FMML_CLF1_Lab2_2022.ipynb

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      "source": [
        "# **FOUNDATIONS OF MODERN MACHINE LEARNING, IIIT Hyderabad**\n",
        "### MODULE 3: CLASSIFICATION-1\n",
        "### LAB-2 : Implementing KNN from scratch and visualize Algorithm performance \n",
        "#### Module Coordinator: Tanvi Kamble\n",
        "\n",
        "---"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "# **Section 1: Implementing KNN** "
      ],
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        "id": "Q7v6N5-LBHWS"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "In the last lab we had started discussing about KNN or K Nearest Neighbour method for clasification. We used the pre-built scikit-learn library for KNN. Now let's see how to implement this algorithm from scratch  "
      ],
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    {
      "cell_type": "code",
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn import preprocessing\n",
        "from collections import Counter"
      ],
      "execution_count": 10,
      "outputs": []
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      "cell_type": "code",
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        "def predict(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        targets.append(y_train[index])\n",
        "\n",
        "    # return most common target\n",
        "    return Counter(targets).most_common(1)[0][0]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9_kulEM0oWJ-"
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      "source": [
        "def k_nearest_neighbor(X_train, y_train, X_test, k):\n",
        "\n",
        "    assert k <= len(X_train), \"[!] K cannot be larger than number of samples.\"\n",
        "\n",
        "    # loop over all observations\n",
        "    predictions = []\n",
        "    for i in range(len(X_test)):\n",
        "        predictions.append(predict(X_train, y_train, X_test[i, :], k))\n",
        "\n",
        "    return np.asarray(predictions)"
      ],
      "execution_count": 12,
      "outputs": []
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      "cell_type": "markdown",
      "source": [
        "**Note** : If k = 1 then the algorithm will simply return the label of the nearest neighbour. When we give k > 1 the most common label out of the given labels in the k neighbours will be selected.The code for 1 NN is given as follows and does not have to be so complicated. "
      ],
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        "id": "W_saK1gP2zd1"
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    {
      "cell_type": "code",
      "source": [
        "def NN1(traindata, trainlabel, query):\n",
        "  diff  = traindata - query  # find the difference between features. Numpy automatically takes care of the size here \n",
        "  sq = diff*diff # square the differences\n",
        "  dist = sq.sum(1) # add up the squares\n",
        "  label = trainlabel[np.argmin(dist)] # our predicted label is the label of the training data which has the least distance from the query\n",
        "  return label"
      ],
      "metadata": {
        "id": "wa4lAr9p3Tqk"
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      "execution_count": 13,
      "outputs": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Hjf1KHs7fU5"
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      "source": [
        "Let us define a metric 'Accuracy' to see how good our learning algorithm is. Accuracy is the ratio of the number of correctly classified samples to the total number of samples. The higher the accuracy, the better the algorithm. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouuCqWU07bz-"
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      "source": [
        "def Accuracy(gtlabel, predlabel):\n",
        "  assert len(gtlabel)==len(predlabel), \"Length of the groundtruth labels and predicted labels should be the same\"\n",
        "  correct = (gtlabel==predlabel).sum() # count the number of times the groundtruth label is equal to the predicted label.\n",
        "  return correct/len(gtlabel)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Section 1.1 - Iris Dataset\n",
        "Let's try it out on Iris Dataset present in the scikit learn library"
      ],
      "metadata": {
        "id": "97MTzRbMKHfl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "data = load_iris()\n",
        "\n",
        "df = pd.DataFrame(data=data.data, columns=data.feature_names)\n",
        "\n",
        "df[\"target\"] = data.target\n",
        "\n",
        "df.head()"
      ],
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        "outputId": "a36c3ae3-a349-444b-e1dc-b9037937d316"
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      "execution_count": 15,
      "outputs": [
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              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
              "0                5.1               3.5                1.4               0.2   \n",
              "1                4.9               3.0                1.4               0.2   \n",
              "2                4.7               3.2                1.3               0.2   \n",
              "3                4.6               3.1                1.5               0.2   \n",
              "4                5.0               3.6                1.4               0.2   \n",
              "\n",
              "   target  \n",
              "0       0  \n",
              "1       0  \n",
              "2       0  \n",
              "3       0  \n",
              "4       0  "
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              "      <th>sepal length (cm)</th>\n",
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      "source": [
        "X = np.array(df[['sepal length (cm)','sepal width (cm)','petal length (cm)','petal width (cm)']])\n",
        "y = np.array(df['target'])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=17)"
      ],
      "metadata": {
        "id": "r8SHprUbKtuI"
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      "execution_count": 16,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Making our predictions\n",
        "predictions = k_nearest_neighbor(X_train, y_train, X_test, 7)\n",
        "\n",
        "# evaluating accuracy\n",
        "accuracy = Accuracy(y_test, predictions)\n",
        "print(\"The accuracy of our classifier is {} %\".format(100*accuracy))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUYb80o6LHUt",
        "outputId": "7a8c493d-2376-47c9-ec7f-45a1faa340cd"
      },
      "execution_count": 17,
      "outputs": [
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          "name": "stdout",
          "text": [
            "The accuracy of our classifier is 94.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iwQ3qP-UXY5x"
      },
      "source": [
        "Voila! You have implemented your own version of the K-Nearest Neighbours algorithm, which works very well on the Iris Dataset. Congratulations!  \n",
        "\n",
        "Now try out the sklearn implementation and compare your results.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0jJj5_7dg-o",
        "outputId": "b009dce1-744e-4d98-bf69-1b9ae842ec7e"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "sklearn_knn = KNeighborsClassifier(n_neighbors=7)\n",
        "sklearn_knn.fit(X_train,y_train)\n",
        "sklearn_predictions = sklearn_knn.predict(X_test)\n",
        "accuracy = accuracy_score(y_test, sklearn_predictions)\n",
        "print(\"The accuracy of Sklearn classifier is {} %\".format(100*accuracy))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of Sklearn classifier is 94.0 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Section 1.2: Weighted NN\n",
        "For KNN, If k is too small, the algorithm would be more sensitive to outliers. If k is too large, then the neighborhood may include too many points from other classes. We also take the label with max votes in the neighbourhood. Another choice can be that instead of giving equal weights to each of the neighbours we can give weighted points in the neighbourhood. So we weigh points by the inverse of their distance. Therefore, closer points will be given a higher priority as compared to the far off points.\n",
        "\n",
        "An easy way to implement this is by specifying the 'weights' parameter as distance when defining the sklearn KNN function. For more information go through this [site](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html)"
      ],
      "metadata": {
        "id": "8Yi2tX6h0_Ka"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hXZP4wXqtmK"
      },
      "source": [
        "## TASK\n",
        "## Modify the KNN function you wrote to return all the K-nearest neighbours along with their distances,\n",
        "## instead of just the output that was most common. You don't need to find out accuracy, just modify the function\n",
        "## and return the k-nearest neighbours and distances."
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(X_train, y_train, X_test, k):\n",
        "    distances = []\n",
        "    targets = []\n",
        "    distt=[]\n",
        "\n",
        "    for i in range(len(X_train)):\n",
        "        # compute and store L2 distance\n",
        "        distances.append([np.sqrt(np.sum(np.square(X_test - X_train[i, :]))), i])\n",
        "\n",
        "    distances = sorted(distances)\n",
        "    #print(distances)\n",
        "\n",
        "    for i in range(k):\n",
        "        index = distances[i][1]\n",
        "        dist=distances[i][0]\n",
        "        distt.append(dist)\n",
        "        targets.append(y_train[index])\n",
        "    #print(targets)\n",
        "    #print(distt)\n",
        "\n",
        "    # return most common target\n",
        "    return targets,distt\n",
        "def k_nearest_neighbor(X_train, y_train, X_test, k):\n",
        "\n",
        "    assert k <= len(X_train), \"[!] K cannot be larger than number of samples.\"\n",
        "\n",
        "    # loop over all observations\n",
        "    predictions = []\n",
        "    disttt=[]\n",
        "    for i in range(len(X_test)):\n",
        "        disttt.append(predict(X_train, y_train, X_test[i, :], k))\n",
        "\n",
        "    return np.asarray(disttt)"
      ],
      "metadata": {
        "id": "eqyMbwOdqJqK"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dist=k_nearest_neighbor(X_train, y_train, X_test, 9)\n",
        "print(dist)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rSKaUgacpjyF",
        "outputId": "d9db4cbe-56a4-4de2-a11d-9e04233f2d6b"
      },
      "execution_count": 27,
      "outputs": [
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            "   0.47958315 0.51961524 0.51961524]]\n",
            "\n",
            " [[1.         1.         1.         2.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.2        0.24494897 0.33166248 0.43588989 0.48989795 0.5\n",
            "   0.51961524 0.54772256 0.57445626]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.24494897 0.36055513 0.36055513 0.37416574 0.42426407 0.57445626\n",
            "   0.57445626 0.59160798 0.6164414 ]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
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            "  [0.17320508 0.2236068  0.26457513 0.36055513 0.42426407 0.51961524\n",
            "   0.55677644 0.678233   0.7       ]]\n",
            "\n",
            " [[2.         2.         2.         1.         1.         1.\n",
            "   1.         1.         2.        ]\n",
            "  [0.17320508 0.28284271 0.38729833 0.42426407 0.43588989 0.4472136\n",
            "   0.46904158 0.5        0.51961524]]\n",
            "\n",
            " [[2.         2.         1.         1.         1.         2.\n",
            "   1.         2.         2.        ]\n",
            "  [0.14142136 0.36055513 0.42426407 0.45825757 0.45825757 0.47958315\n",
            "   0.5        0.53851648 0.55677644]]\n",
            "\n",
