From 9333d8453d258239bee1671ce51ca90911f848c9 Mon Sep 17 00:00:00 2001 From: Sayani Roy Chowdhury Date: Thu, 27 Sep 2018 01:47:19 +0530 Subject: [PATCH 1/3] Assignment 2 (Lists And Numpy) --- VicktorKrum.ipynb | 712 ++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 682 insertions(+), 30 deletions(-) diff --git a/VicktorKrum.ipynb b/VicktorKrum.ipynb index 9e2543a..de3911d 100644 --- a/VicktorKrum.ipynb +++ b/VicktorKrum.ipynb @@ -1,32 +1,684 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "VicktorKrum.ipynb", + "version": "0.3.2", + "provenance": [] + }, + "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "metadata": { + "id": "l_5Q-bRdQuf0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**ASSIGNMENT 2:**\n", + "\n", + "\n", + "3. NUMPY EXERCISES" + ] + }, + { + "metadata": { + "id": "BrGjHSVZRoJN", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "g0MmK9_aRP_P", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "1).Create a uniform subdivision of the interval -1.3 to 2.5 with 64 subdivisions" + ] + }, + { + "metadata": { + "id": "1-yzUHV6RHqU", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "e5a38b3c-8f68-4b5d-d38a-cb67ebdec662" + }, + "cell_type": "code", + "source": [ + "a = np.linspace(-1.3,2.5,64)\n", + "print(a)\n" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[-1.3 -1.23968254 -1.17936508 -1.11904762 -1.05873016 -0.9984127\n", + " -0.93809524 -0.87777778 -0.81746032 -0.75714286 -0.6968254 -0.63650794\n", + " -0.57619048 -0.51587302 -0.45555556 -0.3952381 -0.33492063 -0.27460317\n", + " -0.21428571 -0.15396825 -0.09365079 -0.03333333 0.02698413 0.08730159\n", + " 0.14761905 0.20793651 0.26825397 0.32857143 0.38888889 0.44920635\n", + " 0.50952381 0.56984127 0.63015873 0.69047619 0.75079365 0.81111111\n", + " 0.87142857 0.93174603 0.99206349 1.05238095 1.11269841 1.17301587\n", + " 1.23333333 1.29365079 1.35396825 1.41428571 1.47460317 1.53492063\n", + " 1.5952381 1.65555556 1.71587302 1.77619048 1.83650794 1.8968254\n", + " 1.95714286 2.01746032 2.07777778 2.13809524 2.1984127 2.25873016\n", + " 2.31904762 2.37936508 2.43968254 2.5 ]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "OBqeZ1lUR3eR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "3).Create an array of the first 10 odd integers." + ] + }, + { + "metadata": { + "id": "SKJxWzAUR-aN", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "8ac2ecd2-155c-44dd-f876-b0494cf624e6" + }, + "cell_type": "code", + "source": [ + "a=[]\n", + "for i in range(1,20): \n", + " if i%2!=0:\n", + " a.append(i)\n", + " if (len(a)!=20):\n", + " i+=1\n", + "print(a)" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 3, 5, 7, 9, 11, 13, 15, 17, 19]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "ntiapz3rSq1s", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "4) Find intersection of a and b" + ] + }, + { + "metadata": { + "id": "dB85INg1Suix", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "db9b0da1-d330-4c9d-b773-2da9f7600959" + }, + "cell_type": "code", + "source": [ + "c = [ ]\n", + "\n", + "#expected output array([2, 4])\n", + "a = np.array([1,2,3,2,3,4,3,4,5,6])\n", + "b = np.array([7,2,10,2,7,4,9,4,9,8])\n", + "#if (for i in a: for j in b: if i==j: c.append[i] i++)\n", + "print (c)\n", + "\n", + "list (set(a) & set(b))" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[2, 4]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "id": "9n4n-5s7S9wO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "5) Reshape 1d array a to 2d array of 2X5\n" + ] + }, + { + "metadata": { + "id": "N-wLyL2nTArS", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "fffa2581-698d-4097-f017-008590f1df6d" + }, + "cell_type": "code", + "source": [ + "a = np.arange(10)\n", + "print(a)\n", + "#b = a.reshape(a.shape[-10:5],5)\n", + "b = a.reshape(2,5)\n", + "print(b)" + ], + "execution_count": 36, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0 1 2 3 4 5 6 7 8 9]\n", + "[[0 1 2 3 4]\n", + " [5 6 7 8 9]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "5L5C1N6RWmyK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "6) Create a numpy array to list and vice versa\n" + ] + }, + { + "metadata": { + "id": "JJtmfaw9WqNE", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "6a8740cd-61f1-484b-ac31-785502c88144" + }, + "cell_type": "code", + "source": [ + "a = ([1, 2, 3, 4, 5, 6, 7, 8, 9])\n", + "#print(a.tolist())\n", + "print(list(a))\n", + "a = [1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "b = np.asarray(a)\n", + "d = np.array(a)\n", + "print(d)\n", + "b" + ], + "execution_count": 42, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5, 6, 7, 8, 9]\n", + "[1 2 3 4 5 6 7 8 9]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1, 2, 3, 4, 5, 6, 7, 8, 9])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 42 + } + ] + }, + { + "metadata": { + "id": "_Uk8P1i2XCTL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "7) Create a 10 x 10 arrays of zeros and then \"frame\" it with a border of ones." + ] + }, + { + "metadata": { + "id": "G4hsEI__XF3N", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "1a00c201-5420-41e8-9d17-a30d172bc4e0" + }, + "cell_type": "code", + "source": [ + "z = np.ones((12,12))\n", + "#z = zeros((12,12))\n", + "#a = ones(12)\n", + "#b = ones((,10))\n", + "#v = [a,b,z,b,a] */\n", + "z[1:-1,1:-1] = 0\n", + "print(z)" + ], + "execution_count": 46, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "SJjZG7jzXZ08", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "8).Write a Python program to create a 8*8 matrix and fill it with a checkerboard pattern." + ] + }, + { + "metadata": { + "id": "xIPYT3vLXdyn", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 170 + }, + "outputId": "8ed4a15a-3929-43c4-dc3d-fbabd505d4c3" + }, + "cell_type": "code", + "source": [ + "x=np.ones((3,3))\n", + "print(\"Checkerboard pattern:\")\n", + "x=np.zeros((8,8),dtype=int)\n", + "x[1::2,::2] = 1\n", + "x[::2,1::2] = 1\n", + "print(x)\n" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Checkerboard pattern:\n", + "[[0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]\n", + " [0 1 0 1 0 1 0 1]\n", + " [1 0 1 0 1 0 1 0]]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "CO89jPWgXtKo", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**1) LISTS**" + ] + }, + { + "metadata": { + "id": "XbcuYw7zXzIx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "1) Create any random list and assign it to a variable dummy_list" + ] + }, + { + "metadata": { + "id": "Mp2udy3oX2lH", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import random\n", + "dummy_list = random.sample(range(20),7)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "uaPTS1TwX9oV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "2) print dummy_list\n" + ] + }, + { + "metadata": { + "id": "JEneP1T0YAzH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "00019f0f-bb2d-4a86-f551-9f7de480a4f2" + }, + "cell_type": "code", + "source": [ + "print(dummy_list)\n" + ], + "execution_count": 69, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0, 0, 0, 1, 1, 2, 2, 4, 4, 6, 8, 9.45, 9.45, 11, 12, 12.01, 12.01, 12.02, 12.02, 14, 16, 16, 17, 45.67, 45.67, 90, 90, 200, 200]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "sk1oKePOYD9L", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "3)Reverse dummy_list and print" + ] + }, + { + "metadata": { + "id": "ceVOBdYQYHWh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "bbb25995-ce9b-4dbc-bda6-02ec86b5e36c" + }, + "cell_type": "code", + "source": [ + "print(dummy_list.reverse())" + ], + "execution_count": 70, + "outputs": [ + { + "output_type": "stream", + "text": [ + "None\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "hYoLIQIqYRpr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "4) Add the list dummy_list_2 to the previous dummy_list and now print dummy_list\n" + ] + }, + { + "metadata": { + "id": "QU-C3c4LYU8u", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54 + }, + "outputId": "5f185b29-dba2-4383-e7ad-281fe4fc9b87" + }, + "cell_type": "code", + "source": [ + "dummy_list_2 = [2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "dummy_list.extend(dummy_list_2)\n", + "print(dummy_list)\n", + "\n" + ], + "execution_count": 71, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[200, 200, 90, 90, 45.67, 45.67, 17, 16, 16, 14, 12.02, 12.02, 12.01, 12.01, 12, 11, 9.45, 9.45, 8, 6, 4, 4, 2, 2, 1, 1, 0, 0, 0, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "edEMLOZNYnk6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "5) Create a dictionary named dummy_dict which contains all the elements of dummy_list as keys and frequency as values.\n", + "\n", + "6) print dummy_dict" + ] + }, + { + "metadata": { + "id": "KuPHVLPHYvSF", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "e7212dc2-d97f-4ad7-b9a3-480e32ea1d4f" + }, + "cell_type": "code", + "source": [ + "import collections\n", + "cntr = collections.Counter(a)\n", + "#j = []\n", + "dummy_dict = cntr\n", + "#for i in cntr.values:\n", + "# j.append(i)\n", + " \n", + "#dummy_dict = {a[i]: a[i+1] for i in range(0, len(a), 2)} \n", + "print(dummy_dict)" + ], + "execution_count": 72, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Counter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1})\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "T731revYebYq", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "7) ) Sort dummy_list in ascending order as well as descending order and print the changed lists\n" + ] + }, + { + "metadata": { + "id": "ZtE4zbvFeZl9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 163 + }, + "outputId": "3d06fa71-1394-4d3f-8c51-61dc06d10783" + }, + "cell_type": "code", + "source": [ + "dummy_list.sort()\n", + "print(dummy_list)\n", + "dummy_list.sort(reverse=True)\n", + "print(dummy_list)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'dummy_dict' is not defined" + ] + } + ] + }, + { + "metadata": { + "id": "N5-LKs4Dfdsz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "8) Remove the first item from the list whose value is equal to x. It raises a ValueError if there is no such item." + ] + }, + { + "metadata": { + "id": "wR4CIdVqfieh", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "x= 200\n", + "for i in list:\n", + " if i==5:\n", + " dummy_list.remove(x)\n", + " " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "KXLlwhLTgA7Z", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "9) Remove the item at position x. x is any random integer" + ] + }, + { + "metadata": { + "id": "73oy80JlgJUY", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "#similar to deleting by index,i.e., del[a : b]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "VIWaH3WgguJA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "10) Let's clean everything clear the list and then print" + ] + }, + { + "metadata": { + "id": "VvvPY34LgxJL", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "dummy_list.clear()" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file From 8e0107a9e5b470832e3719a5a6fbedfc70d8819d Mon Sep 17 00:00:00 2001 From: Sayani Roy Chowdhury Date: Tue, 2 Oct 2018 10:39:36 +0530 