{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![Title picture](./img/title_22.png)\n", "
\n", "
\n", "**Discord:** `#co04-ликбез-по-алгоритмам`" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "*SMTB-2022: A Glimpse into Algorithms | Alexey Bochkarev* ([🖂](mailto:a@bochkarev.io), [🌐](https://www.bochkarev.io/contact))\n", "\n", "# The main goal \n", " \n", "- To *mention* some key definitions and \"buzzwords\", \n", "- To provide a first glimpse on the structure of the material out there. **(not a substitute to a proper CS course)**\n", "- So you could learn more if needed, and know what to google." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Why bother?**\n", "- to create / implement algorithms,\n", "- to understand what is going on in a paper,\n", "- to understand what is possible (or not), and what to use for your problem at hand.\n", " \n", "**Prereqs:** ability to read (pseudo-)code -- we will use Python 3." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Technical: How to open these notebooks \n", "- The updated version is on the School GDrive and [Github](https://github.com/alex-bochkarev/Algo-SMTB-2021) ([how to clone](https://docs.github.com/en/github/creating-cloning-and-archiving-repositories/cloning-a-repository) or [download ZIP](https://github.com/alex-bochkarev/SMTB-Algo/archive/master.zip))\n", "- You'd need Python 3 along with several packages (hopefully `conda install ` or `pip install ` will help you just in case).\n", "- With a (free) Google account, you can [![Open it in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alex-bochkarev/Algo-SMTB-2021/blob/master/T1-2-Algorithms.ipynb)\n", "- Finally, `nbviewer` (see the link in `README`) renders it read-only, but zero-config.\n", " \n", "**Note:** The presentation was made from this very notebook.\n", "\n", "**COMMENTS / SUGGESTIONS ARE VERY WELCOME!**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Course intro \n", "- Welcome!\n", "- Course [outline](./README.org) and logistics/tech.\n", " + 👉 **Topic 1:** we will try *designing* algorithms, taking sorting problem as an example (and using several approaches you might have heared about).\n", " + **Topic 2:** we will discuss more systematically how to compare algorithms, focusing on the concept of runtime.\n", " + **Topic 3:** a specific example of a more \"biological\" algorithm (aligning sequences),\n", " + **Topic 4:** let's talk data structures." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# import libraries for today:\n", "import numpy as np # numbers/arrays manipulation\n", "from copy import copy\n", "from itertools import permutations" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Topic 1: Algorithms intro \n", "


\n", "\n", "
Alexey Bochkarev, 2022, a [at] bochkarev (dot) io
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Content of the first topic (today):**\n", "- What is an algorithm (vs. a computer program).\n", "- Key properties: correctness, time, and space requirements (without details)\n", "- Several examples of sorting algorithms\n", "- Correctness and testing\n", "- A word on algorithmic *frameworks* / \"types\" of algorithms: greedy, D&C, randomized, brute-force, DP, ..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## What is an algorithm?\n", "Well, a recipe. Not good as a strict definition, but as put by [The Economist](https://www.economist.com/the-economist-explains/2017/08/29/what-are-algorithms),\n", "\n", "*An algorithm is, essentially, a brainless way of doing clever things.*\n", "\n", "A clear, step-by-step instructions manual, how to acheive something specific. Like, \n", "- get to the bus stop from some specific point, \n", "- sort an array of $N$ numbers, or\n", "- assemble the original genome sequence from a smaller, potentially overlapping pieces. \n", "- ...and so on.\n", "\n", "(Without too vague instructions.)\n", "\n", "Not a big deal, so far..." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Note:** algorithm $\\neq$ computer program. The former are abstract solution procedures, recipes, while the latter are their implementations in some specific programming language.\n", "\n", "We will talk about **algorithms** today -- but we'll consider some specific examples of **programs** too. I suppose, it will be a little easier this way." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "In order to discuss and illustrate some of these, let us pick a specific problem as an example." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### A specific example: sorting
\n", "**INPUT:** an array of some fixed length, $N$. Say, integer numbers
