ANLP_WS24_CA2/cnn_reg.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from tqdm import tqdm  # Fortschrittsbalken-Bibliothek\n",
    "from dataset_generator import create_embedding_matrix, split_data\n",
    "from HumorDataset import TextRegDataset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# lokal imports\n",
    "import ml_evaluation as ml_eval\n",
    "import ml_helper\n",
    "import ml_history\n",
    "import dataset_generator as data_gen\n",
    "# class imports\n",
    "import HumorDataset as humor_ds\n",
    "import EarlyStopping as EarlyStopping\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: mps\n"
     ]
    }
   ],
   "source": [
    "torch.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "\n",
    "\n",
    "best_model_filename = 'best_cnn_reg_model.pt'\n",
    "\n",
    "device = ml_helper.get_device(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter und Konfigurationen\n",
    "params = {\n",
    "    \"embedding_dim\": 100,\n",
    "    \"filter_sizes\": [2, 3, 4, 5],  # Zusätzliche Filtergröße\n",
    "    \"num_filters\": 150,  # Erhöhte Anzahl von Filtern\n",
    "    \"batch_size\": 32,\n",
    "    \"learning_rate\": 0.001,\n",
    "    \"epochs\": 25,\n",
    "    \"glove_path\": 'data/glove.6B.100d.txt',  # Pfad zu GloVe\n",
    "    \"max_len\": 280,\n",
    "    \"test_size\": 0.1,\n",
    "    \"val_size\": 0.1,\n",
    "    \"patience\": 5,\n",
    "    \"data_path\": 'data/hack.csv',  # Pfad zu den Daten\n",
    "    \"dropout\": 0.6,  # Erhöhtes Dropout\n",
    "    \"weight_decay\": 5e-4  # L2-Regularisierung\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNNRegressor(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, filter_sizes, num_filters, embedding_matrix, dropout):\n",
    "        super(CNNRegressor, self).__init__()\n",
    "        self.embedding = nn.Embedding.from_pretrained(embedding_matrix, freeze=False)\n",
    "        \n",
    "        # Convolutional Schichten mit Batch-Normalisierung\n",
    "        self.convs = nn.ModuleList([\n",
    "            nn.Sequential(\n",
    "                nn.Conv2d(1, num_filters, (fs, embedding_dim)),\n",
    "                nn.BatchNorm2d(num_filters),  # Batch-Normalisierung\n",
    "                nn.ReLU(),\n",
    "                nn.MaxPool2d((params[\"max_len\"] - fs + 1, 1)),\n",
    "                nn.Dropout(dropout)  # Dropout nach jeder Schicht\n",
    "            )\n",
    "            for fs in filter_sizes\n",
    "        ])\n",
    "        \n",
    "        # Fully-Connected Layer\n",
    "        self.fc1 = nn.Linear(len(filter_sizes) * num_filters, 128)  # Erweiterte Dense-Schicht\n",
    "        self.fc2 = nn.Linear(128, 1)  # Ausgangsschicht (Regression)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.embedding(x).unsqueeze(1)  # [Batch, 1, Seq, Embedding]\n",
    "        conv_outputs = [conv(x).squeeze(3).squeeze(2) for conv in self.convs]  # Pooling reduziert Dim\n",
    "        x = torch.cat(conv_outputs, 1)  # Kombiniere Features von allen Filtern\n",
    "        x = torch.relu(self.fc1(x))  # Zusätzliche Dense-Schicht\n",
    "        x = self.dropout(x)\n",
    "        return self.fc2(x).squeeze(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funktion zum Laden und Vorverarbeiten der Daten\n",
    "def load_preprocess_data(path_data='data/hack.csv'):\n",
    "    # Daten laden\n",
    "    df = pd.read_csv(path_data)\n",
    "\n",
    "    # Fehlende Werte in der Zielspalte entfernen\n",
    "    df = df.dropna(subset=['humor_rating'])\n",
    "\n",
    "    # Zielvariable aus der Spalte 'humor_rating' extrahieren\n",
    "    df['y'] = df['humor_rating'].astype(float)  # Sicherstellen, dass Zielvariable numerisch ist\n",
    "\n",
    "    # Eingabetexte und Zielvariable zuweisen\n",
    "    X = df['text']\n",
    "    y = df['y']\n",
    "\n",
    "    # Debug-Ausgabe zur Überprüfung\n",
    "    print(f\"Erste Zielwerte: {y.head(10)}\")\n",
    "    print(f\"Datentyp der Zielvariable: {y.dtype}\")\n",
    "    print(f\"Anzahl der Beispiele: {len(X)}\")\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualisierung der Zielvariablen (Scores)\n",
    "def visualize_data_distribution(y):\n",
    "    print(\"\\n--- Zielvariable: Statistik ---\")\n",
    "    print(f\"Min: {np.min(y)}, Max: {np.max(y)}\")\n",
    "    print(f\"Mittelwert: {np.mean(y):.4f}, Standardabweichung: {np.std(y):.4f}\")\n",
    "    \n",
    "    # Histogramm plotten\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(y, bins=20, color='skyblue', edgecolor='black')\n",
    "    plt.title('Verteilung der Zielvariable (Scores)')\n",
    "    plt.xlabel('Score')\n",
    "    plt.ylabel('Häufigkeit')\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400002\n",
      "vocab_size: 400002, d_model: 100\n",
      "Erste Zielwerte: 0     2.42\n",
      "1     2.50\n",
      "2     1.95\n",
      "3     2.11\n",
      "4     2.78\n",
      "7     1.79\n",
      "11    2.20\n",
      "12    1.50\n",
      "13    2.16\n",
      "17    1.78\n",
      "Name: y, dtype: float64\n",
      "Datentyp der Zielvariable: float64\n",
      "Anzahl der Beispiele: 4932\n",
      "\n",
      "--- Zielvariable: Statistik ---\n",
      "Min: 0.1, Max: 4.0\n",
      "Mittelwert: 2.2605, Standardabweichung: 0.5669\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Daten laden und vorbereiten\n",
    "embedding_matrix, word_index, vocab_size, d_model = create_embedding_matrix(\n",
    "    gloVe_path=params[\"glove_path\"], emb_len=params[\"embedding_dim\"]\n",
    ")\n",
    "X, y = load_preprocess_data(path_data=params[\"data_path\"])\n",
    "\n",
    "# Visualisierung der Daten\n",
    "visualize_data_distribution(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train 3945 3945\n",
      "test 494 494\n",
      "val 493 493\n"
     ]
    }
   ],
   "source": [
    "# Aufteilen der Daten\n",
    "data_split = split_data(X, y, test_size=params[\"test_size\"], val_size=params[\"val_size\"])\n",
    "\n",
    "# Dataset und DataLoader\n",
    "train_dataset = TextRegDataset(data_split['train']['X'], data_split['train']['y'], word_index, max_len=params[\"max_len\"])\n",
    "val_dataset = TextRegDataset(data_split['val']['X'], data_split['val']['y'], word_index, max_len=params[\"max_len\"])\n",
    "test_dataset = TextRegDataset(data_split['test']['X'], data_split['test']['y'], word_index, max_len=params[\"max_len\"])\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=params[\"batch_size\"], shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=params[\"batch_size\"], shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modell initialisieren\n",