Subject: [PATCH 2/3] Assignment 2 (with suggested changes) --- VicktorKrum.ipynb | 250 ++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 207 insertions(+), 43 deletions(-) diff --git a/VicktorKrum.ipynb b/VicktorKrum.ipynb index de3911d..d867d2b 100644 --- a/VicktorKrum.ipynb +++ b/VicktorKrum.ipynb @@ -65,7 +65,7 @@ "a = np.linspace(-1.3,2.5,64)\n", "print(a)\n" ], - "execution_count": 4, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -116,7 +116,7 @@ " i+=1\n", "print(a)" ], - "execution_count": 10, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -159,7 +159,7 @@ "\n", "list (set(a) & set(b))" ], - "execution_count": 13, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -210,7 +210,7 @@ "b = a.reshape(2,5)\n", "print(b)" ], - "execution_count": 36, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -254,7 +254,7 @@ "print(d)\n", "b" ], - "execution_count": 42, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -308,7 +308,7 @@ "z[1:-1,1:-1] = 0\n", "print(z)" ], - "execution_count": 46, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -359,7 +359,7 @@ "x[::2,1::2] = 1\n", "print(x)\n" ], - "execution_count": 48, + "execution_count": 0, "outputs": [ { "output_type": "stream", @@ -430,18 +430,18 @@ "base_uri": "https://localhost:8080/", "height": 34 }, - "outputId": "00019f0f-bb2d-4a86-f551-9f7de480a4f2" + "outputId": "c7d65cf7-4630-42f7-d71a-95fc408d95b9" }, "cell_type": "code", "source": [ "print(dummy_list)\n" ], - "execution_count": 69, + "execution_count": 5, "outputs": [ { "output_type": "stream", "text": [ - "[0, 0, 0, 1, 1, 2, 2, 4, 4, 6, 8, 9.45, 9.45, 11, 12, 12.01, 12.01, 12.02, 12.02, 14, 16, 16, 17, 45.67, 45.67, 90, 90, 200, 200]\n" + "[8, 2, 3, 10, 9, 5, 6]\n" ], "name": "stdout" } @@ -465,13 +465,13 @@ "base_uri": "https://localhost:8080/", "height": 34 }, - "outputId": "bbb25995-ce9b-4dbc-bda6-02ec86b5e36c" + "outputId": "90454f5f-033c-422d-fc59-5cc9e73668dd" }, "cell_type": "code", "source": [ "print(dummy_list.reverse())" ], - "execution_count": 70, + "execution_count": 6, "outputs": [ { "output_type": "stream", @@ -498,9 +498,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 54 + "height": 34 }, - "outputId": "5f185b29-dba2-4383-e7ad-281fe4fc9b87" + "outputId": "25e5ba8f-0401-45a8-a071-9e5b86011247" }, "cell_type": "code", "source": [ @@ -509,12 +509,12 @@ "print(dummy_list)\n", "\n" ], - "execution_count": 71, + "execution_count": 22, "outputs": [ { "output_type": "stream", "text": [ - "[200, 200, 90, 90, 45.67, 45.67, 17, 16, 16, 14, 12.02, 12.02, 12.01, 12.01, 12, 11, 9.45, 9.45, 8, 6, 4, 4, 2, 2, 1, 1, 0, 0, 0, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + "[8, 17, 7, 10, 16, 2, 3, 2, 200, 16, 4, 1, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" ], "name": "stdout" } @@ -540,31 +540,77 @@ "base_uri": "https://localhost:8080/", "height": 34 }, - "outputId": "e7212dc2-d97f-4ad7-b9a3-480e32ea1d4f" + "outputId": "d83e140b-5aba-42c0-acad-6f093725ada5" }, "cell_type": "code", "source": [ "import collections\n", - "cntr = collections.Counter(a)\n", + "cntr = collections.Counter(dummy_list)\n", "#j = []\n", "dummy_dict = cntr\n", "#for i in cntr.values:\n", "# j.append(i)\n", " \n", "#dummy_dict = {a[i]: a[i+1] for i in range(0, len(a), 2)} \n", - "print(dummy_dict)" + "print(dummy_dict) #modified" ], - "execution_count": 72, + "execution_count": 10, "outputs": [ { "output_type": "stream", "text": [ - "Counter({1: 1, 2: 1, 3: 1, 4: 1, 5: 1, 6: 1, 7: 1, 8: 1, 9: 1})\n" + "Counter({2: 2, 6: 1, 5: 1, 9: 1, 10: 1, 3: 1, 8: 1, 200: 1, 16: 1, 4: 1, 1: 1, 0: 1, 9.45: 1, 45.67: 1, 90: 1, 12.01: 1, 12.02: 1})\n" ], "name": "stdout" } ] }, + { + "metadata": { + "id": "5rkH4KpDEnuZ", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 289 + }, + "outputId": "2110c1a3-eb9c-486f-daac-6b7dbfa7adbd" + }, + "cell_type": "code", + "source": [ + "from collections import Counter\n", + "Counter(dummy_list) #added as per suggestion" + ], + "execution_count": 23, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Counter({0: 1,\n", + " 1: 1,\n", + " 2: 2,\n", + " 3: 1,\n", + " 4: 1,\n", + " 7: 1,\n", + " 8: 1,\n", + " 9.45: 1,\n", + " 10: 1,\n", + " 12.01: 1,\n", + " 12.02: 1,\n", + " 16: 2,\n", + " 17: 1,\n", + " 45.67: 1,\n", + " 90: 1,\n", + " 200: 1})" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 23 + } + ] + }, { "metadata": { "id": "T731revYebYq", @@ -581,9 +627,9 @@ "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", - "height": 163 + "height": 51 }, - "outputId": "3d06fa71-1394-4d3f-8c51-61dc06d10783" + "outputId": "1c9b0c17-e56f-4dcb-b90a-59108e9342f0" }, "cell_type": "code", "source": [ @@ -592,18 +638,15 @@ "dummy_list.sort(reverse=True)\n", "print(dummy_list)" ], - "execution_count": 0, + "execution_count": 16, "outputs": [ { - "output_type": "error", - "ename": "NameError", - "evalue": "ignored", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mNameError\u001b[0m: name 'dummy_dict' is not defined" - ] + "output_type": "stream", + "text": [ + "[0, 1, 2, 2, 3, 4, 5, 6, 8, 9, 9.45, 10, 12.01, 12.02, 16, 45.67, 90]\n", + "[90, 45.67, 16, 12.02, 12.01, 10, 9.45, 9, 8, 6, 5, 4, 3, 2, 2, 1, 0]\n" + ], + "name": "stdout" } ] }, @@ -619,7 +662,7 @@ }, { "metadata": { - "id": "wR4CIdVqfieh", + "id": "2iCy6t0VG5qc", "colab_type": "code", "colab": {} }, @@ -629,11 +672,43 @@ "for i in list:\n", " if i==5:\n", " dummy_list.remove(x)\n", - " " + "print(dummy_list) " ], "execution_count": 0, "outputs": [] }, + { + "metadata": { + "id": "wR4CIdVqfieh", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "0c121b1d-172f-46ae-d502-6753e99cd54c" + }, + "cell_type": "code", + "source": [ + "x= int(input())\n", + "for i in dummy_list:\n", + " if i==x:\n", + " dummy_list.remove(x)\n", + "print(dummy_list)\n", + "#working, modified (hardcoding removed as per suggestion)\n", + " " + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "16\n", + "[90, 45.67, 12.02, 12.01, 10, 9.45, 9, 8, 6, 5, 4, 3, 2, 2, 1, 0]\n" + ], + "name": "stdout" + } + ] + }, { "metadata": { "id": "KXLlwhLTgA7Z", @@ -648,14 +723,65 @@ "metadata": { "id": "73oy80JlgJUY", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "e83321cb-ddd8-46d8-d3da-f9863c7b70e1" }, "cell_type": "code", "source": [ - "#similar to deleting by index,i.e., del[a : b]" + "#similar to deleting by index,i.e., del[a : b]\n", + "import random\n", + "x=random.choice(range(15))\n", + "print (x)\n", + "dummy_list.remove(x)\n", + "print(dummy_list)\n" ], - "execution_count": 0, - "outputs": [] + "execution_count": 24, + "outputs": [ + { + "output_type": "stream", + "text": [ + "1\n", + "[8, 17, 7, 10, 16, 2, 3, 2, 200, 16, 4, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "eN74BnbpKT7Q", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 68 + }, + "outputId": "879db616-fec8-4b52-c575-c7046a50ac86" + }, + "cell_type": "code", + "source": [ + "'''modified and added as per suggestion: numpy example'''\n", + "print(dummy_list)\n", + "index = random.randrange(len(dummy_list))\n", + "print (index)\n", + "#el = dummy_list[index]\n", + "del dummy_list[index]\n", + "print(dummy_list)" + ], + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[8, 17, 7, 10, 16, 2, 3, 2, 200, 16, 4, 0, 9.45, 45.67, 90, 12.01, 12.02]\n", + "5\n", + "[8, 17, 7, 10, 16, 3, 2, 200, 16, 4, 0, 9.45, 45.67, 90, 12.01, 12.02]\n" + ], + "name": "stdout" + } + ] }, { "metadata": { @@ -671,14 +797,52 @@ "metadata": { "id": "VvvPY34LgxJL", "colab_type": "code", - "colab": {} + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "3aa6457b-3be3-4b6b-857d-d36ea37b01b9" }, "cell_type": "code", "source": [ - "dummy_list.clear()" + "dummy_list.clear()\n", + "print(dummy_list)" ], - "execution_count": 0, - "outputs": [] + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "InhtPg_KHHGc", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "121925c4-5885-4483-ceb5-e913827cbd3e" + }, + "cell_type": "code", + "source": [ + "print(\"Modified And Completed!\")" + ], + "execution_count": 28, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Modified And Completed!\n" + ], + "name": "stdout" + } + ] } ] } \ No newline at end of file From 70d3c8e1174885b4b2cba833a5bfccf3d4b7293a Mon Sep 17 00:00:00 2001 From: Sayani Roy Chowdhury Date: Sat, 2 Feb 2019 15:33:32 +0530 Subject: [PATCH 3/3] 2/11 --- first_steps_with_tensor_flow.ipynb | 2032 ++++++++++++++++++++++++++++ 1 file changed, 2032 insertions(+) create mode 100644 first_steps_with_tensor_flow.ipynb diff --git a/first_steps_with_tensor_flow.ipynb b/first_steps_with_tensor_flow.ipynb new file mode 100644 index 0000000..f126c43 --- /dev/null +++ b/first_steps_with_tensor_flow.ipynb @@ -0,0 +1,2032 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "first_steps_with_tensor_flow.ipynb", + "version": "0.3.2", + "provenance": [], + "collapsed_sections": [ + "JndnmDMp66FL", + "ajVM7rkoYXeL", + "ci1ISxxrZ7v0" + ], + "include_colab_link": true + }, + "kernelspec": { + "name": "python2", + "display_name": "Python 2" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "metadata": { + "id": "JndnmDMp66FL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Copyright 2017 Google LLC." + ] + }, + { + "metadata": { + "id": "hMqWDc_m6rUC", + "colab_type": "code", + "cellView": "both", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4f3CKqFUqL2-", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# First Steps with TensorFlow" + ] + }, + { + "metadata": { + "id": "qe0A1tNZhw5w", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "" + ] + }, + { + "metadata": { + "id": "Bd2Zkk1LE2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**Learning Objectives:**\n", + " * Learn fundamental TensorFlow concepts\n", + " * Use the `LinearRegressor` class in TensorFlow to predict median housing price, at the granularity of city blocks, based on one input feature\n", + " * Evaluate the accuracy of a model's predictions using Root Mean Squared Error (RMSE)\n", + " * Improve the accuracy of a model by tuning its hyperparameters" + ] + }, + { + "metadata": { + "id": "MxiIKhP4E2Zr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The [data](https://developers.google.com/machine-learning/crash-course/california-housing-data-description) is based on 1990 census data from California." + ] + }, + { + "metadata": { + "id": "6TjLjL9IU80G", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n", + "In this first cell, we'll load the necessary libraries." + ] + }, + { + "metadata": { + "id": "rVFf5asKE2Zt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from __future__ import print_function\n", + "\n", + "import math\n", + "\n", + "from IPython import display\n", + "from matplotlib import cm\n", + "from matplotlib import gridspec\n", + "from matplotlib import pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn import metrics\n", + "import tensorflow as tf\n", + "from tensorflow.python.data import Dataset\n", + "\n", + "tf.logging.set_verbosity(tf.logging.ERROR)\n", + "pd.options.display.max_rows = 10\n", + "pd.options.display.float_format = '{:.1f}'.format" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "ipRyUHjhU80Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll load our data set." + ] + }, + { + "metadata": { + "id": "9ivCDWnwE2Zx", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vVk_qlG6U80j", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We'll randomize the data, just to be sure not to get any pathological ordering effects that might harm the performance of Stochastic Gradient Descent. Additionally, we'll scale `median_house_value` to be in units of thousands, so it can be learned a little more easily with learning rates in a range that we usually use." + ] + }, + { + "metadata": { + "id": "r0eVyguIU80m", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 419 + }, + "outputId": "d4ee35e2-a32f-432b-a200-e55ca38290b5" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe = california_housing_dataframe.reindex(\n", + " np.random.permutation(california_housing_dataframe.index))\n", + "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n", + "california_housing_dataframe" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomemedian_house_value
