Image Classification Chars74K.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZnrWMXYejCA"
      },
      "source": [
        "#Common Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u32DkzzSXXT0"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import roc_curve,auc, roc_auc_score\n",
        "\n",
        "import os\n",
        "from PIL import Image\n",
        "import skimage\n",
        "\n",
        "from google.colab import drive #upload files from GoogleDrive\n",
        "import tarfile #extract tgz files\n",
        "\n",
        "# to make this notebook's output stable across runs\n",
        "np.random.seed(42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VtWKWN46iHTX"
      },
      "source": [
        "#Upload EnglishImg.tgz File from Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8k7rCEoFiFw9",
        "outputId": "c490f1c0-a222-4215-db31-012f838f486d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n",
            "Successfully uploaded 'EnglishImg.tgz' from my Google Drive\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    drive.mount('/content/drive')  # Connect Google Drive\n",
        "    file_path = '/content/drive/MyDrive/EnglishImg.tgz' # Define the file path in my Google Drive\n",
        "\n",
        "    print(\"Successfully uploaded 'EnglishImg.tgz' from my Google Drive\")\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {e}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaVt1tvbfTqm"
      },
      "source": [
        "# Extract the EnglishImg.tgz File"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KcspX8XZRV3",
        "outputId": "2e226e15-b038-4e23-ed4e-5d46d3761a3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Successfully extracted 'EnglishImg.tgz' to 'EnglishImg'\n"
          ]
        }
      ],
      "source": [
        "#extract the EnglishImg.tgz to  EnglishImg\n",
        "extracted_directory = 'EnglishImg'\n",
        "\n",
        "try:\n",
        "    # Create the directory if it doesn't exist\n",
        "    if not os.path.exists(extracted_directory):\n",
        "        os.makedirs(extracted_directory)\n",
        "\n",
        "    # Open the EnglishImg.tgz file and Extract its contents to the new directory (extracted_directory = 'EnglishImg')\n",
        "    with tarfile.open(file_path, 'r:gz') as tar:\n",
        "        tar.extractall(path=extracted_directory)\n",
        "\n",
        "    print(f\"Successfully extracted 'EnglishImg.tgz' to '{extracted_directory}'\")\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {e}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_R2zp0BtndSi"
      },
      "source": [
        "#1.1 Getting Started"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aaRb5xzkBK3K"
      },
      "source": [
        "## Helper Function that assign a Label for each sample according to its FolderName using ascii code\n",
        "\n",
        "#### For numbers 1 to 10, we map them to strings '0' to '9'.\n",
        "#### For numbers 11 to 36, we map them to uppercase letters 'A' to 'Z'. (+54)\n",
        "#### For numbers 37 to 62, we map them to lowercase letters 'a' to 'z'. (+60)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sni5rSJh7zRy"
      },
      "outputs": [],
      "source": [
        "def extract_and_map_number_to_label(filename):\n",
        "\n",
        "    number_str = filename[6:]    # Extract the numeric part\n",
        "    number_str = number_str.lstrip('0')  # Remove leading zeros if any\n",
        "    number = int(number_str)  # Convert the number string to an integer\n",
        "\n",
        "    # Map the number to the corresponding label\n",
        "    if 1 <= number <= 10:\n",
        "        return number - 1         # Map 1 to 0, 2 to 1, ..., 10 to 9\n",
        "    elif 11 <= number <= 36:\n",
        "        return chr(number + 54)  # Map 11 to 'A', 12 to 'B', ..., 36 to 'Z'\n",
        "    elif 37 <= number <= 62:\n",
        "        return chr(number + 60)  # Map 37 to 'a', 38 to 'b', ..., 62 to 'z'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fsn7vXyRar3a"
      },
      "source": [
        "### Test Function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bn4esVFtZ5YV",
        "outputId": "d6d45a00-2be4-4304-e2a0-ab23bcd6ccda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Filename: Sample001, Label: 0\n",
            "Filename: Sample002, Label: 1\n",
            "Filename: Sample003, Label: 2\n",
            "Filename: Sample004, Label: 3\n",
            "Filename: Sample005, Label: 4\n",
            "Filename: Sample006, Label: 5\n",
            "Filename: Sample007, Label: 6\n",
            "Filename: Sample008, Label: 7\n",
            "Filename: Sample009, Label: 8\n",
            "Filename: Sample010, Label: 9\n",
            "Filename: Sample011, Label: A\n",
            "Filename: Sample012, Label: B\n",
            "Filename: Sample013, Label: C\n",
            "Filename: Sample014, Label: D\n",
            "Filename: Sample015, Label: E\n",
            "Filename: Sample016, Label: F\n",
            "Filename: Sample017, Label: G\n",
            "Filename: Sample018, Label: H\n",
            "Filename: Sample019, Label: I\n",
            "Filename: Sample020, Label: J\n",
            "Filename: Sample021, Label: K\n",
            "Filename: Sample022, Label: L\n",
            "Filename: Sample023, Label: M\n",
            "Filename: Sample024, Label: N\n",
            "Filename: Sample025, Label: O\n",
            "Filename: Sample026, Label: P\n",
            "Filename: Sample027, Label: Q\n",
            "Filename: Sample028, Label: R\n",
            "Filename: Sample029, Label: S\n",
            "Filename: Sample030, Label: T\n",
            "Filename: Sample031, Label: U\n",
            "Filename: Sample032, Label: V\n",
            "Filename: Sample033, Label: W\n",
            "Filename: Sample034, Label: X\n",
            "Filename: Sample035, Label: Y\n",
            "Filename: Sample036, Label: Z\n",
            "Filename: Sample037, Label: a\n",
            "Filename: Sample038, Label: b\n",
            "Filename: Sample039, Label: c\n",
            "Filename: Sample040, Label: d\n",
            "Filename: Sample041, Label: e\n",
            "Filename: Sample042, Label: f\n",
            "Filename: Sample043, Label: g\n",
            "Filename: Sample044, Label: h\n",
            "Filename: Sample045, Label: i\n",
            "Filename: Sample046, Label: j\n",
            "Filename: Sample047, Label: k\n",
            "Filename: Sample048, Label: l\n",
            "Filename: Sample049, Label: m\n",
            "Filename: Sample050, Label: n\n",
            "Filename: Sample051, Label: o\n",
            "Filename: Sample052, Label: p\n",
            "Filename: Sample053, Label: q\n",
            "Filename: Sample054, Label: r\n",
            "Filename: Sample055, Label: s\n",
            "Filename: Sample056, Label: t\n",
            "Filename: Sample057, Label: u\n",
            "Filename: Sample058, Label: v\n",
            "Filename: Sample059, Label: w\n",
            "Filename: Sample060, Label: x\n",
            "Filename: Sample061, Label: y\n",
            "Filename: Sample062, Label: z\n"
          ]
        }
      ],
      "source": [
        "# Generate filenames from \"Sample001\" to \"Sample062\"\n",
        "filenames = [f\"Sample{i:03d}\" for i in range(1, 63)]\n",
        "\n",
        "# Extract and map labels for each filename\n",
        "labels = [extract_and_map_number_to_label(filename) for filename in filenames]\n",
        "\n",
        "# Print the filenames and their corresponding labels\n",
        "for filename, label in zip(filenames, labels):\n",
        "    print(f\"Filename: {filename}, Label: {label}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qInNhN1fl2H5"
      },
      "source": [
        "## Reading the Samples and assgin Labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVoUNn-Hw_b8",
        "outputId": "2219402e-8c01-4fb9-ba89-1651bb5954ac"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'Sample005': 4, 'Sample011': 'A', 'Sample057': 'u', 'Sample018': 'H'}"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Define the path\n",
        "english_img_directory = 'EnglishImg/English/Img/GoodImg/Bmp'\n",
        "\n",
        "# Define the paths for the subfolders for digit 4 and characters A,u,H\n",
        "subfolder_paths = {\n",
        "    'Sample005': extract_and_map_number_to_label('Sample005'),\n",
        "    'Sample011': extract_and_map_number_to_label('Sample011'),\n",
        "    'Sample057': extract_and_map_number_to_label('Sample057'),\n",
        "    'Sample018': extract_and_map_number_to_label('Sample018')\n",
        "}\n",
        "\n",
        "subfolder_paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "_gYjqO58mmyb",
        "outputId": "33ca1735-dcfa-4b75-a1d0-817bdc88a306"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Combined array shape: (839, 4097)\n",
            "\n"
          ]
        },
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              "      <td>47</td>\n",
              "      <td>47</td>\n",
              "      <td>46</td>\n",
              "      <td>46</td>\n",
              "      <td>H</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>839 rows × 4097 columns</p>\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
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              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "       0    1    2    3    4    5    6    7    8    9  ... 4087 4088 4089  \\\n",
              "0    152  154  154  155  154  153  153  154  156  155  ...  173  172  172   \n",
              "1    193  193  192  191  189  191  193  194  193  192  ...   64   55   64   \n",
              "2     60   63   63   62   61   62   62   60   59   63  ...   91   62   76   \n",
              "3     62   62   63   63   60   61   63   63   63   63  ...   66   67   66   \n",
              "4     56   56   53   56   56   59   60   62   63   67  ...   51   50   49   \n",
              "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "834   31   37   43   46   39   30   29   30   30   30  ...   42   43   41   \n",
              "835  146  145  144  146  146  146  149  150  148  148  ...  132  133  135   \n",
              "836   72   71   71   65   71   73   71   68   66   74  ...   60   64   65   \n",
              "837   64   64   59   52   77  110  132  153  173  179  ...   88   53   56   \n",
              "838   47   47   47   47   48   49   47   46   45   45  ...   50   49   48   \n",
              "\n",
              "    4090 4091 4092 4093 4094 4095 Label  \n",
              "0    173  172  171  172  173  174     4  \n",
              "1     89  125  157  171  167  156     4  \n",
              "2     71   66   67   68   66   65     4  \n",
              "3     64   63   63   64   64   64     4  \n",
              "4     53   52   51   54   52   50     4  \n",
              "..   ...  ...  ...  ...  ...  ...   ...  \n",
              "834   38   39   38   38   38   37     H  \n",
              "835  135  131  132  135  129  133     H  \n",
              "836   70   63   66   62   67   64     H  \n",
              "837   52   50   56   55   56   56     H  \n",
              "838   46   46   47   47   46   46     H  \n",
              "\n",
              "[839 rows x 4097 columns]"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data = []\n",
        "labels = []\n",
        "\n",
        "# Iterate over the subfolders and their corresponding labels\n",
        "for subfolder, label in subfolder_paths.items():\n",
        "    subfolder_path = os.path.join(english_img_directory, subfolder)\n",
        "\n",
        "    # Iterate over the images in the subfolder of each digits or char\n",
        "    for filename in os.listdir(subfolder_path):\n",
        "\n",
        "      # Load and preprocess the image\n",
        "      image_path = os.path.join(subfolder_path, filename)\n",
        "      image = Image.open(image_path)\n",
        "      image = image.resize((64, 64))\n",
        "\n",
        "      # Convert the resized image to grayscale\n",
        "      grayscale_image = image.convert('L')\n",
        "\n",
        "      # Convert the image to a numpy array\n",
        "      image_array = np.array(grayscale_image)\n",
        "\n",
        "      # Flatten the 2D array into a 1D array and append it to the data list\n",
        "      image_data = image_array.ravel()\n",
        "\n",
        "      data.append(image_data)\n",
        "      labels.append(label)  # Append the corresponding label\n",
        "\n",
        "# Convert the lists to NumPy arrays\n",
        "data_np = np.array(data)\n",
        "labels_np = np.array(labels)\n",
        "\n",
        "# Join the label for each image to its data along the columns (axis 1)\n",
        "data_with_labels = np.column_stack((data_np, labels_np))\n",
        "\n",
        "# Print the shape of the combined array\n",
        "print(f\"Combined array shape: {data_with_labels.shape}\\n\")\n",
        "\n",
        "# Convert the NumPy array to a Pandas DataFrame\n",
        "df = pd.DataFrame(data_with_labels)\n",
        "df = df.rename(columns={df.columns[-1]: 'Label'})\n",
        "df\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTfPzZMu-6ZO"
      },
      "source": [
        "#Helper function to convert X--> from numpy to numeric ,y--> Label encoder 0-->4 ,1-->A\n",