            " [[1.         1.         1.         1.         2.         1.\n",
            "   1.         2.         2.        ]\n",
            "  [0.48989795 0.50990195 0.51961524 0.58309519 0.7        0.70710678\n",
            "   0.71414284 0.73484692 0.73484692]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.41231056 0.81853528 0.86023253 1.00498756 1.0198039  1.13578167\n",
            "   1.22065556 1.24096736 1.28452326]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.42426407 0.4472136  0.52915026 0.54772256 0.58309519 0.58309519\n",
            "   0.64031242 0.70710678 0.71414284]]\n",
            "\n",
            " [[2.         2.         2.         1.         2.         2.\n",
            "   2.         2.         1.        ]\n",
            "  [0.26457513 0.33166248 0.51961524 0.54772256 0.60827625 0.64807407\n",
            "   0.65574385 0.67082039 0.78740079]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.24494897 0.3        0.31622777 0.4        0.43588989 0.47958315\n",
            "   0.54772256 0.57445626 0.6244998 ]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.17320508 0.17320508 0.17320508 0.2        0.26457513 0.26457513\n",
            "   0.31622777 0.31622777 0.33166248]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         2.         1.        ]\n",
            "  [0.31622777 0.33166248 0.45825757 0.45825757 0.46904158 0.46904158\n",
            "   0.6244998  0.68556546 0.73484692]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.2236068  0.28284271 0.3        0.3        0.3        0.3\n",
            "   0.31622777 0.34641016 0.34641016]]\n",
            "\n",
            " [[2.         2.         1.         2.         2.         2.\n",
            "   2.         2.         1.        ]\n",
            "  [0.2236068  0.37416574 0.42426407 0.46904158 0.50990195 0.51961524\n",
            "   0.55677644 0.55677644 0.60827625]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.14142136 0.26457513 0.31622777 0.33166248 0.33166248\n",
            "   0.37416574 0.37416574 0.41231056]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.2        0.24494897 0.3        0.3        0.36055513\n",
            "   0.42426407 0.42426407 0.46904158]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         1.        ]\n",
            "  [0.14142136 0.24494897 0.36055513 0.38729833 0.38729833 0.5\n",
            "   0.50990195 0.51961524 0.54772256]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.42426407 0.50990195 0.55677644 0.60827625 0.64031242 0.64807407\n",
            "   0.70710678 0.74161985 0.76157731]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.1        0.3        0.42426407 0.43588989 0.46904158 0.5\n",
            "   0.50990195 0.50990195 0.6       ]]\n",
            "\n",
            " [[1.         1.         2.         1.         2.         1.\n",
            "   2.         1.         2.        ]\n",
            "  [0.33166248 0.36055513 0.37416574 0.46904158 0.46904158 0.5\n",
            "   0.50990195 0.53851648 0.55677644]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.14142136 0.17320508 0.2236068  0.2236068  0.26457513\n",
            "   0.28284271 0.3        0.33166248]]\n",
            "\n",
            " [[2.         2.         1.         2.         2.         1.\n",
            "   2.         2.         2.        ]\n",
            "  [0.31622777 0.33166248 0.37416574 0.45825757 0.53851648 0.60827625\n",
            "   0.6244998  0.64031242 0.64031242]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.3        0.41231056 0.43588989 0.51961524 0.52915026 0.53851648\n",
            "   0.53851648 0.57445626 0.76811457]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   2.         2.         1.        ]\n",
            "  [0.42426407 0.4472136  0.46904158 0.51961524 0.51961524 0.55677644\n",
            "   0.58309519 0.67082039 0.76157731]]\n",
            "\n",
            " [[1.         1.         1.         1.         2.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.37416574 0.45825757 0.46904158 0.51961524 0.53851648 0.58309519\n",
            "   0.73484692 0.75498344 0.79372539]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.45825757 0.50990195 0.51961524 0.51961524 0.59160798 0.68556546\n",
            "   0.71414284 0.72801099 0.82462113]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.2236068  0.3        0.50990195 0.50990195 0.51961524 0.52915026\n",
            "   0.58309519 0.6164414  0.6244998 ]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.1        0.14142136 0.14142136 0.14142136 0.17320508 0.17320508\n",
            "   0.2236068  0.3        0.31622777]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.14142136 0.31622777 0.46904158 0.48989795 0.5        0.54772256\n",
            "   0.54772256 0.54772256 0.64807407]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.17320508 0.2236068  0.3        0.31622777 0.31622777\n",
            "   0.31622777 0.34641016 0.36055513]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.38729833 0.38729833 0.72111026 0.79372539 0.9        0.97467943\n",
            "   1.161895   1.32287566 1.33041347]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.26457513 0.26457513 0.3        0.31622777 0.33166248\n",
            "   0.34641016 0.36055513 0.38729833]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.33166248 0.34641016 0.37416574 0.38729833 0.41231056 0.45825757\n",
            "   0.47958315 0.52915026 0.53851648]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.34641016 0.50990195 0.6        0.6164414  0.6164414  0.64807407\n",
            "   0.66332496 0.67082039 0.72111026]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.2        0.34641016 0.38729833 0.42426407 0.45825757 0.47958315\n",
            "   0.5        0.55677644 0.60827625]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.17320508 0.2236068  0.3        0.38729833 0.42426407 0.58309519\n",
            "   0.60827625 0.6244998  0.64807407]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.34641016 0.38729833 0.43588989 0.46904158 0.64807407 0.65574385\n",
            "   0.66332496 0.74161985 0.74161985]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.34641016 0.4        0.43588989 0.54772256 0.54772256 0.64031242\n",
            "   0.64807407 0.67082039 0.678233  ]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.14142136 0.14142136 0.17320508 0.2        0.2        0.24494897\n",
            "   0.3        0.3        0.31622777]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.17320508 0.33166248 0.36055513 0.43588989 0.48989795 0.6\n",
            "   0.6244998  0.678233   0.68556546]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.14142136 0.24494897 0.26457513 0.31622777 0.31622777 0.48989795\n",
            "   0.53851648 0.53851648 0.55677644]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.1        0.2236068  0.24494897 0.28284271 0.31622777 0.33166248\n",
            "   0.34641016 0.36055513 0.37416574]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.28284271 0.37416574 0.46904158 0.47958315 0.54772256 0.6\n",
            "   0.6164414  0.64807407 0.72801099]]\n",
            "\n",
            " [[2.         2.         2.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.17320508 0.34641016 0.36055513 0.37416574 0.43588989 0.43588989\n",
            "   0.45825757 0.46904158 0.48989795]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.3        0.38729833 0.38729833 0.42426407 0.51961524 0.57445626\n",
            "   0.6244998  0.65574385 0.7       ]]\n",
            "\n",
            " [[2.         1.         1.         1.         2.         2.\n",
            "   1.         2.         1.        ]\n",
            "  [0.2236068  0.46904158 0.52915026 0.54772256 0.54772256 0.6\n",
            "   0.6244998  0.64807407 0.65574385]]\n",
            "\n",
            " [[2.         2.         1.         2.         2.         2.\n",
            "   2.         2.         2.        ]\n",
            "  [0.         0.31622777 0.36055513 0.47958315 0.50990195 0.54772256\n",
            "   0.54772256 0.63245553 0.678233  ]]\n",
            "\n",
            " [[1.         1.         1.         1.         1.         1.\n",
            "   1.         1.         1.        ]\n",
            "  [0.28284271 0.31622777 0.31622777 0.33166248 0.34641016 0.37416574\n",
            "   0.37416574 0.37416574 0.45825757]]\n",
            "\n",
            " [[0.         0.         0.         0.         0.         0.\n",
            "   0.         0.         0.        ]\n",
            "  [0.2        0.26457513 0.37416574 0.38729833 0.38729833 0.41231056\n",
            "   0.42426407 0.43588989 0.43588989]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SxcBnLl8GHWz"
      },
      "source": [
        "# **Section 2: Visualizing Data**  \n",
        "\n",
        "We will look into something called **Voronoi** diagrams.  \n",
        "\n",
        "**Note**: Ideally, we should perform data visualization to see what the data looks like before we apply any Machine Learning algorithm.  Only for the purpose of this lab session, we're explaining it after you've applied KNN."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4aDsDfmXG79k"
      },
      "source": [
        "### Voronoi Diagrams  \n",
        "\n",
        "In simple terms, Voronoi diagrams help you to visualize the dataset by partioning the plane into regions that are close to a given set of points. These regions are also called Voronoi cells.  \n",
        "\n",
        "Note that the cells/regions depend on the Distance metric being used. One way of interpreting this is by understanding that the distance metric decides the degree to which a 'point' or 'seed' in the Voronoi diagram has influence.  For each seed there is a corresponding region, called a Voronoi cell, consisting of all points of the plane closer to that seed than to any other. \n",
        "\n",
        "This [link](https://en.wikipedia.org/wiki/Voronoi_diagram#Illustration) provides a wonderful illustration of Voronoi plots for 20 points in two cases: (1) Using Euclidean distance, and (2) Using Manhattan distance.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AdcdjthOKwra"
      },
      "source": [
        "Since our Iris data is 4-dimensional (as it has 4 attributes), we need to convert into a form that can be represented in 2-D.   \n",
        "\n",
        "While there are methods to visualize data higher than 2-dimensions, that is beyond scope for now.  \n",
        "\n",
        "For simplicity, we just take the first two columns of the iris dataset attributes and observe the Voronoi diagram generated for that.  \n",
        "Alternatively, one can also perform PCA (Principal Component Analysis), to reduce the 4D data to just two dimensions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZNOPcS3f7sZg"
      },
      "source": [
        "#@title Plotting Voronoi regions\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.spatial import Voronoi, voronoi_plot_2d\n",
        "\n",
        "def voronoi_finite_polygons_2d(vor, radius=None):\n",
        "    \"\"\"\n",
        "    Reconstruct infinite voronoi regions in a 2D diagram to finite\n",
        "    regions.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    vor : Voronoi\n",
        "        Input diagram\n",
        "    radius : float, optional\n",
        "        Distance to 'points at infinity'.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    regions : list of tuples\n",
        "        Indices of vertices in each revised Voronoi regions.\n",
        "    vertices : list of tuples\n",
        "        Coordinates for revised Voronoi vertices. Same as coordinates\n",
        "        of input vertices, with 'points at infinity' appended to the\n",
        "        end.\n",
        "\n",
        "    \"\"\"\n",
        "\n",
        "    if vor.points.shape[1] != 2:\n",
        "        raise ValueError(\"Requires 2D input\")\n",
        "\n",
        "    new_regions = []\n",
        "    new_vertices = vor.vertices.tolist()\n",
        "\n",
        "    center = vor.points.mean(axis=0)\n",
        "    if radius is None:\n",