5595-118.233.812.04409.01401.03068.01262.02.3154.7
12475-121.636.719.09899.02617.011272.02528.02.0118.5
10864-120.837.521.02974.0495.01313.0461.04.5135.4
10084-119.834.427.03143.0537.01760.0570.04.7271.5
10647-120.536.921.01779.0399.01446.0371.02.471.9
..............................
5117-118.133.845.03035.0516.01127.0527.07.1500.0
4183-118.033.819.01991.0528.01202.0460.03.2252.1
8693-118.634.235.02987.0391.01244.0387.07.1500.0
9802-119.736.87.02075.0353.01040.0362.04.0100.2
4730-118.133.933.02263.0511.01626.0457.03.6172.8
\n", + "

17000 rows × 9 columns

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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "5595 -118.2 33.8 12.0 4409.0 1401.0 \n", + "12475 -121.6 36.7 19.0 9899.0 2617.0 \n", + "10864 -120.8 37.5 21.0 2974.0 495.0 \n", + "10084 -119.8 34.4 27.0 3143.0 537.0 \n", + "10647 -120.5 36.9 21.0 1779.0 399.0 \n", + "... ... ... ... ... ... \n", + "5117 -118.1 33.8 45.0 3035.0 516.0 \n", + "4183 -118.0 33.8 19.0 1991.0 528.0 \n", + "8693 -118.6 34.2 35.0 2987.0 391.0 \n", + "9802 -119.7 36.8 7.0 2075.0 353.0 \n", + "4730 -118.1 33.9 33.0 2263.0 511.0 \n", + "\n", + " population households median_income median_house_value \n", + "5595 3068.0 1262.0 2.3 154.7 \n", + "12475 11272.0 2528.0 2.0 118.5 \n", + "10864 1313.0 461.0 4.5 135.4 \n", + "10084 1760.0 570.0 4.7 271.5 \n", + "10647 1446.0 371.0 2.4 71.9 \n", + "... ... ... ... ... \n", + "5117 1127.0 527.0 7.1 500.0 \n", + "4183 1202.0 460.0 3.2 252.1 \n", + "8693 1244.0 387.0 7.1 500.0 \n", + "9802 1040.0 362.0 4.0 100.2 \n", + "4730 1626.0 457.0 3.6 172.8 \n", + "\n", + "[17000 rows x 9 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + } + ] + }, + { + "metadata": { + "id": "HzzlSs3PtTmt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Examine the Data\n", + "\n", + "It's a good idea to get to know your data a little bit before you work with it.\n", + "\n", + "We'll print out a quick summary of a few useful statistics on each column: count of examples, mean, standard deviation, max, min, and various quantiles." + ] + }, + { + "metadata": { + "id": "gzb10yoVrydW", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "d4bba3f1-de64-4313-fb16-8f4ddd9016c8" + }, + "cell_type": "code", + "source": [ + "california_housing_dataframe.describe()" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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50%-118.534.229.02127.0434.01167.0409.03.5180.4
75%-118.037.737.03151.2648.21721.0605.24.8265.0
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" + ], + "text/plain": [ + " longitude latitude housing_median_age total_rooms total_bedrooms \\\n", + "count 17000.0 17000.0 17000.0 17000.0 17000.0 \n", + "mean -119.6 35.6 28.6 2643.7 539.4 \n", + "std 2.0 2.1 12.6 2179.9 421.5 \n", + "min -124.3 32.5 1.0 2.0 1.0 \n", + "25% -121.8 33.9 18.0 1462.0 297.0 \n", + "50% -118.5 34.2 29.0 2127.0 434.0 \n", + "75% -118.0 37.7 37.0 3151.2 648.2 \n", + "max -114.3 42.0 52.0 37937.0 6445.0 \n", + "\n", + " population households median_income median_house_value \n", + "count 17000.0 17000.0 17000.0 17000.0 \n", + "mean 1429.6 501.2 3.9 207.3 \n", + "std 1147.9 384.5 1.9 116.0 \n", + "min 3.0 1.0 0.5 15.0 \n", + "25% 790.0 282.0 2.6 119.4 \n", + "50% 1167.0 409.0 3.5 180.4 \n", + "75% 1721.0 605.2 4.8 265.0 \n", + "max 35682.0 6082.0 15.0 500.0 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 4 + } + ] + }, + { + "metadata": { + "id": "Lr6wYl2bt2Ep", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Build the First Model\n", + "\n", + "In this exercise, we'll try to predict `median_house_value`, which will be our label (sometimes also called a target). We'll use `total_rooms` as our input feature.\n", + "\n", + "**NOTE:** Our data is at the city block level, so this feature represents the total number of rooms in that block.\n", + "\n", + "To train our model, we'll use the [LinearRegressor](https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor) interface provided by the TensorFlow [Estimator](https://www.tensorflow.org/get_started/estimator) API. This API takes care of a lot of the low-level model plumbing, and exposes convenient methods for performing model training, evaluation, and inference." + ] + }, + { + "metadata": { + "id": "0cpcsieFhsNI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 1: Define Features and Configure Feature Columns" + ] + }, + { + "metadata": { + "id": "EL8-9d4ZJNR7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In order to import our training data into TensorFlow, we need to specify what type of data each feature contains. There are two main types of data we'll use in this and future exercises:\n", + "\n", + "* **Categorical Data**: Data that is textual. In this exercise, our housing data set does not contain any categorical features, but examples you might see would be the home style, the words in a real-estate ad.\n", + "\n", + "* **Numerical Data**: Data that is a number (integer or float) and that you want to treat as a number. As we will discuss more later sometimes you might want to treat numerical data (e.g., a postal code) as if it were categorical.\n", + "\n", + "In TensorFlow, we indicate a feature's data type using a construct called a **feature column**. Feature columns store only a description of the feature data; they do not contain the feature data itself.\n", + "\n", + "To start, we're going to use just one numeric input feature, `total_rooms`. The following code pulls the `total_rooms` data from our `california_housing_dataframe` and defines the feature column using `numeric_column`, which specifies its data is numeric:" + ] + }, + { + "metadata": { + "id": "rhEbFCZ86cDZ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the input feature: total_rooms.\n", + "my_feature = california_housing_dataframe[[\"total_rooms\"]]\n", + "\n", + "# Configure a numeric feature column for total_rooms.\n", + "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "K_3S8teX7Rd2", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** The shape of our `total_rooms` data is a one-dimensional array (a list of the total number of rooms for each block). This is the default shape for `numeric_column`, so we don't have to pass it as an argument." + ] + }, + { + "metadata": { + "id": "UMl3qrU5MGV6", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 2: Define the Target" + ] + }, + { + "metadata": { + "id": "cw4nrfcB7kyk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll define our target, which is `median_house_value`. Again, we can pull it from our `california_housing_dataframe`:" + ] + }, + { + "metadata": { + "id": "l1NvvNkH8Kbt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Define the label.\n", + "targets = california_housing_dataframe[\"median_house_value\"]" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4M-rTFHL2UkA", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 3: Configure the LinearRegressor" + ] + }, + { + "metadata": { + "id": "fUfGQUNp7jdL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll configure a linear regression model using LinearRegressor. We'll train this model using the `GradientDescentOptimizer`, which implements Mini-Batch Stochastic Gradient Descent (SGD). The `learning_rate` argument controls the size of the gradient step.\n", + "\n", + "**NOTE:** To be safe, we also apply [gradient clipping](https://developers.google.com/machine-learning/glossary/#gradient_clipping) to our optimizer via `clip_gradients_by_norm`. Gradient clipping ensures the magnitude of the gradients do not become too large during training, which can cause gradient descent to fail. " + ] + }, + { + "metadata": { + "id": "ubhtW-NGU802", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "# Use gradient descent as the optimizer for training the model.\n", + "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n", + "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + "\n", + "# Configure the linear regression model with our feature columns and optimizer.\n", + "# Set a learning rate of 0.0000001 for Gradient Descent.\n", + "linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-0IztwdK2f3F", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 4: Define the Input Function" + ] + }, + { + "metadata": { + "id": "S5M5j6xSCHxx", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "To import our California housing data into our `LinearRegressor`, we need to define an input function, which instructs TensorFlow how to preprocess\n", + "the data, as well as how to batch, shuffle, and repeat it during model training.\n", + "\n", + "First, we'll convert our *pandas* feature data into a dict of NumPy arrays. We can then use the TensorFlow [Dataset API](https://www.tensorflow.org/programmers_guide/datasets) to construct a dataset object from our data, and then break\n", + "our data into batches of `batch_size`, to be repeated for the specified number of epochs (num_epochs). \n", + "\n", + "**NOTE:** When the default value of `num_epochs=None` is passed to `repeat()`, the input data will be repeated indefinitely.\n", + "\n", + "Next, if `shuffle` is set to `True`, we'll shuffle the data so that it's passed to the model randomly during training. The `buffer_size` argument specifies\n", + "the size of the dataset from which `shuffle` will randomly sample.\n", + "\n", + "Finally, our input function constructs an iterator for the dataset and returns the next batch of data to the LinearRegressor." + ] + }, + { + "metadata": { + "id": "RKZ9zNcHJtwc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " features: pandas DataFrame of features\n", + " targets: pandas DataFrame of targets\n", + " batch_size: Size of batches to be passed to the model\n", + " shuffle: True or False. Whether to shuffle the data.\n", + " num_epochs: Number of epochs for which data should be repeated. None = repeat indefinitely\n", + " Returns:\n", + " Tuple of (features, labels) for next data batch\n", + " \"\"\"\n", + " \n", + " # Convert pandas data into a dict of np arrays.