        "###return X(from numpy to float32) ,Encode y (4-->0 , A-->1 ,H-->2 ,u-->3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GKcqXTrf9_7y"
      },
      "outputs": [],
      "source": [
        "def to_numeric (X,y):\n",
        "  X = X.astype(np.float32)\n",
        "\n",
        "  # Transform the labels to numeric values\n",
        "  label_encoder = LabelEncoder()\n",
        "  y_encoded = label_encoder.fit_transform(y)\n",
        "  print(\"Transformed labels:\", y_encoded)\n",
        "\n",
        "  print(\"\\nX dtype:\", X.dtype)\n",
        "  print(\"Y dtype:\", y_encoded.dtype)\n",
        "\n",
        "  return X,y_encoded"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VULJ1wGLEu18"
      },
      "source": [
        "#1.2 Binary Classifiers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iSNFLwr7nxj4"
      },
      "outputs": [],
      "source": [
        "# Define the target classes\n",
        "target_classes = ['4', 'A']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tZoHqdtn_0QY"
      },
      "source": [
        "##Helper Funtions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqpfyRgMzeXg"
      },
      "source": [
        "### Fuction to run LogisticRegression model given any x_train,y_train_x_test\n",
        "### return y_train_pred,y_test_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EJXfcPgAoMBa"
      },
      "outputs": [],
      "source": [
        "def LogisticRegression_Model (X_train, y_train,X_test):\n",
        "\n",
        "  logistic_model = LogisticRegression(max_iter=10, tol=0.1, solver='liblinear')\n",
        "  logistic_model.fit(X_train, y_train)\n",
        "\n",
        "  y_train_pred = logistic_model.predict(X_train) # Predict labels for the training data\n",
        "  y_test_pred = logistic_model.predict(X_test)  # Predict labels for the testing data\n",
        "\n",
        "  return y_train_pred,y_test_pred\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-CBwvVDs1HTI"
      },
      "source": [
        "### Evalution Function that calcualte confusion matrix (without using any scikit-learn function)to calualte accuracy,recall and precision given actual y , the predict y and text(Train/Test).\n",
        "### Return TP, TF, FP, FN ,accuracy,recall and precision,ROC curve"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "snDzaaDJH0g_"
      },
      "outputs": [],
      "source": [
        "def Binary_Evalution(y, y_pred,text):\n",
        "\n",
        "  TP = TN = FP = FN= 0\n",
        "\n",
        "  # Calculate the confusion matrix elements\n",
        "  for true_label, predicted_label in zip(y, y_pred):\n",
        "      if true_label == 1 and predicted_label == 1:\n",
        "          TP+= 1\n",
        "      elif true_label == 0 and predicted_label == 0:\n",
        "          TN += 1\n",
        "      elif true_label == 0 and predicted_label == 1:\n",
        "          FP += 1\n",
        "      elif true_label == 1 and predicted_label == 0:\n",
        "          FN += 1\n",
        "\n",
        "  # Print the confusion matrix and metrics\n",
        "  print(f\"\\n{text} Data Metrics:\\n\")\n",
        "  print(\"Confusion Matrix:\")\n",
        "  print(f\"True Positives (TP): {TP}\")\n",
        "  print(f\"True Negatives (TN): {TN}\")\n",
        "  print(f\"False Positives (FP): {FP}\")\n",
        "  print(f\"False Negatives (FN): {FN}\")\n",
        "\n",
        "  accuracy= (TP + TN) / (TP + TN+ FP + FN)  # Calculate accuracy\n",
        "  recall = TP / (TP + FN)   # Calculate recall\n",
        "  precision = TP / (TP + FP)  # Calculate precision\n",
        "\n",
        "  # Print accuracy, recall, and precision\n",
        "  print(f\"\\n\\nAccuracy for {text} data: {accuracy*100:.2f}%\")\n",
        "  print(f\"Recall for {text} data: {recall*100:.2f}%\")\n",
        "  print(f\"Precision for {text} data: {precision*100:.2f}% \\n\\n\")\n",
        "\n",
        "  # Confusion Matrix\n",
        "  cm = confusion_matrix(y, y_pred)\n",
        "  plt.figure(figsize=(4, 3))\n",
        "  sns.heatmap(cm, annot=True, cmap='Pastel2', fmt='g', xticklabels=target_classes, yticklabels=target_classes)\n",
        "  plt.title(f\"{text} Confusion Matrix\")\n",
        "  plt.xlabel('Predicted Labels')\n",
        "  plt.ylabel('Actual Labels')\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "  print('\\n')\n",
        "\n",
        "  # ROC Curve\n",
        "  fpr, tpr, _ = roc_curve(y, y_pred)\n",
        "  roc_auc = auc(fpr, tpr)\n",
        "\n",
        "  plt.figure(figsize=(8, 6))\n",
        "  plt.plot(fpr, tpr, color='pink', lw=2, label='ROC curve (area = {:.2f})'.format(roc_auc))\n",
        "  plt.plot([0, 1], [0, 1], color='green', lw=2, linestyle='--')\n",
        "  plt.xlim([0.0, 1.0])\n",
        "  plt.ylim([0.0, 1.05])\n",
        "  plt.xlabel('False Positive Rate')\n",
        "  plt.ylabel('True Positive Rate')\n",
        "  plt.title(f'ROC Curve for {text} Data')\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ngadjaonpnhf"
      },
      "source": [
        "## Filtering the images to apply binary classifiers on 'A' and '4' Labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "OFe6AA80LSoG",
        "outputId": "a48c5ad3-4e6c-44c4-dfde-dee9f08db2f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Filtered data shape: (605, 4097)\n",
            "\n"
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>152</td>\n",
              "      <td>154</td>\n",
              "      <td>154</td>\n",
              "      <td>155</td>\n",
              "      <td>154</td>\n",
              "      <td>153</td>\n",
              "      <td>153</td>\n",
              "      <td>154</td>\n",
              "      <td>156</td>\n",
              "      <td>155</td>\n",
              "      <td>...</td>\n",
              "      <td>173</td>\n",
              "      <td>172</td>\n",
              "      <td>172</td>\n",
              "      <td>173</td>\n",
              "      <td>172</td>\n",
              "      <td>171</td>\n",
              "      <td>172</td>\n",
              "      <td>173</td>\n",
              "      <td>174</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>193</td>\n",
              "      <td>193</td>\n",
              "      <td>192</td>\n",
              "      <td>191</td>\n",
              "      <td>189</td>\n",
              "      <td>191</td>\n",
              "      <td>193</td>\n",
              "      <td>194</td>\n",
              "      <td>193</td>\n",
              "      <td>192</td>\n",
              "      <td>...</td>\n",
              "      <td>64</td>\n",
              "      <td>55</td>\n",
              "      <td>64</td>\n",
              "      <td>89</td>\n",
              "      <td>125</td>\n",
              "      <td>157</td>\n",
              "      <td>171</td>\n",
              "      <td>167</td>\n",
              "      <td>156</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>60</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>62</td>\n",
              "      <td>61</td>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>60</td>\n",
              "      <td>59</td>\n",
              "      <td>63</td>\n",
              "      <td>...</td>\n",
              "      <td>91</td>\n",
              "      <td>62</td>\n",
              "      <td>76</td>\n",
              "      <td>71</td>\n",
              "      <td>66</td>\n",
              "      <td>67</td>\n",
              "      <td>68</td>\n",
              "      <td>66</td>\n",
              "      <td>65</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>62</td>\n",
              "      <td>62</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>60</td>\n",
              "      <td>61</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>...</td>\n",
              "      <td>66</td>\n",
              "      <td>67</td>\n",
              "      <td>66</td>\n",
              "      <td>64</td>\n",
              "      <td>63</td>\n",
              "      <td>63</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>53</td>\n",
              "      <td>56</td>\n",
              "      <td>56</td>\n",
              "      <td>59</td>\n",
              "      <td>60</td>\n",
              "      <td>62</td>\n",
              "      <td>63</td>\n",
              "      <td>67</td>\n",
              "      <td>...</td>\n",
              "      <td>51</td>\n",
              "      <td>50</td>\n",
              "      <td>49</td>\n",
              "      <td>53</td>\n",
              "      <td>52</td>\n",
              "      <td>51</td>\n",
              "      <td>54</td>\n",
              "      <td>52</td>\n",
              "      <td>50</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>600</th>\n",
              "      <td>74</td>\n",
              "      <td>74</td>\n",
              "      <td>74</td>\n",
              "      <td>73</td>\n",
              "      <td>73</td>\n",
              "      <td>72</td>\n",
              "      <td>72</td>\n",
              "      <td>73</td>\n",
              "      <td>73</td>\n",
              "      <td>73</td>\n",
              "      <td>...</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>190</td>\n",
              "      <td>192</td>\n",
              "      <td>194</td>\n",
              "      <td>197</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>601</th>\n",
              "      <td>177</td>\n",
              "      <td>181</td>\n",
              "      <td>182</td>\n",
              "      <td>182</td>\n",
              "      <td>183</td>\n",
              "      <td>184</td>\n",
              "      <td>187</td>\n",
              "      <td>187</td>\n",
              "      <td>185</td>\n",
              "      <td>185</td>\n",
              "      <td>...</td>\n",
              "      <td>186</td>\n",
              "      <td>185</td>\n",
              "      <td>184</td>\n",
              "      <td>184</td>\n",
              "      <td>185</td>\n",
              "      <td>188</td>\n",
              "      <td>193</td>\n",
              "      <td>195</td>\n",
              "      <td>195</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>602</th>\n",
              "      <td>183</td>\n",
              "      <td>186</td>\n",
              "      <td>190</td>\n",
              "      <td>195</td>\n",
              "      <td>197</td>\n",
              "      <td>193</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>189</td>\n",
              "      <td>189</td>\n",
              "      <td>...</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>190</td>\n",
              "      <td>191</td>\n",
              "      <td>190</td>\n",
              "      <td>189</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>603</th>\n",
              "      <td>109</td>\n",
              "      <td>109</td>\n",
              "      <td>109</td>\n",
              "      <td>108</td>\n",
              "      <td>108</td>\n",
              "      <td>107</td>\n",
              "      <td>108</td>\n",
              "      <td>109</td>\n",
              "      <td>109</td>\n",
              "      <td>107</td>\n",
              "      <td>...</td>\n",
              "      <td>206</td>\n",
              "      <td>205</td>\n",
              "      <td>206</td>\n",
              "      <td>204</td>\n",
              "      <td>201</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>604</th>\n",
              "      <td>138</td>\n",
              "      <td>140</td>\n",
              "      <td>141</td>\n",
              "      <td>141</td>\n",
              "      <td>143</td>\n",
              "      <td>146</td>\n",
              "      <td>150</td>\n",
              "      <td>151</td>\n",
              "      <td>150</td>\n",
              "      <td>154</td>\n",
              "      <td>...</td>\n",
              "      <td>216</td>\n",
              "      <td>198</td>\n",
              "      <td>197</td>\n",
              "      <td>210</td>\n",
              "      <td>212</td>\n",
              "      <td>207</td>\n",
              "      <td>210</td>\n",
              "      <td>215</td>\n",
              "      <td>213</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>605 rows × 4097 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e12bdd7c-7879-4047-9fc5-c954ae954ced')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e12bdd7c-7879-4047-9fc5-c954ae954ced button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e12bdd7c-7879-4047-9fc5-c954ae954ced');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c2667759-b1ff-46f3-a3c2-acb81b03d65f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c2667759-b1ff-46f3-a3c2-acb81b03d65f')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c2667759-b1ff-46f3-a3c2-acb81b03d65f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       0    1    2    3    4    5    6    7    8    9  ... 4087 4088 4089  \\\n",
              "0    152  154  154  155  154  153  153  154  156  155  ...  173  172  172   \n",
              "1    193  193  192  191  189  191  193  194  193  192  ...   64   55   64   \n",
              "2     60   63   63   62   61   62   62   60   59   63  ...   91   62   76   \n",
              "3     62   62   63   63   60   61   63   63   63   63  ...   66   67   66   \n",
              "4     56   56   53   56   56   59   60   62   63   67  ...   51   50   49   \n",
              "..   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
