Linear_and_Logistic_Regression_using_PyTorch.ipynb

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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/smlra-kjsce/Pytorch-101-2023/blob/main/Linear_and_Logistic_Regression_using_PyTorch.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GR57VDRKjGLX"
      },
      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import numpy as np\n",
        "from sklearn import datasets\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TlhK-cQqoWgU"
      },
      "source": [
        "# Linear Regression \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FAansz0UrWOp"
      },
      "source": [
        "## Step 1 - Making Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgtwp-hyk9ZV"
      },
      "source": [
        "# Making random dataset from sklearn.datsets\n",
        "x_np , y_np = datasets.make_regression(n_samples=100,n_features=1,noise=10,random_state=1)\n",
        "\n",
        "# Since the x_np and y_np are in numpy array form, we convert them to pytorch tensor\n",
        "x = torch.from_numpy(x_np.astype(np.float32))\n",
        "y = torch.from_numpy(y_np.astype(np.float32))"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tDG48t3Bu2iU",
        "outputId": "97e6f337-6092-4c1b-c31f-7060f6002cc9"
      },
      "source": [
        "# We try to see the shape of the s and y tensor so that they are of valid dimension.\n",
        "\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 1])\n",
            "torch.Size([100])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aZkp9umq3gE"
      },
      "source": [
        "We see that y is of shape [100] so we convert it to shape [100,1] so that we can easily feed it to the model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddOeupRfvdID",
        "outputId": "a40ccb47-67d1-4e8c-8401-2f96b91b34dc"
      },
      "source": [
        "# We convert y of hsape 100 to (100,1) and see the shape \n",
        "\n",
        "y = y.view(y.shape[0],1)\n",
        "print(y.shape)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([100, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDRGJlBMrb90"
      },
      "source": [
        "## Step 2 - Designing Model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnlDpd-xvlR0",
        "outputId": "d1a0f176-afcc-4b51-fb4e-cce9d357598c"
      },
      "source": [
        "# We try to understand the input and output size and make a model out of it\n",
        "\n",
        "# the no of featrues that each data-point has becomes out input size as any input will have that many values. \n",
        "# Eg. if no. of features is 2, we will have input of the form (a,b)\n",
        "no_of_samples, no_of_features = x.shape\n",
        "input_size = no_of_features\n",
        "\n",
        "# The ouput size will be the shape of each of the given y.\n",
        "output_size = y.shape[1]\n",
        "\n",
        "print(\"Input size is \" + str(input_size))\n",
        "print(\"Output size is \" + str(output_size))\n",
        "\n",
        "# We construct a linear model with the given input and output sizes\n",
        "model = nn.Linear(input_size,output_size)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input size is 1\n",
            "Output size is 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0H54WX2hso-d"
      },
      "source": [
        "## Step 3 - Defining loss and optimizer "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qdqJAqhYwd6a"
      },
      "source": [
        "# Loss and Optimizer \n",
        "\n",
        "learning_rate = 0.01 \n",
        "epochs = 1000\n",
        "\n",
        "# We take the standard mean squared loss\n",
        "model_loss = nn.MSELoss()\n",
        "\n",
        "# We take the SGD (Stochastic Gradient Descent) \n",
        "optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4TfOtqCs53e"
      },
      "source": [
        "## Step 4 - Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S382rx0xxmQD",
        "outputId": "80135013-7404-461a-9f9f-8f28d70ee0ad"
      },
      "source": [
        "# Here we take all the components together and put them in a training loop.\n",
        "\n",
        "for epoch in range(epochs):\n",
        "  # Forward pass\n",
        "  y_hat = model(x)\n",
        "  loss = model_loss(y_hat,y)\n",
        "\n",
        "  # Backward pass\n",
        "  loss.backward()\n",
        "\n",
        "  # Update weights\n",
        "  optimizer.step()\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  # Printing it at every 10 epochs\n",
        "  if (epoch%10 == 0):\n",
        "    print(\"Epoch \" + str(epoch))\n",
        "    print(\"Loss \" + str(loss.item()))\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 0\n",
            "Loss 5386.3017578125\n",
            "Epoch 10\n",
            "Loss 3933.388427734375\n",
            "Epoch 20\n",
            "Loss 2880.792236328125\n",
            "Epoch 30\n",
            "Loss 2117.4716796875\n",
            "Epoch 40\n",
            "Loss 1563.430908203125\n",
            "Epoch 50\n",
            "Loss 1160.9573974609375\n",
            "Epoch 60\n",
            "Loss 868.3634643554688\n",
            "Epoch 70\n",
            "Loss 655.5015869140625\n",
            "Epoch 80\n",
            "Loss 500.54449462890625\n",
            "Epoch 90\n",
            "Loss 387.6737976074219\n",
            "Epoch 100\n",
            "Loss 305.4141540527344\n",
            "Epoch 110\n",
            "Loss 245.43414306640625\n",
            "Epoch 120\n",
            "Loss 201.67941284179688\n",
            "Epoch 130\n",
            "Loss 169.7476043701172\n",
            "Epoch 140\n",
            "Loss 146.4349822998047\n",
            "Epoch 150\n",
            "Loss 129.40951538085938\n",
            "Epoch 160\n",
            "Loss 116.97154235839844\n",
            "Epoch 170\n",
            "Loss 107.88228607177734\n",
            "Epoch 180\n",
            "Loss 101.23873138427734\n",
            "Epoch 190\n",
            "Loss 96.38138580322266\n",
            "Epoch 200\n",
            "Loss 92.8293228149414\n",
            "Epoch 210\n",
            "Loss 90.231201171875\n",
            "Epoch 220\n",
            "Loss 88.33063507080078\n",
            "Epoch 230\n",
            "Loss 86.93998718261719\n",
            "Epoch 240\n",
            "Loss 85.9223861694336\n",
            "Epoch 250\n",
            "Loss 85.17759704589844\n",
            "Epoch 260\n",
            "Loss 84.63243865966797\n",
            "Epoch 270\n",
            "Loss 84.2333755493164\n",
            "Epoch 280\n",
            "Loss 83.94120025634766\n",
            "Epoch 290\n",
            "Loss 83.72725677490234\n",
            "Epoch 300\n",
            "Loss 83.57062530517578\n",
            "Epoch 310\n",
            "Loss 83.45590209960938\n",
            "Epoch 320\n",
            "Loss 83.37189483642578\n",
            "Epoch 330\n",
            "Loss 83.31036376953125\n",
            "Epoch 340\n",
            "Loss 83.26530456542969\n",
            "Epoch 350\n",
            "Loss 83.23230743408203\n",
            "Epoch 360\n",
            "Loss 83.20812225341797\n",
            "Epoch 370\n",
            "Loss 83.19042205810547\n",
            "Epoch 380\n",
            "Loss 83.17744445800781\n",
            "Epoch 390\n",
            "Loss 83.16793823242188\n",
            "Epoch 400\n",
            "Loss 83.16096496582031\n",
            "Epoch 410\n",
            "Loss 83.15586853027344\n",
            "Epoch 420\n",
            "Loss 83.15213012695312\n",
            "Epoch 430\n",
            "Loss 83.14940643310547\n",
            "Epoch 440\n",
            "Loss 83.14739990234375\n",
            "Epoch 450\n",
            "Loss 83.14591979980469\n",
            "Epoch 460\n",
            "Loss 83.14484405517578\n",
            "Epoch 470\n",
            "Loss 83.1440658569336\n",
            "Epoch 480\n",
            "Loss 83.14346313476562\n",
            "Epoch 490\n",
            "Loss 83.14305877685547\n",
            "Epoch 500\n",
            "Loss 83.14275360107422\n",
            "Epoch 510\n",
            "Loss 83.14251708984375\n",
            "Epoch 520\n",
            "Loss 83.1423568725586\n",
            "Epoch 530\n",
            "Loss 83.14221954345703\n",
            "Epoch 540\n",
            "Loss 83.14213562011719\n",
            "Epoch 550\n",
            "Loss 83.1420669555664\n",
            "Epoch 560\n",
            "Loss 83.14202117919922\n",
            "Epoch 570\n",
            "Loss 83.14198303222656\n",
            "Epoch 580\n",
            "Loss 83.14195251464844\n",
            "Epoch 590\n",
            "Loss 83.1419448852539\n",
            "Epoch 600\n",
            "Loss 83.14193725585938\n",
            "Epoch 610\n",
            "Loss 83.14192199707031\n",
            "Epoch 620\n",
            "Loss 83.14190673828125\n",
            "Epoch 630\n",
            "Loss 83.14189147949219\n",
            "Epoch 640\n",
            "Loss 83.14189147949219\n",
            "Epoch 650\n",
            "Loss 83.14189147949219\n",
            "Epoch 660\n",
            "Loss 83.14190673828125\n",
            "Epoch 670\n",
            "Loss 83.14190673828125\n",
            "Epoch 680\n",
            "Loss 83.14189147949219\n",
            "Epoch 690\n",
            "Loss 83.14188385009766\n",
            "Epoch 700\n",
            "Loss 83.14189147949219\n",
            "Epoch 710\n",
            "Loss 83.14188385009766\n",
            "Epoch 720\n",
            "Loss 83.14189147949219\n",
            "Epoch 730\n",
            "Loss 83.14188385009766\n",
            "Epoch 740\n",
            "Loss 83.14188385009766\n",
            "Epoch 750\n",
            "Loss 83.14189147949219\n",
            "Epoch 760\n",
            "Loss 83.14189147949219\n",
            "Epoch 770\n",
            "Loss 83.14188385009766\n",
            "Epoch 780\n",