\n", + " features = {key:np.array(value) for key,value in dict(features).items()} \n", + " \n", + " # Construct a dataset, and configure batching/repeating.\n", + " ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n", + " ds = ds.batch(batch_size).repeat(num_epochs)\n", + " \n", + " # Shuffle the data, if specified.\n", + " if shuffle:\n", + " ds = ds.shuffle(buffer_size=10000)\n", + " \n", + " # Return the next batch of data.\n", + " features, labels = ds.make_one_shot_iterator().get_next()\n", + " return features, labels" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wwa6UeA1V5F_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "**NOTE:** We'll continue to use this same input function in later exercises. For more\n", + "detailed documentation of input functions and the `Dataset` API, see the [TensorFlow Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets)." + ] + }, + { + "metadata": { + "id": "4YS50CQb2ooO", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 5: Train the Model" + ] + }, + { + "metadata": { + "id": "yP92XkzhU803", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We can now call `train()` on our `linear_regressor` to train the model. We'll wrap `my_input_fn` in a `lambda`\n", + "so we can pass in `my_feature` and `target` as arguments (see this [TensorFlow input function tutorial](https://www.tensorflow.org/get_started/input_fn#passing_input_fn_data_to_your_model) for more details), and to start, we'll\n", + "train for 100 steps." + ] + }, + { + "metadata": { + "id": "5M-Kt6w8U803", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "_ = linear_regressor.train(\n", + " input_fn = lambda:my_input_fn(my_feature, targets),\n", + " steps=100\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "7Nwxqxlx2sOv", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Step 6: Evaluate the Model" + ] + }, + { + "metadata": { + "id": "KoDaF2dlJQG5", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Let's make predictions on that training data, to see how well our model fit it during training.\n", + "\n", + "**NOTE:** Training error measures how well your model fits the training data, but it **_does not_** measure how well your model **_generalizes to new data_**. In later exercises, you'll explore how to split your data to evaluate your model's ability to generalize.\n" + ] + }, + { + "metadata": { + "id": "pDIxp6vcU809", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 51 + }, + "outputId": "9c4683bf-8518-420f-aaa2-efd1ed08f2c6" + }, + "cell_type": "code", + "source": [ + "# Create an input function for predictions.\n", + "# Note: Since we're making just one prediction for each example, we don't \n", + "# need to repeat or shuffle the data here.\n", + "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n", + "\n", + "# Call predict() on the linear_regressor to make predictions.\n", + "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + "\n", + "# Format predictions as a NumPy array, so we can calculate error metrics.\n", + "predictions = np.array([item['predictions'][0] for item in predictions])\n", + "\n", + "# Print Mean Squared Error and Root Mean Squared Error.\n", + "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n", + "root_mean_squared_error = math.sqrt(mean_squared_error)\n", + "print(\"Mean Squared Error (on training data): %0.3f\" % mean_squared_error)\n", + "print(\"Root Mean Squared Error (on training data): %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mean Squared Error (on training data): 56251.090\n", + "Root Mean Squared Error (on training data): 237.173\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "AKWstXXPzOVz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Is this a good model? How would you judge how large this error is?\n", + "\n", + "Mean Squared Error (MSE) can be hard to interpret, so we often look at Root Mean Squared Error (RMSE)\n", + "instead. A nice property of RMSE is that it can be interpreted on the same scale as the original targets.\n", + "\n", + "Let's compare the RMSE to the difference of the min and max of our targets:" + ] + }, + { + "metadata": { + "id": "7UwqGbbxP53O", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 85 + }, + "outputId": "b66d6ba9-392a-4c34-d143-0f0de77d5198" + }, + "cell_type": "code", + "source": [ + "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n", + "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n", + "min_max_difference = max_house_value - min_house_value\n", + "\n", + "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n", + "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n", + "print(\"Difference between Min. and Max.: %0.3f\" % min_max_difference)\n", + "print(\"Root Mean Squared Error: %0.3f\" % root_mean_squared_error)" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Min. Median House Value: 14.999\n", + "Max. Median House Value: 500.001\n", + "Difference between Min. and Max.: 485.002\n", + "Root Mean Squared Error: 237.173\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "JigJr0C7Pzit", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Our error spans nearly half the range of the target values. Can we do better?\n", + "\n", + "This is the question that nags at every model developer. Let's develop some basic strategies to reduce model error.\n", + "\n", + "The first thing we can do is take a look at how well our predictions match our targets, in terms of overall summary statistics." + ] + }, + { + "metadata": { + "id": "941nclxbzqGH", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 297 + }, + "outputId": "acc1f4d1-ca8c-42b2-a4a7-d4c50d102357" + }, + "cell_type": "code", + "source": [ + "calibration_data = pd.DataFrame()\n", + "calibration_data[\"predictions\"] = pd.Series(predictions)\n", + "calibration_data[\"targets\"] = pd.Series(targets)\n", + "calibration_data.describe()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 0.4 207.3\n", + "std 0.3 116.0\n", + "min 0.0 15.0\n", + "25% 0.2 119.4\n", + "50% 0.3 180.4\n", + "75% 0.5 265.0\n", + "max 5.7 500.0" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "E2-bf8Hq36y8", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Okay, maybe this information is helpful. How does the mean value compare to the model's RMSE? How about the various quantiles?\n", + "\n", + "We can also visualize the data and the line we've learned. Recall that linear regression on a single feature can be drawn as a line mapping input *x* to output *y*.\n", + "\n", + "First, we'll get a uniform random sample of the data so we can make a readable scatter plot." + ] + }, + { + "metadata": { + "id": "SGRIi3mAU81H", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "sample = california_housing_dataframe.sample(n=300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "N-JwuJBKU81J", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Next, we'll plot the line we've learned, drawing from the model's bias term and feature weight, together with the scatter plot. The line will show up red." + ] + }, + { + "metadata": { + "id": "7G12E76-339G", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 361 + }, + "outputId": "224cd407-ff6f-413f-90b6-326dfe72e07f" + }, + "cell_type": "code", + "source": [ + "# Get the min and max total_rooms values.\n", + "x_0 = sample[\"total_rooms\"].min()\n", + "x_1 = sample[\"total_rooms\"].max()\n", + "\n", + "# Retrieve the final weight and bias generated during training.\n", + "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n", + "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + "# Get the predicted median_house_values for the min and max total_rooms values.\n", + "y_0 = weight * x_0 + bias \n", + "y_1 = weight * x_1 + bias\n", + "\n", + "# Plot our regression line from (x_0, y_0) to (x_1, y_1).\n", + "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n", + "\n", + "# Label the graph axes.\n", + "plt.ylabel(\"median_house_value\")\n", + "plt.xlabel(\"total_rooms\")\n", + "\n", + "# Plot a scatter plot from our data sample.\n", + "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n", + "\n", + "# Display graph.\n", + "plt.show()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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YTLmxJleNtcfpLp+Z6DD+0BITbGVFoludJrPuOt/WcxMRZYriHvmRI0ewe/du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\nyMP4AwGs33wYuw62SAYrJQFN7higfzlVQ2Mr2ru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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "t0lRt4USU81L", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This initial line looks way off. See if you can look back at the summary stats and see the same information encoded there.\n", + "\n", + "Together, these initial sanity checks suggest we may be able to find a much better line." + ] + }, + { + "metadata": { + "id": "AZWF67uv0HTG", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Tweak the Model Hyperparameters\n", + "For this exercise, we've put all the above code in a single function for convenience. You can call the function with different parameters to see the effect.\n", + "\n", + "In this function, we'll proceed in 10 evenly divided periods so that we can observe the model improvement at each period.\n", + "\n", + "For each period, we'll compute and graph training loss. This may help you judge when a model is converged, or if it needs more iterations.\n", + "\n", + "We'll also plot the feature weight and bias term values learned by the model over time. This is another way to see how things converge." + ] + }, + { + "metadata": { + "id": "wgSMeD5UU81N", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n", + " \"\"\"Trains a linear regression model of one feature.\n", + " \n", + " Args:\n", + " learning_rate: A `float`, the learning rate.\n", + " steps: A non-zero `int`, the total number of training steps. A training step\n", + " consists of a forward and backward pass using a single batch.\n", + " batch_size: A non-zero `int`, the batch size.\n", + " input_feature: A `string` specifying a column from `california_housing_dataframe`\n", + " to use as input feature.\n", + " \"\"\"\n", + " \n", + " periods = 10\n", + " steps_per_period = steps / periods\n", + "\n", + " my_feature = input_feature\n", + " my_feature_data = california_housing_dataframe[[my_feature]]\n", + " my_label = \"median_house_value\"\n", + " targets = california_housing_dataframe[my_label]\n", + "\n", + " # Create feature columns.\n", + " feature_columns = [tf.feature_column.numeric_column(my_feature)]\n", + " \n", + " # Create input functions.\n", + " training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n", + " prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n", + " \n", + " # Create a linear regressor object.\n", + " my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n", + " my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n", + " linear_regressor = tf.estimator.LinearRegressor(\n", + " feature_columns=feature_columns,\n", + " optimizer=my_optimizer\n", + " )\n", + "\n", + " # Set up to plot the state of our model's line each period.