              "600   74   74   74   73   73   72   72   73   73   73  ...  189  189  189   \n",
              "601  177  181  182  182  183  184  187  187  185  185  ...  186  185  184   \n",
              "602  183  186  190  195  197  193  191  190  189  189  ...  190  190  191   \n",
              "603  109  109  109  108  108  107  108  109  109  107  ...  206  205  206   \n",
              "604  138  140  141  141  143  146  150  151  150  154  ...  216  198  197   \n",
              "\n",
              "    4090 4091 4092 4093 4094 4095 Label  \n",
              "0    173  172  171  172  173  174     4  \n",
              "1     89  125  157  171  167  156     4  \n",
              "2     71   66   67   68   66   65     4  \n",
              "3     64   63   63   64   64   64     4  \n",
              "4     53   52   51   54   52   50     4  \n",
              "..   ...  ...  ...  ...  ...  ...   ...  \n",
              "600  190  192  194  197  198  198     A  \n",
              "601  184  185  188  193  195  195     A  \n",
              "602  190  190  190  191  190  189     A  \n",
              "603  204  201  198  198  198  197     A  \n",
              "604  210  212  207  210  215  213     A  \n",
              "\n",
              "[605 rows x 4097 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get the images with Label = A or 4 from data_with_labels (numpy array)\n",
        "filtered_data = data_with_labels[(data_with_labels[:, -1] == 'A') | (data_with_labels[:, -1] == '4')]\n",
        "\n",
        "# Print the shape of the filtered data\n",
        "print(f\"Filtered data shape: {filtered_data.shape}\\n\")\n",
        "\n",
        "# Convert the NumPy array to a Pandas DataFrame\n",
        "df = pd.DataFrame(filtered_data)\n",
        "df = df.rename(columns={df.columns[-1]: 'Label'})\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7x4jJAdeGbU"
      },
      "source": [
        "## is the Data Balanced?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "QwRB3FxWmowj",
        "outputId": "913fac18-b0d2-4f0d-8214-4607aa9f1372"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Count the number of 'A' and '4' cases\n",
        "label_counts = np.unique(filtered_data[:, -1], return_counts=True)\n",
        "labels = label_counts[0]\n",
        "values = label_counts[1]\n",
        "\n",
        "# Create a figure with one row and two columns for side-by-side plots\n",
        "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot the bar chart on the first subplot (axes[0])\n",
        "axes[0].bar(labels, values, color=['lightblue', 'lightcoral'])\n",
        "for i, v in enumerate(values):\n",
        "    axes[0].text(i, v, str(v), ha='center', va='bottom', fontweight='bold', fontsize=12)\n",
        "\n",
        "axes[0].set_title('Counts of \"4\" and \"A\"')\n",
        "axes[0].set_xlabel('Labels')\n",
        "axes[0].set_ylabel('Count')\n",
        "\n",
        "# Create a pie chart on the second subplot (axes[1])\n",
        "axes[1].pie(values, labels=labels, autopct='%1.1f%%', startangle=90, explode=(0.1, 0), colors=['lightblue', 'lightcoral'],\n",
        "            wedgeprops={'edgecolor': 'black', 'linewidth': 1, 'antialiased': True})\n",
        "axes[1].set_title('Counts of \"4\" and \"A\"')\n",
        "axes[1].axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "# Adjust the layout and display the plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0nE6BmWp61k"
      },
      "source": [
        "## Getting X (feature matrix) and y (Label--> Target)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQy1HODRhNb9",
        "outputId": "bc52f7b6-5674-4597-d12a-d360fa8ab086"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X shape: (605, 4096)\n",
            "y shape: (605,)\n",
            "X dtype: <U21\n",
            "Y dtype: <U21\n"
          ]
        }
      ],
      "source": [
        "X = filtered_data[:, :-1]  # features\n",
        "y = filtered_data[:, -1]   # Labels\n",
        "\n",
        "# Print the shapes of X and y\n",
        "print(\"X shape:\", X.shape)\n",
        "print(\"y shape:\", y.shape)\n",
        "\n",
        "print(\"X dtype:\", X.dtype)\n",
        "print(\"Y dtype:\", y.dtype)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_oceQ3y0Pxv"
      },
      "source": [
        "##Splitting 60% Train, 20% validation , 20% Test\n",
        "#### A 60-20-20 split ratio, with 60% for training and 20% each for testing and validation, is ideal for small datasets. It balances effective model learning with rigorous evaluation, ensuring that the model captures patterns, performs well on unseen data, and can be fine-tuned effectively."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VjmQs3C05JD9"
      },
      "source": [
        "###Use train_test_split (Selecting images at random)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIcSn8HU42SW",
        "outputId": "2d0f6296-8127-45c7-e6c5-d75a3ad51bac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Transformed labels: [1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1\n",
            " 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1\n",
            " 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 0 1 1 1 0 1 1 1 1 1 1 0 1 1 1 1 0 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1\n",
            " 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1\n",
            " 1 1 0 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1\n",
            " 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 1\n",
            " 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 0 1 1 1 1 1]\n",
            "\n",
            "X dtype: float32\n",
            "Y dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Combine X and y into a single array\n",
        "combined_data = np.column_stack((X, y))\n",
        "\n",
        "# Shuffle the combined data\n",
        "np.random.shuffle(combined_data)\n",
        "\n",
        "# Split the shuffled data back into X and y\n",
        "shuffled_X = combined_data[:, :-1]\n",
        "shuffled_y = combined_data[:, -1]\n",
        "\n",
        "shuffled_X,shuffled_y=to_numeric (shuffled_X,shuffled_y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nm1NYVJrr6qE",
        "outputId": "4ff1712c-5b50-4aba-e42a-30cf2ee6ca95"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training samples: 363\n",
            "Number of validation samples: 121\n",
            "Number of test samples: 121\n"
          ]
        }
      ],
      "source": [
        "X_train_random, X_temp_random, y_train_random, y_temp_random= train_test_split(shuffled_X, shuffled_y, test_size=0.4, random_state=42)\n",
        "X_val_random, X_test_random, y_val_random, y_test_random = train_test_split(X_temp_random, y_temp_random, test_size=0.5, random_state=42)\n",
        "\n",
        "# Print the number of samples in each split\n",
        "print(\"Number of training samples:\", len(X_train_random))\n",
        "print(\"Number of validation samples:\", len(X_val_random))\n",
        "print(\"Number of test samples:\", len(X_test_random))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFNGfJ68C04h",
        "outputId": "4ce02ae4-3bf2-4790-bbcd-f61f32f5215e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "y_train_pred_random,y_test_pred_random=LogisticRegression_Model (X_train_random, y_train_random,X_test_random)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjXdycVSSUW1"
      },
      "source": [
        "#### Evaluation on training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GK0UpLbLSUW2",
        "outputId": "ccdbc513-4f1b-496c-8bc9-0d23ca2b1347"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Training Data Metrics:\n",
            "\n",
            "Confusion Matrix:\n",
            "True Positives (TP): 337\n",
            "True Negatives (TN): 26\n",
            "False Positives (FP): 0\n",
            "False Negatives (FN): 0\n",
            "\n",
            "\n",
            "Accuracy for Training data: 100.00%\n",
            "Recall for Training data: 100.00%\n",
            "Precision for Training data: 100.00% \n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "Binary_Evalution(y_train_random, y_train_pred_random,'Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEtgI2rmSUW3"
      },
      "source": [
        "#### Evaluation on testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "7Nij_r2CSUW3",
        "outputId": "461ce59e-4713-4523-bd74-545d7f8be108"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Data Metrics:\n",
            "\n",
            "Confusion Matrix:\n",
            "True Positives (TP): 107\n",
            "True Negatives (TN): 3\n",
            "False Positives (FP): 7\n",
            "False Negatives (FN): 4\n",
            "\n",
            "\n",
            "Accuracy for Testing data: 90.91%\n",
            "Recall for Testing data: 96.40%\n",
            "Precision for Testing data: 93.86% \n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "Binary_Evalution(y_test_random, y_test_pred_random,'Testing')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0GjRJ6mDqZtn"
      },
      "source": [
        "###Use train_test_split(Stratified Sampling)\n",
        "####When dealing with imbalanced data containing different classes, such as 'A' and '4', it's important to use stratified sampling during the train-test split. Stratified sampling ensures that both the training and testing sets have a class distribution that closely matches the overall distribution in the dataset. This helps prevent biases in the machine learning model, ensures fair evaluation, and improves generalization to new data. In my specific case, where I have significantly more 'A' samples than '4' samples, using stratified sampling is crucial to achieving accurate and reliable results in the machine learning tasks.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92WS2fIxvpjY",
        "outputId": "7662ac67-b460-404c-ff32-822a94c8c918"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Transformed labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1]\n",
            "\n",
            "X dtype: float32\n",
            "Y dtype: int64\n"
          ]
        }
      ],
      "source": [
        "X,y_encoded=to_numeric (X,y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ly8kqidlkva",
        "outputId": "e26d651b-218b-465e-8a10-381e70cab29a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training samples: 363\n",
            "Number of validation samples: 121\n",
            "Number of test samples: 121\n"
          ]
        }
      ],
      "source": [
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y_encoded, test_size=0.4, random_state=42, stratify=y)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
        "\n",
        "# Print the number of samples in each split\n",
        "print(\"Number of training samples:\", len(X_train))\n",
        "print(\"Number of validation samples:\", len(X_val))\n",
        "print(\"Number of test samples:\", len(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NVN7FLFCjbf",
        "outputId": "9a91715e-4462-4c79-ac90-6bbd28c3cb87"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1244: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "y_train_pred,y_test_pred=LogisticRegression_Model (X_train, y_train,X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_ShC7tiHGzA"
      },
      "source": [
        "#### Evaluation on training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aqCbgb8kG2tW",
        "outputId": "9f399b33-4dc0-4c88-f6a8-7f456d046cd0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Training Data Metrics:\n",
            "\n",
            "Confusion Matrix:\n",
            "True Positives (TP): 335\n",
            "True Negatives (TN): 25\n",
            "False Positives (FP): 3\n",
            "False Negatives (FN): 0\n",
            "\n",
            "\n",
            "Accuracy for Training data: 99.17%\n",
            "Recall for Training data: 100.00%\n",
            "Precision for Training data: 99.11% \n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "Binary_Evalution(y_train, y_train_pred,'Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V_iobiYsHVQT"
      },
      "source": [
        "#### Evaluation on testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KNLYkUsoHDsD",
        "outputId": "a08675c7-d5d6-40ff-9c17-211507445392"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Testing Data Metrics:\n",
            "\n",
            "Confusion Matrix:\n",
            "True Positives (TP): 106\n",
            "True Negatives (TN): 4\n",
            "False Positives (FP): 6\n",
            "False Negatives (FN): 5\n",
            "\n",
            "\n",
            "Accuracy for Testing data: 90.91%\n",
            "Recall for Testing data: 95.50%\n",
            "Precision for Testing data: 94.64% \n",
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "Binary_Evalution(y_test, y_test_pred,'Testing')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsRM3Qshtdbz"
      },
      "source": [
        "#Result Explanation\n",
        "####When shuffling the data without using stratified sampling, there's a risk of creating training and testing sets with significantly imbalanced class distributions. This randomness can lead to variable model performance, where the model may perform exceptionally well on the training data but poorly on the testing data, especially if it overfits to the majority class.\n",