            "Loss 83.14190673828125\n",
            "Epoch 790\n",
            "Loss 83.14189147949219\n",
            "Epoch 800\n",
            "Loss 83.14189147949219\n",
            "Epoch 810\n",
            "Loss 83.14189147949219\n",
            "Epoch 820\n",
            "Loss 83.14189147949219\n",
            "Epoch 830\n",
            "Loss 83.14189147949219\n",
            "Epoch 840\n",
            "Loss 83.14189147949219\n",
            "Epoch 850\n",
            "Loss 83.14188385009766\n",
            "Epoch 860\n",
            "Loss 83.14188385009766\n",
            "Epoch 870\n",
            "Loss 83.14188385009766\n",
            "Epoch 880\n",
            "Loss 83.14189147949219\n",
            "Epoch 890\n",
            "Loss 83.14189147949219\n",
            "Epoch 900\n",
            "Loss 83.14189147949219\n",
            "Epoch 910\n",
            "Loss 83.14188385009766\n",
            "Epoch 920\n",
            "Loss 83.14189147949219\n",
            "Epoch 930\n",
            "Loss 83.14189147949219\n",
            "Epoch 940\n",
            "Loss 83.14189147949219\n",
            "Epoch 950\n",
            "Loss 83.14189147949219\n",
            "Epoch 960\n",
            "Loss 83.14189147949219\n",
            "Epoch 970\n",
            "Loss 83.14189147949219\n",
            "Epoch 980\n",
            "Loss 83.14189147949219\n",
            "Epoch 990\n",
            "Loss 83.14189147949219\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U9z9R8yqtSGJ"
      },
      "source": [
        "## Step 5 - Plot the graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ne71GSJoyyty",
        "outputId": "595218bd-5546-4818-9578-2528ce5aa1a0"
      },
      "source": [
        "# We first unwrap the predictions into a array named y_predicted\n",
        "y_predicted = model(x).detach().numpy()\n",
        "\n",
        "# We plot the original points\n",
        "plt.scatter(x_np,y_np,color=\"red\")\n",
        "\n",
        "# We plot the predictions \n",
        "plt.plot(x_np,y_predicted,color=\"blue\")\n",
        "plt.show()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pN9THn8CwiHw"
      },
      "source": [
        "# Logistic Regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mHQ-vqpMsW1M"
      },
      "source": [
        "## Step 1 - Data Pre-processing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gt687BC8zRLt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b37604e-4c25-42ec-c739-6ccfce3e68ee"
      },
      "source": [
        "data = datasets.load_wine()\n",
        "\n",
        "print(data)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'data': array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
            "        1.065e+03],\n",
            "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
            "        1.050e+03],\n",
            "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
            "        1.185e+03],\n",
            "       ...,\n",
            "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
            "        8.350e+02],\n",
            "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
            "        8.400e+02],\n",
            "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
            "        5.600e+02]]), 'target': array([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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
            "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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,\n",
            "       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, 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,\n",
            "       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]), 'frame': None, 'target_names': array(['class_0', 'class_1', 'class_2'], dtype='<U7'), 'DESCR': '.. _wine_dataset:\\n\\nWine recognition dataset\\n------------------------\\n\\n**Data Set Characteristics:**\\n\\n    :Number of Instances: 178 (50 in each of three classes)\\n    :Number of Attributes: 13 numeric, predictive attributes and the class\\n    :Attribute Information:\\n \\t\\t- Alcohol\\n \\t\\t- Malic acid\\n \\t\\t- Ash\\n\\t\\t- Alcalinity of ash  \\n \\t\\t- Magnesium\\n\\t\\t- Total phenols\\n \\t\\t- Flavanoids\\n \\t\\t- Nonflavanoid phenols\\n \\t\\t- Proanthocyanins\\n\\t\\t- Color intensity\\n \\t\\t- Hue\\n \\t\\t- OD280/OD315 of diluted wines\\n \\t\\t- Proline\\n\\n    - class:\\n            - class_0\\n            - class_1\\n            - class_2\\n\\t\\t\\n    :Summary Statistics:\\n    \\n    ============================= ==== ===== ======= =====\\n                                   Min   Max   Mean     SD\\n    ============================= ==== ===== ======= =====\\n    Alcohol:                      11.0  14.8    13.0   0.8\\n    Malic Acid:                   0.74  5.80    2.34  1.12\\n    Ash:                          1.36  3.23    2.36  0.27\\n    Alcalinity of Ash:            10.6  30.0    19.5   3.3\\n    Magnesium:                    70.0 162.0    99.7  14.3\\n    Total Phenols:                0.98  3.88    2.29  0.63\\n    Flavanoids:                   0.34  5.08    2.03  1.00\\n    Nonflavanoid Phenols:         0.13  0.66    0.36  0.12\\n    Proanthocyanins:              0.41  3.58    1.59  0.57\\n    Colour Intensity:              1.3  13.0     5.1   2.3\\n    Hue:                          0.48  1.71    0.96  0.23\\n    OD280/OD315 of diluted wines: 1.27  4.00    2.61  0.71\\n    Proline:                       278  1680     746   315\\n    ============================= ==== ===== ======= =====\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: class_0 (59), class_1 (71), class_2 (48)\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML Wine recognition datasets.\\nhttps://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\n\\nThe data is the results of a chemical analysis of wines grown in the same\\nregion in Italy by three different cultivators. There are thirteen different\\nmeasurements taken for different constituents found in the three types of\\nwine.\\n\\nOriginal Owners: \\n\\nForina, M. et al, PARVUS - \\nAn Extendible Package for Data Exploration, Classification and Correlation. \\nInstitute of Pharmaceutical and Food Analysis and Technologies,\\nVia Brigata Salerno, 16147 Genoa, Italy.\\n\\nCitation:\\n\\nLichman, M. (2013). UCI Machine Learning Repository\\n[https://archive.ics.uci.edu/ml]. Irvine, CA: University of California,\\nSchool of Information and Computer Science. \\n\\n.. topic:: References\\n\\n  (1) S. Aeberhard, D. Coomans and O. de Vel, \\n  Comparison of Classifiers in High Dimensional Settings, \\n  Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of  \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Technometrics). \\n\\n  The data was used with many others for comparing various \\n  classifiers. The classes are separable, though only RDA \\n  has achieved 100% correct classification. \\n  (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) \\n  (All results using the leave-one-out technique) \\n\\n  (2) S. Aeberhard, D. Coomans and O. de Vel, \\n  \"THE CLASSIFICATION PERFORMANCE OF RDA\" \\n  Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of \\n  Mathematics and Statistics, James Cook University of North Queensland. \\n  (Also submitted to Journal of Chemometrics).\\n', 'feature_names': ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeulQ6wsnLJO",
        "outputId": "50a1e8d1-59b9-46ed-bf8c-8ba95a5a9dce"
      },
      "source": [
        "# We try to understand the size of out dataset\n",
        "\n",
        "# We first load the data and target\n",
        "x,y = data.data, data.target\n",
        "\n",
        "# We find the no. of features and data-points\n",
        "no_of_samples, no_of_features = x.shape\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(178, 13)\n",
            "(178,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z71ssHfSoDgD"
      },
      "source": [
        "We can see that there are 178 datapoints having 13 features each. Since y is of zero dimesnions, we rehshape it to (178,1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SoqYvIgUq6TL",
        "outputId": "4d0b353a-5013-46ca-cdaf-78669fc43e0b"
      },
      "source": [
        "y = y.reshape(y.shape[0],1)\n",
        "print(y.shape)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(178, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XSBUzolAP8Kf"
      },
      "source": [
        "We see that our dataset contains 3 different values of 'y', 0,1 and 2. This will create a problem as logistic regression works well with only binary classification. So, we remove the data points having class '1' and rename the class '2' as class '1'  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "66uDYVQbcmM1",
        "outputId": "c0018482-9e2f-493c-ad76-355adf129218"
      },
      "source": [
        "# We create an empty list consisting of all the indices that need to be deleted, i.e. all indices having y value as 1\n",
        "to_be_deleted = []\n",
        "\n",
        "# In the for loop, we append the indices having y=1 to the above list 'to_be_deleted' and replce those with y=2 as y=1\n",
        "for i in range (len(y)):\n",
        "  if y[i]==1:\n",
        "    to_be_deleted.append(i)\n",
        "  if y[i]==2:\n",
        "    y[i]=1\n",
        "\n",
        "# We delete the indices present in to_be_deleted from x and y using np.delete\n",
        "# Note that axis=0 means rowwise deletion and axis=1 means column wise\n",
        "x = np.delete(x,to_be_deleted,axis=0)\n",
        "y = np.delete(y,to_be_deleted,axis=0)\n",
        "print(y)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0]\n",
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            " [1]\n",
            " [1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x6qZdUDmuRPn",