        "        radius = vor.points.ptp().max()\n",
        "\n",
        "    # Construct a map containing all ridges for a given point\n",
        "    all_ridges = {}\n",
        "    for (p1, p2), (v1, v2) in zip(vor.ridge_points, vor.ridge_vertices):\n",
        "        all_ridges.setdefault(p1, []).append((p2, v1, v2))\n",
        "        all_ridges.setdefault(p2, []).append((p1, v1, v2))\n",
        "\n",
        "    # Reconstruct infinite regions\n",
        "    for p1, region in enumerate(vor.point_region):\n",
        "        vertices = vor.regions[region]\n",
        "\n",
        "        if all(v >= 0 for v in vertices):\n",
        "            # finite region\n",
        "            new_regions.append(vertices)\n",
        "            continue\n",
        "\n",
        "        # reconstruct a non-finite region\n",
        "        ridges = all_ridges[p1]\n",
        "        new_region = [v for v in vertices if v >= 0]\n",
        "\n",
        "        for p2, v1, v2 in ridges:\n",
        "            if v2 < 0:\n",
        "                v1, v2 = v2, v1\n",
        "            if v1 >= 0:\n",
        "                # finite ridge: already in the region\n",
        "                continue\n",
        "\n",
        "            # Compute the missing endpoint of an infinite ridge\n",
        "\n",
        "            t = vor.points[p2] - vor.points[p1] # tangent\n",
        "            t /= np.linalg.norm(t)\n",
        "            n = np.array([-t[1], t[0]])  # normal\n",
        "\n",
        "            midpoint = vor.points[[p1, p2]].mean(axis=0)\n",
        "            direction = np.sign(np.dot(midpoint - center, n)) * n\n",
        "            far_point = vor.vertices[v2] + direction * radius\n",
        "\n",
        "            new_region.append(len(new_vertices))\n",
        "            new_vertices.append(far_point.tolist())\n",
        "\n",
        "        # sort region counterclockwise\n",
        "        vs = np.asarray([new_vertices[v] for v in new_region])\n",
        "        c = vs.mean(axis=0)\n",
        "        angles = np.arctan2(vs[:,1] - c[1], vs[:,0] - c[0])\n",
        "        new_region = np.array(new_region)[np.argsort(angles)]\n",
        "\n",
        "        # finish\n",
        "        new_regions.append(new_region.tolist())\n",
        "\n",
        "    return new_regions, np.asarray(new_vertices)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "9NIVhxz8KvG3",
        "outputId": "6b0e3ec9-2705-4a6c-d0ca-c335f4c77386"
      },
      "source": [
        "## Plotting Voronoi regions for the Iris dataset\n",
        "\n",
        "points = []\n",
        "xpts = np.array(df['sepal length (cm)'])\n",
        "ypts = np.array(df['sepal width (cm)'])\n",
        "for i in range(len(xpts)):\n",
        "  points.append([xpts[i],ypts[i]])\n",
        "# print(points)\n",
        "points = np.array(points)\n",
        "# compute Voronoi tesselation\n",
        "vor = Voronoi(points)\n",
        "\n",
        "regions, vertices = voronoi_finite_polygons_2d(vor)\n",
        "\n",
        "for region in regions:\n",
        "    polygon = vertices[region]\n",
        "    plt.fill(*zip(*polygon), alpha=0.4)\n",
        "\n",
        "plt.plot(points[:,0], points[:,1], 'ko')\n",
        "plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)\n",
        "plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "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": "WSK-GI0Knurk"
      },
      "source": [
        "## Section 2.2: Understanding Decision Boundaries  \n",
        "So you have seen the Voronoi diagram of the dataset, implemented KNN, and also seen your algorithm's performance in terms of accuracy? Impressive!  \n",
        "Wouldn't it also be great to know how exactly these 'votes' or neighbours are decided through some kind of visualization?  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytP_AImALiZX"
      },
      "source": [
        "### Decision Boundary\n",
        "\n",
        "While the Voronoi diagram gave us a good idea of the points present in our dataset, to understand how KNN performed on our dataset we can plot decision boundaries. Decision boundaries, as the name suggests, divide the plane into different regions of classification.  \n",
        "\n",
        "Note that here again, for simplicity, we have only considered first two attributes of the DataFrame (ie, Sepal Length and Sepal Width).  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8P8Pqav4DI4N"
      },
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "def decision_boundary_plot(x_dec,y_dec,k):\n",
        "  h = .02  # step size in the mesh\n",
        "\n",
        "  # Create color maps\n",
        "  n = len(set(y_dec))\n",
        "  cmap_light = ListedColormap(['pink', 'green', 'cyan','yellow'][:n])\n",
        "  cmap_bold = ['pink', 'darkgreen', 'blue','yellow'][:n]\n",
        "\n",
        "  for weights in ['uniform', 'distance']:\n",
        "      # we create an instance of Neighbours Classifier and fit the data.\n",
        "      clf = KNeighborsClassifier(n_neighbors=k, weights=weights)\n",
        "      clf.fit(x_dec, y_dec)\n",
        "\n",
        "      # Plot the decision boundary. For that, we will assign a color to each\n",
        "      # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
        "      x_min, x_max = x_dec[:, 0].min() - 1, x_dec[:, 0].max() + 1\n",
        "      y_min, y_max = x_dec[:, 1].min() - 1, x_dec[:, 1].max() + 1\n",
        "      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                          np.arange(y_min, y_max, h))\n",
        "      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "      # Put the result into a color plot\n",
        "      Z = Z.reshape(xx.shape)\n",
        "      plt.figure(figsize=(8, 6))\n",
        "      plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
        "\n",
        "      # Plot also the training points\n",
        "      sns.scatterplot(x=x_dec[:, 0], y=x_dec[:, 1], hue=y_dec,\n",
        "                      palette=cmap_bold, alpha=1.0, edgecolor=\"black\")\n",
        "      plt.xlim(xx.min(), xx.max())\n",
        "      plt.ylim(yy.min(), yy.max())\n",
        "      plt.title(\"Multi-Classification (k = %i, weights = '%s')\"% (k, weights))\n",
        "\n",
        "  plt.show()\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "bgAO62vWKPHt",
        "outputId": "f6baa438-7e7e-41f3-b229-0a6575c1ed5f"
      },
      "source": [
        "x_pts = X[:,:2]\n",
        "y_pts = y\n",
        "decision_boundary_plot(x_pts,y_pts,7)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TFRJidIgr6gt"
      },
      "source": [
        "## TASK-2\n",
        "## In the above cells, we saw the Voronoi diagram of the data and plotted the KNN decision boundaries\n",
        "## by only considering two attributes of the dataset. You must be already familiar with PCA.\n",
        "## Apply PCA on the dataset above to reduce it to two dimensions. \n",
        "## Plot the Voronoi diagram and Decision boundaries after that."
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting Voronoi daigram using PCA"
      ],
      "metadata": {
        "id": "B1RiB7ZP-gF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler as ss\n",
        "from sklearn.decomposition import PCA \n",
        "scaler=ss().fit(df).transform(df)\n",
        "pca=PCA(n_components=2)\n",
        "pca.fit(scaler)\n",
        "reduced=pca.transform(scaler)\n",
        "\n",
        "df1=pd.DataFrame(reduced,columns=[\"PC1\",\"PC2\"])\n",
        "\n",
        "\n",
        "#Plotting Voronai diagram for the reduced dataset\n",
        "points_ = []\n",
        "xpts = np.array(df1[\"PC1\"])\n",
        "ypts = np.array(df1[\"PC2\"])\n",
        "for i in range(len(xpts)):\n",
        "  points_.append([xpts[i],ypts[i]])\n",
        "# print(points_)\n",
        "points_ = np.array(points_)\n",
        "# compute Voronoi tesselation\n",
        "reduced = Voronoi(points_)\n",
        "regions1, vertices1 = voronoi_finite_polygons_2d(reduced)\n",
        "\n",
        "for region in regions1:\n",
        "    polygon = vertices1[region]\n",
        "    plt.fill(*zip(*polygon), alpha=0.4)\n",
        "\n",
        "plt.plot(points_[:,0], points_[:,1], 'ko')\n",
        "plt.xlim(reduced.min_bound[0] - 0.1, reduced.max_bound[0] + 0.1)\n",
        "plt.ylim(reduced.min_bound[1] - 0.1, reduced.max_bound[1] + 0.1)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "m_EkPci89ZyW",
        "outputId": "dfc26b0d-ee08-46c8-d34c-880879fe9542",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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kiH/tGw9z0VXn8dLf3M1FV53H177x8EYNEQBpucS6TxDf+x2spl9uSRFXnk3gNmbjy6lVtt9YqniGZ6W41hCYHrunfC4e9HlZQmNsnkTiooz191W0vd6pxvP1dcA7gAuEEP8x99+bqnDcspgxpoq+97VvPMwttx9iYKgPrTUDQ33ccvuhDRFzK5wk3vsL4ru+gxk5gRCbs0ZDNfCSm3s1VDNqNCP3lYGrqiTmQuPFBwAwAs0Z41lBf0FSb2q/fHtPb0Xb6501C7nW+nGttdBav0xr/Yq5/75ejcEdf+Q4B99wkLe86C0cfMNBjj9yPG8fV6TxrcKx1nfec4R0Jnd5lc6kuPOeI9UYXkFCTZM0736K6M7vIkMDdRuFUgl+vRlQN4ha9tFMBdULIcw4fSz1f9iB5iVjPhcPB5y2SReYVx2+CTucO8Gww2GuOnxTjUa0vmxaz+DxR45z981346azwfCj/aPcffPdAJx/WW78eCo8TZO3I+8Yg8P9BY9dbPvq0YTbxrCbTiKMmSofe3OjNWRmt/7DalWscwPmUsy6MZqtoaocS4sMXnwUazr3HnNcxStHFGc5kqdaDPo3Ufn+eTt4I2qlxtz/mfsXRHweN+1y/2fuzxPypDVNE/lC3t3Zw8BQvk2su7M61d+2ugOzHJQbpkbBGZseudBSauPDMifTUXqrZyrHjfblCfk8sbTivEHFRNTgySbJ+CYR9Nde+TtbVriXs2l90GMDhSsZFtpezE7+3usP44Ryl1dOKMx7rz+8prFtBwdmuXiNsrUlMWuQ3QkwmY5UNRjUNyZR4dINS1oTAb856PG6SUVT4+G+oWzaGXn7znZG+/MrHLbvzK9wOG8nN71cUbn0oiuArK18cLif7s4e3nv94YXt5aJ1toGDECnCrWNYTSe3he27HLJt3eogjKFGmGhqYWDxtYGnYtiyenGhmaY+wqkXrLhf50zAhbMB/XGTH0YFmYYLZd3ZtEJ+zY3X5NjIAWzH5pobrym4fzE7+aUXXVGxcINGSg9EGh3MEnhTKNfHbgKrabIh4nNoJXETAbUwHdQL2S5BtbnNkn4Ttl09IXetQRzjdESwsu1EaOid8umegRMtJj+OCILGZbJqHM+jc3ai6PubVsjn7eD3f+Z+xgbGaN/ZzjU3XpNnH5+nmJ28PDTScIEUyp8h8KYJlkccSE1sZ7Ih4ksI0mEaxaxLYwpds4TXGS9GSzULUgqF1zKEPbar7I8YCvaN++yeFvyy2eTpxiVTNrbv0zk7QXxqHCtROohi0wo5ZMW8mHAvZ8aYpKvM4wqhENIFnUT50/jeLGKFu615t0IYW6PjdrXwGmVrV8SoodlpMh1ldxUdnpANRbToRVS4CrN8zYvHPE63BE+3mPzKqe64tgp24NMxO0Xz1BjW7HTZZ3lTC3kl9OpO9oRyfbcacptIaI0rMvRP/Sxnv5VOVrhVYEbyysdse7yEpGEfL00tKiDOM5V20FogqriKVCJF0DSBOdO2qs87nublIx5nOJKfNBv0NeYCmEHAjsQUzVPj2LOTq1r0bwkhf5G/j9Nn8kMKBeR13DArrNAnTQh3NUR8Oco38FINEV8Jo4aFxAJt4OoYIVHd3IZM9NSqhXyeWFqxP62YjBg8GZeMbZKQxY1CKkVXYor49Dih6UnEKjsDzVPXQi604KXeC+hNlG8bF7oyF3rzHndLp9evliDVSMsvB0MHNfUFJ/0mQnZ1hdw3x1GhJDKz9spcLcmA30gGjMRMnowLprdwhItUmh3JaVqmxwhNTVa1zHHdCrmhDV6VeTEdqcraocgKQuejnSBDpWNntyte0qZRtnZlatElaCkzmRit62C+cOP9OCNnVu14O2Z9LkhAf5PJk02C9KbNcKkMoTQdqRlap8ZxpifWrZBaXQq5rW1+LXU2zZnK60nM9wZ99Jvf4nP33sfgyAjdO3bw7uuu5ZILL1jYz3TAaStekGu7k9lelQhWjdSqpjPyyUyUPU3VP27G7ickT0eo6k2hhYbeaZ/uWfhFxGDaEkzbglmrziJdNLSnZmibHseZGkcG658dVXdCHtYO5ybOIeqV5/Z+8LGHuOWuT3FqqJ9dXT3ccsOHGMn08fE7Pks6ky22NTA8zMfv+CwAl1x4AVpo4rvTUIMu6PWA8myU15iNl0OtmkvMM5V20EhEtVcGQuE1D2NP7KzuccmGLManAsKBynZ0FwLfEaQdSdKWTNuCGYvNlb6goS09S9v0BOHJMWSwsebYuhLyJh3l3NmXEPLLWys++NhDvOvIB0mlsxUQTw728a4jHyRkWwsiPk86k+Fz997HJRdeQHynRpjbN+1+JfxG2dqyqWUFRACFJKNiOLL6DvtM5NS6CDmAWuL8E1pjpTRWStEEeeKesCUzNRL3llSC9pkJIlNjSK94c+T1pm6EvE238KrpF2Gp8od8y12fWhDxeVLpVN62eQZHRrCbwI5P8/DDj3PkyIP094/S09PB4cNXc8UVr1/Tb9gquAmTRthhedRayCHr8HTs6gu5Egn82ATmbGv1j71CFMdyce9mXtwlqZAgGZILZplqi3s8k6JjZoLoxCjS2xy5JXUh5F26g5dPvQBDV+YBOTVUWbna7h07iO1M8PBXj3Po0OdJpbL/SH19oxw69HmAbS/mWoE7W3txqhfEJigNOZ2J0bZO8dpurK/qQq61Xh41XBZZcQ+wUhAnV9yzM3fBTEgwY1KxuMcyaTpmJ4hNjmFkNt9qfdML+W61k5dM7as4kwxgV1cPJwfzy9i2NjeTSqdzzCtOKMQfHXoHwnA5cuTBBRGfJ5XKbt/uQq68RtnaSqhmiNlqmUxHYR0cngCuOYpjp5Fu9VI1tRBUKyJqUdyDvJl72pEkQiJrlikg7hE3w46ZCZqmxjCKrOI3C5tKyIUWtOsWOlQrLX4TTW4ka0qxRPYJ7Vd2U9xyw4dybOQAYSfMx95/mNn0eE7Uygfe/Xv8zltfAUB/gaqL2e2FS+tuJ/xEo2xtJWwG08p0xkFpiVwH570A3PgAzujpVTtmhQvvillJ3F0xSWxyCDNVP6HHNRVyU5vs0G20By00ezFinoNUy9PsNcxHSAiBMAVaa/BXfmJfffGVAHlRKwcufgMjM88vhBtKE1rOnIG5gqM9PR309eWLeU9Pfgnd7YabaJStrQRRwy5B82gEGdVEuER/27XghvpwxB6oMNmuGLW4uhbF3Sdwn6tZv9XVUhMhj2iH30z8GmEvtBDXXRZaoxdEHYQhQYD2VdGV2NUXX7kg6PMkl6UsL8/ePHz46hwbOUA4bHP48NXlj3ULki1bW18XeM1Rm+N8Jfz1E3ItfLzmEazJ7qocT9UwaFzKdH7l0zqgJkJuKZPIWm1qek7AgWdaLQYc2JGBHWlFazIoaVEXLM4cCmVvztvBs1ErY/T0tDeiVgA/HW4kc1aI1BoLqF1gWpaZTIyOdbSKZSKnqifkNczq1Kp4ze/NzKayka+W9oziyZhkxASiEqNN0u4LdriKnTMBBFlLjKc1XqDxfAcjdA5CBiAn0WoWIXNvtSuueP22F+7lZO3jDSWvhKjTzOmWyc+92ppYJtJRTo+v3/EDOUsQmcJINq/5WDWLCREaP90Q8poRTyuElgtpvIGAYUszbAm8tCSWyBXpbAFEgVYm6YkO3JkWIh0nMSMnN3zs9YSbqF40wXYharUQ05Kf13gcM66D0iZyHQvAZeL9RNYo5K4hmEoFoHMrUG8EUqYI6tT/syVK00il2Vnk+nTLeFQp32R28HQSg+eivMqKcG0XVGDhN8rWVkyEGFFf027U/lZLB+sUgziHawyhrczKOxZBCxhJZ82ihrHxdnIVjG/4d1aL2l9dVaIrU3im6FZwQXjJMNMnX0pm4iVovc0KJK+An2y0dKmUiNOMnPPH7JO1r88661deZK4ShMiGIq6WKRaD0cQGC7kQCj8zuaHfWU22jJB3FJktZpbNyL/2jYe56KrzeOlv7uaiq87ja994eNknBKmJdqaf34+f2LuqDLOtiJ9stHKplJi1uLrr9Gtvx5x113dGDtlWcKsJIHQNwVRm8XMbPR+XMrliu8fNzJYQ8gcfe4j9F7+a3+3dwe/t6eZ3e3fw/v2v5NsPfYX0kif7177xMLfcfoiBoT601gwM9XHL7YcKiDnowGB2aC/Jwf0od23dULYCmZmGWaVSInpROA0Np5m1nZWPp9feCGIltPDwmgsn1BX/TNakshSlZhBy4xzEQR2bVWALCPl8hcP5VHw1V/t3rO8U9x66kX/4u68s7HvnPUdIZ3JTbdOZFHfec6To8b2Uw9TJc0iPvRSttmdWY+CGULWOn6szwk4cKXKFe0+Nb7eEGyLYAJNhJnKqov2XmlQW0C5+5pd50WTrQdasUt+9B+peyAtVOJzHTaV48E8+iZqblQ8OFy6iVWz7PEJAeqqV6ZPn4s/uQ9dVlfu146ca9vFKiZn5TvOYD601cOItZb0dngCBMU0QyU26e/Cxhzj78nOJv6aXsy8/lwcfewgAV+aaVBbRoDz89C+QxmJi3sqm0coRMrGpSpuvhlqb7dbMShUOx/v7+In0+ME3/w4hJLpAxafuzvzGzYXQgWR2eBdmaAfhjmcwQpUtIesVb8agEXZYPlpDpEiVqn3S5HtB7ZY3Ca+JqLn+ZoRMUz+R5AuB4n0BNPD6Cy8vcoS5600HeMlfYEfO4G8fO8Yttx9aWFXPm0YBLr3oilWPNfDqv4ZSVWbkQoh7hRDDQogfV+N4lbCrq7QId+7s5l/+/m+547Y/QhXol+c4Yd57/eGKvtPPhJjpO5v06MtRwdZusqC1wE00RLwSIk4Thig8R+rywRC1O5/TbrTqx3z44cc577z3sHv32zjvvPfw8MOP45mDaDP7wCrWF+CDd3yEN/xOObNrxcOP3MNNn3xfxabRFZEByqv/voXVMq38L+DiKh2rIm654UOEncJialkGbmqGo7e8F7eA+UVKg+sOH2HPJZdxwtFMOIKMXX5TrPR0MzMnX403cya6/q1UBVEZp97qB9WcqFU8F8HUcJpRu9DWajs8H374cQ4d+jx9faNovVi7/+GvHsdrHgSKr5onpiZKBB4sXnSPfvNbfPyOOwmKNC5eyTQKWYeqZwkSIcF4GE46mp/aAafMJIZV/6HGVVEfrfW/ADVx+1598ZX82eFPs7u7FwBjLvGipSUGCCYmZot+VmnF6y6+HI1gUimeVz4/w+dHts9zjmLcEaRtgSphQNNKkhjpYfbUfoJMVzV/2qbAa8SPV4TWmogsnQtfS6dnygsR6Oo57UvV7k87p9CoFVfN8+TMrpfE/X7u3vvyWjMuZalpVAO+IUjZgklH0B+Gn4cUT1oBPxE+v9Q+J4OAcaVwgbDKEHgehmkijdrH+q+WDbuihBDXCyG+K4T47uhkdW1SV198JT/56r8x/Kt/4sSJv+LUqf9NNOrgrVDfoqPoBSaYUpqTyudpfJ40fZ4JacbCgnRIoGS+sgeuzUzfC0mNvBLtr3+YV7URWiADgakktm8Q8kzCnkkwW+9uoI0l7DRh6tKupyZfEy9wDW0UqaB6iUGlavdrkSGIj5dcNS9nYXa9xPw0ODJSdH/HCXP9/3eYYQd+5Wh+ZPs8Zfj8HJ8TymckCEiVSAZx/OxEL/B9giDAtO2Nrw1QBTbM2am1vge4B+BVL355VY2EWgYku57CNxYXBcUusHlCjsNb3/mHZR1fCMGsVswGc8s9EyJC0CIkUS0I+WAEGkMKSMXx3XOJxUfQ9jTKSOIbMyCq6+CSWiC1ROj51wKhmfs7c6919rXSS14rUBqpdfa1Vtn3izgzZ3WMdN379DeOUmaVpZxpmHy/RjGds14TMbM6k6mVavdnon15fQG6O3tIphJMTednUnZ39uBagn7ZSqvURJNjdO/YwcDwcMHvT6dT/PnRI4yo7Oq6klQigcbOzC75O/iuC0JgWhZ+DZspV0pNolaqqeLaTpPo+BGBzC1F29vTwakCFxiAISVf+PA7uOZAK0n570yKOBMiznDQxFgQxi+jQL4pxYKYxw2IBGqxtroGpjqBzsVxygAsD2W6KDONMmcwjMFc8VV67rVeEF8xJ7hCzf2pFWID000jps8k9W9D3OBxvMYAACAASURBVAi01sREeSUGuwOQaFQR4fnXx77Kg3fdzuhQPx1dPVx9w6E5oVo7U5ko3VXy0a9Uu983JgicBFdffCVXXXwl/b7GV3ohOW+p89IJhfl/Dx7mp8LjX489woN33c7YUD9NTXEs08TzC6+wRwf7uPvW7KSsknMUlT6ykBppje95SMNASElQB4JeEyHPCI9nzCEiMoSDTUhb2MrACERFvTmDyAyJ1h+hhYupjaxJwA2wUy5H/uAK/utH/2fBzymtePuB1wCKmJomxjS75t+TkoTRxJSIM67jjKgY00GUDsOkXUiatSAaaMwFv8v8hVB63EIZkDEwMg4hS2HrfqTe/K2kIiINDSHn0W9+K6c14Luvu3ahw9Q8TiiKUea5MhXsNU2e9fMdeP/62Ff5/JHDCw760cE+Pn8kG1m1FjG3rDR2ZJRJMUQGmxBr7wBfTu1+N95POH0WU0LgzzXamA8XvPOeIwwOZ2fpbz/4R7zwt9+c9/unp6cwTIt4cyvT05Nzt1yuAAe+x19+5o/zzo+pAkIEhLSPrQJsHWCpAEv5mEbp5rMqCCAIMEwLjUYteZCUcz1sJEJXYXYnhLgf+E9ABzAE/LHW+gvF9j/77LP1X/7lX+YfB4hIB0fYONrC1hZWIJGFEnDCE/ixpwm5HnY6g1Hgad1x4bsZm84Xy73d7Tz3yKdL/iaNIDB24evd+F51CjlLw8M2fo4Z1E+5XIXgx1VqGFCvZKMmPpvXrPuj739Pzs3bHuulWXSUfdxpE/7Jz5/tvffy1zJaoGl4R3cvd3712xWNXYiAcGQSrGE8Mb2wvdkwOdvJbEx9EW3gDL+WwUThyU7aljyDx/wdXOr3P/iVv+eC159T9KtOPfJ1DN/HCDyMICi5ep1tiTNRpn9To7HsEL7v8eg3vlnW9bAevPyiA9/TWv/a8u1VmZFrra+pynGAhEqTWFpaXkJENCHTWQeN0IAQNLtp2iY1tio+q73zD9/G9bfdRzK9OPOIODafPHhl0c8oEcU3Tsf3ulBudWaiAo1pD2EHP0YEte/hWAkSTdgWpNztG0teKGoincnwuXvvy7lxo2WaVeaJ+9AkBTMq99yOFgnXK7a9ELadxA6P4IoRPJE/85wKfE5lOogKG4FCCoVEIUT2tSBACo1AIUSQfQ8Fc9srQfkWY34/UrahtY2eM11qYNSB/mW+gmK/c2yon1eceqrkd0US5ceEexWEeggEvuvy6De/xc23/3fUshZ+ha6HjaQuMjuTeoawDIM/9/jUmrFUF2N0EbcnaLdOEFH5zpCs+QRuOvoQzw+NsaernU8evHJh+1ICoxdP7ybwmtF+9Zx7hpkiJH6ErOOiPGFLkXK3r8OzWNTE0u22FcEkt0Lkg489lNf4e3n/2DMNkx8sE7KOrp7CM9IVwviE8IlEJ1DmML6YZaXK4EOZCInpXSvsVQCtMaVCSoUhFYYAQygMGWAIkDLAEBpDaNqmhwlmArLRyXP3gLAw7TjDoRYGCtQvKvr7O3cC0NbUxPhMvmC3NVVWfsCr8JKeX5ktF/F5SkXXrDc1EXIlK88w8UPTmH5r3vZpt5Vpt5WwmWRH6ARR3YdcksHy9gOvKSjcAFo4eHIfftCNcqtbplUIhW2dwAx+vqHOyfUgYmQYZ/vGkxeLmujesWPhdczOjVYplpYO5Ij5zkDwH2j0Eh/L1TccyrERA9hOmKtvOFRwfKHQLKYziiuGcSuYLWuxyqZqQmQDAgIDSpuZic8M5HuPtIefGaM1M4bXtJdRcrNNi/3+a294PwCfuvZa3n3XXbhLzKm2afKpa68ta/izLTaDrRKhBPZs+edrxXj2JdfDRlMTIffwcMMudqp88fRUGtPJQLpwMkPKj/C8/2IsuY8dTh9xTmDowt7mQHbjsZfAb6nq7Hse05rA1j9GBpvfmVkOEZmGbSzk777u2oI20XdftygcUZnb4qxYWvotd30qR8hNpdljmpxY4vScd9iViloxDA8nMk5gDhGQWpXbMqBwsblqYVG6CboEumZOYMV6GRCL52/p7x8b6qezq5sb33U91x44B2skyVWvPx+ATzzwAH1jY/S2t/ORt751YXshUjGTkTaTk3GPlJXVBUv5nJ6KIoLyxLxkPHsoxHv+n98v6zjrQVWcnZVyxjln6D/5yp+wx92DdMs3VElhYCd3lBXZIgjoCA/SKp7D0gk0Nr6xDy/YiQrWpxxt1pn5NGZQWRnPzY4GnpraiarvhcWaKBWlYFsOu5wX5uwff00vhe4tIQTTT+SaDSYtwb945UixxnFmMJwRPMbQa6zZorXAmzy3okixQhQLlYzhctrML8s6xpMvyvrvBIvxX7+W/AX//tdfzDONvuWNF5Aat/BnSy8HPMdgtN3iZEvAtF3YN9WRidI+VJ4GXfz2awuuzKSU3HroA7zpjRdkHaKuy3p1pFlXZ+dq0FIzEBqgJ+hBBOVdSEoHEE1CYuXCPxqDkVQvI7qHzmiCiN+ELvN7KqWenZnlIICwDYnVt2Osey658IKijqyonZ8EtKurZ6FG/vLty2nxNDEhmC1y85umSyg8RmAME4j0StaMshFCY8gApVYvA6VCJS+7+KKcfUs9DLXIdluePwPffugr3PCRDzEzuZg0dGJwjOtvuw/Imkyfbd/HeDqKpQOswMdUPmbg44UT9LcqRsIerOApGA0laIq1YK/wUIDiK7OFaBUNfiYDUmKYFr6bQawxS1RLULYksASeVdwkXdNKT65wGY2MVtTXKa1mWBLEDaxQo1gI0kFzjg1ynmI1kivBMFOErX8nFPwHgq0n4vNEzGrJx9YjauR3ji+Ulh52wtxyw4cKHuNllsVppkGnIWmSAgOFE54k2vILROwHuObzBKu1aZfANNd2zT541+15BencdIoH77oda8kjZ95RODA8nC2SNTzMx+/4LI9+81tArgR8+6GvcO+hG3NEfJ5k2s3O0M09PCFO5+lwJz+O7OQHTbv59+bT+V7XXn7Yk5kT8fLoa55Bl6GEl1x4AR99/3vY2dmJEIKdnZ2FQw6VIvBcDMssqyCXFhCEJF7MINkimejQDHb7PNub5Oe7Zvhl5xTPtk5yKjZd9Bg1j1qZFbPYUZvm2fyboTAaPzSD6WdnQcszxArVKA6UzntYlOuMKsZWcmaWQ8R0ga1dsnc19MZdWuU/g7DRhNAihBY2v/ufX4JpHuIjf343JweH2N3Vzcfe9X7ecvFvo3UatEGgJGmSICUdbpSOJfMqJSXftk8xI3L9LMcfOc79n7mfsYEx2ne2c82N13D+ZcVtwythGGsT8lKhkpZePPZKIZxCZwUN4MtHPombKm6/PzE4xqsPXFvQf2A5YxX7C1wjYLIDWgtXAcih1MpsOcoPgADDslGBTyA1gSUIQuCairQRkDZ9UtJdc5PSmgs5wLgcJxQJ4ZRZac9TKUwnAmm7ZPu2eSHPCI/AMDGWmFbKdUYVYqs5M8vBJo1hxQl8r9FjAjAknNY0S1TMhcHpDIJMzrkxVD9Ce2TTy1ws9QvC+jgASgiedbtJZKAp3E7YzDUXSiV5VfLFHI9+HyWyS+rjjxzn7pvvxp3LixjtH+Xum+8GWLWYr7WVWqlQSVMtCvlKIZwimyACwFh//vFyEQvfmZP1euDNuLL8WPulDDszxKItWInqrDy11vgRQSoiSNpJZs0MgVi/etCbpoh2v9FP4JQ+iceOHeOyyy5j//79XPTm1/G1bzy0cvs2qUia4zzDAFN2auGpX6xGcrHtGg3+DI79Yxz1nbpIr68mSR0h8Dy00gjDwLAsDNtCGEZBp95WxrHgrPj4oogX4EvHnuD62+7jxOAYWi/ad7907AkATma6FnwOXlDYZBLxHF7mLTpR7//M/QsiPo+bdrn/M/ev+rcIubY0/atvOIS9zIQ0Hyq5VMiLhebNb1/qt23v6S3xjYLlM4l5U07YmQXpc/yR4xx8w0He8qK3cPANBzn+yPGyfkt/S4K1dHH0HMFMGwztDPjVnhS/2jHDQHSaKSu1riIOm0jIhRD0WX1oq7AoHDt2jNtuu43BwcGsjW1wgFs+fYh4U2GTTHdnT/ap6EwRaB+FZiAY5zljhIzpF62RnLNda7Q/jZr+FUHfdzAyP9pyESnlMpnJRvoIIdBBQOB5BK6HDgKElBiWiWnbSNPMPvS2KM1hzZnRIewVnGg3HX0oJ6MYFu27/V4nU6lFxXC94rbvnYl2eoNsiYSxgcIVC4ttLwfN2uzur7v4cv7b4SN0dPeCEHR09/LfDh/hdRdfjrHEtPLu667FCeVGi82HcHp2CEMKbCGwheRVb7xo+ddk9w9HKLYcHB3qB3tgYdUy2j+K1nph1bKSmMd1nE5/J6qlA4zyDBVeCGbaYHhnwDO7kvyqc5r+2AyTVhK/QDbterIpTCvzBCJg0BlkZ7CT5W16jh49Sjqde9Gl02mcUBgnFM6rovae6w/jxTIEKvuZY8eOcfToUYaGhujq6uLC1/8nHvra3+aYV7LOqMPgTaCSY6iZUfRcLQxhCpym5Dr98s2Nh1U6YkVrgqW13zVIy0RKidagAh9dJBuubtCarqaALrO87L3nhwqL6/ND49z3d/+cF73xrivORmhZMBv0v1xyOROxSdp3tjNaoDxz+872in6KoQ2adTMRP4Lh2ZyUioxa/ZzudRdfXrCYl1jSm3TerlwoauXkrn1kEHMhe5rv/8M3Cn5PrKWVWHNrEVPOTnw5WXLVstz8JLSgTbcRy8SQc/n6AaDiLdjpBHqZnd4LQSYCyVDArJnBk5snAGBTCTlAWqQZi4zRnmjPefgODQ0V3H9qepJP3fzZnCpq1x88zBm//WZ+7mlOtyz+/tG/47bbblt4EAwODvI3X/sql15yKU98+9v0Dw6wq7Obj157HVe8ZCdeX37r0fAOiVjn5dFmZVpVVkNECIHy/dxnsRQYhpl9T2kC36ubKudSaPbEU8TlVNmf2dPVzonBfDGPx2I5IWzz0Rsd4V0Y2ijqgH/Tmw/wtve/jbs+cleOUNmOzTU3rlzqKKzDxFUcx3OyorVwb2nOtKeyTViEQAuBRmajvIRAi7mHMRItxFzFFYHSi38GMPd3CLQk0OBrMJaF4hZyFM627mDcyfUPFLORjw72884/voMvHPlQXtbnf33/9dnPlrFqMbRBh+ogko4UzEzVGjKhKJZlkjRms8JtZXDF5o1K23RCDjAtp3EiDtEl8eJdXV0MDg7m7dvV1cWll1zCJRddwWxIcEr7uMA/zSUpjM3ZvJfbcdPpNI//6+M88sgj7B0YwEym+PLjx3nZO2/IyxYzHIkdrv8GratlMlOF8gVKEyypKSIAaRoIaYDOlgjdjLZ224TTopM4FYb+ffLglXkF25xQCCFEweiNj//FnyAQJR3w7zxwHVrosqJWDG0QJ07Uj2K6ZslcDT03E0Zn6+GLtUaqz2f1tLTCzCQUqSMeGJKTnfm1Xtp7ehnryzdhtvf0csF/uQqByE1Aeucf8to37wX8kquWkA7RHrQTSofyVvyFSIUMToWLh/xtJmou5MXCqYaNYXaFd2GlsnGYBw8ezJlVAziOw8GDB0nYaU4Kg8ycDnzx9pv5h4f+ipXCK4aGhgj7/oKIv++ee0i52Rvv1Ogo77vnHgB+7y3nz3nVtx8eNom0Xpf2V/PhWfMIKZGmiQCUUgSet+aEirUQc2BPaBhzFfbOtx94DQrBTUcf4tTQ2IIp4aY/+dOC+/cNDRQ91rwDftdsJ1ddcmXRCBVHOcR1nLAfRrhisdHJChQrArVWPA2yqQXLc9Gz+YI42rsPt0CfzKsO38S9h27MCUG0w2HedviDmLFZfuOSS3NMOeHINL78KQDX3HhNTmQPQMgJ8a73vIuemfJ6h9YjNRXylcKp+sw+9tjZNP4DBw4A5Ni5Dx48yEVvejPPemECDSFTcvzYw2WJOGRn882JbPTJJx54YEHE50m5Lrf+9QNEOzNlVVDcikyp+Ib1MNRKESz5NxBC5CRUbNSsXWtNpClE3E4wJnbgYeJh4mPhCQNfG3gYeNrAn/vT0zL7GomvJJ4WGL91IUd+6yYEml49SfNMP5+7976Cad49Xd1I5IrZoOfMnMFkfJq0yCC0IK7jRIMotmcjVlk3SCmVdWKv4twu9z0dPHhw4V4FUBoypo3V2oGcmUTPzc7TsTiD0SWBClpj4WPgctHlb8BRN/OFW25nZiJrzrJDBmR+ief+ENkkCQU9eOlOPM8Cc3GlPv+Qm58cdnV38brXvo67/sddfOxDHys4xoLnBMmMbgXdB2WaVKsd47+cTrv4yrhmtVaOPHSEg284WHAZ1NHTwdF/PAqAhcWu5C4KJU0GwuR51YK3xFFTrCh9IW699VZuOPtspOvRfs1bi17ItmXiLnHmRRybez587bYQ81+mdpLcRKn5Qspst3ORFSDl+2uuFTKP1pCOtDJotpFg7fV4ltcg+b2D76clNcqtn7kjL837yB99hCbZkmMjh6wD/s8Ofzont2E8PEu/GMN0zarF9BuGQRBUtvKYjyRbvkr+8Ic/XFAoTcNAqGyzh1TIwZWKtJViRk4SBIkcwTz+yHGOHj5KsKSYmGEaHDxycFEctcDWnbhiaCGhxtAG7bqdaCoKQeVj1EDCaGLYc/C1INI0gGc+v+K5WD4phaz/4g9u/YM1i7lEcGbYokMm2bXrbQVrrdQ0/LAcx4SHx1B4KK9AkELSp3NFHMovvh+Px7n4t38b6Wbttr3txT3/S0UcFsPItjqetkimN5dJSc+ZXALXQ/sBguys3bDs7Ox9FasHBSQj7fwqfhbPmDurJuKfP3I4O6nQmtHBPj5720cYdrr40KHDeWneb3rjG7j64iv5s8OfZnd3L0IIdnf35om4byjGvVnMTPVEHLKFnyqlWCTZ0aNH844tpcQPAjwNLgI7kyE+0E/X85OccQLOGG1i93Qz7ZkotjL54q1fzBFxgMAP+OKtX1zcIDSuHMIxHWxt0+P3sCexh+hsdMFiV+4YAdIyzEk66HfD+HNmKTfZCWVE9KxHjD9Ak2HxioimQ5aOmKupaaXccKqkSDIdnV5I49cIBkUbaT//BBfLNFuK44T46EffTVvbGHrcQkx43HzN23jfPXeXrDe8lGLhZVuJKd28YWaVtbC0Oa7WGmmaSEMiEASBQvmFbe1KSBKRdgZkK26Vb4ViNUj+91/8KXd+9V+5/01X0jLbt7Ca8ILsdXf1xVcWzSxWUvO8GMXX+TPnlUwc60GxSLKl203TJAiCvNVuAJjRJnRiFqHBTCnMFESQdBBmdnKWQhTa7gYue2f3FnywlTNGX1iMEmfay7fX+4FBRO3Ck6Vn5esR478rFKLXTFBON7qazsivufEabCfX7lMsnGpcjpOOpNHAsGhltoCIQ+FMMwAnnN1mGJJ0OsMdd9zNo1//O5Jnp5k6s42Oa97LdR+6veyx7+mqLHa3HqlKtMoGs5Cw5Hr4rosOfISUtEYFZzTPckbLDGc2z3Bm8zQtrSaTVrzqIg4rtWsTDIo4XqR9bjVhgdAkxWzR0rQKzSljDLdAjf3lyXKDg4PcdtttHDt2rOzxrsbE2tXVVXK7ZVn4Jfwavu2sKnmsNRPBXDJL1loXXZ2UGqMSBhNGK7/yW5j2i8tlJrljxVl5sVj+SmP8AWwhOSdssKdMEYcaC/n5l53PH9z6B3T0dCCEoKOno6RNacAcYMyOMuUXv/EKZZp97E8/xqdv/33CYZsgyNrh+vpGOXTo83z1q49jdI1hOQGvu/jy7OdWYKW+n1sBD5tkZnOZVVaN1kwnNQ4JoswSEbNERILT1Aku4XHeYP+UnWbhGeBqKdaWraOrh05meWHyGezkeNZM5Hn4nsvg9DM8P/tTZvQ4akmkjEYzZE+RVIVXi5WYD4ohhMA0K3ugHTx4EMfJrY/kOA43vut9dBqteF7pOi6BBhktnKPQEi++vXPI4IxTUc4cambvVDM7UsXLWhcb43Xv+kOeDdoYcS1WqlgVBCZWULolXiWT0lK0WTYvD/vEZWWOqZo6O1eDmTqHVLqceuQa2zFobUlzhvljXnXOO5gssCzr7e3gu098EXv0ZZzQJl945Mv8z2UJB6Zp0hx1GJ+e3TZRKyNBOwMz9TcjL0VXzC+ZmTkpW3ha7OM5t5lgjXOc5XW6AUKOw80f/CCGly5al3sRQUu0k7jZzriZZjwoHs+8f//+ok0s/u3f/m3FsZqmiT8XTWKaZtaJXGZI4lKTTnd3Nx85+Ee846Kr0VrzZ9/8X3z2zz5X0txjizBGMo7WOmvFm9PUT/z3m/jrr/5V3ve95fK389EP3Lqs5IrGDc/i68Lit3SMnd3dvOWdhzjvt64s2hCjEFL6GE0/gBJtKtcStSKAfWGbrhVs4cWcnTWPI6+UNpEiZRhktEFaGQQIQlITNjS28AkRYGoXU3m4VpTnjHYeeniwoIhDdmbes/fNC+nQH3vT1XQaBp/78yML/8Bvf+cHuONNLXWTiVgMjcjmfQg5VzNUoLWc+3PuPSRozZQbYquVORxNmHQ0S4wi2SAtapLz+D4vNxx+YexjKGhiJIhkz0mF5LUr6+zivb//DvDSBTM7gWVirplMDDHBIH5rZ8nvKpUstxKWZeXMnH3fRwiRt70YBw4c4MpLL6dDxYn62Qf/g489xAc/czPj04v1xOfNPfOfmUeJAAO56IqZu+T+5Yl/LPh9//LEP6G1lXdpWiqKLwoL+YEDB3jjJf+ZUd3EzJwJpVRDjEJirpSJo3bjyRNFz8X5l52/qgiViDR5gaOIiNWXAKmfGbmGVwQGZyVz06RTMsrzYhczQf7s8dixY/z5X/wFQwPFky2WsjTUK2UInhYBz895zi+zv0tTUH6K9kpkz/pcKrQWc68BLee6RM39qUV2H5UNj8sKrkCr7EGW/qk1oHT2tQIdZG2HOshGe1RS2m3iRU2kpgTB2orjbTq6mjy6jHwHe0HMMH7gMiObmRAtDKs4Q36MhC4/qqWbadqTgwudo4q1C9vZ2cljX7qv8EFaOkrW2K40xG6epTPxQhhzyTrFwhIdI8QOHSfqLd57y+v8L6e7u5tHHnlk4e9SGISS+Q+ql/7m7qKrjB/988m87VprvOgogc79PUoYTMg4o66ZE6ZaLEy5o7uXO7/67YJjl9LHiP8HVLEgVo8dYo+ZQpYZq17XM/IWHedF6dPYqU8CuWIaVgnO4ueMWr2c9FoWOgEVurhXYmk6dDjQvALJmYbkae0ynYnjaD9HXBdEVIusgGoJSqGVnCsSJdCBRiuNViL7XiAqFtXc6cdqH7zlf5+OGIQ6Jgl1QJAJ4047pCdBbd5SE2UzMmvS0WxglJOG7iUxrRCt/jitjLMPQEJaRpgUzYyJZoaDOMN+hGCZWypOmp7MIKabO8taqS53IUTggVG800yxZLliIi6EwDCMkiIOiwK+3GnpGDYdupmYmz95KlTnfynLo0iUDtDovFyA7s4eBobyhba7s7DvQQiBTYwU2VWAAhJGM0OujSqQaVDaGV0YpUzCwW5c87mi+5SLJSRnhg1aRXXKYW9qIY/g8MLM6XQns57fNC8hFLKx/Gdy9pNoOr1TNBmTPKt3kVRWQQdQOSyvRx7zA14