZAYN\n",
      "Dredg is most similar to ZAYN\n",
      "The Sins Of Thy Beloved is most similar to ZAYN\n",
      "Vasily Agapkin is most similar to ZAYN\n",
      "Geek Music is most similar to ZAYN\n",
      "Santiano is most similar to ZAYN\n",
      "Children Of Bodom is most similar to ZAYN\n",
      "Yom is most similar to ZAYN\n",
      "Hensonn is most similar to ZAYN\n",
      "Pentagram is most similar to ZAYN\n",
      "The HU is most similar to TAXI KEBAB\n",
      "Turan Ethno-Folk Ensemble is most similar to Glass Domain\n",
      "Dalriada is most similar to ZAYN\n",
      "Dark Tranquillity is most similar to ZAYN\n",
      "Wardruna is most similar to ZAYN\n",
      "Ankobo + Kaboo is most similar to ZAYN\n",
      "Vandaveer is most similar to ZAYN\n",
      "Russ is most similar to ZAYN\n",
      "R.E.M. is most similar to ZAYN\n",
      "The Verve is most similar to ZAYN\n",
      "Sting is most similar to ZAYN\n",
      "The Beatles is most similar to Logic1000\n",
      "Queen is most similar to ZAYN\n",
      "Yusuf / Cat Stevens is most similar to TAXI KEBAB\n",
      "Don McLean is most similar to ZAYN\n",
      "John Lennon is most similar to Glass Domain\n",
      "Simon & Garfunkel is most similar to Glass Domain\n",
      "Bob Dylan is most similar to Kardes Türküler\n",
      "Badfinger is most similar to ZAYN\n",
      "Édith Piaf is most similar to ZAYN\n",
      "Johnny Cash is most similar to Logic1000\n",
      "Paul McCartney is most similar to ZAYN\n",
      "Erykah Badu is most similar to ZAYN\n",
      "Vicky Sampson is most similar to ZAYN\n",
      "Moby is most similar to ZAYN\n",
      "LUUV. is most similar to ZAYN\n",
      "Mishaal is most similar to ZAYN\n",
      "Nouvelle Vague is most similar to ZAYN\n",
      "Camille is most similar to ZAYN\n",
      "Avenged Sevenfold is most similar to ZAYN\n",
      "Led Zeppelin is most similar to Glass Domain\n",
      "The Rolling Stones is most similar to ZAYN\n",
      "Kath Bloom is most similar to ZAYN\n",
      "Lee Hazlewood is most similar to ZAYN\n",
      "Imany is most similar to Glass Domain\n",
      "Sonny & Cher is most similar to ZAYN\n",
      "Barış Manço is most similar to ZAYN\n",
      "Hozier is most similar to ZAYN\n",
      "Kelly Clarkson is most similar to ZAYN\n",
      "Justin Timberlake is most similar to ZAYN\n",
      "Sam Smith is most similar to ZAYN\n",
      "Gary Jules is most similar to ZAYN\n",
      "Michael Andrews is most similar to ZAYN\n",
      "Josephine Foster is most similar to Glass Domain\n",
      "Víctor Herrero is most similar to ZAYN\n",
      "Pink Floyd is most similar to ZAYN\n",
      "Colonel Bagshot is most similar to ZAYN\n",
      "Gipsy Kings is most similar to ZAYN\n",
      "Royal Philharmonic Orchestra is most similar to ZAYN\n",
      "Mikky Ekko is most similar to ZAYN\n",
      "Perry Como is most similar to ZAYN\n",
      "Loreena McKennitt is most similar to Glass Domain\n",
      "Bee Gees is most similar to ZAYN\n",
      "Asaf Avidan is most similar to ZAYN\n",
      "The Mojos is most similar to ZAYN\n",
      "Noir Désir is most similar to ZAYN\n",
      "Creedence Clearwater Revival is most similar to ZAYN\n",
      "The Olympians is most similar to ZAYN\n",
      "Eleni & Souzana Vougioukli is most similar to ZAYN\n",
      "Mélanie Pain is most similar to ZAYN\n",
      "Yasmin Levy is most similar to ZAYN\n",
      "The Klezmatics is most similar to ZAYN\n",
      "Helldorado is most similar to ZAYN\n",
      "Marissa Nadler is most similar to ZAYN\n",
      "Elias Rahbani is most similar to ZAYN\n",
      "The White Buffalo is most similar to ZAYN\n",
      "Женя Любич is most similar to ZAYN\n",
      "Martine Scozzesi is most similar to ZAYN\n",
      "Monaldin is most similar to ZAYN\n",
      "Emma Peters is most similar to ZAYN\n",
      "Hindi Zahra is most similar to ZAYN\n",
      "Waldeck is most similar to ZAYN\n",
      "Patrizia Ferrara is most similar to ZAYN\n",
      "Cigarettes After Sex is most similar to ZAYN\n",
      "Caroline Polachek is most similar to ZAYN\n",
      "The Mamas & The Papas is most similar to ZAYN\n",
      "Kansas is most similar to ZAYN\n",
      "Moriarty is most similar to ZAYN\n",
      "John Legend is most similar to ZAYN\n",
      "The Handsome Family is most similar to ZAYN\n",