        "\n",
        "####On the other hand, when using stratified sampling without shuffling, the class balance is maintained in both the training and testing sets. This approach ensures that each class is represented proportionally in both sets, leading to more consistent and reliable model performance metrics. The model trained with stratified sampling is likely to generalize better to unseen data, resulting in more stable and consistent results."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yYMKRQYmUAJi"
      },
      "source": [
        "#1.3 Multiclass Classifier\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q0egUeiykdok"
      },
      "outputs": [],
      "source": [
        "# Define the target classes\n",
        "target_classes = ['4', 'A', 'H', 'u']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K72hxD_yBV4i"
      },
      "source": [
        "##Helper Funtions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Di6qMZgIBV4k"
      },
      "source": [
        "### Fuction to run Multi classfier model given any x_train,y_train_x_test\n",
        "### return y_train_pred,y_test_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TFjcWupfBV4l"
      },
      "outputs": [],
      "source": [
        "def MLC_Model(X_train, y_train, X_test):\n",
        "\n",
        "    mlc = LogisticRegression(multi_class='ovr')\n",
        "    mlc.fit(X_train, y_train)\n",
        "\n",
        "    y_train_pred = mlc.predict(X_train)  # Predict labels for the training data\n",
        "    y_test_pred = mlc.predict(X_test)    # Predict labels for the testing data\n",
        "\n",
        "    return y_train_pred, y_test_pred"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LUWKeYGLBV4l"
      },
      "source": [
        "### Evalution Function that calcualte confusion matrix to calualte accuracy, average recall precision and F1 score given actual y , the predict y and text(Train/Test).\n",
        "### Return accuracy, average recall precision and F1 score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "74t4MXYPj1K0"
      },
      "outputs": [],
      "source": [
        "def MLC_Evalution(y, y_pred,text):\n",
        "  TP = TN = FP = FN= 0\n",
        "\n",
        "  # Calculate confusion matrices  manually\n",
        "  cm = np.zeros((len(target_classes), len(target_classes)), dtype=int)\n",
        "\n",
        "  for i in range(len(y)):\n",
        "    cm[y[i]][y_pred[i]] += 1\n",
        "\n",
        "  # Calculate accuracy\n",
        "  accuracy = np.sum(np.diag(cm)) / np.sum(cm)\n",
        "\n",
        "  # Calculate average recall, precision, and F1 score\n",
        "  recall = np.mean(np.diag(cm) / np.sum(cm, axis=1))\n",
        "\n",
        "  precision = np.mean(np.diag(cm) / np.sum(cm, axis=0))\n",
        "\n",
        "  f1 = 2 * (precision * recall) / (precision +recall)\n",
        "\n",
        "  # Print metrics\n",
        "  print(f\"{text} Set Metrics:\")\n",
        "  print(f\"Accuracy: {accuracy*100:.2f}\")\n",
        "  print(f\"Average Recall: {recall*100:.2f}\")\n",
        "  print(f\"Average Precision: {precision*100:.2f}\")\n",
        "  print(f\"F1 Score: {f1*100:.2f}\")\n",
        "\n",
        "  # Confusion Matrix\n",
        "  cm = confusion_matrix(y, y_pred)\n",
        "  plt.figure(figsize=(4, 3))\n",
        "  sns.heatmap(cm, annot=True, fmt='d', cmap='Pastel2', xticklabels=target_classes, yticklabels=target_classes)\n",
        "  plt.title(f\"{text} Confusion Matrix\")\n",
        "  plt.xlabel('Predicted Labels')\n",
        "  plt.ylabel('Actual Labels')\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "  print('\\n')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofzHHMd4NdW5"
      },
      "source": [
        "## is the Data Balanced?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "jT5oW6-BMLCU",
        "outputId": "af2ed6c9-0da7-4f4d-a24f-7892d87734b1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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ooFKlSggODkZYWNhL+0VHR8PCwgI6Ojpwc3PDp59+ioyMjEJ9mjZtqvp6wYIFCAkJwdKlS3H+/HkAgKenJ2rWrFlix0JERERERC+SSZIkiQ4hWkZGBkxMTJCens7Zy6nEHTp0CM2aNXvpMplMhhUrVmDw4MEAnhXShw8ffmlfHx8fREZGwtDQEACQm5uL0aNHY9WqVVAoFIX6Dhw4EN9++y2srKyK70CIqFzgOZCIiKhkcaSbqJRpaGigWbNmWLx4Mfbt24f169ejVq1aAABJkjB+/HhkZWUBAGxsbDB+/Hhs2LAB+/fvx/Tp06GtrQ0AuHjxIn744QfVdrW1teHu7g5TU9MX9hkSEoKTJ0+W+LEREREREVFhHOkGP+Un8dLS0uDs7Ky6LzskJAQtW7Z8ad8ZM2Zg9uzZAID69evj2LFjAICZM2di1qxZAICPP/4YX331FW7evIlWrVohOTkZ2traiImJgbOzc8kfEBGVGTwHEhERlSyOdBOpAVNTU7i5uaneP3jw4JV969Sp89J+v/32m+rrzz//HEZGRvDz80PXrl0BAHl5edi7d29xxiYiIiIiojdg0U1UyqKjo19oS0tLw/Xr11Xvra2tcf/+fSQmJr7Q99+XiVtbW6u+fvjwoerrJ0+eqL7OzMx8aTsREREREZU8PjKMqJRNmjQJaWlpGDhwIHx9ffHw4UN8//33qtnILSwsUL9+fZw8eRLt2rVDnz590KpVK5iYmODvv//GN998o9pWp06dVF97eXnh7NmzAIARI0Zg0qRJuHnzJjZv3qzq4+/vXzoHSUREREREAHhPNwDez0al63UzkmtpaWHTpk3o3Lnza2c5B4CGDRsiNDQUurq6AIDdu3ejc+fOL8xc/lyLFi0QGhoKmUz2/gdBROUGz4FEb2f+/PmYNm0axo0bV2hCUyKiV+Hl5USl7Ntvv8X48ePh6+sLc3NzaGpqws7ODr1798bJkyfRuXNnAECtWrXw66+/ok2bNnB2doauri4MDAwQEBCA7777DuHh4aqCGwDat2+Pw4cPo3PnzrCxsYGmpib09fXh5+eHOXPmYPfu3Sy4iYiI3sPp06fx66+/wtfXV3QUIipDONINfspPREQVF8+BREXz5MkT1KxZEz///DO++uor+Pv7V+iRbmdnZ4wfPx7jx49Xtfn7+6Nz586YOXOmsFxE6ogj3UREREREbzB69Gi0a9cOQUFBoqMQURnDidSIiIiIiF5jw4YNOHPmDE6fPi06ChGVQSy6iYiIiIhe4e7duxg3blyhyUuJiN4Gi24iAOmzZomOUC6ZzJghOgIREdF7iY6ORkpKCmrWrKlqUygUOHLkCH766Sfk5uZCQ0NDYEIx5HI5/js1VH5+vqA0ROqNRTcRERER0Su0aNECFy9eLNQ2ZMgQeHh4YMqUKRWy4AYAS0tLJCYmqt5nZGQgPj5eYCIi9cWim4iIiIjoFYyMjODt7V2ozcDAAObm5i+0VyTNmzfHqlWr0KFDB5iamuKLL76osB9AEL0Ji24iIiIiInor06ZNQ3x8PNq3bw8TExN8+eWXHOkmegUW3UREREREb+HQoUOiIwhnbGyMDRs2FGobNGiQoDRE6o3P6SYiIiIiIiIqISy6iYiIiIiIiEoIi24iIiIiIiKiEsKim4iIiIiIiKiEsOgmIiIiIiIiKiEsuomIiIiIiIhKCItuIiIiIiIiohLCopuIiIiIiIiohGiKDkBERERERP9PkiRkZmYiJSUFKSkpyM7OhkKhQEFBgeqlUCiQk5OD9PR0WFhYQFNTExoaGqqXlpYWTE1NYWFhAQsLCxgZGUEmk4k+NKIKiUU3EREREVEJUyqVSE1NRXJyMlJSUgr99/nXiUlJSEl5gAcpycjJySnW/WtpacHMzExVhP/3ZW5uDktLS7i6usLFxQVaWlrFun+iioxFNxERERFRMUlLS8PVq1dx9epVXLlyBZevXMGVK1eRcPcOlEplob46unqoZGEJYzNzGJtbwNTZDZUD6sPU3AIm5hYwMbeEiZk5dPT1/xnB1oRcQwNyDQ1oaGpi5uCeePIwGTu2b4dCoVC9lEol8vPzkZmZibS0NKSlpeHx48eqr9PS0pCYmIirV68iPT0djx8/xtOnT1W5NDQ04OTkhKpVq8LNzQ1ubm5wd3eHl5cXHB0dOWJO9JZYdBMRERERvSWlUokbN27g7NmzOHPmDM6ePYfLly8jMfE+AEAmk8HWqTLsXKqiZsu2aONQGSYWFjAxs4CphSVMzCyga2DwXgWsXEMDcpkMBgYG7308ubm5SE1NRUJCAu7cuaN6hYWFYcWKFaqRdyMjI1SvXh3e3t7w8vKCr68v6tatCyMjo/fOQFResegmIiIiInqDW7du4dChQzhz5gyio8/g/IXzyHryBABgZecA5+peaNC5JxyquMGhihvsXKpAR1dPcOqi09HRgZ2dHezs7FCnTp1Cy5RKJRITExEXF4fY2FjExcXh5MmTWL9+PbKzsyGXy+Hn54dGjRqhYcOGaNiwIWxtbQUdCZH6YdFNRERERPQfaWlpiIiIQGhoKEJCQhEXFwuZTAZ7lypwru6NrqPGw9XTBy4eXjCqZCY6bomSy+Wwt7eHvb09GjdurGpXKpW4deuWarR/+/btWLRoEQDAxcWlUBHu4eHBy9KpwmLRTUREREQVXn5+Pk6ePInQ0FAcOBCC06dPQalUwq6yC3zqN0a3CdPgXbcBDIyMRUdVG3K5HK6urnB1dUW3bt0AAA8ePMDZs2dx9uxZREdHY+3atVAqlTA3N0eDBg3QsGFDBAUFwd/fn0U4VRgsuomIiIiowpEkCTExMc9GskNDcejQITzJzISxaSV412uIETO/hl/9xrBycBQdtUyxtLREcHAwgoODAQBZWVm4cOGCqhCfMWMGPv30Uzg4OKBjx47o0KEDmjVrBh0dHcHJiUoOi24iIiIiqhByc3Nx4MAB7NixAyEhoUhIuAtNLS141KiNjkNHw69BY7h4+kBDQ0N01HLDwMAAgYGBCAwMBPDsioKoqCgcPnwYO3bswM8//wxDQ0PVCPiwYcNgYmIiODVR8WLRTURERETllkKhwJEjR7Bu3Tps3rIF6WlpcKziBv8WrTGoQRN41qoHXX190TErDC0tLVURPmXKFMTGxuLQoUP4+eefsX//fnz22WcIbhWMXj17oWPHjjA25uX8VPax6CYiIiKickWSJJw5cwbr1q3D+g0bkHj/PqwdHNGi10A0atcZTtU8REckPHusmpubG7S0tLBo0SIEDgqEdTVrXNh+AQMGDIC2jjZatWqFnj16sgCnMo1FNxERERGVC0lJSVizZg1+WrQIiffvQ9+0Ehq07YSP23eBm19NTtylpsLDwyGXy9FuejsYmhui6aimeJzwGOd3nseFHc8KcF09XfTr2w9jx46Fn5+f6MhEb4VFNxERERGVWXl5edizZw9WLF+Offv3Q1MuR6CjI+4oleg+eiLa9hsiOiK9QWhoKIysjGBobqhqq+RQCU0/aoqmHz0rwE9vOI2tq7Zi+fLlqN+wPj4e8zG6dOkCbW1tgcmJikYuOgARERER0dtKTU3F3Llz4ezkhK5du+Le+fP4pnVrxEyciK39+8PWyAj7/1wpOia9QXJyMi5fvgzP1p6v7FPJoRKCPwnG9HPTMWTVEDzAA/Tu3RuOlR0xc+ZM3L9/vxQTE709jnQTERERUZkRExODH374AatXrYKkUKCXjw9GdO0KL2vrQv36+ftj4bFjeJKRDkNjzoatriIiIiCTyRA0LuiNfTU0NeDX0Q9+Hf2QeDURR5cfxfzv5mPOnDno0rULxo4Zi4YNG/I2AlI7HOkmIiIiIrUmSRLCw8PRvl07eHh4YOu6dZgQGIhL48fjx44dXyi4AaCXnx8USiU2/7xQQGIqqtDQUBhUMoB5ZfO3Ws+2ui16fNcDMy/PRMevOuLImSNo3LgxfPx8sGzZMmRlZZVQYqK3x6KbiIiIiNRSbm4uVq1aBX9fXwQFBeH22bNY0qkTLn78MT5t0gQWBgavXNfNwgI17OxwdNfWUkxMbyM9PR1RUVFwa+L2ztvQM9ZD4xGNMeXEFIz6axTkjnKMGjUKdvZ2mDBhAmJjY4sxMdG7YdFNRERERGolKysL8+fPR2VHRwwZMgS2eXnYMXAg/h4xAv1q1ICOZtHukOzn74/01Ie4GxtTwonpXRw+fBhKpRLNxzZ/723J5XK4N3PH0LVDMf3sdNQeUhu///E73N3dMWTIENy5c6cYEhO9GxbdRERERKQWcnNzsXjxYlRxccEX06ejrZMTTo0ejY19+6KJq+tb36vb1dsbcrkc6374poQS0/sICwuDnpEeHP0di3W7Zo5m6PBFB8y4NAOd53bG1j1b4ebmhokTJ+LBgwfFui+iomDRTURERERCFRQUYNWqVXB3c8P48eMR5OCAqDFjsLBDB1SztHzn7Zrp66N1tWq4dOxwMaal4pCdnY1jx47Bua5zie1DS1cLjUc0xufRnyPokyD88vsvcK3iitmzZyMzM7PE9kv0Xyy6iYiIiEgIpVKJLVu2wMfLC0OGDIG/sTFOjBqFJZ06oXKlSsWyjz5+fnia/RQnQ/cVy/aoeERGRiIvLw9NRzUt8X3pGOog+JNgfH7mc9QeWBtfzf0KrlVcsWjRIuTm5pb4/olYdBMRERFRqZIkCQcOHEDtgAD06NEDDpKEQyNGYHWPHu81sv0ywW5uMNbRwbbffirW7dL7CQsLg46eDtybuZfaPg3NDdHpy0747PRnqNqqKiZMmAA3dzf88ccfUCgUpZaDKh6hRffMmTMhk8kKvTw8PFTLc3JyMHr0aJibm8PQ0BDdunVDcnJyoW3cuXMH7dq1g76+PqysrDB58mQUFBSU9qEQERERURFERUWhaZMmaN26NbQfP8aewYOxpV8/+NvZlcj+tDU10cvXF7euXER+Xl6J7IPeTn5+PiIiImDvZy9k/5UcKqH3ot749NinMPM1w6BBg+Dj54OdO3dCkiQhmah8Ez7S7eXlhcTERNXr6NGjqmUTJkzArl27sHnzZhw+fBj3799H165dVcsVCgXatWuHvLw8REZGYvXq1Vi1ahW++OILEYdCRERERK/w+PFjjBo1CnXq1MGj2Fhs6tsX+wYPRgNn5xLfd28/P+QXFGDXql9LfF/0ZqdPn0ZWVhYafNBAaA4bdxsMWT0EE0InQGGuQKdOnVC/QX0cOXJEaC4qf4QX3ZqamrCxsVG9LCwsADx7bt/y5cuxYMECNG/eHAEBAVi5ciUiIyNx4sQJAEBISAiuXLmCtWvXwt/fH23atMGXX36JJUuWII+fZBIREREJJ0kS/vjjD7i7ueHPVaswr1UrHBo2DMHVqr31bOTvqqa9PVzMzBC66c9S2R+93sGDB6GlrYUaXWuIjgIAqBxQGR9t/wijto7C/ez7aNKkCXr26omUlBTR0aicEF5037hxA3Z2dnB1dUW/fv1Uz9CLjo5Gfn4+goKCVH09PDzg5OSE48ePAwCOHz8OHx8fWFtbq/q0atUKGRkZuHz5cukeCBEREREVcvnyZTRt0gSDBg1CYzs7nProI3xYrx40NTRKNYdMJkN/f388vHcXj5KTSnXfVJhSqURoaCis3K0glwsvRQpxb+qOCeET0P/X/tgfvh/VPavjzz//5CXn9N6E/p9et25drFq1Cvv378fSpUsRHx+PRo0aITMzE0lJSdDW1oapqWmhdaytrZGU9OyXZVJSUqGC+/ny58teJTc3FxkZGYVeRERERFQ8srKyMGXKFPj7++P+tWvYPmAAlnfrBltjY2GZevr6QilJ2LD4O2EZCLh48SIePXqEuv3qio7yUjKZDLV61MKU41Pg3MQZ/fv3R/sO7ZGQkCA6GpVhQovuNm3aoEePHvD19UWrVq2wd+9epKWlYdOmTSW633nz5sHExET1cnR0LNH9EREREVUEkiRh+/btqO7ujkULF2JKo0Y4NnIkmlapIjoaHE1N0cDZGadC94qOUqGFh4dDU1MT9QfXFx3ltQwtDDHw94EY9ucwREZHwtPLE7/99htHvemdqNU1HaampqhWrRpiY2NhY2ODvLw8pKWlFeqTnJwMGxsbAICNjc0Ls5k/f/+8z8tMmzYN6enpqtfdu3eL90CIiIiIKpjk5GR07tQJXbp0QXUDA5wYNQqTmzSBjqam6Ggqff38kJmehpizUaKjVEiSJCEkJASVnCtBU1t9/r94He823vj0+Kfw7OCJESNGoEVQC9y8eVN0LCpj1KrofvLkCeLi4mBra4uAgABoaWkhPDxctTwmJgZ37txBYGAgACAwMBAXL14sNMlBaGgojI2N4enp+cr96OjowNjYuNCLiIiIiN7N7t274ePlhciICKzu2RMb+/SBs5mZ6Fgv6OjpCW0NDWz86XvRUSqk2NhY3Lt3DwHdA0RHeSv6Jvros7gPRv01ChduXIC3jzd++OEHPtubikxo0f3JJ5/g8OHDuHXrFiIjI9GlSxdoaGigT58+MDExwdChQzFx4kREREQgOjoaQ4YMQWBgIOrVqwcACA4OhqenJwYMGIDz58/jwIEDmD59OkaPHg0dHR2Rh0ZERERU7mVlZeHDDz9Ehw4dUNPcHJEffohOnp6lNiv52zLS0UEnT0/ERJ2AUqkUHafCCQ8Ph4aGBpp+2FR0lHfi3swdnx77FLX618LEiRPRsFFDXL16VXQsKgOEFt0JCQno06cP3N3d0bNnT5ibm+PEiROwtLQEACxcuBDt27dHt27d0LhxY9jY2GDr1q2q9TU0NLB7925oaGggMDAQ/fv3x8CBAzF79mxRh0RERERUIURFRaGmvz/+WLkSC9q1w4Y+fWBlaCg61hv18fNDTm4uDu/YIjpKhRMaGgpjW2PoGuuKjvLOdAx10G1+N4zdMxbxD+Lh7++PuXPnIj8/X3Q0UmMyibMBICMjAyYmJkhPT+el5hVU+qxZoiOUSyYzZoiOQERvwHMgvS2FQoH58+dj5syZ8La2xm9dusDNwkJ0rCJTKJVw//57GNg7YcHO8DevoMam9mqPJymJCAkJER3lje7du4fWrVuj0YhG6Da/m+g4xSIvOw8HvjmAiJ8i4OPrgy2btqBq1aqiY5EaUqt7uomIiIhIfcXHx6NJo0b44osvMD4wEKEffFCmCm4A0JDL0dfPD/firiM764noOBVGeHg4ZDIZWnzcQnSUYqOtp40OMzpgfMh4JGUkIaBWAPbs2SM6FqkhFt1ERERE9EZ//vkn/Hx9cTcmBnsGDcL0Fi2gpaEhOtY76e3nhwKFAlt/XSw6SoURGhoKQwtDmNqZio5S7JxqOGF8+HhUrl8Z7du3x6xZszhnABXCopuIiIiIXik/Px/jx49H//790cbVFUdHjEBg5cqiY70XT2treFlb4/D2zaKjVAipqak4f/48PFp4iI5SYvSM9TBkzRC0/awtZs2ahY6dOr7w6GOquFh0ExEREdFLPXz4EK2Cg7Hkp5/wbdu2+LVLFxjrlt1JsP6tn78/HqUkIfF2vOgo5d6hQ4cgSRKCxgeJjlKi5HI5gj8JxvANw3Ho70MIqB2AS5cuiY5FaoBFNxERERG94Ny5c6hVsyYuRkVh+4ABGF6njto+CuxddPfxgUwmw/ofvxEdpdwLCwuDvok+rKtZi45SKjxbemLCwQnI1c5Fnbp1sHHjRtGRSDAW3URERERUyIYNG1A/MBAmCgUihg1DQ2dn0ZGKnZWhIVpUrYpzh8v2DObq7smTJzhx4gRcG7iKjlKqLFws8PGBj1G9TXX07t0bn3zyCQoKCkTHIkFYdBMRERERgGePA5syZQr69OmDDtWqYf/gwXA0NRUdq8T09fNDVtYTnP07QnSUcuvvv/9GQUEBmo9pLjpKqdMx0MGAZQPQeU5nLPxhIVoGt8SDBw9ExyIBWHQTERERER4/fox2bdviu+++w5fBwfi1Sxfoa2uLjlWiWru7w0BbG1uW/ig6SrkVFhYGXQNduNarWCPdz8lkMjQd1RQfbf8IZy6dQY2AGjh9+rToWFTKWHQTERERVXAxMTGoHRCAk0eP4q9+/TC2fv1ydf/2q+hpaaG7tzduXjjLS39LQG5uLo4cOQKnACfRUYSr2qAqJh6cCC1rLTRs2BArVqwQHYlKEYtuIiIiogosOjoaDevXh2ZWFg4NG4ZmVaqIjlSqevv5Ia8gH/v/XCk6Srlz8uRJ5OTkoOHwhqKjqAVTe1OM2TUGAX0CMHToUMybN090JColLLqJiIiIKqiDBw+iaZMmcDYwwL7Bg+FsZiY6Uqmr5+QEBxMT7F+3SnSUcic8PBzautrwbuMtOora0NTRRM8FPdF6Smt89tlnmDZtGiRJEh2LShiLbiIiIqIKaOvWrWjTujXq2Npie//+MNPXFx1JCJlMhn7+/ki5exsZj1JFxyk3FAoFwsLCYONlA7mcJce/yWQytJ7SGp2+7IT58+djzJgxUCqVomNRCeJPABEREVEF8/vvv6NHjx5o7+6ODb17w1BHR3QkoXr7+UGhVGLjT9+LjlJunDlzBhkZGQgcGCg6itpqNroZei3shaVLl+KDDz7gvALlGItuIiIiogpCkiR8/fXXGD58OIbUrInfunaFtqam6FjCuZiZobaDA47v2yk6Srlx8OBBaGpponaf2qKjqLXAQYHov6w/1qxdg169eyE3N1d0JCoBLLqJiIiIKgClUonJkydj6tSp+LRJE3zXrh00eNmvSv8aNZD++BHir1wUHaXMkyQJISEhsKxqCU1+qPNGAd0C8MEfH2Dnrp3o1LkTnj59KjoSFTP+piUiIiIq5woKCvDBBx/g+++/x9dt2uCzZs0qxCPB3kYnT09oyeVYv+hb0VHKvKtXryIlJQW1etYSHaXM8G7jjeEbhuPQkUNo3aY1MjIyREeiYsSim4iIiKgcUygUGDhgAP5cuxa/de2KkXXrio6klkz19NCuenVcPnGUs0m/p/DwcGhoaqDR8Eaio5Qp7k3d8eFfHyL6fDSatWiG1FRO7FdesOgmIiIiKqeUSiVGjBiBjZs2YXnXrujh6ys6klrr4+eHnJwcRPLe7vcSGhoKU3tTaOtri45S5rjUdcFHOz7CjfgbaNSkERITE0VHomLAopuIiIioHJIkCRMmTMCKFSuwpGNHdPLyEh1J7TWvUgWV9PSw/fefRUcps27duoX4+Hj4dfYTHaXMcvB1wOjdo5H4KBENGzfE7du3RUei98Sim4iIiKgcmj59OhYtWoQF7dqhj7+/6DhlgpaGBnr7+eFuzBXk5eSIjlMmhYeHQy6Xo/mY5qKjlGk27jYYu3csMvIz0LBxQ9y/f190JHoPLLqJiIiIypm5c+di7ty5+DI4GB/U5iOb3kZvPz/kKxTY9vsS0VHKpNDQUBhZGcHQ3FB0lDLPvLI5xuwegyxFFlq35eRqZRmLbiIiIqJyZNGiRfj8888xtWlTjK1fX3ScMsfXxgbVLCxw8K/1oqOUOcnJybh8+TI8W3uKjlJumNqbYsTGEYi7FYdu3bshPz9fdCR6Byy6iYiIiMqJ5cuXY9y4cfi4fn1MadJEdJwySSaToX+NGniUlIiUe3dFxylTIiIiIJPJEDQuSHSUcsXW0xZD/hiCQ4cOYfjw4Zxdvwxi0U1ERERUDqxfvx7Dhw/H0Fq1MKtlSz6H+z308PGBJEnYwGd2v5XQ0FAYVDKAeWVz0VHKHbdGbuizpA9Wr16NGTNmiI5Db0lTdAAiIiIiej/h4eEYOHAgevn64tu2bVlwvydbY2M0cXVF1MEQ0VHKjPT0dERFRcGvE2ctLykB3QPw+N5jfDnrSzg5OWHYsGGiI1ERcaSbiIiIqAy7evUqunXtisbOzljcsSPkcv55Vxz6+vvjSWYGLp2KFB2lTDh8+DCUSiWaf8xZy0tSi49boOHQhvjwww+xd+9e0XGoiPhbmYiIiKiMSklJQbs2bWCnp4eV3btDS0NDdKRyo52HB/S0tLB5yQLRUcqEsLAw6BnpwdHPUXSUck0mk6Hr/K7wDPZEj549EB0dLToSFQGLbiIiIqIyKCcnB507dkTWo0fY2KcPTHR1RUcqVwy0tdHZyws3zkRBqVSKjqPWsrOzcezYMTjXdRYdpUKQa8gx4LcBsKpuhbbt2iI+Pl50JHoDFt1EREREZYwkSfhgyBCcO3sW63v1gpOpqehI5VIfPz/k5uchdNNa0VHUWmRkJPLy8tB0VFPRUSoMbX1tDFs3DDAAWrVphdTUVNGR6DVYdBMRERGVMd988w3Wb9iAnzt2RICDg+g45VbDypVhY2SEvWuWi46i1sLCwqCjpwP3Zu6io1QohhaGGL5pOJIeJqFjp47Izs4WHYlegUU3ERERURmye/duTJs2DZ80bowu3t6i45Rrcrkc/fz9kRQfhycZ6aLjqKX8/HxERETA3s9edJQKydLVEkPXD0XUmSgMGDiAz/BWUyy6iYiIiMqIq1evom+fPmjj7o7PmjYVHadC6OXriwKlElt+/qFQ+8bF36Gbh12h19g2jV67rayMdPw2exqGNvJHLx9njGnVENGHw1XLj+zaihFNAzCwTnWsnDez0LopCXcxplVDPH2S+crt//777/Dx8cHXX3/9yj75+flYunQp2rRpg4CAAHTr1g1Hjx4t1Gf37t0ICgpC/fr18c033xRadu/ePbRv3x5PnjwBAJw+fRpZWVloMLTBa4+dSo5zLWf0X9Yff235C4sWLRIdh16Cz+kmIiIiKgMyMjLQqUMHOBgY4NcuXfhosFJSzdIS/nZ2OLp7KwZPnVFomaObO2as2Kh6r6H56tnj8/PyMOuD3jAxt8DkH5fBzMoWD+4nwMDYGACQ8TgVS6d/gjHzFsLasTLmjBwAn3oNUKtZSwDAstnT0H/SZ9A3NHrp9i9duoQtW7agWrVqrz2exYsXY8+ePZgxYwZcXFwQGRmJ8ePHY82aNahevToeP36MmTNn4quvvoKDgwNGjx6NunXrokmTJgCAr776CuPHj4ehoSEA4ODBg9DS1kKNLjXe8J2kkuTbzhdNPmyCTz/9FI0aNULNmjVFR6J/4W9rIiIiIjUnSRI+HDkSyffvY12vXjDS0REdqULp5++PtIcPcDc2plC7hoYGKllaqV7GlcxfuY2DWzfgSXoapvy0Ah4168DKwRFedQLh7OEFAEi+ewf6RkZo0LYTqvr4w7tufSTcvAEA+Hv3NmhqaqJecNuXbvvp06eYOnUqZsyYAeN/ivhX2b17N4YNG4bGjRvD0dERvXr1QqNGjbB69WoAQEJCAgwNDdG6dWt4e3ujdu3auHnzJgBg79690NTURFBQEABAqVQiNDQUVu5W/BBIDXSY0QE21W3Qs3dPZGa++ooIKn386SAiIiJSc6tWrcL6DRuwsG1buJiZiY5T4XT18oJcLsf6H78t1J54Ox7DGtXAqKB6+OGT0XhwP+GV2zh9MATu/gH4bfZn+KCBL8Z3aIa/flkEhUIBALCt7ILc7GzcvHIRmWmPEXvxPCpX88ST9DRsWPQthv1vziu3PWfOHDRq1AiBgYFvPJa8vDzo/OdDGx0dHZw9exYA4OTkhJycHFy9ehXp6em4dOkSqlWrhvT0dPz000/4/PPPVetdvHgRjx49Qt1+dd+4Xyp5mjqaGPD7ANxLvIfRY0aLjkP/wsvLiYiIiNTYtWvXMGb0aPSvUQPdfHxEx6mQzA0M0MrNDYeOHVK1ufnVxJh5P8DOpQoep6Rg85LvMb1/F/ywMwJ6/1x6/W/Jd2/j0oljaNShCz7/dS2S7sRj2azPoCjIR88xk2BoYoqx83/E4injkJebg6aduqNGo6ZY8vlEtOk3BMkJdzH/o8EoKChAr9GTENi6PYBnz8i+cuUKNmzYUKRjqV+/Pv744w8EBATA0dERJ06cQHh4uKr4NzExwZw5c/DZZ58hNzcXHTt2RIMGDfDFF1+gT58+SEhIwNixY1FQUABHR0doamqi/uD67/9NpmJhWcUS3b/rjjUfrkHLoJYYMGBAie5v8ODBSEtLw/bt2wu1Hzp0CM2aNcPjx49hykcasugmIiIiUlc5OTno3bMnHIyM8HWbNqLjVGh9/P2xNyYGJ8P2o25Qa9Rs3Fy1zNndE9X8auDD5nVwbP9OBHXv+8L6klKCibk5Ppz9LTQ0NFDF2xepyUnYsWIpeo6ZBACo27IN6rb8/3/ny6eO43bMVQyb/hVGBzfAhO9/hqmFJab2bAfP2vWQn5eHjIwM/P777y+MXr/K1KlTMXPmTHTs2BEymQyOjo7o1KlToaKpRYsWaNGiher96dOncf36dUybNg3t2rXD119/DXNzc3Tu3Blmlc2gqc2SQp3U6lkLMYdi8OGoD1G3bt033udPJY8/IURERERq6tNPP8W1a9cQNnQoDLS1Rcep0ILd3GCso4Ntv/2EukGtX1huYGwCW2dXJN2+9dL1K1laQUNLExoa/z/ZmkMVN6Q9SEF+Xh60/vPvm5+Xi2Wzp2Hc14uQeOcWFIoCeNV5dvm4rbMrrp8/g9zsp1AqlejVq5dqPYVCgejoaKxfvx7R0dGF9gcAZmZmWLRoEXJzc5GWlgYrKyssXLgQDq943nteXh7mzJmDuXPn4s6dO1AoFKhduzZu3LgBSZJQOaBykb5/VLq6f9Mdd07fQa/evXDi+IkifyhDJYP3dBMRERGpoZ07d2Lx4sX4MigIPjY2ouNUeDqamujp64tbly8gPy/vheXZWVlIvnsblSytXrq+R83aSLp9C0qlUtV2/9ZNVLK0fqHgBoAtS39EjYbN4OrlC6VCAeU/l38DgKIgH0qlAvqGRrCwsMDmzZtVLy8vL7Rr1w6bN29+oeAudDw6OrC2tkZBQQHCwsLQrFmzl/b79ddf0aBBA3h6ekKpVKKgoAAAEB7+7FFnXsFer9wHiaNjqIMBywfg0uVLmDp1qug4FR5HuomIiIjUTEJCAoYMHoy2Hh4YXqeO6Dj0j95+fvj99GnsXv0bMh49RK1mwbC0c8CjlCRs/Ok7yOVyNGzfBQCwaMrHMLOyQf9JnwEAWvUZiH1/rsSKOf9D2/4fIPF2PLb+ughtBwx9YT93Y6/j2N6d+G5bCADA3rUqZDIZwrasQyULK9y7GYeqPv6Qa2hAS0sLbm5uqnX19PRgamqqavvss89gZWWF8ePHAwAuXLiAlJQUuLu7IyUlBUuXLoVSqcSQIUNeyBEXF4cDBw5g06ZNAAAXFxfI5XJs3boV27ZtAwBUaVClmL67VNwcfB3QYWYH/PDZD2jRogXat29fIvvZvXu36hFyzyn+9SERsegmIiIiUisKhQL9+/WDniThp3/uuyX1EGBvD+dKlRC6cQ2q+vpj4aSPkJn2GMZm5qgeUBvzNu6Gidmzx4Y9vH8PMtn/X1RqYWuP//2+Divnz8TETkEws7ZBuwHD0Hl44VmmJUnCL19MxuCpM6Crrw8A0NHVw5h5P+C3Lz9DQV4ehv3vK5hb2xYpc2JiYqH/h3Jzc7F48WIkJCRAX18fjRo1wty5c1941JgkSZg1axYmT54M/X9y6Orq4quvvsLs2bORkpICt8ZuMLUzfevvI5WexiMb48bhGxg0eBAunL8Ae3v7Yt9Hs2bNsHTp0kJtJ0+eRP/+/Yt9X2WVTJIkSXQI0TIyMmBiYoL09PQ3PtuQyqf0WbNERyiXTGbMEB2BiN6A50D1M2/ePEyfPh27Bg5EA2dn0XHoP747cgRzIyKw7MjZV15KXlqm9mqPJymJCAkJKdX9rlmzBt9++y1mXJzBorsMeJL6BN81/g6+1XxxMPzga287eFucvbxoeE83ERERkZq4fv06Zs2cibGBgSy41VRPX18oJQkbFn375s7lVGhoKAwtDFlwlxGG5obov6w/jv59FHPnzhUdp0Ji0U1ERESkBiRJwojhw2FnbIypTZuKjkOv4GRqivqVK+NUyB7RUYRITU3FuXPn4NHCQ3QUegtVG1RFy0ktMWvWLJw/f150nAqHRTcRERGRGlixYgUOHzmChW3aQE9LS3Qceo2+/v7ISE/D9fPRoqOUukOHDkGSJASNDxIdhd5Sy0ktYeVmhREjR3Cis1LGopuIiIhIsKSkJHwyaRL6+PujaRXOBq3uOlavDm0NDWxc/L3oKKUuLCwM+ib6sK5mLToKvSVNbU30WNADp06ewrJly4plm6tWrXrhfm4AaNq0KSRJ4v3c/2DRTURERCTY+HHjoKFQ4KuWLUVHoSIw1tVFR09PXDt9vNBzt8u7J0+e4MSJE3Bt4Co6Cr0j13quCBwQiKnTpiIpKUl0nAqDRTcRERGRQHv27MHGTZswLzgY5gYGouNQEfXx80NObi6O7PxLdJRSc/ToURQUFKD5mOaio9B7aD+zPaAFjJ8wXnSUCoNFNxEREZEgT548waiRI9HCzQ09fHxEx6G30MTFBRYGBti58lfRUUpNWFgYdA104VqPI91lmUElA3T4sgM2bthY6o+bq6hYdBMREREJMn36dDx88ADft20LmUwmOg69BU0NDfTx88O92BhkZz0RHafE5ebm4vDhw3AKcBIdhYpBrZ61UK1xNYwcNRI5OTmi45R7LLqJiIiIBIiKisKiRYvwWZMmcK5USXQcege9/fxQoFBg228/iY5S4k6ePImcnBw0HN5QdBQqBjKZDN2+7Ya7d+5iwYIFouOUeyy6iYiIiEqZJEmYMG4cPK2tMapePdFx6B15WVvD09oaEVs3iY5S4sLDw6Gtqw3vNt6io1AxsXazRuORjTFn7hzcu3dPdJxyjUU3ERERUSnbtWsXjkZGYnZQEDQ1NETHoffQz98faQ+SkXg7XnSUEqNQKBAWFgYbLxvI5SwfypPgycHQ1NfElKlTREcp1/hTQ0RERFSKCgoKMGXyZDStUgXN+UzuMq+797OR3w2LvhWcpOScPXsWGRkZCBwYKDoKFTM9Yz20nd4Wf679E8ePHxcdp9xi0U1ERERUilasWIFr169jVlAQJ08rB6yNjNC8ShWcPRQmOkqJCQ8Ph5aWFmr3qS06CpWAOn3rwMnPCWM+HlOhnjtfmlh0ExEREZWSJ0+eYMb//oeevr7ws7UVHYeKSV9/f2RlPcG5o4dERyl2kiQhJCQEFlUtoKmpKToOlQC5hhyd53XGmagzWL16teg45RKLbiIiIqJSsmDBAjx69AjTmzcXHYWKURt3d+hraWHz0h9ERyl2V69eRUpKCmr1rCU6CpUg13quCOgWgGmfT+MjxEoAi24iIiKiUpCcnIxvvv4aI+vUgZOpqeg4VIz0tLTQ3ccHNy+cRUFBgeg4xSo8PBwamhpoNLyR6ChUwlpPbY0HyQ+wYsUK0VHKHbUpuufPnw+ZTIbx48er2nJycjB69GiYm5vD0NAQ3bp1Q3JycqH17ty5g3bt2kFfXx9WVlaYPHlyuftlR0RERGXfzJkzoQ1gUiMWL+VRbz8/5OXnY/+68nV5bmhoKEztTaGtry06CpUwyyqW8O/ij3lfz0N+fr7oOOWKWhTdp0+fxq+//gpfX99C7RMmTMCuXbuwefNmHD58GPfv30fXrl1VyxUKBdq1a4e8vDxERkZi9erVWLVqFb744ovSPgQiIiKiV4qJicFvv/2GiQ0bwlRPT3QcKgGBTk5wMDHB/nUrRUcpNrdv30Z8fDz8OvuJjkKlpOWElki4k4C1a9eKjlKuCC+6nzx5gn79+uG3335DpUqVVO3p6elYvnw5FixYgObNmyMgIAArV65EZGQkTpw4AQAICQnBlStXsHbtWvj7+6NNmzb48ssvsWTJEuTl5Yk6JCIiIqJC/jd9OuxNTDC8Nmd/Lq9kMhn6+fsj5c4tZDx+JDpOsQgPD4dcLkfzMZyDoKKw9bSFb3tfzJk3BwqFQnScckN40T169Gi0a9cOQUFBhdqjo6ORn59fqN3DwwNOTk6qZ8gdP34cPj4+sLa2VvVp1aoVMjIycPny5dI5ACIiIqLXuHHjBrb89Rcm1q8PXS0t0XGoBPX284NCqcSmn74XHaVYhISEwMjKCIbmhqKjUClqObEl4m7EYfPmzaKjlBtCi+4NGzbgzJkzmDdv3gvLkpKSoK2tDdP/TDRibW2NpKQkVZ9/F9zPlz9f9iq5ubnIyMgo9CIiIiIqCd9++y0sjYzQ24+X6JZ3LmZmqO3ggMh9O0VHeW/Jycm4fPkyPFt7io5CpczR3xHVW1THl3O+5HO7i4mwovvu3bsYN24c/vzzT+jq6pbqvufNmwcTExPVy9HRsVT3T0RERBVDUlIS/li9GqNq1+YodwXRz98f6Y9SEX/lkugo7yUiIgIymQxB44Le3JnKnZaTWuLKpSvYubPsf4CkDoQV3dHR0UhJSUHNmjWhqakJTU1NHD58GIsWLYKmpiasra2Rl5eHtLS0QuslJyfDxsYGAGBjY/PCbObP3z/v8zLTpk1Denq66nX37t3iPTgiIiIiAD/++CO05XIMqcVnHFcUnb28oCWXY8Oib0VHeS+hoaEwqGQA88rmoqOQAK71XOHW0A1ffvUlJEkSHafME1Z0t2jRAhcvXsS5c+dUr1q1aqFfv36qr7W0tBAeHq5aJyYmBnfu3EFgYCAAIDAwEBcvXkRKSoqqT2hoKIyNjeHp+epLYXR0dGBsbFzoRURERFSc0tPT8fOSJRhSsyZnLK9ATPX00NbDA5dP/C06yjtLT09HVFQU3Jq4iY5CAgVNDMKZ6DMICQkRHaXM0xS1YyMjI3h7exdqMzAwgLm5uap96NChmDhxIszMzGBsbIyxY8ciMDAQ9erVAwAEBwfD09MTAwYMwDfffIOkpCRMnz4do0ePho6OTqkfExEREdFzv/76K3KyszHqn79bqOLo4+eHHVeuIHLfTtRv01F0nLd25MgRKJVKNP+Ys5ZXZNWaVINzLWfM+nIWgoODIZPJREcqs4TPXv46CxcuRPv27dGtWzc0btwYNjY22Lp1q2q5hoYGdu/eDQ0NDQQGBqJ///4YOHAgZs+eLTA1ERERVXS5ubn4YcEC9PLxgS2vqKtwWlStClM9PWz7/WfRUd5JaGgo9Iz04OjHeY8qMplMhpaTWuL4seM4cuSI6DhlmrCR7pc5dOhQofe6urpYsmQJlixZ8sp1KleujL1795ZwMiIiIqKiW7NmDZJSUvBxjx6io5AAWhoa6O3ri+VRUcjLyYF2KU8a/D6ys7Nx7NgxVGlcRXQUUgOewZ5w8HbAl199iSZNmoiOU2ap9Ug3ERERUVmjUCjw7ddfo52HB9wsLETHIUF6+/khX6HA9uVLRUd5K5GRkcjLy0PTUU1FRyE1IJPJEDQxCOFh4Th58qToOGUWi24iIiKiYrR3715cj43FuPr1RUchgfxsbeFmYYGDW9aJjvJWwsLCoKOnA/dm7qKjkJrw7eAL6yrWWLx4segoZRaLbiIiIqJi9NuyZfC3t0dtR94PW5HJZDL09/dHatJ9PEi8JzpOkeTn5yMiIgL2fvaio5AakWvIUbtfbfy19S+kp6eLjlMmsegmIiIiKiaJiYnYu28fBvj7i45CaqCHry8kScKGH78RHaVIoqKikJWVhQZDG4iOQmqmVs9ayMvNw+bNm0VHKZNYdBMREREVk9WrV0NLLke3/zwWlSomO2NjNHZ1RXT4AdFRiiQ8PBxa2lqo0aWG6CikZkztTOHRzAMrVq0QHaVMYtFNREREVAwkScLy335Dx+rVYaqnJzoOqYm+fn7IzMzA5dMnREd5LaVSidDQUFi5W0EuZ4lAL6rVpxaOHzuO69evi45S5vAnioiIiKgYHDlyBLE3b2JgzZqio5AaaV+9OnQ1NbFpyfeio7zWxYsX8ejRI9TtV1d0FFJTPm19oG+ij9WrV4uOUuaw6CYiIiIqBsuXL4erhQUaVK4sOgqpEQNtbXTx8sKNM6ehVCpFx3ml8PBwaGpqov5gzrpPL6elqwX/rv5Y9ccqKBQK0XHKFBbdRERERO8pLS0NmzdtQn8/P8hkMtFxSM309vNDbl4ewjer5+PDJElCSEgIzJzNoKmtKToOqbG6fevifsJ9HDx4UHSUMoVFNxEREdF7Wr9+PfLz89HHz090FFJDjZydYWNkhN1rfhcd5aXi4uJw79491OzOWyPo9ZxqOsHW3RYrV60UHaVM4UdZRERERO9p+W+/oaWbG2yNjUVHITUkl8vR188PiyIj8fRJBvQN1ev/k7CwMGhoaKDph01FR3lnoQtDcWH3BaTcSIGWrhac6zijw4wOsHazVvXZOGEjrh++joykDGgbaMOljsuzPtWsX7ldSZKwb94+nFhzAtnp2XCp64Ie3/WAZRVLAEBBbgE2jNuAi3svwtjaGN2/7Q73pu6q9Q8uOojH9x6j29fdSu7gS5FMJkOtPrWwdf5WpKWlwdTUVHSkMoEj3URERETv4cKFC4g+exYDavAxS/Rqvfz8UKBUYvOSH0RHeUFoaCiMbY2ha6wrOso7izsWh4ZDG2L8gfEYtXUUlPlK/NLtF+Rm5ar6OPo5ou9PfTH1xFR8uOVDSJKEpd2WQql49b324YvCcWTZEfT4vgcmhE6Atr42fun+C/Jz8gEAkasjcffcXYw/MB6BAwOxZsQaSJIEAEi9nYrja46j3eftSvbgS1mtHrWQn5ePTZs2iY5SZrDoJiIiInoPW7Zsgam+PoLd3ERHITXmbmkJP1tb/L3rL9FRCrl37x6uX78O77Zl+9nyH275EHX71oVtdVvYe9uj75K+eJzwGAnnE1R96g+ujyr1q8DcyRyOfo5o93k7pN1Lw6M7j166TUmScOSXIwieFAyftj6w87JDv6X9kJ6Ujot7LgIAkq8nw7uNN2yr26LhsIZ48vAJslKzAACbJ21GhxkdyvSHGS9jYmsCj+YevMT8LbDoJiIiInoP27duRXDVqtDS0BAdhdRcP39/pD18gLtxN0RHUTl48CBkMhlajG0hOkqxys7IBgDom+q/dHluVi5O/nkS5pXNYWpv+tI+qbdTkZGcgWpNq6na9Iz1UDmgMm6dvgUAsPO2w80TN5GXnYdrB6/B2MYYBuYGiNocBU1dTfi29y3W41IXdfrWwYnjJxATEyM6SpnAopuIiIjoHcXFxeHi5cto5+EhOgqVAd28vSGXybDhx29ER1EJDQ2FoYXhKwvPskipVGLbZ9vgUtcFtp62hZYdXX4Unzp+iimOU3A1/CpGbR31yhnbM5MzAQBGlkaF2o0sjZCRkgEAqNevHuy97TE/cD5CF4Ri8IrBeJr2FPvm7UO3+d2wZ84efBXwFZZ2W4q0+2nFf7CCeLX2goGpAZ/ZXUQsuomIiIje0fbt26GrpYWgqlVFR6EywNzAAMHVquHC0QjRUQAAqampOHfuHDxalK8PjbZM3oLEq4kY9PugF5YF9AjA5EOTMXb3WFhWscSqD1ap7s9+FxpaGuj+bXd8ce4LTAqfBNd6rtgxfQcaj2iMexfv4eKei5h8ZDKcazlj69St73NYauX5M7vXrluruoedXo1FNxEREdE72vbXX2hWpQoMtLVFR6Eyoo+fH54+fYrTB0NER8GhQ4cgSRKCxgeJjlJstny6BVcOXMGYnWNeOnqvZ6wHyyqWqFK/CoasGoKUGym4sOfCS7dlZP1shDvzQWah9swHmTC2evkM9Df+voGkmCQ0Gt4IN47egGdLT+gY6MC/sz9ij8W+38GpGa9WXrh7+y6uX78uOoraY9FNRERE9A6Sk5MReeIE2ru7v7kz0T9aVasGIx0dbP11kegoCAsLg76J/msfmVVWSJKELZ9uwcU9FzF6x2iYVzYvwkrP1ivILXjpYvPK5jC2NsaNw/9/D35ORg5uR9+Gc23nF/rn5+Rjy+Qt6LmgJ+QackgKCYp8BQBAUaB47SzpZVGV+lWgqa2JAwcOiI6i9lh0ExEREb2DnTt3QgagdbVqb+xL9JyOpiZ6+vjg1uULyM/LE5bjyZMnOHHiBFwbuArLUJy2TN6CqE1RGLBsAHQMdZCRnIGM5AzkZT/7Hj+89RChC0Nx99xdPE54jPiT8Vg5ZCW0dLXg2dJTtZ25defiwu5nI98ymQyNP2yMkO9DcGnfJdy/ch9rP1oLExsT+LTzeSFDyHch8GzpCQdfBwCAS10XXNh9Afcv38fR347CtW75+F4/p2OggyqBVbD/wH7RUdTey2cNICIiIqLX2rZ1K+o7O8PcwEB0FCpjevv5YXlUFPas+R2dh34kJMPRo0dRUFCAZqObCdl/cTu24hgA4KcOPxVq7/NTH9TtWxdaOlq4efwmDv9yGNlp2TCyNEKV+lUwbv+4QhOlpdxIUc18DgAtPm6BvKw8bJywEdnp2XCt54qRm0dCS1er0H4SryTi7PazmHx4sqrNr5MfYo/FYlHbRbBys8KAZQNK4tCFqtasGsK+CUNubi50dHREx1FbMol3viMjIwMmJiZIT0+HsfHL78+g8i191izREcolkxkzREcgojfgOfDdZGRkwNLCArNbtMCH9eqJjkNljCRJ8F+0CDmGxvg59Pg7b2dqr/Z4kpKIkJC3vz/8k08+weGjhzH/7vx33j/RvUv38G3jbxEeHo7mzZuLjqO2eHk5ERER0Vvat28f8vLz0ZaPCqN3IJPJ0N/fHw8T7uDxg5RS339ubi4OHz4MpwCnUt83lS92XnYwsTLhfd1vwKKbiIiI6C3t27cPPnZ2cDI1FR2Fyqievr5QSBI2/vRdqe/75MmTyMnJQcPhDUt931S+yGQyuDVzw74D+0RHUWssuomIiIje0uGICDR04ighvbvKlSoh0MkJJw/sKfV9h4eHQ1tXG95tvEt931T+eDT3wMXzF5GUlCQ6itpi0U1ERET0Fu7cuYNbd+6gQeXKoqNQGdfX3x8ZaY8Re+Fsqe1ToVAgLCwMNl42kMtZCtD7c2/67LGJoaGhgpOoL/6kEREREb2FI0eOAAACOdJN76mTpye0NTSwYXHpXWJ+9uxZZGRkIHBgYKntk8o3I0sjOPk58b7u12DRTURERPQWjhw5Ak8bGz4qjN6bsa4uOlSvjqunIqFUKktln+Hh4dDS0kLtPrVLZX9UMbg1c8P+kP2l9v9xWcOim4iIiOgtHI6IQH1HR9ExqJzo4+eHnNxc/L1rW4nvS5IkhISEwKKqBTQ1NUt8f1RxeDT3QOqDVJw7d050FLXEopuIiIioiBITE3E9Npb3c1OxaerqCnN9fexcubTE93X16lWkpKSgVs9aJb4vqlhc6rhAx0DnnZ4ZXxGw6CYiIiIqor///hsAUJ9FNxUTTQ0N9PHzQ8KNGOQ8fVqi+woPD4eGpgYaDW9UovuhikdTWxNVG1bF/gP7RUdRSyy6iYiIiIro8OHDqGplBWsjI9FRqBzp7eeHAoUCW5ctLtH9hIaGwtTeFNr62iW6H6qYqjaoilOnTkGhUIiOonZYdBMREREV0ZFDh1DfwUF0DCpnvG1sUN3KCoe2bSqxfdy+fRvx8fHw7+xfYvugis3O2w7ZT7MRFxcnOoraYdFNREREVASpqam4dOUK7+emEtHP3x+PU5KQdOdWiWw/PDwccrkczcY0K5HtE9l52QEALly4IDiJ+mHRTURERFQE0dHRAIDaHOmmEtDDxwcAsOHHb0tk+6GhoTCyMoKhuWGJbJ/IyNIIptamOH/+vOgoaodFNxEREVERnDt3Dka6unCuVEl0FCqHrI2M0LxKFZw5HFrs205OTsalS5fg2dqz2LdN9G82njY4f4FF93+x6CYiIiIqgnPnzsHL2hpyOf98opLR198fWU+e4HzkkWLdbkREBGQyGYLGBRXrdon+y87bDufOnxMdQ+3wrEFERERUBOeio+FjZSU6BpVjbdzdoa+lhc0/LyzW7YaGhsKgkgHMK5sX63aJ/svOyw53b99Fenq66ChqhUU3ERER0Rs8ffoUMbGx8LGxER2FyjE9LS109/FB3PkzKCgoKJZtpqenIyoqCm5N3Yple0SvY+fNydRehkU3ERER0RtcuXIFSqUSXtbWoqNQOdfbzw95+fk4sH51sWzvyJEjUCqVaD62ebFsj+h1rKpaQVNLk0X3f7DoJiIiInqDy5cvAwDcLS0FJ6Hyrp6jI+xNTLD/z5XFsr2wsDDoGenB0c+xWLZH9Dqa2pqwcbfhDOb/waKbiIiI6A2uXLkCJzMzGOroiI5C5ZxcLkc/f38k37mFzLTH77Wt7OxsHD16FM51nYsnHFER2HjZ4Oz5s6JjqBUW3URERERvcOXyZXiYcxIqKh29fX2hUCqx6afv32s7kZGRyMvLQ9NRTYsnGFER2HvZ4/Kly1AoFKKjqA0W3URERERvcOXSJV5aTqXG1dwctRwccGzvjvfaTlhYGHT0dODezL2YkhG9mZ2XHbKfZiMuLk50FLXBopuIiIjoNbKzsxF/5w6qWViIjkIVSD9/f6Q/SsWta1feaf38/HxERETA3s++mJMRvR5nMH8Ri24iIiKi17hz5w4kSYKLmZnoKFSBdPHygqZcjvU/fvNO60dFRSErKwsNhjYo5mREr2dkaQQTKxNOpvYvLLqJiIiIXiMhIQEAYG9sLDgJVSSmenpo6+GBy8f/fqf1w8PDoaWthRpdahRzMqI3M3c2x+3bt0XHUBssuomIiIhe4969ewAAWyMjwUmoounj54fsnGxE7tv5VusplUqEhobCyt0Kcjn/3KfSZ2hliKTkJNEx1AZ/ComIiIheIyEhAeaGhtDV0hIdhSqYoKpVYaqri+2///xW6128eBGPHj1C3X51SygZ0esZWrLo/jcW3URERESvkZCQADteWk4CaGlooJefH+5cu4y8nJwirxceHg5NTU3UH1y/BNMRvZqRpRGSk5NFx1Ab71R0u7q6IjU19YX2tLQ0uLq6vncoIiIiInWRcPcu7AwNRcegCqqPnx/yFQrsXPlLkfpLkoSQkBCYOZtBU1uzhNMRvZyxtTFSH6RCqVSKjqIW3qnovnXr1ksfdp6bm6u674mIiIioPEi4c4eTqJEwfra2qGpujvDN64rUPy4uDvfu3UPN7jVLOBnRqxlZGkGhULx0oLYiequPv3bu/P9JHA4cOAATExPVe4VCgfDwcDg7OxdbOCIiIiLREu7dQzs/P9ExqIKSyWToX6