\n", "**OUTPUT:** a sorted version of the same array (each next number is larger than the previous one).\n", " \n", "For example, [4,6,1,3,2,5,7] $\\rightarrow$ [1,2,3,4,5,6,7]. Obviously, there are many ways to approach the problem. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "That is why we consider it in first place: it is easy to understand,\n", "and fun to solve." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "The key technical staple of the course:\n", "\n", "> **Talk is cheap. Show me the code.**\n", "> \n", "> *Linus Torvalds*" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**First:** let's start from an intuitive definition: a sorted array is such that the smallest elements go left. Well, okay, we can implement this in a **\"greedy\"** fashion." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def swap(array, i, j):\n", " \"\"\"Swaps two elements (with indices ``i`` and ``j``) within ``array``.\"\"\"\n", " if i==j:\n", " return # just do nothing\n", "\n", " old_i = array[i]\n", " array[i] = array[j]\n", " array[j] = old_i" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def selection_sort(input_array):\n", " \"\"\"Sorts ``input_array`` by moving the smallest element at each step.\"\"\"\n", " sorted_array = copy(input_array)\n", "\n", " for first_unsorted in range(0, len(sorted_array)-1):\n", " min_element_i = first_unsorted\n", " for i in range(first_unsorted+1, len(sorted_array)):\n", " if sorted_array[i] < sorted_array[min_element_i]:\n", " min_element_i = i\n", "\n", " swap(sorted_array, first_unsorted, min_element_i)\n", "\n", " return sorted_array" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5]\n", "[1, 2, 3, 5, 7]\n", "[1, 2, 3, 4, 5]\n" ] } ], "source": [ "print(selection_sort([5,4,3,1,2]))\n", "print(selection_sort([1,3,5,2,7]))\n", "print(selection_sort([1,2,3,4,5]))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Another idea: how would you check that the array is sorted?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Well, easy:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def is_sorted(array):\n", " \"\"\"Checks if ``array`` is sorted.\"\"\"\n", " for i in range(len(array)-1):\n", " if array[i] > array[i+1]:\n", " return False\n", "\n", " return True # we haven't found anything suspicious" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n" ] } ], "source": [ "print(is_sorted([1,2,3,4,5]))\n", "print(is_sorted([1,5,3,4,2]))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Oh, let's use this idea!\n", "\n", "First, without applying any thought:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "## A really, really straightforward approach:\n", "def brute_force_enum_sort(input_array):\n", " \"\"\"Just tries everything until the array is sorted.\"\"\"\n", " for perm in permutations(input_array):\n", " if is_sorted(perm):\n", " return list(perm)\n", "\n", " return None # something must have gone very wrong if we are here" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Do you see a problem with this?" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.08 s, sys: 661 µs, total: 1.08 s\n", "Wall time: 1.08 s\n" ] }, { "data": { "text/plain": [ "[0, 1, 2, 3, 5, 6, 7, 9, 10, 15]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time brute_force_enum_sort([3,2,5,7,10,15,0,6,9,1])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 18 µs, sys: 0 ns, total: 18 µs\n", "Wall time: 21 µs\n" ] }, { "data": { "text/plain": [ "[0, 1, 2, 3, 5, 6, 7, 9, 10, 15]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# For reference:\n", "%time selection_sort([3,2,5,7,10,15,0,6,9,1])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "(okay, we won't consider this further -- it is ridiculously slow!)\n", "\n", "*But:* In `is_sorted` we can not just cry with `False` if we found a problem, but fix it! Let's see if we can implement this idea in the code:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def bubble_sort(input_array):\n", " \"\"\"Sorts the ``input_array`` with Bubble sort algorithm.\n", "\n", " (see https://en.wikipedia.org/wiki/Bubble_sort for more info)\n", " \"\"\"\n", " done = False\n", " sorted_array = copy(input_array)\n", "\n", " while not done:\n", " done = True\n", " for i in range(len(input_array)-1):\n", " if sorted_array[i] > sorted_array[i+1]:\n", " # here's a problem!\n", " swap(sorted_array, i, i+1)\n", " done = False\n", "\n", " return sorted_array" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4, 5, 6, 7]\n", "[1, 2, 4, 6, 7]\n" ] } ], "source": [ "print(bubble_sort([4,6,1,3,2,5,7]))\n", "print(bubble_sort([6,4,7,1,2]))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Any other possible algorithms, though?