    "model = CNNRegressor(\n",
    "    vocab_size=vocab_size,\n",
    "    embedding_dim=params[\"embedding_dim\"],\n",
    "    filter_sizes=params[\"filter_sizes\"],\n",
    "    num_filters=params[\"num_filters\"],\n",
    "    embedding_matrix=embedding_matrix,\n",
    "    dropout=params[\"dropout\"]\n",
    ")\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = model.to(device)\n",
    "\n",
    "criterion = nn.MSELoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=params[\"learning_rate\"], weight_decay=params[\"weight_decay\"])\n",
    "early_stopping = EarlyStopping.EarlyStopping(patience=params[\"patience\"], verbose=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 1/25: 100%|██████████| 124/124 [00:18<00:00,  6.78it/s, Train Loss=0.907]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 1, Train Loss: 272.8565, Val Loss: 8.8425\n",
      "Train RMSE: 1.4859, Val RMSE: 0.7446\n",
      "Validation loss decreased (-0.744646 --> 0.744646).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 2/25: 100%|██████████| 124/124 [00:19<00:00,  6.44it/s, Train Loss=0.696]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 2, Train Loss: 98.9154, Val Loss: 9.4178\n",
      "Train RMSE: 0.8935, Val RMSE: 0.7682\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 3/25: 100%|██████████| 124/124 [00:19<00:00,  6.30it/s, Train Loss=1.79] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 3, Train Loss: 93.6001, Val Loss: 7.3193\n",
      "Train RMSE: 0.8653, Val RMSE: 0.6769\n",
      "Validation loss decreased (-0.676914 --> 0.676914).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 4/25: 100%|██████████| 124/124 [00:18<00:00,  6.58it/s, Train Loss=1.12] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 4, Train Loss: 83.3528, Val Loss: 6.1189\n",
      "Train RMSE: 0.8183, Val RMSE: 0.6187\n",
      "Validation loss decreased (-0.618719 --> 0.618719).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 5/25: 100%|██████████| 124/124 [00:19<00:00,  6.20it/s, Train Loss=0.866]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 5, Train Loss: 82.4167, Val Loss: 6.3834\n",
      "Train RMSE: 0.8145, Val RMSE: 0.6317\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 6/25: 100%|██████████| 124/124 [00:20<00:00,  6.13it/s, Train Loss=0.528]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 6, Train Loss: 76.5571, Val Loss: 6.5987\n",
      "Train RMSE: 0.7861, Val RMSE: 0.6421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 7/25: 100%|██████████| 124/124 [00:20<00:00,  6.13it/s, Train Loss=0.135]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 7, Train Loss: 73.0328, Val Loss: 6.4774\n",
      "Train RMSE: 0.7692, Val RMSE: 0.6361\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 8/25: 100%|██████████| 124/124 [00:20<00:00,  6.07it/s, Train Loss=0.5]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 8, Train Loss: 73.0788, Val Loss: 5.5935\n",
      "Train RMSE: 0.7680, Val RMSE: 0.5913\n",
      "Validation loss decreased (-0.591316 --> 0.591316).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9/25: 100%|██████████| 124/124 [00:20<00:00,  6.00it/s, Train Loss=0.747]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 9, Train Loss: 72.9909, Val Loss: 5.7356\n",
      "Train RMSE: 0.7666, Val RMSE: 0.5987\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 10/25: 100%|██████████| 124/124 [00:20<00:00,  6.07it/s, Train Loss=0.33] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 10, Train Loss: 68.4401, Val Loss: 5.6286\n",
      "Train RMSE: 0.7438, Val RMSE: 0.5931\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 11/25: 100%|██████████| 124/124 [00:21<00:00,  5.90it/s, Train Loss=0.135]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 11, Train Loss: 72.3024, Val Loss: 6.8619\n",
      "Train RMSE: 0.7653, Val RMSE: 0.6543\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 12/25: 100%|██████████| 124/124 [00:20<00:00,  5.98it/s, Train Loss=0.353]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 12, Train Loss: 65.9048, Val Loss: 6.5378\n",
      "Train RMSE: 0.7297, Val RMSE: 0.6390\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 13/25: 100%|██████████| 124/124 [00:22<00:00,  5.55it/s, Train Loss=0.4]  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 13, Train Loss: 65.4947, Val Loss: 5.1140\n",
      "Train RMSE: 0.7273, Val RMSE: 0.5665\n",
      "Validation loss decreased (-0.566452 --> 0.566452).  Saving model ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 14/25: 100%|██████████| 124/124 [00:23<00:00,  5.21it/s, Train Loss=0.515]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 14, Train Loss: 62.4101, Val Loss: 6.0987\n",
      "Train RMSE: 0.7094, Val RMSE: 0.6177\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 15/25: 100%|██████████| 124/124 [00:23<00:00,  5.38it/s, Train Loss=0.645]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 15, Train Loss: 64.4443, Val Loss: 9.2568\n",
      "Train RMSE: 0.7204, Val RMSE: 0.7601\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 16/25: 100%|██████████| 124/124 [00:24<00:00,  5.15it/s, Train Loss=0.288]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 16, Train Loss: 58.4627, Val Loss: 5.3123\n",
      "Train RMSE: 0.6874, Val RMSE: 0.5776\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 17/25: 100%|██████████| 124/124 [00:22<00:00,  5.43it/s, Train Loss=0.524]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 17, Train Loss: 58.4355, Val Loss: 5.5252\n",
      "Train RMSE: 0.6863, Val RMSE: 0.5889\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 18/25: 100%|██████████| 124/124 [00:21<00:00,  5.74it/s, Train Loss=0.223]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 18, Train Loss: 55.3765, Val Loss: 5.9419\n",
      "Train RMSE: 0.6692, Val RMSE: 0.6100\n",
      "Early stopping triggered.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Speicher für Trainingsmetriken\n",
    "history = {\n",
    "    \"train_loss\": [],\n",
    "    \"val_loss\": [],\n",
    "    \"train_rmse\": [],\n",
    "    \"val_rmse\": [],\n",
    "}\n",
    "\n",
    "# Training und Validierung\n",
    "for epoch in range(params[\"epochs\"]):\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    train_preds, train_labels = [], []\n",
    "\n",
    "    # Fortschrittsbalken für Training innerhalb einer Epoche\n",
    "    with tqdm(train_loader, desc=f\"Epoch {epoch + 1}/{params['epochs']}\") as pbar:\n",
    "        for X_batch, y_batch in pbar:\n",
    "            X_batch, y_batch = X_batch.to(device), y_batch.to(device).float()\n",
    "            optimizer.zero_grad()\n",
    "            predictions = model(X_batch).float()\n",