MGZoeHIzUpEeY2tq/tHx4eDg0NDTT9sGnpBCR6CSPLZxNPJicnw9LSUnAa8d6q6O7cuTOAZz/8gwYNKrRMS0sLzs7O+P7774stHBEREZFIubm5eJCaynu6SagePj6YFRaG9T9+jTHzfnht35CQEBjbGkPXWLd0whG9hJHV/xfd3t7egtOI91ZF9/Nr8l1cXHD69GlYWFiUSCgiIiIidXD//n0AgB0fF0YC2ZuYoJGLC6LC97+2371793D9+nU0GtGolJIRvdy/R7rpHe/pjo+PZ8FNRERE5V5CQgIAwJYj3SRYX39/ZGZk4GrUyVf2OXjwIGQyGVqMbVGKyYhepGOoAx19HRbd/3jnKQ3Dw8MRHh6OlJSUF2alW7FixXsHIyIiIhItLS0NAGCqy0t1Saz2Hh7Q1dTExiXfY+bKTS/tExoaCkMLQ5jam5ZuOKKXMLY0ZtH9j3ca6Z41axaCg4MRHh6Ohw8f4vHjx4VeREREROVBVlYWAMBAW1twEqroDHV00NnLC9ejT730MUyPHj3CuXPn4NHCQ0A6ohcZWhmy6P7HO410//LLL1i1ahUGDBhQ3HmIiIiI1MbzoltfS0twEqJnz+zecP48Dv61HkE9+hVadujQIUiShKDxQYLSERVmYGmApOQk0THUwjuNdOfl5aF+/frvvfOlS5fC19cXxsbGMDY2RmBgIPbt26danpOTg9GjR8Pc3ByGhobo1q3bC5+W3LlzB+3atYO+vj6srKwwefJkFBQUvHc2IiIioqysLOhpa0Muf6c/mYiKVUNnZ1gbGmL36t9fWBYaGgp9E31YV7MWkIzoRUaWRiy6//FOZ5Bhw4Zh3bp1771zBwcHzJ8/H9HR0YiKikLz5s3RqVMnXL58GQAwYcIE7Nq1C5s3b8bhw4dx//59dO3aVbW+QqFAu3btkJeXh8jISKxevRqrVq3CF1988d7ZiIiIiLKysqDPS8tJTWjI5ejr74/Emzfw9EmGqv3Jkyc4ceIEXBu4CkxHVJiRpRGSklh0A+94eXlOTg6WLVuGsLAw+Pr6Qus/l1wtWLCgSNvp0KFDofdz5szB0qVLceLECTg4OGD58uVYt24dmjdvDgBYuXIlqlevjhMnTqBevXoICQnBlStXEBYWBmtra/j7++PLL7/ElClTMHPmTGjzJElERETvISsri/dzk1rp5euLhUePYsvSH1VtR48eRUFBAZqNaSYwGVFhOoY6yM7OFh1DLbzTSPeFCxfg7+8PuVyOS5cu4ezZs6rXuXPn3imIQqHAhg0bkJWVhcDAQERHRyM/Px9BQf9/X4qHhwecnJxw/PhxAMDx48fh4+MDa+v/v4ymVatWyMjIUI2WExEREb0rjnSTuvGwsoKvrS3+3vmXqi0sLAy6BrqoUq+KwGREhckgEx1BbbzTSHdERESxBbh48SICAwORk5MDQ0NDbNu2DZ6enjh37hy0tbVhampaqL+1tbXqMoWkpKRCBffz5c+XvUpubi5yc3NV7zMyMl7Zl4iIiCqurKwsTqJGaqefvz+m7tsHfRNT5Ofn4/Dhw3Cq4yQ6FhG9gvBZQdzd3XHu3DmcPHkSo0aNwqBBg3DlypUS3ee8efNgYmKiejk6Opbo/oiIiKhsysrKgr7mO41REJWYbt7ekMtkuB9/Ew8fPkROTg4aDm8oOhYRvcI7nUWaNWsGmezVlwscPHiwyNvS1tZG1apVAQABAQE4ffo0fvzxR/Tq1Qt5eXlIS0srNNqdnJwMGxsbAICNjQ1OnTpVaHvPZzd/3udlpk2bhokTJ6reZ2RksPAmIiKiF2RlZcGAI92kZiwMDNDSzQ0Hrl8HAGjrasO7jbfgVET0Ku800u3v7w8/Pz/Vy9PTE3l5eThz5gx8fHzeK5BSqURubi4CAgKgpaWF8PBw1bKYmBjcuXMHgYGBAIDAwEBcvHgRKSkpqj6hoaEwNjaGp6fnK/eho6OjekzZ8xcRERHRfz3NyoIeR7pJDfXx94f0z9c2XjZ8rB2RGnuns8jChQtf2j5z5kw8efKkyNuZNm0a2rRpAycnJ2RmZmLdunU4dOgQDhw4ABMTEwwdOhQTJ06EmZkZjI2NMXbsWAQGBqJevXoAgODgYHh6emLAgAH45ptvkJSUhOnTp2P06NHQ0dF5l0MjIiIiUnndlX1EIrWuVg2aMhkKJAmBAwNFxyGi1yjWj2779++POnXq4LvvvitS/5SUFAwcOBCJiYkwMTGBr68vDhw4gJYtWwJ4VtzL5XJ069YNubm5aNWqFX7++WfV+hoaGti9ezdGjRqFwMBAGBgYYNCgQZg9e3ZxHhYRERFVUFra2shTKETHIHqBjqYm3C0tcS01Fds+24bTG07DwccBdt52sPe2h42HDbR0eWsEkToo1qL7+PHj0NXVLXL/5cuXv3a5rq4ulixZgiVLlryyT+XKlbF3794i75OIiIioqLS1tZGlVIqOQfRSuwYPxtITJ3A6IQHXLz/EyTN3kZubDwCQyWWwcLGAo7/js0Lcyx523nYwtjbmFRxEpeydiu6uXbsWei9JEhITExEVFYX//e9/xRKMiIiISDQtLS3ksegmNWWmr4/Pmzcv1PbgyRPsv34df8fH42JyMmL3XcG57eegVDz7/1jfVB/2vvaFRsWt3Kygqc25C4hKyjv9dJmYmBR6L5fL4e7ujtmzZyM4OLhYghERERGJpq2tzaKbyhRLQ0MMqFkTA2rWVLUVKBQ4cecOQmNjEZWQgNiziTh28hby/hkVl2vIYeVm9WxU3OtZIW7nbQdDc0NRh0FUrrxT0b1y5crizkFERESkdrS0tFDAe7qpjNPU0EBDFxc0dHEp1J6Qno59MTE4dusWLicn4+qOC4jaFAVJ+WxedEMLQzj4OcDex151ebplFUtoaGqIOAyiMuu9riOJjo7G1atXAQBeXl6oUaNGsYQiIiIiUgcc6abyzMHEBMPr1MHwOnVUbXkFBTgcH4+DsbE4c/8+bp68i7i/Y5GfVwAA0NDWgI27DRz9HFWXp9t62ULfRF/UYRCpvXcqulNSUtC7d28cOnQIpqamAIC0tDQ0a9YMGzZsgKWlZXFmJCIiIhJCS0sL+RzppgpEW1MTLd3c0NLNrVB7XGoq9sXE4Pjt27h67wEu3DiLk3+ehCQ9GxU3sTV5Niruba+6PN3c2ZzPDyfCOxbdY8eORWZmJi5fvozq1asDAK5cuYJBgwbh448/xvr164s1JBEREZEI2trayOdINxGqmJtjTP36GFO/vqrtaV4ewmNjcejmTZy9fx+3/o5HTPg1FOQ/+6BKS08LttVtVcW4nZcd7DztoGOoI+owiIR4p6J7//79CAsLUxXcAODp6YklS5ZwIjUiIiIqN7S0tPicbqJX0NfWRgdPT3Tw9CzUfiU5GftjYnDi7l1cu/kAZ69EITI3EpAAyAAzBzM4+BceFa/kUImPMitn8rLzoKPDD1iAdyy6lUoltLS0XmjX0tKCkp8GExERUTmhra2NvIIC0TGIyhRPa2t4WlsXasvIycGBGzdw+OZNXEhMxJ3wG7i87xIUBc9qBx1DnWdFuO//T9pm42EDbT1tEYdAxSDzQSZsbGxEx1AL71R0N2/eHOPGjcP69ethZ2cHALh37x4mTJiAFi1aFGtAIiIiIlHMzc3xKCsLkiRxFI7oPRjr6qKHjw96+Pio2pRKJc7ev4+QGzdw6u5dXL/yEKfO3kXuP48yk8lksHCxUI2KP3+cmbGNMX8ey4DMlEzYWLPoBt6x6P7pp5/QsWNHODs7w9HREQBw9+5deHt7Y+3atcUakIiIiEgUKysr5OTn40leHox4mSRRsZLL5QhwcECAg0Oh9odZWdgfE4O/4+NxMTkZcfuv4vyO81Aqno2K65nowcH32aPMns+gbl3NGpra7/VgJipmWQ+yYOPOoht4x6Lb0dERZ86cQVhYGK5duwYAqF69OoKCgoo1HBEREZFIVlZWAJ4VASy6iUqHhYEB+tesif41a6raFAoFTty5g5DYWEQlJCD2XBJun7qFvH9GxeUaclhVtXo2Ku71/8W4oYWhqMOo8DJTMmHd2PrNHSuAtyq6Dx48iDFjxuDEiRMwNjZGy5Yt0bJlSwBAeno6vLy88Msvv6BRo0YlEpaIiIioNFn/c1/qg6wsuJiZCU5DVHFpaGiggYsLGri4FGq/l56OfTExOHb7Ni4nJ+PazkuI3hwNSfnsUWaG5obPZk9/PiruZQ/LqpbQ0NQQcRgVSsaDDNXv0IrurYruH374AcOHD4exsfELy0xMTDBy5EgsWLCARTcRERGVC89HulOePBGchIhext7EBMPq1MGwOnVUbXkFBTgSH4+DcXGIvncPN0/dRdzRWOTnPZsUUUNbAzbVbODg56AaEbfztoO+ib6owyh38p7mIedJDidS+8dbFd3nz5/H119//crlwcHB+O677947FBEREZE6MDc3h1wux8OsLNFRiKiItDU1EeTmhiA3t0Lt8amp2Hv9OiJv38bV+ym4GHcOp9afUo2KG9sYw9HPUVWE23vbw9zl2e8AejuZDzIBgCPd/3irojs5OfmljwpTbUxTEw8ePHjvUERERETqQENDAxZmZnjAopuozHMxN8fowECMDgxUtT3Ny8PBuDhE3LyJc/fu4dbReMQcvIaCfAUAQEtXC7bVbf9/VNzLHraettA10hV1GGVCRnIGABbdz71V0W1vb49Lly6hatWqL11+4cIF2NraFkswIiIiInVgZWnJopuonNLX1kb76tXRvnr1Qu1Xk5Ox7/p1nLhzBzHxD3D2ahQic/OAZ4PiqORQCY7+joUuTzdzNOOjzP7Bke7C3qrobtu2Lf73v/+hdevW0NUt/OlOdnY2ZsyYgfbt2xdrQCIiIiKRrGxs8CAlRXQMIipF1a2tUf0/BWNGTg5CbtzA4Zs3cT4xEXcO3sDl/ZehKHg2Kq5joAM7bzs4+DqoniluU90G2nraIg5BqMwHmdDQ0IC5ubnoKGrhrYru6dOnY+vWrahWrRrGjBkDd3d3AMC1a9ewZMkSKBQKfP755yUSlIiIiEgEaxsb3L11S3QMIhLMWFcX3X180N3HR9WmVCpxLjERIdev4+Tdu7h+9SFOn7uHnNw8AIBMJoO5s3nhUXEvO5jYmpTrUfHM5EyYW5pDQ4OzxANvWXRbW1sjMjISo0aNwrRp0yBJz66vkMlkaNWqFZYsWcJLCIiIiKhcsba2RhQvLyeil5DL5ahpb4+a9vaF2h9mZeHA9ev4Oz4eF5KSEHfgGs7vPA+lQgkA0DPWg72vPex97FWXp9u420BT+63KM7WV+SCTdeG/vPW/auXKlbF37148fvwYsbGxkCQJbm5uqFSpUknkIyIiIhKqSpUquP3oEQoUCmhy1IaIisDCwAD9atRAvxo1VG1KpRLH795F6PXriLp3D7Hnk3H89G3k5eYDAGQaMlhVsXo2Ku71//eKG1kaiTqMd5aZkgkbaz4u7Ll3/iilUqVKqF27dnFmISIiIlI77u7uyCsowJ20NLjy/kQiekdyuRwNKldGg8qVC7XfS0/H/uvXcfTWLVxOTsa1XZcQvSVa9SgzA3MDOPg6qEbF7b3tYVnVEhqa6vshYNaDLNi6c4Lt58rH9QtEREREJcTDwwMAcCM1lUU3ERU7exMTDK1dG0P/NaCZV1CAv+PjER4XhzP37iHudAJuHotDfl4BAEBDSwPW1azh4OdQ6Lni+qb6og6jkLT7abBrZic6htpg0U1ERET0Gvb29tDX08ONhw/Rqlo10XGIqALQ1tRECzc3tHBzK9Qen5qKfdevI/L2bVxJTMHlm+dxesNp1ai4sbXxs0Lcxx72Xs+KcQtXC8jl8lLLnpOZg9S7qaj+n8ewVWQsuomIiIheQy6Xo5qbG248fCg6ChFVcC7m5vgoMBAfBQaq2nLy8xEeF4eIuDicvX8ft47dwvWIGBTkP3uUmaaOJmw9beHo988M6l72sPW0ha6R7qt2816SYpIAAF5eXiWy/bKIRTcRERHRG3h4euLGiROiYxARvUBXSwvtPDzQ7p9bYZ67lpyMfdev48SdO7gW/wBnr0YhMjcPeDYojkoOlQo/yszbDmaOZu/9KLOka0mQyWQc6f4XFt1EREREb+Du7o6De/eKjkFEVGQe1tbw+M9juzJychB64wYOx8fj/P37uH3wBi7vvwxFwbNRcR0DHdh528HB10E1g7pNdRto62kXeb9J15JQ2aUy9PXV4/5ydcCim4iIiOgN3N3dkZKRgbTsbJjq6YmOQ0T0Tox1ddHNxwfdfHxUbUqlEucTE3Hg+nWcSkjA9asPcPr8PeTk5AEAZDIZzJ3Nn12e7vPPqLiXHUxsTV46Kp4ckwxvL+9SO6aygEU3ERER0Ru4u7sDAGJTU1HLwUFwGiKi4iOXy1HD3h417O0LtT/MykLIjRs4cvMmLiQlIS7kGs7vOg+lQgkA0DPWg72vvepRZnbedrCpZoOUmBR0HtBZwJGoLxbdRERERG9Q7Z9Zy288fMiim4gqBAsDA/T190dff39Vm1KpxIm7dxF64waiEhJw43wyjp++jbzcfACATEMGSSGhSpUqglKrJxbdRERERG9gaGgIJwcHXE1JER2FiEgYuVyO+pUro37lyoXaEzMysC8mBtsuX8bft27B19dXUEL1xKKbiIiIqAhq162LqOho0TGIiNSOrbExPqhdG7kKBU4nJqJGjRqiI6mV0ntKOhEREVEZFhgYiLP37yNfoRAdhYhILZ1PTISvjw80NTm2+28suomIiIiKoF69esjOy8Pl5GTRUYiI1NKF5GTUrFVLdAy1w6KbiIiIqAhq1qwJLS0tnE5IEB2FiEjtPM3Lw7XkZNSsWVN0FLXDopuIiIioCPT09ODv68uim4joJc4nJkIpSQgICBAdRe2w6CYiIiIqosAGDXD6/n3RMYiI1M7ft27BxNgYfn5+oqOoHRbdREREREVUr149xD98iIdZWaKjEBGplaO3b6Nx48bQ0NAQHUXtsOgmIiIiKqLAwEAA4CXmRET/kltQgFN376JZ8+aio6glFt1ERERERVS5cmVYW1oiikU3EZFKVEICcvLz0bRpU9FR1BKLbiIiIqIikslkCKxfH6fu3RMdhYhIbfx96xYqmZjwfu5XYNFNRERE9BYaNmqE03fv4mlenugoRERq4ejt22jcpAnkcpaXL8PvChEREdFbaNu2LXLy83H01i3RUYiIhMvJz8fphATez/0aLLqJiIiI3oKHhwecnZwQeuOG6ChERMKdSkhALu/nfi0W3URERERvQSaToV2HDgiJi4MkSaLjEBEJdfTWLZiZmsLHx0d0FLXFopuIiIjoLbVt2xa3Hz3C9YcPRUchIhLq79u30aRpU97P/Rr8zhARERG9pWbNmkFXRwch16+LjkJEJMyjp09x6s4dBLdqJTqKWmPRTURERPSW9PT00Lx5c4TExYmOQkQkzJ5r16CUJHTu3Fl0FLXGopuIiIjoHbRt1w7Hb99GRk6O6ChERELsvnYNDevXh42Njegoao1FNxEREdE7aNu2LQoUCkTcvCk6ChFRqcvIyUHEzZvo1qOH6Chqj0U3ERER0TtwcXFBdXd3PjqMiCqkA9evI6+gAF27dhUdRe2x6CYiIiJ6R88fHaZQKkVHISIqVTuvXUPtgAA4OjqKjqL2WHQTEb3Go0ePMG3aNDRp0gT6+vqQyWSQyWQYPHjwC30lScKvv/6K2rVrw8DAAIaGhqhXrx7Wrl37Qt+EhAQMHToUvr6+MDc3h6amJipVqoQGDRpgyZIlUCgUpXB0RPS+unXrhpSMDBy7fVt0FCKiUvM0Lw9hsbG8tLyINEUHICJSZ3fu3MH8+fOL1Hfw4MH4448/CrWdPHkSJ0+exJUrVzB37lxV+61bt7BixYpCfdPS0hAZGYnIyEhcuHABv/766/sfABGVqLp168KlcmVsuXgRjV1cRMchIioVYbGxyM7LQ7du3URHKRM40k1E9Bra2tpo3Lgxpk6dig8++OCV/Q4ePKgquK2trbFu3Tps3LgRdnZ2AID58+fj9OnTqv4GBgbo378/li9fjgMHDmDHjh1o166davmKFSuQlZVVQkdFRMVFJpOhb//+2HntGnILCkTHISIqFbuuXoWvtzeqVq0qOkqZwJFuIqLX8PT0xOHDhwEAv/zyywuj08/t27dP9fWoUaPQp08fAEB8fDymTp0KSZKwbNky1K5dGwBQo0YNrFmzptA2GjdujEqVKgEACgoKkJ2dDQMDg2I/JiIqXn379sWcOXMQHhuLth4eouMQEZWo3IICHIiNxcQpU0RHKTM40k1EVAzS09NVX/+7UP7318eOHXvpupIk4eHDh1i0aJGqzdvbGxYWFiWQlIiKm6enJ3y9vbH54kXRUYiISlzojRvIyM5G9+7dRUcpM1h0ExEVA3d3d9XXa9euRUJCAu7fv4/Vq1er2u/evfvCer1794ZcLoelpSVmzJgBAGjYsCG2bt1a8qGJqNgMGDQIe69fR1p2tugoREQl6o+zZ1GnVi14eXmJjlJmsOgmIioGAwcOVI1Mnz9/Ho6OjrC3t0dUVJSqT05OTpG2paWlxdnLicqYfv36oUCpxLbLl0VHISIqMXfT0hB64waGjxwpOkqZIrTonjdvHmrXrg0jIyNYWVmhc+fOiImJKdQnJycHo0ePhrm5OQwNDdGtWzckJycX6nPnzh20a9cO+vr6sLKywuTJk1HAyUyIqBRZWloiLCwMNWrUULXJZLJCs3qampq+sN6sWbNw+PBhbNq0CW3btgUAREREICgoqMhFOhGJZ2tri9atWmHdhQuioxARlZi1Z8/CQF8fvXv3Fh2lTBFadB8+fBijR4/GiRMnEBoaivz8fAQHBxeasXfChAnYtWsXNm/ejMOHD+P+/fvo2rWrarlCoUC7du2Ql5eHyMhIrF69GqtWrcIXX3wh4pCIqALz8/PDmTNnEB8fj9OnTyM1NRXjxo1TLX/ZZVju7u5o3LgxevTogV27dsHln0cO3bt3D0eOHCm17ET0/gYPGYLTd+7g+oMHoqMQERU7hVKJtefPo2+/fjA0NBQdp0wROnv5/v37C71ftWoVrKysEB0djcaNGyM9PR3Lly/HunXr0Lx5cwDAypUrUb16dZw4cQL16tVDSEgIrly5grCwMFhbW8Pf3x9ffvklpkyZgpkzZ0JbW1vEoRFRBebs7AxnZ2cAwHfffadqb9++verr7Oxs6OnpvXY7aWlpJRGPiEpIhw4dUMnEBOvOncPMli1FxyEiKlZhsbG4l5aGESNGiI5S5qjVI8Oez/5rZmYGAIiOjkZ+fj6CgoJUfTw8PODk5ITjx4+jXr16OH78OHx8fGBtba3q06pVK4waNQqXL18udKknEdHbevr0Kfbu3QsAOHv2rKr99u3b2LJlCwCgdu3aqFy5Mjp37owaNWogICAAOTk5WLNmDXbu3Ang2aWnw4YNU63fqVMnmJqaomXLlnB2dkZGRgZWr16N+Ph4AM8uTefvL6KyRUdHBwMGDcKaFSswpWlT6GlpiY5ERFRsVp05gxp+fggICBAdpcxRm6JbqVRi/PjxaNCgAby9vQEASUlJ0NbWfuE+SGtrayQlJan6/Lvgfr78+bKXyc3NRW5urup9RkZGcR0GEZUzKSkp6NGjxwvthw4dwqFDhwA8uwJn8ODBuHXrFnbs2PFCX2NjY2zevLnQ77K8vDxs3rwZmzdvful+J0+eDDc3t2I5BiIqPWPHjsXixYux+eJFDKxZU3QcIqJicT8jAyHXr+OnJUtERymT1KboHj16NC5duoSjR4+W+L7mzZuHWbNmlfh+iKhi6devHzQ1NXHz5k1kZWXBxsYGrVu3xmeffYbKlSsX6jtixAiYmJjg/PnzSElJQUFBAaysrFCrVi0MHToUHTp0EHQURPQ+qlatig7t2+OXU6cwoEYNyGQy0ZGIiN7bn2fPQkdHB3379hUdpUxSi6J7zJgx2L17N44cOQIHBwdVu42NDfLy8pCWllZohCg5ORk2NjaqPqdOnSq0veezmz/v81/Tpk3DxIkTVe8zMjLg6OhYXIdDROWIs7MzJEkqUt/Jkydj8uTJRerbt29fnriIyqkJEyeiWbNmOHzzJppWqSI6DhHRe1EqlVhz/jx69e4NY2Nj0XHKJKGzl0uShDFjxmDbtm04ePCgatbe5wICAqClpYXw8HBVW0xMDO7cuYPAwEAAQGBgIC5evIiUlBRVn9DQUBgbG8PT0/Ol+9XR0YGxsXGhFxEREVFxaNKkCfx8fLD0P4MCRERlUWhsLO48esQJ1N6D0KJ79OjRWLt2LdatWwcjIyMkJSUhKSkJ2dnZAAATExMMHToUEydOREREBKKjozFkyBAEBgaiXr16AIDg4GB4enpiwIABOH/+PA4cOIDp06dj9OjR0NHREXl4REREVAHJZDJMmDQJB2JicOPhQ9FxiIjey8JjxxBYty7q1q0rOkqZJbToXrp0KdLT09G0aVPY2tqqXhs3blT1WbhwIdq3b49u3bqhcePGsLGxwdatW1XLNTQ0sHv3bmhoaCAwMBD9+/fHwIEDMXv2bBGHRERERITevXvDysICv548KToKEdE7i7x9Gydu38a0zz/nHBXvQSYV9WbFciwjIwMmJiZIT0/npeYVVDon1isRJjNmFPs2f3z8Y7Fvk4BxlcaJjkCC8BxYcmbNmoVv5s7FlQkTYKqnJzoOEdFb6/7nn0jW1sa5Cxcglwsdry3T+J0jIiIiKgGjRo1CgSRhdXS06ChERG/tfGIiwm7cwNTPPmPB/Z7UYvZyIiIiovLGysoKffv1w7Lt2/FRYCC0NDRERxLufkYGZoaGIjQ2Ftn5+XA1M8OSTp1Qw95e1SfmwQPMCA1F5O3bKFAq4W5piT969oTjv55k829/nj2L0Tt2FGrT0dBA8v/+p3q/+Ngx/HjsGABgXMOGGFu/vmpZVEICJu3Zg/Bhw6DJfyMilYVHj8LV2Rk9e/YUHaXMY9FNREREVEImTZqEVatWYf25cxgYECA6jlBp2dlotXw5Grm4YEu/fjA3MMDN1NRCl97HP3qE1itWYECNGpjWrBmMdXRwNSUFupqv/5PVWEcHp8eMUb3/972nl5KSMDciAhv+eUxjr3Xr0LxKFXhZW6NAocCE3bvxY4cOLLiJ/iX24UPsuHIFS5cuheYbfv7ozfgdJCIiIioh3t7e6NWzJ74JDUUvPz/oVOA/Xn84ehQOJib4uXNnVZtzpUqF+nwZHo6Wbm6YHRysanMxMyvS9q2NjF7afuPhQ3hZW6OJqysAwMvaWtW2KDIS9StXRs1/jbQTEfDDsWOwtrTEoEGDREcpF3hxPhEREVEJmjV7Nu6np2NlVJToKELti4mBv50dBm3ahKrffINGv/xS6H53pVKJkBs3UNXcHF3XrEHVb75Bi99+w+6rV9+47ay8PHgvXAivBQvQZ/16XE1JUS3ztLZGbGoq7qal4U5aGmJTU1Hdygrxjx7hz7NnMb158xI5XqKyKiE9HRsvXMCkyZOhq6srOk65wKKbiIiIqAS5u7tj4MCB+P7YMWTl5YmOI8ytx4+x4vRpVDEzw18DBmBorVqYsm8f1p07BwB4kJWFJ3l5+OHoUbSoWhVbBwxAew8PDNi4EUdv3Xrldt0sLPBTp05Y17s3fu3aFUpJQqvly3EvPR0A4G5piS9atECXNWvQdc0azGjRAu6Wlhi/axdmtWyJg7GxCFyyBI1++QXHXrMfoorip8hIGBoaYuTIkaKjlBsV9xonIiIiolIyY8YM/Pnnn1h28iQmNGokOo4QSklCDTs7fBEUBADws7XFlZQUrIyKQl9/fyj/eYptW3d3jA4MBAD42tri5N27WBkVhYbOzi/dbh1HR9RxdFS9r+voiDo//YSV0dGqUewPatfGB7Vrq/qsO3cOhjo6qOPoiFqLFyNixAjcy8jA0C1bcH78+Ap9GwBVbPczMrD67Fl8MmUKjF5xywa9PY50ExEREZUwZ2dnjBgxAouOH0dadrboOEJYGxnB3dKyUJu7pSUS/hmRNtfXh6Zc/to+RaGloQFfW1vEP3r00uWpWVn4+tAhfNOmDaISElDV3BxVzM3R2MUF+UolYlNT3/LIiMqPORERMDA0xKRJk0RHKVdYdBMRERGVgs8//xw5SiWWHD8uOooQ9RwdXyhoY1NT4WhiAgDQ1tRETTs73HhNn6JQKJW4kpwMa0PDly6fduAAPgoMhL2JCRSShHylUrWsQKmE4l/viSqSC4mJWHfuHGbOng2Tt/iZozdj0U1ERERUCmxtbTFm7FgsPXUKD7OyRMcpdR8FBuJ0QgK+P3IEN1NTsfnCBayOjsawOnVUfcY2aIBtly5hdXQ0bqamYtnJk9gfE4Oh/7o0fOTWrZgVFqZ6//WhQzgYG4tbjx7h3P37GLF1K+6mp2NgzZovZIiIi0NcaiqG/7O9mnZ2uPHwIUJv3MCqqChoyGRws7Aowe8CkXqSJAn/CwtDtapVMWLECNFxyh3esEJERERUSqZMmYJfli7FD0eP4qtWrUTHKVU17e2xtlcvzA4PxzeHD6NypUqY17o1evr6qvp0qF4dC9q3x8KjRzFl3z5UNTfHH716IbByZVWfhPR0yP/1HO60nBx8vGsXUp48gamuLvzt7HBg6FB4WFkV2n92fj4m792LFd27Qy5/Nu5kb2KCb9q0wejt26GtqYmlXbpAT0urhL8TROonLDYWh+PisGPHDmjxZ6DYySTpn1krKrCMjAyYmJggPT0dxsbGouOQAOmzZomOUC6ZzJhR7Nv88fGPxb5NAsZVGic6AgnCc2DpmzlzJr6eOxdRY8bAgZdwEpFgBQoFGi5bBmt3d0QcOgTZvz7UouLBy8uJiIiIStHEiRNhWqkSPg8JER2FiAhrz57FteRkfL9gAQvuEsKim4iIiKgUGRsb47sFC7Dj8mUcjI0VHYeIKrDM3FzMOXwY/fv1Q0BAgOg45RaLbiIiIqJS1rdvXzRt0gSTDxxAbkGB6DhEVEH9eOwYMvPyMGfuXNFRyjUW3URERESlTCaTYcnPP+P248dYdOyY6DhEVAElpKdjyYkTmDBxIpycnETHKddYdBMREREJ4OnpiQkTJuD7o0dx6/Fj0XGIqAKRJAmf7N0LE1NTTJ06VXScco9FNxEREZEgX3zxBSwsLTF1/37RUYioAtl2+TL2x8Tg519+4ZMrSgGLbiIiIiJBDA0NsfDHH7E/Jgb7YmJExyGiCuDR06f4dP9+dOvaFV26dBEdp0Jg0U1EREQkULdu3RDcsiWmHDiAp3l5ouMQUTn32YEDKNDQwE9LloiOUmGw6CYiIiISSCaT4aclS5CUmYkFR4+KjkNE5Vh4bCw2nD+PBQsXwsbGRnScCoNFNxEREZFgbm5umDJ1Kn44dgzn7t8XHYeIyqEnubkYv2cPglq0wODBg0XHqVBYdBMRERGpgenTp8Pbywsjt29Hdn6+6DhEVM58efAgUnNy8OuyZZDJZKLjVCgsuomIiIjUgLa2NtauW4dbaWmYERoqOg4RlSOn7t7FslOn8NWcOXB1dRUdp8Jh0U1ERESkJry8vPD1N99