        "outputId": "8b5d1ee6-6d54-4b76-ef44-fda6ab2a2450"
      },
      "source": [
        "print(y.shape)\n",
        "print(x.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(107, 1)\n",
            "(107, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y6Sdz1yEnstQ",
        "outputId": "7d7edc24-767d-44ab-e333-49d399c20130"
      },
      "source": [
        "# We split the dataset into training and testing with the help of sklearn library\n",
        "\n",
        "# Note: Do not change the order of x_train,x_test,t_train,y_test as the function returns it in that order itself\n",
        "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size=0.1)\n",
        "\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(96, 13)\n",
            "(11, 13)\n",
            "(96, 1)\n",
            "(11, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMp1i7BCqIX2"
      },
      "source": [
        "Since we are dealing with logistic regression, it is recommened to scale our data. We use standard scaler which will scale the data such that the mean of the data points is zero and the standard deviation is one. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8IY8sOcPpfwl"
      },
      "source": [
        "sc = StandardScaler()\n",
        "x_train = sc.fit_transform(x_train)\n",
        "x_test = sc.fit_transform(x_test)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Up2Qd5fzre_j",
        "outputId": "ce6b1236-dd1e-4fc6-9720-ac7adb4bbb1a"
      },
      "source": [
        "# Having completed the preprocessing, we convert the numpy arrays to torch tensors so that we can use pytorch models. \n",
        "\n",
        "x_train = torch.from_numpy(x_train.astype(np.float32))\n",
        "x_test = torch.from_numpy(x_test.astype(np.float32))\n",
        "y_train = torch.from_numpy(y_train.astype(np.float32))\n",
        "y_test = torch.from_numpy(y_test.astype(np.float32))\n",
        "\n",
        "print(y_test.shape)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([11, 1])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zCKX-9zsb0F"
      },
      "source": [
        "## Step 2 - Model\n",
        "\n",
        "Logistic Regression is essentially Linear regression with a sigmoid layer in it. \n",
        "\n",
        "As shown, torch.nn.Linear layer is a single neuron. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrKZ96xEtWb-"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w36SWv8XtvSr"
      },
      "source": [
        "The sigmoid function is shown as follows. In logistic regression, the sigmoid function is applied to the output of linear layer. \n",
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAArwAAAFUCAYAAADGRe8xAAAgAElEQVR4AeydBdhdxdX9cUjwQAiQpECQ4iS4F2mQFncP7gQtVkLRIoHiEtzdAgR3dw8ECe4ECYG0/drvP//nt7+u23lP7qt55cqa5zl3nzOyZ2bNPTPr7rvPnEmSgxEwAkbACBgBI2AEjIARqGEEJqnhvrlrRsAIGAEjYASMgBEwAkYgmfD6S2AEjIARMAJGwAgYASNQ0wiY8Nb08LpzRsAIGAEjYASMgBEwAia8/g4YASNgBIyAETACRsAI1DQCJrw1PbzunBEwAkbACBgBI2AEjIAJr78DRqCLEPh//+//Rc3IcgeJytOaJqqMZGNllS7ZWD7HGwEjYASMgBGodgRMeKt9BN1+I5AhIPIq+e9//zv961//muD43//93yilfJKZKp8aASNgBIyAEagZBEx4a2Yo3ZFqQQByWTwgpv/zP/8TB+fF9Ob6pvzk0zl6GgsQXg7lzcs1VsbxRsAIGAEjYASqFQET3modObe7qhCAWBJEMCWJk7U171CeP48vd668pEkv5JkwevTo9Oabb6aRI0eWjrfffjv94x//iPScXEeEP4yAETACRsAI1CACJrw1OKjuUuUjADGF6EI8kZDSG264Id14443p9ddfjw6ICOeEtrGeiegicWEgvPrqq2m++eZLM888c5pllllSz54906yzzpp69OiRBg8eXLLw5qS3JXU11gbHGwEjYASMgBGoVARMeCt1ZNyumkJARBIJyeWQy8Evv/ySjjzyyDTJJJPEcfDBB4elFuJKfhHfxgBRHiQ6Kffcc8+lueeeO/RNOumkabLJJivp1/lBBx2Uxo0bF2WoQ3oaq8fxRsAIGAEjYASqFQET3modObe7ahCASHIQJDn/8ccf09NPP5222GKLsMROPfXUQUqHDBkSeWWpzctEQuFDZFX5P/zww9S3b9/QNeWUUyYR3gEDBqS55porTTXVVGmKKaaIdIj2P//5zyDJ0tNcfYXqfWkEjIARMAJGoOIRMOGt+CFyA2sBAZFISOVtt90Wxw477BDuBbLsQk45P+KII6LLEFGCyjaGA+kcWHfx3T3ppJNKZFe6zzjjjPTll1+G2wT1TDPNNJFn9913D7UttSY31gbHGwEjYASMgBGoZARMeCt5dNy2mkBAhJTO4MrQvXv3koUVQorFFSsskmusrgQ9eNYSwguRJnz99ddpttlmK7kwoHPYsGGRBqn96quv0qabblqy+i6xxBLp2WefDbeJ3Jc3CvjDCBgBI2AEjECNIGDCWyMD6W5ULgJFwsuDYzPMMEM8SDbjjDMGyYXoysL75z//OTrTGsKrOr755psgvLLs8oAaRJZj/PjxQWyvuOKKqBPiTT6svwTVJ/JcuYi6ZUbACBgBI2AEWoeACW/r8HJuI9AmBGSlhUx+9tlnCT/bTz75JKyr/fv3D+I5MRZeEd5vv/02zTHHHCUSzQ4Nv/76a7g8iNCed955DQgv15QnXXra1EkXMgJGwAgYASNQoQiY8FbowLhZtYNATiI5V9D5iBEjGhDQ1lh4pRsizTkW3tlnn71EeGeaaaZ4KI00fIKRF1xwQaR369Yt5DnnnBNN0kNv5HEwAkbACBgBI1BLCJjw1tJoui8Vi4BIJBJiiYuB9uC966672kx46TBkV24IEN5evXqVCC8WXuohyMJ7/vnnN6jv3HPPjXQT3oDBH0bACBgBI1CDCJjw1uCgukuVjYDIrwjm7bffHgRUFte2WHjRyTF27Ng055xzNiC82u1BhFcWXvnwmvBW9vfFrTMCRsAIGIGJR8CEd+IxtAYj0CwCIrlIuR+IgA4fPryBxbUthFc7LDz++OPxMJweWsPCa8Lb7PA4gxEwAkbACNQ4Aia8NT7A7l7lISDyKwtvSwivyiDzQ64McltYZJFFgjyzzRmk1y4NlTf+bpERMAJGwAh0PgImvJ2PuWuscwREXltDeIFM5XL4RH5l4b3zzjsTD6rJwuuH1nK0fG4EjIARMAL1ioAJb72OvPvdZQiIuLaG8KoMknK4QyA5ILsE0vDh1WuFZeHVtmR6SI5dGUiTDy8PsUkvksPBCBgBI2AEjEAtIWDCW0uj6b5UBQIilK0lvCKjSLky0GHFc/7FF19M8OIJfHjJL1/eK6+8sgHh9YsnquJr40YaASNgBIzARCBgwjsR4LmoEWgLAm0lvCLIV111Vdpjjz3S4MGD08EHH5zeeuutkoWWbcl48cRkk00WpHaKKaZIp5xySvrhhx/S999/nz744IO0zjrrRPrkk0+ellxyyfTaa69FeblFqH1t6ZvLGAEjYASMgBGoRARMeCtxVNymmkZAhFIE9o477ghyOs0004Q88sgjo/+yyJIfCy1uDJxvueWWkQ+3BLYyu+eeeyKeVwdDWtlmjDTILpJjtdVWS6usskpaYIEF4lp1HXXUUVGXdOeW45oeBHfOCBgBI2AE6goBE966Gm53thIQKBJeHjTDIquHzYYMGRLNFCEmP4eud9ttt8jPDgzTTz994sUVBAgv+T7++OM033zzBbGdeuqp05RTTlkivtSj/X4hzuTFt1fWXQiv2lcJWLkNRsAIGAEjYATaAwET3vZA0TqMQCsQkBVV+/DefPPNJUKKNRY3BRFcJPk5lH+bbbYp5R84cGD69NNPSw+v6QG2d955p2TNlZU3lxtvvHH66aefotUqQ10mu60YSGc1AkbACBiBqkHAhLdqhsoNrRUERCpFfPG7ffjhh9MjjzySHnroofT+++9HV4tEVPlHjhwZ+cj/9ddfT5BXlmBI7/333x+60Y9u1YM/L0GuDCa7AYc/jIARMAJGoEYRMOGt0YF1tyofARHfci1VmiR58vO8jNwRSNch0pvnK54XyzVVR7Gsr42AETACRsAIVBMCJrzVNFpua00gIOIqieUW8pkfIq5FCQB5fll9pYt0nSufJPp13lS5mgDZnTACRsAIGAEjkCFgwpuB4VMjUKkIiMRK5u1UHDI/z/OUO29N3nLlHWcEjIARMAJGoFoQMOGtlpFyO2sOgZxwiqzmkg7rWueSis+lACKuqXwqozwqZ2kEjIARMAJGoFYRMOGt1ZF1v2oKgZyk4p9bzv+2pjrszhgBI2AEjIARaEcETHjbEUyrMgLtjYCIrqR8b6lH25QpDelgBIyAETACRsAITIiACe+EmDjGCFQUApBcyKweOPvwww8Tb0gbM2ZMyeVBRNikt6KGzo0xAkbACBiBCkHAhLdCBsLNMALlEMitt3rV8BFHHJF4g9oLL7xQ2tlB+crpcJwRMAJGwAgYgXpHwIS33r8B7n9FIyDrrnx233rrrTRgwIA0+eSTpx133DEI79///vfog/LaylvRQ+rGGQEjYASMQBcgYMLbBaC7SiPQUgRkucVfF9J71llnxWuFsfDOM8886YEHHghV8ueVa0NL9TufETACRsAIGIF6QMCEtx5G2X2sWgQgvCKxL7/8cppjjjnSpJNOGhbeSSaZJO27776J1wT/4x//KPnziiRXbafdcCNgBIyAETAC7YyACW87A2p1RqA9EJBbAmRX7gyjRo0K6+4UU0wREuLbp0+f9Pzzz0eV8vEVQW6PdliHETACRsAIGIFaQMCEtxZG0X2oWQRya+0OO+yQJptssiC7WHdl6d1rr71ix4acHIsw1yww7pgRMAJGwAgYgVYgYMLbCrCc1Qh0FgIirPLN3WCDDUpEF7LLwYNrSNwc3n///Wia8tvK21kj5XqMgBEwAkagGhAw4a2GUXIb6w6BIuHdaKON0oorrpjmnnvuILlYejn69u2btt122/TZZ5+FDy9vYcutwnUHnDtsBIyAETACRqAMAia8ZUBxlBGoFASK5PWSSy4JwtutW7eQ55xzTjRVll0R5Uppv9thBIyAETACRqASEDDhrYRRcBuMQAEBEVck7gnstctDaWeffXYQ3WmmmSbkqaeemrDqsksDQa4MKl9Q60sjYASMgBEwAnWJgAlvXQ67O10tCEBgIa/agQGLLn67svAOHTo00vXyCRPdahlZt9MIGAEjYAQ6EwET3s5E23UZgVYiAIHlkMvCueee24DwnnbaaaFRFl4T3lYC7OxGwAgYASNQFwiY8NbFMLuT1YqACW+1jpzbbQSMgBEwApWEgAlvJY2G22IECgiY8BYA8aURMAJGwAgYgTYgYMLbBtBcxAh0FgImvJ2FtOsxAkbACBiBWkagRHjl+6cF1vL/fCeNg3Ho6u8AD6419dAaE1Tuw1uuveQpF1+pca1tb54/P1f/8rj8nHRdS5Yro7g8fx6nc+kIpU18NKUn11EuX56en6sNuVS69BSvFZ9LlS/m1XVRtjS/8uVSuojLz3VdLi5PU7qk0nLJuQ7lQxZDuTLkUXwxv6+NgBGoLgSC8OqGlqyuLri1RqB2EdAuDc09tMYuDdy/2pasiEi13dutbW+ePz8XDsQpXjJP47yxeOWTLOZTfK6DPE0ded68fB6veiSVT9fSn5dRHknlVZ7iteJz2VxZ6ZDM8ysOmZ/neXSOVJ7iua7zdMVJNldHsazKSaq8pOKRCtKhPJ0hqVv1qh2WRsAITDwCk+jGkvz3v/8de36OHz8+JAupD2Pg70DHfAe4zyCz2ksXyXW+sGLdHTduXMSdfvrpDfbhPemkk4Lk5hZexkp60C/rMJJrDvJzryO5zsc3Ty+Xhh70U6aYXtRDX6iHfI3lJZ520GflZ2rjXOU4J1C39Kn9SsunQ3SpfY39CKAc/SBo/pM1XdekkYeDujkI5EO/ytNOtSMvG5nLfCgPenJcFK8iuqav6Ne10nNJGu1DJ3kpUy6U67fykUa/VA/6aF8xKL2IQ1P5hB1lqYO6COhQGlJYSxf9ID/5mgrkkx61D8l3i3jFNaZD6dTfkvoa0+N4I2AEKhOBBhbe5iaUyuyCW2UEag8ByICOvHfXXHNNEN7u3buHvOCCCyL5559/jvwiYHkZzhuLL+bryuu8jSJDeXuKcSIo5BFWksW5jHhIj9KRCi2Ny/PnZLIxXbnexs7zPqAfDMrlzesul06c2pT3vVzepnQpDV2UVSinh7i8/VwXMVa5XI/aSVyuIx9/pZGeB+krJ5WvmNZYfDGfrpUfHBXXGRLshA245tiqTZZGwAi0HYGw8HJjcUMT3nrrrbTXXnulXXbZJe28884+jIG/Ax3wHdhpp53Srrvumrbffvt08803p6effjqdfPLJ6aWXXkrDhw9vcEdffvnlkff7779PSy+9dBDdKaaYIuSaa66ZPv7441J+7mXu24ceeiiNGDEirb/++kl79V588cVpm222ifSjjjoqffDBB2nIkCHRBu532kTZHXbYIR133HHp888/j/y77757pO22225p6623TldeeWV69dVXo03l5gn0UIa6brvttujbjjvuGPlVB/XonPpOOOGE6MO1116bXnnllTi/8cYbo8xWW22VHnzwwYjjTXPvv/9+2mKLLdJXX32VjjnmmES+YgCr/fffP2266abpiy++KCbH9R133JEef/zxBmno5vXNeXjsscfSk08+mS677LLEPsiEX375JfSDM4GxBJfWBtq27bbbBhaHHHJIo8Vff/31xI+dpgKWzGHDhqWvv/46vlPPPfdc2ex33313euSRR8qm3XLLLYl2iDSfddZZMZZlM6cUY7D33nunl19+ubEsEc9YXXjhhXGO7sMOOyyBP+HTTz9NekU27X/ttdeiH5GYUnrzzTfTvvvu2+B7rrRcMnaUL4Zjjz028d1vaeA7MXjw4MSPyK4KJrtdhbzrrWUEgvDyy5pJaPTo0Wn55ZePhZS3OfkwBv4OdPx3YPrpp09zzDFHgsTOPvvsacYZZ0wDBgxISy65ZFpmmWUS6YzDb3/727L3ZL9+/YIIk3+JJZaIPOiZZZZZ4nyqqaZKSy21VJp22mkblO/Tp0+D63ysJ5100jTffPOVTVd78/yNndOXXr16ldWTl5lsssmi7bxBjnYtu+yyabrppiuVm2222aIPvFKZ/lKW9lGO9tA/MENyLLTQQqWy888/f8SBD4fyzDDDDAm9/IigLHLeeedNWM/Jo7w9e/aMcaGeKaecMvJJP30jL+1Bn3SpjsYkuslbHNP+/fs3qFttmGuuuWL8dC2Zt3PRRRdNjPUCCywQbeE7QB3k1UH+mWeeOfpDXapP2NEH+rLYYovFd0+vsFY/VK8k2JKfMVNblKZr2kA+2kY96KZMjx49on20l3r4rjP+ffv2LY0BOrgmf/49V3tUB3LuueeO8pyrP/SPsnz31S7JXIfKkMZ4U4YxFn7FvO19zboLRrwqnIAByqS3lqmX+9YVCIRLg6y7/EovLorc+D6Mgb8D/g74O+DvgL8DHfsd4B8fAu4NsrJ3BTFwnUagFhEICy9+Q/ya5G8n/v7ZZ5994u+6/fbbL/7a4e8dH8bA34HO/w7wtzz3IX/pgv/qq68eP0Ann3zykKusskrE614lv8ep88epkjHnu8HBd2PgwIEJazHtxTqO9VTfsUruQ0e2jXsLDDbbbLM055xzpkGDBgVeuqc6sm7pPvDAAxMuQ7iUELQm1yLpcJ+MQFch0OChta5qhOs1AkagZQhcddVVQXT56xdrWzmfxZZpcq56ROCnn34q+dviK96c7209YYTR58UXX6yILtudoSKGwY2oMQQaEF7+QuHBBx/GwN+ByvgOsD0SB9uSMSZnnHFGEF35VvKgG/EQGSR5PXaVMXaVNA76HolIca2gtEpqb2e2Rf3XDgm4+Cmus9sh90LGSWOlcbI0AkZg4hAIwjtxKlzaCBiBjkJAC58WQnYJwLIrC692YGBhJniR7KiRqG69+l5g1ND2X3yndK706u5l21qvviPBI79um0aXMgJGoBIRaGDh5Ub3YQz8Haic7wAEhYOHWAhs35QT3qFDh0Y8FinGjbwev8oZv0oZC74ktKVcUHyltLWz21EOm67EpFx7yo2b44yAEWgdArbwtg4v5zYCnYqAFn9beDsV9pqrrCkCR2eVXnMdb0GH1Hfda/l1C4o7ixEwAlWCgAlvlQyUm1mfCGgRNuGtz/F3r42AETACRqB9EDDhbR8crcUIdAgCJrwdAquVGgEjYASMQJ0hYMJbZwPu7lYXAia81TVebq0RMAJGwAhUJgImvJU5Lm6VEQgETHj9RTACRsAIGAEjMPEImPBOPIbWYAQ6DAET3g6D1oqNgBEwAkagjhAw4a2jwXZXqw8BE97qGzO32AgYASNgBCoPARPeyhsTt8gIlBAw4S1B4RMjYASMgBEwAm1GwIS3zdC5oBHoeARMeDseY9dQewhw3xB