\n", + " plt.figure(figsize=(15, 6))\n", + " plt.subplot(1, 2, 1)\n", + " plt.title(\"Learned Line by Period\")\n", + " plt.ylabel(my_label)\n", + " plt.xlabel(my_feature)\n", + " sample = california_housing_dataframe.sample(n=300)\n", + " plt.scatter(sample[my_feature], sample[my_label])\n", + " colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n", + "\n", + " # Train the model, but do so inside a loop so that we can periodically assess\n", + " # loss metrics.\n", + " print(\"Training model...\")\n", + " print(\"RMSE (on training data):\")\n", + " root_mean_squared_errors = []\n", + " for period in range (0, periods):\n", + " # Train the model, starting from the prior state.\n", + " linear_regressor.train(\n", + " input_fn=training_input_fn,\n", + " steps=steps_per_period\n", + " )\n", + " # Take a break and compute predictions.\n", + " predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n", + " predictions = np.array([item['predictions'][0] for item in predictions])\n", + " \n", + " # Compute loss.\n", + " root_mean_squared_error = math.sqrt(\n", + " metrics.mean_squared_error(predictions, targets))\n", + " # Occasionally print the current loss.\n", + " print(\" period %02d : %0.2f\" % (period, root_mean_squared_error))\n", + " # Add the loss metrics from this period to our list.\n", + " root_mean_squared_errors.append(root_mean_squared_error)\n", + " # Finally, track the weights and biases over time.\n", + " # Apply some math to ensure that the data and line are plotted neatly.\n", + " y_extents = np.array([0, sample[my_label].max()])\n", + " \n", + " weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n", + " bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n", + "\n", + " x_extents = (y_extents - bias) / weight\n", + " x_extents = np.maximum(np.minimum(x_extents,\n", + " sample[my_feature].max()),\n", + " sample[my_feature].min())\n", + " y_extents = weight * x_extents + bias\n", + " plt.plot(x_extents, y_extents, color=colors[period]) \n", + " print(\"Model training finished.\")\n", + "\n", + " # Output a graph of loss metrics over periods.\n", + " plt.subplot(1, 2, 2)\n", + " plt.ylabel('RMSE')\n", + " plt.xlabel('Periods')\n", + " plt.title(\"Root Mean Squared Error vs. Periods\")\n", + " plt.tight_layout()\n", + " plt.plot(root_mean_squared_errors)\n", + "\n", + " # Output a table with calibration data.\n", + " calibration_data = pd.DataFrame()\n", + " calibration_data[\"predictions\"] = pd.Series(predictions)\n", + " calibration_data[\"targets\"] = pd.Series(targets)\n", + " display.display(calibration_data.describe())\n", + "\n", + " print(\"Final RMSE (on training data): %0.2f\" % root_mean_squared_error)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "kg8A4ArBU81Q", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 1: Achieve an RMSE of 180 or Below\n", + "\n", + "Tweak the model hyperparameters to improve loss and better match the target distribution.\n", + "If, after 5 minutes or so, you're having trouble beating a RMSE of 180, check the solution for a possible combination." + ] + }, + { + "metadata": { + "id": "UzoZUSdLIolF", + "colab_type": "code", + "cellView": "both", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "5fd98048-fba6-47ea-a53a-4766867a4877" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00001,\n", + " steps=100,\n", + " batch_size=1\n", + ")" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 236.32\n", + " period 01 : 235.11\n", + " period 02 : 233.90\n", + " period 03 : 232.70\n", + " period 04 : 231.50\n", + " period 05 : 230.31\n", + " period 06 : 229.13\n", + " period 07 : 227.96\n", + " period 08 : 226.79\n", + " period 09 : 225.63\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 13.2 207.3\n", + "std 10.9 116.0\n", + "min 0.0 15.0\n", + "25% 7.3 119.4\n", + "50% 10.6 180.4\n", + "75% 15.8 265.0\n", + "max 189.7 500.0" + ], + "text/html": [ + "
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Zp0JsXBqiDyYLHRoREZFV823thvlTeqFtUyVO/nkbH2yIw42Mu0KHRUREZHRM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\nENo7dE8lQp8HBTw8oUA8JhRZy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mean185.1207.3
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kIjAwkLfeegudTkenTp3o0aOHZb2lS5ei0dTdzmghRN2QlVfKqXMFdGrjV+87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\n0WlVnDs2suMHCIIwaKSmhJOeXcnWg6UsD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4VmF7i9AeJOtiirs3n5\n1e8PYW8KcPct2W2tRiNtf+lqu8bphtKqc/uUfjkEngYdHrsWZAmdMcTD943mvKK4Pjm3qHHQ/9oX\noBU1PAY3sbVJGOpUSgUTCpLZtK+WijonuWl9814iDH7bS63Ym71cMjUbg0490MMRBEE4K+JTWASx\nTOzgzFdIo2VXdCVShsD2w3I4S8LhJe/EZ0h+H8HR0zFoMgl6VZiSAmi0fuatexeFLDPqqQdRajUs\nfbUCnxcmFmtRa0Mxnw8gJdFAslnb1vpTUoYwZTmRFB2PaWj088hTZdga/Cy5PpOrF6Z2eJ7WziJx\nBg2TR6VEHENOqqnbezqYVzq1aiXFRRZ8zWocR814GnRIChlDegtXXxPXZwEJ6FkGz9kYzL+H/tb6\n2hYBicGruww4QRgKigvDgf6SMtsAj0QYSKu2VCABC6ZlD/RQBEEQzprIlIgg1uJ1Z7pCGi27oivt\nMwSCoRCvf1aGMek8EhKg4sBmlKEdyFo9zsK5LPtVGUolJGf7yd6yieTq4xwaVcy/94fI+dM2Nmxx\nMrrAyC9/OIplq1Ss3XEi6vlOH/uI5CTKtrnCrT+zWlCo5Q7HOJwBfvXUIarrvFx7RSrXX5Ue9doi\ndTSZPMrCTfMLCQTliPc02krnYHHkuIt92zjZAlVGl+QhcwRMHZvW5+PsrxoHYsVZGGpE+1ZhuJiQ\nn4RSIbGzzMY1s/MGejjCACivaqL8hIPiQgtpIvtREIRhQAQlIjjTiV3rCmksTk3ErdQ7vCgkTuu+\n4afR6e0wQW+1fE0ZZdUa5uZrqaxy8E3dDiRnkNKkCaxfZaexKYAu2Y2/wcYFX32KV6PjqzmLaGn0\nc7jEgUKh4Ed3jUCpkMJdNpQKth8Mp/8lxmmZMrrryb213se2jX6QJTIKffiUwQ5jdDj9/Mcje7E3\nhNAmeNldd4xlq11RJ6nRgjpKBRHvabQijvffMjWm30FfOb3F5/TieG67PgO9gX5N6Y8W7OktQ62Y\npmiPKoitTcJwYdCpGZWTwP5j9rb3b+HcsmprBQALp4s2oIIgDA8iKNGFvprYnT4R12tVuL2BtslS\nV5Mnrz/I9oNWZs+YiyzLtBzbSI50DL/OxF/3JHF8Xx0qdbgF6IzVH6H1ulk/9xu4jXG4q/XIQQVJ\nWX5SLB33HbbWlYhUX6KVyx3kN38qo7EpwF23ZHPpxckdxujxBvl/j4UDEhqzF32Km4ZmYp6kxhrU\nib7SacXj69xutD8EgzIrPrfy2rvV4RafGVruviWHyePNAzKeM83gOdMJ+1BacRYZHUIr0b5VGE4m\nFVrYf8zOrnIbc4uzBno4Qj9qcHjYesBKdoqJMbkJAz0cQRCEXiGCEl3o6+J17SfirQUiT/9+e01O\nL+b4LBITtFRUNnGLaTeSU+ZTfyEVR5T4AzLGdDdZJ8oZfWA7dalZ7JswE59Tha9Zg1IbQDa2tK0G\nxrrSHQzKPPW3Ixyv8nDlJSlcvSAFSZLaxujzh3j8L+XUW0Oo43wY0twdAhy9OUltcnq7LIxZ7/Bi\nd3j7/QW9e38zzy6raGvx+e2bs7hyfioqVZQoTz/pLtjT0wn7UFpxHmoZHULfEe1bheGkuDCZ1z87\nRMkhEZQ413y2rZKQLHPp9BykaCtKgiAIQ4gISnTjTLZmnK1oK9ZxBg2TzitElmVUVV+SEqzCbUjk\n5Z3J+Jo1FIzUo0ho5sL33kFGYv286wjKSly1BpBkjOkukszh1cBYV7plWeafr1awY4+DqRPN3HVz\ndoc3wEAgHLDYvd+J2ujHmN65o0dvTlL1WlXbdpfTKSQw6FT43J3bnPaFOpuXfy2v4uttjUgSLJiT\nzG3XZ5JgHjoVsHs6YR8qK85DKaND6B/9sbVpIIjtSeee1EQDmRYj+47Z8fqD4vd+jvD6gqwrOYHZ\noOaC81K7P0AQBGGIEEGJQSCWFeu9lSri47Ucr2ziZt0uaIE3mwtwW8OT/e/elkv5Xz4kyV7H3gkz\nsabl4K7RIQcV6JLdKLWhttXAOrsrppXu91fUsfJzGyNz9Pz03jyUSqndmGX+/OxRtpQ0MabIgNdk\no7Gl8/P15iTV7Q1EDEhAOFDh8gT6/AXt9YZ4+5Ma3v2kFp9fZnSBkXtuy6Fg5ODIDIjV2UzYh8qK\n81DK6BD6x3Br3yq2J53bigstfLzxGPuONjC5KHI3K2F42bCnGpc3wDWzR6JWDd2/XYIgCKcTQYk+\ncKarVt2tWAdDMnUtOvQGmYmBTcS1WGkypfLZ7nSCXhUXzUhkhKaJpg/exW82c+iya/DbVfgcWpTa\nAJkjYM7kfBbNzAViW+neuK2RF9+oIilBzX/fX4Bef+o6QiGZZ144xpeb7ehMQWrlE+j8ka+zNyep\n8SYtSXEaGpo7Z0MkxWlJNGtpbnL3yrlOJ8syG7bYefHfVdga/CQlqLnjxiwumpE4JNMnz3bCPhRW\nnIdKRofQ//ozA64vie1J57bWoMTOMpsISpwDQrLMqq2VqJQS86aINqCCIAwvIijRi3qyahXLivXu\nY0oMRhUuh4tLnVsAaDlvAd51frSaELffkMWxn/wXssfLqN//N/+5cC4//dUBlIoAD/6okPGj4snO\nTMBqbQa6X+k+Xunhf5ceQatR8N/3F2BJOlXzQpZlnn+tkrUbGlBqA+jSnKAAjy8IgE6jxOcP9skk\nVatWMmV0asRxTxmdgk6jornXznbKkeMunl1Wyb5SJyqVxPVXpXH9VenodX2zStEfqdhnO2EfCivO\nQyWjQxB6QmxPEvIzzZj0akrK6gnJMoohGCAXYrfncD21DS5mT0gn3qjp/gBBEIQhRAQlelFPVq2i\nFW+0N3uwOzzUuZPQ62XmKr9G4agnmFXIJ3vMNDlqufnaDJSbv6Jx1XriZk8j+bor+PtLFdibAtx0\nTTpTxyVFfO6uVrrnT8rlwf8pJeCX+fmP88kf0XE18V//ruSjz6woNUFM2S1Ip33mNepU/GLJFFIS\nDX3ygbg/V+gdzQGWvXOCVevCLT7PnxzPnTdlk5HaNyvs/ZmK3VsT9sG+4jwUMjoEoSfE9iRBoZCY\nVJjMht01HK1uJj9zYDo+Cf1j5ZaTbUCniTaggiAMPyIo0Ut6smoVDIX4ZNPxLos3JsbpOGozYDCo\n8DQ5SDzyBbIkYc2fz/uP12FJUrNojpnSS59CUqsY+fjP2b2/mZXrbIzI1nH91eldjjfSSnfAD794\n4iCNjgB335LN9OL4DmN95K/72LPTh0IdxJTtRKHsPGh7sxeNWtlnK3T9sUIfDMp8ujbc4rPFFSQ7\nQ8fdt2RT3MctPqMFta6fW0C1rYVgLxY0Oxcm7EMho0MQekJsTxIgvIVjw+4aSspsIigxjFVanew7\namdMbgK5aXEDPRxBEIReJ4ISveRMV62CoRC//tdWKuqcXT5ncZGFBp8OnUJmnrwOydVMMG8CSz/2\n4w/I3HFDFvXPPIevqoaM+74NOTk888v9KBRw2w3phOQuqkK207rSHQjI/PZvZRyv8nDVJSlcvbBj\nVeff/N/JgIQqRFy2E4Uq8nP314fhvlqh37W/mefaWnwquevmbK6Yn9LnLT6jBbW+3FXN9oN12J0+\nkuJ6L3viXJqwD/aMDkE4U2J7kgAwLi8JlVKi5JCN6y7KH+jhCH1k9daTWRLTRZaEIAjDkwhK9JIz\nXbVatqq0y4CEQoK5xZkU5BbhUakINDUQV/Y1slLFgcTZfL21ltEFRqbE17Pvn6+iyckk8767efaN\nKupsPhLT/fz94xKSvjw1gfX4AtTZXREnnrIss3RZBSV7m5k60cy3b+lYQOmTtXWUbPchKUPhDAl1\n18GOofphuM7m5YXlVWw82eJz4UXJ3Hpd/7X4jBbU8viCbTU7+qKQnZiwC8LQdC5kOwnR6TQqxoxI\nZM/hBmxNbizx+oEektDLHC4fX+2pJTVBz6RCy0APRxAEoU+IoEQvOZNVK68/yI5Dti6fSwYWTM2h\npFqHViFzsXcVks+Df/R0/vaGA4Bv35zF8Qd+ghwIMuI3/8n+Cj+frrWh0ASRTeHenK0T2IPHG/H6\ng1jt7oh1Ct472fozL1fPT7+Xh1JxKitg/cYGlr5SiaQIZ0goNaGIY1ZIMHdyVp99GO6r4o8eb5C3\nP67l3U9q8QdkxhQa+c6t/d/iM1pQKxJRyE4QhHMp26lVaWkpP/jBD7jzzjtZsmQJ9913H3a7HYDG\nxkaKi4u59957WbRoEePHjwcgMTGRv/zlLwM57D41udDCnsMN7Cyr55KpoivDcLNuRxWBYIgF07JF\nMVNBEIYtEZToRbGuWjU5vTQ6O7e1bJVg1HLYakBvUKFsqsVwZBuyWsvn3ikcPl7HxTOTSNr2OUc2\n7SDx8osxXDSbp3+5D5AxprmQTsvqb5+RcfpK+9fb7Lz0RhXJiSdbf7brKLFpRyN/evYoep2CxBEe\nXKHIAQmA88emcfulo2O9VTHrq+KPsizz5eZwi896e7jF57cWZzHngoFp8RktqBWJKGQnCEKrcyXb\nyeVy8dhjjzFz5sy277UPNjz44IPceOONAOTl5fHyyy/3+xgHwqRCCy+vLKWkzCaCEsNMIBhizfYq\n9FolsydkDPRwBEEQ+owISvSiWFet4k1akqOsik8qsuAI6NCqZC5yfooUDOAZO5sXXmxAq1FwywIT\nFYv+hEKvI/fXD/DqOyeotfrQJXpR6YMxjXVHqY1JuRn8aenRttafyYmnWkyV7HHw1N+OoFErePgn\nhWw5XMXa7VURn0unUbLkst4PSEDPOpp05/AxF8+9Fm7xqe6HFp+x6hzU0tLi8ePxdQ4GiUJ2giCc\nazQaDUuXLmXp0qWdfnb48GGam5uZOHEilZWxBXeHiySzjtw0EweO2XF7A+i14hW9NdgAACAASURB\nVKPdcLF5fy1NLT4uOz9H/F4FQRjWxF+4PtC6auX1ByPWcYi2Kp6TaqJo5Gi8KhXaxuPoju9G1pt4\nvbyIRkc9t1ybQcvf/kGgoZGch+7jiMfEh6tKyUjTos9yY++6bmYH1novv3vmcFvrz7zcU6ts+0qd\nPPF0ORLw4H0FjCk0UZRfRFllU8Q6GBdOzMDQB2+WPeloEo2jOcCrJ1t8yjJccLLFZ3oftfg8U5GC\nWm+tKxeF7ARBEACVSoVKFfm95qWXXmLJkiVtX9tsNu677z7q6uq49dZbueaaa/prmAOiuNDC8Von\ne480MG1MavcHCIOeLMus3FKBJMElU0QGjCAIw5sISvSBWLYctF8Vb3B4iDdpmFxkYfH8ItYc1KJV\nyMxq/BhJDtFUOJu3/68BS5KaBal1lL3yDvrR+SR+62b+538OAfDju0aw45g6pvR/OQjOKhNBX5C7\nbsnq0Prz0JEWfvOnMoJBmf/6YQETx4ZbTykVCn555zSWrSplxyEbTU4fSebui6qdTS2IM+1o0pVA\nINzi8/X3wi0+czLDLT4njRuc7dPap2KLQnaDT1/VNxEEoWd8Ph/btm3jkUceASAhIYH777+fa665\nhubmZm688UZmzJhBamrXk/XERAMqVe//f05J6Z/2jRdPz+X9DUfZX9HIFXMK+uWcQ0F/3f++sKfc\nxvFaJ7MmZjC2aOgGmoby72A4EPd/YIn7HzsRlOgDsWw56Gqrx1cHQa9XkdBwAPWJUoKmRP70ZRqB\nQDN3fDOdEw/dD8DI3z7Ivz+2UlXjZf6cRPJH6hlVEJ6ofrmruq1bw+lkGZzVRoI+JdoEL1srjnFl\nKAWlQsHRChe//mMZXm+I/3dvXodgReuYb79sDIvndz8p641aEGfa0SSSnXsdPPdaJRUnTrb4vCWb\nK+b1fYvP3tL+daLUqAn6/GIiPED6qr6JIAhnZ8uWLUycOLHta5PJxPXXXw9AUlIS48eP5/Dhw1GD\nEna7q9fHlZISh9Xa3OvPG4lZqyTBpGHLvlpqax0oFEPjPa4v9ef97wtvrC4FYO7EjCF7HUP9dzDU\nifs/sMT97yxakEZ8ku5l3W058Po7BgtaV8W1aiWBoEyTX0soJDO+5iMk4A17AZu2N5NsUVCwZwWu\nfaVYFi+iKrmQdz6tRaUNsaP6CA8t3cjyNWVcOycfgzbypFWWwVWnJ+BSozb60ae4qahzsmz1Iapq\nPDzyhzKcLUF++O0RzD4/sctrbD/mrrQGZuodXmROBWaWrynr9h62P8/kUSkRf9bd9oVaq5ffPl3O\nI38oo7Law6VzLfzfE+exaGHqkAlItKdVK8mwGEVAYgD1xmtaEITet3v3bsaMGdP29caNG3niiSeA\ncHHMAwcOkJeXN1DD6xcKSWJSoQWn209ZVdNAD0c4S3WNbnaUWhmZHkdhVnz3BwiCIAxxfZopcXrr\nrurqan72s58RDAZJSUnh97//PRqNhvfff58XX3wRhULB4sWL26pnD0XRthw0ODxY7S6yUyNHiTYf\nUqDXqzAc34yxsQqn0cKrq9IBUMvHOf7k31EnmEn/+Y/4/pPlIIMuxQWKUxMktyeAvTlyZw+vXYuv\nSYtSG8CY0UJrk4ktu+v5fIWPJkeAe27LYf6FyTFda1dp7L1ZC+JMty94vEHe/qiWdz9t1+LzthwK\nRgz/yvRC3+nt+iaCIJy5PXv28OSTT1JVVYVKpWLFihX89a9/xWq