{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "feature_crosses.ipynb", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RLLI6XbdkOB0" }, "source": [ "I wanted to show how Neural network is better than generic machine learning.\n", "\n", "1. I wanted to show with sklearn, it is difficult when we wanted to show descision boundary in non linear data.\n", "\n", "2. I used SVM to do this, but with a helper function like 'plot_decision_regions', can it be done with logistic regression with straight line to show that it is not possible. See [out] 13.\n", "\n", "3. [Out] shows that with only input 1, output 1 neuron it is not possible. It need atleast 4 input neuron to make it happen.\n", "\n", "3. I could not make [out] 13 look like [out] 16. \n", "\n", "4. Please ignore the text markups.\n" ] }, { "cell_type": "markdown", "metadata": { "id": "JzBIxKCynCim", "colab_type": "text" }, "source": [ "## দুটো ফিচার নিয়ে আইরিশ ডেটাসেটকে প্লট করি\n", "\n", "সরল রেখায় ডিসিশন সারফেস/বাউন্ডারি বানানো যায় সহজে। কোড কমানোর জন্য 'plot_decision_regions' নামের একটা হেলপার ফাংশন ব্যবহার করি।" ] }, { "cell_type": "code", "metadata": { "id": "4BjZLitwXUI6", "colab_type": "code", "outputId": "9359de0c-9ee2-4d92-cd0c-652aa90e5031", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "from sklearn import datasets\n", "from sklearn.svm import SVC\n", "from mlxtend.plotting import plot_decision_regions\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "# Loading some example data\n", "iris = datasets.load_iris()\n", "X = iris.data[:, [0, 2]]\n", "y = iris.target\n", "\n", "# Training a classifier\n", "svm = SVC(C=0.5, kernel='linear')\n", "svm.fit(X, y)\n", "\n", "\n", "# Plotting decision regions\n", "plot_decision_regions(X, y, clf=svm, legend=2)\n", "\n", "# Adding axes annotations\n", "plt.xlabel('sepal length [cm]')\n", "plt.ylabel('petal length [cm]')\n", "plt.title('SVM on Iris')\n", "plt.show()" ], "execution_count": 1, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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uDmUXc9NXyBl+PNgMP3Gt+2wtO5yeKlTrofyLDRzbx7mR1pSTB9MxPzmTyA9u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\neOpq65g8qijuv7jCmeF/if+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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "j6QY3PjWXJs4", "colab_type": "text" }, "source": [ "বয়সের সাথে ওজন বাড়বে, বাড়ি স্কয়ার ফিট এর সাথে দাম বাড়বে, এধরনের লিনিয়ার সম্পর্কগুলোতে ডাটাকে প্লট করলে সেগুলোকে সোজা লাইন দিয়ে আলাদা করে দেখা যায় সহজে। কিন্তু ডাটা যদি এমন হয়? কিভাবে একটা লাইন দিয়ে দুটো ফিচারকে ভাগ করবেন?\n", "\n", "\"নন-লিনিয়ার চিত্রঃ নন-লিনিয়ার ক্লাসিফিকেশন সমস্যা \n", "\n", "এটা নন-লিনিয়ার ক্লাসিফিকেশন সমস্যা। নন-লিনিয়ার এর সমস্যা হচ্ছে তাদের 'ডিসিশন সারফেস' সরলরেখা নয়। এই ছবিতে যদি দুটো ফিচার থাকে তাহলে সেটা দিয়ে এই সমস্যার সমাধান করা সম্ভব নয়। সেটার জন্য প্রয়োজন ফিচার ক্রস। মানে এই নতুন ফিচার কয়েকটা ফিচার স্পেসের গুণফলের আউটকাম নন-লিনিয়ারিটিকে এনকোড করে মানে আরেকটা সিনথেটিক ফিচার তৈরি করে সেটার মাধ্যমে এই নন-লিনিয়ারিটিকে কিছুটা ডিল করা যায়। ক্রস এসেছে ক্রস প্রোডাক্ট থেকে। এখানে x1 = x2x3 (সাবস্ক্রিপ্ট হবে)" ] }, { "cell_type": "markdown", "metadata": { "id": "N9rRARqdXJs5", "colab_type": "text" }, "source": [ "## একটা নন-লিনিয়ারিটির উদাহরণ দেখি আসল ডেটা থেকে" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "rQN_GvIhkOB1", "colab": {} }, "source": [ "import pandas as pd\n", "import numpy as np\n", "import sklearn\n", "\n", "from sklearn.model_selection import train_test_split" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "OCwPfWSfXJs8", "colab_type": "text" }, "source": [ "## ভার্সন চেক করি" ] }, { "cell_type": "code", "metadata": { "id": "dBXgoSoNXJs9", "colab_type": "code", "outputId": "95ad95bb-5cdd-4726-85b2-76bdb5567b61", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "sklearn.__version__" ], "execution_count": 3, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'0.21.3'" ] }, "metadata": { "tags": [] }, "execution_count": 3 } ] }, { "cell_type": "code", "metadata": { "id": "-peF4S1Noc39", "colab_type": "code", "outputId": "6093c833-e479-43a2-fa3f-5b4f3bc9a98e", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "try:\n", " # %tensorflow_version only exists in Colab.\n", " # শুধুমাত্র কোলাবে চেষ্টা করবো টেন্সর-ফ্লো ২.০ এর জন্য\n", " %tensorflow_version 2.x\n", "except Exception:\n", " pass" ], "execution_count": 4, "outputs": [ { "output_type": "stream", "text": [ "TensorFlow 2.x selected.\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "dCGChruQXJtC", "colab_type": "code", "outputId": "1b6de002-4e9a-49ed-c9aa-bc93d001d68e", "colab": { "base_uri": "https://localhost:8080/", "height": 35 } }, "source": [ "import tensorflow as tf\n", "tf.__version__" ], "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'2.0.0-rc2'" ] }, "metadata": { "tags": [] }, "execution_count": 5 } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "BAGl_DL6kOB-" }, "source": [ "## ১. ডেটাকে আমরা লোড করে সেটাকে ট্রেইন এবং টেস্টসেটে ভাগ করি " ] }, { "cell_type": "markdown", "metadata": { "id": "268qDbW0XJtF", "colab_type": "text" }, "source": [ "ধরুন আপনি ডাটা প্লট করে দেখলেন এই অবস্থা। কি করবেন? নিচের ছবি দেখুন। এই ডাটাসেটে অক্ষাংশ, দ্রাঘিমাংশ, ভূমির উচ্চতা ইত্যাদি আছে। আমরা সবগুলোর মধ্যে শুধুমাত্র অক্ষাংশ, দ্রাঘিমাংশ প্লট করছি। দেখুন কি অবস্থা। একটা ফিচার ভেতরে আরেকটা ঘিরে রয়েছে সেটাকে চারপাশ দিয়ে। এখন কিভাবে এদুটোকে আলাদা করবেন? সোজা লাইন টেনে সম্ভব না। চেষ্টা করি নিউরাল নেটওয়ার্ক দিয়ে। " ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "1F4403n3kOB_", "colab": {} }, "source": [ "# df = pd.read_csv('geoloc_elev.csv')\n", "df = pd.read_csv('https://raw.githubusercontent.com/raqueeb/TensorFlow2/master/datasets/geoloc_elev.csv')\n", "\n", "# আমাদের দুটো ফিচার হলেই যথেষ্ট \n", "X = df[['lat', 'lon']].values\n", "y = df['target'].values" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "nhaD099jkOCD", "outputId": "0c74d211-2d2f-442e-f392-711ff6e978cc", "colab": { "base_uri": "https://localhost:8080/", "height": 258 } }, "source": [ "df.plot(kind='scatter',\n", " x='lat',\n", " y='lon',\n", " c='target',\n", " cmap='bwr');" ], "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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+tm6tjCda8/mYf/rJ2Oe77zKnpjILwexyMffqxfzrr7g3s+tUroz73bCBORDQ\n7nM6ma+9lrlxY+32lBTmjz829r14Mc6JNXYi5rQ05kcf1fbp9zO3aWM8tnFj5n/+wT3rrzFoEO7T\n4Yjen9/PXLu2tbE5HMwXXqi9t0OHmCdOZH7xRebXXoveX6NGzFdeiXmqVo15xgzlOllZGK/+nAsv\nxLP3epXxXnQRczhs7V2LhDffxLWi3W9qKvPmzUXrpyggotVcRBrWsUYN5nvvtdQS0V9JttLuxWRF\nH1cmsXkzDLly9SQNmxs2YDVPhIAq6f1DhBXZqFHWdOMOh9F7RKJNG6X28ogR0EP37Anf9GefhS3i\nwAGkrzh1yrj6z8lBbiaZI2n+fHgvyVTZXGAoXrYMK+3hw1FpTa2K8flgUGXGql2/uPL7EQDWVee/\nkZ0NwzcRdOW33Qb7SuPGGPsjj8QOxMrJgXun2taRnQ2JLhBQtsvI89RU4zVzc2HHsVKzOxw2usJG\nO3b7dnhWXX89nmFOjuItFQ5HT2W+Zw9sTDk5mNdbboEU17s3nm+7dtq0J0R4TlOnYvumTTAgP/RQ\n4dR3anTpor2Gw2G0neTnl5NyqOU01UZpv6sFRHSdALoR0XFmLhcOcm638UMPh7X66m+/1Xrs5Obi\nI7eCUAiExgzbtsFTpWtXEPf16+FRNGwYCOwvvyAeIj8/MrGV6aM3b4ae/fhxc8NpVhZcJl99Fcbf\niy6C62qlStheoQIMw2lpirrE6YSqpHlzqIHURMbvR5wGM9RMc+bAvfa774heeQVzVq1a9LnJzzcP\nuqtVC66+6voNEybgHh54QJsQT6bxjgWXC/1FYiQej/aZe70wIPftC2J/8iTOz8uL/jyIlPlTjys7\nGwuLO+9EfIxZBprsbMzhk09C3fnTT1DbWbm/aOjQAQZprxfve+PGeK9SUjAv0kHAat6rUg07WV/i\nIYT4iIjOIfgG7yWiJ4jITUTEzJMJEYIDiGg7wc01Qk7LsodmzbBSW74cH6jfjw+qVSvlmCpV8HGp\nP1R9ZtRIcDrxAZq5qzqdIAaHDysER3o6NWoEo/IXX0QnRnJMK1dGf++ZQQCvvx5GYSJEeh84oDCT\n224DQwgEMBeBADxfPB7kfOrTR/H2uf56GFoPHABjk/r0UAjn5ufDHbZPH+yzssKXczJ0KNKLSMYt\n621PmQLDavfuILANG0KP/uKL5tHoEpKxRVvxu1xg1EuXYj5yc2FzibcanseDhHwZGdrn5vHgHZow\nIbKu3++HDad3bzB8OYYVK8B45X1wgRNDPPmWbroJz+zUKby7zHAC2L4diRIvvDC++yyVsJP1lS2U\nlWR9ubkIVluzBqL/gw9qo4WYo8UeAAAgAElEQVRPnoQ//P79+DDdbqyYL7rI2vXr14caywzRArz0\nmVP1cLnA4IYNA9F5/fXIcRMpKRiDVCNkZ0NaiKUGCgSg9rjqKszT9u2IkK5T4KJw5AiMzWqil5oK\n76FzzsHxzz9P9MEH1lfCcgWuH9u99xKNH6/dtnMnpBvZvyxCtGUL5iccxhxHMlCnpGAuzKSuWKhZ\nE4z8r78w1sxMjP3UKW2aDZkYcO5cqBD1Y5Fup0OHQsLo21f7HP1+GJJllt+RIzGXrVtjAVG3bnzj\nLo1ISLK+WrV49U03Wevv2WfLVLK+0m6DKNfweokefTTy/rQ0eOp89BFUOP36GVNxx7q+HpIxRCNK\nZgRVqleyskCod+1CMZ9AQKmbEAqBYIZCOLZRI6yM1Tpmnw/nRKpPIJGVhVXvVVfhPvRFkqpUQX6f\nzz7DsT4fCLQsrdmkCaSQDz+M3o8aZkzL7wcB1eOOO7THu1yIGJ80CXMTCplLL9IzSFaYKwyqVoXk\nOX06pC+1GlJ9TYcD81+vHhiYnkG4XJAAW7eGyk1vc5B1tNevhyQg+9mwAdHgMqnjaQ/pxVQOUT7l\nolKE33+HHvajjwqX1TQ1FVHL998fH3Mggn5XnRrB7zf3nbeCLl1QrMjhgGpKGnKzsrCaf+kl2ABW\nr1bcGnfsMFZlEwJEOxAwdw9VI1Yyt+nTkefpiitgVP35Z60rZosWkMoCAaSgcLvBWKRrrxUMGmRe\nsEjvupudjbHIGtNmzEbSEclEC4NAAMWOiCBBREup4XDgft1uqBT1CIWgjpPRzrVqKfMnmfIZZ0DV\npEY4jMSC339fuHsolyinNoiyN+IyhDlziHr0AJG6+WYQmsIwicLg0CHozJ95BvEDvXqBGNx6q1LW\nksiYo8nlMn+PlywBUTRbFefnQw101lnQz5tVNQuFQMTr1oXKZsIEGMbbtzdPwhcIRJauQiHYCm6/\nHeOdORPSSteuYKLqRIJPPonV8cyZ0K8fPoygrQkTEFMRy1Nn/nzz7U2aGM9Ve6WZgbloPv9uNxIZ\nymC4jh2N8yzHFAjAQ03arO6/32i/CoXgkEAEu8Ly5XA46NgR/Xz0ESSUt982lyoHDYqekuW0QYLT\nfZcmlE+5qBRg61YkQVO7Um7cCKZx5ZXF2/e77+LD9njwYQeDeDfvuguEVQ2HAwFqqanQbdeqBcJ5\n4oR13Xg4jGR3KSm43pdfKqoeiYcfhq1CzsfddyOr68qV0O+/9BL6lBHeQ4aAAOnBDNXS11/jWtJW\nsXmzcu0xY8B0Lr8cv1u31taTSEmBiqhTJ4w32mo+khro3XdBSNVpKYrTnJeSAoKtnpNBg3CvEyeC\nUdaoAfvUgQOQFO+4Qzn2yBFjoJvPB5dniSpVlMy2x4/DYSIjI7KhPxSCOs2sVsdphzJI/K3AZhDF\ngK++ggFXX7ksGCxatLQZmJGC4sQJfOxHjmBlnZOjXfVJtcDAgVqddSgEI6baJfbnn5E63CztRTRI\ndceQISAs6hX2hx8a4w5mz4Zkc9ZZWhfOUIjo448hYeg9ZrZvx/zKe8jKgp1GTcizsuB5JBmEGXJz\nIRHFsgNESpjn98cfWaw+1+EAI5SxIxJCEL32GhwXvvgCcxoKQfLTM0whwFgfeQQqvTp1IqvOBg40\n5liqUwcMxgxffIGxRfMCC4eLluiw3KAcezGVz7tKMvSSg4TDAffLRCEchhqhc2e4CzZqBFVQJDfE\nUMg8YGvHDiX47uhR6LjNSmhKRKvLQAQmNXYs1Ft792KbPk2006mk/pYurPqxmum4s7KM9kAzNVGs\nMXo8RgKfkqJ4SRHBKSBS3YIxY8wDEWOprBo2hHH3l1/ASPXw+7HynzYNksDXX0OdNWECAt5WrjSe\nU6kSPNYiMYecHNiG9HO8bx9W/zt2GM8JBqNLRELA5lSYGtrlEglKtVHaYDOIYsC/JukHfT589O3a\nJa6fjz7CSi8rC6u9w4exoixMgJPMaDpmDNRjeuIgBIjXyJHIF9S+feRrMcPF9IknQNwefBBN2j6c\nTujDb70Vv3v3NrfNfPSRcVvLliBK6qC6KlW0dpVAIHrK6yVLEJylj2FwOCBVZGaCQH79deRvetMm\nc9VULKLaogXmpFUrSGnqcbtcUJ+pr3X11VCfyRxb/frFL4V6PObMIycH78z11xv39e+P8+TC2O9H\nYOPo0Yjm/+03pfjUaQ/bBmEjHnTpoi1a4/djNdytW2L72bxZ68XCDJ1wrVqxE77pId1OpdFSDZcL\n0bgXXKBs698f8QbLlyvbUlPBrEIhbbDZyy9DWnjnHcxLWhpSQMjVeq1aiLLVZ/Y0kwI8HqSGuOEG\n2HRatgTjPXgQyfaEAJPrFMHTfP9+RPOaGVdzc0H8Ihmm1ejYESo4ydj8fhDcSAzC7cYxr7yibLvs\nMqgHn30W89S/P9Ebbyj79+6FNKZ3XV27Nr4AM4cDHlaPPmqUbGVqDz2qV8ezuusuSBr9+ilZWG2Y\noAwSfyuwGUQxYO5c6HzXrsUH9b//WWMOR45AhZCaCt18LFfM1q2hFlEzicxM69XE1JAFXFq1MtZY\nZgYRV8PlAqHOyVEqpR08iLgFfUlNZhDkL76AN5EZXnoJxvvsbBD5QADBW2aoWxe5lNSoX99ajYF1\n6yLPa16eluFFwxtvgEHJ4jpt20KNo5Yq0tPhFHDwIBjEJZdoVVhCQMp6/HGcp5dWKlY0L36kztjK\nDK+tn36CG/Kdd2qlEom778YYJ06EE4G6RnizZriOXj3WtCkWBjZiwLZB2IgHNWqg+ta6dTAc33EH\nVmTTpkU+548/oHq46iowFytpkC+/HK2oSdXq1VO8m6ZONSZPY46cA8rnAyGsVQvqs6uuMl/5h8PG\nGspE0LNPmwYGM2sWmMQNN2D1qg+OSwRq1IjualyzprXrVK4MNcu6dWCI3bsbVU5eL2I07rwTUo2a\nOagRKc6qYkV4f6WkgMGkpODd6NABRvwBA0DEx4yBdPbkk9Fdqc87DzaVK67AwkUIPJdff4U7dqtW\nsGM99ljh4zROW9gqJhvxYONGqDnkx5qdjQ+5Zk1zz5jrroN/vsSaNfA/l3p6iexsrACdTuju330X\nNoFo/uhCQK1z6pSirpARzdnZ6LdPH6yAa9WCZNCunTL2cBjqieuui50Ir39/1GjQIxAAYVJDEla5\nSq5UCQQ3Wk2DoqJDB6h25s5Fv7m5IL4y6vzdd61fy+kEgSbCc/V6tXaNRNzHk0/i2axdCzXckCFg\n5uqyrBI5OYhyvu46LEp69DBez+GAK+umTbgms5KHS+KVV7D96aeLPv7TBmWQ+FtB+byrUoCnnjKu\n5HJyEAdhBn3OJJlhU4233sIqctgwpH844wzk/om16q1fHyt0tS47JwfG9Oxs2B8yMpBllQgrfb3X\nkRBwO33rLaxeN20y7+uZZ4z3LSN59cbQO+6AcV2qxQ4cQM3js88Gk7GaaC8eyPuYMweeQT/+iP+n\nTAHDKmwpzDFjoPpKSVGY7+TJ5sd+/DEYrc8HiUDv6qrHeech0G3oUIz/uefMveSIIHXOmgV70Sef\nKNuZ0c/x42D+a9ZEtpdkZSnxEDYsIIEFg0obyt6IywjM6voSRc5936kTMmeqDds1auC3ywXXyNtu\nUz5qZvQxahR00P36KXmWAgG0ypXhI3/RRVBXRFMbBIOK7aFVK/MaEJKBCIHV8nPPQbethpkk07Ur\n0mXr8c8/WiIVDIJIM0Oa+fNPc0+mokKIxGcRTU+HymnOHNzXhg2wq2zYAOYh1YC//IK8RpLAL16M\nanaRDOPMYN4yBTqRNS+1rCxIGZddhpQc/frBqM5sTX2kXyDYiALbBmFD4uBBpIwYORIFbyLhmmuM\nyfICASNBlZgxAx45Xq+SBfSpp5Ro3UjJaXfuhJpm0yasgj/+GF4nf/6JFfuUKbALjBiB/qOpQvv2\nxV9mRD2bGTvl/pwc6McPHNDuGzVKS1wCAcyVGfr1M/YhGUZWFohtpJVyaURqKlRAkyZh9f7552CM\n992nHPPdd1o1VG6u0eAukZkJ6aFBA9gvhg3DczaLvzAj6NJZYcAAvCfBoHXm8NxzsY+zgldeAWOr\nWBFSULm1bZRTG0TSS9oVRyuukqOHDjHXqKGUbQwEmF9/PfLxEycyV6+O8pQXXsi8bx+25+Qwv/ce\nSkiuWqUcHw4zDxumLZ3p9TLffDPzN9+YlwO97DJjv7m5zO3bo/SnPM7tZr7iCub//te8DGSFCig7\nOnYs+vH5YpfSTE9nXrMGfZ46xXz11Zif2rWZ69ZFuc5o85OTg/E7nehLX5rU5cJ1yxLefx/lPtX3\n4XSiBCwz8+TJxvKqtWqZX+vWW7WlTgMB5t69tc9Vnr9qlfa5+v3MN92EOY71HGVr3Zr5lluYlyxh\nDgZRknbqVFy/YkXmUaNwPav44APtvQYCzOPGFXmKEwpKRMnRBg1Qb9dCS0R/JdmSPoDiaMXFIF57\nzVibuEqV+K6Rm8vcoQOIiMuFD1nWDZ41y/xjbtgQzOPyy7U1hZs1Yz561NjHZ58p9YX1BDc7G8xA\nv++ee5jnzTMSr1j1hI8dQ5+DBmn7TElh3rbN2pzk5+M+atVS7s/vZ7700vjmtjTgvffM53DuXOzP\nzGRu0QLHuN34O2+e+bXatTN/F/TbKlZknjaNefZs7K9alXnkSBDzcBjPycrznDAB/W7ezFynjvEd\n8vuZx4yxPhdDhhj7aNu2SNObcCSEQTRsiAdgoZU1BlEGZZ7kQSa+UyNaRbGTJxG1u2qVIlrPnQvD\nsozWzc5WIlL/8x/z3EB//w3PpVmzoKJ47z2oLbxeqA+WLMFxS5fCljFihPm4hMA9DBqkdUX1eqHi\nuvRSayod6RW1cKFSJezLL7V9hsMo/2kFLhdUEKtXw223Wzeo4orD/lDc6N/f/Bk+9BD+BgIwEL/2\nGgLkli1DhTwzNG2qtWt6vfBM0quTTp6Eferxx2HzOHQIHnBSXfn+++a1QfSQXnQDB8KOon+HsrNR\nf8MqqlY1alXKRf1pM5RTFVPZG3ESMWiQNpLU7wcxNsNff+EDHzKE6PzzEdeQmwvPIb0eViZki1RE\nJxRC3089BZfHw4cRNfzHH8jVP3AgiMDFF4P4RGIO7dqBEL/1FphBWprinqmP2I0Evx++9P/+i3uS\n0EfYOhyx6z3oUbs2KsCtWAHiWRajdmvWNE8SqA5eDARgq7n//ugpS157DY4Kch7y8uCG/M03mHu5\nXVaV273bPNbmoovwbkRDSgrcaINBeM8xmx+Xnh79OhLZ2aiFon6nUlIQVV/uUI69mGwGEQdatkTA\nWPv2CCi65RZtaoRgEEbsUAiG2UOH4Fp46hQI96RJcOFULyTcbiVqumvXyO9QOAyvmClTkDZBvdKX\n1dcirf4dDnjtfPUVfvv9IMQnTpgncdND1oxwu+Eb37AhJJVq1cC4/v0X7q1yZev1gpDJwjanG26+\n2Wioj5Xifc8evE/DhsHRgAhzOHq0tib0m2/Co+3NN41EPCdHm6cpLw8ODD5f5KSDPh8I9znnYLEz\na1bkRIdeLyKxN28GI/ryy8iLiscfxwJGwuXC+xApBUqZRjnOxZR0HVdxtOKyQUTDN9/AGO3zQSdc\ntapR/3rTTTh2wQIYc71e5vPPZz5yBNsPHmRu1cp4nt7gabbdTF9NBIPzI49EHndWllHX7PPBWD18\nOPPgwczz5zNnZMB+ceAAjNNq43fnzrjWF18w33UX8/PPMx8/XrzzXdrx6acw0teqxXzvvbCzRML+\n/cyVKyvPNhBgHj8e+7p0MT7T7t3N7QoOB3PPnsy//45zzzsv+rtEhHd22jQY0S+/XGtgd7lgEJfN\n54MROxDAcampzBdfrBjg1ejTx9hXt26wkzRpAieGsWPNzy1JUCJsEGecgRuz0BLRX0m2sifzlEIc\nOYIgJqlGyMlRJEppswgEICEQYdWtdw8lQjqO+fORniOSr3skN0F9tTAipWj9Aw9gpbd+PaSMdu2U\nFa7fj9rSjzyi1Ci48UZskwiHkU/qm29wP+pVY34+/P+PH4c9ZMCAyPN0OmHoUPNa1mb48ENImfLZ\nZmVBxXbvvXgnZHwLEZ7RP/+Yx5uEw8gl1aULVuo//xy775MnEa0fDsOmpK4VQgQ1llRZ5uQYg/9+\n+AGSxMUXY4xS2mnTBrY3ea7Hg7ic669X+njlFbxvZuVQyxzKonRgAUm9KyFEfyHEFiHEdiHEQyb7\nbxBCHBJC/FbQRiVjnLGwebNRNeTzIYI5EIBoPnQo9M567NgB9dAbb4DRNGmCVNlS9CcCEZcGRzM4\nnebvZ4MGYAoeD1QNPXrAiNqsmVLvYds2qI2YQeyZwSDUuOMOMJBFi2Ak1ycDZIa66tJLMf4hQ6Bq\ns2EN+flGVY1cWLz4ImxFXq/yTuij7tVgBlG2whwksrKwiNDn/nI4Yuf5Coehdq1YEcdXqgSG9+yz\neM9SUzH+xo2Rll3NgLKyYDtTY+tWRHFHU1+VOpRjFVPSJAghhJOIXieiC4hoLxH9KoRYwMz6JA4f\nM3Opzjxft67RMBwMKtlO/X7zdBirVyPHfl4eiPy4cViN33UXiO3u3fjwtmwBQb/33ujjCAQUO0Qg\ngGC9YBCR0f/8oxyXmQkbybffgjmoczTJiOnvvlPu4623FIIVCimEIxwG87n7bgTZyWCsPXtQcnTT\nprJpaC5pDB0KiU3OscuF51C9OhLnbdgA7yEhYOSN5MxgBU6nuRRqlkgxL0/r/eRyYQzqYkKhEOxi\nkrkcO4b37tNPYXdbtw7Htm+P98rhMBquJRYsgB1EZtvt1QsZgHfsQPDnmWfGzgWWFETKtlgOkEyW\n1oWItjPzX8ycR0SziCiCw1/pRoMGKFATCMDLw++HFFCrFozZkXIl3XkniHNeHlZWR45gxUiEDKu9\neiGl97BhkaOaifCRyhoJXi+MySNGIJX0gw8a1VnhsJKS+99/jSs1ddJAqUHWn6/edugQ6ixIApef\nj/QQGzdGHvPpgLw8EMkNG6Kvhps1g6tynz5KOo3cXMzrQw9BVXPnnZDk1M9GomnTyKkx3G5cf/Bg\n5J0aORIlXq2WS61TB8dKwq5eBPt8kDb198YMxuZ2Q93VtauykEhLUxhAIACHCwmpfjp1Cm3ZMqLh\nwzHeIUPwLcmFS6mDLUEkHHWISC0s7yWiribHDRNC9CGirUR0DzObCthCiNFENJqIqH79+gkeamw8\n+ihsC1u3wtvJSqrqw4e1v4NBIzH/8Ud8FJG8UCQ8HqJ77oGnFJGiGtDXa5b7pHvllVeimJFa8lB7\n3Ljd+EgXLFBqNaiZRl4e1AR6SSEUOr2lh4MHIUVlZGAuunSBKibSnHTuDH1+y5ba90KmHBk+HL97\n9sT7IFfsgQCe+Z498EzbswfSYk4OnpXPB0lRfhK9e+PvzJlYUMRKfbF7N46Rz11Kym433HR79FBS\nxathlotMqjwnT4YUe+WVSp2UcNiYtDA/H+lK8vMV1dSll4JJFjXFfcJRBom/FZT2u1pIRA2ZuS0R\nfUtEEXNMMvNbzNyJmTtVS5Ic2rYtPmSrdQwuucRYKnPgQKgbuneHofHCC/FbX4RHDSFgAHz9deh5\na9bE/0QwSOuJUoUKCKQiQp6m557DOdWqIQBPr8qaMQPBfO3a4