Freelancer/AI_Assigment.ipynb

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  "metadata": {
    "colab": {
      "name": "AI_Assigment.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9pgvwHSmTbRV"
      },
      "source": [
        "# **1- Problem 1**\r\n",
        "\r\n",
        "Problem 1: For the data given, find a decision boundary that separates the two classes using the following models (Use the same initial condition for those two models):\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TszWUX0Yk0K3"
      },
      "source": [
        "**Data Pre-processing**\r\n",
        "- First we make list of each data\r\n",
        "- Then convert each list in np array "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8GneDCCZf6a"
      },
      "source": [
        "list1=[1,-1]\r\n",
        "list2=[1,-2]\r\n",
        "list3=[2,-1]\r\n",
        "list4=[2,-2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOEV7r6oZmNL"
      },
      "source": [
        "import numpy as np\r\n",
        "vector1 = np.array(list1) \r\n",
        "vector2 = np.array(list2) \r\n",
        "vector3 = np.array(list3) \r\n",
        "vector4 = np.array(list4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mljCmMFqlLxW"
      },
      "source": [
        "Then we combine the above Vectors in in one vector "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mYVrkraAZw9h"
      },
      "source": [
        "X1 = np.concatenate((vector1, vector2,vector3,vector4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qj2Bekz8yNeN",
        "outputId": "1fa8fd14-16c5-4d46-ffd7-11408c6c535f"
      },
      "source": [
        "X1"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1, -1,  1, -2,  2, -1,  2, -2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 259
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NT14IuCBxRdU",
        "outputId": "f80e9f64-7395-4426-f0a7-0a67d13e301a"
      },
      "source": [
        "X1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 218
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzUxiLWxlWBD"
      },
      "source": [
        "Using below Code we Covert Data in two-dimension "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tL64yK6iwWsO"
      },
      "source": [
        "X1test=X1.reshape(8,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMQw2veah787",
        "outputId": "79bf1ffe-9def-47b1-88f1-58f31546315f"
      },
      "source": [
        "X1test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 221
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RZrShR5yiJD0"
      },
      "source": [
        "X1test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bReWpKvPlgpS"
      },
      "source": [
        "using below Code we Make class for above Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6Rz4oq7Z2pp"
      },
      "source": [
        "Y1list=[0,0,0,0,0,0,0,0]\r\n",
        "Y1=np.array(Y1list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "046SyigFw66V",
        "outputId": "04fb7471-0234-4818-f3c3-4c25f920d851"
      },
      "source": [
        "Y1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 262
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jY-nos08lpu_"
      },
      "source": [
        "The savem detaisl is applied for second type data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ryFbSEsNZ53j"
      },
      "source": [
        "list5=[-1,0]\r\n",
        "list6=[-1,1]\r\n",
        "list7=[-2,0]\r\n",
        "list8=[-2,1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XtdiEGsMZ-oh"
      },
      "source": [
        "import numpy as np\r\n",
        "vector5 = np.array(list5) \r\n",
        "vector6 = np.array(list6) \r\n",
        "vector7 = np.array(list7) \r\n",
        "vector8 = np.array(list8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "87X52KH-aIQh"
      },
      "source": [
        "X2 = np.concatenate((vector5, vector6,vector7,vector8))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6G_T3WUyG9N",