    "            loss = criterion(predictions, y_batch)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()\n",
    "            \n",
    "            # Speichere echte und vorhergesagte Werte für Metriken\n",
    "            train_preds.extend(predictions.cpu().detach().numpy())\n",
    "            train_labels.extend(y_batch.cpu().detach().numpy())\n",
    "            \n",
    "            # Update der Fortschrittsanzeige\n",
    "            pbar.set_postfix({\"Train Loss\": loss.item()})\n",
    "    \n",
    "    train_rmse = np.sqrt(mean_squared_error(train_labels, train_preds))  # RMSE\n",
    "    history[\"train_loss\"].append(train_loss / len(train_loader))\n",
    "    history[\"train_rmse\"].append(train_rmse)\n",
    "\n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    val_preds, val_labels = [], []\n",
    "    with torch.no_grad():\n",
    "        for X_batch, y_batch in val_loader:\n",
    "            X_batch, y_batch = X_batch.to(device), y_batch.to(device).float()\n",
    "            predictions = model(X_batch).float()\n",
    "            loss = criterion(predictions, y_batch)\n",
    "            val_loss += loss.item()\n",
    "\n",
    "            val_preds.extend(predictions.cpu().detach().numpy())\n",
    "            val_labels.extend(y_batch.cpu().detach().numpy())\n",
    "\n",
    "        val_rmse = np.sqrt(mean_squared_error(val_labels, val_preds))  # RMSE\n",
    "    history[\"val_loss\"].append(val_loss / len(val_loader))\n",
    "    history[\"val_rmse\"].append(val_rmse)\n",
    "\n",
    "    print(f\"\\nEpoch {epoch + 1}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}\")\n",
    "    print(f\"Train RMSE: {train_rmse:.4f}, Val RMSE: {val_rmse:.4f}\")\n",
    "\n",
    "    early_stopping(val_rmse, model)\n",
    "    if early_stopping.early_stop:\n",
    "        print(\"Early stopping triggered.\")\n",
    "        break\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plot-Funktion für Training\n",
    "def plot_learning_curves(history):\n",
    "    epochs = range(1, len(history['train_loss']) + 1)\n",
    "\n",
    "    # Loss-Plot\n",
    "    plt.figure(figsize=(14, 6))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(epochs, history['train_loss'], label='Train Loss')\n",
    "    plt.plot(epochs, history['val_loss'], label='Val Loss')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('Loss')\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    # RMSE-Plot\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(epochs, history['train_rmse'], label='Train RMSE')\n",
    "    plt.plot(epochs, history['val_rmse'], label='Val RMSE')\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel('RMSE')\n",
    "    plt.title('Training and Validation RMSE')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot der Lernkurven\n",
    "plot_learning_curves(history)\n",
    "# Funktion zur Visualisierung der richtigen und falschen Vorhersagen\n",
    "def visualize_predictions(true_values, predicted_values):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    # Unterschied zwischen vorhergesagten und wahren Werten\n",
    "    correct_indices = np.isclose(true_values, predicted_values, atol=0.3)  # Als korrekt angenommen, wenn Differenz <= 0.3\n",
    "    \n",
    "    # Plot\n",
    "    plt.scatter(true_values[correct_indices], predicted_values[correct_indices], color='green', label='Richtig vorhergesagt')\n",
    "    plt.scatter(true_values[~correct_indices], predicted_values[~correct_indices], color='red', label='Falsch vorhergesagt')\n",
    "    plt.plot([min(true_values), max(true_values)], [min(true_values), max(true_values)], color='blue', linestyle='--', label='Ideal-Linie')\n",
    "    \n",
    "    plt.xlabel('Wahre Werte')\n",
    "    plt.ylabel('Vorhergesagte Werte')\n",
    "    plt.title('Richtige vs Falsche Vorhersagen')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test Evaluation\n",
    "model.eval()\n",
    "test_preds, test_labels = [], []\n",
    "with torch.no_grad():\n",
    "    for X_batch, y_batch in test_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device).float()\n",
    "        predictions = model(X_batch).float()\n",
    "        test_preds.extend(predictions.cpu().detach().numpy())\n",
    "        test_labels.extend(y_batch.cpu().detach().numpy())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Konvertierung zu NumPy-Arrays\n",
    "true_values = np.array(test_labels)\n",
    "predicted_values = np.array(test_preds)\n",
    "\n",
    "# Visualisierung der Ergebnisse\n",
    "visualize_predictions(true_values, predicted_values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test RMSE: 0.5820, Test MAE: 0.4757, Test R²: -0.0582\n"
     ]
    }
   ],
   "source": [
    "# RMSE, MAE und R²-Score für das Test-Set\n",
    "test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))\n",
    "test_mae = mean_absolute_error(test_labels, test_preds)\n",
    "test_r2 = r2_score(test_labels, test_preds)\n",
    "print(f\"Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Funktion zur Visualisierung der richtigen und falschen Vorhersagen\n",
    "def visualize_predictions(true_values, predicted_values):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    # Unterschied zwischen vorhergesagten und wahren Werten\n",
    "    correct_indices = np.isclose(true_values, predicted_values, atol=0.3)  # Als korrekt angenommen, wenn Differenz <= 0.3\n",
    "    \n",
    "    # Plot\n",
    "    plt.scatter(true_values[correct_indices], predicted_values[correct_indices], color='green', label='Richtig vorhergesagt')\n",
    "    plt.scatter(true_values[~correct_indices], predicted_values[~correct_indices], color='red', label='Falsch vorhergesagt')\n",
    "    plt.plot([min(true_values), max(true_values)], [min(true_values), max(true_values)], color='blue', linestyle='--', label='Ideal-Linie')\n",
    "    \n",
    "    plt.xlabel('Wahre Werte')\n",
    "    plt.ylabel('Vorhergesagte Werte')\n",