\n", "(also see the mandatory [xkcd](https://xkcd.com/1185/); don't try to run at home :))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def panic_sort(input_array):\n", " \"\"\"Sorts ``input_array`` with a famous (although, non-existent)\n", " panic sort algorithm.\n", " \"\"\"\n", " np.random.seed(1234)\n", "\n", " output_array = copy(input_array) # uh, okay, let's start somewhere\n", "\n", " for i in range(1000):\n", " output_array = np.random.permutation(output_array) ## come on, come on!..\n", "\n", " return output_array" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "A = [3,7,9,8,6,2,5,1,10,4]\n", "panic_sort(A)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 5 7 13 35 47 48 50 50 51 136 200 202 207 1024]\n" ] } ], "source": [ "A = [136,1024,50,47,51,48,5,13,35,207,200,202,50,7]\n", "print(panic_sort(A))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_sorted( panic_sort(A) )" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### or (this one is tricky): \n", "Hey, what if I already know the correct order? Is it worth the hassle at all?.." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def real_quick_sort(input_array):\n", " \"\"\"*Kinda* sorts `input_array` by returning consecutive numbers 1,..,N\n", " (where ``N`` is the length of ``input_array``)\n", " \"\"\"\n", "\n", " N = len(input_array)\n", " # throw away ``input_array`` altogether\n", "\n", " return([i for i in range(1, N+1)])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "real_quick_sort([1,5,4,2,3])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "real_quick_sort([4,6,1,3,2,5,7])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## So, by now we have four sorting algorithms\n", "- selection sort\n", "- bubble sort\n", "- real-quick sort (*)\n", "- panic sort (*)\n", "\n", "Let's compare them!\n", "\n", "(*) have sort of limited applicability :)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### What are some important properties of the algorithms?\n", "Thoughts?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- does it result in a desirable outcome? **(correctness)**\n", "- how much memory does it take? **(space/memory requirement)**\n", "- how fast is it? **(runtime)**\n", "\n", "But also:\n", "- can I run it over several machines (or just cores)?\n", "- is it understandable, after all?.." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Correctness\n", "- this is something you need to **prove** rigorously;\n", "- however, first, let's prepare a tiny **testing unit** to check if our algorithms work. It won't guarantee anything, but would *suggest* that we haven't screwed up anywhere." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([1, 2, 3, 4, 5], [4, 2, 3, 1, 5])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_consec_instance(N):\n", " \"\"\"Generates a test instance of length `N` (consecutive numbers).\n", " \n", " How: creates a random permutation of consecutive\n", " integers 1, ..., N.\n", "\n", " Returns:\n", " A tuple, sorted array and unsorted array.\n", " \"\"\"\n", " input_sorted = [i+1 for i in range(N)] # generate a sequence of numbers\n", "\n", " input_unsorted = np.random.permutation(input_sorted)\n", " return input_sorted, list(input_unsorted)\n", "\n", "get_consec_instance(5)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "([1, 4, 7, 10, 13, 15, 17, 20, 21, 22], [4, 13, 21, 7, 22, 17, 20, 10, 15, 1])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def get_rnd_instance(N):\n", " \"\"\"Generates a test instance of length `N` (random numbers).\n", "\n", " How: creates a sorted array first\n", " (by adding random numbers consecutively),\n", " then returns a random permutation of the sorted array.\n", "\n", " Returns:\n", " A tuple, sorted array and unsorted array.\n", " \"\"\"\n", " input_sorted = [np.random.randint(low=0, high= N // 2)] # the first number\n", " \n", " for i in range(1,N):\n", " input_sorted.append(input_sorted[i-1] + \\\n", " np.random.randint(low=1, high=N // 2))\n", "\n", " input_unsorted = np.random.permutation(input_sorted)\n", "\n", " return input_sorted, list(input_unsorted)\n", "\n", "get_rnd_instance(10)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "def test_algo(label, sort_function, instances_list):\n", " \"\"\"Tests given algorithm against a set of instances.\n", "\n", " Args:\n", " label (str): algorithm label to print,\n", " sort_function (function): algorithm implementation,\n", " instances_list (list): a list of instances, each element represents \n", " a pair of versions (sorted, unsorted)\n", "\n", " Returns:\n", " nothing. Prints summary to the screen. For each instance:\n", " a dot (.) if successful, instance summary if not.