R71bpDhMMYr78Xvx+9WrazNQ3nE\nLbeAWJ88CSK/ahXR+PGxz9PXeHY6tfEkM2eC2TgceK5PP42cVqNGQQrZuhWr986d4TCwcqXCHNS4\n5hqotmIxcclA9FJkfj6kxgsvNHeLlfXG9ahfH/aJ117TFtFyOGDYVhdzYjZKOtnZkRNhJg3l2AaR\nzBHvI6J6qt91C7b9fzDzEWaW2v23iahjCY2tRPDccyDQfj8+qCefxOrw+efxYa9Zo5SxjLbSEwLq\nqLfeQiK3jAyolmbOBGGScQ4OB1RWu3YhAIsIRKxJE6wyMzJgB9G/xx4PVF/r1kFFofaw8vkg/i9a\nhEC+K67AfSxaVApXeSWIDRu08SXZ2Zi/WHjlFejlJaEMhcCgn3gCvytXRvW4rCxc8557tOc7nVDV\nVKiA9+e22yAFSBw9isVGdjbel7ZtldrTVavieVqhYx4PpAe9TUHCzKsuFhYuRM1upxPjeOwx4zEV\nKxbu2sWOcsogkuZfS1Bv/UVEjYjIQ0S/E9GZumNqqf4fSkQrLfklJyEOQo9t25hvuAFxBLNmWTsn\nFNLmWrLaPB4kBdRvb9RIm+DP7Wbu0QP+59u3Iz9QIIDcTH4/80MPRR/fmjXGRH81aiA+woYWw4Zp\nkw/6/YgPsYI//zQ+z5QU5u++i35ebi7zl1/imciYCqcTeZWyspgnTULMS1oanvmKFcjVdOAA4lbm\nzbOet8npROzGtm3m++++29q9BoMYAzPG8eabyAm1dy+2TZigjLlSJeZffrF2XaugRMRBNGvGvHix\npZaI/kqyJbdzogEE28IOInq0YNs4Irqk4P/niGhjAfNYQkQtLD2wJDOIXbsQTCYT7wUC+DhjobAM\nIhAwBuY5nUgkaHa8wwFCoA+QCwSYf/st8vgmTjSe43QqH3iJ4eBBRBv+8EPyI60iICODuXlzzHMg\nwNyvH7LlWoX+PXA6kf03Ek6eZG7TxjxRYFoakuzps+VWrqydvhdesJ75lUgJntNv9/mYv/46+v0d\nOYLMtA4Hxvz00xiP34/zK1Rg3rIFxx4+zLxpE5hcopEwBrF0qaVW1hhEUmUeZl7EzM2YuTEzP1Ow\n7XFmXlDw/8PMfCYzn8XM5zLz5mSO1ypmzNBWcMvKQiqKWHA4oMKJ5JFiprLx+40GPocD3lS9epl7\nq4TDSrJANVwupWiQGWrUMHrzVahQwqqk1avhtnPNNYjOuvDC4ik9V0RUq4ZUE8uXQ7X01VfWPYeI\nkJdKjVAIzgCRvKHGj4f9wSzdyqlTsDnpU6pkZirurZmZUCPGE3vAbAzodDhg/L7gAvNzNmxArYyB\nA6FGDYcx5ieegPE5OxvXPHkSalIi2NVatozuyZdU2DYIG/FABpypYZWGvf46Ppa2bY3vk/6aLhcM\n3S6XNshJCBCnt99G3qS0NPNr6bcFg7BRRMKll8L7KTVVqVoXTw3nhOCaa8ANZZKrFStgbCmFcLth\neG3WLH4manZLO3cqmXv12LrVPEmjZOhm+baEQCK/9u1h94qVk8sK3G4Y6M3u99VX4fI6ahQem7q/\nUEjLnMLhMhZsmUAGESuAuOCYy4UQm4QQG4UQHyb0XlQon9EdScaIEVjRqVdzhw9j1fTJJ9qVUG4u\nPDp+/x3V4269lWj7dgSZxVrNeTyoRqZPu8CMICu3Gyu2devQ75tvKmNyuxFrsXcvjILM2N+kSeT+\nXC4QqAULcD89e1r32EoY9u3T/s7KgtVd4tdfER5+5plwoI+EEyeI1q4Ft+vQodSt7s48E89Xzfgd\nDvM4CCJ4jKlTcgihRC+vWqU9VghcmxmGbCIYrq0sYlwuJaVGOGx8R91uI6P65Re4Uj/+eGQm5Hbj\nmmr33UsuiT2eUoEElhy1EkAshGhKRA8TUU9mPiqEqJ6Qzs2QbB1XcbRk2yCYmVeuZG7aVFv9y+dD\nlTBm5tWrma+/XknaJ20ATZua65GdTuiQpW7a4TBWFpMG63POMY4nHIaet3Zt6Hnlddxu5nr1UC2v\nTKB3b23GwpQU5s8/x75HH8XkpaXhr6yAo8fmzTDaVKiA8/v1U7LprV2LsnsTJjD/+2/J3JMJwmEk\nblTfamqqYrzVo2tX7fvgcDC/8QbsRmrHAvkexWNrkO/uvffCSBwOo917r7ZPIZjr19c6LTz+ON5v\nvf1DNq8X99W+Pa4nbRB33FEy5iVKhA2iRQt88BZarP6IqDsRfa36/TARPaw75kUiGlXUcVtpSSfm\nxdHiYRCHDjFfcAFoSuPGzD/9ZPnUmLj0UuMH0bIlykNGqtxmRvSdTuZzz4UBuV8/EPk2bcw9Tvr3\nN68yJ3HsmPFjTUuD9wsz6OTkycx33qlk+SxV2LcPJdl8PtzI2LHYvm2b0WLq9Zpzvk6dtBMdCDBP\nmYJJCAQw4T4fJlqm2k0C/vkH2Vn9flSK+/nnyMempZm/Nykp2Od243ekbMB6Jwb1O+LzMV9zjXm/\n776L91AITN38+dgeDqNUbrR+AgHmV15BotPc3MTPnxUkhEG0bIkVn4VGRLuIaLWqjVZfi4iGE9Hb\nqt/XEtEk3THzCpjEz0S0koj6F/UeIrXTXsU0cCA0Dfn5MIz1748UEQ0aFP3ajRpp1QQOB9JvXH55\n9NoN6lQW0uC8fj38wj/6CL7w339vXols8ODoRV3M1AIyWVwwiPtfsQLjCwSgwtInVEsqateG/u3g\nQaiHZBTZvn2YbLXV1OVCCHr79og6lIrxnTu1Bp2sLCS8eukl5cGEQsh1cfXV0MV1744AgxJURdWq\nhXQTVtCoEexO6tuShYSIiJo3h+pz3LjY1/L7oQ564QUYjQcORL4lib//RoBeRgYi9WWsTlYWpmvT\nJnxDH3xgfn23W6lRcf751u6v1MP6e3GYmYtaFcNFRE2J6BxC/NiPQog2zBxBAVl4lC7FawkjJ8dY\nMEcIpLdIBB59FIn80tLQKlaE7l5mUtVD2gVSU5X3LRxGcNORI0hc1qEDCPnZZ5vr/8eMITrvPGNm\nTomsLKOxOycHfVSogEA9SSNlmoe9ewt3/8UGIRD6vWoVMsCNHw+3IX2uhsxMJADq3RseT5Iztm2r\nDdlNScHEmuV6WLyYaOpUhJLffHPx3lcR8Npr0WnUli0gyvqoZ59POxVEeA/vugvvXFYW0ezZimfd\nwYPgt++8QzRvHsw/atuFEGBqW7eaB3f6fHhsR4+WI+aQWC+mmAHEBNvEAmbOZ+adhFCBpgm5Fx1O\nawbhdhs/DqLERWpWqoRV3YwZ8Cj65hvYT/UE2uEAfRs4EAa9Nm3MrxcOIyp20CCc8+ij5q5/q1Yh\nP09mpjG//65dxlKggVL4a8YAACAASURBVABon5lU43KZ1x5IOqZOhRXzxRcxETLJkB7Z2bCafvml\nkilu+3ZMuHz4bjd+Dx5snFBJ/bKy4FpklvY0JycxLkBFwAMPxD5m7VosWAIB3HIggAWAdJzw+/HO\nLloUuYb1xx9ra1fowYzvp3Vro0u0wwEHh/btzb+7Mo3EMYhfiaipEKKREMJDRCOIaIHumHkE6YGE\nEFWJqBkh6DjxKC7dVTJbPDaI8eOhC5U61C5d4gtoigcTJ5rre6UaXSKSQU+tE962DbZWfWSzWTv3\nXMW4eeCA8RyfT1slTq3DPuOM4puPIqFCBeNExmN1NVOI//ILwt/T0xG2q58ov5/577+VMZw8iZKA\nTies/g88kISoQXRp5fZbtWI+dYp56lTml1/WBkXm5ODWolW+Y8b3oo7O17c2bZRr/N//KVHQVasy\n//FH8c1BYUGJsEG0asW8YYOlZqU/ih1ALIjoFSLaRER/ENGIot5DxLEU14WT2eL1Yvr2W+YnnoCd\nMicnrlMt47vvjB+TEMxnnWX8KM3KlapbaipKnI4di3KfVuifx8O8bBmuP3s2aF1aGoyYb71lzmh6\n947sNVNiOHkSA374YVg0pWeRnkpZsb5Ga04n3Lwkjh3Dg5CU1+0G9VNb7a+7ThtanpLC/P77JTs/\nBahcOfrtud3MI0YUrY+cHOYrrojch9fLPGOG9px//gFjKK3pWBLCIM48E6HeFloi+ivJlvQBFEcr\nDW6uelx4oflHZeYk89lniruf36+lfS4X6hwXJiVH/fpKH8eOYVFz4gR+P/641kP01VdLZl4iYvly\nJBFS34DLhWLG//7LPGSIMe9HUVuPHsoDOXUKxP6sszDhgwcjWdGsWXDVad/enCldf31SpmvhQrwr\nKSmYFiGU4Xk8yMu1b5+1a4XDWDDJNCG3346cSaNGRZdYAwHk6ypLSAiDaN2aeetWS62sMYjT3oup\npBCp4JSZvWPIEKQh+OEHBDp16wbj86ZNsE/88EPhskuoa01UqKDt+6mnoNLftg1BV23bxn/9IiMY\nhDJ92jTkdGY27s/IQPj2zJnIHW0WPlxYLF+O0PPbbkMNWFkIoWVLjOXqq3GcvuaqhNeL85OAgQMR\nELliBcwpTZuiFO60aaipkJEB3f8PPyBjajS8847Woevdd5Hp9bPPjDYttxu2i7w8+At06IDtR48i\nvcyOHag2eOutigpeFsmqVKkcZfwtZYGWCUOyOVRxtKJKEOoMk2bIzIQNwErysF9+gYZk7lxjjEPf\nvtpjjx/HdaOpuU6ejK5NkatHM3VWKRSstHj44cgBIur26KNIeZpI6SGWfsaKkr9CBWSWKwVYssRc\nVVm7NrL2vvce3nMzDBhgPK99e+YGDbTbPB7mBx9kPu88CHvnnce8Ywe+j8aNFS1gIMA8ejS+qTFj\nIAh6PMzdu0OSTSYoURLEX39ZaonoryRb0gdQHK2wDOLwYeZevZQMk1OnGo/59FPsS0mBCP7ttwjy\nmT+f+cMPtWL8XXfh2PR0iOaPPIJI6Ro1IK5PnszcrRsC9R56CMQ9JYW5YkXmq66CXrlGDW0m2HAY\nH7n+A/b5mP/zHxjCb73VuN/pRJbZUo0zzjAO3IzTrViBsNuSYhDxMJJ+/Zh3707qNO7ejfco2lAD\nAWjNzBZCN96oXYQIARXpp5/iPRYCBL5WLea2bRVG4HAwV6uG70AfuOd04n1X83+Pp+h2kaIiIQyi\nTRt8XBZaSTMIQjqOmNsinl+Sgy2pVlgGccEFWg+iQEAx7DIz799vXOCmpDC3bg1mkZaGtno1MjaY\nLYYdDtDB+++3tliW45g7VxnH+vVI5S0lhQoVmGfOxMd+/LjRwYcIq8lSj/btY0+Gw4FiFom2PySq\nCYGXYfnypE3jRx+ZR1abvVe//45zQiG8k34/ptbnQ5NeSNIDacUKLHRefBHb9MHr6emoBaEXuFwu\n5quvNo6hfn0ldUcykDAGsWePpZYEBrHWyrZIzbZBqLBsmdadPS8PQXM9e+L31q3GlM3BILarA9Nu\nugllQs3sDuEw0V9/oXKY1dTKWVnwP7/4YqL//Q+J1f77X8Q0jB8Pdf011yC4qXVrY0lGIWDH2Lgx\nCcn1rOLoUYT7/vYbaEckuFwohpxI20MiwQwl+8iRMBolAVWqRJ9CCZcL2QOIiCZOhNlF2hj8fpQy\nPfdc2MRkZoFu3ZRSoUeOGN/hcBixDvrtPXrAlOPzKcHuQqAFAviOLruM6L33IsdglFokMFlfoiCE\n6E5EPYiomhBCXUQ4nYgsR6GUrrtKMvT1gL1epTQ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+IKL1ls+Po6NhRPRKQRtakjcZb4vH\nzXXHDqzm5UucmqqUYSwsjh83LoD9fvQVC99+i1WZ14tx/fSTsi8vDx/m8eP43aGD8UORH9L06cx3\n3mm+3+z4ceOKds/FhgUL4D1QWhhE796gXOptQmjH53YjzUaScqmr1YbhMAj3FVcwP/kkiFROjqJG\n8nggoYbDWIRUr25+2088YexD74kcCEAq/u035ptuwvupZgYuF2z3+pT06elQj+qhl8S9XqTa6NrV\nmODvv/8t3FwlgmC3a9fRajLXZDCIBmbN8vklOdiSavEwiGuvNdKeCy+0fHpEfPghPoT0dHyEd99t\nfTEZDMLlNRZ9MVMVyDZgAERyq6Wbi+LBVWyYO7f02SQmT4ZrblqaYuE1Y16PPFLi0/X771BFCgF1\n5sqVSA8lp9DrxTsjI+RDIe072aiR+S37/bDZSbz6Kgi9/rj0dEivVqRWpxPHtWjBvGaN+f3UqKE9\nJyUFqez/+EOJ10lNRcoamUEgXiSCYJ91Vker5SBKnEHIRkTViai+bFbPixr/KIQ4SURstouImJnT\no51fFnD4sDH49ciRol/3yiuJ6tUjuuQSRF1PnYqo54ULY0edOp2IcI6Fs88m2rLFuN3hQERq9+7G\newsEEAXLrD2+bt3Y/ZU4nn9eGw5eGnDvvSjuvWkT0XffIez30Ue1NTn9/hKf0OxslBo9fBi///kH\nv3NylLK0ublE+/YRLVpENGyYNrA3P59o507jdYVQ6lMToTb6I4+YP5ZgEFOSmRl7vOEw0auvEo0a\nFfmYmTMRIe104n3t1Yvo0kvxe8sWoqVLlbEVa9S/BVitL1/SEEJcQkTjiag2IVlfA0LqJEvV6aOS\nKmZOK+oASzuGD0eYv3zhAwF8PInAww8THT+uvDw//kj09ttIb1BY5OUR7dmD1BvduqE+tTqVh8OB\nj3rRIqIvvjC+uJHo7dChhR9TsSHer87jMabvSDRcLuQsGTqU6Prrsa1ePYWCEqEw8003Fe84dNix\nw/hsMzPN021kZ+MvM96TXbuImjTBsaxbDjZoQPTtt6hnTUQ0Z46xH7kgufNO8wWLGZiN6Wr0OP98\n8OKVK/G+n3eewtRq1CC64gprfRU3mEsvgyDkXepGRIuZub0Q4lwiusbqySVaRbs04sYbsdoaPx4P\nefRo88LphcHWrdoXJyuLaMOG+K5x7BgWrRs2ELVogQ86JwerQSHAHIQA3erTBx//mjXYbxXhMNHN\nNxO1b0/UsmV844sbn3+OpFVeL9FDDyF5VCRkZMR37UmT8ACLE/n5SPijRvfuoGQ//kiUnk500UXW\nExwlCFWr4r3QIy0N74J8H4QAoWWGlPv555AwQiEQXyltSBw8iBW7RJUq+K0+rmVLotmz8bdnT+35\nTie2LVum/RacTkgHsdCwIZoZtm2D1NOiBVH9+rGvVZwoxQwin5mPCCEcQggHMy+xlINJIhn6sOJu\nJVVRLhbOP9/cG9IqcnOtpyTy+xG52rOnteP1TRoADx6Ep+nkycUQBPzJJ1qbQiBg7gaal4fAErOB\nSsuqfnvTpjg3EeHn0ZrHA8f8UogmTYzD7dwZqaDq1MH/DzwAu9ttt1mzFehDOXbtgku1dOdOSWFe\ntQr7li0z907etEnJJOBy4dzPPy/avb7wgramdjLdXNu06ch79rClloj+4mlEtJiIUgklGj4ioteI\n6GfL55fkYEuqlRYGsW8fc+PG+Ih8PuRWisex5cUXY3/A6taunXnAHhEIxKhRGIeZTTUQQDK/KlXw\nv98PL6pt2xI4IWZuV9deazzu9tsjx0FccAHqVs6Zo3Wod7tBheIJRY/EAKIVQvZ6UY2nlCEvD2nb\n1bxTvSAJh7UVCmUhICsLj+3btX3t2wdj9NNPo8zpiRO4/rx55te84Qbs/+knVDws6sJj2zZzL8Fo\nsRiRkCgGsWsXW2pJYBDjCXFrLiK6nojuJKJ3rJ6flPyzZQWnTkFrsHp14UTI2rVhmF6zBmrr996L\nL+Pv1q3x9bd5M1J3+/3GfTVrQjsycCDRHXcQjRihHOf1wii+bh3R0aNQhWVnw37Srh3SSZ9zDtGB\nA/GNxwC8sFrodRpEUHRLRbkaQhBNmEDUqhVyTqvVOPn5MLocOIC8z/HA4SCqVQsqrxMnoHiPdg+p\nqfFdv5jxww9QMd1+O6aofn2oXSZMgAqVCHaGb79V7AeRMpNLuN14L158kahxY+2+2rWJbrgBWczP\nPBNatfR0o/ODRHY2xtWrF5w2atUq2v3u2mW0XzidUBUnA8yYSystCTiXmcPMHGTm6cw8kYg6Wz67\nJLlZSbVESBB//QWfcFnFrUcPqHziRUYGFs5yVZ+WhkBh/apMjWPHsFCOlNA0WobXK65AJS61e6vH\no6wsHQ64I+7YwfzNN8jL9Pzz6PO88yJf1+VCoaIiYcYMrYrJ74deQg8zXUmFCliCHjqESkfR8jY1\nbBh/4r9KlZT+p0+P7F7bs2fS4hvMcPKksfJgSooSV7BzJ+Jhhg61NiWBAPOvvyIgbd8+8z5DIeY2\nbSK/J+rfPh8kiz/+wLQuWVL02MG//zZKEGlphStwRQlY0bdu3ZG3b2dLLRH9WWlENIYQFJdFCJCT\nbScRzbR8nZIYbEm3RDAIM13+ww/Hf52+fc3Fbr8fGWWnTWP+9FMt8xk6NDr9i6Qe8HqVgLcFCxDT\n1aePMfLU4WC+7z7jWF99NXrYgdudgCR/H34IbnvuuczffWd+zMKFCgVwu8GpZS70Sy+NnQO9MDUw\na9bUjmHKFGRb/H/tXXd4VNX23Wf6TEJCDL0jTZr4SACxIEWQoigo8ECxIvoARRHbs4CAioDio1iw\niwL6QAVUihQFBR69iYCAoICClADJZJLMzPn9sXJ+t8/cSSaVu77vfilz595z79w5++y19147NRXG\no3ZtFBWUspLzHTu0BkLY0sOH8bvZGkOXC4sAuf1bvx6xLbnqcLQeEkI0r1o1xLLefx9/C5kZQTkV\nBkKgLzER169WXTeLeBmI/fu5qa0YDUQyEdUjxB3qyrZLYjpOcQy2uLd4GAi9vi/Vq8d+HPWXV77Z\n7ThPYiJUorOz8Z6KFY3fI1Zlet6F2w1lCnXATq/95LBheC0cliaEUAgehduNOVa9GnQ6C+ZFFQgb\nNnB+//2ct2gBS/fuuxisUTWXfAlcubJ24Hr7ipsYiz50KcOpU9piSIcDtmzIkOhqrB4PbnGDBvA+\nT5+Wjj1smBRU9vmg7cQ5nLhowrviK5iTox1fQgIMT2GRmYl4hPjeFATxmLCbN0/je/dyU1txGYh4\nbVYMwgAVdCpARL3B0aNoDdq7N9F77+GxN0K1CI1aQyHEOTIzET949138P1W3qaqEnBz9LMqcHKIl\nS5Cy+p//SP8fMgT1HQJeL9HttxNNmwa63uUi6tULMYfhwxGjuP56cM0+Hyh6n4/ohRei567HDVWq\nEM2bh/TRtWuRZD9pElHjxsq8S68XFWGXXEKUkkL0+OP4UHw+EN8uF15r2FB7jrvuQr3C/PmRK7ZK\nMVJTEWvweqXQSDBI9OKLiHnphXgE3G4Unu3YgTq/efNwq4iQtvrGGziWaC369tuIi1WqhOcn0rNQ\nqRJ+ZmRoX4tXvCAhAR9rSRfJcY77ZGYrcyhpC1UUWzw8iClTlKt0mw3a9SdPokGJWJn5fBD6M8K6\ndbHR4U2acD57tpRJpJe1mZIC4bVIx5E3cQmF0CegcWNkOn37LedLlijpJKcT1yi/Zq8Xq8sXXkAT\nmGLFhAnai69cHPWVXAAAIABJREFUGfxGrVoIpCQkcH7NNUoVOYGNGyEy9MorWPL27Kk8ltMJd6kM\n46uvpJa0LVtG9zzlW82aaGlrBD1Hze2WWtGGQqCO9CQ3bDboMYn9atZUvu7zFUuTPVOgOKzomzVL\n4zt3clObmfMRUXdCJ7gDRPRUhP1uJSJOROmFvQbDcxTVgUtyi4eBCIehPik05zt2RBrdm29qA2QJ\nCZGP9ccfmKvq1oWxqFLFOM7KGCb/vXtxrscfV57P4wF9sGdPZPqgUiVoOn3xBeczZ2p79D72mPF7\n1RTEkSOFvp2xY/x47QVWqoTXsrIQ3N60yXxDC72oas+eRTf+IsbPPyufC6NngTE8aw4H9rHZpB4K\nkWLtah0kQV3JVYgPHNDSR3Y759OmKY+1Zw+eaYcD35VFi4rmnhQE8TIQ27dzU1u08xF6RR8koktJ\n6rTZTGe/CkS0htBzusgMhEUxGYAxuNgXLkCbafVq/L51q9Ztj5a+VqsW0aBBROPHS5mYa9cStW2r\nTXvlHFIaaWmgiWbOhHqE1wu5gxEjQPU0bYr0xurVMVa5pILPB8qoRg1IiTzyCKpZP/5Y2qd6dVAM\n0aBXXVsskOfhEuGiHn5Y+v3qq4nS05V0UyS0b6+8YK8XeZelAKEQpJ327DGfCvnjj8rP3OgzYgzU\n5enTRG3a4HYFg6AX+/Uzpkf79NF/PurXJ1qwAL9PnKit2GcMFKUcTZsSHTmCFOoLF4huusncNZYV\ncB7XNNe2RHSAc36Ic55LRPMIneDUGE9ErxCRTv18HFFUlqckt8J4EEuXYvH60UfKhJU1a6SAstpd\nfuCByMecM0eZxTFokJTFsXq1uYpWrxfpinoIh3GcK64AjTR8uL534vEoG7c0bozjGqXTut2ovi2x\nrM4dO1D8dtVVnE+fXrjUlwsXQEe53bg5ffuWioyk8+cR0E1IwNamjTll0i++0E+kUG+JiUgKO3RI\nX9nXqOYvEEC2UVKS9vkQFFGXLvrnlBeaL16MbCaPh/Nu3SB5XZpAcfAgmjZN45s3c1MbER0mos2y\nbaj8WER0GxG9K/t7MBHNUO3TmogW5P/+PVkUU/EYiBdewMNvs+HLKm+FqJcqWrcuOrpFmmeCQW0M\nIiFBShsMh5E9YqQaLd8aNIDc8YwZEsfLOVz41FSJltejBwTdkJmJCu1u3RBT0ZsA7HbwxvfdJ/We\n0MOxY6jXqF4dWaslQkXFgnAYxLte84ESwvDhyonb45GyhSIhLw/JXQkJkdVFEhNRubxokf5CoFOn\nyOf5/Xdt6nNSEiqiJ0/WHs9mk0JCu3Yp3+tyodbGCOEwGmHVrw/llI8+Mn8fC4p4GIjLLkvjGzdy\nU1u080UzEISq6O+JqF7+35aBiHUriIHIytJvhSgmcvWXUHR1i4aMDO17ExOVqajhMGoQoqUkCv17\njwcr/3nz8P60NOWX38gjqFVL2fbZ4dDu6/WihWQ05OTgiyzGbLfj+AUpVrqY0b699nOqWRPG+8sv\nlfsuXQrj/sUXeGYOHoS3e911+p93tWoIKs+fb9wXJCEBJSZGn5vfr13g+HzohZ6XBy9UPENOJ9pg\nZGVhfNOmad9rtxs7gjNmaKW6Fi6M6+3WIF4GYt06bmozYSDaE9Ey2d9PE9HTsr+TiehUvidymEAx\nHS8qI1EiMQjG2CWMse8YY7/m/0wx2C/EGNuevy0qyjFlZmrjATablKbXsqXydYcDFHg06KWjZmcr\n38sY0guNYgJOJ1IK8/IwzkAAxxg8mGjKFKJDh/CVEpD/Lsfx40QffaSUW9Db94or9N8fCEDdMxyG\nrPPff0vcdygElYpY1WovdrRqpf3cjx9HvEikIhNBBeTWW9F6YvBgossvh8zF5MmIRanx3/9CjeSa\na4juvNNY3Tcri6hmTaLkZBybCDGyvXul2NesWfiZlITwz4MP4hlxOIh27iSaPp1o9GiiCRMQ70hK\nQpbyqVPaEFFiIp735csh33LNNcgyJoJ0vVxK3O9Hqm5pB+dxjUFsIqJGjLH6jDEXEf2TiP5/7uOc\nn+OcV+Kc1+Oc1yMEqXtzzjcXwaWVjAdBRJMoP32LiJ4ig/7WRJRZkOMXxIMIh9HdSr6Kr1BBSgU8\nfBgUj8eDldKkSeaOu2iRdhVls2GVJcf//qdfxZySItHmeitAj0dKdTTyPNQdMeWbwyGtLhMSICio\nhxkzMAaPB9TaypXabC6fj/Pdu2O+9Rc1zp3jvFUreIYul9ajS0kBIxatME3tod55J+ejR8MDNitw\n6/Mh5ON24xg1a0qSMPv2wRPZtEn/Oi5cQNW22uNt2VKiwbxeqK2sWqXtif3556DM1M/tHXcU7f2n\nOHgQTZqk8TVruKnNzPmIqCcR7SdkMz2T/79x+YZAve/3VN4oJkKOb/X836sT0T6D/YrNQHAOTv3q\nq/HANmyIYl45wmGk+cVCo4wbp/0i2myS8mRWFmouHn6Y81tvlegjpxOVsGbkvj0e/Wppmy2y4bDb\nkTn63nuo5Zg7V9/9VxsvxlCv0bev9H+fD1XchZVQuBiRlwfK5tFH9enIffvMBaTVnz0RniV1YXmk\nTU6z2mzQETODrVsRm5AfKykJFNd776EcRXyfbrlFe94rr8S+4nliDNdc1AuOeBiIxo3T+Pffc1Nb\nPM5XnFtJNQyqyjn/M//3v4ioqsF+HsbYZiIKEtFEzvlXRTmoGjWQPmgExtDJKhZs1nH8EhNRqZ2b\nS3TVVaBrAgFUhg4ZgkYuTZvCrX///ejncDiIFi5EZui+fZIra+TSMkZUsSJc/KlTI4uXEhFt3Khs\n1MY5Kmp37gQFsHUrqJKhQ/U7mFmIDIcDlI2gc0TFrdeLrmn164O2ycoypg+J8Ez5/aD7xGefnY2G\nO3//bW4c8mrfcJjol1/0992wAVv16kilrlpV28wvNxfPVocOyv/rZSbbbNhvzRqiDz8ErfrAA2jO\nVxZQihsGFQ5FZXkIjSp262w3E1GGat+zBseomf/zUkJApkGE8w2l/NSxOnXqRFs4FBv69dOultq0\nwWuLFmlXhg6HMitKnZHk9SL4KFaaTicChbm5KMhr0QKegdOp31LB54PX8Pvv5q+hWzftceTipxbi\nh3XrOE9PR1uLUaMk7av9+0HX6CUWECGYu2OH/vNWvbo5wT63W5ty3bSpdoxvvYXnyOXC/p07I1tP\nZAEmJODns8/qX+NPP2mD0YsXF+x+HTjAedu28FbS02PvX0JxWNE3apTGly/nprZ4nK84t5I5qUmK\nSfWeD4noNjPHLy0NgzjXfhm8Xik7Ze5crZifw4FUVIGNGyGhkJQEKmnsWKQt9u6NLKI+fSShU85h\nXFavxoShll5wu1E8vGsX9j1+HBIakdz43Fx9msroy2+h6PHQQ9JE7Haj4l7g22+1sSy32zizTb1f\n//54f3Iytk8+UaY6B4PaeEhiIs7LOejIDz4wFuP74w/ERnr2BK3Uq5eyY10syM5WGj+bDYsndXwv\nEuJlIJYu5aa2smYgSopiWkTobjQx/+dC9Q75mU1+znkOY6wSEV1NCG6XKmRkED35JNGuXaiMfvFF\nZb+aq64iWrqU6JVXkIX08MOocibSut4uF6pdXS7o0m3ciOypAwdQXV2tmtRsZaHmjoFO6NgRFbk2\nG47ToAHRH3/g3Dk5RMuWgRqYNg2ifuEwKImBA5WV1l99hcyUrCxtla7Phwwagd27cY6WLVE1bkFC\nXh56JyckRKfyooFzorfeQtZat25E3bsTXXcdmgMJ9OiBfZ58EllMRPoZTE4njienlOrXh2Df7t14\njhcuJBo2DAKO332HZzMQ0K/aPnUKP9u2xaaHP/8EFXnuHI7h80Ep4IYb8Exu344xdOtmjqrcuxdZ\nfXJK1e/H828mwzBe4NyimOK6EVEqEa0kol8JVNQl+f9Pp/wiESK6itDwYkf+z/vMHr+4PIjcXM6b\nNZNWVB4PVkWxBGq3bkUFdJUqCPqeOYPVlaCHPB7kypuRHJowQZkxZbeDHlJ34XQ49DNb3noLx1m7\nVrkKtdmkVRpj8EyELPTo0VJv4MJQBeURf/yB5AFRu/LPfxauKv3xx5U1LFWrKuW55Zg/X/8zrlYN\nrS0efVRKMkhOBmUoii/1qvtr1OB8wQIU8akpK58vcgMsgRdf1NYa1ayJJA0hThlLv4iDB/Vbj+7b\nZ/qWxmVF37BhGv/6a25qi8f5inMrEQ+Cc36aiLro/H8zEQ3J/30dEbUs5qHFhG3bsLIXwblAAIHb\ngwf11aX18I9/4DgChw9D90l03AwE4J3s2EHUurX2/TNnEo0bh5Vq1arYXyAUwuo1oFJrMZIdnjoV\ngcEFC5T56PLVEedYkf78M2SW33gDYxXjHTAA9RBmJZL08PffkC1nDC1SU3SrZEo/7r4bnpVYcS9e\njABs9+7QJmrYEDUQM2cSHTuGlbTQKcrJIdq0CZ5gejoCyK+/js+ZCJ/hiRPwSj79FFJTTieSD4jw\nDOp9zu3bE33xBX7nHM9rRgZW9uK9e/dqV8THj0O7KRyGZ+rxYIyVKsHzVLcl1UN2tnZM2dlE//63\nMsD9+efQc9J73uW49FLUhnz5JTzdhARI8DdqFH0s8UZ59SBKimIqFzBygwuTyZObqy3YY0z6Am3d\nikwXmw1fyueflybzrCzsy2WZLvXro2fAggXSQ+z1YqIxMhRJSdqMFjmysyEA+OSTWkMQCkGUTfQD\niBW//SZRGUSgubZtQ4ZZWcOuXUo6JisLk+nw4TAMeXm412fPYrL94AOisWPRoqJ9exSscY5CNlEo\nqUZmJoT1xOfQvz+KIeX9QOTYupXozBkUZjIGw6BGs2b6z7B4fnJzYYwWLpToUjO49Vai116Tnlef\nD2OfO1dpIJxOopMnzR3z44/x/t27QXsOHFj8mXTcopjK1lacFFOLFlKhmceDOorC1AIEgzimoK0c\nDtAU2dnIcFFTP9ECj+r9GEOgW62j43Ry/tprGMPx49EFBB0O0GLqAHblyoW7/j59lON1ODi/556C\nH68kce21ymsR9S2R7qvLhXqSgnRNFXTPa6+BMtJ7nTFIs0T7jJ56Cs+zurZBHtCeMwd05NKl5lvR\nrlwJSvXSS9HCNxAAZSUPoicmRu5VEU9QHCifSy9N4/Pnc1NbPM5XnJsl910IOJ2om7j3XkgGDBsG\nCYHCrGDsdkgn9O2L5mk33ki0fj1c+hdeMKZ+IkFNEf3wA1bmb7+NoHLNmjj2I49gn19+id79KhjE\nKk++QhbS0oW5/mPHlOMNBkHjlUV89BFoPyFRcfnlSgVzPeTmIpFAz1swA78fUvKDBumfi3MEg8+c\niXycl18GPbl8udajJYLHM24cguL9+4Mu27cv+vg6d4ZHePAg0UsvwZNauRJ0EWPwPBcvjtyJsTQi\njlIbpQoWxVRIJCeDh48EzkEf/Pe/+AKMHRuZs73kErjNasiNQzwwdCg2NcaP1898sduxqQui5Pjp\nJ/DARLju774DD5+erk9nqNG9O+gCOQ3RvXv095VG1K+PDLSdO8GPJyWhADIazE4kTqeUhSbgdmNh\nMX48aKmpU7Xv4zy6oSLC4qFWLaJbbiFatEi7aNi7V/qdMcRc1q83N3Y5mjbFfQoGQW2WRZTFyd8M\nLA+iGDBxIoJuS5cSzZmDZkDHjsV+nKFDlb2lC4phw/Dz++9Ruf3oo+D+BYwMgMNBdOWVxgFozqXY\nAeeo7O7bl2jkSHDqZoTXnn0WwVC7Hee76y6iUaNMX1qpg8+He9ayJQLKM2fCGxRelhBitNtji2mJ\nY7RtC2G8pCRUUjdoANE9pxN8v15PpF69YnuOOneO3jSKc+UzVBCUVePAefn1ICwDUUj4/cqgsB6m\nTJFWxCJXW+4hnD8PiYG1axF0NMIddyD42Lix/uvy+gsj+HygPb78kqhnT6L33sMxmzWDIcvIQNc6\nvRVmTg7ohqQkfSPh8yFISIRr+eYbBGazshDY/te/olNXTicyfXJyYGzeeEOf4iiruOce5PmL+5eX\nB4McChk/R3Y7PjOXSzIWgQDet3MnspjmzkV20tatMBQC06fDyxXvu/FG1LiYxfnzRI89Fv0ZdzqR\nXHCxwjIQFhTYsgVFa0lJyBLSk1wW0Ft9if/t2QMqonNnFM4lJcFTMHqYhgwB1ztsmCQTzRjakS5e\njPcnJ2OCb9NGPyOqQgWkForUVLHyf+45GIrrr0dMQg/Vq4PDfvxxjKVePUwOyckwNldeif3++ku/\nner588b3SQ5BZ8Ub69aheLF5c9Awhf3SFiRWIOgUswgGYWRHj9bKx9vtyILq2ROfm1w6/PRp0HNC\nw8nlIqpdOzaDe+KEsWdjs2E8Xi+K9czohpVHcI7PyMxW1mAZiAIgO5uoa1dMgiKt88Yb8YXUwwMP\nKF16txs0ChGCiWfOSAaDcwQ333kn8himTyd69VXwwyNHgg/u1Alu/sKFEAncuJHovvskzyIhAfvU\nqKEfzwgGURE7aZJkPOQQhqhRI3gbH38sVWn7/aCHBD2Vnq4NYNeoEb2m4exZBDGN7mVBwTmC8h07\ngiffswfX8O9/F+x4P/2EVb3bTVSnDupUPvoIBjQlBZ+5HlWXmwujGmsgPzMT8QS1ZxcMopZGD59/\nDoMsJqbcXKI330R6slnUqWM8Vp8PNTv792PRUKmStubmYkB5pphKPI2qKLaiTnPdvVuroZScDLli\nPYRCnE+cCDGxLl2gkyQkw43SCGvUQJ+HFi3QyzYSDh6E9o28F7BAOMz5++9z3rEj5x06cF6nDlIU\nbTbjPgE33KD//yZNImv6JCZCtlpg8WLcJ5sNLSSjCaktWCD17nY4oNOzY0fk95jF8OH6qaOVKsV+\nrNOntZ9/UpKyqtfr5XzECOX7/voLMvKRUoh9Pki/16ih/3r79qg+drlwjs8/Nx7n1KnGvcnNVD4L\nTJmiP5ZataT2ogsXSm1zzXzWpQUUh7TTunXT+LvvclNbPM5XnJvlQRQAVaroSxsLnSQ1bDas2kaP\nBsVx++1Yfa5aZVxQdvw4PIvdu7Hq/+sv/F9URx89ir8ffRR0SffuWJlu2qQ8jt+PdML16xHn+P13\n8PvhMFaGLpd2rEJXR419+yJz0bm5oDOOHgUfnpyMmIbfj1WmqC6/cAGB5w4diLp0QSeyzZsRY/H7\nsVoOBhHDuPJK6PQUBocOgf7So4P0Ov5Fw+7dWppGXk0u/hYVywIPPogK6qws/eM6HKAXX38dSQzy\nWILAwYPw2v78E/dReKJ6uOkmfZrO7ZaeJzMYNQqxE7db8iZatMDz5HaDMhs4UNJFOnAAcZZIz0p5\nQ3n1IMpo3kDJonJlcPTjxkmVy8OHRy7xP3YMXzL5JHLzzZFTRuVYuxbCbB07YpIPhTB5btwIt164\n9n36gKJ55BFM6MnJMDZ6aat5eaCd5GOw2aJnrBghN1eaGJxOHKdrV1RxCwSDMAx79kjnXbUK8Q89\nZGeDulqxomBjIoJ0h8ulpT88HlSimwXnRDNmYCxqii4cxr2TTwJ//41akSpV8PeuXZFjFsEgspyy\ns0ENqo03ESg40aI2ErKzsWARrT3ln2k4jFiTWTCG+MITT4D6a9ECz5XA5s1KQ8Q5FgnnzknyHeUZ\nnJfNyd8MLANRQDz5JFa/u3fDMFx9deT99+3DF15uIDg3F7jiHKvJIUOwOhOTzE8/aSecY8eI2rXD\nFzQvD6tSownf59PXaWrZEpx6QVaAYuIU17liBWIiffrg723bcA1mDSMRVsrhsDTh7NoFzaoWLVBg\nFQ3NmumvpAOB2FIz09KUulk2G4wMEdKYp01Tfr7hMDKAZs/G3y1awLhHMhJ5eYiVzJ6NhAM1oqWC\nBoNIDf78c/zduzeKOfv2RcA5NRUZbAXRt5KrxspRrZp2grTZ9D2g8oryaiAsiqkQSE9HcVA040AE\n+kc9KYbDCDBG+9I3bQpjtG2bcnIxokxOnVKKuqkneocDXe2MJJFr10ZKajyQl6eshA6FYgvQ+nzI\n9a9YERNRQgIM4ODBmHDfeAPy1m++Kclbq1GhAryUevW0r02aJE3g58/Dy2veHAbt+HFpvyVLlMaB\nCJ/fqFEwghMnwoDIEQohiPu//+Hvt99GLURiolRZLQyMGn6/Pg2kV9cgx8SJSGMVWTNLlsBAHz8O\nauvkSXPPaySEQgiY9+sHzy89HVlUCQnYfD5ca0Gy0PbuhQz9oEGoKC8LEAu98pjFVOJBkKLYSlPD\nIDmmTFFKY3/yCRrSd+yIwHHVqkq5bocD3bqOHeP8ySfxujxIrNfIJyVFG0B1udB1rkoV9L2eOxeN\n44NBbaDabkdwk3POn3jCWO/J6eT81Vc5v+oq/dflQdeffpLuwYUL2vGpt6uvRiC2dm30ylY3wFFv\nXq90X/fvN5bUPn5c//0dOyKY37atpKvlcOD8ovmMWrtKbKJRDudooqSWn2YM/5s9G/vk5CCQv2cP\nzvn229GvT328q65SNomSo1Mn7XvatSvcc6vGgAHSmN1uaCvl5KD51HvvFTyxQPTeFs+4zxc5CB8P\nUByCxrVrp/HXX+emtnicrzi3Eh9AUWyl1UBwjuyR5cs5P3JEXzBtyxZ8yVu14nz8ePSHqF1bysBh\nTGr12LixckJijPOuXZU9Krxezrt3NxZn69FDmhTF/qLjHOeY0PWMxD/+gdc3bFAaGZtNMlyMoQ2l\nHI8+qjSCehPgX39h32CQ84EDzXVDk98bxnBv9u9Xntvv1z9Wv35owaoeV1ISeiNwjs5+6vcypuy2\nFgjgfuplh1WsaPxMjBmjFawzyjATr115pfY4v/+OrDf1GAcMMD53rDhxQr+jnFEGXywYOVJ7jy+7\nrPDHjYR4TNi1aqXx117jprayZiAsiqmYUbcu0fz5iFt4vQj8cS693ro16JDt20GjdO4sxROIpK/O\n6tUI9ArdI/Hajz+C9ujYEYVyo0eDYjCidebNAz1QoQKopQULQN0IJCYii0qNn38GpdOuHY7foAEy\nstxuiY+12Yg++UTpWi9ZEjlXvmFDnJMI2U1ffaW8P5HAOWg8zpHppZaI8HqRTCAHY6DcDh7UnicU\nQtyDc9zLp5+W7qPNhgpmeZzA7Ubm1Zgx2gCzqJD//XckENx9N+7FnDlEkyfjXorz+3yROe1gEMkJ\n8ms7fpzoiivwTKjviei8Fg/k5WkzuGy22GJKRggEtJ9BPI5bHCivWUwlbqGKYivNHsTYsdqG7TNm\naPfLyAAlpLd6dzgkGiXSCpsxzm+/vXDy25xzvmKF0ssQ41bnuo8cqR2DywVPSMhBd+yoP07xu8eD\nzmmco+5C77psNolWiuSNeL3o6qbGxx9D8lp+XuF1CI/MZsP/3G7Idos+4SdOgEKR9w0XWLQI0uyp\nqdrPrXlzeDQVK0oels+HDm96n280bykxUXnuCRMiv2/gQO14DxwAjThyJOebNpl7FgQVJ7wIux3e\nz4UL5t4fCWo5e5+P80mTCn/cSKA4rOhr1kzjkyZxU1s8zlecm+VBFDMWL1amSPr9+J8aa9diRaVe\ndXg8yEgRq7jz541X2Jxjhfrxx7GvXjZsQF/iN9+U0hrFOR0OqHzWry/t/+OPCEyqkZuL4zRvjuC5\nnjKrfPyigx6RcRaMx4OA8dmzkm6Uy6UN9odC+tk6gwdjJS8/L8/3Opo2hXdns+F/orPb6NHYr0oV\nBJfVulcrViBt+fBhpIKq7/eePdDQysiQVv5+v37NiVEw0+3G5vXic5HDqFe0wLffKv/etw/ZapMm\nQYurTRvcK71nUQ7GkDr7z3/ierp3RxBeeH2FQfv28BjbtsUzN2GCdN9LO8qrB2EZiGJG9epKusdu\n1++WJprKq3HHHZB0IMLkIiZTI3CO4qtOnaRaiOxsFK5duKD/nrlzQc88/zzSNK+4Au/hHBPxdddB\ne0qepbJhg/HEFgigJmDKlOhCcV6vlF01aZJSW0jAZsM9dLtBz/j9OEf//pi4vV7QNBMn6gsYrl+P\n8ajBOcTufv1VeS2BAAocI2HMmMhUmNFrbrc5kUUiyKW/8goWD3fcoXztttsiS3jLFyWco4+DWk4l\nIwMT/+7d+sfYuRMUaKNGMM7r1xN9/TWoyXiha1cYnF27UARa3N3hCgLOy28Wk2UgihlTpoC3FpNY\nSgoK7tS47joYDjFB+nxY+b7zjpQaOWSIcWqnHLm5WAW/8gom9mrVkJJZpQo8DDUeegiTRziMn3/9\nBWPCOb6wCQnahi7yseohLw/HiTSJ2e049vvvI52zZk3ERNSeQWYmUjXlvQ4YQ7zj889xj1euxASj\nh1h7Fjgcxgq6AgcPxnZMInymw4bBkDVvHl1ELzUVhrt5c+1rrVphsm7dWn81L68Y37DBuAlTOAwP\nQY2TJ1HguG0bjOvSpWW3T0dRwPIgLMQFTZqAbnjtNUgq/PIL6Bo1PB6spB5+GNTFhAnafgobN5pX\nE83ORsVr796gpTIzsTK+/37IPwhwbuxZEMELWbVK+//+/UENJCaCGnI4lAbD54P0w1NPGctbhEKQ\nFzl+XFJdveoq1Dp4PMrjnTqFCut586T/MYaA+7BhkqqsHqpXN9cwx+mEMa9WzbjHMxEK0GIVF0xO\nRjLByy+DJtuyBZ6h14tzulzaQPeSJaCC6tXT797WqROO89lnynvMmPJ+nDhhbMwDAXiNCQmo6hb4\n6SflBJeXh2LKs2dju+7yCM4tAxFXMMb6McZ+ZoyFGWMG5VpEjLHujLF9jLEDjLGninOMRYkaNaDL\nc//9xlpMRJhEJk0CLfPoo9rCo2jut3x/rxdVx2qe2umUOoNNn479omWO6J3X4UD3uPnzUbi2ezdo\nD5cLk82YMWha360bNIpat9afpMSXiHNkEP3nPyheO3xYS2X4/ZgMzYBzTLBTp4Jnj1ac6HIRDRiA\n69m7F59ZdjaM9NSpoFsELlyI7D2pUasWPI7x43HMbt0kDzE7G5/JwoXoQNi4sdKzyMnBBN+vnzFt\nlZGhfU2RwE76AAAe2klEQVReBZ2WFv3Z8fuRbSWKCBMStMcMh80Z2osBloGIL3YTUV8iWmO0A2PM\nTkQziagHETUjooGMsRgUZMo/IunppKSAKxZVu+3aIaagfkgDAUwWK1dida+n2aRGbi7SXNWw24lu\nuAFVsE2agPLJyYG38sQT0n433oiVbqdOkWmVYBAr7A4dcD01aypft9mgi2UGQ4diUh09GjGWCxdw\n3WJTIy8PE/TXX2Ny9PsxsY4YgfuUlobYzDPPwMOoUsVc5fDQoTA4qamgaxo0QIBb/rmcPg2P7Oqr\n4Y3pTSy7dsHINWkCL1SOhQuVfDfnSq+vdm3IbVxyCa69dm2kX6sRDELkkAif1WWXSQYhIQEekFEl\n+MWG8mogSkSLiXP+CxERi7yMaUtEBzjnh/L3nUdENxPRnkhvuphw881w/eXBRqcTX94lS7BK37MH\nq+EmTTChfvABVuROJybuUAiyEsnJ+j0g9OB2YwUs58IzM8Ht2+2Y2OQr6nAYsY8zZzA2jwcT7LRp\nMFzCY9FTOc3NBZXx6aeILXTsCKNms+FYZvo5/PIL3q++vmhB5ZwcZAulpMAIHD6sPMaOHQj2/+9/\naN96++3wLEIhY8XWypUx7kOHYPiM6hNsNqmXtRHCYQTUO3VCTEFQUnq9PtTZXF27whDl5eFZGD4c\n16q+JyKe4XQiOD5rFsZ+zTXwCPWQkwMJju+/h9f66qta416eIILU5RGlWayvJhH9Ifv7KBG1M9qZ\nMTaUiIYSEdWpU6doR1ZAHDkCnjk3F/LIesHGWDBsGCY/kV56ww2IVTRpIjUoatVK+Z4BAzCBP/00\n6JNAAA93bq55JdfMTMRGVq9GgPX0aUz0589j0qpeHfGRihVx7G7dMInKM6HsdqzoZ8+G4bDbof+k\n13EuEEBc4p574HksWIAJa9AgrII3bsT1Nm+u7w0INVezBlCOvDx4Ma1b61Nv2dkw0kSYQAXuvlvK\nNpND3N+FCyNTeUJoceRIJBgYjV3EjBYsQPMor1erGUWEz+Ltt9F1rkED6f8iVjFuHKhMuf6UxwOa\n8M8/8Zl6PPjciRADWrYMhqdtW+V9798fdGN2NsayZg28Jj3xwfIAEYMoj2A80jKqMAdmbAURVdN5\n6RnO+cL8fb4notGc880677+NiLpzzofk/z2YiNpxzkdEO3d6ejrfvFlzyBLFgQNYNWdlSdztypWR\ng6lmkZGBYOGqVXhYe/eWJKaN0KMHMlHk8HoxUYu+2UK+WvRCttuVq1OvFzUZOTmSQJyA3Y7Vff36\noGX0VrVEmHQWLCC69lpIqE+fjuPIv3A+H6gedUX3kSMQOxRB9Y4d4Tmp4wtnz8IDKExVrsuF+6FX\nBe71gnKT14VkZMBQnzypvI5Nm0ANzpiBtq3q4zGGe1KjBvo+2GwISl9zDYxGs2ZEY8dqDUZiIozA\n+vWob8jI0L8Oux2f+/XXa1/LygKltGyZVK3vcuG+TZ6Mz5EIyQ5dumCswSB+//JLjDUzE0ZD/ixU\nqICFwM03G93dkgNjbAvn3DAOagbVqqXzwYPNzTdTphT+fMWJIotBcM6v55y30NkWmjzEMSKShyVr\n5f+vTOKllyRKh3NMmLG0flTju+/gLTRrhkD3pZdiBT5yJIq9oslYt2yp5I+dTnyBZ89G9sq6dQii\n33gjJrMzZzChyd+TnY200p9/1rrYoRDG9847xsaBCBNkr164BpE143RiUrTZMKHddZe+3Me112Ly\nFznmK1dirGqcPKnvWVSqJNEyDge8kTFj9NNE3W5kTQmqRhzP4ZB6k8tRsSJon9Gj4dl07QqaTcSN\n+vfHeUQMxuXCpD1lCnpB/PknJuZAAJTO+fNEH36IWM6AAaCe5IYwMxNpxLfcEjkLLRQypoYSEuAh\nfPGFVKh5/jx+PvEE6Cwi1EqcP48kgqws3HeRLGDEGpeFeobCwIpBFD82EVEjxlh9gmH4JxENKtkh\nFRxnz2ofED06xQyeegoTod+v5Izz8rDl5GCfSBk+Y8bgi71/P768Vati9S7PqmrfXvkevf7EeXn6\nKZdEGNvGjdIqNBLUr4vMIM5RCW63Y3wCwSBW2OrzffMNsm/k2LdPG0BOTMRK+a23sOpu3BjHFxlm\nl12GCZAI1+z14v+DBuF6hg2DV0OEiblZM+hnVa+OcX36KcY4ZAhW3wIZGRhLxYqg+hYtwvHbt5e8\nn8GDlUY1N1dZu/H++6B+xowB5Sa/J1u3RteuimRATp9G7EBt8F0u3MdGjdBzRA55X42EBIxt0SJc\ng9MJo9qlS+QxlWWUZ4qppNJc+zDGjhJReyL6hjG2LP//NRhj3xIRcc6DRDSCiJYR0S9E9DnnXCd3\npmxg0CApLkCE3wcOjP04J06gfiIry3giCIWUXLIeEhIQF1i1CoVRu3dHTrklAp2kl6kT6cvBGCbT\nWHH6NDyUQECiPvbvl143Cgqqw0+HDhHde6/Wi3G5MKnPmoWMoAULpIr2atVwT0TXNJ8Px6hdG57X\nZZdhNR0OYxx+PzysceMQvG/ZEkHasWNBK27Zgmu54QZQf6mpyIBavlxSS9q0CZ8rkf49Pn5cMhKM\nweu6807lMyW6G0aDUeB7+XJkM917r5b6ys0FZUYEGQx59pnHgxiNwOzZiHF16YK40ebN5qvFyyri\n6UFES+9njI1ijO1hjO1kjK1kjOnkoMUJJS0GVRRbaRXre+MNNKOvWhV9Doz6FkTCL79AqC2SkJvb\nzfnEibEf+/RpiL498ggE+vTQvn3kc6vHcc89eN+aNZy3bIlrT06O/D7GtMJzycmcr12LY50/z/mS\nJRCJU4vcHT2qHG+3blrhPK8X4nSHD6N/wZw5Ut8Hgc6dJSFAo54Y6u2mmzi/+27t/p07cz58uFJY\nUE9g8frrIe+ekKB//Fq1lGMMBjnv00crpBhps9k4X7hQ+7nm5ek/V4mJGPe0adK+hw9zXr8+xPRc\nLvTBKKugOIjnVa6cxocN46a2aOcjIjsRHSSiS4nIRUQ7iKiZap9OROTL//1fRPRZYa/BaCvNFFO5\nw7/+VfhObZdeCnokkgfBOaphY0FGBkTo/v4bq8VZs0Bj3XOPcr/mzbEiNqKMEhNB1+Tmgg9/9lkE\nqz/+GCvN6dNRHd2uHagZvawphwOBzTNnpP+Fwzj3mjWolpankTKGle/ixdp0ykOHtCu3G2/EeZs1\nkwQRGUOdyNixoGDWrJG8FDMrP1Ep/vXX2v3PnpU4fQH1Z+dwIO5y4IBxbcjx45LcCRE8jQULULj3\n3HORYz1E8FymTVNKxAucOqX1yipUwGd3112gzgTq1oUHdewYvCx5f+qLFXGkmKKm93POV8v230BE\nKmWu+MGS2ihjcLnAETdtikklJUUrXSE0lGLB7NmgdcTEbxREf/llTMIVKkib241JokIFZMhs2QLa\nZvx4ZCZNnozJ5OBBiMzt349txQrUDsjH73QiLXbOHGmiZAwBZCJMbuoaA56fhy7vYyFw7bXKmgzG\nMMYuXXCN8srtF19EgNbhMEfVyPHQQ4g39O+vpRIHDIBhl1NHTicMpthSU5EUcMcdxjECzlGLQYR7\nV68e7vsnn2hjQ3Y74lByY+P34xxjxmiPXbmytho8GMT9lhsH+fHr1LGMA1HMUhuVGGObZdtQ1eH0\n0vsjVZHcR0RL4npBMlgeRBlEkyZSJfO2bVIKpEBqauzyy1lZ2hWk3MicPw9eeccOiLR17YqJrUMH\nrD7//BPcfMWKymPMnKk8bm4udKgWL0ZaaseOCHxOmIDXW7dG/cC990qTHufwNp55xrjSW+3RnDwJ\n47NtGyb8vDx8QTk3noCDQRiPvn1R8fzRR5hU3W7cz/Pn9bWvvF7UgxDhnL/9BuMYDGISv/dexKA2\nbJA8v8REXEt2tiSf0c6wykc6z9q1uNabb5Y8ht27pcZHwaAUM/nmG6XhEJ/npEkwZnLpErsdn0mv\nXnhPbi7EHSNV61uQEIMHcYrHKc2VMXYHEaUT0XXxOJ4eLANRyrBqFSZQIugvRcv++Mc/sPIVIngu\nF7T/Y00r7NkTQVYxmXu9qLAmwv86dECxU04Ogo5btiBoKiqa9aQaiPRpD3UWzJgxoDICAakHxMGD\nSvopJwed9fQCuB4PaBCBo0fRmc6MbIgcDodEUc2ciSLD1atRWPbkk6B4Bg3SFqINHiz9/vvvMBbC\nYP36K4zgjh3IAvr0Uxjav/82V5Qoh80GAyzqXQREl7chQ5BB1aMHJvr339c/h8uFoPhvv4G2S03F\n/6+9Ftd48CAC9mZlTC52CA8iTjCV3s8Yu56IniGi6zjnMT7pMaCoghsluZXWIHU0rFih7DHt9XL+\n3Xfm3nvmDOd793KenV3w8y9fznmjRpxXrsz5kCHSsbZu1QYwExI437Mn+jFr1tQGPh96KPr7hg5V\nBnV9PnSmq1dPeazkZASH586Vxtu1q/acZrb69dHJLxomTEBnuORkzkeMkJINcnIQSFYfNyGB8927\nsc/ll5vvse10Ighss+EYV16JYPKnn2oD2eoOc3/+aRy8drlwb5OT8T7Rd/tiBMUhSJ2amsbvvpub\n2qKdj7BoP0RE9UkKUjdX7fMPQiC7UWHHHm2zYhClCJMnK2md7GzQAWaQkgLqqaDiabm5qAn47TcE\nVU+fVsYA9GDGS5k1S+K2GcM4zegnvfoqKBeXSyri275d6X04HKBsVq0Ct966Nf42qstQj13w/y4X\nguk7d0qc+tq1KA576SWtlPczz+AeZWQg6C7u0549+rLfoZBU1HbgQPT4hssFCuq66+ANjhuH4PIP\nP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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "id": "OYyAr4ijeyRl", "colab_type": "code", "outputId": "d665771a-12f0-41b5-9eb1-10ed81d57e7e", "colab": { "base_uri": "https://localhost:8080/", "height": 87 } }, "source": [ "from sklearn.svm import SVC\n", "\n", "clf = SVC(gamma='auto')\n", "clf.fit(X, y)" ], "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)" ] }, "metadata": { "tags": [] }, "execution_count": 8 } ] }, { "cell_type": "code", "metadata": { "id": "C1ExvWGje9jx", "colab_type": "code", "colab": {} }, "source": [ "# Loading Plotting Utilities\n", "import matplotlib.pyplot as plt\n", "from mlxtend.plotting import plot_decision_regions" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "84b_2PR_Yerq", "colab_type": "code", "outputId": "450a1ca6-c6c4-4b67-d3d6-0d537f35d48a", "colab": { "base_uri": "https://localhost:8080/", "height": 284 } }, "source": [ "plot_decision_regions(X=X, y=y, clf=clf, legend=2)\n", "plt.xlabel(\"x\", size=5)\n", "plt.ylabel(\"y\", size=5)\n", "plt.title('SVM Decision Region Boundary', size=6)\n", "plt.show()" ], "execution_count": 10, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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cP3PG7e0k9sxdON1xQOCm+gN3UbAa9QyNn/Fz3A/WDWqCUv/gt2hedQ1OdxuR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"text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "id": "ZaADNuarXJtK", "colab_type": "text" }, "source": [ "মেশিন লার্নিং এ ফিচার ক্রসের সুবিধা থাকলেও আমার পছন্দের ব্যাপার হচ্ছে মডেলে নন লিনিয়ারিটি ঢুকানো। মডেলে নন লিনিয়ারিটি ঢুকাতে গেলে নিউরাল নেটওয়ার্ক ভালো একটা উপায়। আমরা একটা ছবি আঁকি লিনিয়ার মডেলের। তিনটে ইনপুট ফিচার। ইনপুট ফিচারের সাথে ওয়েটকে যোগ করে নিয়ে এলাম আউটপুটে। আপনার কি মনে হয় এভাবে মডেলে নন-লিনিয়ারিটি ঢুকানোর সম্ভব? আপনি বলুন।\n", "\n", " \n", "\n", "চিত্রঃ তিনটা ইনপুট যাচ্ছে একটা নিউরাল নেটওয়ার্কে " ] }, { "cell_type": "markdown", "metadata": { "id": "5Upc4g4pXJtL", "colab_type": "text" }, "source": [ "পরের ছবিতে আমরা একটা হিডেন লেয়ার যোগ করি। হিডেন লেয়ারের অর্থ হচ্ছে এর মধ্যে কিছু মাঝামাঝি ভ্যালু যোগ করা। আগের ইনপুট লেয়ার থেকে এই লেয়ারে তাদের ওয়েটগুলোর যোগফল পাঠিয়ে দিচ্ছে সামনের লেয়ারে। এখানে সামনে লেয়ার হচ্ছে আউটপুট। ইনপুট থেকে ওয়েট যোগ করে সেগুলোকে পাঠিয়ে দিচ্ছে আউটপুট লেয়ারে। ইংরেজিতে আমরা বলি 'ওয়েটেড সাম অফ প্রিভিয়াস নোডস'। এখনো কি মডেলটা লিনিয়ার? মডেল অবশ্যই লিনিয়ার হবে কারণ আমরা এ পর্যন্ত যা করেছি তা সব লিনিয়ার ইনপুটগুলোকেই একসাথে করেছি। নন-লিনিয়ারিটি যোগ করার মতো এখনো কিছু করিনি। \n", "\n", " \n", "চিত্রঃ যোগ করলাম প্রথম হিডেন লেয়ার, লিনিয়ারিটি বজায় থাকবে?\n", "\n", "এরকম করে আমরা যদি আরেকটা হিডেন লেয়ার যোগ করি তাহলে কি হবে? নন লিনিয়ার কিছু হতে পারে? না। আমরা যতই লেয়ার বাড়াই না কেন এই আউটপুট হচ্ছে আসলে ইনপুটের একটা ফাংশন। মানে হচ্ছে ইনপুটের ওয়েট গুলোর একটা যোগফল। যাই যোগফল হোকনা কেন সবই লিনিয়ার। এই যোগফল আসলে আমাদের নন লিনিয়ার সমস্যা মেটাবে না। \n", "\n", " \n", "চিত্রঃ যোগ করলাম দ্বিতীয় হিডেন লেয়ার, লিনিয়ারিটি বজায় থাকবে?" ] }, { "cell_type": "markdown", "metadata": { "id": "1EcN9ynXXJtL", "colab_type": "text" }, "source": [ "একটা নন লিনিয়ার সমস্যাকে মডেল করতে গেলে আমাদের মডেলে যোগ করতে হবে নন লিনিয়ার কিছু ফাংশন। ব্যাপারটা আমাদেরকে নিজেদেরকেই ঢোকাতে হবে। সবচেয়ে মজার কথা হচ্ছে আমরা এই ইনপুটগুলোকে পাইপ করে হিডেন লেয়ারের শেষে একটা করে নন লিনিয়ার ফাংশন যোগ করে দিতে পারি। এই ছবিটা দেখুন। আমরা এক নাম্বার হিডেন লেয়ার এর পর একটা করে নন লিনিয়ার ফাংশন যোগ করে দিয়েছি যাতে সেটার আউটপুট সে পাঠাতে পারে দ্বিতীয় হিডেন লেয়ারে। এই ধরনের নন লিনিয়ার ফাংশনকে আমরা এর আগেও বলেছি অ্যাক্টিভেশন ফাংশন। " ] }, { "cell_type": "markdown", "metadata": { "id": "v9VnlkqDXJtM", "colab_type": "text" }, "source": [ "আমাদের পছন্দের অ্যাক্টিভেশন ফাংশন হচ্ছে রেল্যু, রেকটিফাইড লিনিয়ার ইউনিট অ্যাক্টিভেশন ফাংশন। কাজে এটা স্মার্ট, অনেকের থেকে ভালো আর সে কারণে এর ব্যবহার অনেক বেশি। ভুল হবার চান্স কম। ডায়াগ্রাম দেখলেই বুঝতে পারবেন - যদি ইনপুট শূন্য হয় তাহলে আউটপুট ০ আর ইনপুটের মান ০ থেকে বেশি হয় তাহলে সেটার আউটপুটে যাবে পরের লেয়ারে যাওয়ার জন্য। \n", " \n", "চিত্রঃ ইনপুটের সবকিছুর 'ওয়েটেড সাম' থেকে ০ আসলে সেটাই থাকবে বেশি হলে ১, মানে পরের লেয়ারে পার " ] }, { "cell_type": "markdown", "metadata": { "id": "b_vktYi3XJtN", "colab_type": "text" }, "source": [ "আমরা যখন অ্যাক্টিভেশন ফাংশন যোগ করব তার সঙ্গে বেশি বেশি লেয়ার মডেলে ভালো কাজ করে। একটা নন লিনিয়ারিটি আরেকটা নন লিনিয়ারিটির উপর থাকাতে মডেল অনেক কমপ্লেক্স সম্পর্ক ধরতে পারে ইনপুট থেকে আউটপুট পর্যন্ত। মডেলের প্রতিটা লেয়ার তার অংশে কমপ্লেক্স জিনিসগুলো এক্সট্রাক্ট করতে পারে সেই কারণেই। অ্যাক্টিভেশন ফাংশন ছাড়া একটা নিউরাল নেটওয়াক আসলে আরেকটা লিনিয়ার রিগ্রেশন মডেল। এদিকে অ্যাক্টিভেশন ফাংশনে ব্যাক-প্রপাগেশন সম্ভব করে কারণ এর গ্রেডিয়েন্ট, তার এরর, ওয়েট এবং বায়াসকে আপডেট পাঠায়। শুরুর দিকের অ্যাক্টিভেশন ফাংশন হচ্ছে সিগময়েড। যার কাজ হচ্ছে যাই পাক না কেন সেটাকে ০ অথবা ১ এ পাঠিয়ে দেবে। \n", " \n", "চিত্রঃ ইনপুটের সবকিছুর 'ওয়েটেড সাম' পাল্টে দেবে ০ থেকে ১ এর মধ্যে এই সিগময়েড \n", "\n", "আবারো বলছি - সিগময়েড অ্যাক্টিভেশন ফাংশন লেয়ারগুলোর ইনপুটের/আউটপুটের যোগফলকে ০ অথবা ১ এর মধ্যে ফেলে দেয়। হয় এসপার না হলে ওসপার। লিনিয়ারিটির কোন স্কোপ থাকবে না। একটা ছবি দেখুন। ইকুয়েশন সহ। " ] }, { "cell_type": "markdown", "metadata": { "id": "4Qg-wNIHXJtN", "colab_type": "text" }, "source": [ "আমার আরেকটা পছন্দের অ্যাক্টিভেশন ফাংশন হচ্ছে সফটম্যাক্স। এটা সাধারণত আমরা ব্যবহার করি দুইয়ের বেশি ক্লাসিফিকেশন সমস্যা হ্যান্ডেল করতে। সিগময়েড ভালো যখন আমরা দুটো ক্লাসিফিকেশন করি, তবে মাল্টিপল ক্লাসিফিকেশন এর জন্য সফটম্যাক্স অসাধারণ। যখন আউটপুট লেয়ার একটার সাথে আরেকটা 'মিউচুয়ালি এক্সক্লুসিভ' হয়, মানে কোন আউটপুট একটার বেশী আরেকটার ঘরে পড়বে না তাহলে সেটা 'সফটম্যাক্'স হ্যান্ডেল করবে। আমাদের যেকোনো শার্ট অথবা হাতে লেখা MNIST ইমেজগুলো যেকোন একটা ক্লাসেই পড়বে তার বাইরে নয়। " ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CFszInLxtAz6" }, "source": [ "## ট্রেনিং/টেস্ট স্প্লিট করা " ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "pKVNZo7FtYNX", "colab": {} }, "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, \n", " test_size = 0.3, random_state=0)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ea_iDHeYNeI_" }, "source": [ "## একটা লজিস্টিক রিগ্রেশন করি " ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "IhGTb_eiNoKE", "outputId": "340fd2b3-a754-482d-b578-2d947c948309", "colab": { "base_uri": "https://localhost:8080/", "height": 159 } }, "source": [ "from sklearn.linear_model import LogisticRegression\n", "lr = LogisticRegression()\n", "lr.fit(X_train, y_train)" ], "execution_count": 12, "outputs": [ { "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n", " FutureWarning)\n" ], "name": "stderr" }, { "output_type": "execute_result", "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", " multi_class='warn', n_jobs=None, penalty='l2',\n", " random_state=None, solver='warn', tol=0.0001, verbose=0,\n", " warm_start=False)" ] }, "metadata": { "tags": [] }, "execution_count": 12 } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "9aJnyQCFOZlD" }, "source": [ "## একটা প্লটিং দেখি " ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "MDvAOwa5Ohqz", "outputId": "b5d1c63c-236f-4b69-9556-76fe4b02849b", "colab": { "base_uri": "https://localhost:8080/", "height": 275 } }, "source": [ "hticks = np.linspace(-2, 2, 101)\n", "vticks = np.linspace(-2, 2, 101)\n", "aa, bb = np.meshgrid(hticks, vticks)\n", "ab = np.c_[aa.ravel(), bb.ravel()]\n", "\n", "c = lr.predict(ab)\n", "cc = c.reshape(aa.shape)\n", "\n", "ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n", "ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)" ], "execution_count": 13, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 13 }, { "output_type": "display_data", "data": { "image/png": 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z/VbnrLRjoVwue/ZQKC1cGG0dzJlDjT4UMjRx1XAvHpzmCyjEKoTTdefuoGb4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"text/plain": [ "
" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gw_IZFTjkOCG" }, "source": [ "## ২. একদম বেসিক নিউরাল নেটওয়ার্কে দেখি\n", "১টা ইনপুট লেয়ার, ১টা নিউরন, ১টা আউটপুট" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "DTcyMT39kOCO", "outputId": "da7dab4d-935e-424a-ada7-4d76a54e00e5", "colab": { "base_uri": "https://localhost:8080/", "height": 800 } }, "source": [ "model = tf.keras.models.Sequential([\n", " tf.keras.layers.Dense(1, input_dim=2, activation='tanh'),\n", " tf.keras.layers.Dense(1, activation='sigmoid')\n", "])\n", "model.compile(tf.keras.optimizers.SGD(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])\n", "result = model.fit(X_train, y_train, epochs=20, validation_split=0.1)" ], "execution_count": 14, "outputs": [ { "output_type": "stream", "text": [ "Train on 945 samples, validate on 105 samples\n", "Epoch 1/20\n", "WARNING:tensorflow:From /tensorflow-2.0.0-rc2/python3.6/tensorflow_core/python/ops/nn_impl.py:183: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Use tf.where in 2.0, which has the same broadcast rule as np.where\n", "945/945 [==============================] - 1s 1ms/sample - loss: 0.6506 - accuracy: 0.6476 - val_loss: 0.6544 - val_accuracy: 0.6381\n", "Epoch 2/20\n", "945/945 [==============================] - 0s 66us/sample - loss: 0.6435 - accuracy: 0.6582 - val_loss: 0.6544 - val_accuracy: 0.6381\n", "Epoch 3/20\n", "945/945 [==============================] - 0s 59us/sample - loss: 0.6439 - accuracy: 0.6582 - val_loss: 0.6560 - val_accuracy: 0.6381\n", "Epoch 4/20\n", "945/945 [==============================] - 0s 66us/sample - loss: 0.6433 - accuracy: 0.6582 - val_loss: 0.6539 - val_accuracy: 0.6381\n", "Epoch 5/20\n", "945/945 [==============================] - 0s 61us/sample - loss: 0.6422 - accuracy: 0.6582 - val_loss: 0.6535 - val_accuracy: 0.6381\n", "Epoch 6/20\n", "945/945 [==============================] - 0s 90us/sample - loss: 0.6430 - accuracy: 0.6582 - val_loss: 0.6551 - val_accuracy: 0.6381\n", "Epoch 7/20\n", "945/945 [==============================] - 0s 71us/sample - loss: 0.6407 - accuracy: 0.6582 - val_loss: 0.6505 - val_accuracy: 0.6381\n", "Epoch 8/20\n", "945/945 [==============================] - 0s 63us/sample - loss: 0.6351 - accuracy: 0.6582 - val_loss: 0.6458 - val_accuracy: 0.6381\n", "Epoch 9/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.6225 - accuracy: 0.6582 - val_loss: 0.6180 - val_accuracy: 0.6381\n", "Epoch 10/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.5999 - accuracy: 0.6466 - val_loss: 0.5931 - val_accuracy: 0.6381\n", "Epoch 11/20\n", "945/945 [==============================] - 0s 62us/sample - loss: 0.5808 - accuracy: 0.6582 - val_loss: 0.5776 - val_accuracy: 0.6381\n", "Epoch 12/20\n", "945/945 [==============================] - 0s 56us/sample - loss: 0.5644 - accuracy: 0.6212 - val_loss: 0.5599 - val_accuracy: 0.6381\n", "Epoch 13/20\n", "945/945 [==============================] - 0s 59us/sample - loss: 0.5542 - accuracy: 0.6466 - val_loss: 0.5478 - val_accuracy: 0.6381\n", "Epoch 14/20\n", "945/945 [==============================] - 0s 56us/sample - loss: 0.5443 - accuracy: 0.5958 - val_loss: 0.5449 - val_accuracy: 0.6381\n", "Epoch 15/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.5408 - accuracy: 0.6328 - val_loss: 0.5355 - val_accuracy: 0.6381\n", "Epoch 16/20\n", "945/945 [==============================] - 0s 56us/sample - loss: 0.5347 - accuracy: 0.5873 - val_loss: 0.5313 - val_accuracy: 0.4476\n", "Epoch 17/20\n", "945/945 [==============================] - 0s 58us/sample - loss: 0.5318 - accuracy: 0.5884 - val_loss: 0.5291 - val_accuracy: 0.6381\n", "Epoch 18/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.5307 - accuracy: 0.6159 - val_loss: 0.5268 - val_accuracy: 0.6381\n", "Epoch 19/20\n", "945/945 [==============================] - 0s 63us/sample - loss: 0.5262 - accuracy: 0.6529 - val_loss: 0.5245 - val_accuracy: 0.6381\n", "Epoch 20/20\n", "945/945 [==============================] - 0s 57us/sample - loss: 0.5248 - accuracy: 0.6095 - val_loss: 0.5244 - val_accuracy: 0.6381\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sM0uZCaBQ-eF" }, "source": [ "## অ্যাক্যুরেসি প্লটিং দেখি " ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "HWNaNWmOkOCS", "outputId": "aba2c2c8-1b9e-4a63-f934-9ae47c7242f9", "colab": { "base_uri": "https://localhost:8080/", "height": 287 } }, "source": [ "pd.DataFrame(result.history).plot(ylim=(-0.05, 1.05))" ], "execution_count": 15, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 15 }, { "output_type": "display_data", "data": { "image/png": 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hDXellIpCGu5KKRWFNNyVUioKabgrpVQUknAdji4iB4BNYVl423QB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\n4a6UUlFIw10ppaLQ/wearSZCALUzLgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "k_IrXnd4kOCy" }, "source": [ "## ডিসিশন বাউন্ডারি কি ঠিক হলো?" ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "kM3NKQijkOCz", "outputId": "f08ea65e-1e69-4517-c38a-7ccf8b3b27c6", "colab": { "base_uri": "https://localhost:8080/", "height": 275 } }, "source": [ "hticks = np.linspace(-2, 2, 101)\n", "vticks = np.linspace(-2, 2, 101)\n", "aa, bb = np.meshgrid(hticks, vticks)\n", "ab = np.c_[aa.ravel(), bb.ravel()]\n", "\n", "c = model.predict(ab)\n", "cc = c.reshape(aa.shape)\n", "\n", "ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n", "ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)" ], "execution_count": 16, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 16 }, { "output_type": "display_data", "data": { "image/png": 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939MbysmhwnrlFSqyUJh1FzNnGhTS/Pn8TnbvNmI0XnFcBqGPehCegujFaGmhVV9UlP7e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1jqun9/4JlnrMebNsVfRoLy3mqqG0C5ErvzEWDHt5Lj1uj60CymCwcJ4O9CCAngeYduukY74Kw/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\nvHBK/VZk1QAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "tags": [] } } ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "fKokeS3XkOC5", "outputId": "38db7138-cb8e-4033-be3d-2806307a6fb9", "colab": { "base_uri": "https://localhost:8080/", "height": 748 } }, "source": [ "model = tf.keras.models.Sequential([\n", " tf.keras.layers.Dense(4, input_dim=2, activation='tanh'),\n", " tf.keras.layers.Dense(1, activation='sigmoid')\n", "])\n", "model.compile(tf.keras.optimizers.SGD(lr=0.5), 'binary_crossentropy', metrics=['accuracy'])\n", "h = model.fit(X_train, y_train, epochs=20, validation_split=0.1)" ], "execution_count": 17, "outputs": [ { "output_type": "stream", "text": [ "Train on 945 samples, validate on 105 samples\n", "Epoch 1/20\n", "945/945 [==============================] - 1s 640us/sample - loss: 0.6562 - accuracy: 0.5937 - val_loss: 0.6172 - val_accuracy: 0.6381\n", "Epoch 2/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.5760 - accuracy: 0.6571 - val_loss: 0.5294 - val_accuracy: 0.6381\n", "Epoch 3/20\n", "945/945 [==============================] - 0s 62us/sample - loss: 0.4803 - accuracy: 0.8138 - val_loss: 0.4245 - val_accuracy: 0.9238\n", "Epoch 4/20\n", "945/945 [==============================] - 0s 67us/sample - loss: 0.3789 - accuracy: 0.9429 - val_loss: 0.3197 - val_accuracy: 0.9810\n", "Epoch 5/20\n", "945/945 [==============================] - 0s 55us/sample - loss: 0.2817 - accuracy: 0.9884 - val_loss: 0.2379 - val_accuracy: 0.9905\n", "Epoch 6/20\n", "945/945 [==============================] - 0s 64us/sample - loss: 0.2037 - accuracy: 0.9989 - val_loss: 0.1719 - val_accuracy: 1.0000\n", "Epoch 7/20\n", "945/945 [==============================] - 0s 63us/sample - loss: 0.1492 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 1.0000\n", "Epoch 8/20\n", "945/945 [==============================] - 0s 53us/sample - loss: 0.1146 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 1.0000\n", "Epoch 9/20\n", "945/945 [==============================] - 0s 63us/sample - loss: 0.0919 - accuracy: 1.0000 - val_loss: 0.0858 - val_accuracy: 1.0000\n", "Epoch 10/20\n", "945/945 [==============================] - 0s 59us/sample - loss: 0.0766 - accuracy: 1.0000 - val_loss: 0.0722 - val_accuracy: 1.0000\n", "Epoch 11/20\n", "945/945 [==============================] - 0s 61us/sample - loss: 0.0651 - accuracy: 1.0000 - val_loss: 0.0630 - val_accuracy: 1.0000\n", "Epoch 12/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.0569 - accuracy: 1.0000 - val_loss: 0.0549 - val_accuracy: 1.0000\n", "Epoch 13/20\n", "945/945 [==============================] - 0s 61us/sample - loss: 0.0502 - accuracy: 1.0000 - val_loss: 0.0488 - val_accuracy: 1.0000\n", "Epoch 14/20\n", "945/945 [==============================] - 0s 61us/sample - loss: 0.0449 - accuracy: 1.0000 - val_loss: 0.0442 - val_accuracy: 1.0000\n", "Epoch 15/20\n", "945/945 [==============================] - 0s 59us/sample - loss: 0.0407 - accuracy: 1.0000 - val_loss: 0.0400 - val_accuracy: 1.0000\n", "Epoch 16/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.0371 - accuracy: 1.0000 - val_loss: 0.0366 - val_accuracy: 1.0000\n", "Epoch 17/20\n", "945/945 [==============================] - 0s 56us/sample - loss: 0.0341 - accuracy: 1.0000 - val_loss: 0.0338 - val_accuracy: 1.0000\n", "Epoch 18/20\n", "945/945 [==============================] - 0s 60us/sample - loss: 0.0315 - accuracy: 1.0000 - val_loss: 0.0315 - val_accuracy: 1.0000\n", "Epoch 19/20\n", "945/945 [==============================] - 0s 57us/sample - loss: 0.0294 - accuracy: 1.0000 - val_loss: 0.0292 - val_accuracy: 1.0000\n", "Epoch 20/20\n", "945/945 [==============================] - 0s 55us/sample - loss: 0.0274 - accuracy: 1.0000 - val_loss: 0.0274 - val_accuracy: 1.0000\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "TAttI_euXJtl", "colab_type": "text" }, "source": [ "## একটা কনফিউশন ম্যাট্রিক্স তৈরি করি \n" ] }, { "cell_type": "code", "metadata": { "id": "aionVhhKXJtm", "colab_type": "code", "colab": {} }, "source": [ "from sklearn.metrics import confusion_matrix, classification_report\n", "\n", "y_pred = model.predict_classes(X_test)" ], "execution_count": 0, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "ReiF4Z_0XJto", "colab_type": "code", "outputId": "d3a85966-ef77-4a12-801f-2da0fab8434f", "colab": { "base_uri": "https://localhost:8080/", "height": 110 } }, "source": [ "cm = confusion_matrix(y_test, y_pred)\n", "\n", "pd.DataFrame(cm,\n", " index=[\"Miss\", \"Hit\"],\n", " columns=['pred_Miss', 'pred_Hit'])" ], "execution_count": 19, "outputs": [ { "output_type": "execute_result", "data": { "text/html": [ "
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pred_Misspred_Hit
Miss3110
Hit0139
\n", "
" ], "text/plain": [ " pred_Miss pred_Hit\n", "Miss 311 0\n", "Hit 0 139" ] }, "metadata": { "tags": [] }, "execution_count": 19 } ] }, { "cell_type": "code", "metadata": { "id": "UCp6QtU2XJtq", "colab_type": "code", "outputId": "747ff72c-c1d7-4854-8b28-cbd8f8d8db64", "colab": { "base_uri": "https://localhost:8080/", "height": 69 } }, "source": [ "train_score = model.evaluate(X_train, y_train, verbose=0)[1]\n", "test_score = model.evaluate(X_test, y_test, verbose=0)[1]\n", "\n", "print(\"\"\"Accuracy scores:\n", " Train:\\t{:0.3}\n", " Test:\\t{:0.3}\"\"\".format(train_score, test_score))" ], "execution_count": 20, "outputs": [ { "output_type": "stream", "text": [ "Accuracy scores:\n", " Train:\t1.0\n", " Test:\t1.0\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "TkA1ITQYvSn4" }, "source": [ "## নতুন ডিসিশন বাউন্ডারি, অল্প লেয়ারেই " ] }, { "cell_type": "code", "metadata": { "colab_type": "code", "id": "3H0Eum25uDWv", "outputId": "af7b54cc-0082-4798-a106-864b27458fcf", "colab": { "base_uri": "https://localhost:8080/", "height": 275 } }, "source": [ "hticks = np.linspace(-2, 2, 101)\n", "vticks = np.linspace(-2, 2, 101)\n", "aa, bb = np.meshgrid(hticks, vticks)\n", "ab = np.c_[aa.ravel(), bb.ravel()]\n", "\n", "c = model.predict(ab)\n", "cc = c.reshape(aa.shape)\n", "\n", "ax = df.plot(kind='scatter', c='target', x='lat', y='lon', cmap='bwr')\n", "ax.contourf(aa, bb, cc, cmap='bwr', alpha=0.5)" ], "execution_count": 21, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": { "tags": [] }, "execution_count": 21 }, { "output_type": "display_data", "data": { "image/png": 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BNNdvnZ6Vdi3mzeP9VlZGoTR/frx1cNll1OjDYUMTTzbTGbC3AzfDqRAuGtV3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\nxG9WUoJItE8/tf92FRWYhS9YoCqg5+ZizDoFMXo0Jgzl5bgWz52D8J8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E4Mepq4Nz2eAO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\njIIwGAwGgxajIAwGg8Gg5f8A6ljUHV5CDCAAAAAASUVORK5CYII=\n", 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