0/0iWi8vTium6RhIa05vrUJ7/FLEwAkagQhAw4a2QgXAzjEA5BLSQeh/ecug4rlIREOnT97crZY6R2pXH6Vxt1DVScUWZ58nPi/nacq16c70+NwJGYOIRMOGdeAytwQh0GAJaME14OwxiK/4PAnzXCPrOtUQq/39UNBDS1yCyky5UN5J751//+lfJOss5cbyG+9///ncpXU0jnjxIgnTl56Shg/LKr3yS0tdaObHlW1uf8xuBekHAhLdeRtr9rEoEWPw4THircvgqttEiVfp+FWVLGw7ZK3eICCJFMJGddYiMUp+C2qHrolTbFK/7TvGS6BZ+5KWPkqQpX2ukyuXjgM68nqjEH0bACLQZARPeNkPngkag4xHQAsiCSDj33HPTJJNMkrp16xbytNNOi/h//OMfIb1ABgz+KCCg74W+T7ksZI1LyBrfueYOyF6uKz+HCCugj7Ry5Li5uFxnS86lj7y0784770xPP/101E9/rrvuunTxxRenjz/+OD322GPpwgsvTK+++mqpH++++2664YYbEveU2qx6pZv+XH/99emdd95J3333XXrqqafS3//+91J+8qlMU1L5JMlLkBR+lkbACEw8Aia8E4+hNRiBDkNAi6UJb4dBXJOKRZj0/ZEs11m+W5A1DkjeP//5z3LZysb9+uuv6dtvv03ff/99GjNmTIND8cOHD0977LFHOuCAA9K+++6b9ttvv1YdlGnNsc8++yQdu+++e5p88slT3759o8699947fijyo3GFFVZIc889d1zPO++8afDgwVFP//79I27zzTePOHSp3ZzT/q233jryLL744mmDDTZI0047bRo0aFCkqe6WtFn69tprr3T66acHxiLVXGgcy4LvSCNgBFqFgAlvq+ByZiPQuQiIqJjwdi7ulV6biJC+H0VJ+5Un7wtkavz48SWCC8ktF95+++308ssvp1deeWUC+dJLL0U81tHtttsurbzyymm11VZLq666avrd734XxyqrrJI4uJ5llllKJBOi2dnHNNNMk6accspSvfw7wjHZZJOlqaaaKnXv3j1NMcUUpXTi9Q9KU20lD2SaPOhpKm9L0pZffvkYM+51uUkwXg5GwAi0DwImvO2Do7UYgQ5BQETGhLdD4K0qpSKw+k5I5p1QnP4iR2KxleW2HIHiL/zLLrssXXvttemaa65JZ555ZsLi2adPnzh69+6ddBDHObJnz54tInmQSUgnh8hmc1J524NItoRsVkKe5ZZbLobShDf/RvvcCLQfAia87YelNRmBdkdABMaEt92hrQqFjD+hKPPG5w9HlSO0lJWv7UcffZT4m/+QQw5JBx54YNp///3TgAEDwlIJucQSioUTAoicdNJJQ+q8SAzzdKyd5ENyoIuDPMVyLb2efvrp0+GHH57wVT/11FPT0KFD45zriTlwH1D5/FxxnS1POeWU+MHBuDKGGkeNez7ePjcCRqBtCJjwtg03lzICnYIACx6HCW+nwF0VlfB9wGKrI2/0uHHj0ueff56++OKL9OWXX6aHH344DRw4MFwOfv/73yd8TotkE5I69dRTx9/ykF7O9Vd9MS8EFOvuHHPMkeacc840++yzp169epUOrL+zzTZbkMlnnnkmPfjgg9GGRx55JLX0oM2PPvpoeuihh8KdQn/v5/2s5XOT3FoeXfetKxEw4e1K9F23EWgGgeYILxYvAuSH4MUyYKiKD42VxjiXGkvlweLX2ENl7DAASbz//vvTH/7whzTzzDMHIYWMTjfddA0ILtZW3AlwGcj9VovElmt8cuWLKx/d22+/Pf3444/ps88+S1999VWQamR+QLjbk6SiK7di1+q5MNOY6ztQFV9mN9IIVAECJrxVMEhuYv0iIBIkC+8555wTBAbCAinhr1fymPBW7ndEBEZjKUmLlabWKw0JAdKhdMmzzjor/t7n73gsquUIK3EQXKy1jZFbdgdgdwJ2UMC9QW4OF1xwgaqaQBbbPEGG//wtDzHlezsxBJX+qz7JcvXVWlw99bXWxs79qVwETHgrd2zcMiMQiz2LnwhvcR/ek046KVDS0/ZeKCvrS6PxQOpcLVQc1ltIYU7ulEcSiyrbXm255ZZpk002mYDg5rsQNEZ++Tfg5ptvjn1o2Yv2gQcekPqyskhYRV5Fwmk358j8aKofZStqQaSwE2a1KoFCfW0BLM5iBIxAKxAw4W0FWM5qBDobAS3sIryy8OJniT/lpZdeGgskT+Irb2e30fVNiEBOWornIocQw+Ket+Rl/9rXX3894XPLbgkLLLBAuCjkRJbx58jjsOLOOuus4U+L1ffyyy9P7733Xho1alRiJ4Zyge8VB/8Q0BYOxZUjtOqLZDmdiiNPex3obEmdqtvSCBgBI1BEwIS3iIivjUAFISDCUCS8kBu2MYLMyOJGsyEpDl2DQE7ING651F/7jJfOaenXX3+d7r333vT444+nE088sbQ7Qk5mOS+3RRdkeK211oqH0thWDL2QV2Qx8N1QvUhZYvM25ufF8romDyHPW+5ceVTO0ggYASPQlQiY8HYl+q7bCDSDgIhEkfBCfn7729+m++67L0gM6crbjEondwACYE8ojgHXIpmqVj9Kfvnll3T++eenddddt4GlFp9bdk4oEl6u11tvvdim67DDDgufW37w5CFvB/VwQGyRalsu87LF8zxffk4+rh2MgBEwAtWEgAlvNY2W21p3CIhoiPCeffbZQYQgRPhzsg1VTnZNRDrnKyKcNT7UqjjO5R6QW1o/+OCDeCgMH1xeWwt5Fall54SiHy4uK7gm9OvXL/EQ2dVXXx07JBR7iEU3t+rm7cjzKj6XnDd2FPuU6/K5ETACRqDaEDDhrbYRc3vrCgGRERFe+fBi4WU/1BEjRgQeSpf1sK5A6uTO5oRRVROXW1MVj8R/9uijjw5/XBFcSV5rm+95i5vKCiuskDbYYIPYh3bkyJHhhyt91KFXA//6669hvVUaMm9bfp7n8bkRMAJGoB4RMOGtx1F3n6sGAUgLhwitCC8kCXL0xhtvlIiW8lZN56qooTl5zHHGXYCxkU8s11h3hw0blthR44QTTkjzzz9/bAvGFmH8UMGSiw+2fHIXWWSR2Bbs0EMPTWPGjEk///xzkNocHurAiov+YlB78jYW8/jaCBgBI1DvCJjw1vs3wP2vaAREZkR4tS0ZlkGshMccc0wQYm9L1nHDmBNJnVMb5FMEFML73XffpYsuuii2DZPVFqlzWXVnmmmm2E1hyJAhiR8wTz75ZKnx6JPPb06i83r1nZCkcJ5eUuYTI2AEjIARKCFgwluCwidGoPIQEKkR4ZWFVy+e+Nvf/haNxvpHMPFpnzEs4phfMxa4E+BewBZizz33XLrpppvirWSQWYht0VVhiSWWSMsss0zaeOON461oTz/9dIOGMn5YhlWPpDJxnR8eayFjaQSMgBFoGQImvC3DybmMQJcgIJIjwisLb054yQNhUt4uaWiNVAqGCjoXrhBcjQN5xo4dmzbddNPUs2fPNOOMMwbRxVVBY9OrV6+05557piOPPDJ99NFH8fpd3BUU0MW4YclVUF2SxKsdymNpBIyAETACrUfAhLf1mLmEEeg0BER8RLREeOXScNxxx0Vb5NIAKXOYOASEOVo4zwkpccOHD0+77y1gJkAAACAASURBVL57GjhwYJBcXBaK24ideuqp6YknnoiG5ISV8dHLHfKxUp3KKzlxPXFpI2AEjIAREAImvELC0ghUIAIiQiK8cmmAYPHCgS+//DIIGeRJeSuwGxXbJBFLYadrSC7WV5HSzz//PL366qtpnXXWSXJbkE+uJBZddmNgVwaVkx+upHx+BYjq5Vp1K83SCBgBI2AE2g8BE972w9KajEC7IyBCJMKrfXghWdtvv33UBzEjiGS1eyNqUGFOLvNzMBSe6jYuCVhx2RcX3NltAdcFziG5q622Who0aFDskatxQkJu0V0cF42p9BevFW9pBIyAETAC7YeACW/7YWlNRqDdERAZEpGShRfSxYsnSNfDTsrb7o2oEYXCB0nIryGlIqikPfPMM+mkk05KBx98cInkQnBFdLGw77TTTg12WKAcOnI95erK46Ih/jACRsAIGIEOR8CEt8MhdgVGoO0IiJSJ8NrC23osRTDzksI1J6ekf/LJJ/FDYvbZZw+iC8ktbiu2zTbbxEshpI8fHOiBNKsu6SeP4pTf0ggYASNgBDofARPezsfcNRqBFiMg4iTCKwsvFkbexsVT/xAukS2Tq/9Cm2MhHCUhqHoYDReGr7/+Ou2www6pd+/eJaKr3RbAeqGFFkq33357GjVqVOmlEJTnkE7VXKxX8ZZGwAgYASPQdQiY8HYd9q7ZCDSLgMhUkfCySwNuDfztTh7v0tA4lCKg/CiAoApL4t9+++3YNmy66aYrEd2pp566dL7qqqumM888s1RGtaAHfXnQWBGnOvN0nxsBI2AEjEDXIWDC23XYu2Yj0CwCIlEiadqWTNbH0047LXRAeJW3WaU1nkFkU3ggwS93X3jvvffS4Ycfnuacc84gt/jm8gOCAzeGP/7xj+mvf/1rCSnKym2hHNEtZfSJETACRsAIVCQCJrwVOSxulBH4PwRE2kR45dLQrVu3IGZDhw5tYOEV2as3/IST+o8UMRV2yPfffz+2F1t55ZUDPwjulFNOGedsN4bv7nrrrZfGjBkTEPJDgnJF/SSqrnrD2v01AkbACFQjAia81ThqbnPdICCiJdImC68Iryy82kqrHkmY+pxLzrHI4t9MeOqpp9Lxxx+f5p133vSb3/wmCK4wxJ0BAnzXXXelkSNHph9++CHK8PrgXKe+dIrTtaURMAJGwAhUPgImvJU/Rm5hHSMAueIw4Z3wSyBsSNE5Vl1ILj62EF7ktddeG3voyl2Bh9BwW5hqqqnSmmuuma655pr0/ffflyqQ+wIR6EO36ihl8okRMAJGwAhUFQImvFU1XG5svSEgImfC23DkRUKJ1XlOTseOHRuvAN5uu+3SzDPPHAQXv2dtMbbUUkulm2++OXZnQIdIrmSut3jOtYMRMAJGwAhUFwImvNU1Xm5tnSFgwjvhgIvgChty4GsLWYXoPvjgg+GHO8sss5SIrnZe6NGjR6S9+eaboRgd+Y4Lue4Ja3aMETACRsAIVCsCJrzVOnJud10gIFJnC2/D4RYukFz56SJ33nnn0iuAcVsQ0eV8//33T7feemtYhCG55JdVWPqopXjesGZfGQEjYASMQDUiYMJbjaPmNtcNAiJf9Ux4wYAgLJAQXUir4q+++urEnrm4LGjnBfns8npgHkhToFxeVvqlS/ksjYARMAJGoHYQMOGtnbF0T2oQAZG8eiS85YioyK6G+oknnkiLL754mnbaaUsPomHN5dh4443TG2+8UXopBxhClBuz6kqnpREwAkbACNQeAia8tTem7lENIVCPhLdIdHWN1Bvlvvzyy3BPYN9cyC3WXFl02XaMh9V47TIBksshLIkrntfQV8ZdMQJGwAgYgTIImPCWAcVRRqBSEBAxqxcLL/0lqN+cY5GFsAoDyO4yyyxTIrq8JQ3Sy0Nqe+21V+ylq/ET0ZVOybwe5bU0AkbACBiB2kXAhLd2x9Y9qwEERPxE9mr1xRMioAyZziG6+NrqmrRddtkl9e/fPwiuXhwB2e3Zs2fsp6shzx9Iy3XmupTX0ggYASNgBGofARPe2h9j97CKEYCgcdQy4RUJVV8l9WDZL7/8krDqbrTRRkF0IbjaT5fzgw46KH300UdhCYboiiTnevkK6LqKvw5uuhEwAkbACLQRARPeNgLnYkagMxAQ+SsSXl6iANmr9lcLi4Sqn0gIK69KxsL73HPPxXZi2l4MKV/dpZdeOh133HGlHRfIzyFdjI/0d8ZYuQ4jYASMgBGoXARMeCt3bNwyI1AibyK855xzToO/86uZ8IqM5gRVD5gx9DfccENpH11eByyiO2DAgDR06ND06aefxjcEgiyym39lpD+P87kRMAJGwAjUJwImvPU57u51lSAgMshf9YTzzz8/CG81W3jVJ/qjcwgrVl3CmDFj0uWXX55mnHHGILnacoy3pPGw2siRIyMfPwLARTqKMjL5wwgYASNgBIxASsmE118DI1DBCOgvell4//a3v1W1hRdSqiCCmlt1X3nllbTyyiuHZReLrnZgmHPOOeOVwfjyEsaPHx9WXc5zndJtaQRqHQF973UfNSXBQuk6l1S8pOKRtRjoJ0H9zaXim+t3e+horg6ntz8CJrztj6k1GoF2Q0CTsbbXGjVqVFpyySWD9PI3P3/tE2Qd1UTcbg1oR0Vqm/qEVLvHjh2bjj766IRfLr7J8tnVVmNPPvlktAQcRP6lR02Ufl1bGoFaRCD/3rf0O5/ny89rER/3yQg0hoAJb2PION4IVAACLE5YeSF6hPvuuy/16tUryae12nx46Y/6JDeNH3/8MW244YZBdCG7supCei+++OLoN2Xw1RUOXCvk54qzNAK1joC+98wPvJCFH4/Fg3juGwL5VUaStLxsnldlagnHIgbCCwzAUX0WPsW+K156kMxjwrA4Pyl/UY+vuwYBE96uwd21GoEWISCXBhYiJs977703zT333GmqqaYK/9ZqsfDmCwR9oj9sNzZixIh4BTBElz6x3RiuDFh6RXax6LKQ5DoAz4tJi75CzlRDCOT3AOcQNpGspropQpeXbyp/Ld9bwqCx/jfXd9JFjvVvU1FXczqK+X3dOQiY8HYOzq7FCLQJAU3OLGpMsu+//35afPHFq8KlQZO+pMi7Fujdd989+gHBhejKsrvnnnumb7/9NvBiQREG0kNCft4mYF3ICFQRAvn3XfeRrLE//PBDOvHEExP+/aeffnpsVcg/P1yfcsop6bXXXiv1VGWRhKeffjr95S9/SWeeeWaUw4eeuvQDW/UidVCuGJ/HheIyH8UyxWvpUD2Sii+jcoJ2qIxksSzxCvxYuPDCC6PfJ598cvr6668jSWWVN7/O44T/jTfemCh/1llnxfyMEs1xyq86LbsWARPersXftRuBJhFgwmRxkiWBhQlraKXv0qCJXlILrTq72267RT8guvlLJHbcccew/JI/34FBC7T0SY+lEah1BPSdR+oQoYKsrrjiinEvMS+UO/r165f23Xff9M0338RcQlndW8cff3yUwUWKskOGDAk4eSiUULxvicvbE5myuPxabZXMy+b5yp0rrlimJXU3VTafR/ihMOWUU5Ywe/bZZ6Mo+OT5ivpoA/MxeXjzpZ43AD+2UiRApgnSExf+6HIETHi7fAjcACPQOAJaLLRAsQ8vFlG9VrcSXRqKi5IWECZ/LLc52dWCgwsD242NGzcuwChadoVD40g5xQjULgL6/nMP6X465JBDSmRNRJet/HSO1L8mnG+xxRZRlrkEPS+++GJs/af8v//97+P+g6yRrjpBlXPF6VpS+SQbGwXSVUa68rhimvIoPgpn5Dqvj3Plz89VVnmRmkv32muvNNNMM5Xw4iU3pGO5la68vOLIIwMEmIIf8zHbJ95xxx3RTBFe8jpUDgImvJUzFm6JEZgAASbMfILFosAEW4kW3nxyV7tZJDT5Q9a1AEPa8dmlL+w6gfWJQDkWdJ3nOiPSH0agzhAo3ks85HnooYfGvaN/R9jNZN11102vv/56Wn/99dPvfve70hzBj0rut4UWWii9+uqrQeYgfdxn/KW/0korpVVXXTXcH4BW96vuRe5hCB5lkCLcxEMO8zjKq73KJ0n+xnRRRvqoJz9UTrpzqTTaofapLO1Sel5GdZHGrjf0nePdd98lW/RPeWg7h3RSD9dghBw0aFBgy1wG6b399tuj/6QLh1Dqj4pAwIS3IobBjTAC5RHQpMnkTajUN63RToLaK8kCQeCFGVqcWXw5RHbZW5f85FU5pILidG1pBOoJAYgZAdJFwBLJD17dRzPPPHN67LHHIi3/OO+888KCyX2nf4R22WWXyKL7Ms/PuerinoPQcY3Mg+KK8VzrvkWSLz+K+aWTeOlUXC6lV/OApMogGwv0k3SVQRJUtlguz6d2FfNQVi4f4Mk8poeI8ecl8JAgoam2RQZ/dCoCJrydCrcrMwKtQ0ATsAivLLxawCphWzItIrlkoVGbL7roovBzw09QpHeOOeZI1157bfrkk08CkJzsCiHp07WlEahHBLgPIE4c+J3usMMOJestbx986aWXApZff/01iB2Sew/3oQUWWCAImXx0Dz744BIpfeaZZ8IavN566yV85yFxIsKSWCqp96qrrkobbbRRguCxuwpt4sCivMkmm6Q777wz2gApJ576aQcuShwigD/99FP4CbMN4eabb57uueeeKEcZwttvvx36Nt1006hv1113DaJP21QnUmSUttHGt956K3Z7oS0qK3cv8hZJc1T2nxfYfPfdd6XnBlQH+QnU+8UXXyTcR2gzLgtyu3riiSdSnz59wg+YHx/bbLNN5KXveX2qy7LrETDh7foxcAuMQKMIaAIWeaw0wkv7CGonUosl8ZdddlnprWny14Xsvvnmm6U+FxcH6Sxl8IkRqGMEuB9EwCBfCy+8cJBYSBaE96OPPgp0IH/ce+SFBEJM+Yt9ueWWizLbbrtt0psKkbPNNlvowULJmwwhqyqPQtyM/vCHP4QrRPfu3Ut5IdE8LLf33nun2WefPeL79u0bPsG0gePjjz9O/fv3T/PPP38ca665ZrryyisTD9DJrYl68Tn+4x//GLtFQORpB/H58dvf/jZdeuml0UdtryY8/vSnPyXSIZ55Gc4h+Yssski66667omxORD///PPEy2zY8Wa++eYrPWwGBpprIbeUn2eeeUq6aS/9B0se8KMeuWb99a9/jXpk/QUHh8pCwIS3ssbDrTECDRAQkdQkLMJbCT68IqZqI1J/u/IAGm9Ow7eQhVkWaRZGkV0WBpPdBsPtCyMwAQLcV7pPvvrqq7TssssGmdMPSEgZZHL06NFRVkRLPzxFfhVPJggpZA0dENCllloqiB7zDPnYomuttdYqET0RSGTxQAf3ud6GCCkdNmxYA2JbLEOdOfHNzyGQSlcft9tuu4R1mD6hn+PPf/5zg7ZQhvz8i8Qhq/ass84apJd+yTo7ePDgkmWWtl1wwQXxo50fCWBw9913J8rl7c7bSDy7M8ithOuDDjoodMgaTX2aIycYVEd0CQImvF0Cuys1Ai1DQGRShLfSfHjVPnrDwkrgL1asJiwCLAhaNLCUaE9Q5c0XYS8OLftOOFd9IcB9IfIEEV1iiSXinhIZ1D02cODAdNhhh6V99tkn3XLLLYnXdUOQCbJcSg/xkEKV5WU2kEjyjRkzJlwdROogetNNN1068MADw5o6/fTTl8qKVPKvDS4StJWH6hZccMFoI2Wpg/IQddyb2AoNgqz5Qe3AfYG9bPlRv/rqq0c6+tVPSCiBueOEE04o6UfPvPPOG3sQn3HGGfG6demHlJK+wQYbRP9ws+DHA+SUeAwH1HH11VeHbvB5/PHHw3JNuznYfYGHBNkS8uyzzy65iQg79GB1x7ca3czV+bwYiv1REQiY8FbEMLgRRqA8Apo4WYg45+EvTdRI+amJQJKno4PaJEl9suyy1ZH+AmQxYTHjb8qVV1655GuYt1U6OqPdHY2L9RuBjkBAJBXrJmRq+PDhJXcECB2EEFLGfKCDv97XXnvttMIKK8RLKWgXeiBkBPxPc8LGrg4QXtLxq2W7Llk08YsljnR8ZY877rioR2QWwnjUUUeF9ZR7G6LNzg+0hTpIv/XWW9N7771Xggd3AeJFSLHg6kc9mQ4//PAGdfzmN79JL7/8cvQBP1/IM/ppA2T3qaeeKunmhP1wSacO2tC7d+90+eWXRx5ez84/Teof7hb48YIvZP2AAw6Isrhx8Bp3SG4eIPb6hw391IN/LwGMCGDtUHkImPBW3pi4RUaghABEkMlTiwFvT2KClYtAVz60pkldBJZFh4WE9skqwzmWIRYUgv5mFdEtddQnRsAIlEVA9wpS8wCkjd0ZuL/yAyLG3CAiRhrn+Nzydz4/TLlv9bZG0nEheP7556NuHnTjITbIIMfGG29cegMZ1lECb3uEZKKXH7Qc+OoT5BLANl/oFqnmYTsCOn7++efYbYY0+Qbvv//+MTdgXWaO4Jrymkd4kxx9p+1YWikn0ok1W7pxJyAfpJjyah/nEHUwvOSSSyJNZHuPPfaI8uh+5513wq9YurfeeutIQy8Hcx04zjDDDA36h68z5UnPxysK+6NiEDDhrZihcEOMwIQIaPLE8sI5+2jis8diwdHZFl61R1IWDbZF0gMsWkj465O/AllMyS8rsMrSW84djIARaBoB3TNI+aHee++98UpbfvQyDyB5EA2Sx9wAYRVhxFL5xhtvxP0GMctdjsjHHrToxjqLxROCCKnUdmfamQHSR3kIKHlEDHG1ojyE7/rrrw/LqEg3rxAX2aaXPGRHO2kjOvhHiIfrILoQYvTzI5k0bfeFmwMBd6nll18+0jTPqCxzJGUJ+BNTXqScc82VN910U1iWhQ3WZcrRd3arULm55pordp+AQOuHOroh5bhokE99gOATNB+qHRHpj4pBwIS3YobCDTECEyLAIkLQYsJ5bv049dRTI11WVuWPyHb+kG5N5qrzkUceST179owFgAWKRYbFRE9Wk1+EHR3SI9nOzbQ6I1ATCOj+0D0D6dI9p7hiR/m7/aGHHkp77rlnkEXILMSMXQx4kJRy+XZlkGMezvrggw9CFT6wuEdw/xJ///33BxmE9FGWH62QOrY3Qy+kk/xYeLnPObbffvtIExmmTQTpwNoLmRQhxqIMoaasrMhyaRCphWBTPw/nUa8sw9SFP7LwACPORXjpH/Vg9cYyTDoP1NE/DtLZcozw6aefRj7iqGONNdYoPSiHTtqH5N8qEV7ygdPTTz8dacpDPofKQ8CEt/LGxC0yAiUEmDg12TJZs28tEzGWBSZmWS3yhbBUuB1PNIFLylr78MMPlx5A0eKC5G9DAu0y2W3HgbCqukNAc4A6zsNR7LJA4P6CSOpQHiyquVsBPrBYeAl6WYLIsNwRuE953S4kjvkFv1zuc5FISfx5ySPCCukkH3VC+CDbpENWyUN++kAbScdqDEkUseRNi5BW0tDDdmjs+YsOSCn/HGGVJSBpmwivtgJDN4E2EiDwlBfp3nLLLQMrLNnam5h0HjbDxYL28UMA32dZbVdcccWw5tIuDs194CSXBnRA3kXYqZu8DpWJgAlvZY6LW2UEAgEmWSZQTeg8QJFP5B3tw0v9mugly5FdFjYtFLLskk/lVZZO5eceZiNgBMojoPsEgsUcwN/5kFDcAdipgYe4IHikK3DP6W91SB5zBcQWIvfCCy/EvccDVsTzbwySh+CoC11yJSCeH9PE6z7WfS/rqcgkJJnAA18E6VB68aUUPCCHfggvltJjjz02LLu4SxCuu+66Ujr5INQEMODhN+KkG0uw5kckWPAj4Igjjoh86qMeKnvllVdKD/yhZ+mlly7phvDSHhF5Hrz7/vvvQye6wQeJhVh60cFzC/QdrPIjFPujohAw4a2o4XBjjEBDBDSBatHBV44JWQ+tdaSFl7oJagNSluQHH3wwNr1nwpelhnM9Ca32qmyup2EPfWUEjEA5BCBXBBFN3Je4x/Q3P3vHKsiyqWv8aNmaS1ZWCCDkDZ34rKJHpE0PfZEmskr6KaecEup0z3OBFVc+tLrv8XvlPudH+QMPPBAPtGGZZZ6CdGsHBNLJxz9U6OfAOorVlSA/4WuuuSZ+PKt9EF6ILMdtt90WaZr/IMvFQD/UNn6E487BlmbUzcNsWLtVP89DEE/AssxzByK8WMhpU/6Dgnz5iz/QgzsX/r/So3ErtsvXXY+ACW/Xj4FbYAQaRYBJlEMW3s568YQmb9WP1MKHz67e0oT1iEWFRQL/OgIWprwccdLXaEedYASMQAMEdA+JKPJjFyIJEeS+g9DirwsJJfBiBsglf/uLLELIOMf1SPfgoEGDgvChC9cAuRzwRjJcDYjnnobIyTrLPQ0hxCIqsoiEWOIDK5KX7xOOjtNPPz3aRnlIOW0Q4SVd240RD7kkUCe6aQeSnSIIEE9tN6Z5B4sscbw5jofJ8C3GBQILMHkoj0UZtwXaiHVaD9dSP/v9EqgfCy/bsalf4MZ+vQTS0M+b3fQjgnwc7JiBbtqHHg6HykTAhLcyx8WtMgKBgCZQEd58QWGy7QiXBk3YqhspKxObsrPJPHWzIEF0WRiuuOKKaK8su1zkejycRsAItA4BkUjde1hcue+432SF5JqXPPBgGHvucs0BmeOA+OGjCxnjgTAezGIXBsggOnCRIGh3BF7+QHn5yOKrilWV1/7mulU/fq7c55BuJO5MItLkP/LIIyOePsgKve6660bbaAOkGlcLAqQYPbxBDf3oof1YttFNH9j6kNcVg4GsuGpXLkWWBwwYEP681A1hXWWVVaJu0ukbRFbzFC/1QCcH2EkfO81o31/F0T7l05sjqUNjFh3yR8UhYMJbcUPiBhmB/yLAZMwhwisLr/7Sa2/Cq8lf9dIS1c2iqn12NdmzaF177bXRYFmiuMj1/Lc3PjMCRqClCOgehOhxsBPAQgstVCJiIl+NSf6e50U1BFmBhwwZEuXlFgGZI4jw8kIIrLyN6RTRFSHEl5j7Xu4I1Ece5oV+/fqlRx99NNrOv0P0B/Is/eTD2ox1VnMMfsnUrXrY75dAughz/uKLxtpJ/GKLLVbabo368bMVcWX+gjRTN0E/CNiTtymdeZraiG+1dGjMIsIfFYeACW/FDYkbZAT+i4AmUC0IHWnhpS6C6kSqXp4Mx9+OCZ/FTPLGG2+MMrJC2cLx37HzmRGYWAR0L+q+4qUPWHp5FXCPHj2CPEIg+ZseaynnvJACiyz/xnD/ijBzj7LLy/rrrx/3MBZhXrQAkeSQyxJuElhR0YM+3lKG6wD14hvMvQ/Zg/Ti50pZuSNgTeYfIMg2++8SRFbJh0sBPsBYV3kD44gRIyIP1l3aiR4ILeVxm8LNgnj6z4EuHjzDFYE2kQcccO/g4BwseFUwWKl+dFCW+YpdGrBgM5fSZ+nX/MermTEosHcxmKJzueWWC9cJfhDwAx9XDPLg4oD/Ljoor3GKiv1RcQiY8FbckLhBRuC/CGjBE6GUhVdPKbeXhVeTveqjBVoAeXEE1hoWOlmG+EtQZJfFSuWk57898JkRMAJtQUD3kkgUhFGBlzfwsBev65XUOdfaz5YyujchZQT2weUVwfyFT0C/6vr888+DBLNNGIRx1KhRoR9iTBg4cGDMA5BdDqzOBBE+yJ/K4T5B/dKv+nEtQB/5aAvpOtDFXEe9+auI1QfyEXgAjwfQ0EG+vO/aU5h8wkz9I452sScxQXqRtE8H6ehBL20FjzyMHj06dGiOVB/zevL8Pq8MBEx4K2Mc3AojUBYBTchYJzjXtmRyaWjPXRpUFxISS+AvRv5WhOyqTiSWHoLevKSyEekPI2AE2gUB3VeSEDLmguYCBIx8Ioh5ec4VRAjxZWXXB/x7sZzy9rJiwNrKD135x7JXLg/KSXeuV2UVpzy0X3HKg1Q6kjwK6qvSiReJV55yEtKsulS2nG6libAi8/pz3eikPWpT3hbpyfP7vPIQMOGtvDFxi4xACQEmUiZhLUzsAQn5xP8M2Z4WXuoiyGoB2dUrSLHsYtHhrUrsk6l8muhVVjIy+MMIGIF2Q0D3FpI5oakjz5uf0xiuIXU5ueOlFNzb+LYyr/A3Pm4R+Keyw8PgwYPDfUD/8ODSQDwBAqg2SapteefVDqUhFad2SSpPXl5pSKU3JlVO+nOpMnmc8qsO5cllnkfxRR26zvP6vHIQMOGtnLFwS4zABAgwgXIwwbKw8FfeZpttFosSvrQTa+GVfk3Usm7gxsA77ln8sOiI7F511VXRRkgxbVJ5IqVjgk44wggYgTYjoPtK91prJRU3VYZ7nvuZuYT7nXlFfvr48uofHtI4llpqqdhJga2+KIvufC5oqq5yac21r7n0cjq7Kq7Ng+yCnYKACW+nwOxKjEDbENDEzcKClRf/PL0yFGvMxBBedCtwTh0sXGwez4MqIrtIbW9EfpNdoWZpBKoXgXxuoRevvvpqvCiCt7KJ3Eouuuiiabfddkv7779/+uKLL8KVifI50a1eJNzyekHAhLdeRtr9rEoEtKjIb+yMM86IxUh/LU6sS4MWPaQedNl8881LdWDZZQG85JJLAj+T3ar8GrnRRqABAtzvBEmIKz6q+O7zIgp2MGCLMe57znFv4gcxeSjDfFQku9LVoCJfGIEKQsCEt4IGw00xAkUEWEQ4WFxYcNhPc5NNNglCyt+ObMpOgIgSWrroSK+kHlLjwRVtbK+HU/Dj5clmWZmph/a0pr7I7A8jYAQqBgHd+5IisI01kPtfR/H+R4eDEah0BEx4K32E3L66RkCLEQsNVhW2E+JJatwZ2uLSkC9MWuCw2hAgu/oLUw+vsA8n+3JSTvlULtdV14PkzhuBKkZAcwySexvXqeLB/FO873X/S1YxBG56nSBgwlsnA+1uVicCWoy04Lz44ovxKlGsu7gbtMaHVwuTdIKIdn+48MILg+yikwPiy5Pa8aX7fwAAIABJREFUbBRPkEtFXrY6EXWrjYAREAKaE7jOz5VelMpTlMV8vq5OBPJx5VwHvVFadfbs/1ptwlvNo+e21zwCmnAgplhYHnvssdg9QTsntJTwarKSRJcstpBdCDRbDel1mbzpiJ0aCPLbU1uIk56aHwB30AjUAQK6n3WPNyaBQnnrABZ3scYQMOGtsQF1d2oLARaXnJzykBrWV70EoiUPrWmB0iKGPvn8XnTRRSWSK59d9uN84YUXAkgsu3l5InVdW0i7N0bACBiB+kVA6wMIcM46waH5Pk+vVpRMeKt15NzuukBAk4yI53nnnRcuB3q1cGssvNIlNwaewNYLLCTx2X322WcDW1l2ucgnvboA3p00AkbACNQJAuXmd8gurnTl0qoVFhPeah05t7suEBBJlQ/tueee2yoLr8pr0kIPxxVXXBFWYvx1Zdmdc8450zPPPBO4imCrPJHSURfAu5NGwAgYgRpFQHN5ufmdOIwd/AtYJLwqV62wmPBW68i53XWBgCak1hLefGLilzqBSYzze++9N+Gjm/vs9u3bN14jqnxIlYvC/jACRsAIGIGqR0BrgyQd0joDyeX8lVdeSWuvvXYaNGhQvGiEPKwH1b4mmPBW/dfXHahlBDQRtZbwgonKcs4vdek44IADwkosN4YFF1wwJjjyybdXZZEORsAIGAEjUDsIaF5H4uLG+iBXN3p59NFHxxrRp0+f9OGHH0bHc2tvtSJhwlutI+d21wUCIp4iqy11aVA5JBOVJismMt7ShnVXD74NGzYssNTLJ/gVTzkHI2AEjIARqC0E8rWBc4ju+PHjQ950000JgwhrA3uxL7TQQumTTz4JAGphXTDhra3vsntTYwhocmoN4aWMJickrgzfffddOuaYY+JXO2RXryZeY4014mUW+pWv+oCRcwcjYASMgBGoHQRYEwjM+RxHHHFE2mabbdIee+yReI6DXYD0XAdv2fz4448jv9aUakbChLeaR89tr3kEREBbQ3gBRZOTyt1yyy0lNwa5Mqy66qrps88+CwyZ+ESUiTDZrfmvljtoBIxAHSKQryn8q6cdf/K3bGo/9vnnn98W3jr8jrjLRqBLEMgnJxrQEpcGlZEbAxbeP/7xj0F4NbmttNJK6fPPP48+kU5QOaSDETACRsAI1B4CsvCyPmDoOPHEE9P++++fDjzwwLTffvuVdu+BAJvw1t74u0dGoGIREAmVpbY5wivLbj6piezik8Ukttxyy6VPP/00+lwkuxULhBtmBIyAETACE42A1pRcEXFaM3r37h3rhAlvjpDPjYAR6HAENDm1hPAqL7/cmbx+/vnn9Pvf/z4mL8guflnLL798+uijj6LdkF2VQRIkO7xjrsAIGAEjYAS6BIF83pcvL2sM53PMMYcJb5eMiis1AnWOgCamlhBeoJIbww8//FAiuxBdCC++uy+//HIgqu3H9KueSJPdOv+yuftGYCIR0ByieSuXqFa6qtF1c/mUvzWynO48rjW6ailvEQOuWQd0zD777Ca8tTTg7osRqBYEmIw4miO8PHzAhEU+3BXWXHPNmLSmmGKK2IKMv6e22GKL9M0330Qe8kq3ZLVg4nYaASNQeQgwjxAkm2phU3mUJtmUnnJpebn8XHkVh9S50upFqt/CQGQXaQtvvXwL3E8jUGEIaEJqivCSB4ut8jz66KNBdrHs8upgyO6mm24aLg5MaLICa9KrsC67OUbACFQZAppLJJln+HtcD0YhCUpX93Sd5ydO8eXKqGxzUjqkW+0p6lS+5vTVcrowR5rw1vJIu29GoIIR0EQkMlt8aG3o0KHR+l9++SXk22+/neadd95wYdDWMpBdNhZn0SmSXU/2FTz4bpoRqAIENIdormKOgWSWC8ojSR6Vz/OXi8vTmzpX2byOPL/a1lh6nrdezoUF0oS3Xkbd/TQCFYaAJqLGCO9pp50WCwYPqL322muJjcKx6EJ2se7i0qCdGJjopY9ucu5gBIyAEZhYBDSvaI7Bxeq2225LN9xwQ+LtXSNGjIgqimQTqyuBH+rkvf/+++OfKvTleVvTPrVFP+7HjBmTbr/99nTrrbcmnm1Ar9qpvMh6DjkOJrz1/E1w341AFyKgiagxwouFl4mdsOeee5b8drUFGa8Szi0umtglu7BrrtoIGIEaQEBzFJK5hn+TDj300JiL+PHNwZsdlY4UmdU/T8cdd1zk69mzZxo7dmwpL/A0Rk7RQ0Dmh3RrznzmmWdKzzHstddeady4cWEEUD7JGhiKNnchx8+Et80wuqARMAITg4AmIk3ecmnQCyROOeWUUH/dddfFJuFYdEV2TzjhhEgT4UUXQTIu/GEEjIARmAgERBi1zSHzDiSXXWGYp3hd7cCBA6OGfC5iTqPsG2+8kZZZZpnYNnGttdZKv/76axBn8hKamq/KpRGnAx3oO++889IMM8yQ2IHgnXfeCb1qr/JGZJ1+CAOkCW+dfgncbSPQ1QhoIioS3m7dusWigkvDF198kfr06RPXIrunnnpqNJ2H2VhUpAfpYASMgBGYGAQ0jyA1v2Ct5Xr77bcPdyr9KMeVgFeY52SXfJrTzj777LDA8mMdayz6cHVQHZQjL3GSpOlQfqUh1Sal0dd99903XLzWXnvt9Oabb0YekWrlnxhMqrlsjmW+LRkuch9//HF0rRYwmqSaB8ltNwK1joAmIiZxQtHCe9lll6UrrrgiJnIWGP46POOMMyKvtirjgsnKwQgYASPQnghofkKnnhU44IAD4se3CO9zzz0XVfLjm/wioeTH8jrzzDPH/LXddtsl/G1J13wl/ZRlPuMgKD4uUgoiTJpcJIiXDv3o33vvvUv/fp100klRlHlVbZKuepTCE9m3b98YD36ALLTQQqW3coIn6dUcTHirefTc9ppHQBNRkfDydyEPpS2xxBJp1llnjQmKvxHl4iArBwBpkpKsedDcQSNgBDoUAc1Lklhfv//++3hArV+/fkF4tUsM7gqjR4+O9ii/rLbsICM/Xx5uI11zHXm4vu+++2Jf8c033zytt9566dJLLw3iSz4O3hy50047RdrGG2+c1l9//diCkQopT9vQBfFedtllo74TTzwx/fTTT5EmIqe2NQYc6dKpvK2VKt9YHV0Vr36ABT9ANCZYez/88MNolsajq9rYHvWa8LYHitZhBDoIAU1EWgRk4cWSq0kJ4osrQ69evcI/jUmLSZ7AOQE9DkbACBiB9kBA84pIEC4LkNHu3buX5iXNT8gBAwakhx56KOYj5jLKQYLXWWedyI9Vcfjw4dE0zV2SEF7mNumbdtpp07333lua43bddddSGnkWXnjheDCNNnIw96GLnWx4tTp5evToETtDUCFtIahPcVH4aO/5s731FZrb6kthBRa8oIhX0nNstdVW4TKHQuVptfIKKmDCW0GD4aYYgSICTIwcRcKLhZeJWy+X4OlmFgb+JmRyVznJol5fGwEjYATaioCIJJL5hp0V2AGBOUnPEXCeH1hmCbgdEP7617+W0nGDgGzlc5d0k1c70Ew33XQJwnv33XeHDl6VjkWZeRAjACTtq6++ijT0Mf+hh3P2Kv/zn/+c0EG7qJM40jmamytJJyDVVtrb0iMvLz2hsAI+1HckD/mBCwduInm7dV4BTW5TE0x42wSbCxmBjkdAkxCTMYSX67POOisma7k08LfhbLPNlu68885oUL5gVPvk1PEIuwYjYATaggBzi0iiiCU/tvGThUzqXyfODz/88LDuUg/l5Ot75plnlgjvQQcdFPo0f0k315w///zzQWyZ7/AthQCPHDkyLbnkkqFD/3gNGzYsuqM6qI9DBoNRo0aFjyrtg/h+/vnnkZ8+EKirXJAe0nRO3pYc5CcfMi9frp48rphf9Xa0pK3gwaF2d3Sd7alfGOdY6tyEV0hYGoEKQoAJgKAJhwmcOB5SYxHRAyEsANdcc03k5de48qu8ZAV1zU0xAkagBhBgbtH8wrzDAUnaZpttGsxRTz/9dPQW8ko6ZZ566qn4oS4XiF122aVUPp/DOBdZ3XDDDUsEGSvyTDPNFNecMx8OGjSo9Ir1XAeVi0jzcp7evXtHOXxVsQbTnmL+4vDkfcWavdRSS8UDXbhPNHXw0Neiiy4aLwSCbBNaUldeP20Hg846eMhPB+tOZ9XbHvWALUHfS0nhacIrJCyNQIUgoJtUkyw3MRMQAcsG1gm5NCy99NKxjyULCROGypBXeiqkW26GETACNYJAPreIZGgHBbb/4ke5rK733HNPicwyRxFwv9I8xoNR7DSDHhFizWNI4kj79NNP0xprrFEivdShB+PwASYoP+eUkR4R3tdffz32BaYshPnrr7+OcuoD+csF0pWHnSQo39rjxRdfDNV5H8vVpbjG2qJ0y9YjYMLbesxcwgh0CAKanFGucySTNYEnkZlkWSg4OD///PMjTYuNJmVPlgGLP4yAEeggBPI5inP5e8rfVv9C8bpg0iF6Ip4PPvhgaesrLK7s1as8zGGcEzSfiSiji7lPfsKc47+LP3CxPNfSozkUwisLL4RX/r6qR/mLkOW66OfJJ5+cjj322MQb4po6yHP88cenIUOGlMh1rqtYD9dqA3gReFsmD+bhLoKfNPjmMj8nrZje0riiHq5bElcJ9fEja/fdd0/5j4ocywAypWTCKyQsjUCFIKAJUZYNmsXfdRBc/NeQsmywDRmTNRZgldOEWSHdcTOMgBGoYQREUOV2BQFijpKFF2sugXwinhBXiCrElb/8H3/88UgvWj9FRPXvFQ+r5YSXeniDGg9aMe8pv6TmQtXLW91ywttSC6+GT/p0PTGyMV2KVx9uvvnmdNFFF8V2bDz456MhBpdcckm6/PLL08UXX1za/k7YCUuNkwmvkLA0Al2MADenDm5YTf5syK4FRJZdEd6hQ4dGq+XyULzBu7hLrt4IGIEaRkDzFV3UHLT//vvHfJVbeElnThPxfOCBB0qEeIUVVkiffPJJifCSV3olZeHlHy3mQoiy5kKINRZjgqyiKidJvZzjw8urjtHRGpcGtQlJPyDYHOw40ZKDvEUSRnuaCs2lN1W23tMaw86Et96/Ge5/RSDADcrBpMjB4sG+kXoABGuISC6TtSZ7Xi1M0GLT2I1eEZ10I4yAEagpBDRvQTQhle+880742TI/6TkDrLkE8shSK9cEvSKdv86Z90RMNY8RJ7KLJY8tydDNwXyoORE/4CeffDJ0qA61jbpFtHML74wzztgqlwb0qF3RoVZ85OXydpVTobyStJ0+IevpoM868n4rTpjkku8LQdhJCmcTXiFhaQS6CAHdlLpZ5Y/LRu2yZiA5tEBoojfh7aJBc7VGwAgEwQQGzVl/+9vfSkSUH