\n", " \"\"\"\n", " print(f\"Testing algo: `{label}`\")\n", "\n", " n_failed = 0\n", " for instance in instances_list:\n", " arr_sorted, arr_unsorted = instance\n", " out = sort_function(arr_unsorted)\n", " if np.all(out == arr_sorted):\n", " print(\".\", end=\"\")\n", " else:\n", " print(f\"\\nFAIL: expected {arr_sorted}, returned {out}\", end=\"\")\n", " n_failed += 1\n", " print(f\"\\ndone. FAILS: {n_failed} out of {len(instances_list)} ({n_failed*100 / len(instances_list):.1f}%)\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "OK, let's see if our algorithms work:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": false, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==================================================\n", "Testing algo: `Selection sort`\n", "....................................................................................................\n", "done. FAILS: 0 out of 100 (0.0%)\n", "==================================================\n", "Testing algo: `Bubble sort`\n", "....................................................................................................\n", "done. FAILS: 0 out of 100 (0.0%)\n", "==================================================\n", "Testing algo: `Panic sort`\n", "\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [10 9 5 2 7 6 8 1 4 3]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 5 8 6 7 1 9 3 4 10 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 5 2 9 7 1 6 3 4 10 8]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 5 9 8 2 1 6 3 10 7]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [10 1 9 7 6 2 4 3 8 5]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 1 5 3 2 8 7 9 10 4 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 7 6 4 9 2 1 8 10 3 5]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 7 8 3 6 5 2 10 1 9]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 2 9 4 3 6 8 1 7 5 10]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 2 1 7 6 5 10 8 9 4]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 9 2 3 4 5 10 7 8 1 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 2 7 3 5 9 6 1 10 8 4]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 2 9 7 10 5 6 1 8 3 4]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 10 1 9 8 5 3 6 7 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 5 3 8 2 9 1 7 6 4 10]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [10 1 5 2 4 6 9 3 8 7]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 7 8 6 9 5 10 4 2 1 3]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 1 9 7 2 8 6 4 10 3 5]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 2 6 5 3 1 10 4 9 8 7]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 9 1 5 2 7 10 4 6 8 3]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 9 10 7 6 5 2 8 1 3]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [10 6 1 4 2 3 7 9 8 5]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 8 10 7 9 5 1 4 6 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 5 9 3 1 4 2 7 10 8 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 1 4 10 5 6 8 2 7 9]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 1 5 6 7 4 2 9 8 10]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 3 9 10 1 2 6 7 5 8]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 8 5 10 4 6 3 9 7 2 1]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 4 7 9 1 5 2 8 6 10]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 8 2 9 7 4 1 3 10 6 5]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 5 10 7 4 8 3 9 2 1 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 7 5 6 8 4 9 10 1 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 2 8 7 4 6 3 10 9 1 5]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 1 5 6 7 2 4 9 10 8 3]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 7 4 9 10 1 2 5 8 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 7 9 8 5 1 10 3 2 4 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 6 5 9 8 1 10 3 7 4 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 2 3 7 10 8 9 1 5 4 6]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 1 5 4 2 7 10 6 9 8]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [10 3 1 9 8 6 5 7 4 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 9 10 6 7 4 3 5 1 2 8]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 7 1 5 6 9 4 2 3 10 8]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 6 9 10 1 3 7 4 5 8 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 