1kpub2/a4adOm8e6773LTTTcR\nDAb57ne/S1pa2gCOvH8UF1pYV3KCkjIbo3ISBno4Qg/JsswrKw4iA5eenzMgHcEEQRD6W58FJbpq\n3XXrrbdyxRVX8Mc//pE333yTa6+9lmeeeYY333wTtVrNDTfcwMKFC0lIGJpvqNG2HMjAn9/cFTHl\n2x+QcQR0qKUgU+2rAHjxRBFBrxqN2cecze+i8Hiouvk2dn/hxtEUQhvvRW0IdDjHgeN2EuM0NJwW\nmPA1q3Hb9EiqEKbMlra2oaGARNURNSG/nztuzOTKSyJnJrTXXRp7b9WCgNg7msiyzKp1dTz9XBn1\ndj/JiWq+dWMWFw5Qi09heOnN17QgCD0zfvz4iG0+H3744Q5fq1Qqfvvb3/bXsAaNsSMS0agU7Cyz\nsXieqDs0VH22rZI9RxoYn5/EBWOHfzBNEAQB+nD7RmvrrvZ7ODdt2sQll1wCwLx58/j666/ZuXMn\nEyZMIC4uDp1Ox5QpU9i+fXtfDavPRdtyAJFTvr3+IOv2htDplaRVrcPQUk+9KYOPd6aCJDPKVULh\noV3UpI/gTfUY3l9Rh0IVQp/ijvj8RaetkATcSlpqDCDJmDJbUKhlIByQaK40EfIrue6qNL55RXpM\n19hVGvsLHx/A6w+2BWYiOdNWlq0dTHz+rludHj7m4r9/W8qjT+3H0RzgxqvTefrx85gzI0kEJIRe\n0ZuvaUEQhL6gUSsZl5dEdb2L2ober5Eh9L0qq5M3Pi/HpFdz95VjxWcYQRDOGX2WKRGpdZfb7Uaj\n0QCQnJyM1WrFZrORlJTU9pikpCSs1shp0q0Ge5XsHy2ejEGv4evdJ7A2eiI+Zld5Pd+5VsuyFQfZ\nuKeWObMuQhnwM6HhC2QknjlUhBxUYExwcNEn7xCSFHxx8Tdx1ZqQZcgsCOCSI5+/pNTGyPQ4XN4A\ntVYvrmoTyGDKbEGlC0/uQ0EJZ5WRkE/J6LEafnLv6Jje/Dy+ALvK6yP+7Ks9NRyqamLm+AxmTszk\nwy+PdHrM7EmZZGd2nwUTDIZ4/oO9bNxTTZ3djUIBoRCkJOiYOSGTuxaNw+EM8M+Xj/LhympkGS6a\naeFHd+WTma7v9vmHquFexXcwX9/sSVm8v/5whO/H9pqGwX19vWG4Xx8M/2sc7tc33E0qtLDjkI2S\nMhuXnZ/b/QHCoOEPhPjnB/vwB0J875pxItgtCMI5ZcC6b8hy5Bl1V99vbyhUyb529kimjbLwq+c2\nE+mKbI1u/vr6DjbsqWFswTiMBhVZRz5E43ZQaRzBxoNJSKoQM8s/Jb6pnp3Fc6hS5oW7ZsR7mTDW\nzKZ9ke+DNxDiaE0zGYlG9C0WGgM+9Cku1KbwVg85BM4qI0GvCk28l9uuz8Vmc8Z0XXV2F1Z75wyN\nVla7m/fXH2b+1CwWTMvuVAti0czcmO7zstWlHTqYhEInn7/Rw3tfHGbnDiel+4O43EFysnR855Zs\nLpmbhdXaPGwr3Q73Kr6D/foWzczF5fb1+DU92K/vbA3364Phf42Rrk8EKYaWSYUWJKDkkAhKDDXv\nfHGYijonF03KjJpxKwiCMBz1a1DCYDDg8XjQ6XTU1taSmppKamoqNput7TF1dXUUFxf357DOSlfF\nHgFSEvQR6zsAJJi0HDhuRyEpGDcmF8nvYWz918gKJU/tLARZIkNzjCnb1tJiNLNx8mV4qnVIqhCZ\neTKXX5DDpn21XY5LlqF0NwRcPvKLlNil8BjaAhIeFZo4H6Y0NyPTzTFfb7SaGe3tPFTPb+65oNta\nEJFEKyrob1Hhsuop8fkwGpTceVMm50+NIyleF/M1CEJPxFrfRBAEYaDEGzXkZZo5VNmE0+3HpFcP\n9JCEGOw/2sCKzcdJTdRz8yWiHoggCOeefm0JOmvWLFasWAHAypUrmTNnDpMmTWL37t04HA5aWlrY\nvn0706ZN689h9UgwFGLZ6lIeWrqRB/+xkYeWbmTZ6lKCJ5f0g6EQb60rx+WNXAthzIhEGhxeRheM\nxWBQUXD8A5Q+N6XaPEorzCi1fuZtfRNlKMiXc66hqSEJkDCmuZg8Jpn0JCPJXexxl2Vw1YZbfxrM\nAVwae9v3ndVGAm41apMPQ7qLDIsBtzfQqVVpV7qrmdGqtfhfLO1DTxepqGDQp8BZZcBZZSLkU6CN\n9zJrnoovy8t56NlNPLR0I0vf3d12/wWhr/TkNS0IgtBfJhVaCMkyuw9H3mopDC4tHj/PfrQfSZL4\n7qJx6DQDlsQsCIIwYPrsL1+k1l1PPfUUP//5z1m+fDmZmZlce+21qNVqfvrTn3L33XcjSRI//OEP\niYsb/OmircUeW7UWewS4dcGoTj9vpdMouXBiBtfOyaP0eBPjx+Si8jWRV7+NkErN7zYWADDRuZHc\n46Uczx3FvuRpBOtVaM0+1MYAOw9ZUSokdFoV0DljwWvX4nOEW39qUp14/eGAREu1gUCLGpXBjzHd\nhSSBw+nh5//YSFKchimjUzt1BYmktR3n9oNWGpojZ0ycTfG/9tkYcgg8DTo8di3IEip9AH2KG5MZ\nth46lSlS7/Dy/vrDuNw+bl0wqkfnFQRBEIShbnKhhXe+OMzOMhszx8VWwFoYGLIs89KnB7E3e7l2\nTh75mbFnrgqCIAwnfRaU6Kp11wsvvNDpe5dffjmXX355Xw2l10XbXrCj1MaiWSO7/LlRp+L6uQVo\n1UomjJ2AXq9iTNl7KAI+tqrPo7rBgEnfxJz17xJQqvhi1nV4GvRIyhC6k902Gpp9rN5aiUbZuTBl\nh9afWS0kxGlodPpw1RrwOzWo9IEOLUGdnlCH5wzJMksWjo56/e3T2F9ZcZANe2o6PWbyKEuPV5K1\naiXFRRY+XluH26ZHDiiQVCEMFhfqOD/hepyRn3tHqa3t/gqCIAjCuSYrxUiyWcfuww0EgiFUyn5N\nihXOwNd7a9hyoI7CrHiumjlioIcjCIIwYMQ7VQ9E2l7Qyt7sobLOGeXnXpqcXrx+mdQ0C3p3HZm2\n3QQ1en6/Ph8kmYvK38PY0sz2afM44ckFWcKQ5kKh7Fgy0xfs+HWH1p9ZThQqmclFyfjqDfgcGpS6\nAKYsZ1tAIpKvdte0beVobcfZ1dYOrVrJnVeOYcG0bJLNOhQSJJt1LJiW3ZZN0RPlx1zs2QKuG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2LpfBa8t3UVntYMYv+hClYtgiIk2ipEQTHHu3312rytqlB8MGnkfPqu34FhymxDeSr78u5eZ9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\nlindGXtRGBa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" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "metadata": { + "id": "ajVM7rkoYXeL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Solution\n", + "\n", + "Click below for one possible solution." + ] + }, + { + "metadata": { + "id": "T3zmldDwYy5c", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.00002,\n", + " steps=500,\n", + " batch_size=5\n", + ")" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "M8H0_D4vYa49", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This is just one possible configuration; there may be other combinations of settings that also give good results. Note that in general, this exercise isn't about finding the *one best* setting, but to help build your intutions about how tweaking the model configuration affects prediction quality." + ] + }, + { + "metadata": { + "id": "QU5sLyYTqzqL", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Is There a Standard Heuristic for Model Tuning?\n", + "\n", + "This is a commonly asked question. The short answer is that the effects of different hyperparameters are data dependent. So there are no hard-and-fast rules; you'll need to test on your data.\n", + "\n", + "That said, here are a few rules of thumb that may help guide you:\n", + "\n", + " * Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.\n", + " * If the training has not converged, try running it for longer.\n", + " * If the training error decreases too slowly, increasing the learning rate may help it decrease faster.\n", + " * But sometimes the exact opposite may happen if the learning rate is too high.\n", + " * If the training error varies wildly, try decreasing the learning rate.\n", + " * Lower learning rate plus larger number of steps or larger batch size is often a good combination.\n", + " * Very small batch sizes can also cause instability. First try larger values like 100 or 1000, and decrease until you see degradation.\n", + "\n", + "Again, never go strictly by these rules of thumb, because the effects are data dependent. Always experiment and verify." + ] + }, + { + "metadata": { + "id": "GpV-uF_cBCBU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Task 2: Try a Different Feature\n", + "\n", + "See if you can do any better by replacing the `total_rooms` feature with the `population` feature.\n", + "\n", + "Don't take more than 5 minutes on this portion." + ] + }, + { + "metadata": { + "id": "YMyOxzb0ZlAH", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 955 + }, + "outputId": "6c9ab3a7-f939-47f1-d311-ac15fc4c7b6f" + }, + "cell_type": "code", + "source": [ + "train_model(\n", + " learning_rate=0.0005,\n", + " steps=1000,\n", + " batch_size=5,\n", + " input_feature=\"population\"\n", + ")" + ], + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Training model...\n", + "RMSE (on training data):\n", + " period 00 : 225.63\n", + " period 01 : 214.62\n", + " period 02 : 204.67\n", + " period 03 : 196.10\n", + " period 04 : 189.52\n", + " period 05 : 184.13\n", + " period 06 : 180.26\n", + " period 07 : 177.96\n", + " period 08 : 176.84\n", + " period 09 : 176.26\n", + "Model training finished.\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + " predictions targets\n", + "count 17000.0 17000.0\n", + "mean 115.8 207.3\n", + "std 93.0 116.0\n", + "min 0.2 15.0\n", + "25% 64.0 119.4\n", + "50% 94.5 180.4\n", + "75% 139.4 265.0\n", + "max 2890.3 500.0" + ], + "text/html": [ + "
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aFP4ikGhIwFHPJpzKN3Ek24JBD4Pa2YgMUJq0DY2loFhlyYflfP2ji/YReu6d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\nyipsLapVxeaQWtyahdujKAqZL/+Rkq17Cbh7AJ3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by2rWbDaZL3fn8+nWPMwWmahwPbOn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\nnZ1d9bPFY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pIk4ZZl1m87wfjcjYSpKvi3vTt5VyI5fuHmWO8bFMLABBlFpUFqoCDhluFU\nnp7CCg2hOjcDEm3omvH7ToVN4aOtNr677CY6XOKRewx0iAtMkv/dOU91RHaencQ4PU8/1ok+PULr\n3Lc1T3o58V0py1dncf5yBRq1xPSJRubem0BEuJjq0FwoisK5ixVs3lXA/q/NOJwKWo3EuFHRTE0z\n0qNLsGiLaQWoVCq6du0KwIQJE3j99df51a9+xaRJkwIcmUDQfEwalkJ+kZUdR67zl88zeXbOAGEK\nLhAIBA2kWdKbzz77TIgStfCVTFdWIfgyqvR2bF0iQqXnhMMlExWmq1PA0GtV2J03l/uPHpDI7LFd\nayTnbllGkjwl8kWlNiJD9AzyUSLvTXyp/bgagy9D0Ma2kzRERLn+2p8pXL2BkMF96fb3N1FpPW+Z\ntdtPcnfWJuK0FXxe2pFPLR3AlH3TtWYMDmXG4FAKSl1IUcnENkCQcLghM8eAxa4mKshN3wQbmmb8\nnpNV4GbpBhtFFoX+3fTMTdMQEnTrEzm7XebDtdl8sc1THXHf5DgWzEpCr29bX+quXLeyIj2Lw8c9\nnhejR0Qx//4kEuNavwFnW8Fmd7PnoJktGQVcvGoFIDFOz5RxsaSNjiE89PZeoWxt1BaGEhMThSAh\naJc8OLEb+cVWTlws5KPt53h4Ug8hjAoEAkEDaJZvcP6O4bqdaMh0jdor/r6OdTjdvPDwEHRaNRGh\nejRqqUaCrfNi4DZ6QGKV0FC7VUStUtVI7NUqFQ+M74bbLXP0nAlzmZ3j502oVdJNibvvSoYCvyoZ\n7E43OaZy3E73Tfv6MgRt7MQFf0WU3L9/SM77yzF07USP5X9EHezpSbdXlDM6ewsp2nK2lCXzqaVL\n1TEq6YaZ6Kwhodw3KJR8i4t/7C3nPx+ue9W/LqxOieM5BqxOFXGhLnrF2WnOiYWHvnOyeqcdlxsm\njdCyYHoUhYVlzXcBPzl1tox3P7hCTp6dxHg9zzzWid7d/X+eWgOmIgcfr80m40ARigL9eoWyaG4y\n3TuHBDq0dsPVLCtbdpnYdaCQCquMSgV3DIlgapqRAb3DUIlxnm0CkaQJ2itqlYr/mNGX11ceJuNI\nFglRwUwaLjzXBAKBwF+aRZQQXzRuxlcyXZvaK/71JeLGqGCvbR61qyFialUBzB7blQJzBUgSxsgg\nryWGq3aeJ+PojZV/b4m7LwGl0GJnxZYzPHpPrzqvU6NiodROdNjNFQu+DEEbM3HB33YQ02ebufo/\nv0cbH0vPj99FG+NxgMflQJfxIZ3UJewuT2BFSXfgxuu/UpC4f2go9w4MJc/i4q1NRQztneR3rGV2\nFcdz9DjcKlIiHXSJdtJcbzGXW2H9Xgf7jzsx6GDRNAN9u2hueVJnt8usXJPFhh2eezFjShwPzWxb\n1RHlFS7WbMhjw/Z8HE6FTh0MLJyTzJD+4eIzsRlwumQOHi5mc4aJU2c9gll0pJb7JsUxaWwsMVH+\nVx0JAsPRo0cZN25c1e+FhYWMGzcORVGQJIldu3YFLDaBoLkJ0muqRoV+suMcxsggBnWPDXRYAoFA\n0CYQta4thK9kujaVK/52p5uCYisoCgO6xZJxxLevg68Eu5IBXWOqRAS3LLNm94V62xYa4uNQn/hy\nIDOXYIOmzjYOfysWKttGmmPigj/tIPrjx7n03P+gDg+l54d/Rt8h0bOD24Vm9yeoi65x1BnPP4t7\nolAz+YwO07P47mj6xSvklrj4575yhvZO8jtWc4WKzDwDblmia4ydlEhXgx+jN0rKZJZttHElVyYh\nRsXi6QaMkbdeBDh5ppR3P7hKbr6dpHg9zzzeiV7d2k51hNMps3FnAelf5FJW7iYmSsv8WUmMHRWN\nWqzYN5l8k52tu01s31tIicXz+h/YN4yp44wMGxgRsIkwgoazefPmQIcgENxSYiIM/HTOAN788Ah/\nW3+S/1owhE4JYiKMQCAQ1IcQJVqQ2sm0Tlv3VItB3WNI33We/Sdyq7brNSqC9SqsdhkFT1tAsjGU\nOeNutAr40yJy/EIR9u/bIryJAG5ZYcrwlCpPiYb4OPgjvtRlSNkQ4UOtUjF/Yo+bvC8aQ31VKNpL\nFzn3w+dBpaL7B78juE93z0bZjWbfatTZ53Andee4PBw5P+emczw2Lpo+RgVZpUUT04FfPhzid6z5\nZWq+y/O0o/SJtxEXevNrpbFcyHKzYpON0gqFwT00zJ2gR6+9tcmdze5mZXo2G3YUoJJg5tQ4HpyZ\nhF7XNqojZFlh71dmPlqbTb7JQXCQmkVzk7hnQlybeQytFbescPSEhc0ZBRw5YUFRIDREzYwpcUwe\nF0tSvBiz1xZJTk4OdAgCwS2nc2I4P7qvL++vPcGf1nhGhUaFCW8hgUAg8EWziBKhoW1nlbMxNHb8\nYO1k2tsIUFlR2Flr2oXdJUO1RXJZgWv5ZaTvulhVReBPi0iliBARqvcqAuw+mkXGkayqVo+ZY7o0\nyMfhgfHdsNpc7M/M9RlDdd+KxhhYVp+40Nh74ktEGRHu5NIjzyFXWOn2jzcJHznUs0GR0Xz5Oeqr\np5DjU3GNfYi5KjWypK5xLx8fG0UvowJqHarITsSq/Z+2cK1Yw4VCPWpJoV+Cjajgm01JG4OiKOw5\n5uSLfQ6QYMbdOsYM1N7y9oLMM6W8u+QKeQUOkhP1PPNYKj27th3PhWMnLaxYncXFq1Y0GokfTI5j\n9r0JwlixiRSXONm+t5Ctu00UFHoMent0DWHquFhGDY8SYo9AIGiTDO1pZG5aNz7NOM8f07/lvxYM\nwdCco7MEAoGgneH3J2RBQQEbN26kpKSkhrHls88+y/vvv98iwQWKyoQ3NFjLur2XmjzqsnoyXXvF\nH+DFfxz0+1zVqwj8qVKoFBF8iQCVPgjV2ye8nXdA1+ibRAC1SsXDU3ry3ZWiOid/1CVkNNbAsjnG\nj9bVDjIsTkP3376Ko9BM6hv/RfQ94z07KwqabzaivngUOaYDzrSHQaNFTbV7WWojWm1BYzd7xn1G\npoLav7eWosDFIi3XinXo1DIDEu2E6ptHkLA7FD7dYefYORdhwRKLphnoknxrx31abW5WpGezaaen\nOmLWtHgenJno1ZC1tXHpagXLV2dx7KRn1vTdd0ax4P4k4mLFqldjURSFk2fL2JJh4uDhYlxuBYNe\nxeSxsUwZF0uXTq1z1KtAIBA0hCkjUsgtqmDPt9n8ff0pnr6/vzDlFQgEAi/4LUo88cQT9OzZs12X\nY9ZOePW6mu0WzTXqsrpIkW+u8HtKB0CRpWYVQWWCve94Tp2tIZUeFA0x3jx61sQrjw8H4MgZjwll\n5VSJ4xcK+Wj72ZtEAL1WzZCecX4bUjbWwLI5xo/eVMGiOLn4wE+ouJpN0nM/Im7RnBv7Ht2G+sxX\nyJHxOCcsBG3NZFSvURGnLQWrGdR6iOoEKv/eVrICp/P15JdpCNLKDEi0EaRtnkk2BcUyS7+wkVsk\nk5qoYtE0AxGht1YIOPFdKe99cIU8k4MOiQaeeawTPdpIdUS+yc5Ha3PYc9AzUWNg3zAWzUkWCXMT\nKK9ws+tAIVt2mbiWbQMgJdnA1HFGxo6MJiT41gpmAoFA0JJIksTDk3tgKrFy7LyJTzPO8+CE7oEO\nSyAQCFolfosSwcHBvP766y0ZS8CpnfDWleRD3R4JjaUhYoFnf12NKoLKBHvmmM58tO0cp6+YKS6z\n32QG2RDjTXOpjbIKJ/Mn9sAtK2QcyaqzmqK2CNBQQ8qG7t