      "Alela Diane is most similar to ZAYN\n",
      "Neil Young is most similar to ZAYN\n",
      "S+C+A+R+R is most similar to ZAYN\n",
      "Bozkurt is most similar to ZAYN\n",
      "Kenan Doğulu is most similar to ZAYN\n",
      "Altyn Tuu is most similar to ZAYN\n",
      "Vindkaldr is most similar to LYNN\n",
      "Gondar is most similar to ZAYN\n"
     ]
    }
   ],
   "source": [
    "## FIND SIMILAR ARTISTS\n",
    "# each artist is represented by a vector, and you can think of this vector as something that describes each artist, for example it may be that in which each value of the vector represents the traits of their genre of music\n",
    "# e.g. 10% pop, 20% techno\n",
    "# each user is also represented by a vector, in which they also have percentages of how much of their music taste is pop, techno, etc.\n",
    "# these similarities were calculated by examining \n",
    "# still woozy related to orion sun --> most similar recommendation score for each user\n",
    "# each artist is a point in an n-dimensional space graph, we're finding the artists that are closest to each other on this graph\n",
    "# for example if user A has really high recommendation scores for taylor swift and low for central cee, this indicates that these artists aren't very similar to one another\n",
    "\n",
    "data_matrix = user_new.drop('User', axis=1)\n",
    "artists_transposed = data_matrix.T\n",
    "\n",
    "# Calculate pairwise Euclidean distances using the transposed data\n",
    "# distance in space is how similar the type of people who like the artist\n",
    "distances_artists = pd.DataFrame(squareform(pdist(artists_transposed, 'euclidean')), \n",
    "                                 index=artists_transposed.index, \n",
    "                                 columns=artists_transposed.index)\n",
    "\n",
    "np.fill_diagonal(distances_artists.values, np.inf)\n",
    "\n",
    "for artist in distances_artists.index:\n",
    "    most_similar_artist = distances_artists.loc[artist].idxmin()\n",
    "    if most_similar_artist != \"Rosario Dawson\":\n",
    "        print(f\"{artist} is most similar to {most_similar_artist}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw Euclidean Distances:\n",
      "Sara is most similar to Alperen\n",
      "Ebu is most similar to Sara\n",
      "Alperen is most similar to Sara\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/lm/dmlc5y790cq1twzy4dtrk2rc0000gn/T/ipykernel_16682/1860731175.py:30: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  ax.scatter(x, y, z, s=250*(1/scaled_variances[i]), alpha=0.5)  # Alpha = opacity\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SIMILAR USERS: Calculate pairwise Euclidean distances\n",
    "print(\"Raw Euclidean Distances:\")\n",
    "distances = pd.DataFrame(squareform(pdist(user_new, 'euclidean')), \n",
    "                         index=user_new.index, \n",
    "                         columns=user_new.index)\n",
    "\n",
    "# To avoid selecting the same user, set the diagonals to a high number\n",
    "np.fill_diagonal(distances.values, np.inf)\n",
    "\n",
    "# For each user, find the name of the closest other user\n",
    "for user in distances.index:\n",
    "    closest_user = distances.loc[user].idxmin()\n",
    "    print(f\"{user} is most similar to {closest_user}\")\n",
    "\n",
    "data_matrix = user_new\n",
    "variances = data_matrix.var(axis=1)\n",
    "scaled_variances = (variances - variances.min()) / (variances.max() - variances.min()) * 100\n",
    "\n",
    "# PCA to reduce to 3 dimensions\n",
    "pca = PCA(n_components=3)\n",
    "reduced_data = pca.fit_transform(data_matrix)\n",
    "\n",
    "# 3D plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Plot each user as a point in 3D space\n",
    "for i, user_name in enumerate(data_matrix.index):\n",
    "    x, y, z = reduced_data[i, 0], reduced_data[i, 1], reduced_data[i, 2]\n",
    "    ax.scatter(x, y, z, s=250*(1/scaled_variances[i]), alpha=0.5)  # Alpha = opacity\n",
    "    ax.text(x, y, z, user_name)  # Label each point with the user's name\n",
    "\n",
    "# Creating dummy points for legend\n",
    "for point in [20, 50, 80]:\n",
    "    ax.scatter([], [], [], s=point, c=\"blue\", alpha=0.5, label=f'Variance {point}')\n",
    "\n",
    "# Label the plot\n",