1 3 10 7 6 2 4 5 9 8]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 1 7 6 5 9 3 4 10 8 2]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 3 4 9 8 1 6 2 5 10 7]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 9 8 3 6 7 2 1 5 10]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 4 9 8 3 6 2 5 7 1 10]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [10 5 9 7 2 8 6 4 1 3]\n", "FAIL: expected [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], returned [ 8 3 5 6 9 4 1 10 2 7]\n", "FAIL: expected [2, 4, 8, 11, 12, 14, 15, 18, 21, 23], returned [12 4 2 8 23 21 18 15 14 11]\n", "FAIL: expected [2, 5, 9, 12, 16, 19, 23, 26, 29, 33], returned [16 19 23 29 26 12 2 5 33 9]\n", "FAIL: expected [2, 5, 7, 8, 9, 12, 15, 16, 19, 22], returned [19 16 15 2 7 12 9 5 8 22]\n", "FAIL: expected [2, 6, 8, 10, 11, 13, 17, 20, 23, 24], returned [20 17 8 10 23 11 2 13 6 24]\n", "FAIL: expected [1, 4, 6, 10, 13, 17, 18, 20, 21, 23], returned [ 6 20 18 21 23 17 10 1 13 4]\n", "FAIL: expected [3, 4, 8, 11, 15, 17, 20, 24, 27, 29], returned [ 8 24 27 3 29 17 15 20 4 11]\n", "FAIL: expected [4, 7, 10, 12, 15, 17, 21, 23, 25, 27], returned [23 15 27 17 10 21 7 25 12 4]\n", "FAIL: expected [4, 7, 11, 12, 14, 15, 17, 21, 25, 28], returned [28 11 14 25 17 7 4 21 15 12]\n", "FAIL: expected [4, 5, 6, 9, 13, 14, 17, 20, 21, 25], returned [14 17 5 4 13 9 6 20 25 21]\n", "FAIL: expected [3, 7, 9, 13, 14, 18, 20, 23, 27, 29], returned [14 7 20 13 9 23 27 18 3 29]\n", "FAIL: expected [3, 6, 10, 14, 18, 20, 22, 24, 26, 28], returned [14 18 10 3 28 24 20 6 26 22]\n", "FAIL: expected [3, 6, 9, 10, 13, 14, 17, 18, 22, 26], returned [26 13 3 22 14 10 6 18 9 17]\n", "FAIL: expected [4, 6, 9, 12, 15, 17, 20, 23, 25, 26], returned [ 6 25 15 17 20 23 26 4 9 12]\n", "FAIL: expected [1, 2, 6, 7, 9, 11, 14, 16, 18, 19], returned [16 7 1 19 14 2 18 6 9 11]\n", "FAIL: expected [0, 4, 5, 8, 12, 16, 20, 22, 24, 27], returned [22 0 16 27 24 8 20 4 5 12]\n", "FAIL: expected [1, 2, 3, 6, 8, 12, 13, 14, 18, 19], returned [19 3 2 14 8 6 12 1 18 13]\n", "FAIL: expected [2, 6, 10, 11, 15, 17, 19, 20, 23, 25], returned [17 20 2 15 10 23 19 11 6 25]\n", "FAIL: expected [1, 4, 7, 9, 11, 15, 18, 22, 24, 27], returned [ 1 22 7 4 9 24 27 11 15 18]\n", "FAIL: expected [1, 4, 8, 11, 14, 17, 19, 20, 23, 27], returned [ 1 14 17 8 23 27 4 19 11 20]\n", "FAIL: expected [2, 3, 6, 9, 10, 11, 15, 19, 21, 22], returned [21 15 9 2 19 10 22 11 3 6]\n", "FAIL: expected [4, 6, 8, 10, 13, 14, 18, 21, 24, 26], returned [ 8 26 6 14 13 4 21 10 24 18]\n", "FAIL: expected [0, 2, 6, 10, 14, 15, 16, 17, 19, 21], returned [10 16 0 6 21 14 17 2 19 15]\n", "FAIL: expected [2, 5, 8, 11, 14, 17, 21, 23, 27, 29], returned [17 14 23 29 11 27 21 5 8 2]\n", "FAIL: expected [4, 7, 8, 9, 12, 15, 16, 19, 21, 24], returned [15 21 7 16 24 9 19 12 4 8]\n", "FAIL: expected [3, 5, 7, 8, 11, 14, 16, 18, 21, 25], returned [25 7 14 3 5 8 21 16 11 18]\n", "FAIL: expected [0, 4, 5, 9, 13, 15, 19, 21, 24, 28], returned [24 13 21 4 19 28 9 5 0 15]\n", "FAIL: expected [4, 7, 8, 10, 11, 12, 16, 18, 19, 22], returned [18 12 8 10 11 16 22 19 7 4]\n", "FAIL: expected [2, 6, 7, 11, 14, 17, 20, 21, 25, 26], returned [20 11 7 2 14 21 26 17 6 25]\n", "FAIL: expected [0, 3, 5, 7, 9, 13, 16, 18, 20, 24], returned [18 0 7 20 9 13 3 16 5 24]\n", "FAIL: expected [0, 3, 4, 6, 7, 11, 14, 18, 21, 24], returned [ 0 21 14 4 3 7 11 18 6 24]\n", "FAIL: expected [1, 4, 8, 10, 14, 16, 18, 20, 22, 23], returned [18 23 8 1 14 16 20 22 4 10]\n", "FAIL: expected [1, 2, 5, 8, 10, 14, 16, 20, 22, 25], returned [16 2 10 20 25 1 8 14 5 22]\n", "FAIL: expected [1, 5, 8, 10, 13, 15, 16, 19, 21, 22], returned [21 19 13 5 16 8 1 15 10 22]\n", "FAIL: expected [2, 6, 8, 9, 12, 14, 15, 17, 18, 21], returned [12 18 9 15 8 2 14 21 17 6]\n", "FAIL: expected [4, 8, 10, 12, 15, 18, 21, 25, 26, 27], returned [ 4 26 8 15 18 27 10 12 21 25]\n", "FAIL: expected [0, 1, 2, 5, 8, 11, 13, 17, 19, 21], returned [13 2 11 21 5 0 19 1 17 8]\n", "FAIL: expected [1, 3, 4, 8, 12, 13, 14, 17, 20, 22], returned [17 12 3 4 14 20 13 8 22 1]\n", "FAIL: expected [4, 5, 8, 9, 11, 15, 18, 22, 25, 28], returned [ 4 22 5 9 28 25 15 18 11 8]\n", "FAIL: expected [2, 3, 7, 11, 14, 18, 22, 25, 28, 31], returned [18 22 14 31 28 3 7 2 25 11]\n", "FAIL: expected [4, 8, 10, 14, 16, 17, 20, 22, 26, 27], returned [10 26 14 8 17 4 27 22 16 20]\n", "FAIL: expected [0, 3, 4, 8, 9, 10, 13, 16, 20, 23], returned [13 20 9 23 0 3 8 10 4 16]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "FAIL: expected [0, 4, 8, 11, 15, 17, 20, 22, 24, 27], returned [27 11 22 24 15 20 4 17 0 8]\n", "FAIL: expected [0, 2, 5, 6, 7, 11, 12, 13, 14, 17], returned [17 7 6 11 13 12 2 5 14 0]\n", "FAIL: expected [2, 6, 10, 12, 15, 18, 19, 21, 23, 26], returned [ 6 2 10 18 15 21 19 26 12 23]\n", "FAIL: expected [0, 3, 4, 7, 11, 13, 16, 18, 22, 26], returned [26 22 13 7 3 4 11 18 16 0]\n", "FAIL: expected [1, 2, 4, 8, 9, 10, 11, 14, 18, 20], returned [20 10 8 14 4 18 1 11 2 9]\n", "FAIL: expected [3, 5, 9, 11, 14, 15, 18, 22, 23, 27], returned [22 27 14 5 18 9 3 23 11 15]\n", "FAIL: expected [1, 2, 3, 4, 8, 10, 14, 18, 20, 21], returned [ 4 1 3 8 21 10 18 2 14 20]\n", "FAIL: expected [2, 4, 5, 7, 9, 10, 14, 18, 19, 20], returned [19 14 7 10 5 9 18 2 20 4]\n", "FAIL: expected [3, 6, 10, 12, 15, 19, 21, 23, 27, 30], returned [19 30 12 3 15 27 10 6 23 21]\n", "done. FAILS: 100 out of 100 (100.0%)\n", "==================================================\n", "Testing algo: `Real quick sort`\n", "..................................................\n", "FAIL: expected [2, 4, 8, 11, 12, 14, 15, 18, 21, 23], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 5, 9, 12, 16, 19, 23, 26, 29, 33], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 5, 7, 8, 9, 12, 15, 16, 19, 22], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 6, 8, 10, 11, 13, 17, 20, 23, 24], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 4, 6, 10, 13, 17, 18, 20, 21, 23], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 4, 8, 11, 15, 17, 20, 24, 27, 29], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 7, 10, 12, 15, 17, 21, 23, 25, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 7, 11, 12, 14, 15, 17, 21, 25, 28], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 5, 6, 9, 13, 14, 17, 20, 21, 25], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 7, 9, 13, 14, 18, 20, 23, 27, 29], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 6, 10, 14, 18, 20, 22, 24, 26, 28], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 6, 9, 10, 13, 14, 17, 18, 22, 26], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 6, 9, 12, 15, 17, 20, 23, 25, 26], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 2, 6, 7, 9, 11, 14, 16, 18, 19], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 4, 5, 8, 12, 16, 20, 22, 24, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 2, 3, 6, 8, 12, 13, 14, 18, 19], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 6, 10, 11, 15, 17, 19, 20, 23, 25], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 4, 7, 9, 11, 15, 18, 22, 24, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 4, 8, 11, 14, 17, 19, 20, 23, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 3, 6, 9, 10, 11, 15, 19, 21, 22], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 6, 8, 10, 13, 14, 18, 21, 24, 26], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 2, 6, 10, 14, 15, 16, 17, 19, 21], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 5, 8, 11, 14, 17, 21, 23, 27, 29], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 7, 8, 9, 12, 15, 16, 19, 21, 24], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 5, 7, 8, 11, 14, 16, 18, 21, 25], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 4, 5, 9, 13, 15, 19, 21, 24, 28], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 7, 8, 10, 11, 12, 16, 18, 19, 22], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 6, 7, 11, 14, 17, 20, 21, 25, 26], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 3, 5, 7, 9, 13, 16, 18, 20, 24], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 3, 4, 6, 7, 11, 14, 18, 21, 24], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 4, 8, 10, 14, 16, 18, 20, 22, 23], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 2, 5, 8, 10, 14, 16, 20, 22, 25], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 5, 8, 10, 13, 15, 16, 19, 21, 22], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 6, 8, 9, 12, 14, 15, 17, 18, 21], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 8, 10, 12, 15, 18, 21, 25, 26, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 1, 2, 5, 8, 11, 13, 17, 19, 21], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 3, 4, 8, 12, 13, 14, 17, 20, 22], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 5, 8, 9, 11, 15, 18, 22, 25, 28], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 3, 7, 11, 14, 18, 22, 25, 28, 31], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [4, 8, 10, 14, 16, 17, 20, 22, 26, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 3, 4, 8, 9, 10, 13, 16, 20, 23], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 