    "    plt.title('Richtige vs Falsche Vorhersagen')\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rRnshddvrdTuu6bVdPWTVTcqhSfCkSZNMAqMTHOgJq/Zg6FTqF198senF0BvoahKp15Do8aD3otJHfcSok2/oPX8ee+wxc7Kv19TpceC611RN6Im3TtShrx0d4qpTgGtvlfaQ1jW9v5P2kGiPsU5tr69hfT3ovZBcE0soTc41sdFpzHW6/XPPPdf02mkPmvYIzZkzx/QSVUeXo+8P+rrRfemaxt06JE/3jQ6z0/2s+073f1XT5h/t614nK9Fri3QiC+0R0t4xTcL0fVDLdZpynUwikPc3ACEi2NPrAWh8U4C7HjpldXJysvOcc84x02lbp5r2NwX40qVLnRdffLGzffv25u/1f53id+PGjR5/969//cvZp08fZ3h4uMeU2zot73HHHeczPn9TgOtUy/fdd58zKSnJGRMTY6bA3rZtW6W/f/LJJ8104Trt8Omnn+5cvXq1z+mO/cXmPQWyys3NdU6cONG0MyIiwkwr/fjjj7unp3bR5YwfP75STP6mKPa2e/du56hRo5ytW7c221WntfY1TfnRTgFeXV3XFODaJl82b97svOGGG8xxou3X7fvb3/7W+dZbb3nUW7t2rdnO0dHRps60adOcL7zwQrVTgLumgL7//vudnTt3NuvQdek067pulxUrVjj79etnto33FNm1HaMvGRkZZhrvFi1amGnDf//73zszMzMrxeJ6vXhPIe167VnXU1hY6LzjjjvM1ODNmjVzXnTRRc4dO3bU6hTg/qay1unmu3TpYrbnSSed5Pzoo498Hv/queeeM9teX3vx8fHm2LznnntM+wN19913m3iuvPLKSs8dOHDAfezrlPcjRoxwrl+/vtJrx9cU4Efzutdp9WfMmGHef7Ruy5YtTbumTJniPHTo0FG9vwEIPpv+E+xEDQAAAABCBdckAQAAAIAFSRIAAAAAWJAkAQAAAIAFSRIAAAAAWJAkAQAAAIAFSRIAAAAANKWbyTocDsnMzDQ3S9Q72AMAAABompxOp+Tm5kr79u3Fbrc33SRJE6SOHTsGOwwAAAAAIWLHjh2SkpLSdJMk7UFybYiEhIRghwMAAAAgSHJyckwHiitHaLJJkmuInSZIJEkAAAAAbNVchsPEDQAAAABgQZIEAAAAABYkSQAAAADQlK5JAgAAQP1MrVxWVibl5eXBDgVNWFhYmISHhx/zrX9IkgAAAHBMSkpKJCsrSwoKCoIdCiCxsbHSrl07iYyMrPEySJIAAABQYw6HQ9LT0803+HqDTj0xPdZv8YGa9mZqwp6dnW2Oye7du1d5w9iqkCQBAACgxvSkVBMlvfeMfoMPBFNMTIxERETItm3bzLEZHR1do+UwcQMAAACOWU2/sQdC8VjkaAYAAAAAC4bbAQAAoE7otSE5OTn1sq6EhARp06ZNvawLjR9JEgAAAOokQRo99lbJLSyql/XFx0TLgufmN8lEKS0tTSZMmGAeqB0kSQAAAKh12oOkCdKQ68dJYruUOl3XvqwM+fzlZ8w6A0mSqpt976GHHpLJkydLXTvhhBPk9NNPl/nz51d67uWXX5abbrpJdu7cKa1bt67zWOCJJAkAAAB1RhOk5E6dJZToPZ1cXn/9dXnwwQdlw4YN7rK4uDiPaaX1Brl6g9LaNmbMGJOMzZo1y8zKZrVw4UL53e9+R4IUJEzcAAAAgCYlOTnZ/WjevLnpWXL9vn79eomPj5cPP/xQ+vXrJ1FRUbJ8+XK58cYb5ZJLLvFYjg5vGzJkiPt3nQp9+vTp0rlzZ5P09O3bV9566y2/cVx33XVSWFgob7/9tke53uPn888/N0nU5s2b5eKLL5a2bdua5G3AgAHy3//+1+8yt27datrz/fffu8sOHjxoynSZLuvWrZPzzz/fLFOXff3118vevXvdz7/11lump0vbkZiYKMOHD5f8/HxpKkiSAAAAAC9//vOf5dFHH5VffvlFTjzxxID+RhOkl156yQyf++mnn2TixIkmEVq2bJnP+tpLpAnQggULPMoXLVokKSkpcu6550peXp5ccMEFsnTpUlmzZo2cd955ctFFF8n27dtr3DZNmoYNGya/+c1vZPXq1bJkyRLZvXu3XHnlle6etmuuuUZGjx5t2q/J1WWXXWZ61ZoKhtsBAAAAXqZOnSrnnHNOwPWLi4vlkUceMb08gwYNMmVdunQxvVDPPvusnHXWWT7/TnuLtEdHe4+0B0oTkRdffFFGjhxp7vejvVH6cJk2bZosXrxY3nvvPbnttttq1La5c+eaBEnjddFETW8IvHHjRpOYlZWVmcSoU6dO5nntVWpK6EkCAAAAvPTv3/+o6v/6669SUFBgEisdwuZ6aM+SDpnzR+trr5Feg6S0x0h7iUaNGmV+14Tlrrvukt69e0uLFi3MMrV351h6kn744Qf57LPPPOLs1auXeU5j7du3r5x99tkmMfr9738vf//73+XAgQPSlNCTBAAAAHhp1qyZx+/aq+M93Ky0tNT9syYz6oMPPpAOHTp41NPrmvzR5er1Ttp7pJM4aLI0dOhQ0wulNEH65JNP5IknnpBu3bqZa4SuuOIKKSkp8bs8ZY3VGqcrVh2yN2PGjEp/365dOwkLCzPrXLFihXz88cfyt7/9Te6//35ZtWqV6e1qCkiSAAAN8saT3DgSQH3S9xud7MBKJ0eIiIgwP/fp08ckQ9rD429onT/aa/Twww/LO++8Y4bSPf/88+7nvvrqK5NEXXrppe4ERydnqCpO13VFOqTOFafVySefbCaL0Psr+Zu1z2azmenJ9aGz/+mwO41t0qRJ0hSQJAEAQiZBGjdqlBTlBpYkRccnyDMLF5IoASFO72HUGNahEx08/vjjZvicXnP0yiuvmKTJlYjojHja66OTNegsd4MHD5ZDhw6ZJEe/1NFrjPzR3hld/tixY02ipdcCuXTv3t0kT9rzo4nLAw88YJbvj/Y0nXrqqWbSCV3unj175K9//atHnfHjx5shdDo5wz333COtWrUywwVfe+01k6CtXr3aDPvTiSOSkpJMD5K+R+uQv6aCJAkAEBK0B0kTpIlnDpSUxFZV1s3Yt19mfbEq4BtHAqh/mhjEx0Sbm7zWB12XrrOujBgxwiQomlQUFRWZmd9uuOEG+fHHHz0mVdD3JJ3lbsuWLeYaIu21+ctf/lLt8nUCB01M/vjHP0p0dLS7fObMmWZdp512mpkN79577622x10nYdDl6RTmPXv2lMcee8wkPC7t27c3yZsuS8t10gntKdKZ83S4XkJCgnzxxRcye/Zssy597sknnzQTTDQVNmcjn8tPd6zOf6+ZfF2+cAAAx0YvFv7T6FHy5KXnS9e2SVXX3b1H7lz8ocxZsFC6du1abzECqEwTBtfMbNaT+6MdQnusGIKLQI7JQHMDepIAAABQJzRpIXFBQ8QU4AAAAABgQZIEAAAAABYkSQAAAABgQZIEAAAAABYkSQAAAABgQZIEAAAAABYkSQAAAABgwX2SAAAAUCe4mSwaKpIkAAAA1EmCNG7UKCnKrZ8kKTo+QZ5ZuDAkE6Ubb7xRDh48KO+++675fciQIXLSSSfJ7Nmz6zWOzz//XIYOHSoHDhyQFi1a1Nl6bDabLF68WC655BJpqEiSAAAAUOu0B0kTpIlnDpSUxFZ1uq6Mfftl1herzDoDTZI0cXnxxRfNzxEREZKamio33HCD/OUvf5Hw8Lo9RX7nnXfMOkMpsSkpKZH27dvLXXfdJX/+858rPT9t2jSZO3euZGRkBBx7Q0aSBAAAgDqjCVLXtkkSis477zxZuHChFBcXy3/+8x8ZP368SQDuu+8+n0lEZGRkray3Vau6TRprQtt23XXXme3xZ68kyel0yqJFi0wS2RQSJMXEDQAAAGiSoqKiJDk5WTp16iTjxo2T4cOHy3vvvefuadLhYv/3f/9nelh69uxpynfs2CFXXnml6dXRZOfiiy+WrVu3updZXl4ukyZNMs8nJibKPffcY5IMKx1uN2HCBPfvmqTde++90rFjRxNTt27d5IUXXjDL1V4k1bJlSzOMTeNSDodDpk+fLp07d5aYmBjp27evvPXWWx7r0cSvR48e5nldjjVOX8aMGSMbN26U5cuXe5QvW7ZMtmzZYp7/5ptv5JxzzpHWrVtL8+bN5ayzzpLvvvuuyp4wjVuHG7p8//33pswaj67zjDPOMLHqdrjjjjskPz/f/fy8efOke/fuEh0dLW3btpUrrrhC6hJJEgAAACBiTtC1x8hl6dKlsmHDBvnkk0/k/fffl9LSUhkxYoTEx8fLl19+KV999ZXExcWZHinX3z355JOm12XBggXmxH///v3m+pyqaA/NP//5T3nqqafkl19+kWeffdYsV5OFt99+29TROLKysmTOnDnmd02QXnrpJZk/f7789NNPMnHiRNMTpAmNK5m77LLL5KKLLjJJyU033eRzGJ3VCSecIAMGDDCxW2nv0mmnnSa9evWS3NxcGTlypGnb119/bRKXCy64wJTX1ObNm802vPzyy2Xt2rXy+uuvm+Xfdttt5vnVq1ebpGnq1KlmOyxZskTOPPNMqUsMtwMAAECTpj09mhB99NFHcvvtt7vLmzVrJs8//7x7mN0rr7xienC0THtCXAmE9hppj8m5555rJmPQ4XqaoChNYnS5/mjPzRtvvGESMe3JUl26dKk0NC8pKcl9TZL2PD3yyCPy3//+VwYNGuT+G00sNMHS3p1nnnlGunbtapI2pT1hP/74o8yYMaPKbaG9RXfddZdJ2DRR0+RHe6j0dzVs2DCP+s8995yJS5Oz3/72t1ITmvBde+217t41Tbx0fa52bN++3ewLXb4mqNrz95vf/EbqEj1JAAAAaJK0d0gTAR3Cdf7558tVV10lkydP9uhZsV6H9MMPP8ivv/5qTtT17/ShSUxRUZHpDTl06JDp7Rk4cKD7b3QSiP79+/uNQXt5wsLCTEIQKI2hoKDADHtzxaEP7VnSOJT2SFnjUK6EqirXXHONGTKoiZvSXh273W62jdq9e7fcfPPNJpHR4XY69XpeXp5JZGpKt6v2vlnboj12mpCmp6ebdmpipIng9ddfL6+++qppf12iJwkAAABNkl6noz0VmgjpdUfes9pp74WVJgP9+vUzJ+neajr1uA7xO1oah/rggw+kQ4cOHs/pNU3HQpOeK664wvSQjR492vyv12Bp4qJ0qN2+ffvMsD9NXHR9mnxZhylaaYKlrNdl6bBF7/bccsstZkidN511UPePXvekvXUff/yxPPjggyaZ1euj6mrGP5IkAAAANEmaBOkkCYE6+eSTTc+KDn3TZMKXdu3ayapVq9zXzJSVlcm3335r/tYX7a3SHhMdruYabmfl6snS3h2XPn36mOREe2/89UD17t3bPQmFi15DFIgxY8aYySW0p23FihXy+OOPu5/T67B0EgW9Dsl17dPevXv9LsuVPGoPm04+4eo9s9Jt8/PPP1e5LzSB1e2jj4ceesgkR59++ql7WGNtI0kCAABAnd7DqDGsQ+l1M5ow6Ix2OolASkqKbNu2zdz3SGex09//9Kc/yaOPPmqGo+lEBzNnzvSY2c1bWlqa6Z3RXhu9DkdnqdNl7tmzx/TgaG+NXv+kCYsmJtrzpMP99LohnaxBE6zBgweboX6awGjypsu79dZbzfVId999t5m0QRM1HdIWiDPPPNMkLDqhhLZBJ21w0Xa9/PLLZgih3pdKl19Vb5guRyeg0J4fnSlQr8FyXSflojP7nXrqqWaiBo1Vk1dNmvQ6Lb03k7ZdZ9fTuDTR0ln7tN2uGQfrAkkSAAAAap2erEfHJ5ibvNYHXZe/3p3aEhsbK1988YU5qdceDJ3UQIe7nX322e5133nnnabXRBMVHWqmyc+ll15qkhh/dMif3sT2j3/8oxnKpkPM9Hely58yZYqZmW7UqFEmcdFkR2/uqr00OumBJhDas6I9Mq6/02XozHiaSP3tb3+TU045xUz2oPFUx2azmXq6LO97RunU5GPHjjXr0uRHl6kJmz96XyWduU+nWD/xxBPN7HkPP/yw/P73v3fX0XLtSbv//vvNNOA6NE8nnXBdB6Vt00RUEy29/ksTNV3mcccdJ3XF5vSeuL2R0QxXLyrTA7OuXzgAgJrTi43/NHqUPHnp+dXeeHLz7j1y5+IPZc6CheaDFEDw6EmrXlyv9+vRCRCssrOzzblYfdDzvJpeF4Smc0zmBJgb0JMEAACAOqFJC4kLGiKmAAcAAAAAC5IkAAAAALAgSQIAAAAAC5IkAAAAHLNGPhcYmtixSJIEAACAGtMpnlVBQUGwQwE8jkXXsVkTQZ/dbufOnWau+Q8//NA0SG84tXDhQnODKlcmqHfV/fvf/25uxHX66aebueR1fnQAAAAEV1hYmLmPjd781HUvIb3PDlDfNG/QfEKPRT0m9dhskEnSgQMHTNIzdOhQkyTpFJGbNm0yd9J1eeyxx8zdh1988UUz1/kDDzwgI0aMMHfh9Z73HAAAAPUvOTnZ/O9KlIBg0gTJdUw2yCRpxowZ5k692nPkoomQNRucPXu2/PWvf5WLL77YlL300kvStm1beffdd+Xqq68OStwAAAA4QnuO2rVrJ0lJSVJaWhrscNCERUREHFMPUkgkSe+9957pFfr9738vy5Ytkw4dOsgf//hHufnmm83zeqfcXbt2yfDhw91/o3fIHThwoKxcudJnklRcXGweLq67PJeVlZmHstvt5uFwOMzDxVVeXl7uccGXv3LdAfqm4FqutVxp/UDKw8PDzXKt5bpcre8do79y2kSbaBNtauhtcsWla/OsLaJL0WddkThsNhOf6+8a4n7av3+/ueO79wXGrv1kpfX1URvlynud+tmqozma6rFHm2q3TTrSp7G1qTHup8bcpjJLu7xj925zSCZJW7ZsMdcXTZo0Sf7yl7/IN998I3fccYdERkbKyJEjTYKktOfISn93Pedt+vTpMmXKlErla9askWbNmpmf9YOga9euJgnLzs5210lJSTGPjRs3mg8uly5duphvRtatWyeFhYXu8l69epnuPF229eA48cQTTRtWr17tEYNeZ1VSUiJr16712HEDBgww61u/fr27PCYmRvr27St79+4128n6Qda7d2/JzMyUjIwMdzltok20iTY19Da5vn0uiGkm30UluMsTHGXSs7RAssKiJDM8ypTlJ0VJ7759zc8NcT/pN52TH35E2qd0kF7durnLt2VkyPc//iQnnXCcdEpJcZev//VX2bBpswwa0E+SWrd2l6/5cZ1sz9gpw844XeLj4tzlK79ZLXv27pMLzznbnMS4fPrlciksKpILzzny5aNa9uVymTt7pscx05SOPdpEm2hT02lTfn6+BMLmDOJ8jRqwbvQVK1a4yzRJ0mRJe4q0XK9Z0o2pXbguV155pckUX3/99YB6knRI3759+yQhoeJDlyycNtEm2kSbQq9N+gE58aYx8sSl50vntklV9iRt2ZMt9767RGY9/4IZph2qbfK3P7StN98+QYZcf6u0Tu7gLneKTf9Iu3rE5m5tzcttTs+eJFOucVnq7tu1Uz57eb48P3eOpKWlNcljjzbRJtrUdNqUk5MjiYmJJvFy5QYh15OkiU+fPn08yjTDfPvtt83Prguudu/e7ZEk6e8nnXSSz2VGRUWZhzfdidZv06wb2ZtrYwZa7r3cmpTrTvVV7i/Goy2nTbTJXzltok2h0iZXXHoa72ut1qjtTqf7wzSU2+SO1ytG18+J7TpK205HrsUNBqfNXmXsTeHYq2nstIk2HW05bZKgt8nf85XikSDSXqINGzZ4lGmXWqdOnczP+u2gJkpLly51P6/Z36pVq2TQoEH1Hi8AAACAxi+oPUkTJ06U0047TR555BEzhO5///ufPPfcc+bhykwnTJggDz/8sLkvkmsK8Pbt28sll1wSzNABAAAANFJBTZL0Yq/FixfLfffdJ1OnTjVJkE75fe2117rr3HPPPeYCq7Fjx5qbyQ4ePFiWLFnCPZIAAAAANL4kSf32t781D3+0N0kTKH0AAAAAQF0L6jVJAAAAABBqSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAswq2/AADQGGVnZ0tOTk5AdUtKSiQyMjLgZSckJEibNm2OIToAQKghSQIANPoEadyoUVKUW32SVFxSIlt37JCunTpJeHhgH5HR8QnyzMKFJEoA0IiQJAEAGjXtQdIEaeKZAyUlsVWVdf+3aYs8sm2r3H5aP+nWoX21y87Yt19mfbHKrIMkCQAaD5IkAECToAlS17ZJVdbZvnef+b9Dy5bV1gUANF5M3AAAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFuHWXwAAAIIpOztbcnJyJBQkJCRImzZtgh0GgCAgSQIAACGTII0ee6vkFhZJKIiPiZYFz80nUQKaIJIkAAAQErQHSROkIdePk8R2KUGNZV9Whnz+8jMmJpIkoOkhSQIAACFFE6TkTp2DHQaAJoyJGwAAAADAgiQJAAAAACxIkgAAAADAgmuSAABNQmlJiRTk51dZp6iwSJxOpxQVFVVbVxUWFEpxSbFs27btqGLR+mVlZUf1NwCA+kOSBABo9DQh+Xn9BtkfH19lvc07dkpJSalsTk+XkkOHql3uzoM5sn1Hhtw3earExDYLOJ7CgnzJ3LVbSktLAv4bAED9IUkCADR65Y5yKS93SuuUThIRHe23XmK5XcIiwqVVh1Rp16FDtcstzd4rLdr+KiPG3ilt2ncMOJ5N338jb897QsrLywP+GwBA/SFJAgA0GZogRcfE+n8+KlpsNrtERkZVWc8lMjpGIiIjTYJ0NFNWZ2fuCLguAKD+MXEDAAAAAFiQJAEAAACABUkSAAAAAFhwTRIAAE1cSQ2mMa8LTI0OIFSQJAEA0ITlHtwv6Zu3yP3THpGoqKigxsLU6ABCBUkSAABNWFFBvtgjIuSs68dJh7SuQY2FqdEBhAqSJAAAIInJ7Y9qGvO6wNToAEIFEzcAAAAAgAVJEgAAAABYkCQBAAAAgAVJEgAAAABYkCQBAAAAgAVJEgAAAACESpI0efJksdlsHo9evXq5ny8qKpLx48dLYmKixMXFyeWXXy67d+8OZsgAAAAAGrmg9yQdd9xxkpWV5X4sX77c/dzEiRPl3//+t7z55puybNkyyczMlMsuuyyo8QIAAABo3IJ+M9nw8HBJTk6uVH7o0CF54YUX5B//+IcMGzbMlC1cuFB69+4tX3/9tZx66qlBiBYAAABAYxf0JGnTpk3Svn17iY6OlkGDBsn06dMlNTVVvv32WyktLZXhw4e76+pQPH1u5cqVfpOk4uJi83DJyckx/5eVlZmHstvt5uFwOMzDxVVeXl4uTqez2vKwsDAzRNC1XGu50vqBlGuiqMu1lutytb53jP7KaRNtok20qaG3yRWXrs2ztoguRZ91ReKw2Ux8rr+rqk36nK5PbLaK+l7Lth1+mHK7XSIiIsRps7vX5fRR31XutNnMsm3Ow0vVbeX62R1QWEWZZTvaXEvVMoe1tTYTg5j2WNassdu03GvL+C23Vzznq9ys1xFALK7YA2uT//LA26SxuPbrsbSpyvIA26T71HX8NsTXU2N8j6BNtKk22uT9fEgmSQMHDpRFixZJz549zVC7KVOmyBlnnCHr1q2TXbt2SWRkpLRo0cLjb9q2bWue80eTLF2OtzVr1kizZs3Mz23atJGuXbtKenq6ZGdnu+ukpKSYx8aNG01PlkuXLl0kKSnJxFVYWOiRtGl8umzrwXHiiSea2FevXu0RQ//+/aWkpETWrl3rseMGDBhg1rd+/Xp3eUxMjPTt21f27t0rW7ZscZc3b97c9Kbp0MOMjAx3OW2iTbSJNjX0NukXY6ogppl8F5XgLk9wlEnP0gLJCouSzPAoU5afFCW9+/Y1P1fXpoKCAhlxyaVi37XTPJ8RlyAlekJ8WPuCXIktK5Ot8S0ksncfue7msVKU2lFKy0sk3OGQLQktPdrUJeeAlNntsj2uuRRGN5OLLvmdtCraJyJdxVaUJ2F7trrrOiOipLx9D7HlHZSw/RXrV8mRFR/s8c5iCd/xs7vcEddSHIkpYj+QKfa8A0fKmyeJo0VbCcvebtbhUt6qgzjjW0nYrs1iKz3yBWF5Upo4Y+IlLGP9kQROTw7adRcJj/BYZ5cYp0kMw8XhUa6JYnnqcQG3yRkdJ+VtO4v9ULbYD+2pUZs0lp7dulYcJ8fQJlPesY9IWamEZ22qUZtaFxbIKSefZH5uiK+nxvgeQZtoU220KT8/XwJhc1pTsCA7ePCgdOrUSWbOnGk28KhRozx6hdQpp5wiQ4cOlRkzZgTck9SxY0fZt2+fJCRUfOiShdMm2kSbaFPotUk/ICfeNEaeuPR86dw2qcqepC17suXed5fIrOdfkM6dO1fZJl3uHaNvlBu6dpL+/fpLZEys356klRs2ybTX35A5o0ZKj44p1fYkbcveKw/+5xO5YvIsaZvWNeBel3Wrlsvzk++WCXMWSlqPXkHtSfIfS/33JGksC6beK3fMXiBp3XvWuE1VlgfYpt3bt8rimZPluadm+T3GQvn11BjfI2gTbaqNNmluoJPCaeLlyg1CcridlWZ/PXr0kF9//VXOOecck7Fq4mTtTdLZ7Xxdw+QSFRVlHt50J5rhFhaujezNtTEDLfdebk3Kdaf6KvcX49GW0yba5K+cNtGmUGmTKy5NQnyt1Rq1/fAwukDapM+ZD83DH6b+Ziwy5Q6H6dHSngpXMuT6v1L8Fd80mmVrD8XhRonYfERvTvCP/Op0/aL1Lb1aR4LxE6WvujUpt8RYbSwBtqna8gDapLG4T5KOoU3VlgfQJtPrdPikqiG+nqqLkTbRpqONvbG0yd/zleKREJKXlyebN2+Wdu3aSb9+/Uz3/9KlS93Pb9iwQbZv326uXQIAAACAuhDUnqS77rpLLrroIjPETscnPvTQQyYrvOaaa8y4xTFjxsikSZOkVatWpjvs9ttvNwkSM9sBAAAAaJRJkl60pQmRXi+kF20NHjzYTO+tP6tZs2aZbje9iaxeZzRixAiZN29eMEMGAAAA0MgFNUl67bXXqnxepwV/+umnzQMA0PDoTEWuWzFUZ9u2bVJcUiyFBYVSUM3sQ1onNz/P3BJC/64qOlJBZ0ByVpr8GwCABjBxAwCgcSVIo8feKrmFRQHVLyzIl4M7MuSXDZskZ/fuKuv+smuPbNz0q0y55y6x+7uQ/7ASnVihpESKeqSJ0xEyE7oCAEIYSRIAoE5oD5ImSEOuHyeJ7Sqm065KduYO+WjWVEnq1FnatWldZd1t5XaJi4mWv/zut5Ka5DlduLfvt22TZz78yEwVG0J3vQAAhDCSJABAndIEKblT54DqRkRGSmR0jER73cuoUr2oaLHZ7CZB6pGaWmXd7PzCKibyBgAgxKcABwAAAIBgI0kCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAItw6y8AANS23IP7A6qXnblDysvK6jweAACqQ5IEAKgzJSXF8u6TUyTSUX3yU1hUJLl7sqSstLReYgMAwB+SJABAndGeIVtRgdwx7Axp17JllXXXpKfL3Pd3iqOc3iQAQHCRJAEA6pwmSGlJbaqss3NfYMPyAACoa0zcAAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAWJEkAAAAAYEGSBAAAAAAW4dZfAAAAUKGkpFi2bdsmoSIhIUHatGkT7DCAJoEkCQAAwEvuwf2SvnmL3D/tEYmKipJQEB8TLQuem0+iBNQDkiQAAAAvRQX5Yo+IkLOuHycd0roGOxzZl5Uhn7/8jOTk5JAkAfWAJAkAAMCPxOT2ktypc7DDAFDPmLgBAAAAACxIkgAAAADAgiQJAAAAACxIkgAAAADAgiQJAAAAACxIkgAAAADAgiQJAAAAAEIxSXr00UfFZrPJhAkT3GVFRUUyfvx4SUxMlLi4OLn88stl9+7dQY0TAAAAQOMWEknSN998I88++6yceOKJHuUTJ06Uf//73/Lmm2/KsmXLJDMzUy677LKgxQkAAACg8Qt6kpSXlyfXXnut/P3vf5eWLVu6yw8dOiQvvPCCzJw5U4YNGyb9+vWThQsXyooVK+Trr78OaswAAAAAGq/wYAegw+kuvPBCGT58uDz88MPu8m+//VZKS0tNuUuvXr0kNTVVVq5cKaeeeqrP5RUXF5uHS05Ojvm/rKzMPJTdbjcPh8NhHi6u8vLycnE6ndWWh4WFmSGCruVay5XWD6Q8PDzcLNdarsvV+t4x+iunTbSJNtGmUGuTdT1Om00cXt/Q6VKPLFmDr/jeTsusdX3Wt9vd6/Suazv8cJfb7RIRGakbp/r6WjciQpw2u3tdHjEerusq13bp/rE5Dy9Vt5XrZ/f6wyrKLNvR5lqqljms+9tmYhCz7Sxr1th1+3jUrarcXvGcr3KzXkcAsbhiD6xN/ssDb5PGosedcQxtqrI8wDZpLLpv3cuoYZtqbT8d/llfj96vv4b6HlFVOW2iTfY6apP38yGZJL322mvy3XffmeF23nbt2iWRkZHSokULj/K2bdua5/yZPn26TJkypVL5mjVrpFmzZubnNm3aSNeuXSU9PV2ys7PddVJSUsxj48aNpifLpUuXLpKUlCTr1q2TwsJCj6RN49NlWw8OHTaosa9evdojhv79+0tJSYmsXbvWY8cNGDDArG/9+vXu8piYGOnbt6/s3btXtmzZ4i5v3ry59O7d2ww9zMjIcJfTJtpEm2hTqLWpoKDAfIDGJiRIXsdOskUTFf2AczqlS+5BKQwPl8zYeHf9iK5dzf/l8c1lS8KRkQWxZaXSviBPDkRFy/6oGFMW2buPnHrmmebnvTGxkhMR5a7fqrhQWhUXya7YOCkIjzB1rx97i0TszTTPZ8QlSImeEB/WviBXYsvKZGt8C1P3upvHSlFqRyktL5Fwh8MjFrNtcg5Imd0u2+OaS2F0M7nokt9Jq6J9ItJVbEV5ErZnq7uuMyJKytv3EFveQQnbv9NdnhxZ8cEe7yyW8B0/u8sdcS3FkZgi9gOZYs87cKS8eZI4WrSVsOztZh0u5a06iDO+lYTt2iy20iNfEJYnpYkzJl7CMtYfSeD05KBdd5HwCI91dolxmsQwXBwe5ZoolqceF3CbnNFxUt62s9gPZYv90J4atUlj6dmt4jg4ljaZ8o59RMpKJTxrU43apLGcd/Yw8/OxtKm29lPrQn09hZkvkK2vs4b8HtEY3/doU+i3KT8/XwJhc1pTsHq0Y8cOs8E/+eQT97VIQ4YMkZNOOklmz54t//jHP2TUqFEevULqlFNOkaFDh8qMGTMC7knq2LGj7Nu3TxISEkwZWThtok20iTbVfZv0A++6MTfrJ5I8fsXF0qlN6yp7klau3yQPvvqqzLt5tHRPTfVYvnf9lRs2yZR/viZzR4+Ubl51vXuSXHXvOq2fDD7tTImOi/NbX+tOe/0NmTNqpPTomFJtT9K27L3y4H8+kSsmz5K2aV0D7nVZt2q5PD/5bpkwZ6Gk9egV1B4K/7HUf0+SxrJg6r1yx+wFkta9Z43bVGV5gG3SWBY9fJ/cNvP5iu0S5J6k3du3yluPPyDPz50jaWlpjeI9oqpy2kSb7HXUJs0NdFI4TbxcuUFI9STpcLo9e/bIySef7C7T4L/44guZO3eufPTRRyZjPXjwoEdvks5ul5yc7He5UVFR5uFNd6K729xrI3tzbcxAy72XW5Ny3am+yv3FeLTltIk2+SunTbSprtpkXY/N6ax0EawrOXE7fEJo83PBrEd9h8P9gefv4lp3ucMhpSUl7pPcKutr3dJS862+a10eMXrHc3jok/ZQHDkZ9rHNzMmwpamuX7S+pVfrSDB+ovRVtybllhirjSXANlVbHkCbNBb3SdIxtKna8gDapLG4T7aOoU21tp8OH2P+3gsa4ntEdeW0iTbVRZv8PV+pvgTJ2WefLT/++KNHmfYcaVfZvffea3p/tPt/6dKlZupvtWHDBtm+fbsMGjQoSFEDAAAAaOyCliTFx8fL8ccf71Gm1wxp95erfMyYMTJp0iRp1aqV6Q67/fbbTYLkb9IGAAAAAGjws9tVZdasWabbTXuS9DqjESNGyLx584IdFgAAAIBGLKSSpM8//9zj9+joaHn66afNAwAAAABC9may1un8AAAAAECaepLUrVs3Mw33K6+8IkVFRbUfFQAAAAA0pCRJbwCr9zbSSRV0Ou5bbrlF/ve//9V+dAAAAADQEJIkveHrnDlzzB1zFyxYIFlZWTJ48GAzK93MmTM97pYLAAAAAI0+SXLRmzFddtll8uabb8qMGTPk119/lbvuusvc4+iGG24wyRMAAAAANJnZ7VavXm16kl577TVzjyNNkPTeRhkZGTJlyhS5+OKLGYYHAI2QjhjIycmpss62bdukqLBQIsvL6i0uAACCliTpkLqFCxfKhg0b5IILLpCXXnrJ/K/3NFKdO3eWRYsWSVpaWq0ECQAIrQRp3KhRUpRbdZJUXFIsOTu2SrjNJmWlpfUWHwAAQUmSnnnmGRk9erTceOON0q5dO591kpKS5IUXXjjW+AAAIUZ7kDRBmnjmQElJbOW3XmFBoby9bLm89sM6cdCbBABo7EnSpk2bqq0TGRkpI0eOrMniAQANgCZIXdsm+X2+ID9fEpvF1mtMAAAEbeIGHWqnkzV407IXX3yxNuICAAAAgIaTJE2fPl1at27tc4jdI488UhtxAQAAAEDDSZK2b99uJmfw1qlTJ/McAAAAADSpJEl7jNauXVup/IcffpDExMTaiAsAAAAAGk6SdM0118gdd9whn332mZSXl5vHp59+Kn/605/k6quvrv0oAQAAACCUZ7ebNm2abN26Vc4++2wJD69YhMPhkBtuuIFrkgAAAAA0vSRJp/d+/fXXTbKkQ+xiYmLkhBNOMNckAQAAAECTS5JcevToYR4AAAAA0KSTJL0GadGiRbJ06VLZs2ePGWpnpdcnAQAAAECTSZJ0ggZNki688EI5/vjjxWaz1X5kAAAAANBQkqTXXntN3njjDbngggtqPyIAAAAAaGhTgOvEDd26dav9aAAAAACgISZJd955p8yZM0ecTmftRwQAAAAADW243fLly82NZD/88EM57rjjJCIiwuP5d955p7biAwAAAIDQT5JatGghl156ae1HAwAAAAANMUlauHBh7UcCAAAAAA31miRVVlYm//3vf+XZZ5+V3NxcU5aZmSl5eXm1GR8AAAAAhH5P0rZt2+S8886T7du3S3FxsZxzzjkSHx8vM2bMML/Pnz+/9iMFAAAAgFDtSdKbyfbv318OHDggMTEx7nK9Tmnp0qW1GR8AAAAAhH5P0pdffikrVqww90uySktLk507d9ZWbAAAAADQMHqSHA6HlJeXVyrPyMgww+4AAAAAoEklSeeee67Mnj3b/bvNZjMTNjz00ENywQUX1GZ8AAAAABD6w+2efPJJGTFihPTp00eKiorkD3/4g2zatElat24t//znP2s/SgAAAAAI5SQpJSVFfvjhB3nttddk7dq1phdpzJgxcu2113pM5AAAAAAATSJJMn8YHi7XXXdd7UYDAA1Udna25OTkSChISEiQNm3aBDuMJqO0tFSyM3cEVDc2Ll4SElvXeUwAgCAkSS+99FKVz99www01jQcAGmSCNHrsrZJbWCShID4mWhY8N59EqR4czM+XXTu2ykezpkqE14yvvthi4+XG6U/XS2wAgHpOkvQ+Sd7fohUUFJgpwWNjY0mSADQp2oOkCdKQ68dJYruUoMayLytDPn/5GRMTSVLdyy8qlii7XW4/fYB0Sm5XZd2sAwfkqeWrpCAvt97iAwDUY5KkN5H1phM3jBs3Tu6+++4ahgIADZsmSMmdOgc7DARBuxYtJC2JpBQAmvQU4L50795dHn300Uq9TAAAAADQJJMk12QOmZmZtblIAAAAAAj94Xbvvfeex+9Op1OysrJk7ty5cvrpp9dWbAAANMqZ8PbvzpKy0hJzDVl0tO9bZzATHgA0sCTpkksu8fjdZrOZC4SHDRtmbjQLAAD8z4RXVlIiMQW58uncRyUyOrrKmfBIlACggSRJDoej9iMBAKCJzISXGBsjWem/SsfuvSU6tlmVM+GRJAFAA7qZLAAAqNlMeMnxcSL7dktq69YSGxcX7JAAALWRJE2aNCngujNnzqzJKgAAAACg4SRJa9asMQ+9ALVnz56mbOPGjRIWFiYnn3yyx7VKAAAAANDok6SLLrpI4uPj5cUXX5SWLVu6bzA7atQoOeOMM+TOO++s7TgBAAAAIHTvk6Qz2E2fPt2dICn9+eGHH2Z2OwAAAABNL0nKycmR7OzsSuValpubWxtxAQAAAEDDSZIuvfRSM7TunXfekYyMDPN4++23ZcyYMXLZZZfVfpQAAAAAEMrXJM2fP1/uuusu+cMf/mAmbzALCg83SdLjjz9e2zECAAAAQGgnSbGxsTJv3jyTEG3evNmUde3aVZo1q3xDPAAAAABo9MPtXLKyssyje/fuJkFyOp21FxkAAAAANJQkad++fXL22WdLjx495IILLjCJktLhdkz/DQAAAKDJJUkTJ06UiIgI2b59uxl653LVVVfJkiVLajM+AAAAAAj9a5I+/vhj+eijjyQlJcWjXIfdbdu2rbZiAwAAAICG0ZOUn5/v0YPksn//fomKiqqNuAAAAACg4SRJZ5xxhrz00kvu3202mzgcDnnsscdk6NChtRkfAAAAAIR+kqTJ0HPPPSfnn3++lJSUyD333CPHH3+8fPHFFzJjxoyAl/PMM8/IiSeeKAkJCeYxaNAg+fDDD93PFxUVyfjx4yUxMVHi4uLk8ssvl927d9ckZAAAAACouyRJE6KNGzfK4MGD5eKLLzbD7y677DJZs2aNuV9SoPSapkcffVS+/fZbWb16tQwbNsws76effnJPEPHvf/9b3nzzTVm2bJlkZmaa9QAAAABAyEzcUFpaKuedd57Mnz9f7r///mNa+UUXXeTx+//93/+Z3qWvv/7aJFAvvPCC/OMf/zDJk1q4cKH07t3bPH/qqace07oBAAAAoFaSJJ36e+3atVLbysvLTY+R9krpsDvtXdKEbPjw4e46vXr1ktTUVFm5cqXfJKm4uNg8XHJycsz/ZWVl5qHsdrt56HVU+nBxlWss1hvj+isPCwsz12O5lmstd7UpkPLw8HCzXGu5Llfre8for5w20SbaFLw2uX/W/x2WeGx2XYlnmavc1HcEVm4Pq1i2z3JHxXNHgnHHZN2WtbmfzP/h4eKw2aTca1iCNcJysVU0y2YTp83u8ZzW16U6fbRfy7xaWrm+3e6Oxbuu7fDDXW63S0RkZMW+qK6+1o2IMPG61uV9m3SbtdxS31Xm9BO7ta5uuyrbarOZY9mm+1ePH62v66h0LPkrD/zYs4mf4/dojz2/5TbTdjHHnbPK2DUWPe6MY2hTleUBtklj0X3gXkYN21Rr++nwz96vbcV7OW2iTRJwm7yfr9UpwK+77jrTy6ND5Y7Vjz/+aJIivf5IrztavHix9OnTR77//nuJjIyUFi1aeNRv27at7Nq1y+/ypk+fLlOmTKlUrkMBmzVrZn5u06aNGRaYnp4u2dnZ7jrae6UPHUp46NAhd3mXLl0kKSlJ1q1bJ4WFhR5Jm8any7YeHHqdlcauQwit+vfvb67hsiaZuuMGDBhg1rd+/Xp3eUxMjPTt21f27t0rW7ZscZc3b97c9Kbp0MOMjAx3OW2iTbQpeG2Kjo42/7cs3i/hO/Ld5eVJaeKMiZewjPXuExxV1q67SHiEhO/42aNNZR37iJSVSnjWJneZnlCXpx4ntqI8Cduz9Uh5RJSUt+8htryDErZ/55E2lVS8+Wt81m1Wm/tJDRlxnuxMai8HD89oenxJnkQ6HfJdVMKR9ofFii0sTJq3aClFXbrJlpgYU253OqVL7kEpDA+XzNh4d/2Iw8O1y+Oby5aElu7y2LJSaV+QJweiomV/VMUyInv3kVPPPNP8vDcmVnIijsys2qq4UFoVF8mu2DgpCI8wda8fe4tE7M00z2fEJUiJnhAf1r4gV2LLymRrfAtT97qbx0pRakcpLS+RcIfDIxazbXIOSJndLtvjmrvrF6d0ECkvqdSmSEe5pOblSG5EpMeyD5SXmudzmsVLVsKRz7mE0mJJKiyQ4pat5KJLfietC/dK+I4CcTRPEkeLthKWvd0cC+5t3KqDOONbSdiuzWIrLa7RsdclxmmSt3BxeJQf7bHnjI6T8radxX4oW+yH9rjLHXEtxZGYIvYDmWLPO3Ck3EebNJae3SqOg2NpU228njSW886uGMlyLG2qrf3UurBAwsPDzBfI1tc27+W0iTYdXZu0QyYQNqc1BQvQ7bffbma30/si9evXz518uMycOTPgZekG15vSauPfeustef755831R5okjRo1yqNXSJ1yyilmBj1/E0T46knq2LGj7Nu3z0wOocjCaRNtok212aatW7fKTbf9SS6/+2FJTu0U1J6kXdu3yduP/1WenztH0tLS6mQ/6YfepLE3y6O/O1e6JLWpqH/4eWuEBfkF8uJ/PpJ5K7+ReWNvlu4dU6rsSVq5fpM8+OqrMu/m0dI9NdWzqV71V27YJFP++ZrMHT1SunnV9e5JctW967R+Mvi0MyU6Ls5vfa077fU3ZM6okdLjcLxV9SRZ6/fsmFJlT9IKS9228c0kY8NP0vm4kyTGKx6tn569Vx78zydyxeRZ0jY1rU57KNatWi7PT75bJsxZKGk9KpLgYPUkaSwLpt4rd8xeIGnde9a4TVWWB9gmjWXRw/fJbTOfr9guQe5J2r19q7z1+AOVXtuK93LaRJsk4DZpbqCTwmnu4coNjrknST8Y9YWpmdvJJ59syjS7s9LgjoZmdt26dTM/a8L1zTffyJw5c+Sqq64yCdTBgwc9epN0drvk5GS/y9P7NPm6V5PuRHe3uddG9ubamIGWey+3JuW63XyV+4vxaMtpE23yV06bjr1N7vc9/d/SQ3FkQb7bKrajKDcnWb7K9STLq14NtsHR7qfysjLTI+T9rPX3sMMnj/ohpt+Se6/VlZy4HT4htPmZVcijvsPh/sDzNwORu9zhkNKSEvdJbpX1tW5pqYnXtS5/n2o2r/o+2+Snrm67Ktt6eEiVGcZnPX78HUtHW245lpxSzfEb6LFXXbmP4847Ro3FfZJ0DG2qtjyANmks7pOtY2hTre2nw4mTv9c27+W0qarYaZO4y/09X6m+HAXtOcrKypLPPvvM/K6JzFNPPWWGwNUWfXPUniBNmLT7f+nSpWbqb7VhwwbT66TD8wAAAACgLhxVkuQ9Mk/vaRTouD5f7rvvPnOvJZ2MITc318xk9/nnn8tHH31kxi2OGTNGJk2aJK1atTLdYTrMTxMkZrYDAAAAUFdqNHGDSw0uZ/KwZ88eueGGG0zvlCZFemGVJkjnnHOOeX7WrFmm2017krR3acSIETJv3rxjWicAAAAA1FqSpGMOva85OtprkKx0hryq6IxRTz/9tHkAAAAAQEgOt7vxxhvdEyPotN233nprpdnt3nnnndqNEgAAAABCMUkaOXJkpfslAQAAAECTTZIWLlxYd5EAAAAAQAjwd7sIAAAAAGiSSJIAAAAAoLamAAcAAED9KCkplm3btkko0PtXtmnTJthhAHWGJAkAACDE5R7cL+mbt8j90x5xzzIcTPEx0bLgufkkSmi0SJIAAABCXFFBvtgjIuSs68dJh7SuQY1lX1aGfP7yM5KTk0OShEaLJAkAAKCBSExuL8mdOgc7DKDRY+IGAAAAALAgSQIAAAAAC5IkAAAAALDgmiQAaGTqeppgXXZxSbEUFhRKQX6+33oFhYXiFGedxQEAQF0hSQKARqQ+pgkuLMiXgzsy5JcNmyRn926/9RzlZVJcXCzkSQCAhoYkCQAakfqYJjg7c4d8NGuqJHXqLO3atPZbL//QQZH00LjxJQAAR4MkCQAaobqeJjgiMlIio2MkOibWb52SoqI6Wz8AAHWJiRsAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwIIkCQAAAAAsSJIAAAAAwCLc+gsAoOnK2bdXCvJyq62XnblDysvK6iUmAACCgSQJAGASpEX3jRdnQfVJUmFRkeTuyZKy0tJ6iQ0AgPpGkgQAMD1ImiDdMXigtGvZssq6a9LTZe77O8VRTm8SAKBxIkkCALhpgpSW1KbKOjv37a+3eAAACAYmbgAAAAAAC5IkAAAAALAgSQIAAAAAC5IkAAAAALAgSQIAAAAAC5IkAAAAALAgSQIAAAAAC5IkAAAAALAgSQIAAAAAC5IkAAAAALAIt/4CAGhccvbtlYK83GrrZWfukPKysnqJCQCAUEeSBACNOEFadN94cRZUnyQVFhVJ7p4sKSstrZfYAAAIZSRJANBIaQ+SJkh3DB4o7Vq2rLLumvR0mfv+TnGU05sEAEBQr0maPn26DBgwQOLj4yUpKUkuueQS2bBhg0edoqIiGT9+vCQmJkpcXJxcfvnlsnv37qDFDAANjSZIaUltqnwkJTQPdpgAAISMoCZJy5YtMwnQ119/LZ988omUlpbKueeeK/n5+e46EydOlH//+9/y5ptvmvqZmZly2WWXBTNsAAAAAI1YUIfbLVmyxOP3RYsWmR6lb7/9Vs4880w5dOiQvPDCC/KPf/xDhg0bZuosXLhQevfubRKrU089NUiRAwAAAGisQuqaJE2KVKtWrcz/mixp79Lw4cPddXr16iWpqamycuVKn0lScXGxebjk5OSY/8vKysxD2e1283A4HObh4iovLy8Xp9NZbXlYWJjYbDb3cq3lSusHUh4eHm6Way3X5Wp97xj9ldMm2kSbgtcm98/6v8MSj82uK/Esc5Wb+o7Ayu1hFcv2We6oeM61CDkSi83pqNgeNpvoX+rSvZYgtsMPXXdERIQ4bXZTx1Xuq76y1rWWH4lExKFtP7ydrXVN6IfrOn203+ljvZXq2+3uY8Bfm9zldrtEREZW7Ivq6mvdw21zrcsjRu+2Wuq7ypx+YrfWdW0bv2212cy+031ojh+tr+uodCz5Kw/82LMeM5XqH8Wx57/cZtou5rXkrDJ2jUVfY8YxtKnK8gDbpLHoPnAvo4ZtapT7yekwr2vVmN7LG+PnE22SSrF7Px/ySZJulAkTJsjpp58uxx9/vCnbtWuXREZGSosWLTzqtm3b1jzn7zqnKVOmVCpfs2aNNGvWzPzcpk0b6dq1q6Snp0t2dra7TkpKinls3LjRnbCpLl26mB6udevWSWFhoUfCprHpsq0Hx4knnmjiXr16tUcM/fv3l5KSElm7dq3HjtPrsnR969evd5fHxMRI3759Ze/evbJlyxZ3efPmzU1Pmg47zMjIcJfTJtpEm4LXpujoaPN/y+L9Er7jyHDh8qQ0ccbES1jG+oqT3cPK2nUXCY+Q8B0/e7SprGMfkbJSCc/a5C7TE+ry1OPEVpQnYXu2HimPiJLy9j3ElndQwvbvdJcnR1Z8YMQ7i6VFYZ5cdMnvJK9tkuy1OSWpsED2xsRKTkSUu36r4kJpVVwk4ampct3NY6UotaNsiYmRpMJ8SSgtkYy4BCnRE6rD2h+eKe+qG2+Uoi5dTV2VmndIwh0O2ZJwZIKIspg4sYWFS/OWLaWoSzd3XbvTKV1yD0pheLhkxsa760d07Vqx3eKbeywntqxU2hfkyYGoaNkfVbGMyN595NQzzzQ/+2vTrtg4KQiPMHWvH3uLROzNNM/7alNsWZlsjW9h6rq2Q2l5SaU2qS45B6TMbpftcc3d9YtTOoiUl1RqU6SjXFLzciQ3ItJj2QfKK2YRzGkWL1kJRz7jEkqLzX4qbtnK7LvWhXslfEeBOJoniaNFWwnL3m6OBfcx1qqDOONbSdiuzWIrLa7RsdclxmmSt3BxeJQf7bHnjI6T8radxX4oW+yH9rjLHXEtxZGYIvYDmWLPO3Ck3EebNJae3SqOg2NpU228njSW886uGMVyLG1qjPupdWGBdOzQ3vzcmN7LG+PnE22SSm2yXtZTFZvTmoIF0bhx4+TDDz+U5cuXmw2idJjdqFGjPHqG1CmnnCJDhw6VGTNmBNST1LFjR9m3b58kJCSYMrJw2kSbaFNttmnr1q1y021/ksvvfliSUzsFtSdp3arl8vzku2XCnIUSEx0tb02eKFMvOEc6tWldZU/SV+s3ybQ33pA5o0ZK944pVfYkrfhlg0x78y13XVe5Cd1SN+fgAfngs6Wy4NsfZd4tN7vrmtB99LqsXL9JHnz1VZl382jpnprq2VSv+is3bJIp/3xN5o4eKd286nrH7qp712n9ZPBpZ0p0XJzf+lp32usV26HH4Xir6kmy1u/ZMaXKnqQVlrpt45tJxoafpPNxJ0mMVzxaPz17rzz4n0/kismzpG1qWp32UFiPmbQevYLaQ6GxLJh6r9wxe4Gkde9Z4zZVWR5gmzSWRQ/fJ7fNfL5iuwS5JymU9tPu7Vvl7ScelOfnzpG0tLRG817eGD+faJNUil1zA50QThMvV24Qsj1Jt912m7z//vvyxRdfuBMklZycbLLWgwcPevQm6ex2+pwvUVFR5uFNd6K729xrI3tzbcxAy72XW5Ny3am+yv3FeLTltIk2+SunTcfeJtewE3MyYemhOLIg320V21GUmxMVX+V6knXkV6frl8ND3PTDwuZ0umfp8Ttbj9Nhhjfrt9nWOv7q+6prVmv928MfXvoh5q+utb7rBM/mZ70e9R0O9weevxjd5Q6HlJaUuE8Iq6yvdQ+3zbUujxi922qp77NNfuq6to3ftjqdZt+ZYXzW48ffsXS05ZZjyXrM+Kwf4LFXbbmP15J3jBqL+yTpGNpUbXkAbdJY3Cdbx9CmRrmfdDjq4WO4Mb2XV1dOm6RRtMnf85XikSDSF5gmSIsXL5ZPP/1UOnfu7PF8v379TNfy0qVL3WU6Rfj27dtl0KBBQYgYAAAAQGMX1J4knf5bh9T961//MvdKcl1npGMWdTyj/j9mzBiZNGmSmcxBu8Ruv/12kyAxsx0AAACARpckPfPMM+b/IUOGeJTrNN833nij+XnWrFmm601vIqvXGo0YMULmzZsXlHgBAAAANH5BTZICmTNCZ416+umnzQNAcOmsM65p9YNNr1fU2WpCwbZt2wKeUhQIlF6/lJ25I6C6sXHxkpDYus5jAlxKSorNe18o0JFGOkMaUJtCYuIGAA0jQRo99lbJLSwKiQ/nHVu3SqcuXQO+ALMuFRbkS+au3VJaWhLsUNBIHMzPl107tspHs6ZW3OOpGrbYeLlx+tMkSqgXuQf3S/rmLXL/tEd8TpZV3+JjomXBc/NJlFCrgn92AaBB0B4kTZCGXD9OEtsdmYUyGDZ9/41sm/eEDP7DWOmQVnFPlWDH8/a8JypNjQrUVH5RsUTZ7XL76QOkU3K7KutmHTggTy1fJQV5uSRJqBdFBflij4iQs64fF/T34H1ZGfL5y8+YzyiSJNQmkiQAR0UTpOROnjNR1jfXEKTE5PZBj8UaD1Db2rVoIWlJnPghNIXKezBQF4I6BTgAAAAAhBqSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAItw6y8AgODI2bdXCvJyA6pbXloiYRGRPp/bvztLykpLZF9WhkRGRUt5WVktRwoAQONHkgQAIZAgLbpvvDgLqk+SSktLZdfODGmf0lHCwiu/hRfm5UlMQa58OvdRKReR3D2aNJXWUeQAADROJEkAEGTag6QJ0h2DB0q7li2rrLsmPV3m7twu4wf1k07J7So9n5dzULLSf5WO3XvLL7v3yNz3d4qjnN4kAACOBkkSAIQITZDSktpUWWfnvv0VdVu08Fk3JyJMZN9uSW3dWvYVFtdZrAAANGZM3AAAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAFiRJAAAAAGBBkgQAAAAAoZIkffHFF3LRRRdJ+/btxWazybvvvuvxvNPplAcffFDatWsnMTExMnz4cNm0aVPQ4gUAAADQ+AU1ScrPz5e+ffvK008/7fP5xx57TJ566imZP3++rFq1Spo1ayYjRoyQoqKieo8VAAAAQNMQHsyVn3/++ebhi/YizZ49W/7617/KxRdfbMpeeukladu2relxuvrqq+s5WgAAAABNQVCTpKqkp6fLrl27zBA7l+bNm8vAgQNl5cqVfpOk4uJi83DJyckx/5eVlZmHstvt5uFwOMzDxVVeXl5ukrTqysPCwswwQddyreVK6wdSHh4ebpZrLdflan3vGP2V0ybaVNdtcv+scTisy7FpoFpBn7QU20RsWu65Tv/l9ornfJWb9Vq2r2s9lWLRjRZWUW6pf6TcUfFcteVH2abDdWze8QTYJpvTUbEvDi/J6aPL311ut0tERIQ4dbk+6jss5Ufq2sVxeDleW0Vbah4ak7Wuq9xXfWWtay33FYseU9a6ldrktV2cPtZbqb7d7j6u/bXJXa7bITKyYl9UV9+yzVzr8t4fHm211Jdq9p/H/rDsJ19t1ViPZn+Y9wQ9nlzHWgN9PWks+p5nHMN7RJXlAbZJY9HXpXsZx/Ie0cje90JqPzkdR94LGtFnbmM8j7CFSJu8n29wSZImSEp7jqz0d9dzvkyfPl2mTJlSqXzNmjVmuJ5q06aNdO3a1SRi2dnZ7jopKSnmsXHjRjl06JC7vEuXLpKUlCTr1q2TwsJCd3mvXr2kRYsWZtnWg+PEE0+UyMhIWb16tUcM/fv3l5KSElm7dq3HjhswYIBZ3/r1693leg2WDkXcu3evbNmyxSNR7N27t2RmZkpGRoa7nDbRprpuk+uNK7asQMJ3/HykPK6lOBJTxH4gU+x5B46UN08SR4u2Epa9XWxFee7y8lYdxBnfSsJ2bRZb6ZEvNMqT0sQZEy9hGesrTvYOK2vXXSQ8wmOdXWKc5gQyXBwe5XpCWZ56nFlf2J6tR8ojoqS8fQ+x5R2UsP07j5RHx0l5285iP5Qt9kN7atym+Ir3X2njyPOIJ9A2tS4skIsu+Z1ISaGU2u2yPa65u67d6ZQuuQelMDxcMmPjJbJ3H7nu5rFSnKzvjQ7JjYiUPTHNjuxXPYHQL4iaad2Wpm5RakfZG26XpMIC2RsTKzkRUe76rYoLpVVxkYSnprrrbomJkaTCfEkoLZGMuAQpObxM1b4g1/x/1Y03SlGXrqauSs07JOEOh2xJaHmknTFxYgsLl+YtW0pRl27uut5tcono2rViu8U391hObFmptC/IkwNR0bI/qmIZuh1OPfNM87O/Nu2KjZOC8AhT9/qxt0jE3kzzvK82xZaVydb4Fu7tq9uhtLykUptUl5wDUnZ4P7n3R0oHkfKSSm2KdJRLal6O2U/WZR8oL3Xvp6yEFu76CaXFZj+FtWvvsT+82+Si+0kNPXuYtC7cK+E7Co7q2Au115PG0rNbxXFwLO8RprxjH5GyUgnP2lSjNmks5509zPx8rO8Rje19L5T2k75/nnLySebnxvSZ2xjPI5qHSJv0cp9A2JzWFCyINPNbvHixXHLJJeb3FStWyOmnn242pE7c4HLllVeauq+//nrAPUkdO3aUffv2SUJCgikjC6dNtOno26RvXjffPkEuv/thSU7tFNRvVNetWi7PT75bJsxZKGk9egW9J+nHVcvlhcl3y8Q5C6WTNZ4A27R7+1Z5a/JEmXr+cElLalNlT9LKDZtk2utvyJxRN0jPjh0r9VzkHDwgGRt+ks7HnSTf78w6XHekdO+YUmVP0lfrN8m0N47UrarnYsUvG2Tam2+567rKxUcsH3y2VBZ8+6PMu+Vmd13vNrmsXL9JHnz1VZl382jpnprqdxuYuhs2yZR/viZzR4+Ubl51vWN31b3rtH4y+LQzJTouzm/9I9t3pPQ4HG9VPUnW+j07plTZk7TCUrdtfDP3forxikfrf7V+o0x7482A9se2Pdny1/98Ir+fMlvapqY16NeTxrJg6r1yx+wFkta9Z1B7KDSWRQ/fJ7fNfL5iuwS5J4n95Dt2ff9cPHOyPPfULOncuXOj+cxtjOcRthBpk+YGiYmJJvFy5QYNqicpOTnZ/L97926PJEl/P+mkim8MfImKijIPb7oT3d3mXhvZm2tjBlruvdyalOtO9VXuL8ajLadNtOlY2+T+WT/8LN/CWyr4XKfPujUptx0pd7pOU/3FYj7UfZXrh7cEXh5wmw4PnfK7bapuk35rqm/qrhNhnyG6yh0OKS0trRja56O+9tJIpboO9yw9fmfrcVauW1V9X3XFTyz6IeavrkdbXcMP/azXo77D4f7A8xeju1y3Q0mJ+6SqyvqWbeZal6/94S631PfZJj91rfvJZzxO51HtD3OyoMex97HWwF5PGov7JOkY3iOqLQ+gTRqL+2TrmN8jaljOfqp+P2mvk+u9oBF95ta0nDZJtbH7e75SPBKi9NsATZSWLl3qLtPMT2e5GzRoUFBjAwAAANB4BbUnKS8vT3799VeP4Tzff/+9tGrVSlJTU2XChAny8MMPS/fu3U3S9MADD5h7KrmG5AEAAABAo0qS9AKqoUOHun+fNGmS+X/kyJGyaNEiueeee8zFVWPHjpWDBw/K4MGDZcmSJRIdHR3EqAEAAAA0ZkFNkoYMGeJxwZWvMY5Tp041DwAAAACoDyF7TRIAAAAABANJEgAAAABYkCQBAAAAgAVJEgAAAABYkCQBAAAAgAVJEgAAAACEyhTgANBY5ezbKwV5uQHVzc7cIeVlZXUeEwA0RiUlxbJt2zYJBQkJCdKmTZtgh4FaQJIEAHWQIC26b7w4CwJLkgqLiiR3T5aUlZbWeWwA0JjkHtwv6Zu3yP3THpGoqKhghyPxMdGy4Ln5JEqNAEkSANQy7UHSBOmOwQOlXcuW1dZfk54uc9/fKY5yepMA4GgUFeSLPSJCzrp+nHRI6xrUWPZlZcjnLz8jOTk5JEmNAEkSANQRTZDSkqr/oNy5b3+9xAMAjVVicntJ7tQ52GGgEWHiBgAAAACwIEkCAAAAAAuG2wE+ZGdnmzHFoaCkpEQiIyODHYaZOaiMGdiAkFRaWmpmSQxEbFy8JCS2rvOYAKAhI0kCfCRIo8feKrmFRSExremOrVulU5euEh4e3JdrYUG+ZO7aLaWlJUGNA4Cng/n5smvHVvlo1lSJCOALFVtsvNw4/WkSJQCoAkkS4EV7kDRBGnL9OElslxLUWDZ9/41sm/eEDP7D2KDP2qOxvD3vCSkvLw9qHAA85RcVS5TdLrefPkA6Jbersm7WgQPy1PJVZgZGkiQA8I8kCfBDE6Rgz5TjGj4TCrP2BDqUB0BwtGvRIqDZFAEA1WPiBgAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAAuSJAAAAACwIEkCAAAAAItw6y8AAKBxKy0tlezMHe7f9+/OkrLSEtmXlSHR0TEedWPj4iUhsXUQogRwrLKzsyUnJ0dCRUJCgrRp00YaCpIkAACaiIP5+bJrx1b5aNZUiYiMNGWFeXkSU5Arn859VCKjoz3q22Lj5cbpT5MoAQ0wQRo99lbJLSySUBEfEy0LnpvfYBIlkiQAAJqI/KJiibLb5fbTB0in5HamLC/noGSl/yodu/eW6Nhm7rpZBw7IU8tXSUFeLkkS0MBoD5ImSEOuHyeJ7VKCHY5oT/XnLz9j4iJJAgAAIaldixaSllRxopITESayb7ektm4tsXFxwQ4NQC3SBCm5U+dgh9EgMXEDAAAAAFiQJAEAAACABUkSAAAAAFhwTVITno6xoU3FCARbzr695iJ2b95TKOv0yuVlZUGJEQAQPCUlxbJt27Zgh2FiKONz6JiQJDXh6Rgb2lSMQLATpEX3jRdnQeUkyXsK5cKiIsndo4lTaVBiBQDUv9yD+yV98xa5f9ojEhUVFdRYCgvyJXPXbiktLQlqHA0ZSVITnY6xIU7FCAST9iBpgnTH4IHSrmVLj+e8p1Bek54uc9/fKY5yvsUDgKaiqCBf7BERctb146RDWtegxrLp+2/k7XlPSHl5eVDjaMhIkoKA6RiBhksTJNfUyS7eUyjv3Lc/aPEBAIIrMbl90M/zdNg3jg0TNwAAAACABUkSAAAAAFgw3A4AAPhUWloa8LCd2Lh4SUhsXecxAUB9IEkCAACVHMzPl107tspHs6ZKRGRktfVtsfFy4/SnSZQANAokSQAAoJL8omKJstvl9tMHSKfkdlXWzTpwQJ5avsrMAkmSBKAxIEkCAAB+tWvRotKMjgDQ2DFxAwAAAABYkCQBAAAAgAVJEgAAAABYcE0SQkZ2drbk5OQEOwzZtm2blJWVBTsMAAAABAlJEkImQRo99lbJLSwKdihSWJAvmbt2S2lpSbBDAQAAQBCQJCEkaA+SJkhDrh8nie1SghrLpu+/kbfnPSHl5eVBjQMAAADBQZKEkKIJUnKnzkGNIdC7ywMAAKBxYuIGAAAAALAgSQIAAAAAC5IkAAAAALDgmqQmrKSk2Ex3HQqYdhu1KWffXinIyw2obmxcvCQktq7zmAAAQMNBktRE5R7cL+mbt8j90x6RqKioYIfDtNuo1QRp0X3jxVkQWJJki42XG6c/TaIEAADcSJKaqKKCfLFHRMhZ14+TDmldgx0O026j1mgPkiZIdwweKO1atqyybtaBA/LU8lXmb0iSAACAC0lSE5eY3D7oU24rpt1GbdMEKS2pTbDDAAAADVCDmLjh6aeflrS0NImOjpaBAwfK//73v2CHBAAAAKCRCvkk6fXXX5dJkybJQw89JN9995307dtXRowYIXv27Al2aAAAAAAaoZBPkmbOnCk333yzjBo1Svr06SPz58+X2NhYWbBgQbBDAwAAANAIhfQ1SSUlJfLtt9/Kfffd5y6z2+0yfPhwWblypc+/KS4uNg+XQ4cOmf/379/vnmJal6EPh8NhHtZl60MnD3A6ndWWh4WFic1mqzR1tZYr70kIcnJypKysVDI3b5CivBx3udNmF3E6xSZHlq2/ic1WZ+V7tqeLw1EuWem/ipSVVqpvcx7ZLu5ynQnMsowqy4+yTRqPlu+yxFPb2yDQNmksTqfDI5aatKmx7afsHVXHUp/7yRVLplcsau/uLCkqKpL1O3bI/hzX68wpNqezYgnaZtf2PXRI8nJzZd3KZbJz8/pK67Wu82D2bikqKJD127fJ/hx9X6mISxXm5si+ffslb+tWiYyOkS27s8Rmt8uGjAzJLy6u2M4eCz/cxsPl1vp5+v7lVV+3iSt2d93MzIple7WpMC/XHcuWvXsP182SvJKSim2g294Su2s/bNmVaalbamLUWpXrO2Rz5k7Puj7aZGLJz5WsQ7nmfdajrlebXDSG8vIy2bBzp0fdI/vv8LFzeJtpidlmJSU+2+SKXevaw8Nkx6Fc+TE9XSJiYiq1qaKt1u2bVbEvvGL0bqu1vq/9YT32rHWzo6M8jhnv+puzvPeHZ5u894cWee8PV5ustK7Zxpa6up9MLOnpEhkTe2R/7M6qqGu2sa/9caSt+lrS20pkbN5gjsGavhfoa1t/19e2s6zsmN7Lj/V9T2Ox22xVxhIq73v1/fnEfvIdu8YSZrf7jKW+95NHLHo+eoznRs5j3E/79T3b4ZDc3Fw5ePCgz/Pv+jov1/NxE6flb32xOaurEUSZmZnSoUMHWbFihQwaNMhdfs8998iyZctk1apVlf5m8uTJMmXKlHqOFAAAAEBDsWPHDklJSWmYPUk1ob1Oeg2Ti2ak2ouUmJhosstQpVltx44dzQ5LSEgIdjgIAPus4WGfNTzss4aHfdbwsM8aHvZZzWn/kPZotW/fvsp6IZ0ktW7d2nSR7d6926Ncf09OTvb5N3pjVO+bo7Zo0UIaCj3QOdgbFvZZw8M+a3jYZw0P+6zhYZ81POyzmmnevHnDnrghMjJS+vXrJ0uXLvXoGdLfrcPvAAAAAKC2hHRPktKhcyNHjpT+/fvLKaecIrNnz5b8/Hwz2x0AAAAANLkk6aqrrpLs7Gx58MEHZdeuXXLSSSfJkiVLpG3bttKY6BBBvReU91BBhC72WcPDPmt42GcND/us4WGfNTzss7oX0rPbAQAAAEB9C+lrkgAAAACgvpEkAQAAAIAFSRIAAAAAWJAkAQAAAIAFSVI9evrppyUtLU2io6Nl4MCB8r///c9v3UWLFonNZvN46N+hfnzxxRdy0UUXmbsx67Z/9913q/2bzz//XE4++WQz00y3bt3MPkTo7jPdX96vMX3oLJqoH9OnT5cBAwZIfHy8JCUlySWXXCIbNmyo9u/efPNN6dWrl3lPPOGEE+Q///lPvcSLmu0zPs+C65lnnpETTzzRfdNRvc/khx9+WOXf8BprWPuM11jdIEmqJ6+//rq555NO1/jdd99J3759ZcSIEbJnzx6/f6MvjKysLPdj27Zt9RpzU6b34tJ9pIltINLT0+XCCy+UoUOHyvfffy8TJkyQm266ST766KM6jxU122cueoJnfZ3piR/qx7Jly2T8+PHy9ddfyyeffCKlpaVy7rnnmn3pz4oVK+Saa66RMWPGyJo1a8xJuj7WrVtXr7E3VTXZZ4rPs+BJSUmRRx99VL799ltZvXq1DBs2TC6++GL56aeffNbnNdbw9pniNVYHdApw1L1TTjnFOX78ePfv5eXlzvbt2zunT5/us/7ChQudzZs3r8cI4Y++TBYvXlxlnXvuucd53HHHeZRdddVVzhEjRtRxdKjpPvvss89MvQMHDtRbXKjanj17zD5ZtmyZ3zpXXnml88ILL/QoGzhwoPOWW26phwhRk33G51noadmypfP555/3+RyvsYa3z3iN1Q16kupBSUmJ+TZg+PDh7jK73W5+X7lypd+/y8vLk06dOknHjh2r/QYBwaX70bp/lfYUVrV/ERr0BtXt2rWTc845R7766qtgh9OkHTp0yPzfqlUrv3V4rTW8fab4PAsN5eXl8tprr5mePx3C5QuvsYa3zxSvsdpHklQP9u7daw7ytm3bepTr7/6uf+jZs6csWLBA/vWvf8krr7wiDodDTjvtNMnIyKinqHE0dD/62r85OTlSWFgYtLjgnyZG8+fPl7fffts89INlyJAhZjgs6p++x+kw1dNPP12OP/74o36tcS1Z6O4zPs+C78cff5S4uDhzzeytt94qixcvlj59+visy2us4e0zXmN1I7yOlotjpN8WWL8x0IO9d+/e8uyzz8q0adOCGhvQGOiHij6sr7HNmzfLrFmz5OWXXw5qbE2RXuei1zwsX7482KGglvcZn2fBp+91er2s9vy99dZbMnLkSHN9mb+TbjSsfcZrrG6QJNWD1q1bS1hYmOzevdujXH9PTk4OaBkRERHym9/8Rn799dc6ihLHQvejr/2rF1LGxMQELS4cnVNOOYWT9CC47bbb5P333zczFOoFyzV5rQX6Xor632fe+Dyrf5GRkWbWVdWvXz/55ptvZM6cOeYk2huvsYa3z7zxGqsdDLerpwNdD/ClS5e6y7QrVH+vanyplQ7X065XHSKE0KP70bp/lc78FOj+RWjQb+14jdUfnWNDT7Z1GMmnn34qnTt3rvZveK01vH3mjc+z4NNzkOLiYp/P8RprePvMG6+xWlJHE0LAy2uvveaMiopyLlq0yPnzzz87x44d62zRooVz165d5vnrr7/e+ec//9ldf8qUKc6PPvrIuXnzZue3337rvPrqq53R0dHOn376KYitaDpyc3Oda9asMQ99mcycOdP8vG3bNvO87ivdZy5btmxxxsbGOu+++27nL7/84nz66aedYWFhziVLlgSxFU3L0e6zWbNmOd99913npk2bnD/++KPzT3/6k9Nutzv/+9//BrEVTcu4cePMjEyff/65Mysry/0oKChw1/F+b/zqq6+c4eHhzieeeMK81h566CFnRESE2YcIzX3G51lw6b7Q2QfT09Oda9euNb/bbDbnxx9/bJ7nNdbw9xmvsbpBklSP/va3vzlTU1OdkZGRZkrwr7/+2v3cWWed5Rw5cqT79wkTJrjrtm3b1nnBBRc4v/vuuyBF3vS4pof2frj2kf6v+8z7b0466SSzz7p06WKm5ETo7rMZM2Y4u3btaj5IWrVq5RwyZIjz008/DWILmh5f+0sf1teO93ujeuONN5w9evQwrzWdev+DDz4IQvRNU032GZ9nwTV69Ghnp06dzPZv06aN8+yzz3afbCteYw1/n/Eaqxs2/ae2eqUAAAAAoKHjmiQAAAAAsCBJAgAAAAALkiQAAAAAsCBJAgAAAAALkiQAAAAAsCBJAgAAAAALkiQAAAAAsCBJAgAAAAALkiQAAAAAsCBJAgCEvOzsbBk3bpykpqZKVFSUJCcny4gRI+Srr74KdmgAgEYoPNgBAABQncsvv1xKSkrkxRdflC5dusju3btl6dKlsm/fvjpZn64rMjKyTpYNAAh99CQBAELawYMH5csvv5QZM2bI0KFDpVOnTnLKKafIfffdJ7/73e/cdW655RZp27atREdHy/HHHy/vv/++exlvv/22HHfccaYXKi0tTZ588kmPdWjZtGnT5IYbbpCEhAQZO3asKV++fLmcccYZEhMTIx07dpQ77rhD8vPz63kLAADqG0kSACCkxcXFmce7774rxcXFlZ53OBxy/vnnm6F3r7zyivz888/y6KOPSlhYmHn+22+/lSuvvFKuvvpq+fHHH2Xy5MnywAMPyKJFizyW88QTT0jfvn1lzZo15vnNmzfLeeedZ3qx1q5dK6+//rpJmm677bZ6azsAIDhsTqfTGaR1AwAQEO0Juvnmm6WwsFBOPvlkOeuss0zSc+KJJ8rHH39skqRffvlFevToUelvr732WnNNk9Zzueeee+SDDz6Qn376yd2T9Jvf/EYWL17srnPTTTeZROvZZ591l2mSpOvW3iTtsQIANE70JAEAQp725mRmZsp7771nenc+//xzkyxpb9D3338vKSkpPhMkpcnT6aef7lGmv2/atEnKy8vdZf379/eo88MPP5jlu3qy9KGTRWjPVXp6eh21FAAQCpi4AQDQIGjPzTnnnGMeOhxOe3oeeughueuuu2pl+c2aNfP4PS8vz1znpNchedNZ9gAAjRdJEgCgQerTp4+5TkmH3GVkZMjGjRt99ib17t270lTh+rvWdV235Iv2VOn1Td26dauT+AEAoYvhdgCAkKbTfA8bNsxMyqATKOhQtzfffFMee+wxufjii801QmeeeaYZkvfJJ5+Y5z/88ENZsmSJ+fs777zTTBeus9dpIqXTiM+dO7faHqh7771XVqxYYSZq0CF9OjzvX//6FxM3AEATQE8SACCk6bVAAwcOlFmzZpkZ50pLS8103DqRw1/+8hf3xA6a9FxzzTVmUgXt/dEZ7lw9Qm+88YY8+OCDJlFq166dTJ06VW688cYq16s9VMuWLZP777/fTAOu8xx17dpVrrrqqnppNwAgeJjdDgAAAAAsGG4HAAAAABYkSQAAAABgQZIEAAAAABYkSQAAAABgQZIEAAAAABYkSQAAAABgQZIEAAAAABYkSQAAAABgQZIEAAAAABYkSQAAAABgQZIEAAAAAHLE/wOOsw9Zh/OWkwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot distribution of predicted values and true values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.hist(test_labels, bins=20, color='skyblue', edgecolor='black', alpha=0.7, label='True Values')\n",
    "plt.hist(test_preds, bins=20, color='salmon', edgecolor='black', alpha=0.7, label='Predicted Values')\n",
    "plt.title('Distribution of Predicted and True Values')\n",
    "plt.xlabel('Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}