8QHwp/0GvVxAZrOLvwF1ScPIvx4Vkk\n/+ePq7arT+xGc3IvclgMzomLQV8rIVUUKMttlCDhkuFkrgGzVU243k2/RBu6ZsrJMi+4+HibDZsD\nRg/Uct9oHRr1rVudsdrcLF+dxeYMEyoJ7r8nngdmtI3qiNIyF+lf5LJxZwEul0LnjkEsmpvMoL7h\ngQ6tzXLhSgWbMwrYe9CM3SGjUUuMuSOKqWlGencPEZNKBAJBu0WjVvHkzH7838ojbP3mGvFRQaQN\n6RDosAQCgaDV4bcoMXDgQC5cuEDXrl1bMp6AYXO46p1kUYk3z4PG0BCxAGBw97qrCIL1Wn54bx+f\nXgsPjO/mMZQ7W0BJmQNJutG6UZ3q00COnzfVGUddIkBDDSmr76/WaXE7nD73b4wPhS8UWebiT/8b\ny75viJo6jtT/+1VVgqQ6fRDNse0oIRE4Jy2GoFq+KdUFCY0eIv0XJBwuOJ5joMyhJibYRZ94O+pm\nyNdlWWHzQQc7DjnRamD+ZD1De/nva9EcHP++OiLf5CAl2VMd0b1z66+OsDtkNu7IZ82GPMor3Bhj\ndMy/P5G774gW5baNwO6Q2f+1mc0ZBZy7VAFAXKyOyWNjmTAmhsjwW/u6FAgEgkARbNDy7JwBvLr8\nEB9uO0dsZBD9u8QEOiyBQCBoVfgtSuzdu5elS5cSFRWFRqNpd3PGzZb6J1lU4svzoDHMHNOFCpuL\nw2fzsTu8+wmkxIUye1w38s0VXhP+6q0h1XHLMh/vOMeXmTnYvr+Gt1Qr2KBBo5YoLLH5FAEKzBXo\nvm8NqR5L7RjqM6XUa9UYY0MoKCj1+tih8T4UdaEoCldf/h1F/95G2B2D6freq0gaz9tBdf4I2m82\noBhCcU58FEIiax8MpTlgKwaNASI7+i1IVDgkjucYsLlUJIQ56WF00Bw5b7lVYeUWG2evuokJl1g8\n3UCS8daVw1utbpatzmLLLhMqFcyeHs8DP0hE28qrI9yywu4DRXy8LhtTkZPQEDWLH0hm2nhjm6js\naG1k5drYsstExv5CysrdSBIMGxjO1DQjg/qFi5GpAoHgtsQYGcQzswfw1kdH+cu6TF54eCgd4tq3\nSbxAIBA0BL9Fib/85S83/c1isTRrMIEkKtz/NgpfngcNobaHRVSYjsHdIpk0PIV9J3I5fr6QolIb\nkSF6BnaPQaWS+O9/fdUog8dVO8/fNOGjrioJ8Ez5WLXzPLPHdvX6nOi0av6YftxnLM1hSlmdxvpQ\n1EXOu0vJW7KKoF5d6f7B71AFeUYOqq5kojm4DkUXhHPiYpTwWqsZNwkSnUDl33UtNhUncgw4ZYlO\nUQ5So5w0R+X6tXw3yzbYMJcq9E5VM3+ygWDDrUv+vj1p4b2lVykodNAx2cBPH0+la2rr9l5QFIUj\nJyysSM/iynUbWo3ErGnxzJ4eT0iwcEhvCC6XwjfHitmcYeL4dx5hMSJcw+zp8UweGytMQQUCgQDo\nlhzBD+/tzV8/P8kf07/lxUXDmnWBSyAQCNoyfn/7Tk5O5vz585jNZgAcDgevvvoqmzZtarHgbiUG\nncZrwmvQqXE43fV6HjSU2h4WRaUODp7KJzRYx8LJPbGn3agwWLP7gk+Dx7qqESr/FqTXcORMfoNi\nq2zP8Pac2BzuKs8Nbz4TzWFKWZuG+lDURcEn67n++nvokuLpufJPaCI9fgGqrLNo9q4GjQ7nhEUo\nUfE1D1QUKM0GW0mDBYnCCjUnc/XICvSItZMU4ar/ID/46qSTz3bZcbthyh06Jo7QorpFPfoVVjfL\nPs1i625PdcTcexOYe19Cq6+OOH+pnGWrs8g8XYYkwfi7onloVhKx0bpAh9amMBU52LbHxLbdhZhL\nnAD07RnK1LRY7hgSiVbTul8HAoFAcKsZ0TuefLOVz/Zc5E9rjvP8/CHNssglEAgEbR2/RYlXX32V\n/fv3YzKZ6NixI9euXeOxxx5rydhuOd4S3pljulBW4ajXI6Eh+GvaGBcV7HPfI2cKcMsKx8+bqqoR\nBnaPRQKOnfP8LTJUj7ns5lGdvqj0aKj9nESG6qmwu+o0Aa0ed3ObUlbSUN+Kmx7Xtr1c+uVrqKMi\n6Pnxu+iSPMKDlHsJze6PQaXGmfYwSmwtI6omCBK5Fg1nCnRIEvRNsGMMqdtAtSG4XApr99g5mOki\nSA+LpxvondrwFf5K4SosIqhBxx3LtPDe0iuYipx06mDgmcdT6drKJ1Pk5tv58LNs9n3tEVaHDghn\n4ZxkOnVo2GO/nZFlheOnStmcUcA335YgyxAcpGb6RCNTxsWSkiSeS4FAIPDF9JGdyCuqYH9mLv/8\n4hQ/mdnvli0mCAQCQWvF7yzmxIkTbNq0iYULF7JixQoyMzPZtm1bS8Z2y/GV8Abrm7ekuyGmjb72\nLSq1k3HkRltGocV+U5uGucz/kaOVVHo01H5OHC6Z//7X1/XG3dymlLXx5p3hi9JDx7nwxH+h0mro\nufwPBHXvDIBUcA1txkpQFJxp81HiU2seqChgyQZ7CWiCvveQqF+QUBS4WqzlUpEOjUqhf4KNiCDv\nniH+Yi6VWb7RxtU8maRYFYunG4iJaNiqdO3WGmNUEAO6xtTbWlNe4Wbpp9fZvqcQtRrm/SCBOfcm\ntOpV8RKLk9Vf5LIlw4TLrdAtNZhFc5Pp3zss0KG1GSylLnbu94zzzM33vK+7dgpmaloso++IwqAX\nK30CgUDgD5Ik8ci0XphKbBw+U8Ca3ReYO655KnAFAoGgreJ3pq3TeUqbnU4niqLQr18/3nzzzRYL\nLJA0JuFtKBGheqLCdBSV3lzBUNu00ZfBo8rLBI2mUtujoXrVhj9mk/6YUlZvOWlprOcucfaR55Cd\nLnp88DtCh/YHQDLnot25AtxOXHc/gJJUa4a4ooAlC+yWBgsS5006sixa9BqZAYk2QnRNv1HnrrlY\nudlOmVVhaC8Nc9L06LQNX2Gp3VqTb7bW21pz5EQJ7y+9SqHZSWpKEM881okurbg6wm6X+fe2fD7b\nmIvVJhNv1PHw7CRGDYsSEzX8QFEUzlwoZ3OGiQPfmHG6FHRaifGjY5iaFtsmpqoIBAJBa0SjVvHU\n/f15bcVhNh28SnxUMHcPTAp0WAKBQBAw/BYlOnfuzIcffsiwYcN49NFH6dy5M6WlvqclvPXWWxw+\nfBiXy8UTTzxB//79ef7553G73RiNRt5++210Oh3r169n2bJlqFQq5s2bx9y5c5v8wFozbllmze4L\nVNjrLuOvSxDw5u3QUEFCp1XhcHpW6w06NaP6xYMk8e25Qr88GuozmwSqpoN4229g9xg+zTjPsbMm\niss8LSd3DUzmvpEdG2WAWR+O7DzOPPQ0bnMJnd95mciJowGQLCa025ciOaw4R92P3LFvzQOrCxLa\nIIjwT5Bwy3A6X09BuYYQnUeQ0GuaJkgoisKuI042HPBM67h/nJ5R/TVVI0wbQkNba8orXHzwSRY7\n9nmqIx6ckcj90+NbbXWE262wc38hn6zLoajYSXiohvkPJTElLbbVxtyasFrd7D5YxJZdJi5fswKQ\nnKBnyjgjaXdFExoijEAFAoGgqYQGafnZ3AG8uuwQK7acITbCQJ/U6ECHJRAIBAHB72+Xr7zyptLH\nmQAAIABJREFUCiUlJYSHh7NhwwYKCwt54oknvO5/8OBBzp07x6pVqzCbzcyaNYuRI0cyf/58pk2b\nxjvvvEN6ejozZ87kvffeIz09Ha1Wy5w5c5g0aRKRkZFez93Wqb1KXR2DTo2iKLhluUaCXpffxYCu\n0Ry/UOjXxBCAmHADLy8eRkmZHSQJY2RQVfI5d5zvsZ3VqSuWgd1jUBSFF/9xsMrbYlD3WMYPTa4h\neAzqHsOZa8Vczy+vOl+hxc76vRepsDoabYDpDVexhTMLnsGRnUeH//c0xgd/4NlQVox221IkWznO\nEfcidx1c88BGChJON2TmGiixqYkwuOmXYKOpNiQ2h8KqbTaOX3ATHiLxyD0GUhMbf9KGtNYcPl7C\nX5Z5qiM6d/RUR3Tu2DqrIxRF4dC3JaxIz+Zatg2dTmLOvQnMmhZPcJBoL6iPK9etbM4oYPeXRVht\nMmo1jBwWydQ0I/17hTZKABMIBAKBd+Kjgnlm9gB++8lR3lubya8XDiUpVlShCQSC2496RYlTp07R\np08fDh48WPW32NhYYmNjuXTpEgkJCXUeN3z4cAYMGABAeHg4VquVr776ildeeQWAtLQ0lixZQufO\nnenfvz9hYZ7+7iFDhnDkyBHGjx/f5AfXGvG1Sg2eqRY7DmchSVKNBF2tUjF7bFdPeZ+iYIwKRq9V\ns2LrmRqeEr4Y3COWsGAdYcE3TxloSMtKbZ+JIL2GT3eeZ39mbtU+hRY7Ow5nMXFYB1790R1Vgsen\nO8/VECSq0xQDzLqQrTbOLv451jMXiX/8QRKffsSzoaIU3fYPkCpKcA2ZjNzzjpoHKgpYroO9FLTB\n3wsS9a+w210Sx3MMlDtUGENc9Iqzo27iwnxekczSDVbyzQpdklQsnGYgPKRpJ/Wntaa8wsWSj6+z\nc38RGrXEQzMTuf+eBDSa1pmYnr3gmahx6mwZKgkm3h3DgzMSiYkSEzV84XTKHDhUzOaMAk6f97wv\nY6K0zJwaz8S7Y4mO1AY4QoFAIGjf9EiJ5NFpvfnHF6f4/aff8p8PDSK+hVuIBQKBoLVRryixbt06\n+vTpw/vvv3/TNkmSGDlyZJ3HqdVqgoM9H6rp6encfffd7Nu3r8qbIiYmhoKCAkwmE9HRN8rVoqOj\nKSjwnrS3Feoa0Qm+V6mrUz1Br21KGB3uaY14YHw3Jg7tUK8oEVNt/+ZEo5bYfvg6R87k1+mN4Xkc\nBdw9IBHj9/9gj54zeT1fUTMYYFaiuFycf/LXlH19jOgfTKLjKz/3rPTaKzwtG6VFuPqNxd13TK0D\nFSi5Do7vBYnIjiDVLwKUOzyChN2lIjncSbdYB01dWD5+3sUn22zYnTB2sJbpo3So1U0XBeprwTlx\nqoy/LLtKUbGTLp081RGpKa3zC1JWro0P12Tz5eFiAIYPimDh7CRSksUUCF/k5tvZutvEjr2FWMo8\n42kH9wtnSloswwZENMvrTCAQCAT+MbJfAkWlNtbsvsj/rTjMz+YOpHNieKDDEggEgltGvaLECy+8\nAMCKFSsadYHt27eTnp7OkiVLmDx5ctXfFaXuHntvf69OVFQwGk3zl2MbjU1343e7ZZb8+yQHM3Mo\nKLZijAzizn6JPHZfX9RqFWERQRijgsg3W32ep8hiQ63TYowN4R/rTtRIIAstdrYfuk5wkI6F9/Qm\n2othZiUvPDqCHh2bv0+xdlx1UWix8/KSb4iLCqJf11iKfYwmjQ430DU1BoOuaT3riqJw4icvU7xl\nNzHjRzL8o3dQ63Uodhvl6SuRS/LRDb6bsHEza5SkK7KM5fo5HI5StCHhRHTsgeRHy4apVOHYZQWn\nG/qnSPRM0iFJjTfvdLsV0reXsmGfDb1O4sl5EdzZv3mT7KfnDSY4SMfBzBxMxVZiI4MY3D0ec5aO\n1z66gEYj8aOHU1kwOwVNK/RhKDI7+OCTK6zfkoPbrdCnZxhPPdqFgX3bbttXc3z++MLtVvjyUCFr\nN2bz9VEzigIRYRrm39+BGVOTSE68fYWcln7uBQKBoD6mj0wl2KBl5dYzvPXRUZ6a1Y9+XWICHZZA\nIBDcEurN/hYuXOizl3j58uVet+3du5e//vWv/POf/yQsLIzg4GBsNhsGg4G8vDzi4uKIi4vDZLqx\nep6fn8+gQYN8xmQ2V9QXdoMxGsMoKPBt3OkPH20/e9NUg9p+CQO6xtSbzEeE6nA7nFzPLmb/t3VX\nQuz/NptpI1LonhLFV6fyvJ6rvNTW6MfmreLD7nR7jasu8s1Wdh66hkGnwuaoeyzmgK4xmExlfntb\neOP6238j+1+fEtyvJ6l/ed1TmeIqRbtjOar8a7i7DqG07wRKTWU3DlLk7yskykAbgjM4CVNh/a+z\ngnI13+XpkRXoZXQQo3Nh8l4MUi9lFQorNts4f91NbKTEo9MNJMS4muW1WZuZd6UybUQKJWV28gsk\n3vnLecwlxXTtFMwzj3eiU4cgzOa6W20ChdXmZv3WfNZtysNml0mM17NwdhJ3Do1EkqQWeZ5uBc31\n+VMX5hIn2/eY2LrbhKnICUCvbiFMSYtl1LAodFoV0DKvsbZASz737RUh4ggELUPa4GTCg3X8bf1J\n/ph+nEfv6cWofomBDksgEAhanHpFiSeffBLwVDxIksSdd96JLMscOHCAoCDvK2ulpaW89dZbLF26\ntMq0ctSoUWzZsoUZM2awdetWxowZw8CBA3nxxRexWCyo1WqOHDlSVZ3RFqg91tKfqQYPjO+GW1bY\nfTTL6/SMwd1j0aglPth42quRZaUp4cIpPTh0Og93Hbm+Qaeuap1oCHW1jAzoFsvEoR2IDjf43YZy\nM3ULXKlJ4UgSNYwyK1tOGjKRI29ZOtm//wf6Tsn0/PBPqMNCwe1Cu+tjVPlXcHfqh+vOGTVbMqoL\nEroQiEjxq2Uju0TDWZMOlQT9E+zEhNQ9TcVfrua6WbrRRkmZQt8uah6aZCBI37Jl9A67wsfp+ez6\n8v+z9+YBUZ7n+v9n9hmYGRgYdnCJiguKuGvUCIpKtsY2iUmMxixNmzRpmzY9J+fkdD399rT5tac9\np2lPm6ZZjFlq1mZVExMwGo3GBRRUwBUElG1gZhhmfd/fHyOILMOAIKjP5z8HZt5n3ncc3vt+ruu6\nG1GrFay+NZkVeQnDTr7v98ts2VbPhndraLL7iTKrWbsyhdyF1mGbczGUyLJM8REnm/Lr2LW/iUAA\n9Doly7Ot5OVYh60dRyAQCK52ZoyP40d3ZvHHNw/w9w8O09ziJW/2CBE2LBAIrmh6bUq0ZUY899xz\n/P3vf29/fNmyZTz88MM9Pu+jjz7CZrPx2GOPtT/2m9/8hh//+Mds2LCB5ORkVqxYgUaj4fHHH+eB\nBx5AoVDwyCOPtIdeDme6K9rHj7CENdVApVSyZtl4kGXy91d3+d20eCOrlqaz4bOj7OgQHtkZrUaF\nMUJDhE7DomkpfLa3q3Lh2imJ/VIcdJ4Q0mD3kL+vivx9VcSadWSOie0xLDEUXl+AaycnUlrRRKPd\nTZRRy7RxViIjdXyw/cQFx2s7frgTORo//JRTTz6F2hrD+Ff/hCYuFqQA6m2vo6w5SiAlHf/8Wy8M\nrZQlaK4Eb0vYDQlZhpM2DadsWjRKmSlJbsz67tUf4SDLMl+W+HmnwIMkwfXztCyeqUE5yDcgu/c3\n8deXKrA1+5k4zsRD96QyYphlMciyzK59zax/s4rqsx70OiV3fC2RW5YnYBATNbrQ4vLz2ReNbC6o\no6om+H9zVKqB5TlWFs2NEedMIBAILgPS06L599XT+f3rRbyRf4wmh5c7lowd9PsCgUAgGCrCNu+f\nOXOGEydOMHr0aAAqKiqorKzs8ffvuOMO7rjjji6Pv/DCC10ey8vLIy8vL9ylDAu6K9p3FJ9Bp1Hi\n8XUtUNumGsB5dcWt2WNRqZTsL6un0eEmOlJHVrqVVbnj8AfkkFM6IDip45/bTrAqN527loxDqVCw\nr7QOm8ODxaRj+vjwwi07WzR6mxDSYPeQv7+a1LhIoG9NCYtJz5rl4wEuUJj87Pnd3f5+uBM57Dv3\ncuyRH6OMMDD+5f9FPzoNZAn1zndQVR5GShiN/7o7QdXhI39BQ8IIUam9NiQkGcrrtNQ4NOjVEplJ\nbiK0veeg9ITPL/N2gYfdh/xE6GH1cj3jR15cpkYoPL4AVWddvPNBHdt3N6FWK1hzWzIPrB6LrdHZ\n+wtcQg6XO3npjSqOHG1BqYS8HCsrv5aEJUpMhOhM+YkWNufXs213I16vjFqtYNG8GPJyrIwfEyl2\n2AQCgeAyIyXOyH+smcHvXy/ikz2VNLd4eODGSWiGYc6TQCAQXCxhVz+PPfYY9957Lx6PB6VSiVKp\nvKxsFgNJqKK9u4YEBKcaqFUKXt1S1mWKxi8emIXT5bsgR6Gh2dXnKR0dx3SGk8nQ01SPnGkpYR27\nqi6YN6BUBIv1WLOOFrevx8wIgMyxse3rapuyUWtzUdfUffCnLYyJHK6SMsrv/SHIMuOe+y2RmRNB\nllHv/hDV8SIkayq+nLtB3aGYlSVoqgRf+A2JgASHzupocKkxagNkJrm5mEzORrvEuo/cnK6VSI1X\nsvYGPTHmwbnZaLvW23Y1cPakBjmgJCZWyU++n86o1AjUvdg1esoWGQwqq1t5+a1qdu9vBmDujGhW\nfyOZlCT9oB73csPjkdi2q5FN+fUcOxXMP0mI07I8O44lC2IxmwavuSUQCASCwSfGrOffV0/nj28e\nYPfhWhwuH49+YwoGnfh+FwgEVxZhf6vl5uaSm5tLU1MTsixjsVgGc13DmnDyFPRaFV5fAItJz7R0\nK3csHtutuqIni0KUUReWPaJz0a7TqMIeqdnTegIBKaxjt+kD2nIxxqVGhwzcBMidkQoEi9y6plaQ\nZaKMOuKiu59I0lFh0h2eympK7/4uAUcLY/7vV0RdNwdkGdX+j1GV7UayJOBbfA9oOqhUHK1YqUfp\nd4XdkPAGoLhGj92jwmIIkJHo5mI2K0or/Ly8yY3LDbMnqflGtg7NIGYjvPRRORs/seFz6EAhY7C2\nIlk87DhymlGpPdtjQo2j7UvWRzg0NvnY8G4NWz6vDwaHjo1k7coUJow1DuhxLndO17jZlF9H/heN\nuFoDKBUwe1oUeTlxTJ1kQqkUqgiBQCC4UojUa3j8jiz+9v4h9pXV8ZtX9vGDlVOJDnFvJBAIBJcb\nYTclqqqqeOqpp7DZbKxfv5433niDWbNmMWrUqEFc3vAkyqjD0ssYzgidmifXzCAu2tCrJaI7i4JO\no2JaelyvUzp6K9p7ItR6DhxrJHOslfx94U/XACg/3RTyvMSYdEQZdbzySSlfHDyD2xsMhtRrlSTF\nRnb7nGnp1h535n0NNkrvehRfbQMjfvFDYlcsB0BVvBV1yXYkcyy+JfeCztBeXBcfq+OeuRHEJ+mo\nbFaQfE0Kql4aEq0+BQdq9LT6lMQb/UyI99Dfuk+SZT7b42PTTi9KJdy2WMfcDPWgyuu37Wrgw/ec\nBPxaVHo/kQkuVLqgmqXts9cTfWmk9YWOyouAH/658SzvfVyLxyuRkqRjzW0pzM6KEraDc/j8Erv3\nNbOpoI7iI0GbjSVKzY25iSxbZMUao+33a19KFYxAIBAI+o5Wo+I7Kybz8idlFOyv4r/W7+UHK6f2\neO8kEAgElxthNyV+8pOfcPfdd7dnQowaNYqf/OQnrF+/ftAWNxwJSBJvbT2GyxN60kKT04NWrWy/\nyQ+lrujJotCWB7G/rJ4Gu7vb54Yq2kPR23pyZ6SiUipCHrvr8zzMzUjsMZxz+vg4/rntOJ92CuR0\neyVO1DhIizficvuxOdwXKEy6I+Bqpeyex3AfryDpkbUkPrgKANXhnagLP0WOjMaXex8YgrvsGz47\nyrbCKh5bamF8kpY9J908k99EzgxVyOLa6VFyoEaHN6AkLdrLNTE++lsnt3pkXvvETcnxAFFGBffe\noGdE4uAVgc12H39/9TTbd9tAAQZrKzqL54L1t332Urt5fl8baeHQUXnR0OxB5Y7AUavD45GxRKm5\n/65UliyIHXbTP4aKugYvH2+tZ8vn9TTZ/QBMmWgiL8fK7Kzoi5o8cilVMAKBQCC4OJRKBWuWpWMx\nanln2wl+/fI+vn97JmOSo4Z6aQKBQHDRhN2U8Pl8LFmyhBdffBGAWbNmDdaahjWdd457orOCIZQd\noye1g0qpbM+JaLS72fxVBQeONtDs9BJjDl2090Zv64kx6y849pY9lRQdbaDR0bOlw2LSs2rpOAw6\nVSclhIprpySyYuFofvZc94GWAC2tPn523yxaPf6Qu7aSz8/RB5+gZX8J1pU3kfrkowAoj+5Fvecj\nZIMJ79L7IDL4h9rjC3DoeB0/WGYhPVHLVyda+VtBMwE5dHFtcykpPqsnICkYE+shLdrf8wnthTMN\nAV780E1dk8zYVBVr8vQYIwav8N6xx8Yz6yuxO/yMuyYCT0QjDm/4nz3oXyOtNzZ8dpRPvjqNz6mh\ntd6E5FOBUiIjU8ePH56IXid26iVJZn+xnc0F9ewtakaSITJCxc1L41mebR2wbI3BUsEIBAKBYHBQ\nKBTcPH80UUYd6zYd4bev7efhWyYzdax1qJcmEAgEF0WfknLsdnu7nLq8vByPp2+TFy53eptK0ZHO\nCoZQdoze1A5qlYL8/VWUHG+k2ekl2qgjc2zsRe1ohrsenUZFUmwka5ZPAEVpSEvHtHQrEToNdy8d\nz23ZY9szI+IsEeg0KmptocM7bQ4PrR5/yEJXliROPP6fNOfvIGrJfEb99scoFAqUJw+i3vkusi4C\nX+5aMMW0P8fuaGXtvEjGJWjZdbyVZ7c2t+dg9FRc1zpVHD4bLNYnJbiJN4ZWxoRif5mP17d48Poh\nZ4aG6+dpUQ2S77/J7uPZlyvZsacJrUbBvXekcNPSeDZ8Vt7nz15/Gmmh8PgCfLGnAUeFkYBHDcjo\noj3oY9x4tLreYj2ueJrtPj7d3sDHBfWcrQ9aoMaOjiAvO44Fsy3odAN3ggZDBSMYehptXgoPOSgq\nsVNS6mTpIit3fC1pqJclEAgGmOumJmOO0PLXd4t5+q2DrL1+PAszk4d6WQKBQNBvwm5KPPLII6xc\nuZK6ujpuvvlmbDYbv/3tbwdzbcOO3gIuFYDFpGPCSAsrFo7u8vOOdoxwLAptdN7RtDk95O+rQqVU\nXNSOZl/W4/EFOHC0vtvXUSpgUVbyBc/TaVSkxl0YUNhbeKflXOZEKCp/9TQNb36ENnMSaX/6FUqN\nGuXpUtTb3wSNFt+