x/BQqNcuicWauNkUeatmfS9FL/tzcRhodXzQlGKEU4R0pnHYI0lEy0yEyUyr7YKpDlBaM+m3lVTEUAoIAhJFd1szhqCTdJOlmgJeQrRkyazQzQTMjOo5K+TjJ8YIhj907dhSckRer1w1AJg2R0i3DDhw4UFa44Xy8+EoivhRvrlSCY4XoCJqIucVXJMXq+c9TyNwjTAXLokbee/3hnM5fkK1q+t7rDxc9tk6FkGFJQoYZ8sO4JRq7l2qIUYp0sgPZdHJNs/IW0+JM28UW1Vvubpp65EuxsHixfwbnT75qQcTnyWTOwjXOLvi5cDDLi9TT9JqTReNH5ymWDZhbjzwgGPkxKlXdiIbNjo7lnxshwYwkiHaP03rWFPE9Pk6zzO0IUCeMJIy8GXRRdACy9HzHQNEcTHBa8Bznqid5hfMcThE/cam63EWH4GaQa47n1zQ3J2hrG1lVbRWtNWkvg6VLn7didf5hsTZSHkb+eC696ApuOXQ7O7uyyVE7u3q55dDtC12/CiG0ZIIdnHSjuCuEC5ZqiFGKIDCw/N0l9ymKhtMcmxfbKewq1yvfVDNyoQWn6V2cMdOLVaKzt+vuQdsh7OCHiCWz3y8de2KhJooUkqCI/b+5pY2rLriML339wQL1yOeb4wYEI0+hkvVR/ayaBOHS4iykxorOYEUh3CUJkjHSUxaZmaBq6fLridKCEb+N7jJrixNkwAqDV9xksJSQTnJWLMOA285YIlf4StXlhuy5NR2JEQIzFCBDPobl4mmDifFWVuPSikYzxGLDSJldoTY3O0xNrRz5VYgBOcle2ov+O99yw4cK2sibm5v5wAc+UHjVIAMgf8Vx6UVXlBTuQqR1fgehQix1RpcTtZLzHcl2jHhls3JHGrzAgdgaHJql2DTOzp2qkxck9xDxyuvbCWBY4zj6ewgdLLTWWt6VpRA7u3r53iPf5djX/yaviH82q07hjzyFTmTtbV9+/DifeOCBvPK2W5X0fgNlV77sU76Jl4iSmTTwkpt7pi6F5kXxkcoqGxoWBJX1uJyimeenw3mmKGGA6UjMkMYIBRi2h7R9pFn4vGsNkdFfx1WCjPTJ4JHRLpkS4wmFPJqbxzGM3MmI1oLJyb2k0yt3eC9Ep9FKmxsp+v7y5hg3H/wjXlUiXNcRcURydQ+W5fzSUSTU+l97kdgQnvVcWft2Wjan2WnMcmrnrkAxZ2fNhbxVN/Oi1Om0ZGKrOpZhTOGI73P6pe8pWMi/EPOe7x1hg0iw/Pcr/NGfoGcnAPLK2wKEbZv/cf31W1LMNZrUa9WajW7Ks/BmY6QmBMEmTSzqjPnlz8oB/RtJ0AAAIABJREFUpDUfAlTR97giRJ9oR4cCDNtH2hmkWZnnOJTejTN6Rt52jcY1AzLSIyN80trFl2lizeNY1ljR9ZHSIUaGd6PUYnBAuc5SKSSn606soNy8Qzhpj5Eo0ps0JKPIRGXNS4rxU0fhboCQS+ljxn+ILtFsQgJnhS3aZXkruXLYdEWzIoR5ZeZsfn3ypasS8Qcfe4izLz+X6P6z2XvpB8sWcVj0fI8kfVxj6aWuCMZ+tiDiULy87SceeKDiMdcDKhJBq7Y1R5BLyyPUOkHLvnFazkgR3QHS2lxml9GEgV+JdVF5YBR29H3p2BOcdtkHkfuv47TLPrjQZBnA1hma26YJtUxhRhILIv7ww49z3nnvYffut3Heee/h4YcfL/rVoenCmYUCQcg3ibthdmSa2O22s9uMYpcQcQApMrS1Ze+ZSlP8lVa4w0/iPf8E/sD38IefJBj7GcHUM6iZE6hkHzozjHbHwJ+CYJZW3yjqlVBrbWSxBG8DRByyESylsj2bDJNXRFiViFdyXcxTFRu5EOJi4E7AAD6vtS4ZJO7oEL/xf9l79zjJqvLe+7vWvtW17/fuuXAXRAwIgxAw3uOoow4BAvGY1+QlJI5Eo58jGSGeoAYdyQmJN6IeP74xJzkSUEiiyXj3KIiiJt5F7sxMd0/39L3utS9rvX9UV0933bqqunqmesyPzwCza9fau6r2fvZaz/N7fr+Fi5rOp5ZyVY9MTSIQVXQbSnNmgktf9HKODtu4huBxAwZ9OGPJw5l6FJVc+0CYmKv8gKi2fasjFx5i6fAI0vRwOhaworNIc25DRU3DzmH05XB6NSofJb9MZ9Rlq6ETi0KuvJvhRmblfhZMB/zjzSelab1DU3Pc+L4CJ7zYAVxKE77//ge5+eZPks0W3lOUjADYu/eKNfva7giiBlOkFGaqAzqNmjnc++9/kAMH7mFycm7Zfm/tCRZb/CvNym2lsJMFAoDOFx5K6/2SDrATCGwLNxLBc0K4toVrmvjCRZougWdtyEgkECfW9jub7sPsGC+blW9zHEbNTLEzpCE0cl2sxoYDuRDCAD4KvAwYB74vhPhXrfUvqr3HUdaGimKVuKpFLurqn9IJhzjrol387KEHOF4l0vz7v9zN0K/v4vKrrgYgacKEAS95rLxRZLS3l/HZyttPReTsbgCUb5GdHyA7P4CQAU7nAnZ0DmnN1C20XwohBEYoQySUIdwHfi6Ku2STW9IN8upbh9m0QX+nhUkDue/AA2mAKgTKW++6r6w2U5Q6fv3uy/BkjJw3AM5xat6BA/es3KxFZLMuBw7cU3bDOontDX4qie334VqVmVulwaJaerUa86sz23yqwHA9wu4S4VX9IO6FFn70UbQWaBXiZ1lnpQsZShlmYmWbQKCXzasEAqXBzo/g5luTb18PShmY/thKrtwRBmeFoEM2zw1v5LpYjVakVnYBT2itn9Jau8DdwGtbMG5VVOOqavSKlnPf6Bi/d8dfc/SZpygt9bvZLPceuH3NtqxlkezspBTvuu46wiXOHGHb5l3XXbfBT9F+UIZNnvJis1YGuYU+EuPnsPjMZeTmzkflh9G6+XmAkGBF0kSHF+g5O0HHNh+nQ5xwOqPWghmvu8E3qUK1chnVJI0PT88xK87l8fRlzM+cjvaPB5jJCvLNhe1rx7K8AWSufgJAEWa2r+prlYJFJVTkfAPR+fmq77n3wQe44KY303v9dVxw05u598EHah5DDVn40ULuXAiNNLIIM09aJEnLwp+UTKz6s0RKLpGUiyTkAkljgYRcYEnOkzTm0eGfE44s1DxmK5HL9CG0Sa9l89yw17DYVSnqvS5K0YpAPgqs7pkdX962aajGVd02NMov/vn7HHx6hju/90Muv+rqqo7clbbPDAyVbbvmiiv5mxtvZKyvoNA41td3yhY68x1D6+uPa4PcUg+JibNYevoysjMXEORGQTcvsCWkwooliY3O0312gviowo7JE2bGPJuReOs8lMpy4F/4JliFAFtN0lhruOBVV/P5r/wzaEl27lkrr42MVA60IyNrxwoldpTtU6wPdVw2ynmvu4R7vlhucmKmeqqucqoFizXHrcL5jvo+hlt59VIkBozPzqK1XvG9rRbMtdS4O8rTPxG5gdWZ0Pj2Y0Ri5dozmwGlDEaNQc6xM5hNrlRXo97rohQnrNgphLhRCPEDIcQPZhc3ll++7Y/eSbiEzF/kgGutWTCPB4DekcrPlErbpzorz8yuueJKfvKRjzL3mbv5yUc+ekoGcYCc0+DMFEE+2UVy8gwWnrqUzLELCbLb0KrxGWQRUirsjiXi2+boOTtFbFhhhjc37aK1YCboqfp6MQd+aGoOrY/nwP/xX78ORkHSOFKlA+jo9AS33XEzX/jK/bjpKH6m0Eyyf/+1hMMlK72wzf7916783fR7kCUSrcX60JGpCbTWHJma4KYD7ygL5kIZmKryZ6oWLIroHx3l5tveXTE/Hk9Wb45rlBgQnOagrPKHQqx+MkxlCPCsQ0Q7n6Fyl3XrIIRi2KlPy6ke1HNdVEIrAvkEsLrVaWx52xporT+htb5Ya31xX9fG8su1LLFSIQNv1X1/zf5bscMlHVzhMNfsv7Vs3LlQBNfZuM3XVoSWkpyozg1eD0II3FSc5NHTWHpmF+mpi/DTO9BB82NK08fpWqJz5zzdZ6SJDoLhbE5Qn0sZeMaZFZUOq+fA78fXglfteQ0f+bM3MTo0XHHsXD7LBz9RqP+nZ3aglcPevVdwxx03MDpaWOmNjvZxxx03rORBlddHsHhm2ViV6kNFjaBSWLnK99mb3/nmitsBEIK/+t6POPMNv8+RwTHSsc4V/SJDa8IL1dMqjRADtCNwByunIcKyNSwWV04T6XoCUYMiuFEMRlNYsnWt9utdF9XQCtbK94GzhBCnUQjg1wG/04Jxa6KaJdZ8eO2NWCxo3nvgduYmJ+gdGeWa/beubF8NIWBmYJjRI89syjm3M9z4QEHetkXwMjG8TAzYgRnKEOqcxwjNIIxkU+NJ2yXU4xLqgSAXxk2FyS1qVGP9OdXHlwZ590xcsRPLmsIKnkBQyN3WyoH/9cHHufMjHyvocddoT586Vqjr6ECSn38Wob4fs3fvFWtuUK0NguwI2YVh/OW8eOBA96oMU7X6UKXtVrqX1RP6rIjzuHg2Y3tfRuxdHyZVISivXqmmDJNURzd2rJO+XJpt01M1SxiNEAP8syyoQs2L4NGqZIEnFgh1/RI3cTZBBYXUjWI4WlsKpBmUXhf1YMOBXGvtCyFuAr5EgX74Ka31zzc6blPngibvLTKYkhSK3gIt4LWv+E1es/sVK3/XCMhnUFosXy8CLQoV8MV4fHMT/G2KXLj2cnsj8HMRUrkIMIbh5Ah1zGOEZ5Bmc7LARihLOJQl1KtRuRj5hENuqaAs2Szksrqg1iauO4bHCKa1gMUTbB/srdin0NHRwf/4iwMr4mxHpyYop7sWMDQwgkVBJ9+YDzB0nKC/8FDTQQQvNUp2oR9dIk2RyCsCW9K7PPLY4AhHKog9VaobCdfBUHFcmWZcPoun1PDKiuO/ved2PnXz23FXMVCqrVQ9BASKSA1VQygQAyo1z5USA3SngduVrcpbM4XCERb5Ct6kzcAnjRX/BWb2bPL55leIpYhYHnGzXADtZKAlPHKt9b8D/96KsZpFljSz2SOYyTzlJcvGkOjppGO+ddrjWwE52VxnbaMI8iHSMyPACNLyCHXOY4ZnEeZcwz7PQgiMcJpIOE1oAFQmRj5RpDOW7/9vX/t6VY0To0QY65+++M8rbeY9HZ1YpoG3ytW9YB8mKihs6gqdC7D7gvPon/z+8Y3HQM2EWeg/i1yicw1/+gtfuZ8PfuIAU8cmGRoY4a037uea3b9Fv1FZy2StRtBa5PNn8INIJxltrxHdrGel2p3Lsm32GMMTR7CWg7Ps6EalEivUy9Uo1o7Wk7Nwz6ys/3mc2z5LrDMOQGop1bDbTiUEIg/hnxOSZ5PLlrPTmsFwfL5tvMlPSov+Rec+V3/r77644XG00OSlRzI/TTJbOXdX6+athSiS7rmFLSECtVF40R5mOs89qecgjeMNSMKaQ2yAhqiVxEtHcRM2uURByOsvPvQR7v38v63ZL+Q4/I+3vYVXveTFRENdDFoFhkglcwTbsoiFoywklxgaHORN+/bx53/+53UzayrJOig7zFTfRWv2+8JX7q8o33rbzXdw1SuuYtAUfPbgfVU0gsox2WHwcGf9aYqQ77NtYY6xySNEE+WCcea25yGkTbDwBCrRQCPVMoJRi/xp5a36pdz2Utghu6afb93QYPmnkUnVdl1aD0Jonj/0n9hG41LZG0Fbaa2cef6Z+iOf+zAxIoRViFgQJhw4RLwQoZI8ViAVedMjL10yRo60zJKWWZIiRVoULvb+XIzuWVHWp/JvX/v6GlstWHvzrocwgt75pa2o1NoQUgPnkDA3L7XSKIT0cTqWsGOzCGsGsQFalw4MPnv393nbn/7Piq8PDwxw8B/+jmhsBwOyGwmc97pLKqYvtg2N8vP7v8eT1jS+DtizZ09Fi7VqGOvr4ycf+ejK34NwB9Pdz1mzz8uuubSiocLw4ChfufdhTCkYsiVGna3oS2HJ1/tq00Ck0gynlhibmqT32BSyytBG1yCy6+zjG7wFvKOPVJydVx5Ak71YoK3y/S+99C1MTNSmRfaN9HHXN+o3lK4FOxgltTTadCfpQDTFud0/bsm5NIK2cgjSaJIiRZJUIatevM7CILUkQoiIDLOoErhi/WrWTChFethmZDaMkT9+03/4U59eE8QBcvk8H/7Up+sK5Fk0M72d9M0nkU1oOG8VZIzWCBa1ClqZ5BZ7yS32gjiTUEcCOzaHtGegjuthNYQR8D8/9HdVX5+amcGOnEbejzNtagYMWbOgKIQgKkMsBWn27dvXkAtVKXtDm+Xqg8WiaLXtvtJM5hRDYYlVh8RBuIZgWU8uy7aZKYYmxrHW8+cUAtlR0mFqdWNt21X37Nw/3UFblfPs9XDb5462ThbDNSaIduXIJk5Dr6OxXglD0RPDU68XbaVHDqCEIkWGlM4QMkN1y4ZmDJen+322JTpwEoUnfjO2WqXIa82xng76EykM9xTwOiuB70TxReur+S2DNsgtdZNb6gbOwIkvYcfmkaFjiDodVmoFiaGBYf71S19dyUkPD4zQFe9kYZVxcBHFgmJMh1givcKz/tu/vYupqel10yyl7I1AlN9+9VicKTRHswFDIQN7nZm5rTS2Bnd54hn2PLYtzjE6cYRosjqDqFS6+c//331ct6MCk0KYGD3PQsYH8Y/+soIfoMLqsLDiinxHgFdFjWFkpG/dGXnvcGtlMTw5R6gzj5s6i8Cv/x4ImT5dVnsUOYtoS4egInJBjoiMYNfZNRhIxTNdiywMR3HjnVXts2raaq2CkAam04UIjzLTfyGJgXNxI402zbQ33Pj6Tuvtg+UGpKOns/jUpaSnL8TPbF+3AalaA4wQ8ILLXsptd9zM0elCg83k9ASpbBqrZLa8uqAY9Y/3Guzd+3Ie/t4HOXLkHxkdrZ6eqsjeqODD+dYb9xNy1vY9VLI408DRrE/OWD81EPcVY0sLPP/Rn/Pihx/g7F/+Yt0gXtqh+QcfeA87Xn5exS5SAGF1I7dfwuLg+SwM/RpzwxcxM3wx5pk9xPoTOKEUHYsJBuZ8Il75567UCLMadsjm+rdfv+5nbRS+SGHFHsGqslKohJHYwoZqOJuBtpuRlyKjMowEI5jKZN6aJyUqd5Y98PkH+Mydn2Hu6Bx9IwNc8JJXkqjQSlzTVksITCuOMGJoomgVQlOwbQRImT2kunowuzyi3jyhpUkMd3McP04UstbWfDAJIfDScbx0HNiJFUnjxOcwwjOIEtGi/fuvLSukCQHXvvYNfOs7X1tTWISCR2VXZzexcJSJ6Yk1BUUh8oSsaU4Ti6TMEEbX1EoOv9JxALq6Yrz/j2/gmmevNVcIjPLAVXTEKWWtVHTKEYLpbEBfyCRaLfWnfZ7/n99DuPVrgFTq0ASYTyxy04F3AFQsriaEScZYywhJ637iq/oDTc+nezZJzHFY6rDJm4V7tMibLioydnZG0UKQWGwNa6UWApFDRH9OKHcOuVy85r4azUC4suzHycRJN5aoB1prtnvbMXMmylYk7AQLYmGFw/TA5x/g43/2cdx13IHi3d3c9M5beN2LXoCdXEIEAaYVQZpx0DGUDtOQ27yGsJjBzE1jBgHSDzA9H8PztkSBVJk2UwOXnOzTaDnMUBanYx4zfGylAWm1bOvISC9ve9PNvOIFv8NzfmNbxZTIivlIyCCqfEx7BpOjyOAYYhW5MO3ESWsL7SiU5fO5L3yTA3f808px9u+/lr17r0ADockwxlPHr9Fk/9kkrfpWh+uhO2TSUSGYq+QzBHNHKryjOnqvv65mmqioabQaniGYzJYXMR0zz5nWN6uOlY+EWYobeLI8bTkVRHiqDsevlkGD6Z1BNl19ZdUXSfPsnh+duHMqQVsVOxuFEIIJa4LtajvSlXS5XXQanWScDPf/+/18dP9HUcH6xUg7EuXc17+RR4HerGbHVA6UpJk6phAKwRTp/Fwh9puy8CdkoXUIS0oMLTABS2mMIMD0fUzPR3rtkWvPxzfKuG9P+Lkwfm4UGMWwc4Q653ntni5et/eKwk+VO5383HbQMDI4ysTUeNkYwwMjhExBVqXoN7+LqFKrsVMB7uHjF9DrB1/A73z4heioREcEhARqAbSt8Pp9tGFjPl4IToFozmqtEhZyPoEj13SBCpIEC+WfbT1U69AsorwY7LGUD4DyFUbed/DtMKaunLpwMln605psR5REpJAeLSLSYoPiWghJSdQwiYaOIMMZFjO9zGXDKL02+zwUaa/ceBFbIpBDoQg67owzpsYQvkAEgm997lt8/H0fryuIw3HFw7GUpn/apdkSgWGkcbOHQVcOyEIIfK3x0eRZPoyUYNkQtgGNJQxMNKYCU2sMrxDoDdc7YQyZnFNdKOpUQeCubUDqjfpEMlGKneu7L38Jn7zv02Xve+Wvv5RBKXDsmZUgvtrce/tgL7fvu4rfftmLAVHT13U1J0KTB9NEGGZBy7eFWOkClT4h62lM/2kiO8Fzw/gZCy/po7z1l4qVOjRXY3UXqc7P4E8/RqhjiHTktIr7pxiii6crfn+v330ZQggiyQzhJKQ74yTDAUoowqJ1rfpFWEISM0wiBkQERKRPCB9D+MDy5zUzjISOEGiDhNfHfK6P2UwMpQXdofZiqxSxZQI5gC98psJTDKeHQcFdd91VN/ULCjoS21aCeOMQMkCro7iZjeodCzytClYGYvmPYxT+RB2EEFhCYGowlcZUqpCy8X2k6yJbYMKghSQnW9eu3E6o1Bn56pftRXkW6bRDZNXX96WHvlpxjML29yMpNMVUcwFSyiI7K9cEvqJ8K1DW1SgQ4AdoP6Bj6SmyPRdUFOpqFnk/Tyj0PSw/XTwgtpPGdoBu8L0wXs7CS0JQIRWy+pzf+ff/wHwJe2el6Ktd/NlHVwzK7eQ0MrwdJYyy7/+WfX9Mt3hyfRclILaUJJIUJLtipG0PW5i4DXqkQkHWPmZaRA1JRGoiIiAkfWxcVgL2OjBEQLc9Tbc9zRkd4KlYU64/JwJbIkdeipiO0Z/qZ9clu+rXrBYCtGZwZIwX7nox3/rO19YvJq2CYSSXZ+Enl0+utcYUAktIDL08o1cK0/MwPB/penU1OeQ7hpiLlZv5bnXU6ox89cv2gtZsDxkrNYyOy0ar5sgT35kgan4DofPs3FPZF3b7UB/Kp2IqorQBqBLc7hEydm+hsV8UJHURy1l4XXjgIsTyORY0gaDk9cIZg1CcHv0hjiqnTlaCUhZeLoyXEngpf42ugHDCmEPP454v3V/SRbqfa154Bd7044iS7y3Vfxb/5/9+u+L3H3Ukc4ly55wdQ7088/m/rHh+gWnyw0iUp2t5emqIGCZRQxKWEJGKiPBx8DelfV5Kh0DlT1q/95bOkcNaVkrvcC83vO0GBgcHK3bWSSl5zbW/zYMPPsDs+PhKEAeYnhznn/7571f2LepFAxWDuZA+OpjEzbSH9ooQggAIig8UCUgB5vG0jSkkFgJTL8/mA4V0C4HeWE5D5SLt08nZSnzwEwfKWChFGdlXv2wvCEFeCkLLzTS1RKik9BG6wPaopoB4ZHqWagXydX1dRWEma/uVm4AahRk1cMLlbfXVIKWHE/FwIqD7JZ4bxksXUjBGz04QYq3KqM4RzDyKP/UYgnKu+a1veCMf/D+fqfj956qQZqp9rwCG73NxYomeSJwfGyClIGoYRAxBVGrCBISFjxQnrk1eqTyGDKHUiW3NXw9tzSMvoshKmZ0s8FpnJ2e58113cukLL10WLzqOUCjEbbfdxk3vvZ07H/4hvaNjZVZvpVitF30cGsNI4GcfJXDbI4jXh0J+PqsVSTQLUjBjGUxHQ0x2xRjvjTPV20nWODXTKut1RgKkVy2qapmUCHmcWlrNBWjbYG9V/9b1fF2F44DfuoJekG9+LCEUtpMm2rNI544U4Y4ZpFmMvhqdncQ99H1UpvCgqMQ1f/tHP1SxmakWqn2vq7HDzfL8cMDFEZdznSw7zAx9MktUusgWuPI0CqVySNm8ecpmYEsE8s/c+ZkyaqGbc/n2t77Nze++maGhIYQQDA0Nccstt/CqV+9hMlzgg1azeivF6htdSh/UIdzMITbbYeREQyPw0CBPrc9VxOoOyGrb026w0l1YNCkZGxorCPkPj/Hu9/wVV1z9Wg6lJjniDpCgi/fuu7rMBSgSsvmLN13blK+rcGx0tWlqk9D+cmpmgxCA6R8ior+BY/2CYPpH+NNPrll3VHMDMmTlkNLd2VXx+7t9X2WxryICy2K210EZ7XW9BiqHaKOO6C2RWqmmsTB3dI5nv/bZ3P/K+4lnJNFcjsjSEtZjjxFLJPnZjjPoHRllbmJ9ClbhRtdIYwk3M76GK3wqQuADp54b0ltv3F8xR7u6MzIAvjdgkZGQl2Df8Nu87w9+e804j+UzbPfyuJ7BQibMcy97Dbe/I85f/a9PMzE9s8K6uP7ll7H0TOF7XE++dQWGRPubQ0HVWIg6i3lQmYlTLD4CWOow0ugom85USxsFShFywmXf/zvf8m5GrJ9z612fq3qsUvi2zUy3iWqRY1ArIQCtfYQw0VXYaycSWyKQ9w73MltBL6N3uHeZunSMkcPHc1arc3fxeAemZeN71S9uJxTmpjf9d3TwNF4+/SsgXAtat8hap81QT2fk+KjDbMlk6qH7PrtGm/sN//1P+b3zz6wgg/xGXvWSFxMLabocF49CwLrmiivr8nLVaKRhoqtQ+zYKpUykUd/Y1Zg4wJoA63Qq/JKG6mpc87G+Pt7xB3/CgU/97Zrv/5Uv28tpkdN5/e7n13Vunu0w222g2nrlqEAYy+IxJ3fityVYK5U6N4v6xK/5zRdz0c/zK7rhxdzd6mWfZRj4SqMrME6kNHjrbX/JG379fAwVNK1fvtVghYdQqjVdhVsJM/0WRzrWLv8fuu+zFd1yXvvSF/P5L391XRnkXl8TWarPwk6EQ+hs7UJZLU76eohuF9hWdYPk1ajGxFnNJAm04uHcFG5WcEZmG7G8s3KOldyA/ubGG7nqpa9kuueCsnEHwhP0s755mOc4zHRL9FZoj+bEMlm2NGulqLGwmrVy/duv54WvfgHnPRasMX+olLvzgoCueJyM5+OuMgtwQiHe/a79vOYFF+DlvDL98qPHjvGev/4QwCkXzHWrzC63ENJxgyPx8hzuvQduXxPEAdxsls/920FUSXNWJRnkOVMQdHcQX1iilsSDsC10Nltzn9IAWYuTXgnKk1Bnw2gtL1IAH3goM8F0vtA3MRM5xnBkhNPTo7XdgHJJHJUhX9KnkPK76F8n4rihELNdbJkgDu3BZNkSM/JqOHfWYfTI2i+vmk6EEIK/uflP+cDf/38cnTpGT0cUtGY+mWZssI83vfH3+PCnPs3RY+UtuMMDA3zxH8u7/7YypBVDyMqdeKciPEfyyIiFX6EW97tjA+sym1ZDCMGPvlzubBjXgs6FRUSlgqMQhe7eoHa+94Kb3tw0Jx3A6TeJdNTHsqo1I3/883fwUPoIx/KVOenbGGNncgjHr/zUyHeNMhfZuWabQPOs8DeQVM4pu5EIsx16SwXx1ZAnIJhXm5FvCdZKJfTk7bIgDtUpX6N9vbx+7yVM/Ov7+d/vvoFs3mUukUZrODI1y3v/+kMVgzg0pl++VaCC1jIm2hZGANEMbmiBHjeDVUH+YLVz/GrIKgyMajLISaFZ6O5Cy/JALhx73SAO1YuI63LSl6Hc+nPKt++7qiKT5D379vJA+jDH8ot88sNf57rdH+Ta3/wbrtv9QT754a8DcIRxvh3/T57omsQ1yj+XnZxClKQyNYK0qCybnI9GmOlU6wbx++9/kEsvfQvbtv0Ol176Fu6//8G6P+9mI1A5hDw5TJYtGchNJTjv6cpP9UpUMIDxmVnOu+oP+YeD3+XWu+5bKfAUkc1XD2z16pdvKSiPk12g2TRIhQ7n8KPzZO1jZNUS0s3QO3+MM6eOcNrSLL1uDmN5Fn7N/luxw2u55HY4zFWv2UPIWcvsqSmDDKSFZra7A20cV1gRjlM31bBZTnoRQQ1HoFK8fvdlfOKW/4cdQ70IUZiJ33XL7zJy5TBz+SU++eGv8+Uv/AS1bF6hlObLX/jJSjBXKJ4Rh3io8z94pmt6jeCVCAJi/hxojWUIIrak0zFQ4jRc89l4xtn4xun4chu5aB+z8fUfQEVfz4mJWbSGiYlZbr75k20TzAWgVYHJcsKPfTJSK2c/+0z9l5/7AG6TFelnH3MYnqi+hPmnhx7kPf90N0enZyi1NA+FHHI1birTMPBXzZwa8fjcStBCE+sfwU1bhOniAAAgAElEQVR3oqosj7cSNBrpePhmFk9l0XU8pLSQLHUZJEyXB/7lK/zDHR9hbnKK3pFhrt9/M/9t14V87f4vNFX8thAMJFIYGnQQ1P3MrFVErJUjVxKWekNMdCnOcHxGsvUVX1cjqwMeSD3Dkldopb9u9wdXgvhqSCm4++Bby7bb2JymdjKaHsbqGUOE++viWmvbZWngu4h1ejaq+XqOjvbx8MMfWvc4JwpCWmgVsBk9KG1V7IzmNC/4cUBggBsyyDkG+ZAkZwvyliZnanKmIicDPKkRFFpz44ZBTGgGIhrdaSKW1s7KE10W4wMGA7/2Aj745ivZ96J9ZbTFXC6PISVBFYXBWCxK2Amd8qyV+AA4Pb8g1As6iKHcbrxMJ/lkJ1o17mF4siBsj8DK46k0CtXQvSO0IpLN8a9funttIf1tv8WVLx/hsD7G5de9iN0vexFSNcZJ8NBMd8QYTmUb6t6sWUSsgGzcYrrX4FCHi2cUJihTwEWikzMakJXIoPhW8imS/vGib6UgXrpdCEnE7CdqjBGWoyg9hNHbjazTHBpAuDaR9Flko4/W3K+aZd/kZOu8PFsBrbwTrslyUlkrRgDhdEA4Xf1CDwxB/nmAfXwGnh8EBkEEBmbKJpuUPBILSFo+rCqkVGskqhbEAZYSSR78xn0E65nRbmEYNtjdxzU5hJHCCKcwwkdweoEgTtYb5LDeRoBRyL/p40KNxTSmKG5fVnCSCIpqTkIffx1AFld+SiCEPm68oQoP6sK+euU46OIYy7eC1svu7hqpFNLK4JEl0F5TE5+DBw9y1113FbR6Vq3aZidn+fiffRwosKWOhVPMj0pGk92EEno5JVUffDTa9xu+mdfjpAemYL7P4XB3wEJo7TVfxH+aAdloJ+dlEse/+ypI64BvJp8k7a9d5UopqszIJSPhKwmJUWw1iCzqqmuwDIHZQBAvwl4cIh+aQBnVqZPVfD1HRlrr5dkKnGgmS9vTD41A4xyzyI+V30DaCPA6sxidMBBESJZ8Z9UaiQaHBshnciwmygWGhvr7CTwPYRigVP3qilsI8VFvxZ6sFALASBIxkpwpJjgkzuSQrseAQlT5/02A1vS7mu6lDEYTTXUHDx7kfe9733EJ5JKf2M25fObOz3Dlnivp8Hvoz45hZ6NgCqThIUUW5SfxvMWygl7peYo6tfLXhybV5TDZKxiPuihj/Zz7I0ZANtbBRakkRpXzTBLwzcQTZCsUv1/6yufw5S/8pGz7rgsvp4tLC99byU8dtyQ0EcjRgujieSR7v1d1l0pWeuGwzf791zZ+vBOAoibLiQjmW6LYaYy7CFX9VAUwaGS4IGJirrqyrn/79dglVXk7ZPP7b/ldbt73hzULWToIQEpEFebCVoXTCUaovoYRU2c5Q/2Uy/k+A6I+adQTAiGYccI83jfKXO8ggdnYfKQeHfvZyVne+ht/wo/ufgQnEyusGgRoZREEHWgximmdhxU+Byu8DcPuovR2MltgHOE5BpNjYb5zvsV3T/M43OHSSObrGRHw7VgMT5a/aUl5/N/Fx1eCuBQmcWsbw+HLOC1yNX958308/3lXlL3vRz/7D77wlfsrHi9ccWt9kNkIodzOqq/v3XsFd9xxA6OjfQVdnNE+7rjjhhW/z3bEiWKytP2MHAAfzAUHr7e203VM5LgwavHLrEFSBVUbiS5/5RVsnygE8VqFrEIwF0jTRG2SNsaJhBaa6GDjZtEhvcj5+vsk5CCP6rNIbuh2bR20FBxzwsz0jdLvZulamscI1v+dpqen6xr/6NRkTYljhEApG7BBdGE4Gml4oNME/hIiaLzgCKAlLPWEmOjVHA3nYYMyrdNC8c1ojCsyWUJBYTa7oH0eTh8jYp1OrxwjLEYwg15kMeAvT6oPjT9dNt4aWeBVaDatshr2wnbyg0fRsvKKY+/eK9o6cJdiNZNlMzVZTgpr5cIzztBff9/7G3qPjkkyv+atWckdN9SdZWSkb8XoViE45IU5WkXPQmvN2ePx+p12hMAwzS2fN4+PKOzOjUnyagRzcju/VKfjngSaVS2EhCCaz9KdSWJlM1WbfPbs2VNRx74ahgdH+cq9D9fcRwuNF8qSt9OkjEUSxgIKn11PG8QT9d3AuajJdL/F4Q6X/Cao/UW05AVZFy/YztHsEOj1pVjXM6dejZ6QQbxCIL/ni/eVmFO887jGeQUEsUVSXSfP4Hgz0ComS1uxVpqBSCmsdAg/WpidFDmlxXxZkVMKhaf2aVaGmBHmiYxHabwWQqAciczV+RDTmsDzMCyboIb4VjvDcMDuaG6GuBoCTZ86xGVikqPiDJ5QY6jNsGJpAjmlCewwc3YIu0MxkEsTTSeRJb/Zvn371ubIgTKe6ipU0jgPTB/PyZKxEqTMRZJyqaIjzc92CHb9olDYr4TAKBQuj/QEzId8qOWGQ7nByvVvv35l5bkeMkLxrVA3O2d21rU/FFRBK2mMV5ILrrROu+eL93HTgXeQXZbGODI1wU0H3gFQNZgbqS7syBCu3Z7+mM1AKw8hHdQmMVm2VALYXHU/HThwz5qiB0A263LgwD0rf++XWZ4bM7Eq3GG+0/hH9908htU+GsSNID6ShxaK8BvaY0z9kl8XD7FNtEnnqwC5/Fu7UjIeifNo/wiT/SPkYh2FFnlg9+7d3HLLLSs69sODoxx414cYHqzc4Tk0MILv5EnF5zjW8zRP9P8nj/Z+j6diP2XKOUTKqBzEAdKmz+NnlIe4VKfN42c4fOt8wY9H8stBvDYqGax8/M8+zgOff6DOLwgyMomq9lSpgLfeuJ+Qs/b8S2WBoXpa5baPvX8liBeRzWW57WO1V+Sh+TNAb5l5Zl3QKo9hbI4hRdt+U5UU4K6+4grkTgtleXVzSiPkuDBi8mjOZGlV/jRvBTT6lQohCDx3y83Mw91ghBrPjdcDS2c4S/+IMdHD4+IsZnXHphynXuS1whGC/Kp0wJJls2T1IOPd9OczdGRS7N69m927X0k4P1DopllGqZY5wAUvPp/Huv6j6XMaj+boGXToWfQ51mdyqMsnbTeepqtmsFJk2NQLz8niZGJ17VuPLDBUZ6uMT1d2bKq2vQjhW0RS55CJr6+WuJWggs1hsmwokAshrgFuA84Fdmmtf9CKk6qlAHfV6S8lP+I1xCk18Tk3FHDED3M076HQZE2fziY/fuC5GLaN7x6Xz21bSE14oNz0ttUI63meox8mIYf5pT6L9Ek0rQiWue2lKTUlBNOhKNOhKBHfZ1vSh6wgsDxcO8Pl11zCix9/Mf9+97+ted837v8GZz/v7IaCZSkeGVH4wwotmp8A1DJYaQQZO1l3IIdCMF/PnLxa+buWJ+p6sBJ9mOFufHOhntPcMghUDmM5zdIqbDS18jPgKuBbLTiXFVSzkXrv3Xcjj7igBPv3X0s4XGKvVYFTKoSFlGEMYbLdyLAr6vPciGA4HuDEmw/CgetiWnbbq5XERzRCnpgirQA61VEu0Q9yvngSs4rK3WbDR+MYhd/2ofs+y9t2Xcjvjg3wtl0X8tB9nwUgY5r8ssvhqaFHeLTn+zwd+zlTzmG+963yomZx1ruxcwrKHiyNone4cuNLte3VkDTnN3Yiq6EVjvCrslVqeaKuP7YgsnBOS+zr2gkCUMprqSbLhkbSWj8ChZRDK1FLAU54GmsptEJBKrBW5hgZ6WX//mt53d4rEMJGCInWPlp7K244QhSKdVHhETU9GMsQcR1yi1Gy87phDanA8zAti8D32lJ/yoqAHavfVb1VkCgG1FP0MM6kPJMn1Qj6BBZEV9x+JsYLP/pymmVuYpxP3fx2AC6/6upCqswKg3t8xteqWW8pNBqhxYYkWq9/+/UVDVauf/v1DY2Tkgm0UAhd3zzOwMfUHqZyMVQe089ieFkMN43I5xACVO82ZHw7pXPDYkGzEdbKaoh8iHD2DHKRJxr6jO0PReG7krRCk+WE5ciFEDcCN0JBW7kWqtlIFRXgzCMBXvdqTqlASgfQKOWhtVu3vLS080QG8oR7JW6ig+ycJPDqv9kCzyvwzJWCGq3/JwOx4dzxfvqTABOX7eoXDIpDPC3OYVJvfit1mdtPyYXgZrPce+B2Lr/q6sIGfwRbaQK5SEC2pq3gRmFIA38DXOJqfRENp3yWqZJ2NgqA1AGm8DGVi6nyGMuBWroZDDeDqHUzLT+fg7kj6OwiRv95UCKUde0rrqo7cFeCsziC6xxFGZufIjyRKHh+2ijtbjhBu24gF0J8FajUo32r1vpf6j2Q1voTwCegwCOvte+7rruuogJc0ZVcJALMbBgV1YBCKXfDxQNhKJzuRewugZ+OkZ2z8DL1BUHl+whDIgwDVYfm9IlApA+kXbuB6kTB0Wmepf+TbbKXR/XZLFJ/frZRVHL7KcXcZCFnK5Qmu2Cg1XZgO6aV57+95U/429veS36VQmYzs95SSC3p9/vJGBkSOoGWzT1gr9xz5YZy9UVEcofpWQgw8imEas01qzJJ1PgPMIfPQ5hdLRmzMLAkunQeyZ7vt27MNoHWLoYRQgUbi1/rBnKt9Us3dIQmsK4CnCVRhodSa2c31RqEGoEQGiuWxIpBkAuTW4iQXVTrPjF1oNqmC1QYEOrdOGe81YiqOS7kOywaY/xSnUGW1lM5i0G6FopGEuGsIr9qEfXNzx/kno/9LflcHikNlAroHx7i9W9/A5fvuaT5k9Iw5A/hZB1ChOgW3ShHkTWyLMkl3A0UQJtBxDfon5zfnMVaEOCP/xSjd/tyqqU1KTWZieJEt5F3jqy/8xZDK5gsbUs/rKUA5z5Louy1Fd/1GoSagRHKEh3OEu63cRdjZOY1usbkRSuN1gGGZZ3ULtCOkQAh21NSQADdwTiXcpRpeRqP6e0EtE42t3dktJAbrwI7HOaa/beCBj9xPIB++4v/zCcP7F/xdFUqwA6FueYP38klV76OYMknFEoirCUCsUgg6mcc9Kt+nOxxFo/QAiNnEFv+R5uavJUnZaRIiuSma46dOWMgmkzx1GsMHcwdXk61nFuWamkWzsJO8oNTILZ2h3UlbJTJsiHWihBirxBiHLgM+DchxJc2Ml49CLY7+J3lH7aeBqFmIU2XUN883WcuER9RmLWaiVZ1gZ4M2DGBcRIKnI1CEjCsnuByvs1OcbQhz8xaqOT2U+zW6R0d4/fvuJPLr7qacFYR+MePec/H7lhjzA3g5rLc87E7AFDKJJPpJr20k9zir0H6Amx/J7buLuc5rkKH7iCaitY8Z+ELQtkQfak+dqZ3ss3dRq/qxarXRbkBhAJJ37HmVgBFWvD4bKEhqUgLvvfByg1JKpPAG/8B2m+N4JrwDaKJc1syVrtho0yWjbJW7gcqy6BtAnSXRW5bpuKE5USIzgtZ0CqxOjRBNk52zsZNVQ5ARa55UEXvZbMQG678/bQrLJ3ndP0zRsRhnhRnM627NzResYh574HbmZucoHdklGv238qLf+sacqseFiqx9neZrdKgUm2754bx3DAwiBQKK5TEtJIExgIBheYrB4eeVE9DrC6hBGbOpGP5H2Ur8maepJEkLTZe7DtzzkI2uYSvRQuuqp++nGox+3YgYtvY6HLDTPZgRfrxrDbpJm4pmmeytG1qpQyWIH+WX7UV+kSKzgshMCMp4hEI3BD5hQjZhXL6YuC6mLaNl8+3nKJZCdEBjTBPjJB9qxHSCZ6tf8B2OcCjnEVCR5oe6/Krrj7OSllGTmksITCEINAa2WVjJz3cXCFX1jc4wmyFxpW+OhpXlJbks53ks53AGKbp4jgJBn0PsUEOtHQlYTdMmDAY4NkeaZkmIRMEorEipRUIBqaan1hsxBjanz2EzBRTLRtbaYTnz8YdnFvXGm4rolkmy5bRWvHOMVFO9bxevQ1CrYZh54gMztN9ZoLYoEaWPBp918VynE2nmUsTnJ72K3A2irg6xvPUQ1wgH8WhRblQDWFD4qHJaYWHJhuSZPsdjKEwTszi2jfdjF3SuGKHwlz7Rzc3fDjft4l4nRh+iy3zArCyFl3pLrantrM9t50Bf4Cwrk9W+Kx5B7kBo4uNGkOrzBLekR+Av7HUn/AsoqmzNzRGO0NrF0M21hm9JWbkapuD11VbK6Rag9CJ0i6WZoDTs4jVDUGqg8yciZ8thG/fdTEtC9/3No3WHR/zEQ3O0NoVAk1fcJgeJjkqT+dxtQ3VpEmDIwQBkK3C8Xctgdtt8eu/dz12xOQf7nwfc1OT9A6N8DtveycvePVvFXqKCicGQhSMcYoaAMuvCQFaCLTWCCGIJzfZiEOD4RpE3ShRomCAa7srBVNVIpBmKsHg9MZawtejBdcF5eON/xizfyciOkazqRZraRAjNElgtn89qBkolUc2QEtsez1y3WGSeU6+akqlneHnIuTmw+QWA0RR0zwIWlbYK8LpEMRGW9h23WbwRITD4iwO6YG632MikELgVrE4W+n+XJ1Hv/paci1q6nrW0cO1beA2EwICOyBrZknIBHmR55w5h22HN552q5e1UtdpRrsw+8+l2fmkCmVJ9tXWid/K0ICUDnoVk6WaHnl7B3JTkLuQmimVrQDl2eQXo2TnKZC8tUa3KGBooek5K4usw8NxqyMru3hCn8MM1RUWJWBLSS5QVSd7Zd2fFGiJL7jmOn74ta+sCe6lufZ6YGjN2UcPNfy+TYOl2TY5iZ1ow2vEsLBGng1GvKm353sOkYuUuxidOpAIYazIjFQL5G2dI18vL75VIC2XcP8CXWctERvwMWxRMHduAeKD+lciiAOE1SLP0Q9zsfgpMdZSBbWGkJBoKMyqa6zgKnV/utksX/3ff1fgoGu9ostSFNlqBGYbGXZHoy4D3eOoc1yw2nBZG3h4R36EykzQjGCRs7gNodrDenBzoFiem9fcq20DuRq18bq3JgOjGqRU2F2LdOyYJT6Sw4pt7Os3bLC7tn6Bs1F0qCku1t/mOfIJIjpPRPvYUpDTqq5QULX7s4ouS6OwN5BSOXjwIHv27GHXrl3s2bOHgwcPNjVOJOLTPzBJR8chDCOHcnzy5wuQbRjMgeDYU/izP4dGG5WUPGW55UVo7SOlRa3ZSVsWO3XcJLfj1AriqyEkWLEUViyFn4mSPuasFEYbQXzUQ7TQ9WcrQaLpD57m7PRhQsksc+EBngmN8YQzgC9qr3bW6/5cjXpa/kthNqldcvDgwTUWdFNTU7zvfe8DCq5G9SAUCojFZ7DM8gd8EPXwznOwfuax6e2jTUCnFvCyP8AcOR9h1K/HY6Q6sCMjuHZts4qtDKXySFndCqf9ZuSGIH9OAE2KCm01mJE0nTvn6dzmYzRgPxfqBiOU2sQz2xqQgUZqRX9mikvmf8A101/mJUs/Zqc3V5UhVKv7sxRFXZZGYDZZ/7jrrrtKfEQhl8tx1113rftex1H09c3Q3f1UxSBehNeVxz/75Jl+rIvAwz/yw4ZTLaH500G1vhO2nVBLi6XtArl3tomqw7/wVIMZS9J52hwdYwGGvc5sSUKk/9SS9GwWpbxoU/mMpA9z5cxDXHvsy1yZ/AXD/tKafS6/6mp+/4476R0dAyHoGx3jJW94Y1lwX9FlaRDNzsinp6cb2g5g24re3nl6ep7EsuqjPLoDWdSO9vaeDY49RTD3i7pTLcI3iaaetcln1b5oq9SKGnHwejfHW3IrQAiw4gk6YwIv0UF6xkBV0EaPDymEsXU8QzcTtRpc7CDPzuST7Ew+ScaK8cPwc5gS3WhD8MJXXcVv7PktAgmmKXG15qxLdpVREpthrZhBcxORwcFBpqbKneMHBwfLj2EqOjqWsO2ZprqGc9uyOG4UNadWagNCawTH/3/1dvSJT8ao5Dwq+x9Yw+eDUVuvBsBM9GJGevBb6YC0RdA+gTxmktvZHvrZJxtCaOzOJZyogbEQQeQVcnm7ISHTmW9HQ6KTAlln0Ix4KYbUNIfcta3/AtC2INIZ4vK95a39dUNrwjqB4Y2zpBeJmMNIv7EF7759+9bkyAFCoRD79u3DQBKSNrawEPEp7PDR5aDbfHg9fNYAPz/zzMbfqAv/Esv/L4pnoXXBhat4cQpWTClWXj/+0vEHBWLlAQKV39MpLAZzmu68IppXVdNmkYVzWOp/+JRs36+F9gjkUpA7R/3K5MWrQWhByDdwXHByLqabA9KUqrwauTiJ0KnRxVmKRjTlBQLZQD46TuXVnnI1+ZkshgS72yEfMlB1xmCDgFAwi8o/g1aZgkupgPHwOKPuKEa+Tpqp1rzmlXsIS5u//uiHODp1lNHBEd715j/lupf/FjIQEIByciTDrSnqpURHcxaFovCvYrerXvvCpiABHLEFYGApg2EPBl1Nd7YQ2FfOIO8QSZ9JNvrYpp1LO6ItGoL8Z9m4fb+Cs3ENTmAQcgVO3sfM52vbahXfJgTTg5GGRZPaHaWa8lDQy7njjhsqBnOpJcNTtVNx/3jwO9x6130cnp5jbKiPPX/4LqAgWzs7PUnf4AjX/tHN/PorXrfqXRq708GPmngV4rCpFNEgh2IaP38ETZXfQcOIP7KiRS6AkLRxhI2NiYWJpQ1MJTGCwry0iHu+eF9Fn8t87zPkws/U/Mz14sfycub0+imLdoetYMSFAVfTnVNE3IDk0H+gjFOPDNC2nZ162CF7xq9OXtxaCdwBVj7f0IxyNTIdURaip1Ygv/TSt1RUsBwd7ePhhz9Utt1UJoPT1W/Wfzz4HW5836fJrDIrNi0LrSkYZi/DDoW5Yf+BkmC+/FrMIhQK6Mkk6Uwm6FycJ5JMMj0S4meD9TVineeexVi2H6POaf49X7yPmw68g+wqffRwKMyHb/kAr/i9QWjRA/xb4sX4LTT1aBc4AfSJHCPGI8RVZXnrrYpqgfykplZ0xCR72qk9E5dKEvYMQq7CzuWRfmv48ZFEmlQ4jidPHbeURjXljXXmILfedd+aIA7gV3BuKhpIVArkHYkEFz3yM4wSL1bXrH8C9KR5iG30173/bR97/5ogDpDNZQvbu/du2M4QIEfslAziAHkDJggxoS+kXybZyePEVet8CdoRJy+QS0H+3FMvLy60IOybOHmNnS/muTcHnUmf2c5NG/6Eo1FNebmO1vfh6fpv3koGEn0qy0UT5UEcwDXrzwfnpctEZIZtqXL2SSWMVzGzGJ882jI7w5Tsbi4/vsUwo+PMcBH9Mslp+nFi+tQM6CeNR+6faaHCp8Zs0vFMOjMmAwswPJ2jezZJJJnC3GR3ICeTZWnmdGZnz2BhYSdLS9tJp8fIZofJ5wfwvF58v5NARdHaQeu2axtYg0Y15aWqHUy3D9ZvKlJqIFEriAO4DU6BHjWfxpf1pUTGqphZGIZsmZ1hSpxCM4A6MKPjfI+L+Kl8PinRerOZk42TMiPXpsAd2LopFSswCHkSJ+cv57lPnpxAt0gz4XXgefUFaSE0hqEwTY1hBEhDFf4rfKQMkDJACA+Ej8Ar6G6fIDSqKS9V7XO7fd9VZTlyyzJRWpTlyFcbSKwXxAG89fI6pfsLn8PRKU5Prt8petsfvbM8Rx52yGYr5+SbsTNM6vpb4E8lFGfoAzLBafpxovrU4JyfnEDubK01nVSSiGfg5Jfz3HWKvZ8IdAczTNSQdS2F1gLfN/B9qOfnl7IQ+AvBX60Ee8MIEMJHyMJDQAgP8Dcc+PfuvaLuNIFcp078+t2XAaywVrYP9rL/j9/IT73tVVkr9QRxgHyDgRzgceMQ28xBLL/2937tK64CWMNaecctr+UDH7inZXaGyTpdhUpRSce9ae79ScQx3cExnsegXGKnfpyoXjjZp7QhnJwc+Qmc5TUDoQVhz8RxNXbOxfTaJ3CXIqQyRI2AdLA5hSulBEoZeJV4eBUg5fHZfvEBIKWPlIX/ClH4g/AQVPdgretYdTB+Xr/7spWADpCREf7Zu6xiYbNPZblo/KcYJeNWMlMYueBFDZ+vEoqnwhOck9yx7r7XvuKqlYCe7x0nF34CIahIz2zUztAjRK6JW79Ux70o9QtsyWAOMK07meZihowldqitG9DboyGoDRDyTByPZT53ri4+d7ugW6ZIB+2R81RKUigN1Bf4j8/29cpMXxqqMMuX/vJsvxj8/TUtJzJo/DcKqRwChS4pD9UK4qvtzcZnZ/mTT3yCP9gh0ULzmTs/w9zROXqHe7n+7ddz5Z7abjlPG+PssIYJeXVqnQhFzimYVLTKzrDZQmc1Hfd7D9y+ZQN5EVOqk6nlgL5TP0ZEbbJVX4vxKxvIrcAk7IKTDzBzeaRu31n3eugOZhinPQJ5owgCSRAUg//66nWmqVaCf9SfI1SlW7MaJIouI89CcDy1UC2IA7z37rvXeFQCZF2XT93+Kdyci7ucf5+dnOXjf/ZxgJrBXAvN4+HDPMerrzXejy/CKoppI6mnakjT1dT7qkn6NiP1266YUp1M6UsYkouFgK6X1n9TG6C9aQwthKkMYnmT3oRk+JjPwLEU8cUUdjaLPFneii2Co7LEjF8NxUjfl+TzJpmMzVPeENkmLMK65PFZZa0gDjAxV7mQmFpMrQTxItycy2fu/My6xx83pkjb9U0ccpFn6tqvESRorpuzmqRvM1K/bQ0BU7qL7+pdPCIvJrMFGD6nbCAXWhBxLbqTBoOzAYPTaTrnU4TSmbqFlrYSeuSvnlOQ0oLH1TZ82Zi+dqcozOLXC+IAo72NFRLnjtbHIHk09My6+6hwZlNc4pNNtuVX0nFvVup3S0DAUd3Nd/UufikvJivqJxWcaJxSgTxU5HPPa4ancnTPJYmk0pgVuvlONXT5M23o+VI/mrU4C+UDEsc68PKR9XdeRpxMXUEc4F3XXUfYXpvPDjkO8a7KK4He4foC/7QxS8KprSmfj7be8SbAJK2b0yIv1XHvHR3j9++4c8vnx9eFgEndzXf0pTxqXExWNGcUvZnY0jlyOzCXdUs8zJy7pfPcG4Wt88RMj6S/9VxSmrE4i7suPctDLlAAABA9SURBVPMLmOlCMEwtCayOTsI9WYx1tNq7vWRdQRzgmisK+e7VrJU/ftPvMt7p8fE/+/ia9Iodsrn+7dev/4GX8cvw0+zKn1/xNW345J2Jlj+c07JrQx2dl1+1AanfrQ4BE6qbCX0po8Yi29WjhHV7rIS3VCA31bLglKuwc25b8bnbAT0kSLL1utZqWZyVBvKY59Ezv4CVKhfL8hI+XtLC6Q0T7kghKohLaSTB0aCuIF7ENVdcuRLQAQ7tDPP4sjF4o6yV1ZgTi8yHEvTkypfsXsfMpjRjpelu+Zi/chCCCdXNpL6UEbnADvUoIU6u0mJbB3KpRYEW6GqcXB6jjfnc7YCuYJbD9G45CY16LM6ivk/P3Dx2hQC+Bhrysz7uYpjwgMAOJ9fManOJKCq3seJ2flkw68o9VzYUuCvhkdBTXJ5/LmK1bozQLZOqLUWS9ksLbFVoIZjQPUzyfEblPDv0Yzj65AT0tgrkBWOF5UacvFfQ595yYenkwdIuHabH0hZLr9SyOIv6Pj3zC9jJxpaw2tdkJjX5SAfhXh/LzuAHEXIzG2co1dkbVRcSIsVMeJGBzPGZchBdQsv6JHIbRZL6awn/hfqghWBc9zKhn8+YMc92deID+oaKnUKIvxRC/FII8RMhxP1CiIYJqrZv0pG16F8UDE3n6ZlNEk2ksP4riDeFblrPcths7Nu3j1AotGZbKBTiXW94A0OHDjccxFcjyASkjgjSc12kjramtu820Z5fC7+wn0StSqPkY0daOn4RSgsSqjGGz3+hfmghOKJ6eUg/n8eNC8mLE6dns9Er+yvA+VrrC4DHgHfWdVAl6EtIhqc9+mdSxBeTpwSfux3QFWw99sru3bu55ZZbGBoaQgjB2MAAH7zhBsxslgtuejO911/HBTe9mXsffKDpY7iLHojWBDHXaO11mhU5pqIF2qJ2snjm5kitZmUnWpxSRLW2RCGg960EdFdsvgvThlIrWusvr/rrd4G6ytlGEOCkf3VcgU4kTO3Rabos+s1RzE4W9r785dywaxehxUVAVG2NB9YUHhuB9txlt+WNnau7CRr6j5hPMih78OOV6wWtQEp28yvmSXxSUQzoE/QyJmfZph/D0ZsT91r5eP59oCr5VwhxoxDiB0KIH8wsnHpeeu2EHrE12ooBHD9gdH6e0WcOEVpcomjgW601/r133938wQKFKEnhNANXtD4ausLjSGySrHO45WMXkWpAJfO/0DooBId1P9/hcp6UzyUvWl+nWHdGLoT4KjBU4aVbtdb/srzPrYAP/GO1cbTWnwA+AXDxeTv/K/m9iej8/9u71xDJ8rOO49/nnLpXdV36Mp3Z6Z5EJYQEryiBEBEhUScSsnHBZcK+Cb4IwxKML2RcHGJQmRBXEEFZVkkCKwzKwq7BF9mQiAE1ZjUxrDFmk80kmUvvTFd3nerq7uq613l8UdWzMzt9qTp1qs85Xf8PNEzVzDTPman+1ann/M//6W5iyRLHbNcdqKTrslDbJrVVfWDo8L7Dbo0/7PlRabsDIuBxU7ROXNAp7d75w9hNfnEKbxL76qdg0HKUuQg39Qy3dJHzdoUV9we+naEfG+Sq+v6jfl9EPgp8EHifBjHJ2XiITZ+C3WKrN/nZp98Srsvi9japra3hbsYHd/TPLSywVjlgEPOYt8w/xHWRZBJte1sV0kvawHSGXndUaWmStExnxcqOhu/1MItULG66Z7itS6zam6z2XyPBZIN2Jl21cgG4DHxIdUrNH8OTecLVXom7LmdrNVZu3CBd3Tp2S/qDbo1PJxJ88uLFiWvRTgcsb5eEO4npXizcmtK+8i1ydE/psOWockW46Z7hP3gvP7R/ji7e32gnXUf+10AS+IoMJgS8rKqXJvyehg/yvQo2Z+gHvIYlrsrCzg4ZxxlrnshBt8Z/8uJFzxc6H6CKpFJoc/wbzLqJ6f57rnd6nI0d9jnFu70ZGbYcRfuBfpslzlsbrLqvEWe81+akq1ZG21TZOHE2LsV4B6cbzLrhmOuyuFsnU3UQj836N98a7ydttcG2oD9eT7oTm26Qt9SlTZIU/rZXds2FztBzRbihy9yWJc7LBivuD0YO9FDd2Wn4q6RbOAdep56emCoLu7tkq1VkzJA8UapILIH2xwvMaQc5DNorZ33ugphb86Ojj8WP9S3ckjO81Sqz0n+NGEdvBGeC/BTL9yrEZJmeTj98bFUW6nVyjhPuAL+Pdtpg23DMoOX7dePT70+UO33OepuNfChzoTN6+lj8yD3LLVnm/DDQD2OC/BSzUIqxNpXu9H6IBwG+R67qIL3prOaYll4sQS2/yNxulWRntI+wbZ9vzz9Iw+3T0jgp8Wcf/S4p2iOM0TPCqXdfoB/GBPkpV9IqFR7x/ftaqizs7ZFzHKyIBLhrWdSzRZxMkY3EHFV7cNorc6ss9xus7G6yWFs/8kLjNO7qPMi2Gydl+xPk5kLn6dA7YpGhCfJjXHvp61x55kVulR3OLy9w9cnHeOID7wm6rJHN9Rxicta39oqlynyjwZzjYHXDPzKvmcmxlSmykSqwEcvQP2AJniKs21nWi1lSxVVW2jXObd0h03p4go/fG2Ydptzts+xTn7we0cHcxuhMkB/h2ktf52Offo7GcArMzXWHj336OYDIhLkFlGJNNruT3RYsqiw0msw5lVAHeDeRZDtbpJIucjc2R8sa7yXewuZ6coHrb1lgwW2xurfJ8tZdrOEgio51Mv3/er9PR+MkfGiv7HJyu/AZwTBBfoQrz7x4L8T3NVodrjzzYmSCHGDerbLpcR9qUWW+2STvOFid8M0+dW2b3UyBarbIeiJPzfLveoBjpXDmVonPneNcZ5tz23foWie3T9C2m2DJh/bKjpo9yE87E+RHuFU+eF+Pw54Pq1yvStx+hK47+l2Jokqp1aLgOFjto5c+nbRGdo6tTIlyIs9mLIM75a1Zu1jcSJS4sVQi1dzFSq3Rlenv+17u9lmasL0yGLac9P8OIyNUTJAf4fzyAjfXHw7t88vRmospIszbLcru8WdmAhSbTQqOgx2SAO8kU2xni2ymCqzH52hLcC/bVmsOWu8klZp+oO/0e3SJE8f7WXljwmHLRjSYID/C1Scfe6BHDpBJJbj65GMBVuVNyXUoH9VeUaXUbg8CvDWdTZtG1bdtdoerS9YTeXas8E21uRfo6R2s5Bpdmc409W03zqLlPcjrZtjyTDBBfoT9PniUV63sy/VrJK1ztA9orxRbbQpOhVhAAa4iNDI5tjLzlJNzbMayaER6Aa1mHprvIpXexkq9Thd/A32zqyxO8D5WlzlzRj4DTJAf44kPvCeSwX2Qkt1g3X1jBUOh3aboOMQ8bB41qXYyxU62xEaqwHo8RyfAdokfWs0CNAukM9tI0r9A3+p16SVjxPC2Ushc6JwN0f7pMcZSch3WyZFvdyhVq8QaJ7fzcD8WYzdbopLKczeZpx7Cdokfmo0CNAqk09uQXKMnk69y2XYTLFjjB/m9YcvR+HBjTMAE+QzJ9nd4m1PGrj18o4vfVIRGNk81U6SczFOxM5Fpl/ihee8MvTYIdLz/m292XRY8vO81rQLq6zRHI6xMkM+YtLSP2UfNu3YqQy1bYiOVZz2WoydmkEGzUYRGcRDoiTV6Mn6gV7tdekmL2JiTk+tiVqzMChPkM8ZO+veT3YvF2M2VBndRxufYsxLH/6UZNQj0AplMDTe5Rp8x2loi7LoJStZ448DqUjBBPiNMkM+YWKIDHkd+qWWxl3mwXWKMQ2g0StAojh3omz2lNOb7pBm2PDtMkM8YO94GyYx8ptZKZ6lli2wkCpTjOXpTvovSq6996Qs8/+zTVMp3WFx+hMcvXea9Fz4cdFmHeCPQ09ktNLFG/5jhu06nRz9hYY/RXjF7kM8OE+QzRlDstE2/cfDWs714nO3cPJXUHOuJPA0Jf7vka1/6Ap/9zFN0WoMwrKy/zmc/8xRAiMMcQGjuzcNeiUx2C/eIQFeBXTdJccT2SlvMsOVZYoJ8BsXSQn/4id61LPayBZxMkXIif2+P7ih5/tmn74X4vk6ryfPPPh3yIN8nNPbmkb3BGfog0B9e21/pQXHE99W6mD3IZ4kJ8hnUT1vcXVxhI5WnHMseuEd3lFTKd8Z6PqwUi8beArJXIp2t4sZfpy9vBHql0+UnExbWCO0VM2x5toSz4WlM1e3sMq/kVrgTy0c+xAEWlw+egHTY82E3CPRF2rWfId79Kexhr9sVqOtoC8rNHuSzxQT5DKq5p2s1w+OXLpNIPdgSSqTSPH7pckAV+UOxaNQfDHSnN9pNVbsavRaZ4Z1prcygqnu6fsj3++DRWbUynv1AF+ap5hxW8z8mdsSOiF2StMyw5Zkiqid/RURENoGbB/zWIlA54XKmwRxHuJjjCBdzHN69VVWX3vxkIEF+GBH5pqr+UtB1TMocR7iY4wgXcxz+Mz1ywzCMiDNBbhiGEXFhC/K/DboAn5jjCBdzHOFijsNnoeqRG4ZhGOML2xm5YRiGMSYT5IZhGBEXqiAXkT8VkW+LyCsi8mURieQ91iLy5yLyveGx/KOIFIOuyQsR+W0R+T8RcUUkFMusxiEiF0Tk+yJyXUSeCroer0Tk8yKyISLfCboWr0RkVUS+KiLfHb6mPhF0TV6JSEpE/ktE/md4LH8ceE1h6pGLSF5Vd4a//l3gXap6KeCyxiYivw78i6r2ROTPAFT1DwIua2wi8k7ABf4G+H1V/WbAJY1MRGzgNeDXgDXgG8BHVPW7gRbmgYj8ClAH/k5VfzroerwQkbPAWVX9lojMAf8NfDii/x8CZFW1LiJx4N+BT6jqy0HVFKoz8v0QH8oS0Y04VfXLqro/9vxlYCXIerxS1VdV9ftB1+HRu4HrqvojVe0A/wA8GnBNnqjqvwLVoOuYhKreVdVvDX+9C7wKnAu2Km90oD58GB9+BZpVoQpyABG5KiK3gSeAPwq6Hh/8DvBS0EXMoHPA7fserxHR4DhtRORtwC8A/xlsJd6JiC0irwAbwFdUNdBjOfEgF5F/FpHvHPD1KICqXlHVVeAa8PGTrm9Uxx3H8M9cAXoMjiWURjkOw/CLiOSAF4Dfe9Mn8EhR1b6q/jyDT9vvFpFAW14nvvuhqr5/xD96Dfgi8KkpluPZccchIh8FPgi8T8N0IeJNxvj/iJrXgdX7Hq8MnzMCMuwnvwBcU9UXg67HD6paE5GvAheAwC5Gh6q1IiJvv+/ho8D3gqplEiJyAbgMfEhVRxuTbvjtG8DbReQnRCQBXAT+KeCaZtbwAuHngFdV9S+CrmcSIrK0vxJNRNIMLqgHmlVhW7XyAvAOBislbgKXVDVyZ1Eich1IAs7wqZcjuvrmt4C/ApaAGvCKqv5GsFWNTkR+E/hLwAY+r6pXAy7JExH5e+BXGWybWgY+paqfC7SoMYnILwP/Bvwv3JtV94eq+sXgqvJGRH4WeI7B68oCnlfVPwm0pjAFuWEYhjG+ULVWDMMwjPGZIDcMw4g4E+SGYRgRZ4LcMAwj4kyQG4ZhRJwJcsMwjIgzQW4YhhFx/w9inoSAeGeLCwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another way by using Decision boundaries plot"
      ],
      "metadata": {
        "id": "ykaR6el491f-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.colors import ListedColormap\n",
        "import seaborn as sns\n",
        "\n",
        "\n",
        "def decision_boundary_plot(x_dec,y_dec,k):\n",
        "  h = .02  # step size in the mesh\n",
        "\n",
        "  # Create color maps\n",
        "  n = len(set(y_dec))\n",
        "  cmap_light = ListedColormap(['pink', 'green', 'cyan','yellow'][:n])\n",
        "  cmap_bold = ['pink', 'darkgreen', 'blue','yellow'][:n]\n",
        "\n",
        "  for weights in ['uniform', 'distance']:\n",
        "      # we create an instance of Neighbours Classifier and fit the data.\n",
        "      clf = KNeighborsClassifier(n_neighbors=k, weights=weights)\n",
        "      clf.fit(x_dec, y_dec)\n",
        "\n",
        "      # Plot the decision boundary. For that, we will assign a color to each\n",
        "      # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
        "      x_min, x_max = x_dec[:, 0].min() - 1, x_dec[:, 0].max() + 1\n",
        "      y_min, y_max = x_dec[:, 1].min() - 1, x_dec[:, 1].max() + 1\n",
        "      xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                          np.arange(y_min, y_max, h))\n",
        "      Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "      # Put the result into a color plot\n",
        "      Z = Z.reshape(xx.shape)\n",
        "      plt.figure(figsize=(8, 6))\n",
        "      plt.contourf(xx, yy, Z, cmap=cmap_light)\n",
        "\n",
        "      # Plot also the training points\n",
        "      sns.scatterplot(x=x_dec[:, 0], y=x_dec[:, 1], hue=y_dec,\n",
        "                      palette=cmap_bold, alpha=1.0, edgecolor=\"black\")\n",
        "      plt.xlim(xx.min(), xx.max())\n",
        "      plt.ylim(yy.min(), yy.max())\n",
        "      plt.title(\"Multi-Classification (k = %i, weights = '%s')\"% (k, weights))\n",
        "\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "mg90dRqAq-8T"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_pts = X[:,:2]\n",
        "y_pts = y\n",
        "decision_boundary_plot(x_pts,y_pts,7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "9cCjZXBYwDsc",
        "outputId": "93cb18f5-2223-4fd8-cba3-3bd0d5392ccd"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gti-Jgu_VBDl"
      },
      "source": [
        "## Section 2.3: Confusion Matrix  \n",
        "In classification problems, a confusion matrix, also known as an error matrix, is a table that allows visualization of the performance of an algorithm, typically a supervised learning one. Each row of the matrix represents the instances in an actual class while each column represents the instances in a predicted class."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPfc8YFBA8Oh"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XMXIM9apA99s",
        "outputId": "e920330a-ce63-4f11-c983-c451e411b58f"
      },
      "source": [
        "# print(confusion_matrix(y_test,predictions))\n",
        "pd.crosstab(y_test, predictions, rownames=['True'], colnames=['Predicted'], margins=True)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Predicted   0   1   2  All\n",
              "True                      \n",
              "0          13   0   0   13\n",
              "1           0  22   0   22\n",
              "2           0   3  12   15\n",
              "All        13  25  12   50"
            ],
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              "    .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",
              "    [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-c223ce75-dc4d-45ff-a65f-190f5c459d33 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-c223ce75-dc4d-45ff-a65f-190f5c459d33');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "5cgYG0E5UHdy",
        "outputId": "37626145-f3f3-4e96-d5b9-ef0b062d7844"
      },
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"viridis\" ,fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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bCewXER8Cngb+s8jxh0XEsIgYnuViWRL5KmBdwfa6tMzMrG5EkzIvJc8VMQtY3aLsjohoTDfvBwaUK/Zic618LV1dBDwg6RaS76KxwCPlCsDMrDZk740iaTwwvqBoYkRMbMfFvghMbWNfAHekXb5/m+W8xR52Ng/6+d90aXZLlijNzHKlHQ870+TansS9iaRvAo3A9W1U+VhELJHUF5gp6cn0Dr9NxSbN+k5HgjQzy6Uu6LUi6QvAscAREa13eIyIJem/KyTdBIwAOpbICy68E/AfwL7AFgUXOzxr8GZmNa/CA30kjSbJpYdGxBtt1NkaaIiIden6UcClpc6d5WHn9cCTwPuB7wDPA3OzhW5mlg8R2ZdSJE0B7gOGSFosaRwwgaTJembatfCKtO6ukm5LD90ZuEfSwyTdvv8WETNKXS/LgKAdI2KSpHMj4h/APyQ5kZtZfSnjXCsRcVIrxZPaqLsUGJOuPwvs397rZUnkG9J/l0n6JLAU6N3eC5mZ1TLleGRnlkT+35K2By4AfgVsB5xf0ajMzLpaPSfyiLg1XV0DHFbZcMzMqqQGZzXMqtiAoF9R5DsqIs6pSERmZtVQp3fkmSZrMTOrC03VDqDjig0IurYrA8mz4UcP48xfnEZDjwamT7qTqT+6udoh5d6yFXDh92DVK4Dgs5+Czx8PP74c7von9OoJA3eF718I2/nFg2XTrX+Xc9y0kqUfuRXR0NDA2RPGcdGY73H6vudz2Ikj2e0DZZsLp9vq0QP+46tw6+9g6uXwh5tg0fNw8HCYdjXccjUMGggT2xrkbO3W3X+XFdmXWuNE3klDRuzF0kXLWf7cCho3NHL31Hs5eGymmSetiL47wr57J+tbbwV77g4vvQwj/wV6pn9H7j80KbPy6Pa/y+WdxrZLOZF3Up/+vXl58Tuz+q5cvJo+/XesYkT1Z8kyeOKZJHEXuvE2+PiB1YmpHvl3Ob+6vNeKpNMi4uo29m2aGnIfPsIA7dGRS1gdef0NOOdiuPBs2Gbrd8qv+H3S/PKpI6sXm9WXWmwyyaoavVa+A7SayAunhjyy4V9z8bGuXLKanQa8c9fSZ0BvVi7xezfKYUMjnHsxfOoTcNQh75TfNB3u/idc/XNQfp9P1Zxu/7tcxiH6Xa0ivVYktfXiCZFMClM3npq7iP6D+7HLoL6sXLKaUSeM5AenXFbtsHIvAr71I9hjd/jCCe+Uz34AJk2B3/0Sttyi7eOt/br973Iubh1bl3Ua228AQ8k+je3OwNHAKy1PB/yz/WHWrqaNTUw4exI/mPFNGno0cPvVd/HCwsXVDiv35j8K0+4Qe+8RHDcuKTvvS/D9X8L69TDugqRs/6Hw7QuqF2c96e6/y/XatNLsepJXEn0SOAM4FSjVV+BWYJuIWNByh6S72xljzZsz/SHmTH+o2mHUlQM+BE/8473/sw49qArBdCPd+ne5zhN5u6exjYhxRfad3N4gzcwqrs4TuaexNbO6l+emlSz9yAunsf06cBWextbM6k2Tsi8lSJosaYWkxwrKekuaKemZ9N8d2jj21LTOM5JOzRJ6yUQeEbdGxJqIeCwiDouIAyJiWpaTm5nlRZmH6F8DjG5RdiFwZ0QMBu5Mt98dg9QbuAQ4kOSly5e0lfALZem1cjWttB5FxBdLHWtmlhtlbFqJiFmSBrUoHguMStevBe4m6RFY6GhgZkSsBpA0k+QLYUqx62VpI7+1YH0L4DiSdnIzs7rRnjbywlHoqYnpgMZido6IZen6clofU9MfeLFge3FaVlSWNwTdULidvh36nlLHmZnlSjsSeeEo9A5dKiKk8j1e7cikWYOBvuUKwMysFqgp+9JBL0nqB5D+u6KVOkuAgQXbA9KyokomcknrJK1tXoC/8t52HTMzK24ayYBK0n9vaaXO7cBRknZIH3IelZYVlaVpxe9fMbP6V8aHnWkT9Cigj6TFJD1Rfgj8SdI44AXgs2nd4cAZEXF6RKyW9F2gedDlpc0PPovJ0mvlzog4olSZmVmelXNAUESc1Mau9+TNiJgHnF6wPRmY3J7rFZuPfAtgK5JvlB1IJrwC2I4MT1HNzHIlxyM7i92Rfxk4D9gVeJB3EvlaYEKF4zIz61r1mMgj4jLgMklnR8SvujAmM7Mu14neKFWXpfthk6T3NW+kT1PPrGBMZmZdrsxD9LtUlkT+pYh4tXkjIl4BvlS5kMzMqiDasdSYLEP0e0hSRASApB7AZpUNy8ysi9Vggs4qSyKfAUyV9Nt0+8tpmZlZ3ajFJpOssiTyb5BMDvOVdHsmcGXFIjIzq4YcJ/Is85E3RcQVEXF8RBwPLATci8XM6koXzLVSMVnuyJH0YeAkkiGlzwE3VjIoM7Mul+M78mIjO/cmSd4nASuBqYAi4rAuis3MrMvUaxv5k8Bs4NiIWAQgye/qNLP6lONEXqyN/DPAMuAuSVdKOoJ3humbmdWXHPcjbzORR8TNEXEisA9wF8m8K30lXS7pqK4K0MysK9T1yM6IeD0i/hARnyJ5W8VD+MUSZlZn6jqRF4qIVyJiouciN7O6U49NK2Zm3UqZErmkIZIWFCxrJZ3Xos4oSWsK6lzcmdAz9SM3M6t35WoyiYingGGwaW6qJcBNrVSdHRHHluOaTuRmZlCpJpMjgP+NiBcqcvaUm1bMzGjfEH1J4yXNK1jGt3HaE4Epbez7qKSHJU2XtG9nYvcdeTc2ZvSJ1Q6h7j09cdtqh2AZtadpJSImAhOLnk/aDPg/wH+2sns+sHtEvCZpDHAzMDh7BO/mO3IzM6hEr5VjgPkR8dJ7LhWxNiJeS9dvA3pJ6tPR0J3IzcygEon8JNpoVpG0iySl6yNIcvGqjobuphUzM8o70EfS1sCRJC/iaS47AyAirgCOB74iqRF4Ezix+S1sHeFEbmYGqKl8mTwiXgd2bFF2RcH6BGBCua7nRG5mBjU5YjMrJ3IzM2pzDpWsnMjNzMB35GZmeec7cjOzvHMiNzPLNzVVO4KOcyI3M8NNK2Zm+dfx8ThV50RuZobvyM3M8s+J3Mws3/yw08ws55zIzczyzg87zczyzQ87zczyzonczCzfyvxiieeBdcBGoDEihrfYL+AyYAzwBvCFiJjf0es5kZuZUd4XS6QOi4iVbew7huRly4OBA4HL0387xO/sNDODSryzs5ixwO8icT/wPkn9OnoyJ3IzM5KmlcyLNF7SvIJlfIvTBXCHpAdb2QfQH3ixYHtxWtYhbloxMwNoR9NKREwEJhap8rGIWCKpLzBT0pMRMauzIbbFd+RmZlDWppWIWJL+uwK4CRjRosoSYGDB9oC0rEOcyM3MaF/TStHzSFtL2rZ5HTgKeKxFtWnA55U4CFgTEcs6GrubVszMKGuvlZ2Bm5IehvQE/hARMySdARARVwC3kXQ9XETS/fC0zlzQidzMDMo2ICgingX2b6X8ioL1AL5anis6kZuZASDPtWJmlnOe/dDMLN98R97NDT96GGf+4jQaejQwfdKdTP3RzdUOqe702qwnP5n8RXr16kmPng3M/p/Hue7yu6odVu79+OOjOXy3PVj15hscdeM1AFw04lCO2G1PNjQ18cLaV/n3WdNZu/7t6gbaFfKbx939sLMaGho4e8I4LhrzPU7f93wOO3Eku31gQLXDqjsb1jfyjS9dw5kn/IYzT/gNww8ezD4f9OfcWX9+5jFOnfGXd5XNXvICR91wNaNvvIbn1qzmzP07PAVIrqgpMi+1xom8k4aM2Iuli5az/LkVNG5o5O6p93Lw