4, 8, 11, 15, 17, 20, 22, 24, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 2, 5, 6, 7, 11, 12, 13, 14, 17], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 6, 10, 12, 15, 18, 19, 21, 23, 26], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [0, 3, 4, 7, 11, 13, 16, 18, 22, 26], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 2, 4, 8, 9, 10, 11, 14, 18, 20], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 5, 9, 11, 14, 15, 18, 22, 23, 27], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [1, 2, 3, 4, 8, 10, 14, 18, 20, 21], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [2, 4, 5, 7, 9, 10, 14, 18, 19, 20], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "FAIL: expected [3, 6, 10, 12, 15, 19, 21, 23, 27, 30], returned [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", "done. FAILS: 50 out of 100 (50.0%)\n" ] } ], "source": [ "np.random.seed()\n", "instances_list = [get_consec_instance(10) for _ in range(50)] + \\\n", " [get_rnd_instance(10) for _ in range(50)]\n", "\n", "for sorting_func, label in [\n", " (selection_sort, \"Selection sort\"),\n", " (bubble_sort, \"Bubble sort\"), \n", " (panic_sort, \"Panic sort\"), \n", " (real_quick_sort, \"Real quick sort\")]:\n", "\n", " print(\"==================================================\")\n", " test_algo(label, sorting_func, instances_list)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "(This \"real quick sort\" is not completely \"fake\", though. It is okay assuming you know something about your `input_array`. If interested, see [Bucket sort](https://en.wikipedia.org/wiki/Bucket_sort), [Radix sort](https://en.wikipedia.org/wiki/Radix_sort) and such)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "**Bottom line:** \n", "- correctness can be tested with a simple testing framework. Just generate a bunch of random (or not random, but carefully engineered!) instances, and see if it fails.\n", "- by the way, there are convenient \"testing frameworks\" in different languages, e.g., see [pytest](https://www.pytest.org) for Python.\n", "\n", "- **HOWEVER:** it does not guarantee anything, strictly speaking. Correctness is a property of an algorithm that needs to be proved." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "For **bubble sort**, for instance:\n", "- first, make sure that our `is_sorted` criterion is valid,\n", "- then, observe that `bubble_sort` stops only after the array is sorted (otherwise `is_done` would be `False`).\n", "- finally, note that at each pass the algorithm \"fixes\" at least one \"wrong\" pair of elements, and the number of such pairs is finite.\n", "\n", "Therefore, the algorithm finishes in finite time, and the result will be the sorted array." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- **HOWEVER-2:**\n", "> ``...Beware of bugs in the above code; I have only proved it correct, not tried it.''\n", ">\n", "> *Donald E. Knuth*" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "Note that we have just designed several \"types\" of algorithms to sort an array of numbers:\n", "- **Selection sort** -- a \"greedy\" algorithm (grabs the smallest element every time).\n", "- **Brute-force enumeration** -- just all possible orderings.\n", "- **Bubble sort** -- a natural first implementation based on a correctness criterion. We started with what we had, and incrementally \"fixed\" the solution, until it was good.\n", "- ... and some other variants (including a really fast version that leveraged some prior knowledge about a problem -- e.g., that the numbers are consecutive)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- there are other \"keywords\" relevant to the algo design:\n", " + we will consider **\"Divide and Conquer\"** momentarily,\n", " + in this course we will also consider something called **\"Dynamic Programming\"**, which is about smart problem decomposition.\n", " + sometimes, e.g., finding \"best\" solution (in some sense) you can start the \"enumeration\" route, but find smart ways to disregard whole groups of solutions. (See **branch and bound** and the like) \n", " + sometimes, you can design a surprisingly efficient algorithm that... well, just **flips a coin** from time to time :) (**randomized** algorithms)\n", " \n", "The list is not exhustive, of course." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Take-aways so far\n", "We considered several sorting algorithms (bubble sort and selection sort).\n", "- Algorithms are (theoretical) \"recipes\", which can be *implemented* in code (e.g. in Python, C++, or Java).\n", "- They can (and should) be analyzed in a systematic, scientific way\n", "- ... and are characterized by runtime, space requirements, and correctness.\n", "- (Of course, there are more characteristics.)\n", "- Correctness needs to be proved, but making numerical tests is handy.\n", "\n", "**thinking with ☕ :** how in the world our `panic_sort` worked for two instances?..\n", "\n", "**Any questions at this point?**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Topic 2: Runtime \n", "