    "ax.set_xlabel('PCA 1')\n",
    "ax.set_ylabel('PCA 2')\n",
    "ax.set_zlabel('PCA 3')\n",
    "ax.legend(title='Point Size Represents')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean distance of PCA results:\n",
      "Sara is most similar to Alperen\n",
      "Ebu is most similar to Sara\n",
      "Alperen is most similar to Sara\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparison of distance on PCA results:\n",
    "print(\"Euclidean distance of PCA results:\")\n",
    "data_matrix = user_new\n",
    "variances = data_matrix.var(axis=1)\n",
    "scaled_variances = (variances - variances.min()) / (variances.max() - variances.min()) * 100\n",
    "\n",
    "# Perform PCA to reduce to 3 dimensions\n",
    "pca = PCA(n_components=3)\n",
    "reduced_data = pca.fit_transform(data_matrix)\n",
    "\n",
    "# Calculate pairwise Euclidean distances using the PCA-transformed data\n",
    "distances_pca = pd.DataFrame(squareform(pdist(reduced_data, 'euclidean')), \n",
    "                             index=data_matrix.index, \n",
    "                             columns=data_matrix.index)\n",
    "\n",
    "# Set the diagonals to a high number to avoid self-comparison\n",
    "np.fill_diagonal(distances_pca.values, np.inf)\n",
    "\n",
    "# Find the name of the closest other user based on PCA-transformed distances\n",
    "for user in distances_pca.index:\n",
    "    closest_user = distances_pca.loc[user].idxmin()\n",
    "    print(f\"{user} is most similar to {closest_user}\")\n",
    "\n",
    "#HEATMAP\n",
    "np.fill_diagonal(distances_pca.values, np.nan)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(distances_pca, annot=True, fmt=\".2f\", cmap='viridis')\n",
    "plt.title(\"Pairwise Euclidean Distances Heatmap\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Joey Bada$$'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m/Users/john/john/projects/dealing_with_data/MusicRecommendation/create_playlist_matrix.ipynb Cell 14\u001b[0m line \u001b[0;36m4\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/john/john/projects/dealing_with_data/MusicRecommendation/create_playlist_matrix.ipynb#X22sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# kmeans by ARTIST\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/john/john/projects/dealing_with_data/MusicRecommendation/create_playlist_matrix.ipynb#X22sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m artists_transposed \u001b[39m=\u001b[39m playlist_new\u001b[39m.\u001b[39mT\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/john/john/projects/dealing_with_data/MusicRecommendation/create_playlist_matrix.ipynb#X22sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m artists_transposed \u001b[39m=\u001b[39m artists_transposed\u001b[39m.\u001b[39;49mdrop(\u001b[39m'\u001b[39;49m\u001b[39mJoey Bada$$\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/john/john/projects/dealing_with_data/MusicRecommendation/create_playlist_matrix.ipynb#X22sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m artists_transposed \u001b[39m=\u001b[39m artists_transposed\u001b[39m.\u001b[39mdrop(\u001b[39m'\u001b[39m\u001b[39mStephan Bodzin\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/john/john/projects/dealing_with_data/MusicRecommendation/create_playlist_matrix.ipynb#X22sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m kmeans \u001b[39m=\u001b[39m KMeans(n_clusters\u001b[39m=\u001b[39m\u001b[39m5\u001b[39m, random_state\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/core/frame.py:5399\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5251\u001b[0m \u001b[39m@deprecate_nonkeyword_arguments\u001b[39m(version\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, allowed_args\u001b[39m=\u001b[39m[\u001b[39m\"\u001b[39m\u001b[39mself\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[1;32m   5252\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mdrop\u001b[39m(  \u001b[39m# type: ignore[override]\u001b[39;00m\n\u001b[1;32m   5253\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5260\u001b[0m     errors: IgnoreRaise \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mraise\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m   5261\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataFrame \u001b[39m|\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   5262\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   5263\u001b[0m \u001b[39m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   5264\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5397\u001b[0m \u001b[39m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   5398\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5399\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mdrop(\n\u001b[1;32m   5400\u001b[0m         labels\u001b[39m=\u001b[39;49mlabels,\n\u001b[1;32m   5401\u001b[0m         axis\u001b[39m=\u001b[39;49maxis,\n\u001b[1;32m   5402\u001b[0m         index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m   5403\u001b[0m         columns\u001b[39m=\u001b[39;49mcolumns,\n\u001b[1;32m   5404\u001b[0m         level\u001b[39m=\u001b[39;49mlevel,\n\u001b[1;32m   5405\u001b[0m         inplace\u001b[39m=\u001b[39;49minplace,\n\u001b[1;32m   5406\u001b[0m         errors\u001b[39m=\u001b[39;49merrors,\n\u001b[1;32m   5407\u001b[0m     )\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/util/_decorators.py:331\u001b[0m, in \u001b[0;36mdeprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(args) \u001b[39m>\u001b[39m num_allow_args:\n\u001b[1;32m    326\u001b[0m     warnings\u001b[39m.\u001b[39mwarn(\n\u001b[1;32m    327\u001b[0m         msg\u001b[39m.\u001b[39mformat(arguments\u001b[39m=\u001b[39m_format_argument_list(allow_args)),\n\u001b[1;32m    328\u001b[0m         \u001b[39mFutureWarning\u001b[39;00m,\n\u001b[1;32m    329\u001b[0m         stacklevel\u001b[39m=\u001b[39mfind_stack_level(),\n\u001b[1;32m    330\u001b[0m     )\n\u001b[0;32m--> 331\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/core/generic.py:4505\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m \u001b[39mfor\u001b[39;00m axis, labels \u001b[39min\u001b[39;00m axes\u001b[39m.\u001b[39mitems():\n\u001b[1;32m   4504\u001b[0m     \u001b[39mif\u001b[39;00m labels \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 4505\u001b[0m         obj \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39;49m_drop_axis(labels, axis, level\u001b[39m=\u001b[39;49mlevel, errors\u001b[39m=\u001b[39;49merrors)\n\u001b[1;32m   4507\u001b[0m \u001b[39mif\u001b[39;00m inplace:\n\u001b[1;32m   4508\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/core/generic.py:4546\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m         new_axis \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39mdrop(labels, level\u001b[39m=\u001b[39mlevel, errors\u001b[39m=\u001b[39merrors)\n\u001b[1;32m   4545\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 4546\u001b[0m         new_axis \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39;49mdrop(labels, errors\u001b[39m=\u001b[39;49merrors)\n\u001b[1;32m   4547\u001b[0m     indexer \u001b[39m=\u001b[39m axis\u001b[39m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4549\u001b[0m \u001b[39m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4550\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/pandas/core/indexes/base.py:6934\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m \u001b[39mif\u001b[39;00m mask\u001b[39m.\u001b[39many():\n\u001b[1;32m   6933\u001b[0m     \u001b[39mif\u001b[39;00m errors \u001b[39m!=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[0;32m-> 6934\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00m\u001b[39mlist\u001b[39m(labels[mask])\u001b[39m}\u001b[39;00m\u001b[39m not found in axis\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m   6935\u001b[0m     indexer \u001b[39m=\u001b[39m indexer[\u001b[39m~\u001b[39mmask]\n\u001b[1;32m   6936\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdelete(indexer)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Joey Bada$$'] not found in axis\""
     ]
    }
   ],
   "source": [
    "# kmeans by ARTIST\n",
    "\n",
    "artists_transposed = playlist_new.T\n",
    "artists_transposed = artists_transposed.drop('Joey Bada$$')\n",
    "artists_transposed = artists_transposed.drop('Stephan Bodzin')\n",
    "kmeans = KMeans(n_clusters=5, random_state=0)\n",
    "clusters = kmeans.fit_predict(artists_transposed)\n",
    "\n",
    "# Apply PCA to reduce the dimensions to 2 or 3\n",
    "pca = PCA(n_components=3)\n",
    "reduced_data = pca.fit_transform(artists_transposed)\n",
    "\n",
    "# Find indices of min and max for each PCA component\n",