2OGlD7R2e+vN9QD07NmDnj0b8vwegJoxZ/liXn37rXeVzV7yPBvTD/ehFcvot/W21Qit60VkX2qMm1Y6qU//3ry8eNWm7ZWLV7PPgYOrGFH9amgQv5pyBrsO7M1fp87hqccWVzukuvfZIftx67NPVTuMLpHnV71V7I5c0j6SjpC0TYvy0ZW6ptW3pqbgqydczr8d/VOG7DeA3ffsW+2Q6tpZww6isSm4adHCaofSNXJ8R16RRC7pHOAW4GzgMUljC3Z/v8hxm2YUWxzPViK0slu5ZDU7Ddhx03afAb1ZuWRVkSOss15f9xYPz32O4SP9l0+lHD94X44YuCfn3nVrtUPpOl07jW1ZVeqO/EvAARHxaWAU8F+Szk33qa2DImJiRAyPiOEDtEeFQiuvp+Yuov/gfuwyqC89e/Vk1AkjuW/avGqHVXe232Ertt52CwA227wnHzloT1587uUqR1WfDh0wiDM+NIJxM2/krY2N1Q6ny6ipKfNSayrVRt4QEa8BRMTzkkYBf5G0O0USeR41bWxiwtmT+MGMb9LQo4Hbr76LFxa67bbcevfZlgu++xl6NAg1iFl3PM6c2U9XO6zc++Vhx/LRfgPZYYstuf+kM/j5g/dy5v4HslmPHlx3zGcBeGjFUr5578wqR9oFai8/Z1apRP6SpGERsQAgIl6TdCwwGfhgha5ZNXOmP8Sc6Q9VO4y69twzL3HWiZdXO4y6c04rTSdTn360CpFUX54HBFWqaeXzwPLCgohojIjPA4dU6JpmZh2X44edFbkjj4g22xYi4t5KXNPMrFNqMEFn5X7kZmbgNnIzs7yrxd4oWXmIvpkZlK2NXNJASXdJWijp8YKu14V1RklaI2lBulzcmdB9R25mBuVsI28ELoiI+em7Ox+UNDMiWg6RnR0Rx5bjgk7kZmZQtjby9CXKy9L1dZKeAPoDFZvrwE0rZmYk/cgzLwXTiaTL+FbPKQ0CPgw80Mruj0p6WNJ0Sft2JnbfkZuZQbuaViJiIjCxWJ10wsAbgPMiYm2L3fOB3dPBkmOAm4EOTx7kO3IzM4CNTdmXEiT1Ikni10fEjS33R8TagmlMbgN6SerT0dCdyM3MoJy9VgRMAp6IiJ+1UWeXtB6SRpDk4g5Pm+qmFTMzKGevlZHA54BHJS1Iyy4CdksuE1cAxwNfkdQIvAmcGNHxAJzIzcwAyvQuzoi4hxKzvEbEBGBCWS6IE7mZWSLyO7LTidzMDDI9xKxVTuRmZuDZD83Mcs+J3Mws55zIzcxyLsfT2DqRm5mB78jNzHLPvVbMzPIt3I/czCznyjSysxqcyM3MwG3kZma5514rZmY55ztyM7N8i40bqx1ChzmRm5mBH3aameVejrsf+lVvZmZANEXmpRRJoyU9JWmRpAtb2b+5pKnp/gckDepM7E7kZmaQ3JFnXYqQ1AP4NXAMMBQ4SdLQFtXGAa9ExF7Az4EfdSZ0J3IzM5KHnVmXEkYAiyLi2YhYD/wRGNuizljg2nT9L8ARzS9j7oiabSOf2fTnDv9Q1SJpfERMrHYc9cyfceV118+4PTlH0nhgfEHRxILPrD/wYsG+xcCBLU6xqU5ENEpaA+wIrGxv3OA78nIbX7qKdZI/48rzZ1xCREyMiOEFS1W/+JzIzczKawkwsGB7QFrWah1JPYHtgVUdvaATuZlZec0FBkt6v6TNgBOBaS3qTANOTdePB/4e0fGhpTXbRp5T3a5dsQr8GVeeP+NOSNu8zwJuB3oAkyPicUmXAvMiYhowCfi9pEXAapJk32HqxJeAmZnVADetmJnlnBO5mVnOOZGXQanhuNZ5kiZLWiHpsWrHUq8kDZR0l6SFkh6XdG61Y7Js3EbeSelw3KeBI0k6/s8FToqIhVUNrM5IOgR4DfhdROxX7XjqkaR+QL+ImC9pW+BB4NP+Xa59viPvvCzDca2TImIWydN9q5CIWBYR89P1dcATJCMQrcY5kXdea8Nx/ctvuZbOxvdh4IHqRmJZOJGb2btI2ga4ATgvItZWOx4rzYm887IMxzXLBUm9SJL49RFxY7XjsWycyDsvy3Bcs5qXTqM6CXgiIn5W7XgsOyfyToqIRqB5OO4TwJ8i4vHqRlV/JE0B7gOGSFosaVy1Y6pDI4HPAYdLWpAuY6odlJXm7odmZjnnO3Izs5xzIjczyzkncjOznHMiNzPLOSdyM7OccyK3oiRtTLuhPSbpz5K26sS5rpF0fLp+laShReqOknRwB67xvKQ+Wctb1Hmtndf6tqSvtzdGs3JzIrdS3oyIYemMg+uBMwp3pi+ObbeIOL3ErHqjgHYncrPuyInc2mM2sFd6tzxb0jRgoaQekn4saa6kR2HinNUAAAKYSURBVCR9GZKRgpImpHO1/w/Qt/lEku6WNDxdHy1pvqSHJd2ZTth0BnB++tfAxyXtJOmG9BpzJY1Mj91R0h3p/NlXASr1Q0i6WdKD6THjW+z7eVp+p6Sd0rI9Jc1Ij5ktaZ9yfJhm5eKXL1sm6Z33McCMtOgjwH4R8VyaDNdExL9I2hy4V9IdJLPnDQGGAjsDC4HJLc67E3AlcEh6rt4RsVrSFcBrEfGTtN4fgJ9HxD2SdiMZSfsB4BLgnoi4VNIngSwjPr+YXmNLYK6kGyJiFbA1yctxz5d0cXrus0heRnxGRDwj6UDgN8DhHfgYzSrCidxK2VLSgnR9NslcHAcDcyLiubT8KOBDze3fwPbAYOAQYEpEbASWSvp7K+c/CJjVfK6IaGvO8U8AQ5PpQADYLp2l7xDgM+mxf5P0Soaf6RxJx6XrA9NYVwFNwNS0/DrgxvQaBwN/Lrj25hmuYdZlnMitlDcjYlhhQZrQXi8sAs6OiNtb1CvnPB0NwEER8VYrsWQmaRTJl8JHI+INSXcDW7RRPdLrvtryMzCrJW4jt3K4HfhKOgUqkvaWtDUwCzghbUPvBxzWyrH3A4dIen96bO+0fB2wbUG9O4CzmzckNSfWWcDJadkxwA4lYt0eeCVN4vuQ/EXQrAFo/qviZJImm7XAc5L+Nb2GJO1f4hpmXcqJ3MrhKpL27/lKXo78W5K/9m4Cnkn3/Y5k9sJ3iYiXgfEkzRgP807Txl+B45ofdgLnAMPTh6kLeaf3zHdIvggeJ2li+X8lYp0B9JT0BPBDki+SZq8DI9Kf4XDg0rT8FGBcGt/j+FV+VmM8+6GZWc75jtzMLOecyM3Mcs6J3Mws55zIzcxyzonczCznnMjNzHLOidzMLOf+PzNfHoZbplMQAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TTPQOPStVtmI"
      },
      "source": [
        "## Section 2.4: Classification Report\n",
        "\n",
        "Precision, Recall, and F1-Score are other metrics besides accuracy that one might look for in an algorithm.  Depending on the use-case, one might consider one metric more important than the other.  \n",
        "\n",
        "Note: *T-> True, F->False, P->Positive, N->Negative*\n",
        "    \n",
        "Mathematically, Accuracy is :  \n",
        "\n",
        "$Accuracy = \\frac{TP+TN}{TP+TN+FP+FN}$  \n",
        "\n",
        "**Precision**: The accuracy of positive predictions\n",
        "\n",
        "\n",
        "$Precision = \\frac{TP}{TP+FP}$ \n",
        "\n",
        "**Recall**:Fraction of positives that were correctly identified\n",
        "\n",
        "\n",
        "$Recall = \\frac{TP}{TP+FN}$\n",
        "\n",
        "\n",
        "**F1-score**: Harmonic mean of precision and recall  \n",
        "\n",
        "\n",
        "$F1 = \\frac{2*Precision*Recall}{Precision+Recall} = \\frac{2*TP}{2*TP+FP+FN}$  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VH3KEfEYW190",
        "outputId": "b5c460f5-9f69-4458-f9ac-4559d71e9d5c"
      },
      "source": [
        "#import classification_report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        13\n",
            "           1       0.88      1.00      0.94        22\n",
            "           2       1.00      0.80      0.89        15\n",
            "\n",
            "    accuracy                           0.94        50\n",
            "   macro avg       0.96      0.93      0.94        50\n",
            "weighted avg       0.95      0.94      0.94        50\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtYNFoJh0LU-"
      },
      "source": [
        "### **Car Evaluation Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RsBukCMi4UjJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "aa1b9362-0117-436d-dfa5-1e513cb0d0d3"
      },
      "source": [
        "# Upload the Car evaluation data CSV file that has been shared with you.\n",
        "# Run this cell, click on the 'Choose files' button and upload the file.\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d3f961cc-83a7-4218-b820-c36dd9517bed\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d3f961cc-83a7-4218-b820-c36dd9517bed\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving car_evaluation.csv to car_evaluation.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0T5yvzVH4jrQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "4f12c303-9280-4960-ba0e-0673bc3ae26c"
      },
      "source": [
        "car_df = pd.read_csv('car_evaluation.csv')\n",
        "car_df"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      vhigh vhigh.1      2   2.1  small   low  unacc\n",
              "0     vhigh   vhigh      2     2  small   med  unacc\n",
              "1     vhigh   vhigh      2     2  small  high  unacc\n",
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              "...     ...     ...    ...   ...    ...   ...    ...\n",
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          "metadata": {},
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    },
    {
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        "outputId": "5e2fe8c8-842b-4eb7-f3b1-b7e6d65505de"
      },
      "source": [
        "for x in car_df.columns:\n",
        "  # print(x)\n",
        "  le = preprocessing.LabelEncoder()\n",
        "  le.fit(car_df[x])\n",
        "  car_df[x]=le.transform(car_df[x])\n",
        "\n",
        "car_df"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      vhigh  vhigh.1  2  2.1  small  low  unacc\n",
              "0         3        3  0    0      2    2      2\n",
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              "...     ...      ... ..  ...    ...  ...    ...\n",
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              "\n",
              "[1727 rows x 7 columns]"
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              "\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",
              "    [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-85ecf10e-4a42-4c2b-b895-a8ce3aba6dd3 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-85ecf10e-4a42-4c2b-b895-a8ce3aba6dd3');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5Klx4yMCgKa"
      },
      "source": [
        "dataset = car_df.values\n",
        "X = dataset[:,0:6]\n",
        "y = np.array(dataset[:,6])\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=19)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1HL5ufCHDANh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3cecc08-1286-4601-90a4-a47f4d2ece12"
      },
      "source": [
        "knn = KNeighborsClassifier(n_neighbors=5)\n",
        "knn.fit(X_train, y_train)\n",
        "predictions = knn.predict(X_test)\n",
        "score = accuracy_score(y_test, predictions)\n",
        "print(\"The accuracy of the classifier on Car evaluation dataset is {:.2f} %\".format(100*score))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the classifier on Car evaluation dataset is 89.88 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYCimUC_A56C"
      },
      "source": [
        "## TASK-3\n",
        "## Plot a Confusion Matrix for the results of the Car evaluation dataset"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, predictions)\n",
        "p = sns.heatmap(pd.DataFrame(cnf_matrix),annot=True,cmap=\"viridis\",fmt='g')\n",
        "plt.title('Confusion matrix', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "DWztPzlSruuF",
        "outputId": "33d71773-3124-489d-97a6-adb4ef8a9b3c"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 15.0, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ST89yx1r2og",
        "outputId": "b66670a3-0200-4a49-d3df-de131c2c0fba"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 56,   2,  14,   0],\n",
              "       [  9,   6,   0,   0],\n",
              "       [  3,   0, 239,   0],\n",
              "       [  5,   0,   2,  10]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a8TRknp0XTqJ"
      },
      "source": [
        "## TASK-4\n",
        "## Print a Classification Report for the results of the Car evaluation dataset"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#import classification_report\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUL--bwVr6aY",
        "outputId": "3d3d6b9b-dc55-4cd5-ae18-114649258357"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.78      0.77        72\n",
            "           1       0.75      0.40      0.52        15\n",
            "           2       0.94      0.99      0.96       242\n",
            "           3       1.00      0.59      0.74        17\n",
            "\n",
            "    accuracy                           0.90       346\n",
            "   macro avg       0.86      0.69      0.75       346\n",
            "weighted avg       0.90      0.90      0.89       346\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xr9dI1Kqsprz"
      },
      "source": [
        "### Questions to Think About and Answer\n",
        "1. In the section on Decision boundaries, you must have seen that we ran the KNN algorithm twice: first with the _weights_ set to 'uniform' and then set to 'distance'. Find out the difference between these two.  \n",
        "2. What do you think could be the drawbacks of using KNN ?"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Answer for Question 1**\n",
        "\n",
        "Uniform : All the points in each neighbourhood are weighted equally. \n",
        "\n",
        "Distance : Weight points by the inverse of their distance. \n",
        "\n",
        "In this case, closer neighbours of a query point will have a greater influence than neighbours which are further away. With uniform weighting, you do not take into account the distance between the new data point and its k-nearest neighbours. With inverse distance weighting, you assign higher weights to the closer training."
      ],
      "metadata": {
        "id": "t4DhmoRVsZZs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Answer for Question 2**\n",
        "\n",
        "1. Accuracy depends on the quality of the data\n",
        "2. With large data, the prediction stage might be slow\n",
        "3. Sensitive to the scale of the data and irrelevant features\n",
        "4. Require high memory – need to store all of the training data\n",
        "5. Given that it stores all of the training, it can be computationally expensive"
      ],
      "metadata": {
        "id": "vheJzsgwtU2Y"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TRlrn4EctZrC"
      },
      "source": [
        "### Useful Resources for further reading\n",
        "1. Interactive KNN visualization, with class boundaries: http://vision.stanford.edu/teaching/cs231n-demos/knn/  \n"
      ]
    }
  ]
}