        "outputId": "fdba0377-9d35-4ef0-b6a2-738ed4bf4321"
      },
      "source": [
        "X2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1,  0, -1,  1, -2,  0, -2,  1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 266
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8IaTJVGbva8Q",
        "outputId": "5e7bdecd-e3dc-4838-aa79-9b20c98a2888"
      },
      "source": [
        "X2.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 267
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DU2YV9AKijED"
      },
      "source": [
        "X2test=X2.reshape(8,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1Sbh6wxiuIa",
        "outputId": "2b8dffe5-bbc5-46a6-9e9f-f64bd4063150"
      },
      "source": [
        "X2test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esM07gRraNPz"
      },
      "source": [
        "Y2list=[1,1,1,1,1,1,1,1]\r\n",
        "Y2=np.array(Y2list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "azt9mTWvl8h6"
      },
      "source": [
        "In this section we combine two type input X and Y "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hsmz57b7aWn5"
      },
      "source": [
        "X1 = np.concatenate((X2test, X1test))\r\n",
        "Y1 = np.concatenate((Y1, Y2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSFaY6AWjU6b",
        "outputId": "ddef6f96-db03-4dee-abfe-07e6b29cbc62"
      },
      "source": [
        "X1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkMZAEBpsd9y",
        "outputId": "01f574fa-1abc-45b3-fbca-d17ae7452250"
      },
      "source": [
        "Y1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 251
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dl9t9HlEeSxW"
      },
      "source": [
        "#from sklearn.datasets import make_classification\r\n",
        "#X, y = make_classification(n_samples=200, n_features=2, n_informative=2, n_redundant=0, n_classes=2, random_state=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXVrLkIMmEFS"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-tNvPiNhmFV1"
      },
      "source": [
        "**Data Normalization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hk7MQhgRly6K"
      },
      "source": [
        "In section We convert the input data between 0-1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4kFKDtic5Fa"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "sc_X = StandardScaler()\r\n",
        "#sc_y = StandardScaler()\r\n",
        "Xnew = sc_X.fit_transform(X1)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkviXSb9gqDG"
      },
      "source": [
        "Y2.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_l6lBOPymQwc"
      },
      "source": [
        "**Applied Machine learning Models**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECLvsKRea-zu"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\r\n",
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "from sklearn.svm import SVC\r\n",
        "from sklearn.naive_bayes import GaussianNB \r\n",
        "from sklearn.neural_network import MLPClassifier\r\n",
        "from sklearn.linear_model import Perceptron\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNegeht2bC2E"
      },
      "source": [
        "# Initializing Classifiers\r\n",
        "clf1 = LogisticRegression()\r\n",
        "clf2 = DecisionTreeClassifier()\r\n",
        "clf3 = RandomForestClassifier()\r\n",
        "clf4 = SVC(gamma='auto')\r\n",
        "clf7 = GaussianNB()\r\n",
        "clf5=Perceptron()\r\n",
        "clf6 = MLPClassifier()\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "4tD4zUojbKXx",
        "outputId": "99dd4bad-eeca-445c-fb85-71191ef63f23"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "from mlxtend.plotting import plot_decision_regions\r\n",
        "import matplotlib.gridspec as gridspec\r\n",
        "%matplotlib inline  \r\n",
        " \r\n",
        "gs = gridspec.GridSpec(3, 2)\r\n",
        " \r\n",
        "fig = plt.figure(figsize=(14,10))\r\n",