g2alTOBgbKzoOEZUDuQUF+Hj3btQOCMDHH38sOk6FxKKbiIiISI2MHTsWQS1aYPSuXXj89KnoOERUxs0MC8PNR4/w+4oV0NDQEB2nQmLRTURERKRG5HI5Vq1ejRyZDBP27IEkSaIjEVEZtffaNSw9cQLffvcdfHx8RMepsFh0ExEREakZe3t7/PLrr9h++TI2XbggOg4RlUF309IweudOdOrYEWPHjhUdp0Jj0U1ERESkhnr27Il+ffti8v79uJOWJjoOEZUh+QoFhm3bBiMzM6xYuZKzlQvGopuIiIhITf20ZAlMzMwwascOKJRK0XGIqIyYGxGBqIQEbNi0CWZmZqLjVHgsuomIiIjUlKmpKf5YuxbHb9/GnIMHRcchojIgPDYWC48exdy5cxEYGCg6DoFFNxEREZFaa9q0KebNm4cFR49ix+XLouMQkRpLyszEyO3b0bpVK3zyySei49A/NEUHICIiIqLXmzx5Ms5ER+OjHTvgZmEBT2tr0ZGISM0olEoM37YNWgYG+GPNGsjlHF9VF/yXICIiIlJzMpkMy1esQBU3N/TbtAlp2dmiIxGRmvn2yBEcu3UL6zZsgKWlpeg49C8suomIiIjKAAMDA2zbsQNpCgWGb9vGidWISGXX1auYf+gQZsyYgaZNm4qOQ//BopuIiIiojHB1dcWGjRsRHhuLuRERouMQkRq4mJSED7dvR/du3TB9+nTRceglWHQTERERlSHBwcGYN28evv/7b+y4ckV0HCISKOXJE/TZsAHVPDywavVq3setpjiRGhEREVEZM3nyZERHReGjHTtQzcIC1a2sREciolKWW1CAAZs3o0BbGzt374aBgYHoSPQK/CiEiIiIqIyRyWRYsXIlXKtWRd9Nm/DgyRPRkYioFCmVSozZuRPnEhOxfedOODg4iI5Er8Gim4iIiKgMMjAwwI5du5Atl6PH+vXIzM0VHYmISsnciAhsvnABa9auRd26dUXHoTdg0U1ERERURrm4uGB/SAhuZmSg/6ZNyC0oEB2JiErYH9HR+O7vv/HNN9+gR48eouNQEbDoJiIiIirD/Pz8sGv3bpy4excj+SgxonLtYGwsJuzZgw8//BCffPKJ6DhURCy6iYiIiMq4xo0bY8PGjdh59Sqm7NsHSZJERyKiYnb67l0M3LwZrVq1wuLFiyGTyURHoiJi0U1ERERUDnTu3BnLli3D76dP4+vDh0XHIaJidO7+fXRftw7+AQHYtHkzNDX5EKqyhP9aREREROXE0KFDkZKSgs8++wyWBgYYWru26EhE9J4uJSWhy59/wsPbG3v27eOjwcogFt1ERERE5cjUqVORkpKCT378Eeb6+ujs5SU6EhG9o5gHD9B57Vo4V62K/SEhMDY2Fh2J3gGLbiIiIqJyRCaT4fvvv8eDlBQM37gRBtraaOnmJjoWEb2luNRUdFqzBjZOTggJC4OpqanoSPSOeE83ERERUTkjl8uxYuVKtGnbFn03bMDuq1dFRyKit3D78WN0WrsWJtbWCDt4EBYWFqIj0Xtg0U1ERERUDmlra2PLX3+hc5cuGLR5M/66eFF0JCIqgnvp6ei4di10TE0RHhEBa2tr0ZHoPbHoJiIiIiqntLS0sG79evTr3x/Dtm7F2rNnRUciotdIysxEx7VrAX19HDx0CPb29qIjUTHgPd1ERERE5ZimpiZWrlwJXV1djFm2DDn5+RhWp47oWET0H7cePULXdeuQq6WFwxERcHJyEh2JigmLbiIiIqJyTi6X45dffoGenh4++fFHZBcUYGz9+qJjEdE/LiQmovu6dTC2tMTfYWFwcXERHYmKEYtuIiIiogpAJpNh4cKFMDAwwP/mzkV2fj4mN24MmUwmOhpRhXYkPh79Nm6Eu6cn9uzbBysrK9GRqJix6CYiIiKqIGQyGebMmQN9fX1Mnz4dOfn5+F+LFiy8iQTZfvkyRmzbhiZNm+KvrVthZGQkOhKVABbdRERERBXM559/Dj09PUyaNAkPnz7F9+3aQUtDQ3Qsogrl91OnMHnfPvTp3RsrV62Ctra26EhUQlh0ExEREVVAEydOhLm5OYYNG4a76elY1aMHTHR1RcciKvf+r707j6uqTvw//r4sAaLghiiTiZr7FuPCOPobNXkoyFgmmjqW5vrIUYuodPxWpk1ZWaa5kctFsymXcSmHHMmddFBAQUnFhRQ0QUUEBBWFe39/NN2G1LTycuDyej4e58HlbPd9OY+kN+ecz7FarZqxY4fei43VCy+8oPfff19OTjxUypFxdAEAACqp4cOHKyYmRknZ2QpZvlxncnONjgQ4tOKSEoX/6196LzZW7777rmbNmkXhrgQ4wgAAAJXYo48+qv/Exemam5uCoqJ04LvvjI4EOKS869c1dM0a/ePgQS1fvlyTJk1iPIVKwtDSHRsbq759+8rPz08mk0mff/55qeVWq1VTp05VvXr15OHhoaCgIJ04caLUOjk5ORo6dKi8vLxUvXp1jRo1SgUFBWX4KQAAACq2Fi1aaG98vBo2b64+y5drbUqK0ZEAh5J64YJ6ms3am5mp6OhoDR8+3OhIKEOGlu7CwkK1a9dOCxYsuO3ymTNnau7cufroo4+0b98+eXp6qnfv3rp+/bptnaFDh+rw4cPasmWLoqOjFRsbq7Fjx5bVRwAAAHAIderU0Y5duzRw0CCNXrdOf9+2TRaLxehYQIX3xZEjCoqKkruPjxL371dwcLDRkVDGDB1ILSQkRCEhIbddZrVaNWfOHL366qt6/PHHJUkrVqyQr6+vPv/8cw0ePFhHjx7V5s2blZCQoA4dOkiS5s2bpz59+uj999+Xn59fmX0WAACAis7d3V0fr1ihNm3bavLkyTpy8aIWP/GEqrm5GR0NqHBKLBa9uX27Zu/erScHDpQ5KkpVq1Y1OhYMUG7v6T516pSysrIUFBRkm+ft7a3AwEDFxcVJkuLi4lS9enVb4ZakoKAgOTk5ad++fWWeGQAAoKIzmUx6+eWXFR0drT3ffacgs1mHz583OhZQoVy+elUDP/tMH/7nP5o5c6ZWrV5N4a7Eym3pzsrKkiT5+vqWmu/r62tblpWVpTp16pRa7uLiopo1a9rWuZ2ioiLl5+eXmgAAAPCjPn36aF98vB7w8VHPpUu1LDFRVqvV6FhAuZeSlaXuZrMOXrqkmJgYvfzyywyYVsmV29JtT2+//ba8vb1tU/369Y2OBAAAUO40b95c+xIS9MyoUXohOloj1q5V7rVrRscCyq01hw6pV1SUaj74oBIPHCh11S4qr3JbuuvWrStJOv+Ty5nOnz9vW1a3bl1duHCh1PLi4mLl5OTY1rmdKVOmKC8vzzadOXPmPqcHAABwDB4eHoqMjNQ///lP7ThzRn9askSJZ88aHQsoV64UFem5jRs1dv16DXjySe2Ji5O/v7/RsVBOlNvS3bBhQ9WtW1fbtm2zzcvPz9e+ffvUuXNnSVLnzp2Vm5ur/fv329bZvn27LBaLAgMD77hvNzc3eXl5lZoAAAAquri4ODk7Oys0NPS+73vAgAFKPnhQfg8/rOBly/Th7t2Mbg5I2puRoa6LFmldaqoWL16sj1eskIeHh9GxUI4YWroLCgqUnJys5ORkSd8PnpacnKyMjAyZTCaFh4frzTff1MaNG5WSkqJhw4bJz89P/fr1k/T9MyWDg4M1ZswYxcfHa8+ePZowYYIGDx7MyOUAAKDSMZvNmjhxomJjY3Xu3Ln7vn9/f3/F7t6tF196Sa9v3aqBK1fqYkHBfX8foCK4UVys6Vu3qs/y5fpd06Y6eOiQxowZw/3buIWhpTsxMVEBAQEKCAiQJEVERCggIEBTp06VJE2aNEkTJ07U2LFj1bFjRxUUFGjz5s1yd3e37ePTTz9V8+bN1bNnT/Xp00ddu3bV4sWLDfk8AAAARikoKNDq1as1btw4hYaGavny5XZ5H1dXV73zzjvavHmzDuXkqOvixdp28qRd3gsor46cP6+eZrPm7d2rN998U7G7d6tx48ZGx0I5ZbIyDKXy8/Pl7e2tvLw8LjWvpPKmTzc6gkPyfv31+77PDy9/eN/3Cen5Gs8bHQEG4Xeg44iKilJkZKQSEhIUHR2t8PBwnThxwq5n3TIzMzV82DBt2bpVg9u104zevVWzShW7vR9gNIvFogV79+rv27fr4Ycf1j8++8x2AhG4k3J7TzcAAADundls1lNPPSVJCg4OVl5ennbt2mXX96xXr55ivvpKZrNZm0+fVqeFC/XPQ4d4tBgcUkZurvp+8ommbtmiCc89p/1JSRRu3BNKNwAAQAV37NgxxcfHa8iQIZIkFxcXDRo0SGaz2e7vbTKZNHLkSB1NTdWjISEas369Bq1cqTO5uXZ/b6AslFgsWhofr66LFinjxg1t27ZNs2bNKnXLK/BzKN0AAAAVnNlsVnFxsfz8/OTi4iIXFxdFRkZq3bp1ysvLK5MMdevW1eo1a/TFF1/o8JUr+kNkpBbt26cSRjhHBRZ/5ox6LF2qlzZt0sC//EUphw+rR48eRsdCBUPpBgAAqMCKi4u1YsUKzZo1y/ZUmOTkZB08eFB+fn5auXJlmeZ57LHHdCQ1VcNGjtTkf/9bvZct05Hz58s0A/BbXSwo0PgvvlAvs1muvr7au3evli5dKm9vb6OjoQKidAMAAFRg0dHRunz5skaNGqXWrVuXmsLCwsrkEvOf8vLy0sKFC7V7924VVqmibosX6+/btulKUVGZZwF+iR8uJe+4cKE2nTqlyMhI7UtIUGBgoNHRUIFRugEAACows9msoKCg256BCwsLU2Jiog4dOmRAMqlLly5KOnhQ//fqq1oQH6/2CxYoKiFBxSUlhuQBfs7/XkoeNmSIjp84oWeffVbOzs5GR0MFR+kGAACowP71r3/pyy+/vO2yTp06yWq1qm3btmWc6kdubm6aNm2ajh0/rt6PP64XN23SHxct0qbUVEY5R7lwp0vJa9eubXQ0OAhKNwAAAOzuoYce0ooVK7R//37Vb91af1m1Sn9esUJJ331ndDRUUrnXrunN7dsVMH8+l5LDrijdAAAAKDMBAQHasnWrNm3apFx3d/VYskSj161T+uXLRkdDJXGlqEjv7dqldnPnakF8vP763HM6cfIkl5LDbijdAAAAKFMmk0khISFKPnRIS5Ys0Z4LF9RxwQK9GhOjnKtXjY4HB3X1xg3N27NH7ebO1Xu7d+uZsWP17alTevfdd1WrVi2j48GBUboBAABgCBcXF40ePVon0tL0ymuvadnBg2o9Z46mbN6ss2X0fHE4vqLiYi3at08B8+dr+o4dGjB0qE6mpWnOnDmqW7eu0fFQCVC6AQAAYChPT09NnTpVp06f1ouTJ2vV0aMKmDtX47/4QscvXjQ6HiqomyUl+nj/frVfsEBTYmIU0r+/jh07pkWLFql+/fpGx0MlQukGAABAueDj46M33nhD6WfO6J2ZM7UjM1OBCxfqqdWrtf/sWaPjoYLILizU+7Gxajt3rsKjo9W1Vy8dOXJEy5YtU6NGjYyOh0qI0l2JhIaGymQy2abU1FRJ0s6dO0vNv93k7+9vbHgAAFBpVKtWTREREfr29GktXbpUx2/eVM+lS/XYihXakZbGo8ZwW4fPn9fEL75Qqzlz9P6ePQodOFApKSlauWqVmjVrZnQ8VGKU7kri008/1aZNm3719q6urvcxDQAAwN25ublp5MiROpKaqrVr16rQ21tPfPKJui9dqk+TklR444bREWGwEotFX6amqu+KFeoSGantWVma9sYbOvvdd1qyZIlatWpldERALkYHgP1lZ2crPDxcJpNJrq6uuvGTX1ABAQH6+uuvb9nugw8+0IYNGyRJ/fr1K4uoAAAAt3B2dlZYWJj69++v7du3672ZMzVh40b9LSZGA1q10vD27fWIn5/RMVGG8q5f1z+SkrQkMVGnL11S58BArZoxQ/379+dkEcodSnclEB4eruzsbI0dO1YxMTFKT08vtdzb21tdu3YtNa+oqMhWxJ2cnDRu3LgyywsAAHA7JpNJPXv2VM+ePXX69GlFRUUpaulSLdu/X239/DTskUc0oE0bVffwMDoq7MBqtSrh7FmtTE7WPw8f1vXiYj05cKBWh4erU6dORscD7ojLyx3c5s2b9emnn8rPz08zZ8685+3WrFmj7OxsSVJwcDCDTgBwGHca30KSdu3apVGjRqlFixZycnKyrbNz507jAgO4LX9/f73xxhs6nZGh6OhoNerYUZNjYtRi9mw9u2GD/pOezr3fDuJMbq7ej41Vh4UL1cts1tasLL3w8stKT0/Xp599RuFGuceZbgdWUFCgZ599VpK0cOFCeXt73/O2CxcutL0eP378fc8GAEa42/gWGzZsUFRUVBkmAvBbubi4KDQ0VKGhocrMzNTHH3+spYsXa9WyZWri46MnW7dW3xYt1MzHRyaTyei4uEc5V69q45EjWnv4sPacPi0Pd3eFDRigRcOHq0ePHnJy4twhKg5KtwN75ZVXlJ6eroEDB+rxxx+/5+2SkpK0d+9eSVKjRo0UHBxsr4gAUGbuNr6FJPn6+mrAgAHq3LmzFi1apOPHjxuQFMCvVa9ePf3tb3/TpEmTtGvXLi1dskQfbtyot3bsUJM6dfTnpk3Vt0ULBfj5UcDLoYKiIm06dkzrvvlG29LSZLFa9WiPHoqaOlVhYWGqVq2a0RGBX4XS7aBSU1M1f/581ahRQ/PmzftF2y5YsMD2ety4cfwlEYBDuNv4FpI0ZcoU2+s1a9aUZTwA95GTk5N69OihHj166Pr169q2bZvWr1+vFRs2aPbu3XqwRg31adJEfVu0UOeHHpKLs7PRkSslq9Wqk5cu6asTJ7T15EntSU/XjeJi/fEPf9DsiRM1YMAA1a1b1+iYwG9G6XZQWVlZslgsunz58h3/sWrRooXatWun5ORk27zc3FytXLlSkuTh4aGRI0eWRVwAsKufjm8RExNjdCQAZcTd3d12+fmiRYu0e/durV+/XhvWrdPi+HjV9PRUn6ZN1adZM3Xx95e3u7vRkR1a4Y0b+vrUKW09eVJbv/1Wpy9dktsDD6hbt256d8IEPfHEE2rQoIHRMYH7itKNUpYvX66rV69KkgYPHqyaNWsanAgAfpvfMr4FAMfi4uKi7t27q3v37vrwww+VmJio9evXa/3atfrHqlVyMpnU1s9PXR56SF39/dX5oYcYCf03slqtSvvhbHZamvakp6vo5k01bNBAoYMHKyQkRN27d5enp6fRUQG7oXQ7qIcfflizZ8++Zf4bb7yhy5cvS/r+MspWrVrZllmtVkVGRtq+ZwA1AI7g145vAcCxmUwmdezYUR07dtSMGTP07bffateuXdq5c6c2bt+uBXFxMplMauPnpy7166urv7/++NBDqlGlitHRy7XCGzeUdO6cEs+eVeJ33ynx3Dll5eXpAVdXdevWTe+MH6+QkBA1bdqU++pRaVC6HdSDDz6o8PDwW+bPmTPHVrqHDRum5s2b25Zt3brVNmhQYGCg2rdvXyZZAcBefsv4FgAqD5PJpMaNG6tx48YaOXKkrFarTp8+rZ07d2rnzp36cvt2Re7dK5PJpNb16qmTn5/a1quntnXrqkWdOnJ3dTX6IxjCYrHoeHb29+X67Fntz8zU4cxMWaxWVfX0VMeOHTWiXz916dKFs9mo1CjdsOExYQAcza8d3wJA5WYymdSwYUM1bNhQI0aMkKRSJTxu3z4t+/JLWSwWOTs5qbmvr1r7+KhlnTpqXqeOWtSpo/re3g5zJtdisei7/HylXbqkE5cu6eSlSzqWna0D584p/9o1mUwmtWrRQoEhIXruD39QYGCgWrZsKWcGqAMkUborndOnT99x2YYNG8ouCAAAQAXi7++vZ555Rs8884wk6erVq0pJSVFSUpKSkpJ0MClJX8bFqaCwUJJU1d1dzXx81NDbW7/z9tbvvLx+/OrlpdqenuWqlFssFl2+dk3f5uTo5H+L9clLl5R2+bLSLl3Stf8+ZtHV1VWN/f3VvHVrTR45UoGBgerYsaO8vLwM/gRA+UXpBgA4rF86vsWRI0d05MgRSVJOTo5t/V27dik7O1uSNGDAAHvHBlABVKlSRYGBgQoMDLTNs1qtysjI0OHDh23TqbQ07c/I0NnMTN28edO2rpurq/y8vfW7atW+n7y8VMPDQ1Xd3OT5wAPyfOABVf3v159+/4Czs0wmk6xWq4otFhVbLLpZUlLq9U2LRSUWi26UlOjytWvKLizUpatXdbGw8MfXV6/q0n+XZRcUqMRiseV70M9PTZs10//r1k2jmjZV06ZN1axZMzVo0EAuLlQI4JcwWa1Wq9EhjJafny9vb2/l5eXxV7pKKm/6dKMjOCTv11+/7/v88PKH932fkJ6v8bzREcqUv7+/7TndR48etY1vMW3aNE2/y78HjvZrk9+BQNmwWCy6ePGizpw5c+uUnq6zZ84oNz9fBYWFsvxP+b2dH54rXlxS8osyODk5yadWLfnUri0fX1/V8fWVj4+PfHx8VKdOHdWpU0eNGjVSkyZNuP8auI/4MxUAAABgZ05OTvL19ZWvr686dOhwx/WsVquuX7+ugoKCn51MJpNcXFzk6uoqV1fX277+4WvNmjXl4+OjGjVqyMnJqQw/NQCJ0m03649lGh3BIfVvVs/oCAAcwJ3Gt5g2bZqmTZtWplkA4H+ZTCZ5eHjIw8NDPj4+RscBcB/wpy4AAAAAAOyE0g0AAAAAgJ1QugEAAAAAsBPu6QYA2M0333xjdASH1Lp1a6MjAACAe8SZbgAAAAAA7ITSDQAAAACAnVC6AQAAAACwE0o3AAAAAAB2QukGAAAAAMBOKN0AAAAAANgJpRsAAAAAADuhdAMAAAAAYCeUbgAAAAAA7ITSDQAAAACAnVC6AQAAAACwE0o3AAAAAAB2QukGAAAAAMBOKN0AAAAAANgJpRsAAAAAADuhdAMAAAAAYCeUbgAAAAAA7ITSDQAAAACAnVC6AQAAAACwE0o3AAAAAAB2QukGAAAAAMBOKN0AAAAAANgJpRsAAAAAADtxmNK9YMEC+fv7y93dXYGBgYqPjzc6EgAAAACgknOI0r169WpFRETo9ddf14EDB9SuXTv17t1bFy5cMDoaAAAAAKASc4jS/cEHH2jMmDEaMWKEWrZsqY8++khVqlRRVFSU0dEAAAAAAJVYhS/dN27c0P79+xUUFGSb5+TkpKCgIMXFxRmYDAAAAABQ2bkYHeC3ys7OVklJiXx9fUvN9/X1VWpq6m23KSoqUlFRke37vLw8SVJ+fv59y3W14Mp92xd+lJ/vaZ/9Xr9ul/1Wdqb7+N/UD67nc6zsId/5/h8rSSooKLDLfiu7+/n76od9Wa3W+7ZPAADwowpfun+Nt99+W9OnT79lfv369Q1IAziwd94xOgHu0d/0N6MjwGBXrlyRt7e30TEAAHA4Fb50165dW87Ozjp//nyp+efPn1fdunVvu82UKVMUERFh+95isSgnJ0e1atWSyWSya97yJj8/X/Xr19eZM2fk5eVldBzcBcer4uBYVSyV+XhZrVZduXJFfn5+RkcBAMAhVfjS/cADD6h9+/batm2b+vXrJ+n7Er1t2zZNmDDhttu4ubnJzc2t1Lzq1avbOWn55uXlVen+R7Mi43hVHByriqWyHi/OcAMAYD8VvnRLUkREhIYPH64OHTqoU6dOmjNnjgoLCzVixAijowEAAAAAKjGHKN2DBg3SxYsXNXXqVGVlZemRRx7R5s2bbxlcDQAAAACAsuQQpVuSJkyYcMfLyXFnbm5uev3112+53B7lE8er4uBYVSwcLwAAYC8mK88IAQAAAADALpyMDgAAAAAAgKOidAMAAAAAYCeUbgAAAAAA7ITSDb3zzjsymUwKDw83Ogp+RlxcnJydnRUaGmp0FNzBM888o379+t0yf+fOnTKZTMrNzS3zTAAAADAWpbuSS0hI0KJFi9S2bVujo+AuzGazJk6cqNjYWJ07d87oOAAAAADuAaW7EisoKNDQoUO1ZMkS1ahRw+g4+BkFBQVavXq1xo0bp9DQUC1fvtzoSIBD8Pf315w5c0rNe+SRRzRt2jRD8gAAAMdD6a7Exo8fr9DQUAUFBRkdBXexZs0aNW/eXM2aNdNTTz2lqKgo8bQ/AAAAoPxzMToAjLFq1SodOHBACQkJRkfBPTCbzXrqqackScHBwcrLy9OuXbvUvXt3Y4PhFtHR0apatWqpeSUlJQalAQAAgNEo3ZXQmTNn9Pzzz2vLli1yd3c3Og7u4tixY4qPj9eGDRskSS4uLho0aJDMZjOluxzq0aOHIiMjS83bt2+f7Y8mAAAAqFwo3ZXQ/v37deHCBf3+97+3zSspKVFsbKzmz5+voqIiOTs7G5gQ/8tsNqu4uFh+fn62eVarVW5ubpo/f768vb0NTIef8vT01MMPP1xq3tmzZw1Kg7txcnK65VaNmzdvGpQGAAA4Ikp3JdSzZ0+lpKSUmjdixAg1b95ckydPpnCXI8XFxVqxYoVmzZqlXr16lVrWr18/rVy5Us8++6xB6YCKz8fHR5mZmbbv8/PzderUKQMTAQAAR0PproSqVaum1q1bl5rn6empWrVq3TIfxoqOjtbly5c1atSoW85oh4WFyWw2U7qB3+DRRx/V8uXL1bdvX1WvXl1Tp07lD48AAOC+YvRyoBwzm80KCgq67SXkYWFhSkxM1KFDhwxIBjiGKVOmqFu3bvrzn/+s0NBQ9evXT40bNzY6FgAAcCAmK88dAgAAAADALjjTDQAAAACAnVC6AQAAAACwE0o3AAAAAAB2QukGAAAAAMBOKN0AAAAAANgJpRsAAAAAADuhdAMAAAAAYCeUbgAAAAAA7ITSDeC+MJlM+vzzz42OAQAAAJQrlG4A9yQrK0sTJ05Uo0aN5Obmpvr166tv377atm2b0dEAAACAcsvF6AAAyr/Tp0+rS5cuql69ut577z21adNGN2/eVExMjMaPH6/U1FSjIwIAAADlEme6AdzVX//6V5lMJsXHxyssLExNmzZVq1atFBERob179952m8mTJ6tp06aqUqWKGjVqpNdee003b960LT948KB69OihatWqycvLS+3bt1diYqIkKT09XX379lWNGjXk6empVq1aadOmTbZtv/nmG4WEhKhq1ary9fXV008/rezsbNvytWvXqk2bNvLw8FCtWrUUFBSkwsJCO/10AAAAgDvjTDeAn5WTk6PNmzfrrbfekqen5y3Lq1evftvtqlWrpuXLl8vPz08pKSkaM2aMqlWrpkmTJkmShg4dqoCAAEVGRsrZ2VnJyclydXWVJI0fP143btxQbGysPD09deTIEVWtWlWSlJubq0cffVSjR4/W7Nmzde3aNU2ePFlPPvmktm/frszMTA0ZMkQzZ87UE088oStXrujrr7+W1Wq1zw8IAAAA+BmUbgA/6+TJk7JarWrevPkv2u7VV1+1vfb399dLL72kVatW2Up3RkaGXn75Zdt+mzRpYls/IyNDYWFhatOmjSSpUaNGtmXz589XQECAZsyYYZsXFRWl+vXr6/jx4yooKFBxcbH69++vBg0aSJJtPwAAAEBZo3QD+Fm/9gzx6tWrNXfuXKWlpdmKsJeXl215RESERo8erU8++URBQUEaOHCgGjduLEl67rnnNG7cOH311VcKCgpSWFiY2rZtK+n7y9J37NhhO/P9v9LS0tSrVy/17NlTbdq0Ue/evdWrVy8NGDBANWrU+FWfAwAAAPgtuKcbwM9q0qSJTCbTLxosLS4uTkOHDlWfPn0UHR2tpKQkvfLKK7px44ZtnWnTpunw4cMKDQ3V9u3b1bJlS23YsEGSNHr0aH377bd6+umnlZKSog4dOmjevHmSpIKCAvXt21fJycmlphMnTuhPf/qTnJ2dtWXLFv373/9Wy5YtNW/ePDVr1kynTp26vz8YAAAA4B6YrNzoCOAuQkJClJKSomPHjt1yX3dubq6qV68uk8mkDRs2qF+/fpo1a5YWLlyotLQ023qjR4/W2rVrlZube9v3GDJkiAoLC7Vx48Zblk2ZMkVffvmlDh06pFdeeUXr1q3TN998IxeXu1+sU1JSogYNGigiIkIRERG/7IMDAAAAvxFnugHc1YIFC1RSUqJOnTpp3bp1OnHihI4ePaq5c+eqc+fOt6zfpEkTZWRkaNWqVUpLS9PcuXNtZ7El6dq1a5owYYJ27typ9PR07dmzRwkJCWrRooUkKTw8XDExMTp16pQOHDigHTt22JaNHz9eOTk5GjJkiBISEpSWlqaYmBiNGDFCJSUl2rdvn2bMmKHExERlZGRo/fr1unjxom17AAAAoCxxTzeAu2rUqJEOHDigt956Sy+++KIyMzPl4+Oj9u3bKzIy8pb1H3vsMb3wwguaMGGCioqKFBoaqtdee03Tpk2TJDk7O+vSpUsaNmyYzp8/r9q1a6t///6aPn26pO/PTo8fP15nz56Vl5eXgoODNXv2bEmSn5+f9uzZo8mTJ6tXr14qKipSgwYNFBwcLCcnJ3l5eSk2NlZz5sxRfn6+GjRooFmzZikkJKTMfl4AAADAD7i8HAAAAAAAO+HycgAAAAAA7ITSDQAAAACAnVC6AQAAAACwE0o3AAAAAAB2QukGAAAAAMBOKN0AAAAAANgJpRsAAAAAADuhdAMAAAAAYCeUbgAAAAAA7ITSDQAAAACAnVC6AQAAAACwE0o3AAAAAAB28v8BDezVnF1TyEwAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Count the number of occurrences for each target class\n",
        "label_counts = np.unique(data_with_labels[:, -1], return_counts=True)\n",
        "labels = label_counts[0]\n",
        "values = label_counts[1]\n",
        "\n",
        "# Initialize filtered_values with zeros\n",
        "filtered_values = [0] * len(target_classes)\n",
        "\n",
        "# Iterate through target_classes and update filtered_values if the class exists\n",
        "for i, cls in enumerate(target_classes):\n",
        "    if cls in labels:\n",
        "        index = np.where(labels == cls)[0][0]\n",
        "        filtered_values[i] = values[index]\n",
        "\n",
        "# Create a figure with one row and two columns for side-by-side plots\n",
        "fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