    "min_indices = reduced_data.argmin(axis=0)\n",
    "max_indices = reduced_data.argmax(axis=0)\n",
    "\n",
    "# Combine these indices (they might be not unique if min and max are the same for some component)\n",
    "label_indices = set(min_indices) | set(max_indices)\n",
    "\n",
    "# Plotting\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], c=clusters, cmap='viridis', marker='o')\n",
    "\n",
    "# Annotating points with min and max PCA values\n",
    "for i, artist_name in enumerate(artists_transposed.index):\n",
    "    # Label the min and max points for each PCA component\n",
    "    if i in label_indices:\n",
    "        safe_name = artist_name.encode('ascii', 'ignore').decode()\n",
    "        ax.text(reduced_data[i, 0], reduced_data[i, 1], reduced_data[i, 2], safe_name, usetex=False)\n",
    "\n",
    "ax.set_title('K-means Clustering of Artists')\n",
    "ax.set_xlabel('PCA 1')\n",
    "ax.set_ylabel('PCA 2')\n",
    "ax.set_zlabel('PCA 3')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The top artists \u001b[1;4mEbu\u001b[0m should listen to are:\n",
      "1. Jessie J\n",
      "2. JAY-Z\n",
      "3. French Montana\n",
      "4. Swae Lee\n",
      "5. Josh Lippi & The Overtimers\n"
     ]
    }
   ],
   "source": [
    "## top 5 recommendation\n",
    "\n",
    "# recommend 5 top artists from the highest recommended complete matrix per user, that has No value in complete df per each user\n",
    "def recommend(type, user):\n",
    "    if type == 'playlist':\n",
    "        orig = playlist_original_df\n",
    "        complete = playlist_new\n",
    "        col_name = 'Playlist Name'\n",
    "    else:\n",
    "        orig = users_original_df\n",
    "        complete = user_new\n",
    "        col_name = 'User'\n",
    "\n",
    "    if orig.index.name != col_name:\n",
    "        orig = orig.set_index(col_name)\n",
    "    if complete.index.name != col_name:\n",
    "        complete = complete.set_index(col_name)\n",
    "\n",
    "    # Check if user exists in the original df\n",
    "    if user not in orig.index:\n",
    "        raise ValueError(\"User not found in the original DataFrame\")\n",
    "\n",
    "    # Extract user's data from original and complete matrices\n",
    "    user_orig_data = orig.loc[user]\n",
    "    user_complete_data = complete.loc[user]\n",
    "\n",
    "    # artists that the user hasn't rated/listened to in original df\n",
    "    unrated_artists = user_orig_data[user_orig_data.isna() | (user_orig_data == 0)].index\n",
    "\n",
    "    recommendations = user_complete_data[unrated_artists]\n",
    "    top_recommendations = recommendations.sort_values(ascending=False).head(5)\n",
    "\n",
    "    return top_recommendations.index.tolist()\n",
    "\n",
    "    # old\n",
    "    # return top_recommendations\n",
    "\n",
    "## USER \n",
    "top_artist_recs = recommend('user', 'Ebu')\n",
    "\n",
    "print(\"The top artists \\033[1;4mEbu\\033[0m should listen to are:\") # print out the score of each artist\n",
    "for i, artist in enumerate(top_artist_recs, start=1):\n",
    "    print(f\"{i}. {artist}\")\n",
    "\n",
    "# OLD\n",
    "# print(\"The top artists you should listen to are:\")\n",
    "# for i, (artist, score) in enumerate(top_artist_recs.iteritems(), start=1):\n",
    "#     print(f\"{i}. {artist} - Score: {score:.2f}\")\n",
    "\n",
    "## PLAYLIST\n",
    "# print(recommend('playlist', 'Alperen/jawngspec'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    NaN\n",
      "1    9.0\n",
      "2    NaN\n",
      "3    NaN\n",
      "4    NaN\n",
      "5    NaN\n",
      "6    NaN\n",
      "7    NaN\n",
      "8    NaN\n",
      "9    NaN\n",
      "Name: PawPaw Rod, dtype: float64\n",
      "User\n",
      "Cece      -8.087846e-15\n",
      "Tyler      1.951220e-01\n",
      "Sara      -1.204765e-13\n",
      "Jeong      5.652301e-16\n",
      "Malia     -7.514822e-17\n",
      "Carter    -2.831506e-15\n",
      "Leo       -1.312388e-15\n",
      "Aleena    -1.273547e-14\n",
      "Rhea       1.271859e-15\n",
      "Alperen    7.679276e-05\n",
      "Name: PawPaw Rod, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(users_original_df['PawPaw Rod'])\n",
    "print(user_new['PawPaw Rod'])"
   ]
  }
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