Se5CjE84/QQoQK9cSl6Dly2Ot/P3z8w2J4DG7FteVTWqONehQKWQmJ7qxRPQ8\nTSQUgYDMB194+bzQh04Da2/Qkzl2cBKzZVlmx1dN/O3lSuxOPxPGRvLo/SNJSQzuqvfns3cxjbTO\nnDrdynOvVVBdFjzXGqMXg9WNShs8t/1VXlzuyLLM4fIWCtadJv+LOvx+Ga1WQe7CWPJy4hgzanDO\nx2CoYASXHo9H4lC5k8JiO4UldiqqzlvtYqI1JMWLIDyB4Eola5yVH901jf99o4gXPjpCs9PLjfNG\niiwmgUBwWRJ2hTR69Gi+/vWv4/P5OHLkCIsWLWLv3r3MmzdvMNc3rAhVVMeadYxLjab8dBM7i89Q\nWmHr4s/2B2RyZ6Ry87WjerUotOFwedl7ZHB2NDvaQ3oLugtVxMjA8tkjelVt9BbeOX18XMj3Uv3M\ny5z5y3rssfG8M+d2Il4p4oaRfpY3FYBShS9nNXJsyvknSAFoqkAZcHPCpujSkIALi2tZhuONGiqb\ntGhVEplJHoy6/jUkHC6J9RvdHKuSiLcouPdGAwkxgyMFaGr28beXK9m5twmtVsH9d6ZyQ27cBWqM\nvlxrOB9+2NZc62sjrY36Ri+v/bOGgi8akGQwmAKoLS7U+guVJ/0NbL1ccbUG2LqzkU35de2FZGqS\nnuXZVnLmxxAZMbjj3gZaBSO4NEiSzKnTrRSWBNUQh8qc+PzBLzWtVsG0yWayJpvIyjCTlqwXxYlA\ncIUzNiWKJ9fM4PcbCnn78+M0OT2syk0XU5gEAsFlR9h3vg8++CAZGRkkJCQwdmywIPH7+++xvxwJ\nVVRH6DV82WEcZkd/dts40O4C5XqiLYRuz5FampzdT7MYqB3NcMaIhipiYvpQxNyxeCyyLHfJnMid\nPYJbrh3Z4/Pq397I6V/8Dy2RZt67+QFaDZGktNaSXV+EpJQJLFmDnDDq/BOkADSdAr8b9FGMGJvI\n4hmqHotrSYYjtTpqnWoMGonMJDcGjdz9YnrhZE2AdR+5sbfIZI5RccdSPXrtwN8gyLLM9t02nn2l\nEoczwMRxQXVEckLPmQO9Xeuewg9/8cBsnC5v2BMaWlx+3v7oLB98UovXJ5OWomft7SkcOXuGT/c6\nuvx+fwNbLzdOVLjYVFDP5zsbcXskVCqYPyuaO74+ktQE5SUrIgdSBSMYXBqbfBw4ZGd/sZ2iQw6a\n7ef/7o4eYSArw0xWhokJ44xoNVe5B0oguApJio3kyTUz+cPrhXy2r4rmFi/funkSGrX4HhcIBJcP\nYTcloqOj+fWvfz2Ya7ks6E4Gnzk2lqLyntUMAUm+IIshnEC5cAI1L+WO5kAVMSqlstvMidTkaOrq\nuharAM0FX3L8sZ/j1Rn48JYHcJotjNQ4+FfrATQKiRdas1gZN5r2M9GpIYEpGZVC0aNSwC9ByRk9\ntlYVZl2AyUlutP34Wy7LMjsO+nn3cw+SDDfN15I9XTMohaat2ccz6yvYta8ZrVbBA3elcsOSuIve\nHbnY8EOfT2JTfj2vv1+DsyVArEXDXSuSyZ4fg0qpIEsyoVAo+q28uBzx+iR2fGVjU349pcdaAIiL\n1XLrjVaWLIzFEqUhLs7U4+d/sOivnUwwuHi8EofLnBSWBC0Zp06ft2RYotRkXxtDVoaZqZNMREdp\nhnClAoFguGAx6fi3u6fz9FsH2Vtax3+7ivjerVOI0IvvCIFAcHkQdlNi6dKlvPfee0ybNg2V6nzF\nlpx8dQXrdCeDb3Z6KOghALLR7qawrPsshp7sF+EGal7qHc2BLGK6y5zoDmfRIcq/+S+gVLHpprU0\nWpNIUrfwRGwRBoWfv9omstMdzfI2xcgFDYloMCXRcQZmZ6WA1w8HavQ4vSpiI/xMSvCg6sdmo9cn\n81a+hz1H/ETqYc31esalDbwEX5Zltu0KqiOcLQEmpRt59L4RJIVQR4TLxYQfSlJQtfHK29XU1nuJ\nMKhYfWsyNy2NR6c9f0L7aiO5nKk562ZzQT2fbm/A2RJAoYAZmWaWZ8cxPdM8aGGn4XI1XYvhjCyf\nt2QUltg5VNrBkqFRkJURtGNkTTYzIkVYMgQCQfdE6DX88I6pPPv+IfaU1vHrV/bxw5VZWEzCjicQ\nCIY/YVdNpaWlvP/++0RHR7c/plAoKCgoGIx1DXs6FrehrA1RRi1NzvAD5Ty+AMermnsMgwSwGHXM\nmBDa/jEYXOoixn28grLV30dyexj5f/+F57QBa0sTT1qLiFL5eM6WzheticSazylGJD80VfTYkOiM\ny6vgQI0et19JoslHepyX/tSJDc0SL37oprpeYkSCkntu0GMxDbyMurEpqI7Yvb8ZnVbJg3enkpdz\n8eqINvobflhUYuelN6s4fqoVtVrBzcviue2mRMzGnr9ewrEMXY4EAjJfFTazqaCOopKg8sFsUvON\nGxJYtshKQtzwuzm8Uq/FcKap2UfhITtFxQ6KDtmxNZ+3ZIxKNTD1XC7ExHHGC5p6AoFAEAqNWsVD\nt0zmtS3lfLrvNL9av4cfrswi2Ro51EsTCASCkITdlCgqKuKrr75Cq9UO5nouS0JaG8ZZOXCsoddA\nuc5efqWCLqGMANFGLT+/fxamiKG7DpeiiPHW1lO66rv4G2yk/tcTKBZey9ydR1hytoAYlYdXmsfw\nmSsYajkt3YpOJZ9TSHjAYAFjYsiGhN2t5GCNHp+kYKTFyyiLL9Sv98jhk35e2eym1QPzJqtZcZ0O\ntXpgdzJlWWbrl4089+ppnC0BJk8w8si9I0kc4GT9voYfnqhwsf7NavYX2wG4bq6FVV9PHpaF92DT\naPPyyecNfPJ5PQ02HwCT0o3kZVuZOyMajfD6X9V4fR0tGQ5OVra2/yzarGbRvBiyMkxMzTBjEZYM\ngUBwESiVClYtHUe0SctbW4/z65f38v3bpjI2NWqolyYQCAQ9EnZTYvLkyXg8HtGU6IGQ1gZF+QWZ\nEm10tF909vLLPWQszpwQP6QNiUtBwOGk7O7v4amoonHFN3jDlYz32c/5WXwRCWo3Gz1j2NQygljz\nuXOcPQpspyAQXkOiwaWi5IwOSYZ0q4fkqL4HtkqyzJbdPj7e5UWlgjtydcyeNPDFRKPNy1/XV/JV\nYTN6nZJvrU5jebZ1UJK1w80Nqa338No7NWz9shFZhqmTTKy5PYUxI6+u3XZJkjl42MGmgnp2729C\nksCgV3L94jiWZ1sZmWoY6iUKhghZlqmoclNYYqeoxEFJqQOvL/ilrlErmHrOkjF1komRqQaRlC8Q\nCAYUhULBjfNGERWp48WNR/jtP/bz0C0ZTBsXN9RLEwgEgm4Juylx9uxZFi9ezJgxYy7IlHjllVcG\nZWHDmbZxiR3tC91ZG9QqBRs+O9oegtmmfojtNH0jlJdfqQg2KGLMV0cIneTxUv7Av+AqKaM5ZzGv\np83B4GjhP6xFJKmcbHSmUnXNXP7r1hHB86+S+9SQOGNXU1qnRaGAjEQPcZGBbq9nKFxumVc/dnP4\nZACLScHaG/WkxYdnYwn3WLIsU7CjkedeO02LK6iOePS+kYOuQgjVXHM4/bz14Rk++rQOn19mVJqB\ntbenkDXZPKhrGm44nH4++6KBzQX11JwNqkpGjzCQlx3HwrkWDHqRy3A10mT3ceBQMBeisNiBrdnX\n/rORqfpzUzLMTEwXlgyBQHBpWJCZhDlSy//98yB/evsg9ywfz6KslN6fKBAIBJeYsJtlWdmEAAAg\nAElEQVQSDz300GCu47Kgp3GJdywei0oZvMnsaG14dUvZBbvObXaMjNGWCyYZhPLyy8CP7szimpSo\nsPMbOhe+Hl+AOpsLFAriog39fp3BRg4EOP69n2Lf/hVRy7N5Y8bN6Frc/EvsAUZrneS3JPFy81hi\njzWycvE4dEoZbCch4AVDDBgTemxIyDJUNGk40ahFrZSZkujGqPPz6pbQ17Mz1fUBXvzQTUOzTHqa\nirvz9BgNve9yhvPZaaPR5uUvL1Wwp8iOXqfk22vSWLZocNQRnemuuaZAwXub63jrwzO0uALExWpZ\n9fUkrpsbc9Xs8MqyTPlxF5sK6vhitw2vT0ajVpB9bQx5OXGkXxMhAgivMnw+icNHWygstlNUYud4\nxXlLhtmk5rq5lnY1RIzlyla3CQSC4UvmmFj+9a7p/M8bRazbVEqz08vN80eJv1kCgWBYEXZTYvbs\n2YO5jsuCvoxLDKV+2H7gDCqVilW541AplSG9/DEmfdgNie4KX4NeTW2jC++5NHedRsmCzCTuXDKu\nx8K7LwV0b/RFGXDqp/9N4/tbMM2ZRtSv/wP7i3t5PPYg43XN7HDF81zTeECBzeHG4WhBJ9eGbEi0\nHdscqaOy2UCVXYNOLZGZ5CZSK/Pqlr6Nv9x7xMcbn3nw+WHJTA15c7VhF+XhfHZkWSZ/RyPPn1NH\nZE408ch9I4i3XvqMBp1GRWyUgc93NvLqO9XUN/owRqq4d2UK1y+JQ3uVZCS4PQE+/9LG5vy69qIz\nKV7H8mwrOQtiQ4Z5Cq4sZFnmdLW7fUpGcakDrzf4vapWK8icaCLrXEClsGQIBILhxDXJZp5cM4Pf\nbyjkn9tP0OT0sHrZePE9JRAIhg3ijjpM+jouMZT6QZIhf18VKqWCVbnpYXv5e6O7wpdOa/D4JD7d\nW4UMrF46PuzXCVWsd0d3jY0JIyzctTSdCJ363FrONyyOPfUMtS+8jmHCGMa9+HsCeh0/iD/MFI2N\nva2x/NU2EZngH8+RcQZipLMg+fDrLDT6TET5pfbz1PHYTU4fi+fPICnRTIQmwNRkDzq13Kfr6Q/I\nvL/dy/YiH3otrL5Rz+Qx4f/XCedYTmeAv6yrYO+BoDri4XtGsHRR7JDsZMiyzP5iO+vfqObk6VY0\nagUr8uK59cZEjJFXx1dGRVUrmwvqKdjRgKtVQqmEOdOjyMuJI3OiSdzIXSU0t1kyDjkoKrG3h5gC\npKW0WTJMZKSb0OmGV6PuUivdBALB8CYxJoL/WDODP7xeREFhNc0tXr79tQy04vtBIBAMA66OCmMA\n6Ou4xFDqhzY6Fr8hgzJ7oONNZ/C53Re+3bHjYA23Z4/tcrPa1+ZLT3TX2Pii+Ax7y2q5dkoSCqCw\nvJ5Gu4fpx/cz64PX0KYkMv6Vp1GbI9F/8RZTNbUUu6N5ujGDAMEb/ugIJd/PjUIp+SipVfDi58e7\nqDnajq3RqFly3VwS42I5U9eA7Kpi9ogxQPjX094ise4jNydrJBJjlNx7o544S9+Kj1DHarS7+eiz\ns7z5Xh2u1gBTM0x8Z+3QqCMAjp10se6NKg4edqBQQM78GO5akUxc7JUvP/f5Jb7c28Sm/HoOlTkB\niInWcPPSeJYushIrJPhXPD6fxJGjLeemZNg5fqqDJcOoZuGcc5aMDNOw/TwMpNJNIBBcWUQZdTxx\n93T+9PZB9pfX87sNhXzv1kyMBjH1RyAQDC2iKREmfR2XGEr90EbH4rc7L39bHkRDs+uC3a7ubjrH\nj7D0WPh2h9srUVXn4Jrk6Ase72vzpTtCNTbcXonP9p6fRDLy+CFmfLgBtz6CU999nKzEONS73kd1\n4gABaxr7VfMxeZtodHiINSr5UV4MZj18Xu7lxW2N7a/TpuYIBCQOHGvAoNeTe90cLFFmTlZWs333\nfixGLd9YOAqdRhXW9TxeFeCljW4cLpmscWpWLtGh0/Z9h7ynY0k+Bb4GIy9tOINBr+Q7944gd+HQ\nqCOqz7Ty9N9PsG2XDYBpk83cc3syo9Ku/IkatfUePt5az5ZtDTTbg5NYpmaYyMuOY+bUqAEf8SoY\nPsiyzMnKFj7bVktRiZ3iI048XgkAtUrB5AnGoBpispnRaZeHJWMglG4CgeDKxaBT84OVU3nuw8Ps\nOnSW37yyjx+unEqMWT/USxMIBFcxoikRJqGaDBF6NWpV15vVOxaPJRCQ2FpY3R5y2ZGemhnxlggC\nksSrW8q63e3q7qZzR/EZ9FoVbm8g7Pf0p7eLmTkh/oIdtL42X7ojVGOjIwk1J1m68WUCKhUf3Xwf\nAaeW2/dsQlX+FZIlEf+SNazUGvBQSlFpDf+SF0O8Wc37hU7e2efs9jX3l9eDQssNS+YSGWHgSPkJ\nviosRubCpkqo65k1zsqukgDvb/eCDF9bqOW6LE2/mwWdjyXL4LVrcdUZQFIwbbKZh9eOGBI1gt3h\n5433a9hUUI/fLzNmZAT3rEwhc6Lpkq/lUhKQZPYftLMpv459B+3IMhgjVdyyPJ5l2VaSE8TN2ZWK\n3eHnwOHghIzCTpaM1CQ9WRkmsiabyRhvRK+7vGTNA6V0EwgEVzZqlZIHb55EVKSWj7+q5Ffr9/KD\nlVNJjTMO9dIEAsFVimhK9IHbsq/hy5IzOFv9FzxeWetkw2dHu+xCqZRK1iyfAAoF+fuq6EyovIie\ndrsCksyBo/UD8G6gyentsoPWn+ZLRzy+AF5foFfriqXhLNe/9wJKSWLTTWupTRrJN+Qj6I6cRDJb\n8S1ZC1oDHl+AyupGnrghhjiTmnf3O3l3f/cNCQCtLpLFC+ag1WrYd+AwxaVHzx+zU1OlO8tM5tg4\npEAq737uxWhQcM/1esakXvxNfNuxdh+sp/qYCp9Lg1oDD65JY+l11kuujvB4JD7YUsvbH53B1SqR\nlKDnrlsSmT/bclnsBveXpmYfW7Y18PHWeuoavACkj4kkL9vKtbMsYlTjFYjPL1F6rG1KhoNjp1zI\n55rEJqOKJQvjmDDWQFaGGWvM8LRkhMtAKN0EAsHVgVKh4M4l44g26ng9/yi/eXkf3711CuNHWIZ6\naQKB4CpENCX6wIZPj3ZpSLQRahcqOGVDEXZeRKjdrsKyemzO7m86Pd4A8ycncqTCFrIh0Jm9R+q4\n+dpRmCKCN+R3LB5LaUUTlbUXFv89NV+Ac8qOcgrL6mlyekIWd5GOJm589+/oPa3k566kYvRE8iIr\nudV8EikyGl/uvWAIdusdDiffWmjEalLxzj4H7xe29Pi6acmJXDd3Okqlku2793P81IWNlc5NoM6W\nGX9Aw6ubfdQ0BBiZqGTtDXqijANTpCoVCqyaGGrLXfjcElMzTDx638hLXgQFJJn87Q38490aGmw+\nTEYV99+VyurbR9Pc1PO5vZyRZZmSMieb8+v5cm8T/oCMXqdk2SIreTlWRo8QRdqVhCzLVJ/xtOdC\nFB9x4vact2RMSjcybbKZrAwzo0cYSEgwU1fnGOJVDwwDoXQTCARXF3lzRhBl1PL8h4f57w1FfPtr\nk5gxPn6olyUQCK4yRFMiTDy+QNAa0AONIXahesqL6IlQu11NLR6ijVqanN4uP4sx61m9fDx1Nhc/\nff6rMN5VEJvTw8+e391u5fAHZFxuX7e/213zJSBJ/OeLey5oYrjP+bJVSgWBDt4VndvFje8+h9HZ\nzJfXXk/ppJlkR1SzJvooLqUB1dL7IDLq3At7iZXOojCpeHuvgw+Kei6ax10zkjnTp4AsMTmxlZo4\naLbpw2oC6TQq6mxaXv3YjdsL8zM1fG2htldVSLjU1nv4v3UVFJU4iDCoePS+kSxeEHNJ1RGyLLOn\nyM76N6uorHaj1Sq49cYEvn59IpERqityxGeLK0DBjgY2F9RTWe0GghMT8rLjWDQvhsgIIWO/UnA4\n/Rw4HLRjFJU42lUwAClJunNTMoKWDIP+yr3uAzXJSSAQXF3My0jEFKHhz+8U83/vFLN6WTo501OH\nelkCgeAqQjQlwqTZ6em2EdBGdKSu112otryI3gi12xVj0pM5Job8/dVdftZ20xlniSC2F/tEZzpa\nOXJnpPZJAvzqJ2VdVBVtqJTBxoTXL6Py+7jhwxeJaTyLbVkelbPyuNZfwQPRpbiVehQ33A+mmOAT\n/V5oOolC8lNYo+y2IZEWb8Tl9pOWNoLMSeMJBHzMSPMSbSDsJpAkyWze5WXLVz7UKrhrqY6ZEwcm\nhVqWZT7eWs+LG6pweyRmZJp56J4Rl1wdUXashXVvVHGozIlSAbkLY7lzRdKwnR5wsRw75WJTfh3b\nvrTh8UqoVQoWzrGQlxPHxHGRQxIkKhhY/H6ZsuNBS0ZhiZ2jJ89bMoyRKubPij43JcN8VUyO6Uh/\nJjkJBALB5NGxPLFqGv/zehHrPy7D5vTy9YWjxd9MgUBwSRBNiTCJMupCFvpZA7gL1dtu1x2Lx6JS\nKXu86Qxn8kdP7C+r5+ZrR4UtAe5NQeL1BysFhRQgd+MrJFSdxDF3Hsue/09yK45g2P4xqHXErnyE\nRuW5SSB+DzSdAskPkfFMmRJDbl3X97syZyxldTpqW7ToVBJT03xEdKg/emsCudwyr2x2c+RUgBiz\ngntv1JMSNzDXsLbew59fqODA4aA64rsPjCTn2kurjqg+6+blt6rZuacJgFlZUay+NZkRKYZLtoZL\nhccr8cVuG5vy6yg/4QIg3qpl2SIrSxbGEm0W484uZ2RZpqbW0x5OefCwo92SoVLBxHHG9oDKa0ZG\noLqCc1F6o6/KPIFAIGhjVKKZJ9fM4Pcbivhgx0manB7W5o0X44QFAsGgI5oSYRKq0E+LN7Iqd9yA\nHi/Ublc4N51tz99XWkejI3zFhM3hptXjD1sC3JuCBABZ5rr8dxh94hCn08ayK/s2FlUdI+KLN0Cp\nwrd4DaqENKhzXNiQMCZARCwquiof1CoVh87qaHCpMWoDZCa50fbyafb4Au3Pr7PBuo/cNNplJoxU\ncfdyPRH6iy9kJCmojlj3+nl1xMNrR1xSVUKT3cfr753h4611BAIwbnQEa1emkDH+ypuoUXXGzeaC\nevK/aMDZEkChgJlTzeTlxJE12XxVF6eXO84WPwcPOygsCTYiauvPf88kJ+jImmwmK8PE5PEmDAZR\ndHcmXGWeQCAQdCTeEsGTa2bwhzeK2H6gBkeLl4dWTBbNTYFAMKiIpkQf6NgoaLS7iTJqmTbOyqql\n6QPeRQ6n8RDqprPt+ddNTeZnz+2mm4mk3dKmhAhXAtybggRg5q6PmViym7q4FDbfcA/X+BsxbNsK\nyPhyViPHjwz+YjcNie7erzcARdV67B4VFkOAjEQ36hCnPyBJbPjsaPt4VYsxEeQ0ZFnBstkals7R\nohwABcPZOg9/euEUxUecREao+P43R7Jo3qVTR7g9Ad7bXMs7G8/i9kgkxetYfVsy82ZEX1HyS79f\n5qvCJjbl13PgcDCgMMqs5tYbE1i2yEq8VYT5XY60WTKKDtkpLHFw9HhL+yjlyAgV82ZGn8uGMIlr\nLBAIBIOIOVLLE6um8ed3iik61sDvXtvP927LbA9EFwgEgoFGNCX6wFDIYi92tysu2tDreM6OtI39\nDPe99mYVyTiwg5m7P6U5KpaPbrmfZKOPH1kPoJAC+BfdhZw0BgC/pxWaToIU6LYh0UarT8GBGj2t\nPiXxRj8T4j30thl+fryqAoNmJLKUgCT7SR/RzPK5I8M6L6GQJJlN+fWsfzOojpiVFcVD94wgJvrS\nWAYCAZkt2+rZ8G4NtmY/ZpOaNbelsGyRFbX6ymlG1Dd6+eTzej7Z2oCtORjEmjHeSF6OlTnTo9GE\n6kwJhiU1Z93tSoiDhx20uoOWDKUSxo+NbA+oHDP66rZkCAQCwaVGr1Xz/dsyeeGjw+wsOcuvX97H\nD1dOxRp95VlABQLB0COaEv1gIGWxHS0Fg9Hg0GlUZI2z8uneqi4/M+rVON0XjjjtPPYznPe6YuE1\nbD9Q3T5xo41ryg+woOBdXAYjH97yTSxmJf8Wux+Dwo9//m1IaRODv+h303SiAqQAPkM8mh4aEk6P\nkgM1OrwBJWnRXq6J8dGbAKBtvKpCocGoHYdaZcQvuWjxlHO8RoHHl3pR5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vIAACAASURB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\n4TJnuCRDp5VjzPBY5OUYkTfIhKQuthGNJp8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predictionstargets
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mean119.2207.3
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50%97.3180.4
75%143.5265.0
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" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "Final RMSE (on training data): 176.04\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "image/png": 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C04FtyBQcvUY0zpSJqnKfRcdc5T6DW7oWtJRyn81KbHQwVw/pRHG5lQ83HPB1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pyGjBgQXa/Y4ItoZnP4WqwRIpiNU33wGAVLHsFpKaFg4i1sHDX9OteL/AILYdh5wpxPf30lx6Vw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nmEtuCZMetqzVsXSBmn5vcFrRJPE5wwlhZUDil6/0cOx0ouVg6WIzn9mcRVH+zGLRlcaaCsWY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\nkPSj/6UFZ1e4ErUTyNelUOIowzAoWbCYQ7l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