        "\n",
        "# Plot the bar chart on the first subplot (axes[0])\n",
        "axes[0].bar(target_classes, filtered_values, color=['lightblue', 'lightcoral', 'lightgreen', 'lightgrey'])\n",
        "for i, v in enumerate(filtered_values):\n",
        "    axes[0].text(i, v, str(v), ha='center', va='bottom', fontweight='bold', fontsize=12)\n",
        "\n",
        "axes[0].set_title('Counts of Classes')\n",
        "axes[0].set_xlabel('Classes')\n",
        "axes[0].set_ylabel('Count')\n",
        "\n",
        "# Create a pie chart on the second subplot (axes[1])\n",
        "axes[1].pie(filtered_values, labels=target_classes, autopct='%1.1f%%', startangle=90,\n",
        "            colors=['lightblue', 'lightcoral', 'lightgreen', 'lightgrey'],\n",
        "            wedgeprops={'edgecolor': 'black', 'linewidth': 1, 'antialiased': True})\n",
        "axes[1].set_title('Counts of Classes')\n",
        "axes[1].axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "# Adjust the layout and display the plots\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcBPeYPlCD0R"
      },
      "source": [
        "## Getting X (feature matrix) and y (Label--> Target)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_CR-1EBJO3G",
        "outputId": "2d4fd840-f7e9-4b43-a0fa-ab0e910ddac1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "X shape: (839, 4096)\n",
            "y shape: (839,)\n",
            "X dtype: <U21\n",
            "Y dtype: <U21\n"
          ]
        }
      ],
      "source": [
        "X_MLC = data_with_labels[:, :-1]  # features\n",
        "y_MLC = data_with_labels[:, -1]   # Labels\n",
        "\n",
        "# Print the shapes of X and y\n",
        "print(\"X shape:\", X_MLC.shape)\n",
        "print(\"y shape:\", y_MLC.shape)\n",
        "\n",
        "print(\"X dtype:\", X_MLC.dtype)\n",
        "print(\"Y dtype:\", y_MLC.dtype)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1sGe484e5TlZ"
      },
      "source": [
        "##Splitting 60% Train, 20% validation , 20% Test\n",
        "For the same reason above\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64LSNM0a5y88"
      },
      "source": [
        "###Use train_test_split (Selecting images at random)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WewFV_b_5y8-",
        "outputId": "28d28e56-a835-4a47-dd4b-faf031f84bde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Transformed labels: [1 2 3 1 2 1 1 2 1 2 1 1 2 1 1 1 1 1 1 1 3 2 1 0 1 1 1 1 1 1 1 2 1 3 2 3 2\n",
            " 1 1 1 1 2 1 1 1 3 2 2 1 1 2 2 1 1 1 0 2 2 1 1 2 1 2 1 1 1 1 1 1 1 0 1 1 2\n",
            " 1 1 3 3 3 1 1 2 1 1 2 1 1 1 2 1 1 0 1 1 1 2 1 2 1 1 2 1 1 1 0 1 1 1 1 2 1\n",
            " 1 2 1 1 3 1 1 1 1 1 0 1 1 2 1 1 2 3 0 1 0 1 2 1 1 1 2 1 3 2 1 2 2 1 2 2 1\n",
            " 2 2 1 1 1 0 1 2 1 0 2 1 1 1 1 1 1 2 0 1 3 2 3 1 2 0 3 2 1 2 1 1 1 1 1 0 1\n",
            " 1 0 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 1 1 1 1 1 2 2 1 1 1\n",
            " 1 1 2 2 3 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 3 1 1 1 2 1 1 1 2\n",
            " 1 0 0 1 1 0 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 2 2 3 2 1 1 1 1 1 2 1 0 1 1 2\n",
            " 1 1 1 3 1 2 2 1 1 2 1 1 2 1 2 2 1 3 1 1 1 2 3 1 1 3 1 1 1 1 1 1 1 2 1 1 1\n",
            " 1 1 1 1 1 2 1 1 1 0 1 1 1 1 1 1 3 1 2 1 1 1 2 1 1 1 1 1 1 3 1 1 1 1 1 1 1\n",
            " 3 3 1 2 1 2 1 1 1 2 1 1 1 1 1 1 1 2 1 2 1 1 2 1 1 1 1 1 1 1 1 2 2 2 1 1 1\n",
            " 1 0 1 1 1 3 2 2 0 1 1 2 1 2 2 1 2 3 2 2 1 1 2 1 1 2 1 1 1 1 1 1 1 1 2 0 1\n",
            " 1 1 1 2 1 1 2 1 1 3 2 3 1 1 2 2 1 1 1 1 1 1 1 1 1 1 2 0 1 1 3 1 1 2 2 2 1\n",
            " 2 1 1 1 1 1 0 2 1 2 2 1 1 1 0 1 1 1 1 1 1 1 3 1 2 2 1 1 2 3 2 2 1 2 0 2 1\n",
            " 1 0 1 2 0 1 1 1 2 1 1 1 1 2 1 1 1 1 1 2 2 1 2 2 1 1 1 2 2 1 1 1 1 0 1 1 2\n",
            " 1 3 1 1 1 1 2 1 1 1 0 1 1 3 1 0 0 1 1 2 1 1 1 0 1 2 1 2 2 2 1 0 1 1 1 1 1\n",
            " 1 2 1 1 2 2 1 2 3 0 1 1 2 2 2 1 1 1 2 1 2 1 1 3 1 1 1 1 2 2 1 1 0 2 1 2 1\n",
            " 1 1 2 2 2 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 2 2 1 1 1 1 2 1 1 3 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 2 0 1 1 1 1 2 1 1 1 2 1 1 2 1 0 1 3 2 1 1 1 2 1 1 3 2 2 2 1\n",
            " 1 1 1 1 1 1 1 1 0 1 1 0 1 2 1 1 1 2 1 1 1 2 1 1 1 2 0 1 1 1 2 2 1 2 2 1 0\n",
            " 2 2 1 1 2 1 0 1 2 1 1 1 1 2 1 1 1 1 0 1 1 1 2 1 2 1 2 1 1 1 1 2 2 2 1 2 1\n",
            " 1 1 1 1 1 1 1 1 1 0 1 1 1 2 1 3 1 2 2 1 1 1 0 1 2 1 1 1 1 2 1 1 1 2 1 1 1\n",
            " 1 1 2 1 1 1 1 2 1 1 1 0 1 1 3 2 1 1 1 2 1 1 1 1 1]\n",
            "\n",
            "X dtype: float32\n",
            "Y dtype: int64\n",
            "Class 0: Count = 47\n",
            "Class 1: Count = 558\n",
            "Class 2: Count = 193\n",
            "Class 3: Count = 41\n"
          ]
        }
      ],
      "source": [
        "# Combine X and y into a single array\n",
        "combined_data = np.column_stack((X_MLC, y_MLC))\n",
        "\n",
        "# Shuffle the combined data\n",
        "np.random.shuffle(combined_data)\n",
        "\n",
        "# Split the shuffled data back into X and y\n",
        "shuffled_X = combined_data[:, :-1]\n",
        "shuffled_y = combined_data[:, -1]\n",
        "\n",
        "shuffled_X,shuffled_y=to_numeric (shuffled_X,shuffled_y)\n",
        "\n",
        "\n",
        "# Count the occurrences of each class (0, 1, 2, 3)\n",
        "class_counts = np.bincount(shuffled_y)\n",
        "for i, count in enumerate(class_counts):\n",
        "    print(f\"Class {i}: Count = {count}\")\n",
        "# class 4-->0 , class A-->1 ,class H-->2 ,class u-->3\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OkYqMnMK5y8_",
        "outputId": "1cdbbeda-9b1f-4d7d-a8d7-f727d0865fc8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training samples: 503\n",
            "Number of validation samples: 168\n",
            "Number of test samples: 168\n"
          ]
        }
      ],
      "source": [
        "X_train_random, X_temp_random, y_train_random, y_temp_random= train_test_split(shuffled_X, shuffled_y, test_size=0.4, random_state=42)\n",
        "X_val_random, X_test_random, y_val_random, y_test_random = train_test_split(X_temp_random, y_temp_random, test_size=0.5, random_state=42)\n",
        "\n",
        "# Print the number of samples in each split\n",
        "print(\"Number of training samples:\", len(X_train_random))\n",
        "print(\"Number of validation samples:\", len(X_val_random))\n",
        "print(\"Number of test samples:\", len(X_test_random))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggUhlayT6Veo",
        "outputId": "0d0480da-8fda-49c9-d56f-b2c154194ee5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ],
      "source": [
        "y_train_pred_random,y_test_pred_random=MLC_Model(X_train_random, y_train_random,X_test_random)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l_D_Xkxy9Ge-"
      },
      "source": [
        "#### Evaluation on training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "yNIkAyQj9GfA",
        "outputId": "5c088686-15dd-41c8-d0a0-03b71cb42035"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Set Metrics:\n",
            "Accuracy: 99.60\n",
            "Average Recall: 99.56\n",
            "Average Precision: 99.85\n",
            "F1 Score: 99.70\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "MLC_Evalution(y_train_random, y_train_pred_random,'Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5R88yvan9GfB"
      },
      "source": [
        "#### Evaluation on testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "3KYVN3Av9GfC",
        "outputId": "a1ffc8df-b9eb-44d2-f2d4-12ea6d116ac1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Testing Set Metrics:\n",
            "Accuracy: 58.33\n",
            "Average Recall: 39.08\n",
            "Average Precision: 37.92\n",
            "F1 Score: 38.49\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "MLC_Evalution(y_test_random, y_test_pred_random,'Testing')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wfAGshap9cqu"
      },
      "source": [
        "###Use train_test_split(Stratified Sampling)\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qQMYOGZ79cqw",
        "outputId": "588d5835-a05d-4d9b-8f36-79a9ae903e3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Transformed labels: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
            " 1 1 1 1 1 1 1 1 1 1 1 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n",
            " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
            " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n",
            "\n",
            "X dtype: float32\n",
            "Y dtype: int64\n"
          ]
        }
      ],
      "source": [
        "X_MLC,y_MLC_encoded=to_numeric (X_MLC,y_MLC)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaV_dx-k9cqx",
        "outputId": "7bb31afd-ef89-497c-9a4d-aed6587ab6db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of training samples: 503\n",
            "Number of validation samples: 168\n",
            "Number of test samples: 168\n"
          ]
        }
      ],
      "source": [
        "X_train, X_temp, y_train, y_temp = train_test_split(X_MLC, y_MLC_encoded, test_size=0.4, random_state=42, stratify=y_MLC_encoded)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42, stratify=y_temp)\n",
        "\n",
        "# Print the number of samples in each split\n",
        "print(\"Number of training samples:\", len(X_train))\n",
        "print(\"Number of validation samples:\", len(X_val))\n",
        "print(\"Number of test samples:\", len(X_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUkDH9FJ9cqy",
        "outputId": "4cd1e8c7-ce58-4ad7-da6e-877f754d6747"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ],
      "source": [
        "y_train_pred,y_test_pred=MLC_Model (X_train, y_train,X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50fMgUd19cqy"
      },
      "source": [
        "#### Evaluation on training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "vUVkCP2Z9cqz",
        "outputId": "759e5d42-6109-400e-ab74-9e906fee4b7d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training Set Metrics:\n",
            "Accuracy: 98.81\n",
            "Average Recall: 98.71\n",
            "Average Precision: 99.56\n",
            "F1 Score: 99.13\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "MLC_Evalution(y_train, y_train_pred,'Training')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RdVMpX8r9cqz"
      },
      "source": [
        "#### Evaluation on testing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "id": "Rk-wUAMs9cqz",
        "outputId": "143602d8-c209-4545-e443-031d147144f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Testing Set Metrics:\n",
            "Accuracy: 49.40\n",
            "Average Recall: 36.81\n",
            "Average Precision: 34.98\n",
            "F1 Score: 35.87\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 400x300 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "MLC_Evalution(y_test, y_test_pred,'Testing')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R2e-amy2ucEa"
      },
      "source": [
        "#Result Explanation\n",
        "####In the multiclass classification task using a Logistic Regression classifier with the 'one-vs-rest' (OvR) strategy and four target classes ('4', 'A', 'H', and 'u'), the performance of the classifier is influenced by class imbalance.\n",
        "\n",
        "####-Class Imbalance: The dataset exhibits significant class imbalance, with 'A' having the largest number of samples (558), 'H' having 193 samples, '4' having 47 samples, and 'u' having 41 samples.\n",
        "\n",
        "####-Easier vs. More Difficult Classes:Easier Classes: The classifier performs very well on the majority classes 'A' and 'H' in both training scenarios. These classes have larger sample sizes, and the model effectively learns to classify them.\n",
        "\n",
        "####-More Difficult Classes: The minority classes '4' and 'u' are more challenging for the classifier. In both training scenarios, the model struggles to correctly classify these classes, resulting in lower accuracy, average recall, average precision, and F1 scores for these classes.\n",
        "\n",
        "####-Confusion: The classifier tends to confuse the minority classes ('4' and 'u') with the majority classes ('A' and 'H'). This confusion is evident from the lower average recall and precision scores for these minority classes.\n",
        "\n",
        "####-Impact of Stratified Sampling: Stratified sampling helps maintain class balance in the training set without significantly impacting overall performance. It results in slightly lower accuracy on the training data but maintains good average recall, average precision, and F1 scores.\n",
        "\n",
        "####-Challenges on Testing Data: On the testing data, regardless of stratified sampling, the classifier faces difficulties in generalization, especially for the minority classes. This indicates that the model struggles to perform well on unseen data, particularly for underrepresented classes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wjwih2kOuyKx"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "oZnrWMXYejCA",
        "VtWKWN46iHTX",
        "qaVt1tvbfTqm",
        "_R2zp0BtndSi",
        "aaRb5xzkBK3K",
        "qInNhN1fl2H5",
        "nTfPzZMu-6ZO",
        "pjXdycVSSUW1"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}