+nMWSK8IrTkZ27DAiwr8IEHHhgkBVJMPuZEDpHkH374obQFmeY+zYlyndhkk01iRPjxr7lUeiBFnL/00ksll4ZZZpkl/GqJz+ssN6x5OtbaQw45JPbxpd0tOQYPHlzaAi3XVa4ux02IAGPUHsGEtz1QtA4j0EYEdCMjOZiYCbwu83e/+13JZ5fJHdcGbdauhcKEt43Au5gRMAITjYDmLUgcVrjvvvsubbbZZkF0RURFeCGv5Ef++OOPQRT1TxUPZBHPIUKI1H66F1xwQfjqikQPHDgwfFl5rTpzI882QGDPOeecKCMd1Mc5etlOjDkUHdRL+1qzDy96CN9++23UKcLdUvnCCy9EeXAiSF9clPmg7QRhXG8SfPjxIrzUf9ZI4vUjRvGSwqwMpH5orRwojjMCnYEANyhBNyo3Njc57y7XO9+1IDCpjhs3Ll155ZUx2crSa8LbGSPlOoyAEWgMARE3WXmPOOKIIJSao3hojTlOxEUSciqyeNRRR4V6kRh06sC/V9uRoRP/Xe09jmRLMuJwoUDitkCA5Gpu5ZztxJZbbrmok3l13XXXTT/99FPkoS7lLdfPPJ03kLFv8A477BAPE/NAcXPHtttuG37K6M51laurnuM0Bsg88J1RWh6vuGL+PE9+bgtvjobPjUAnI6Ablhua47333ksrrrhiTMpYILQrw8EHHxwt4y9DFglbeDt5oFydETACEyCg+UtE9dFHH42XLDBHae7KLbwoUF628yIPFtf+/fuHuwH6RAhFjK+++upw5eLNaMyJvD4YowBklbDOOusE2SUdXRtttFFYAIu6sPBSViQbn18CZJhAvY0F9ZN0zukD1ueWHuXcLBqrqzXxtEVtUhvzuNbo6sy8eRvzdnOu8WDLOFxBnn322WgaPzTAkS3s+MFx4YUXRry+J9LZVD9MeJtCx2lGoIMQKN7k+IURrrrqqrBSsBDIVw2LCZMAE7KsIrKe2MLbQQNktUbACDSLgOYx5ieODz74IPFGtH79+qW555479o6VFVV5mcc4+HEviysk9LzzziuRSeVFfvnll/GiiocffjixuwPXBEgnel555ZVIh1jjG3zTTTeV9gSmPHkgprRt5ZVXDuszrhCPPPJI6CE9r6+xTpOHgJSBQv1uTpKfelRe9UWEP0q4cAJW4MO49urVKyz4TzzxROThRwo/bLDm8+OFfJBggvCNi0Y+THgbAcbRRqAjEMgnOt2g8lN75pln4jWVEF3+muMp4r/85S/RDOU599xzw0JhwtsRo2OdRsAItBYBzWmaz3Bt4K1oWGHl5kAa+SRFAE844YTYHoz5jj12CRBZgvTFReEDgql6C0mlS5XX3MkLILRzBP+YES89ylsqXDihrkoKao8kmIE1h/DL8VG+ru6D2iEJ7hBWtZ0t3mi/xuzMM8+M9Y4fKHfddVc69NBD4xp/7XfffbfBd0o6m+qjCW9T6DjNCHQQAtycHEz8BJ4exiKCpUOTMu9hVx4tHLbwdtCAWK0RMAKtRkAkQ/MZBAYSqSBCqXTlF+F9//330/zzzx/z3s477xzbhKGDQ2U4hwRRBin9ua48nXOVJy8H++/KVYx5FUsh5dGpeqRPbS9Kpef5W3uOTpUp6m/JtdqgvE3pKuZVmY6QqkvtKUrqVJ7iebn2aDzffvvt0r8A/CjSQ4s77rhjkGSNH+PdkmDC2xKUnMcItAMCuuE1GXCzMhnz102fPn1i0uemFum97rrrYuLm5ucg2MLbDgNhFUbACLQbAvm8hlLIB3Mbh4iI8oiIco0Vj+OMM86IH/m4cbGPL4Gy0hUnhQ/pkywkl+rV391XXHFFyZCw9dZbl9qo9qidRT2Vcp33k3MO1g71jzexbbDBBmn11VePB5tpNxjm5TqqL6pDslw9SkNyCG/2mueBP9q++eabx4OEl112WajgIW0C1nj+9cR/m7WRVwhjACp+v1RHufoVZ8IrJCyNQCcgoBudyYoblieHZdnVAxy8KlNPIWvSMuHthMFxFUbACLQJAZENyVyJ4pD5Adnl+sUXXyy9/WzxxRdPPJzEPMkcSTrneTnOCbkspuuaeZO/yXGdgCwNGDAgjRw5MnRLv/Lmba6Uc/VR7eGaNUF/+ROPT7O2gqOP7BFMgAxrvSnqkb72ktKP1HghdU49SuMc7Am82pk2Fw9en0x+CDFbx/G9kDHo2muvjbL611N9jMhmPkx4mwHIyUagPRDg5s0PTVi33HJLPIGsfSG58bUjAxMbgXLKbwtve4yGdRgBI9DeCDBPEfJ5Lo/L61O85rURI0aktddeO+HWAEElXURGefPyTZ0rv+ZP3CYghBtvvHHiL3KC0pS3KX2VkEY7i23lob9TTjklHuJi3eC5DiyhPORMgPCqjLAs1xflUR26Jm9+3lhZlaMOcNU/kkjGFwnBJT3PyzkPNLK92/rrrx9WXl4estZaa6V77rmnNEZHHnlkEGJ2JmKdxMLLfs/oznU211bab8JbbhQdZwTaEQHdiLrZ9ev29ttvL5FdrLtMWr/5zW/ivfDk4aAMNzWTBqHowzt06NCI169d1dWOzbcqI2AEjEC7IKA5EGWaqyTLVdBUWrn8ilM5ScUjFZdLnef5KuGcdnFozcD6zc48kMD55psv1gzt5iP/VhFeCKH6JdmaPqmMZF6WOMUXZZ4vP9d6pvx5Wrlz2s+eyksvvXTJuqt96bVVmdZF1siWBBPelqDkPEagjQjo5kZy6Ma844470vTTTx83sh5Sw4/3sccei5r4paz8SN3YsvBqH97TTz898unXPHkdjIARMAKVjIDmKUnIkKyBxOmgD8rT0v4ovyRzLro5NP8qTbKlujs7n9oLPpzjAjfHHHNM4AKAsUTrSEsJr/ouSR2sO7J+twR7lQVb3A/YIxcL7R/+8IfYH1lWe/lmk596pJtrjQ1rmA6s/ITzzz8/+tq9e/c0bNiwkq83enGHABMO9KgtUbCRDxPeRoBxtBGYWAR0A+pm1ETCa4O5gfm1Kr+k2WefPSy71Im1VmUkRXhl4aU8k9yxxx4bzZSFVxPkxLbd5Y2AETACRqBrEdD8jxTpZccJ9qBdYYUVYgeDeeaZJ9aCtlp40V0u5HUX05XGeiMCy9tBp5122rJknP10DzzwwCDT5Ff5vG4RV/UTP94ePXqEvoUXXjiacPbZZ5f084AeAZJMaMnaZ8IbUPnDCLQvArqRdWNzE0NauT766KPjpoXs8ndUz549k95GVI7sUqZIeHlilb+02Ihd6aqrfXtibUbACBgBI9BVCGheR4rUYTzBCsq6wq4GuMSxJmAEaY2FF53oIDz33HOJfW95s10ez3kxEMchIw6WXdWPLzHnWJyRctejbfvtt1/UR1kRXOmXTuJx3aAfvFENv+6PP/448rNX/XbbbZf22Wef2N2DMlobpafY1vzahDdHw+dGoB0Q0I2nG1hkFykHfKy7HLgmsKE2oZxbgnTops5/4e60006lcpxoMoxIfxgBI2AEjEBVI6C1hE7oXOuJtu1iS7J8267GCK/Ka00R4dS/g3/605+CMGOAIUBmyat8eRukg3UJ4o2fLYSWY7311ovX/kKecUng5UkzzzxztJF0XkhCkF61K9cfGbIP1ZdFlU6bSitl+s+JCW8REV8bgXZAQDchk5N8x9h9gRueyUl/P/FqTV4rTB6CSKvKS4rwyqUBssyTx9KvfEgHI2AEjIARqA0ENLfTm/xcBhIsvPxb2BIf3nx9kC7WHs7555G1aYklloh1hbVFdRaRZJ3SmsRzJbzul7VtrrnmCktxnp+dGOR2gbWXPXepD/1a71SP2oSkf5BxDrUFqTitmXmZvN5y5ya85VBxnBFoIwL5zcfNzK9kJgb8l5gQILoiu0sttVRstM6Ny41MWcogFaRPk0tOeLfaaqvIlt/4KmdpBIyAETACtYOA1gWtEbLM4uvaFOEtritca11C4j5A4HkQ1qhFFlkkrtGvfCKmagNS9bPvL+U4Vl111XT99dcn/onEunvBBReEhRdfXMg0hpp555039KMbPdIZkU18tDRfEyq8LVlT4DjNCLQGAd2QmhwgovzdM3jw4BLZ1a/wZZZZJvyS0J/f+NKhernmKEd4t9xyy0gz4RValkbACBiB2kZAa4Qe1sLCm7s0HHbYYbEukK61SOuIJGmsTZBd5JtvvpkWW2yxWKfYPejiiy9ukM4aRZA+GWjeeuuttOCCCwaRhcxizNGD2CLBRcmrpAla9+LiPx/qm9qZS7Lk18W8uZ7Gzm3hbQwZxxuBViBQvPl0M3/yySfxNw83vSaC5ZdfPrZUQT1ENr+Ji1UqrRzhtYW3iJavjYARMAK1jYDWmpzwysLLQ2Ls00vAEKL1AymS+s0336Qdd9wxdnhgp4eVVloprK6sUZBWJM+WrLzyyom1it0XLrzwwpJO6UbedNNNJWOOiC1tWGCBBcKNoV+/fqGbB6whulh32WGCQHtEoCOiEz5MeDsBZFdR2whoAtLkAtnl4G0wOPDrrxwkf/kw4RBEilWeuPxc18SZ8AZk/jACRsAI1DUCWiNywgvJhHCuttpq4WoAkRSZlGS9IfDmOe3lKyMMRFdkV8QVqXQetiaoTv2reM0110S9/HPJ+ta7d+90ww03JB6o++qrr2Lf4G+//TbOeQUyrwnW+kc/1JdQ3gkfJrydALKrqF0EdMPq5mVSYYL5/vvvwzlfk4e2ZmHrF0KR7Kp8ESnFm/AWkfG1ETACRqD+ENCaI/KJS4NeRLTuuusGIKwXrENaP5Bcs+7wgogDDjggiKrWp+bkoYceGnpVJ9ZZDl5vvOKKK5aIMduOEeTfq7ZGZCMfLcnTSNFWR5vwthoyFzAC/4eAblQkBxMAYezYsWnTTTct/dUjsrvbbrvFZMNkRH798paecrhKtwlvOXQcZwSMgBGoLwS0Xoh8Qnhl4V1zzTVjHYLYsh5p/dBaozUK390HH3wwtsR8/PHHS3vDa63ClYH44cOHp3vuuSeNGjUqQFZ5LlT/kCFDYq2jLJbjO++8M/Kqzs8++yxtvfXW4cqwxhprpG222aa0/qkvnTWCJrydhbTrqSkEdKMWJxS2GONhMn4x5w78u+66a+kXNxOByklPY+AonwlvYwg53ggYASNQPwhozZAVlV0aRHhxoSPIwsu5iKfKSeaIQX5l5WULMe3xm+fJz9EJ4UXuu+++UVZW5llmmSWIMi+LeOONN4LoSjeSh+MIxX85c/0ddW7C21HIWm/NIyAyiuTm5T3n/HrlpubXLj5N+DZh2RXJzX91twQg1WHC2xK0nMcIGAEjUNsIiLDKwnr55ZeHSwNrzpJLLhnWWNaZnFCqjCTrEXkgzcjbbrut9KzJQgstFACK0JJOfoLKI1mTSLvyyiuDcOMDLAtxTnA511acPMOih+m0JkpnZ4yaCW9noOw6agYBbs7iIQf+Rx55pGTZ5SaH8Hbv3j0eEgAAkdbi5NEUOKpLZfN9eLfYYotoiyYQ5W1Kn9OMgBEwAkagehFgnifIwstetzKyIOVHy7pQXGu0Rihea9cdd9wROiiPpVa7MmjdUX7VLT1Kv+WWW9K0005b0oEeHXoYbvXVVw+s0Av9AAAgAElEQVSXPnRBlKWjM0fChLcz0XZdNYOAJgDd8Px9w69XSC6HbvYTTzwx/fDDD/FrmzK6yTVxNAeI8qsev1q4OcScbgSMgBGoXQS09ojwXn311alv377x+t611147Xt3LepGvN0U0tK6Qh7z8O4nbHQaa/v37J4w3kNLGiKnKI7U2PfXUU/HyCl4jTHvmnHPO1KtXr9j+7LrrrosHualPOtUPdHRWMOHtLKRdT9UjoBtTN7t+HfNucPYqhOTyl45+0UJ2CdzYHCrXGiBURpOKLLxsF8PTsW+//XaTE1Nr6nJeI2AEjIARqGwE8nWIlvIAGvu9sw79+OOP0XiRSq0fKpP3TGkinjx/wpZlbCNGUHq5ssV0rYXEjx49OtpDmz766KPSnvPUQ7uKZSOikz5MeDsJaFdT3QjoptfkoBucbVl4RTBkl7+CRHiPP/746LD8oDR5ECldLUFE5YqEF99gHi7gCVrykK68LdHrPEbACBgBI1C9CJRbR8qRynI9VFmtGXk58sv/V+ud8ktXfi0dlOEoF1if0KW8efly+TsqzoS3o5C13ppBQDenpMguW7UMGDAgyK6c8iG+xxxzTPSdfPkvbSKlo6XgkJ9DhDd3adh5551DjR5e0OTUUt3OZwSMgBEwAtWFgNaQXDL3i1DSG6UVz/OeKk8ucx3Kq3Rd57KYxrXaIlnMo/KNxSu9I6QJb0egap01gQA3pG5KSZFd3iG+6KKLBtnV22jYjoU9CQntQXbRozaI8MqlAbeJzTffPAg1dSmf2lkTA+BOGAEjYASMQFkENNfnc3+587KF/xNZLn8xrqnypBXzN3etMs3p7Yh0E96OQNU6awoB3cByG3jttdfSggsuWCK7ekhtkUUWCQKqrV5UDjA4b0uQjnKEl/1+STfhbQuyLmMEjIARMAL1hIAJbz2NtvvaYgRENPUXD24D+CdBdhdYYIEgu/LXxY0B0suTqLgwiJxSltBWsquylJfO3KWBPX8JdmkIGPxhBIyAETACRqBRBEx4G4XGCfWOgEhvTljPOuusBpZdiC5bubAPIaE5Z//WYqo2iPDmLg2bbLJJvMaYNOXL29raupzfCBgBI2AEjECtImDCW6sj6361CQERRkkstlhQX3311XTmmWfGvoKy6GLh7datW7ylhso6gniKyIrwnnvuuUG4Idm04y9/+Uv0c/z48SFlVW5T513ICBgBI2AEjECNImDCW6MD6261HQGRTKT2DdT7wiGZPDDGgXX33nvvjYr0MJvKEsn5xAbpE+GVhZddIZZeeun00ksvhVVZluX2qHNi2+zyRsAIGAEjYAQqDQET3kobEbenSxAQUcwJJudsxL3pppumPn36xB672pEB4nvfffdFW7EAq1yupz06Ir3lCG+/fv3SQw89FNvAmPC2B9rWYQSMgBEwArWKgAlvrY6s+9ViBIoklWu9tvHuu+9OIrm4MEB0p5566nT//feH/o4ku1TQGOGlLcsss0xiezTIsCzRdmlo8bA7oxEwAkbACNQRAia8dTTY7uqECBTJLsRR7gmPP/54WHYhvJBcyO6MM87YwI2B8trJAe3SN2FNbYtpjPDypjWsznfccUfUn29N1raaXMoIGAEjYASMQO0iYMJbu2PrnjWBgIgkWXSOWwDhm2++SQcccECae+65g+RCLiG7PXr0SCNGjIg8OcEUyZWMDO30obbJpSHflsxvWmsnkK3GCBgBI2AEah4BE96aH2J3sCkEioTyu+++S7feemsQXEiuXiox00wzpeHDh4eqcm4MTdUxMWnF9umhNR6a22yzzeKBtXLke2LqdFkjYASMgBEwArWGgAlvrY2o+9MsArLEikzKhWH06NFpxRVXTD179gwXBll2p59++tI+u/j2qlyup9lK25hBdcnCmxPerbbaKrSq/WpPG6tyMSNgBIyAETACNYuACW/NDq07Vg4BkUI93CWy+O6776Yll1wyLLtYdUV22e/2xhtvDFWdTXaptCnCq1cLY3FW3jjxhxEwAkbACBgBI9AAARPeBnD4olYREHGkfyK9IrvvvPNO6t+/f5Bd7cSAOwOW3euvvz4gkRtDRz6gVg57tdsW3nLoOM4IGAEjYASMQMsQMOFtGU7OVcUIiODSBZ2L7LKt1yKLLBJkV9uP4dKwzjrrpMsuuyx6jWVXRFcEVHo6GhbVZ8Lb0UhbvxEwAkbACNQyAia8tTy67luJ4Io4IuUC8Prrr6cFF1ywRHb1gNpcc82Vxo0bF2UhmnnZzoZUdZvwdjbyrs8IGAEjYARqCQET3loaTfelhABEUUH+uuyxyznylVdeSQsvvHADNwZ2PmC/3euuuy6K5m8vkz5J6e5oacLb0QhbvxEwAkbACNQDAia89TDKddxHEUZILudjxoxJ22+/fZphhhmC7OYPqJ100kmJbcnIq/wqD4Scd3ZQ/bbwdjbyrs8IGAEjYARqCQET3loaTfelhICIIhJLLQT2559/TptvvnkQXR5Kw6I75ZRTxvXJJ58cZclfKWSXBqkfJrylofWJETACRsAIGIFWI2DC22rIXKASEYAYEkQQJSG7uDH88ssviX1rIbp6OA3Cy7XILqQSsisXiFxnV/VZ/TDh7aoRcL1GwAgYASNQCwiY8NbCKNZ5H8oRU0ir4n/44Ye04447BrmVRReiy3HKKacEeuzaIHIpSYJ0dBXEaosJb1eNgOs1AkbACBiBWkDAhLcWRrGO+yBCKqmH0rDsEk4//fQ066yzBrnFX1eEd6eddkqfffZZ5Cn3cBoJ0hmZuujDhLeLgHe1RsAIGAEjUFMImPDW1HDWV2dESEUKkZBXkd3TTjstiC6W3Jzsbrvttmn8+PEBVjmyK72VgKb6ZgtvJYyG22AEjIARMALVioAJb7WOXJ22W2Q0lzrH/xZi+Ouvv6ahQ4cG2cVPV766EN8tttgi9tjFEpyTXcEpXbruakl7OEx4u3okXL8RMAJGwAhUMwImvNU8enXWdpHRXHIO0eVtaISrrroqrbbaammaaaYJq64eUMOtYdCgQaVtx4pkVzorDVLaxWHCW2kj4/YYASNgBIxANSFgwltNo+W2BvkTCcRKKyIINNdcc02afvrpw7KLr678dXl18AsvvBCkGKKbbztGuUolu2qbCa+/+EbACBgBI2AEJg4BE96Jw8+lOwEBEVwkJJcAaWVnBcJjjz2WDjrooBLB5W1puC9MNdVU8ergkSNHRj5eKUw5gvRUMtmlneq7iP0555xTctVgmzWCcKj0vkRj/WEEjIARMAJGoAsQMOHtAtBdZcsRyEmcSCrkD+I6duzYdM8996R55503SCAkt1u3bnHes2fPdP3116d33303KoMUUl4Ekshcd8tb1Lk51V4T3s7F3bUZASNgBIxAbSFgwltb41lTvREhFelD4qsLcf3yyy/ThhtumLp37x4EF/eFKaaYIs579+4dVl+BUfTXJV66ladSpfpuwlupI+R2GQEjYASMQDUgYMJbDaNUx20U4cOiK3cEXiSxzjrrBLlluzEOrLuQ3z/96U/poYceCsRwYSiS3Wohuhpy9d+EV4hYGgEjYASMgBFoPQImvK3HzCU6AAERURG8XEJaFY499ti08MILB8HFoiuyC+G9/PLLlS0eZst1kKA6Spmq4ER9MOGtgsFyE42AETACRqBiETDhrdihccMgulhpCaNGjUpHHHFEEF3ILS4MkF322O3Xr1+J7EIMsQSLKIrkSlYbquqHCW+1jZzbawSMgBEwApWEgAlvJY1GHbZFRFTETlLuC0CC5RaSq0N763I9ePDgEikuPpQmOFWHrqtJCg8T3moaNbfVCBgBI2AEKg0BE95KG5E6aI9InIgoUjswILXN1sMPP5wOOeSQILry1ZULwworrJAuueSS9MsvvwRiudsDEcU6qhVW9cOEt1pH0O02AkbACBiBSkDAhLcSRqGO2iCSqy6L0EF0IXVcc/7ggw8m3o6GFVe7L8jCu9JKK6UvvvgiVCi/9EhKf7VL9ceEt9pH0u03AkbACBiBrkTAhLcr0a+zuiFvBJE4JK4LInOkffjhh2nZZZctbTfGK4IhutNOO21abrnl0nrrrZdGjx4derRFmfRGZI19CCth5BdP1NgAuztGwAgYASPQKQiY8HYKzPVdSU5IReCQkDi5MnzyySfxoogllliiZNWVry6kd9iwYQGiyucPppGQ11FLaKu/Jry1NKruixEwAkbACHQ2Aia8nY14Hdcn8gZZ1e4LX331Vbr00kvT6quvHkQ3d2Hg1cCzzTZbuvjiiwM1fHtzkiySK1mL0AozE95aHF33yQgYASNgBDoLARPezkK6juoRARVZy695uExWXV4LjJvC1FNPHWQXyXZjWHYXWGCBdNxxx6Xnn38+3B70UFquC0h1XavwCkMT3lodYffLCBgBI2AEOgMBE97OQLmO6hABFVGj61h0c1/dd999N5144olpnnnmCaLLXrpYc/VQ2nbbbZdwcRg/fnwgh2VX+iRJUF21DK/6a8Jby6PsvhkBI2AEjEBHI2DC29EI14n+nHzqHAlZhewSfv311/TOO++kxRZbLMgtltxu3brFyyM4X2211RIPZY0bNy7yQ/I4cn11Amepm/Sdw4S3BIlPjIARMAJGwAi0GgET3lZD5gJFBHJCmhM0dlEgjB07Nr355ptp4403jreiYcnVm9I47927d/jxfvPNNyXVuDBIlySJqquUscZP1HcT3hofaHfPCBgBI2AEOhQBE94Ohbe2lYuM0UsRUfxz9eII4h944IG08847px49epRcFnKyu+GGG6bbb789gEIHxA6LcDndqqO2UW3YO+FgwtsQF18ZASNgBIyAEWgNAia8rUGrjvOKbIqAAYXiILkcuibt0UcfTbvvvvv/b+9cgK2q6jDeQ1KQR4Y2PhJJ1JLsYZMKCE5GMwWmgZUoZuFoUYEWmkU+yiy1py/M1AoESRhFBZnCSQkflKKmQIozWimaCioPMbMmndX8lvMd1t3sfe65955z7jnnfntm37X2evzX2r+1113fWXvtvUPfvn1LQpcPSOhLaczsXnjhhaXlDqnQTW2nNnsifvG24O2Jre9zNgETMAETqBYBC95qkWxBOxKbWZdTJQyRixDD1cYDaRMnTgwDBw4sCd30gTTW6k6YMCE8+OCDgSUPephNwk6uypDdnuqKhwVvT70CfN4mYAImYALVIGDBWw2KLWYDkaUt9RPGMQI3XbZA+OOPPx6mTp0a1+bqbQu8Zow3MHDMkoaDDjoosE5X7+BF7KbLF9KyUr/q0hNdOLBb8PbE1vc5m4AJmIAJVIuABW+1SLaAnVRkSmhxWvglThFeEl984nf+/PnhRz/6UWk2F3HLsgWWL+AfMGBAfFiNGd10Sx9KS8PTOqThPdWvdhBzf1q4p14JPm8TMAETMIGuELDg7Qq9Js4rYSlBlT3m1JjJRehmP/rA+3GnT58eDjzwwJLQZSaX5Qqa3cU96aSTwuLFi0uUsiI3W6aOSxnsiT824GLB64vBBEzABEzABDpPwIK38+yaMqdEJa78OhEELmG4EqeKYynCli1bwhlnnBGGDRtWErasz2XpgoQub2A49thjw9KlS5W1tFaXAJWrsuWWEtvThoB4WfC2weIDEzABEzABE+gQAQveDuFqvsSpoJR4SsPSWVxEFTO6hLFt3rw53HXXXeH6668P++yzT9h9991LwnaHHXZoM6Pbp0+fMG7cuHDbbbeVvpCGaMZmXrnNR7J7aix2Frzdw9+lmoAJmIAJtAYBC97WaMc2ZyGRRKD8uOmGGOXBMwkp4gjjzQl86WzJkiXhmGOOKa3FZQaXtbnaOWZ2d+zYseH0008Pv/3tb0vm0xlila/IbD0UbjefgPipnbyGN5+TQ03ABEzABEygHAEL3nJ0mihOQlICSW56CogmBC1vSVB6xCkPnz355JNRuLIc4TOf+Uzp/bl8+pfZXC1Z6NevX5zpPfTQQ6PIfeaZZ0pF6DPCeWWrvFJieyoiIJYWvBXhciITMAETMAETyCVgwZuLpbECJRYlfrKuapuXDhGKwNWrwJT2H//4R7jjjjvCNddcEw444IDwwQ9+sCRqEbesxU0fQvvwhz8chg8fHmbOnBkeeOCB8Nhjj0VTmhXWa8pUNyLlV71Utt3KCYihBW/lzJzSBEzABEzABLIELHizRBrgWAJRYkduXtXSNbh6owIilF12lO/OO+8MP/7xj8OVV14ZRowYEd+Ny4ytZm/1AJq+hrbLLruEk08+OZx11lnh6aefju/QTW3qwxGyn8alfsXb7TgBOLJb8HacnXOYgAmYgAmYgAhY8IpEN7gShRI1cqmK4tJqEcZMqmZtcfPS8dqwTZs2hZdeeilcddVVcS3uiSeeGHbdddcobtOZ29SP8OXhsyOPPDLMnTs3vmkB4ayNshBe7IhrjtO9qN7Kb7fjBMTXgrfj7JzDBEzABEzABETAglckGshF5LAhaJlF1Z6KT1V348aNYeXKlWH16tXhoYceiu748ePD/vvvHz7wgQ9EAasZXMRt+vUzid0PfehDYejQoeHggw8Oy5cvDxs2bJD5OFOsGWNmk9m1qZ4cp37F2+06AQverjO0BRMwARMwARNoWMErAaUBv9ldLrWic2C2VDO3qZt3eS5cuDBcd9114dprrw3z5s2LrwLjwTKWJvTt2ze6Eri4LE9gqUIaxmd+J06cGMO+9rWvxbIRucwMa5PITdtBcToPjhWvOLvVJSDWnuGtLldbMwETMAET6FkE2hW8EjQaeOvt0hyqQz2bptIyUx6aAS1yK7Wp8+QVVF/5ylfCtGnTwmmnnRY/6MAXzVLxip8w7YrLpmMW9+c//3m48MIL47tyKYMH1niYTcsTqDfCCjc9r7TeqV/1tFs7AmoHC97aMbZlEzABEzCB1idQVvBK3MjtDhwa8OvpSrAiMtrbNRNaKRvOQ4JSa3D5uMPo0aPDqFGj4s4DZYcddlicnZWAlcsyBD1cprCsK7FL2nPPPTfce++9gbcypJsEFGEIXp2z0qjNs67i7daHgK57tZffw1sf7i7FBEzABEygtQiUFbycqgSPRJqEUa1diTAJSlwEYnsCtKvxKqejzbxu3brw8MMPhzVr1oRHHnkk7hzjZ30t77rlDQksO9h5551D//794/ID3PTTvKl4RbDyDlwJXAlZpdluu+3Ce97znrhe9/3vf39497vfHZc68AngF154IT64lp4HbDg/XNoVl3ZM21kCKw1LbdhfXwJqD9qKzYK3vvxdmgmYgAmYQGsQKCt4NdgiiiQ8EaISo7USvWkZ3YX5iSeeiOJx9uzZYc6cOdvshLPPmjUrXH311fFdthKinXERt+y8/7YoP+L36KOPDpMmTYofh6DcSja1l9oTly3PzYZVYt9pakdAbWbBWzvGtmwCJmACJtD6BHIFbyp65BeKesyy6sEtBnnEJh9IePTRR8PUqVPDlClTarpPnjw5fOMb3wh8aKFIeBaFI1bzdmZii/IQTjwPl0ns8hUzZvIuuuiicOmll4bLL788+m+++WY1Q8nVjC2s2PkRQpvJlWCqxC0ZtadhCKjdLHgbpklcERMwARMwgSYkkCt4dR4abHn11Re+8IWA4GJ9KWtNEWW13EeOHBnYuZ2/xx57hH333besaCwnKDsbx4yqdt6EoF1h1C27zKCorD333DMuORg8eHBgZ/kBrw674oorAmt4ly1bFu6+++74QNnatWvVBNu4PGTGa8p4o4LWAJOItvLWegTUBy14W69tfUYmYAImYAL1I5AreCWeEFZsX/3qV6PYHDhwYN1Fp776hZBkrSu7BGdXXNnR+ljK0bIC3CLhmg3n4bIjjjgijBkzJowdOzZ3P+qoo+Ia3ueeey48++yzQe769esLW1rCFld7KnDTjGoviSPiFJams7/5CKhNLXibr+1cYxMwARMwgcYhkCt4uR3OhuBlwD3uuOMqFoBZQdiRY82WIkZ5mIsHvPjy14477hh3CVxmWuXvqitbfIXszDPPjJ/RPfvsswP7Oeecs81O+He/+92Y7uKLL45fM4OR1skWrWuWcMFl07HWRiNo5MdW0aZ8qSt7RXkc3rwE1M4WvM3bhq65CZiACZhA9xNoV/BSxVNPPTXstddeYb/99gt77713zfZ99tknDBo0KArKW265JVx//fVhwYIFNd8p589//nOnWkNraIvWNms9stbUypWQwc3b0visn/RF+fJsOax5CajtLXibtw1dcxMwARMwge4nkCt4JaY008tt+FWrVrV55ZZevVVNl1d6UU76xa96ImJGO28pgZYUpK7SilV79ZRwqdTFXqW22yvb8c1LQNeLBW/ztqFrbgImYAIm0P0EcgWvqtVdgovBPRWXtfQzA4t9CQqde6WuBEklLja7i2ml5+N0jUVA15WuT7+Ht7Hax7UxARMwARNoDgK5gleiTC5rShlwtca0li7lqNx6I5S46KhLPburzvVm5PLqS0DXogVvfbm7NBMwARMwgdYikCt4G+EUNdDX2+XcLV4b4QpwHXQtcj1a8Pp6MAETMAETMIHOEygreCX86i0603I7f2rOaQLNT0B9z4K3+dvSZ2ACJmACJtB9BMoK3u6rlks2AROAgAWvrwMTMAETMAET6DoBC96uM7QFE6gZAQvemqG1YRMwARMwgR5EwIK3BzW2T7X5CFjwNl+bucYmYAImYAKNR8CCt/HaxDUygRIBC94SCntMwARMwARMoNMELHg7jc4ZTaD2BCx4a8/YJZiACZiACbQ+AQve1m9jn2ETE7DgbeLGc9VNwARMwAQahoAFb8M0hStiAtsSsODdlolDTMAETMAETKCjBCx4O0rM6U2gjgQseOsI20WZgAmYgAm0LAEL3pZtWp9YKxCw4G2FVvQ5mEA+Afo3m/p5epyfw6EmYAKdJWDB21lyzmcCdSCggdBfWqsDbBdhAnUkUCRus+E6rmPVXJQJtCQBC96WbFafVKsQsOBtlZb0eZjAVgISsXJff/318Nprr4X//e9/pdnerantMwETqAaBsoJXnVGDblddKlwNG1k72eOOlqH8csmf+vOOKykja6O9PEXpY2UqYFeUv1y5WdvlbGTj0uPUn5ZHOFs2jON003FeunJxqQ2VIzfNl4bllaF4uWne1E98V7bUVupPbabhDITs7c3wkifNl9rL+tN0qT+bTsdpmtSv+I64af5y/jSuI/ZJm+aVX+EcV7Jn0+sYt2hTWal90lZynKZL/anNtNw0XPaVL02X58/mrTRfni2HdZ5A2g5Y+e9//xuNqb9n4ztfknOagAlAoKzgrTYideA8u+Xi0vSky6bNHqfpK/Erv1zyyI+rvRJbSlNJHpWh8tJj2WkvLpsuPW7Pr/LktldWmk5pVUY2Lg3PxnFctCtfnv3UjvxZO0X5lD5rP82fl1dhabpq+duzncZL8F522WXhTW96U3jzm98cjj32WJK0GSg7UreYOSPIyuXvaPo8W9ggXFs2jcKVLhtfybHyylaaR2FFblHabHh6nOeXfeLSLT1WPuKLwtM4pU/drO00rhJ/e/YrsZGXRnbz4hz2xv8/zerC48477wxHHnlkWLFiRenH7X/+8594XZAuZSa2advbbwIm0D6BsoKXTsZGh0t3wtVZuQ2jDpmmyfOTll350zTcykmPU7/S65ZPWmY5m6mNcn7ZoA6pnzyEKbycDcWprkV5FK9ylC97rHC5sqf8Cper/EXxSoerNNjET178xKmcbHrZT8PTtKk/WwZ503wqT+HkRdClNlReWlelT/PLj33lSe0QzjF7Wi/sKkzxyi9bhCtM5eg4L0+aXvGpm9aFcB3r3JVWx6oHZTL7w7EEL6J3woQJsf1effXV6Cp/e67sZ8+lKJ/SU35RncvZSuPkx0567akM4onDpT6EKyytn+ykYfIrr+qqcFzC8vbsdaYylZbjtI4qQ7ZJp3hcjpUGN2ufePISp3RpWYRlbagsuYqXbfKoDkqT56ZpZCOtg/xpXtKlx+X85Gcrl6anx6kN/v3vf0dOw4cPjz9k6deTJ08ON9xwQ2SodPD3ZgIm0DUCuYKXf6BscrtWRLGdPPt5YZWUT76O5u1M+nJ58uIIywsvOqc0feovSk94ar9a/xhlU25afl5YGp/nVx65eWkUlp5D6ic+e6w8eW65tJXUI89mNcMqqYPSSET8+te/Ls3wTpw4MVZHaapZt3rYKmqfovBq1CmPVV4YZRWFV6MeReeYllmUJlt+micb113HjVin7mJRSbkf+9jH4l2bXr16lYTvjBkz2mTlehBXuW0S+MAETKAsgVzBqxzqVPwKfemll8KWLVvCyy+/HDZu3BgI+8UvfhEuuuiiOPu0YcOGGEeadFe+devWha9//evhrrvuiuk3b94c023atCnaO+GEE8ITTzwRbSiPysM25V1xxRXh4IMPjr+An3/++cDM1qpVq8LJJ58cq0y90rLL+SmDnTzf/OY3w5IlS8Lhhx8eFi9eHG699dZw+umnh3/+859h0qRJscyrrroqzsS0VwbnRX0/8YlPhDPOOKNNHrGj3rNmzQrnn39+rC+zd+ecc06YN29ePCeYUHds8Qv/2muvDcOGDYvn+sorrwTisaU0/CO8/fbbY50BQflF5646/Otf/woIJtJdfvnl4eqrrw4vvvhiGDFiRDymXI4pC7G1cOHC8J3vfCeWy/E999wTufz+978Pf//732M+zolzIR/lPPXUUzH8pz/9aRSqhGP3yiuvjHnhvmjRonDIIYfEdLTtnDlzYlvC4txzz43+xx57LKbhemOjfeBLW02ZMiXa+t3vfhfOPvvscMstt8TjZcuWxbQ33XRTPD7mmGNiPTlfrh02mN94443hi1/8Ykwzd+7cyHDNmjVxhoV6Pfzww/E6R2iecsopsc1mzpwZ60N9f/jDH4ZLLrkkLFiwIMpzKywAABCrSURBVHz2s58NDFIcp+d06KGHRvvUbdq0adF/1llnxTocd9xxMS3tSx5s4o4ePTo88sgjgXaiPbm9ed9994VBgwYFDYqf+9znYjx9C64/+9nP2tjATtH+0Y9+NKxcuTLWOy03TU9dVC9mnbgW1q5dGyiXdMQxM0U6+PzgBz+IfoXJ1nXXXVeKmz9/fpg+fXr8PwAAZqzJz3VHeq4hNvKMGjUqhtFPrrnmmpJt0tFWtB/+bHlcU6eeemp47rnnIhPss9Mf2eCp/pP2k8cffzymoS9QL/7/kZY+9/3vfz+WNXLkyFg3ysYm5Y8ZMyYsX7488iCMevN/Yvbs2fG8Vq9eHduP64rr9a9//Wusx7hx4+L/Ml2zpKMvX3DBBbE/kojrafz48WHp0qWxHJ0L/0t/9atfhV/+8pfRFm1z8cUX5/KgjtkdOyyJefLJJ+O50S5ci7jf+ta3SudGuqOPPjrecsevdsra41jXOf9P2D75yU+W7OSl78lhsOT85fbv378kdLfbbrvwlre8JR7Dm/8F999/f7wemRnnuqz0x1BsCP8xAROIBHIFr4QuwowN0ff2t789DBw4MAwYMCDuHHP7hR2/wotcdegddtghNz12+vbtmxuHTcpg3aLKxB5hffr0iWHveMc72q1DUd2w+ba3vS3aQUzI369fv1J5/APaaaedKi5D9czLQ731D03nRvq3vvWt25w/+fkHSDznmseaNNtvv32HOGAHm5SPC1u1EX5sckw8foks8nHcu3fvmA9WtBs2OCfi1Tbil9ojr86dPLKLXzZoS50zftnHDsdqnzRvNozrjLRpGuxwPtSPOMpTOfjhj7vjjjuW8uGnXHbi2NP6c0xcaidNqzy4qqPCVAcdZ13KVnvjirnsc27iDdds/vaOuZ7SereXHnZq02zaLJM0XlwJU3lqH52L0mMHLmke4rL2s/HKj6s21/WcxmGb88jb0+uMesGUdDBObeSVzfmkacindOq3tCdpOE7bXtcF4WpHcZBN9W8d45JG6SgvjavUr3NWXdU+2fzZ88vGp8ewS88vjbP/jf8h5TjAj/ZIr/njjz8+Tvrww5bNgjdi8B8T6BCBXMGrzsSsKhszV+U6aD3j0n8C1S43O/jKfmfKLLIlmx11O1OHjpZB+o6Uk6ZN/Wm5lYSTRnuaN/Vn7aR8s3F5x2n61K782TyE54Wl6bPxaRnEZfc0r/wqJ5s2tZWmLQpXmqydouPUTlGaNFz2ccmbxuFXfDY8G6d0qZvmyQtXWF66NEx+pZdbFK74St2sHR2LpY5xZTP1K0yu8nFcSbqUu2xk3bQO7fmzedPjNK/qmYaV88tOWt9y6XtanLjIFS9c/fDADxeOuTvFHQPurDE2MyGlSakOjfZObAI9nECu4FVn0rpBliGwnIDbjdzO9W4Gvgbqdw385je/iX2PJQMMhJqF4xYy7UB86rpt6tc2Zm3WHb0G1F9ZJsdSnU996lOBGXSJXfyDBw+Oy1oYc9evXx9lipYzaHzu4drFp28CHSaQK3hlxR1LJOyaQPcTePTRR6Pg1e3lE088sfsr5RqYgAl0iQDrxIcMGRKXPA0dOjQ+Q6F13hhmVjc7s+uxuUvInbmHEsgVvOpMclk3xHpeHpzxbga+Bup3DdDvWFpEH+Rdnczwas0nD4/RFjx8ies+Wr92cR8w665cA+qrPJTKxkOR7DxoiV02pdGdVo3HcmMi/zEBE6iYQK7grTi3E5qACdSUAIOb1tTfe++9UfDqASaeqmfTF5o8ENa0KWzcBGpCILs2lx+3Wr6gAtW35SrcrgmYQOUEygpedS5c72bga6D+1wBiVzM8vAqOGV4JXl4rRZsgeHHT255uq/q3lZmbeWeuAYZrRC57UR8mDba9mYAJdJ5AWcHbebPOaQImUA0CDHLtzfDqFmg6IMovt6guiseVvyitw03ABLYKT/WZ1IWPjlO/wlJXLAlLNx1n3TSN/SZgAh0nYMHbcWbOYQJ1I8CglxW8WsPLDC9b0ZIG8rLrlqkqrYE0nU3KxunYrgmYQFuRCw/1S/qQ+pf6W8orTZemlQ25yoursOjxHxMwgaoRsOCtGkobMoHqE9CAieVK1vCmAyd+1gJq4+E2vhrGV8b0jm2tFVQ+pbVrAiaQT4C+krdp6VHal4rSKn978Upn1wRMoOsELHi7ztAWTKBmBBgQNcO7YsWK+IYGvtjFLO/nP//5WG52hleDKGsC2fhkLZ9dZu3v7rvvHr+YuPfeewc+C83GkgjypHuM8B8TMIFIIO0b+OkzvCaQvvXggw+GZ555JqZTXyUNG31z1apV8TPecvlkPfGkld2Y2H9MwARqSsCCt6Z4bdwEukZAt0Gxcvfdd8eH1nhwjf2II46IxrOClUDNNs2dO7dNHuXF3XXXXcPtt98ebWimNztgx0j/MYEeTkD9QndMeE9u2pf233//oHfn0vdSIZumwz9mzJhIU2vvZbuHI/bpm0DNCVjw1hyxCzCBzhNIB85169aFSy+9NPCFphkzZoTbbrstGpa4ZeAkPbNK+GfPnh0HZb7Mps/DavDls6X4d9ttt8CXFNmUHxveTMAEthJQP6SP0N82bdoUpk6dGvuQPgTz3ve+N6xZsyb2I91dOfPMM+OXEbkjw5fUuMOyePHimEY/Mt3ftnK2zwRqScCCt5Z0bdsEqkCgkgFRAzKuBtvRo0dHoYu47dOnT3jnO98ZlzPssssubT5lSji3W8mbnZ2qQvVtwgRaggBil039iw/BsLwIIdurV68ofvkUMOn4aASClr7FD0vFH3744bGPqZ/JZksA8kmYQIMTsOBt8AZy9Xo2AQlZKDA4MnurXbdXSaOBU8sbLrvsstC7d++4M+BOmTIlbNy4MX7J6cUXXwwnnXRSHIgRwsT/8Y9/jKBlU/Z6Nn2fvQm8QUD9kH6Bnz7Ig5+nnXZa7D+sj+eH5eDBg8NTTz0VM33pS1+Ka+0ldolftmxZFMKp4MWeNxMwgdoTsOCtPWOXYAI1JyCBKsHLkgeErF5hdsopp8Q68ClT0k6aNCnGczuW5Q4PPPBAaYaXhBrga15xF2ACTURA/UKCddGiRWGPPfYo3UlB3LLkaO3ateHAAw+MfYwlRYR/+tOfDuvXr4/9T8LZYreJGt9VbXoCFrxN34Q+gVYnoEFRg23qcu7psW63nnfeeXGwRfTuu+++cckCs7fEM9jysNqQIUPi7VgE79KlS6MdDcS43kzABLYSUD9UH9HdkIULF4b+/ftH0UtfYhkDD7HR9xC7uMOHDw8bNmyIyxnIpz671bp9JmACtSZgwVtrwrZvAjUmIHGqAfgvf/lLfDhGgy1redk0+8vsFA/dHHbYYXEw5lbrRz7ykbB58+Y2olcDfI2rb/Mm0DQE6BPqb/Qj9meffTaMHTs2/nikLyFw2dMHRbnjwqb3X8uG+1jTNL0r2gIELHhboBF9Cj2bgGaLNHN0xx13xAFXgnfkyJFxYNbbGxhseW/oQQcdVBqYBw4cGNf4QpJBnE2Dcs+m67M3gbYE1N9w+RHJXZM5c+bE5UOIXHYeZJPoPf/88+MHX/SDNM3f1rKPTMAEaknAgreWdG3bBOpAgAGUTQPqn/70pzaDLjO5bOkM7/PPPx+GDRsWB2UGZt7Jy6wvthC6GpRjRv8xARNoQ0D9gx+H9Lunn346jBs3rtSfNMvLWxxYN8+mPLjeTMAE6k/Agrf+zF2iCVSVgAZQrd/VByo0wztq1KhYHjO8pEXQ8vDMIYccUhqgeVUZb3Fg08yu7Fa1sjZmAk1OQP1C/USC9pJLLon9KV3W0Ldv39if6JvkU165TY7C1TeBpiJgwdtUzeXKmsC2BDR4aoZ3+fLlceDVbdXsDG9W8HIL1jO823J1iAkUEZB41TreLVu2hAkTJsR+l67d5S0pS5YsiWa0VEj9VW5RGQ43AROoLgEL3urytDUTqDsBDb4IXvwI3nQdYTrDi9jNCl5uv/br1y/wfl42Dcyawar7CblAE2hgAupv6ksvvPBCOOGEE+J7eOl32rWsYcCAAeGmm27Knelt4NN01Uyg5QhY8LZck/qEehoBCVMtaeALUAy26ZIGRGy6hpf1urwqiXQM0DNnzoxCF1sa0HG9mYAJbCVAn1Afob/hf+ihh+InuulL6XKGVPgiiEn/yiuvxP4lG+5jW9naZwK1JmDBW2vCtm8CNSYggaoZXg3AWtLw8Y9/PNaAwRbhy8C7cuXKcMABB0SxyyDNMZtnd2vcWDbf9AQkVulv9CWWMiBu+YFJX3rf+94Xdt555/hjEhFMPySc9/Wy8cOTDTveTMAE6kfAgrd+rF2SCdSMgAZPDabM2GrGab/99gurV6+OZSN62bKfFmYZBMJZolkiumYVtmETaDIC6hO4/DCkz/GA6NChQ6OgRdjutNNO4eWXXw73339/2GuvvaLY1Z0WPjXMsiFEcmqryTC4uibQtAQseJu26VxxE3iDQDp4vvrqq3Ew/clPfhIF7/bbbx/dQYMGhfvuuy9m4CtrfAmKAVqfHuZVZmye4X2Dqf+agAiof3GsH5Z6p/W0adNi/+IT3fzA5Itrulvy5S9/OYZJ8BK/YsWKaJYflrKHfW8mYAK1J2DBW3vGLsEEak4gHYgpbPbs2XGw5VZrr169on+33XaLX1fT7VatN/z2t78dZ6U8u1vzZnIBTUxAwldi9Z577gl77rln/NHIj0ceTps1a1bgRyf73/72t/j2Ey1r4Mfn+PHj49fWmOVlU79tYiyuugk0DQEL3qZpKlfUBIoJaDDGZTBlaQNfeGKwZU9nmRTGO0JHjBgR/vCHP0TDmt31jFMxZ8f0PAJp30Kg0k/oY9/73vdi3+LjEvSp6dOnl/qR+tLkyZNjHIKYNO9617sCX0Iknl22ex5Vn7EJ1J+ABW/9mbtEE6gJAQ2euBpwZ8yYEY4//vg4C4XAZWeA5tbreeedF+uRN/hiw5sJmMAbs7D0B8Qus7u43BVhORD9iZnbIUOGxPft0pd0p0R9kKUNpKXf9e7dOxx11FERq9bbe5bXV5kJ1IeABW99OLsUE6gpgVSg4mdnnSEbA++8efPCzTffHG688cawYMGCcOutt8aBmZkqduWRW9PK2rgJNBEB+gQbwlSilz50ww03hEWLFoX58+eXHgqlr5GedOpLCGD1P/KsWrWqlAa7st9ESFxVE2hKAha8TdlsrrQJ5BPQ4KlZI2aRELR5m2aiiFO+vHQOMwET2NpHivpK+sMxFbz64ZlliJ0iW9m0PjYBE+g6AQverjO0BRNoKAIaSDWYMvgifNNdT5lTcaVTvoY6GVfGBLqZQLZ/cEz/SftT+qNS/SjNl5c2m66bT9PFm0DLE7Dgbfkm9gn2ZAIadIsYKF5uUTqHm4AJlCcgAZumcr9KadhvAt1LwIK3e/m7dBOoCYF08JU/z6VwD8o1aQIbbTEC6id5/Uhh6k9Fx3nhLYbJp2MCDUvAgrdhm8YVMwETMAETMAETMAETqAYBC95qULQNEzABEzABEzABEzCBhiVgwduwTeOKmYAJmIAJmIAJmIAJVIOABW81KNqGCZiACZiACZiACZhAwxKw4G3YpnHFTMAETMAETMAETMAEqkHAgrcaFG3DBEzABEzABEzABEygYQlY8DZs07hiJmACJmACJmACJmAC1SBgwVsNirZhAiZgAiZgAiZgAibQsAT+D/G37nadvzwiAAAAAElFTkSuQmCC)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UJmdt-V4xCkp"
      },
      "source": [
        "Before diving in t the model, lets understand the __ init __ and self used exensively in pytorch models. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6VoL850FwEc8",
        "outputId": "f78b1962-5eed-4381-c242-de5de22a08ef"
      },
      "source": [
        "# We define a class named smlra and the __init__ function in it.  \n",
        "class smlra(object):\n",
        "  x = 100\n",
        "  def __init__(self):\n",
        "    self.x = 200\n",
        "\n",
        "# We import the class variables directly without initializing the class\n",
        "print(smlra.x)\n",
        "\n",
        "# When the object of the class is created i.e., here A is object and smlra is the class, then __init__ gets automatically called\n",
        "# __init__ is similar to class constructors in other object oriented programming languages\n",
        "A = smlra()\n",
        "print(A.x)\n",
        "\n",
        "# self is a hidden parameter which is passed evrytime the methods of the object are called.\n",
        "# In other programming languages, self is automatically passed, while in python, we have to declare it. "
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100\n",
            "200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94532jSfsS9C"
      },
      "source": [
        "# We define our logistic regression class here\n",
        "\n",
        "class LogisticRegression(nn.Module): \n",
        "\n",
        "  # __init__ method of our LogisticRegression class is the simple linear regression layer \n",
        "  def __init__(self,no_of_input_features):\n",
        "    super(LogisticRegression, self).__init__()\n",
        "    self.linear = nn.Linear(no_of_input_features,1)              #This is because the output size is 1 (essentially between 0 and 1 as it is Logistic)\n",
        "  \n",
        "  # The forward method of our LogisticRegression class is built on the top of __init__ and is essentailly applies sigmoid layer on the linear layer\n",
        "  def forward(self,x):\n",
        "    y_predicted = torch.sigmoid(self.linear(x))\n",
        "    return y_predicted"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9BYFPCVl0G-j"
      },
      "source": [
        "model = LogisticRegression(no_of_features)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9H3V2rAD-w_"
      },
      "source": [
        "## Step 3 - Optimizer and Loss"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4EuP_dKEFGe"
      },
      "source": [
        "Since it is a logistic regression, we take the binary cross-entropy loss for back-propagation "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6ejogio0Qpp"
      },
      "source": [
        "# We first define the learning rate and the number of epochs\n",
        "learning_rate = 0.01\n",
        "epochs = 100\n",
        "\n",
        "# We then define the loss and the optimizer function\n",
        "loss = nn.BCELoss()\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0c59Czt8Eu1g"
      },
      "source": [
        "## Step 4 - Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2GGn_EvREuHk",
        "outputId": "981247b5-c265-471b-81fb-d09dc9bebbce"
      },
      "source": [
        "for epoch in range(epochs):\n",
        "\n",
        "  # Forward pass\n",
        "  y_predicted = model(x_train)\n",
        "\n",
        "  # Backward pass\n",
        "  l = loss(y_predicted,y_train)\n",
        "  l.backward()\n",
        "\n",
        "  # Weights updated\n",
        "  optimizer.step()\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  if (epoch%10==0):\n",
        "    print(l)\n",
        "  "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0.7708, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.6208, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.5122, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.4322, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.3721, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.3258, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.2894, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.2601, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.2361, grad_fn=<BinaryCrossEntropyBackward0>)\n",
            "tensor(0.2162, grad_fn=<BinaryCrossEntropyBackward0>)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lfzqyc11HU5j",
        "outputId": "10d5c106-c27a-42fe-f7e5-760c6b11c446"
      },
      "source": [
        "!pip install torchviz"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torchviz\n",
            "  Downloading torchviz-0.0.2.tar.gz (4.9 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (from torchviz) (1.13.1+cu116)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (from torchviz) (0.10.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch->torchviz) (4.5.0)\n",
            "Building wheels for collected packages: torchviz\n",
            "  Building wheel for torchviz (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torchviz: filename=torchviz-0.0.2-py3-none-any.whl size=4151 sha256=81c4815aceb6308a22d20109d42422e2a43fbe86df38a3be989a3e1232684945\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/7d/1b/8306781244e42ede119edbb053bdcda1c1f424ca226165a417\n",
            "Successfully built torchviz\n",
            "Installing collected packages: torchviz\n",
            "Successfully installed torchviz-0.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "JTP5es2lIqUR",
        "outputId": "6505df01-3798-4360-a28c-588258739653"
      },
      "source": [
        "from torchviz import make_dot\n",
        "make_dot(y_predicted)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<graphviz.dot.Digraph at 0x7f4ea0845eb0>"
            ],
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mr5HlrWYHafK"
      },
      "source": [
        "## Step 5 - Test and Plotting"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jCW8kgjHibD",
        "outputId": "ec6ce891-ccf5-475d-fe32-dc15899820aa"
      },
      "source": [
        "# We predict the x_test things and since the sigmoid function gives a vale between 0 and 1, we round it off to get the desired classes\n",
        "with torch.no_grad():\n",
        "  y_pred = model(x_test)\n",
        "  y_predicted_classes = y_pred.round()\n",
        "\n",
        "# We print the actual classes and the predicted classes and see that the accuracy is 100% (due to small dataset)\n",
        "print(y_test)\n",
        "print(y_predicted_classes)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.],\n",
            "        [0.],\n",
            "        [0.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [0.],\n",
            "        [1.]])\n",
            "tensor([[0.],\n",
            "        [0.],\n",
            "        [0.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [0.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [1.],\n",
            "        [0.],\n",
            "        [1.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OPeMcxuPSy_r"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sO-jcKSQPDI0",
        "outputId": "1cde9253-0342-4879-c691-479ebc674fd0"
      },
      "source": [
        "# We print the confusion matrix.\n",
        "# In confusion matrix, the values along the diagonal are correctly predicted and others are wrongly predicted, which in our case is 0.\n",
        "print(confusion_matrix(y_test,y_predicted_classes))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[4 0]\n",
            " [1 6]]\n"
          ]
        }
      ]
    }
  ]
}