        " \r\n",
        "labels = ['Logistic Regression', 'Decision Tree', 'Random Forest', 'SVM', 'Perceptron', 'Neural Network']\r\n",
        "for clf, lab, grd in zip([clf1, clf2, clf3, clf4, clf5, clf6, clf7],\r\n",
        "                         labels,\r\n",
        "                         [(0,0), (0,1), (1,0), (1,1), (2,0), (2,1)]):\r\n",
        " \r\n",
        "    clf.fit(Xnew, Y1)\r\n",
        "    ax = plt.subplot(gs[grd[0], grd[1]])\r\n",
        "    fig = plot_decision_regions(X=X1, y=Y1, clf=clf, legend=2)\r\n",
        "    plt.title(lab)\r\n",
        " \r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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smcvC4WMYNqyX3mHBf/3o1tLlNdXAXLNvCbFwDmdc+kuGDetlg2HJvKtOK11eU40++BMs6o9llnfyXPv7lrDBknksGj66Y9/XAfdPfZID33MW1579XnaeuGXpcqSW6sY+1U1zHayT59oN72ujqVOncuZ3/pe37b+7vaqBe0xWYkl/MumkC9h02x3YZfNhbLrNDkw66YLlrizWd41zHbdxsOk2O7DDu7+53D/g13eL+oMd3v3NZb46ea6Nv8OdOtcBH/nyZWzW+xwfOvt7pUuRWq5b+1S3zHXwVyfPtRve10bn/teZbDRsib1qEIPJKiyaNY2RM+7h3MNGM3LGPSya9Vjpklpm0axpbDzjHg7ZZQM2nnFvR8+1mzw/6zE2nnEv5x42uuPf1/unPsnd9z3E+W8cyd33PcQD054qXZLUct3Wp7plrt2k297XJ/70MFPvu4s3/vlwe9UgBpNVePbGc5myWy+7ju9lym69PHvjOaVLapmBuW45chhTdutl3g3nli5JTTD/hup9nVz/Dnfy+/qRL1/GlMm9/MVWw5kyudctUeoK3dinumGu3aTb3tcrzzmNKZN7GD+qx141iMFkJfr7ljByxj2cuPsIAE7cfQQjZ9xDf/+SwpU13+C5nrD7CDaecW9HzrWb9PctYeMZ93JCF7yvA3tL3rVHNdd37THCLVHqeN3cpzp5rt2k297XJ/70MDPvv4137rERYK8azGCyEhv1L6j2IIzqAWDLUT1M2a2XjfoWFK6s+QbPdXw91w37ni1cmYZiw/5nmbJbL+O74H0d2FsyoZ7rBLdEqQt0c5/q5Ll2k257X6885zSOmtzLVvaq5fKqXCvRk32cf0cf59+xcNDyQgW1UONc5y9Kzqvn3Ilz3WBY8vDZxy53eafpyT6+cUcf3+iC3+E775/GbYsWc96ds5daPnyDaYUqklqvW/vU0ssLFdRCvcOCqV86ZrnLO003va8A0+6/m/MXL+L8Oxcxb2EfX/vNIsBeNSAy1/6d/9rPHu7QX5vutVXvfO4478N88viDS5ciaXWM3xV2/OvO+2ulSexTktrRbhs+wU++enp3/L21ySR42WGr1ac8lEuSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVF5m59k+OOCEzv9rEetqWc+1MzrUzddNcofvmuya66WfjXDuTc+1c3TTf1Z3rUPeYnDDE569PnGtncq6dqZvmCt033zXRTT8b59qZnGvn6qb5rtZcPZRLkiRJUnEGE0mSJEnFDTWYdMVxcTXn2pmca2fqprlC9813TXTTz8a5dibn2rm6ab6rNdchnfwuSZIkSc3goVySJEmSijOYSJIkSSpuSMEkIj4dEb+LiN9GxLURsXWzCms3EfH5iLivnu8VEbFJ6ZpaKSLeEhH3RkR/ROxeup5WiIgDI+L+iHgwIj5Sup5WiYhvRMRTEXFP6VpaLSImRsQNEfH7+vf3PaVrapWIGBERt0XEXfVcTytdUzvqpj4F3dWr7FOdwz7VmdamTw31AxbHZObc+vuTgZdl5jvXesA2FhH7A9dn5pKI+CxAZn64cFktExEvBfqBc4EPZObthUtqqojoAf4PeAPwKPBr4KjM/H3RwlogIvYC5gMXZObk0vW0UkRMACZk5m8iYjRwB/DGDn1fAxiZmfMjYjjwc+A9mXlL4dLaSjf1KeiuXmWf6hz2KfvUgCHtMRlY2ddGAh17Jn1mXpuZS+qbtwDblqyn1TLzD5l5f+k6WmgP4MHMfDgzFwHfAQ4vXFNLZObPgKdL17EuZOYTmfmb+vt5wB+AbcpW1RpZmV/fHF5/dew6eG11U5+C7upV9qnOYZ+yTw0Y8jkmEXFGREwD3gZ8fKjjrSfeAVxduggNyTbAtIbbj9KhK4ZuFRHbA/8PuLVsJa0TET0R8VvgKeC6zOzYuQ5Fl/YpsFet7+xTHc4+taxVBpOI+GlE3LOcr8MBMvPUzJwIXAyc1IxJlLKqudaPORVYQjXf9drqzFdaH0XEKOBy4L2Dtph3lMzsy8xXUG0V3yMiOvoQiBXppj4F3dWr7FPqVPap5etdjQFfv5qvfTFwFfCJ1Xx821nVXCPiWOAQYL/sgA+AWYP3thM9BkxsuL1tvUzrufo41suBizPz+6XrWRcyc3ZE3AAcCHT8yaODdVOfgu7qVfYp+1Qnsk+tuE8N9apcOzfcPBy4byjjtbOIOBD4EHBYZj5buh4N2a+BnSPiJRGxAXAk8KPCNWmI6hPtzgP+kJlfLF1PK0XEFgNXXIqIjahOkO3YdfDa6qY+BfaqDmOf6kD2qZWvg4d6Va7LgV2orooxFXhnZnZkmo+IB4ENgVn1ols6/MouRwBnA1sAs4HfZuYBZatqrog4GDgT6AG+kZlnFC6pJSLiEmAfYBzwJPCJzDyvaFEtEhF7AjcDd1OtlwBOycyrylXVGhHxF8C3qH5/hwHfzcxPla2q/XRTn4Lu6lX2qc5hn7JPvfCc9XwvryRJkqQO4Ce/a70VEZ+MiItK1yFJkqShM5ioqSLijxHxXETMj4jpEfHN+soT662I2CeqTxae3/D143X4+ttHREbEKi9WIUlqfxGxZ0T8MiLmRMTTEfGLiHhdRCxYXs+MiDsj4qSGfnDnoPvHRcSiiPjjOpuE1AIGE7XCoZk5CngF1fW5P1q4nmZ4PDNHNXwduqYDRPUpvpKkLhYRY4Arqc6P2Yzqs0lOA+ZQfVbJ3w56/GTgZcAlDYs3HnTZ1SnAIy0sW1onDCZqmcycDlxDFVAAiIiPRMRDETEvIn5fn7w4cN+xEfHziPhCRDwTEY9ExEEN978kIm6qn3sd1UlyNNx/WETcGxGzI+LGiHhpw31/jIgPRsTv6i1S50XE+Ii4uh7vpxGx6ZrOMSJeWr/W7Pq1D2u475sRcU5EXBURC4B9I2LriLg8ImbU8zu54fF7RMTtETE3Ip6MiIGrdfys/nd2vbfmtWtapySpbfwZQGZeUn/Gw3OZeW1m/o7qROFjBj3+GOCqzJzVsOxC4O8HPeaCVhYtrQsGE7VMRGwLHAQ82LD4IeB1wFiqLUQXRcSEhvtfDdxPFTo+B5xXX1oP4NvAHfV9n6ZhpRwRf0a1Nem9VFdouQr4cX2JxQFvprpU3Z8Bh1J9IvIp9eOHASezBqK6DvmPgWuBLYF3AxdHxC4ND5sCnAGMBn5ZP/4uqi1k+wHvjYiBq8icBZyVmWOAHYHv1sv3qv/dpN5b86s1qVOS1Fb+D+iLiG9FxEGDNopdCOwVERMBImIYVR/51qAxLgKOjOpTtV8GjKKDPz1c3cNgolb4QUTMA6YBT9HwYWaZ+b3MfDwz+zPzUuABYI+G507NzK9lZh/VingCMD4itgNeBfxrZj6fmT+j+iN/wFuB/8nM6zJzMfAFYCPgrxoec3ZmPllfKvRm4NbMvDMzFwJXUB12tiJb13tFBr7+DngNVTP498xclJnXU+2eP6rheT/MzF9kZj+wG7BFZn6qfvzDwNeork0PsBjYKSLGZeb8zLxlpT9lSdJ6p/6U7z2BpOoBMyLiRxExPjOnATcCb68fvh/V5Z//Z9Awj1JtxHs91d6SC9dB6VLLGUzUCm/MzNFU1yT/cxoOuYqIYyLitwN/4AOTWfqQrOkD3zR8ONgoYGvgmcxc0PDYqQ3fb914uw4C06j2TAx4suH755Zze2Un6T+emZs0fH23fs1p9Ws11tT4mtMavp/EoIBDtcdmfH3/8VR7c+6LiF9HxCErqUeStJ7KzD9k5rGZuS1VH9ya6vNKoNooNxBM3g58p97gNtgFwLFUG8MMJuoIBhO1TGbeBHyTau8FETGJauvQScDmmbkJcA8QKxqjwRPAphExsmHZdg3fP071hz/1awUwEWjlB6k9Dkysd7U31tT4mo0fFDQNeGRQwBmdmQcDZOYDmXkU1WFhnwUuq+frhw1JUofKzPuoeuXAyezfB7aNiH2BN7HsYVwDLgf+Bng4M//U6jqldcFgolY7E3hDRLwcGPgjewZARBzHiyvilcrMqcDtwGkRsUFUn5zaeGWs7wJ/ExH71ed+vB94nuq8jla5FXgW+FBEDI+IfeqavrOCx98GzIuID0fERvWxwZMj4lUAEXF0RGxR74GZXT+nn+rn1Q/s0MK5SJLWgYj484h4f30eJvX5JEcBtwDURwZcBpxPdXjz7csbp37cXwP/sE4Kl9YBg4laKjNnUO1u/nhm/h74D+BXVIdR7Qb8Yg2Gm0J1cvzTVOetvHAFksy8Hzia6vKLM6kCwqGZuagJ01iueuxDqU7wnwl8BTim3vq1vMf3AYdQXaXskfo5X6e6EADAgcC9ETGf6kT4I+urtTxLdQL9L+pDwF7TqjlJklpuHlUvu7W+YuMtVEcPvL/hMd+iOgpgpVfayszbM/OhVhUqrWuR6VEikiRJkspyj4kkSZKk4gwmkiRJkoozmEiSJEkqzmAiSZIkqbjeIT371nM9c77DzO/ZhNN/1c+f/6UXfuo0m9xzMW98xRaly1CzbflSeMleq/NZQF3pm794xD4lrSc2uud7vPUVY1f9QK1fNtkOdjlotfrU0IJJX8uuxKpCkue59zd3sOPLX126FDXZL27/HW/c7XWly1Cz9S8pXUFbW9xnLpHWF7+9/be8dbfXli5Dzda3eLUf6qFckiRJkoozmEiSJEkqzmAiSZIkqbihnWOyHP0EC3o2o693BNCO52MmPUsWMrLvaYbhsceS1G2CZOzwfkb0QET79anMZGEfzFk8jGzLPipJrdH0YLKgZzOGj9qEUdFHG67vyYTncwQL5sPovlmly5EkrWNjh/ezycgR9Ecv7dqoRuQSWLCQ2Yt7SlcjSetM0w/l6usdwYZtGkqg6kEbRl+9R0eS1G1G9NC+oQQggv7oZYSZRFKXacE5JtG26/oBVX1tXqQkqSUion1DyYCItjzMTJJayZPfJUmSJBXXkcHkJzffwS4Hv4udDjiBf//aZaXLkSRpKbf//HqOP3RPjjv4tVz69bNLlyNJbaHpJ7+viT2OPpWZc55bZvm4sRtx20VnrNWYfX19/PPp53Ld1z/FtuM351VvfT+H7bsHL9tpu6GWK0nqMu855gjmzJ27zPKxY8Zw1gVXrNWYfX19fPmMU/jMVy9l3FYTOPnIg3jNvvszacddhlquJK3XigaTmXOeY9cT/3OZ5fee+761HvO2ux9gp+0msMPErQA48qDX8cPrbzWYSJLW2Jy5c9n5hC8ts/yBr5601mPef/edTNhueyZMnATA3gcdzq9uuMZgIqnrddyhXI89OYuJW4174fa2W43jsae8LLAkqT3Memo6W2y1zQu3x42fwKwnpxesSJLaQ8cFE0mSJEnrn44LJtuM35xp02e+cPvR6TPZZsvNC1YkSdKLNt9yK2ZMf+yF2zOffILNx29VsCJJag8dF0xeNXlnHi+L3mcAACAASURBVJj6OI88Op1Fixbznatv5rB9X126LEmSANhl8it4fOojTH/0TyxevIibrv4hr9nngNJlSVJxRU9+Hzd2o+We6D5u7EZrPWZvbw9fOvVEDvjHT9LX3887jng9u+7sie+SpDU3dsyY5Z7oPnbMmLUes6e3l3865TOc+s6j6O/rY/8jjmT7nTzxXZKKBpO1vSTwqhy89+4cvPfuLRlbktQ91vaSwKuyx177scde+7VkbElaX3XcoVySJEmS1j8GE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScV1ZDB5x6lnseWeb2fyYctee16SpNK++K/v4617T+bEI/YpXYoktY22CCYzn5nLm0/6FLNmz23KeMcesR8/+eonmzKWJElznpnFGScfzdzZTzdlvDcc/necfs63mzKWJHWKtggmF3z/Gp557EG+dfk1TRlvr90ns9nYUU0ZS5Kk639wMf2P38X/XnFRU8bbbffXMnrspk0ZS5I6RfFgMvOZuVx53Q2c86bxXHndDU3bayJJUjPMeWYWd153GWe+aVvuvO6ypu01kSQtrXgwueD713DIjsEu40dwyI7RtL0mkiQ1w/U/uJhDd4Kdx2/EoTvRtL0mkqSlFQ0mA3tLjnnlGACOeeUY95pIktrGwN6SKa8cC8CUV451r4kktUjRYDKwt2TcqF4Axo3qda+JJKltDOwt2XzUcKD6170mktQavSVf/Mbb7uLxJ57n23c/sdTyrWfexb8c/5a1HveoD3yeG2+7h5mz57Ltvsdx2klHcfyb9x9quZKkLnP3bTdz8xMLueR3jy61fJMZN3PEcSev9bj/9qF38btf/5K5s5/m6P3+kqP/+QMc+KYpQy1XktZrRYPJj849vSXjXvKFD7ZkXElSd/n4Od9rybgf/dw5LRlXktZnxU9+lyRJkiSDiSRJkqTiWhBMkszmj9pMVX1tXqQkqSUyk/WhUWW71yhJTdb0YNKzZCHPZ0/brvMz4fnsoWfJwtKlSJIKWNgHw3JJ+4aTTIblEhb2lS5Ektatpp/8PrLvaRbMh4W9I4Bo9vBNkPQsmcfIPq9BL0ndaM7iYbBgISN6IKL9+lRmsrCvrlOSukjTg8kwktF9s8AtPZKkNpQEsxf3wOLSlUiSGrk5RpIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxkZlr/+xfnj2EJ7e/0Qd/gkX9QX/fEjZYMo9Fw0czbFgvGwxL5l11WunymqpxriycS/9GYxk2rJfeYcF//ejW0uU11cmHvZol/cv+6nbyXPv7ljBs4RzYaEzH/g4PuH/qkxz4nrO49uz3svPELUuX03rjd4Ud/zpKl9Guvvazhzu6TzX+Hx+xZC4Lh4/p+HV3N811sE6eazf1KeiyXrXJJHjZYavVp9xjshKL+oMd3v1NNt12B3bZfBibbrMDO7z7myzq77y/AV6c646M2zjYdJsdmHTSBctdMa7vlvQnk066YJmvTp7rptvu8ML72qm/wwM+8uXL2Kz3OT509vdKlyK1XOP/8YE+1Q3rs26Zq32qc9mrls9gsgrPz3qMjWfcy7mHjWbjGfeyaNZjpUtqmWqu93DILhswcsY9HT3XbrJo1jRG1u9rp/8O3z/1Se6+7yHOf+NI7r7vIR6Y9lTpkqSWG/g/fu5hozt+3d1Nc+0m3dSnwF61MgaTVZh/w7lM2a2XyeN7mbJbL/NuOLd0SS0zMNctRw5jym69PHvjOaVLUhM8e+PS72sn/w5/5MuXMWVyL3+x1XCmTO51S5S6wsD/8V3rPtXJ6+5umms36aY+BfaqlTGYrER/3xI2nnEvJ+w+AoATdh/BxjPupb9/SeHKmm/wXE/cfQQjZ9zTkXPtJv19Sxg54x5O7ILf4YEtUO/ao5rru/YY4ZYodbzB/8c7ed3dTXPtJt3Up8BetSoGk5XYsP9ZpuzWy/hRPQCMH9XDlN162bDv2cKVNd/guW5Zz3WjvgWFK9NQbNS/oNoK1QW/wwNboCbUc50wqsctUep4g/+Pd/K6u5vm2k26qU+BvWpVeksX0M56so9v3NHHN+5YOGh5oYJaqHGu8xcl59Vz7sS59g4Lpn7pmOUu7zQ92cf5d/Rxfhe8r3feP43bFi3mvDtnL7V8+AbTClUktV7j//GllxcqqIW6aa72qc58X8FetSpeLlhLmdczlin/fQeHHfee0qWoyf7vwo/w+eNeV7oMNZuXC16pTr9csNRJ7r3wY5x53GtLl6Fm83LBkiRJktYnBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUnMFEkiRJUnEGE0mSJEnFGUwkSZIkFWcwkSRJklScwUSSJElScQYTSZIkScUZTCRJkiQVZzCRJEmSVJzBRJIkSVJxBhNJkiRJxfUO6dk9GzSpDLWL6NmQnoDhPVG6FDVbDPP/bCcaNrTVeKdzXSatP8I+1Zl6hq/2QyMz1/p1IuKEzPzqWg+wHnGuncm5dqZumit033zXRDf9bJxrZ3Kunaub5ru6cx3qoVwnDPH56xPn2pmca2fqprlC9813TXTTz8a5dibn2rm6ab6rNVfPMZEkSZJUnMFEkiRJUnFDDSZdcVxczbl2JufambpprtB9810T3fSzca6dybl2rm6a72rNdUgnv0uSJElSM3golyRJkqTiDCaSJEmSihtSMImIT0fE7yLitxFxbURs3azC2k1EfD4i7qvne0VEbFK6plaKiLdExL0R0R8Ru5eupxUi4sCIuD8iHoyIj5Sup1Ui4hsR8VRE3FO6llaLiIkRcUNE/L7+/X1P6ZpaJSJGRMRtEXFXPdfTStfUjrqpT0F39Sr7VOewT3WmtelTQ/2AxTGZObf+/mTgZZn5zrUesI1FxP7A9Zm5JCI+C5CZHy5cVstExEuBfuBc4AOZeXvhkpoqInqA/wPeADwK/Bo4KjN/X7SwFoiIvYD5wAWZObl0Pa0UEROACZn5m4gYDdwBvLFD39cARmbm/IgYDvwceE9m3lK4tLbSTX0KuqtX2ac6h33KPjVgSHtMBlb2tZFAx55Jn5nXZuaS+uYtwLYl62m1zPxDZt5fuo4W2gN4MDMfzsxFwHeAwwvX1BKZ+TPg6dJ1rAuZ+URm/qb+fh7wB2CbslW1Rlbm1zeH118duw5eW93Up6C7epV9qnPYp+xTA4Z8jklEnBER04C3AR8f6njriXcAV5cuQkOyDTCt4fajdOiKoVtFxPbA/wNuLVtJ60RET0T8FngKuC4zO3auQ9GlfQrsVes7+1SHs08ta5XBJCJ+GhH3LOfrcIDMPDUzJwIXAyc1YxKlrGqu9WNOBZZQzXe9tjrzldZHETEKuBx476At5h0lM/sy8xVUW8X3iIiOPgRiRbqpT0F39Sr7lDqVfWr5eldjwNev5mtfDFwFfGI1H992VjXXiDgWOATYLzvgA2DW4L3tRI8BExtub1sv03quPo71cuDizPx+6XrWhcycHRE3AAcCHX/y6GDd1Kegu3qVfco+1YnsUyvuU0O9KtfODTcPB+4bynjtLCIOBD4EHJaZz5auR0P2a2DniHhJRGwAHAn8qHBNGqL6RLvzgD9k5hdL19NKEbHFwBWXImIjqhNkO3YdvLa6qU+BvarD2Kc6kH1q5evgoV6V63JgF6qrYkwF3pmZHZnmI+JBYENgVr3olg6/sssRwNnAFsBs4LeZeUDZqporIg4GzgR6gG9k5hmFS2qJiLgE2AcYBzwJfCIzzytaVItExJ7AzcDdVOslgFMy86pyVbVGRPwF8C2q399hwHcz81Nlq2o/3dSnoLt6lX2qc9in7FMvPGc938srSZIkqQP4ye+SJEnrWER8MiIuKl3HUEXE9hGREbHK85alVTGYqLiI+GNEPBcR8yPiyYj4Zn21iuI6pXFIUrere81TETGyYdk/RMSNBctarojYp/5j/yuDlv+8vrjB6oyREbFTSwqUWsRgonZxaGaOAv4S2B342Oo+MSpFfpdLvrYkaY31AO9p9Ys0ae/BAuDt9WddtCX3kqjZ/INKbaU+KfVqYHJEvCYifhkRsyPirojYZ+BxEXFjVB+a9gvgWWCHiNg1Iq6LiKfrPS+n1I8dFhEfiYiHImJWRHw3Ijar7xvYBX1CRDweEU9ExAfq+w4ETgHeWu/NuWslr/1XEfHriJhT//tXg2r9dET8IiLmRcS1ETFunfxAJUmNPg98YOBKQYNFxJ839JH7I+LvGu67MSL+oeH2sRHx84bbGRH/HBEPAA/Uy86KiGkRMTci7oiI161BrbOBb7KSy1tHxDsi4g8R8UxEXBMRk+rlP6sfclfdv94aETdFxJvr+/+/ut6/qW/vF9WH4A30zI9FxNR6D9MFETG2vm+gZx4fEX8Crl9OTW+u90515ecqaWgMJmorETEROBh4Avgf4HRgM+ADwOURsUXDw98OnACMprqKx0+BnwBbAzsB/1s/7t3AG4G96/ueAb486KX3BXYG9gc+HBGvz8yfAJ8BLs3MUZn58hW89ry61v8CNge+CPxPRGze8PgpwHHAlsAG9XwkSevW7cCNLGcdXB/idR3wbap19ZHAVyLiZWsw/huBVwMDz/k18AqqPvZt4HsRMWINxjsDeHNE7LKceg+n2nj2Jqork90MXAKQmXvVD3t53b8uBW6iuvIVVP3wYWCvhts31d8fW3/tC+wAjAK+NOjl9wZeCix1FbSIOA74LPD6zOy6z1TS0BlM1C5+EBGzgZ9TrRwfBa7KzKsysz8zr6NqKAc3POebmXlvZi6h+jCx6Zn5H5m5MDPnZeat9ePeCZyamY9m5vPAJ4G/HbQL+rTMXJCZdwPnA0etot7G194feCAzL8zMJZl5CdV1ug9tePz5mfl/mfkc8F2qRiVJWvc+Drx70IYuqPrIHzPz/HpdfifVh+C9ZQ3G/rfMfLpe15OZF2XmrHq8/6C6lPMyIWNFMnM68N/A8i6x+s769f5Q96LPAK8Y2GuyHDdRBQqoAsm/NdxuDCZvA76YmQ9n5nzgo8CRg3rmJ+ue+VzDsvcCHwT2ycwHV3eOUiODidrFGzNzk8yclJn/BIwH3lIfxjW7Di17AhManjOt4fuJwEMrGHsScEXDOH8A+urXWN5YU6n2rKxM4+O3rp/TaCqwTcPt6Q3fP0u1BUqStI7VW/KvBD4y6K5JwKsH9Z23AVutwfCNvYGI+EB9qNWceryxVJ/VsSY+CxwQES8ftHwScFZDrU8DwdK9p9GvgD+LiPFUG8cuACbWhxbvAQwc/jW4p00FellxzxzwQeDLmfnoas9MGsSTltSupgEXZuY/ruQxjR/CM41qt/uKxnpHZv5i8B0NJxVO5MVPI90OeHw5r7Gi136cqkE02o7qsDJJUvv5BPAb4D8alk0DbsrMN6zgOQuAjRtuLy+wvNAb6vNJPgTsB9ybmf0R8QxVeFhtmTkrIs4EPj3ormnAGZl58WqO82xE3EF18v89mbkoIn4J/AvwUGbOrB86uKdtByyhOmR628HzbLA/8JOImJ6Zl69OTdJg7jFRu7oIODQiDoiInogYEdXlE7ddweOvBCZExHsjYsOIGB0Rr67v+2/gjIaTAreoj81t9K8RsXFE7Ep1Lsil9fInge1j5VfeuopqK9SUiOiNiLdSHV985ZpPW5LUavWhRpcCJzcsvpJqXf72iBhef70qIl5a3/9b4E11r9gJOH4VLzOa6g/6GUBvRHwcGLOWJX8R+Cuq8zoG/Dfw0bpvERFjI6LxsLMnqc4RaXQTcBIvHrZ146DbUJ2n8r6IeElUl+4fONdyySpqvBc4EPhyRBy2uhOTGhlM1JYycxowcGLfDKotQx9kBb+zmTkPeAPVeR3Tqa6Ism9991nAj4BrI2IecAvVyYmNbgIepDph/guZeW29/Hv1v7Mi4jcreO1ZVMcmvx+YRbWF7JCGrU+SpPbzKeCFzzSp+8j+VHvfH6fqJZ+lOi8E4D+BRVR/8H8LWNWeimuo9pz/H9XhUAtZ/iFQq5SZc4HPUZ1EP7Dsirq+70TEXOAe4KCGp30S+FZ9qNfA1cVuogpMP1vBbYBvABfWyx6p6373atZ5F1U//FpEHLSqx0uDReaKjlSROl99KNcjwPDV2BokSZKkFnGPiSRJkqTiDCaSJEmSivNQLkmSJEnFucdEkiRJUnFD+hyTr/3s4a7Y3fKqEdOYfdvl7POXO5cuRZKWNn5X2PGv1+hzEbrKL8/uij71ZO/WfP5Xz7PL/3tN6VKktTb63ks58uV+/nDH2WQSvOyw1epT7jFZTTfe+UDpEiRJWqH7f3tr6RKkIbnlN3eXLkGFGUwkSZIkFWcwkSRJklScwUSSJElScUM6+X15gmTs8H5G9EBE+52PmZks7IM5i4eRtF99kqTW6idY0LMZfb0joC37QNKzZCEj+55mGF1x7r4kAS0IJmOH97PJyBH0Ry+0YTAhkxG5BBYsZPbintLVSJLWsQU9mzF81CaMir52bVM8nyNYMB9G980qXY4krTNNP5RrRA/tG0oAIuiPXkaYSSSpK/X1jmDDNg0lULXPDaOv3qMjSd2j6cEkIto3lAyIaMvDzCRJ60KsD22K9jzMTJJax5PfJUmSJBXXkcHk9p9fz/GH7slxB7+WS79+dulyJElayk9uvoNdDn4XOx1wAv/+tctKlyNJbaHpJ7+vifcccwRz5s5dZvnYMWM464Ir1mrMvr4+vnzGKXzmq5cybqsJnHzkQbxm3/2ZtOMuQy1XktRl9jj6VGbOeW6Z5ePGbsRtF52xVmP29fXxz6efy3Vf/xTbjt+cV731/Ry27x68bKfthlquJK3XigaTOXPnsvMJX1pm+QNfPWmtx7z/7juZsN32TJg4CYC9DzqcX91wjcFEkrTGZs55jl1P/M9llt977vvWeszb7n6AnbabwA4TtwLgyINexw+vv9VgIqnrddyhXLOems4WW23zwu1x4ycw68npBSuSJOlFjz05i4lbjXvh9rZbjeOxp7wssCR1XDCRJEmStP7puGCy+ZZbMWP6Yy/cnvnkE2w+fquCFUmS9KJtxm/OtOkzX7j96PSZbLPl5gUrkqT20HHBZJfJr+DxqY8w/dE/sXjxIm66+oe8Zp8DSpclSRIAr5q8Mw9MfZxHHp3OokWL+c7VN3PYvq8uXZYkFVf05PexY8Ys90T3sWPGrPWYPb29/NMpn+HUdx5Ff18f+x9xJNvv5InvkqQ1N27sRss90X3c2I3Wesze3h6+dOqJHPCPn6Svv593HPF6dt3ZE98lqWgwWdtLAq/KHnvtxx577deSsSVJ3WNtLwm8KgfvvTsH7717S8aWpPVVxx3KJUmSJGn9YzCRJEmSVJzBRJIkSVJxBhNJkiRJxRlMJEmSJBVnMJEkSZJUXEcGky/+6/t4696TOfGIfUqXIknSMt5x6llsuefbmXzYsp/lJUndqi2CyZxnZnHGyUczd/bTTRnvDYf/Haef8+2mjCVJ0sxn5vLmkz7FrNlzmzLesUfsx0+++smmjCVJnaItgsn1P7iY/sfv4n+vuKgp4+22+2sZPXbTpowlSdIF37+GZx57kG9dfk1Txttr98lsNnZUU8aSpE5RPJjMeWYWd153GWe+aVvuvO6ypu01kSSpGWY+M5crr7uBc940niuvu6Fpe00kSUsrHkyu/8HFHLoT7Dx+Iw7diabtNZEkqRku+P41HLJjsMv4ERyyYzRtr4kkaWlFg8nA3pIprxwLwJRXjnWviSSpbQzsLTnmlWMAOOaVY9xrIkktUjSYDOwt2XzUcKD6170mkqR2MbC3ZNyoXgDGjep1r4kktUhvyRe/+7abufmJhVzyu0eXWr7JjJs54riT13rcf/vQu/jdr3/J3NlPc/R+f8nR//wBDnzTlKGWK0nqMjfedhePP/E83777iaWWbz3zLv7l+Les9bhHfeDz3HjbPcycPZdt9z2O0046iuPfvP9Qy5Wk9VrRYPLxc77XknE/+rlzWjKuJKm7/Ojc01sy7iVf+GBLxpWk9Vnxk98lSZIkyWAiSZIkqbimB5PMhMxmD9tcmVWdkqQulOtDmwLavEhJarKmB5OFfTAsl7RvOMlkWC5hYV/pQiRJJfQsWcjz2dPObYrns4eeJQtLlyJJ61TTT36fs3gYLFjIiB6IiGYPP2SZycK+uk5JUtcZ2fc0C+bDwt4RQPv1KUh6lsxjZJ+f6SWpuzQ9mCTB7MU9sLjZI0uSNHTDSEb3zQL3nEtSW3G3gSRJkqTiDCaSJEmSijOYSJL0/7d3vyGW3WcdwL/PzOzf2Y1JTTemSYjdEoJtUhRiRBAR/4ZQNvVFQVeQViVtIW36QmqwYqxSUASprqWskqRGgqKNSl9EiGJKFElbt41mt8lqGgjbIMmyEpPtuq4z8/PF3Gk32+3uZGdufzPnfD5wYe5h7p3nmTPnPOd75t5zAehOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuqrV2yQ/+48efu/QHbwIf3PcDWVhqaYsLqdP/ney4LDMzc9k60/LqIx/tXd662n37vTmzVFlaXMjWhVdzZsvuwfd6riH3Oob1uuLo8y/mtrt/P48e+FBuuG5P73Km76q3JW/50W/+g2bZPx8Y9Jw6exvP6VeytOM7MjMzl7mZyh985nO9y1tXKzN5aXEh2xdeyektlw2+13MNudelxYXMDPxY62yjmlWXX5+8dd+q5pT/mFzAwlLL9Xc9mDdcuzdX7qxccc3e7P3Ap857ULvZnVmq7P3Ap3LFtXtz43fOjKLXc29D7nUM63XFPZ/4dN4w9z/58IG/7F0KTN03tvG3fH1OXX/Xg+c9qN3sVmby2fuzofd67m3IvV4xgmOts5lV5yeYXMSZE8ey4/jhvOPGrdl5/EjOnHihd0lT878nXsjO40dycN/uwfc6JmNar0effzFPPfOVPPDO+Tz1zFfyH8de6l0STN3yNr48p+aPHx70Nn7mxLHMHz+cg/t2D77XMVlZr2M41krMqgsRTC7i1GcPZv/Nc9kzP5P9N8/l1ccO9i5pak4+ttzrTVfNDb7XMRnTer3nE5/O/pvm8vbv2pL9N805E8UorGzjK3Pq1Gc/2bukqVmZyW+b7M+G3OuYjOlYKzGrLkQwuYClxYXMHz+c996yPUly5y3bs/P4kSwtLXSubP0tLS5k5/EjuXMEvY7JmNbryhmo99+63Ov7b93uTBSDd+42/t5btmf++OFBbuPnzuQh9zomYzrWSsyqixFMLmDH0tey/+a5XLVrNkly1a7Z7L95LtsWT3WubP1tWzo1ml7HZEzrdeUM1NWTXq/eNetMFIN37ja+Z7KN71j8WufK1t/KTN4zgl7H5Nz1OuQ5lZhVFzPXu4CNbLYt5oFDi3ng0OmcPNNy36HTk+WdC5uC2baY+w8t5v5Jj99Y3qmgKdo60/LcgXefd/nQjGm9funosXz+zP/lvi+9/JrlW7Ye61QRTN/Z2/gY5tTKTH7t8k4FTdHcTOX5P/z58y4fmjEdayVm1cW4XPAqfP/2Y/mbg7+d3/jF23uXAvBaLhd8YQO/XPCKF+felF/65OPZ9567e5cCl+zIn/5aPv6eH+xdBuvN5YIBAIDNRDABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTic1fZQAABY1JREFUTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO4EEwAAoDvBBAAA6E4wAQAAuhNMAACA7gQTAACgO8EEAADoTjABAAC6m1vLg6/cvXW96tjQts/tSrbMJ/N7epcC8FrbLutdwcY2kv327Ozl2bl1djRzmWGa3bJtNNvsqOy4fNXfWq21S/45VXVna+2PLvkJNhG9DpNeh2lMvSbj6/f1GNPvRq/DpNfhGlO/q+11rS/lunONj99M9DpMeh2mMfWajK/f12NMvxu9DpNeh2tM/a6qV+8xAQAAuhNMAACA7tYaTEbxurgJvQ6TXodpTL0m4+v39RjT70avw6TX4RpTv6vqdU1vfgcAAFgPXsoFAAB0J5gAAADdrSmYVNVvVdW/VdWTVfVoVb1pvQrbaKrqd6vqmUm/f11Vq/+0mE2oqt5VVUeqaqmqbuldzzRU1W1VdbSqnq2qe3rXMy1VdX9VvVRVh3vXMm1VdV1VPVZVX578/d7du6ZpqartVfX5qvrXSa8f7V3TRjSmOZWMa1aZU8NhTg3TpcyptX7A4mWttVcmX38wyVtba++75CfcwKrqJ5P8Q2ttoap+J0laa7/SuaypqarvSbKU5GCSX26t/UvnktZVVc0m+fckP5Hkq0m+kORnW2tf7lrYFFTVDyc5meTB1tpNveuZpqq6OsnVrbUvVtXuJIeSvHOg67WSzLfWTlbVliT/lOTu1toTnUvbUMY0p5JxzSpzajjMKXNqxZr+Y7Kys5+YTzLYd9K31h5trS1M7j6R5Nqe9Uxba+3p1trR3nVM0a1Jnm2tPddaO5Pkz5Pc0bmmqWitPZ7kv3rX8e3QWvvP1toXJ1+/muTpJNf0rWo62rKTk7tbJrfB7oMv1ZjmVDKuWWVODYc5ZU6tWPN7TKrqY1V1LMnPJfn1tT7fJvELSf62dxGsyTVJjp11/6sZ6I5hrKrqu5N8X5LP9a1keqpqtqqeTPJSkr9rrQ2217UY6ZxKzKrNzpwaOHPqm100mFTV31fV4fPc7kiS1tpHWmvXJXkoyV3r0UQvF+t18j0fSbKQ5X43tdX0C5tRVe1K8nCSD51zxnxQWmuLrbXvzfJZ8VuratAvgfhWxjSnknHNKnOKoTKnzm9uFU/446v82Q8leSTJvav8/g3nYr1W1buTvCPJj7UBfADM61i3Q/RCkuvOun/tZBmb3OR1rA8neai19le96/l2aK29XFWPJbktyeDfPHquMc2pZFyzypwyp4bInPrWc2qtV+W64ay7dyR5Zi3Pt5FV1W1JPpxkX2vtVO96WLMvJLmhqt5cVVuT/EySz3SuiTWavNHuviRPt9Z+r3c901RVb1y54lJV7cjyG2QHuw++VGOaU4lZNTDm1ACZUxfeB6/1qlwPJ7kxy1fFeD7J+1prg0zzVfVskm1JTkwWPTHwK7v8dJIDSd6Y5OUkT7bWfqpvVeurqm5P8vEks0nub619rHNJU1FVf5bkR5JcmeTFJPe21u7rWtSUVNUPJfnHJE9leb+UJL/aWnukX1XTUVVvT/InWf77nUnyF6213+xb1cYzpjmVjGtWmVPDYU6ZU19/zCb/Ly8AADAAPvkdAADoTjABAAC6E0wAAIDuBBMAAKA7wQQAAOhOMAEAALoTTAAAgO7+H+JYMWjXDjPrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1008x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r3398uKGTWJ1"
      },
      "source": [
        "# **2- Problem-2**\r\n",
        "\r\n",
        "Find an optimal linear decision boundary that maximizes the margin of separation in a nonlinear transform space.\r\n",
        "\r\n",
        "Find an optimal nonlinear decision boundary that maximizes the margin of separationin the original feature space.\r\n",
        "\r\n",
        "Detaisl and coding is similar to Problem 1 but online Data is changed "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ik8GkastTxPl"
      },
      "source": [
        "list1=[-2,3]\r\n",
        "list2=[1,-1]\r\n",
        "list3=[2, 1]\r\n",
        "list4=[2,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxYB1aFET3A6"
      },
      "source": [
        "import numpy as np\r\n",
        "vector1 = np.array(list1) \r\n",
        "vector2 = np.array(list2) \r\n",
        "vector3 = np.array(list3) \r\n",
        "vector4 = np.array(list4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kepWkaaLUGyS"
      },
      "source": [
        "X1 = np.concatenate((vector1, vector2,vector3,vector4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BczfrjkhdFbF"
      },
      "source": [
        "X1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cm29jy6XdMqb"
      },
      "source": [
        "X1.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1uJ3fNCldSrd"
      },
      "source": [
        "X1test=X1.reshape(8,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I6epezTNdbp7",
        "outputId": "7471deb4-2beb-4990-ce74-c0861dd4259d"
      },
      "source": [
        "X1test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 302
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzhj8MO6diKj"
      },
      "source": [
        "X1test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfGOIg5ydso1"
      },
      "source": [
        "Y1list=[0,0,0,0,0,0,0,0]\r\n",
        "Y1=np.array(Y1list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyesUqKYdzof",
        "outputId": "449948ce-f3f2-4a4c-bb84-fa856ca13c33"
      },
      "source": [
        "Y1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uUtpKfpod90S"
      },
      "source": [
        "list5=[0.5,1]\r\n",
        "list6=[-0.2,0.5]\r\n",
        "list7=[-1,3]\r\n",
        "list8=[0,2]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ActBbG7peaad"
      },
      "source": [
        "import numpy as np\r\n",
        "vector5 = np.array(list5) \r\n",
        "vector6 = np.array(list6) \r\n",
        "vector7 = np.array(list7) \r\n",
        "vector8 = np.array(list8)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLlMqfmJemf9"
      },
      "source": [
        "X2 = np.concatenate((vector5, vector6,vector7,vector8))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XOGxaXXMesHl"
      },
      "source": [
        "X2test=X2.reshape(8,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J-H6Uxq9e7pC"
      },
      "source": [
        "Y2list=[1,1,1,1,1,1,1,1]\r\n",
        "Y2=np.array(Y2list)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcKZDPOKfFDo"
      },
      "source": [
        "X1 = np.concatenate((X2test, X1test))\r\n",
        "Y1 = np.concatenate((Y1, Y2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puBFAfC_fSId",
        "outputId": "f7b083c0-9870-40c2-d43f-cd33cf8ac584"
      },
      "source": [
        "X1.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 312
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vfvXefe2fbtt"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\r\n",
        "sc_X = StandardScaler()\r\n",
        "#sc_y = StandardScaler()\r\n",
        "Xnew = sc_X.fit_transform(X1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bBoureAqfjxk"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\r\n",
        "from sklearn.tree import DecisionTreeClassifier\r\n",
        "from sklearn.ensemble import RandomForestClassifier\r\n",
        "from sklearn.svm import SVC\r\n",
        "from sklearn.naive_bayes import GaussianNB \r\n",
        "from sklearn.neural_network import MLPClassifier\r\n",
        "from sklearn.linear_model import Perceptron"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKLBDyM-fsHS"
      },
      "source": [
        "# Initializing Classifiers\r\n",
        "clf1 = LogisticRegression()\r\n",
        "clf2 = DecisionTreeClassifier()\r\n",
        "clf3 = RandomForestClassifier()\r\n",
        "clf4 = SVC(gamma='auto')\r\n",
        "clf7 = GaussianNB()\r\n",
        "clf5=Perceptron()\r\n",
        "clf6 = MLPClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "DIOWaQFlf0ZR",
        "outputId": "e47588bc-ab41-4302-dac0-c3a2b1819c24"
      },
      "source": [
        "import matplotlib.pyplot as plt\r\n",
        "from mlxtend.plotting import plot_decision_regions\r\n",
        "import matplotlib.gridspec as gridspec\r\n",
        "%matplotlib inline  \r\n",
        " \r\n",
        "gs = gridspec.GridSpec(3, 2)\r\n",
        " \r\n",
        "fig = plt.figure(figsize=(14,10))\r\n",
        " \r\n",
        "labels = ['Logistic Regression', 'Decision Tree', 'Random Forest', 'SVM', 'Perceptron', 'Neural Network']\r\n",
        "for clf, lab, grd in zip([clf1, clf2, clf3, clf4, clf5, clf6, clf7],\r\n",
        "                         labels,\r\n",
        "                         [(0,0), (0,1), (1,0), (1,1), (2,0), (2,1)]):\r\n",
        " \r\n",
        "    clf.fit(Xnew, Y1)\r\n",
        "    ax = plt.subplot(gs[grd[0], grd[1]])\r\n",
        "    fig = plot_decision_regions(X=X1, y=Y1, clf=clf, legend=2)\r\n",
        "    plt.title(lab)\r\n",
        " \r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  % self.max_iter, ConvergenceWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/mlxtend/plotting/decision_regions.py:244: MatplotlibDeprecationWarning: Passing unsupported keyword arguments to axis() will raise a TypeError in 3.3.\n",
            "  ax.axis(xmin=xx.min(), xmax=xx.max(), y_min=yy.min(), y_max=yy.max())\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x720 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bGxSnsPYkCRL"
      },
      "source": [
        "# **References**\r\n",
        "\r\n",
        "[Decision Boundary In Python](https://predictivehacks.com/decision-boundary-in-python/)"
      ]
    }
  ]
}