\n", "\n", "
Alexey Bochkarev, 2022, a [at] bochkarev (dot) io
" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Runtime\n", "\n", "In order to discuss this, let us first introduce another algorithm, **merge sort**. It will be *faster* -- and this will allow us to discuss what a \"faster\" means." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "# We will need more libraries for this part\n", "from time import time\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "import pandas as pd\n", "from math import log, log2\n", "\n", "plt.style.use(\"dark_background\");" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- Imagine an array, split it into two halves.\n", "- What if we had a *magic* function that would sort each half?\n", "\n", "![halfsorted](img/merge_sort.png)\n", "\n", "Now, we could easily *merge* these two into a sorted array: just use two pointers `i left` and `i right`. Scan left to right, picking the smallest one and increasing pointers as necessary." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let us just fix this in the code, while we are at it:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def merge(left, right):\n", " \"\"\"Merges sorted arrays ``left`` and ``right`` into a single sorted array\n", "\n", " Notes:\n", " - the only slightly tricky part here is handling edge cases\n", " - this is not the best implementation in the world.\n", " E.g., fixed-length ``result`` would be more logical.\n", " \"\"\"\n", " result = []\n", " i_left = 0; i_right = 0\n", " while len(result) < len(left) + len(right):\n", " if i_left == len(left): # no more elements in the \"left\" list\n", " result.append(right[i_right])\n", " i_right += 1\n", " elif i_right == len(right): # no more elements in the \"right\" list\n", " result.append(left[i_left])\n", " i_left += 1\n", " else: # there are elements in both lists\n", " if right[i_right] < left[i_left]:\n", " result.append(right[i_right])\n", " i_right += 1\n", " else:\n", " result.append(left[i_left])\n", " i_left += 1\n", "\n", " return result" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "merge([1,3,5,7],[2,4,6])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "OK, you might say, but we don't have any \"magic function\", do we? " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Let's just cheat now, and pretend we do:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def merge_sort(input_array):\n", " \"\"\"Sorts `input_array` with merge sort.\n", "\n", " (See, e.g., https://www.geeksforgeeks.org/merge-sort/ for quick description,\n", " or Wiki)\n", " \"\"\"\n", " result = []\n", "\n", " N = len(input_array)\n", " if N == 1:\n", " return input_array # nothing to sort\n", "\n", " m = N // 2 # find the middle\n", " left = input_array[:m]\n", " right = input_array[m:]\n", "\n", " # here is the magic\n", " left = merge_sort(left)\n", " right = merge_sort(right)\n", "\n", " return merge(left, right)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5, 6, 7]" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# In case you were hesitating:\n", "merge_sort([4,6,1,3,2,5,7])" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "... oh, what has just happened?.. 👀" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "- **Merge sort** -- a \"divide and conquer\" algorithm (divides a problem into smaller pieces and then reconstructs the solution)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### So, Runtime\n", "\n", "Everyone is data-driven these days, right? -- so let's just make some data (again) and see how these different approaches to sorting perform." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "def get_runtimes(sort_function, gen_function=get_rnd_instance, N=100, no_inst = 100):\n", " \"\"\"Makes instances and tests ``sort_function``.\n", " \n", " Args:\n", " sort_function: sorting function to call,\n", " gen_function: instance generation function to call,\n", " N (int): instance length (# of numbers),\n", " no_inst(int): number of instances to generate.\n", " \n", " Returns:\n", " List of runtimes in seconds (per instance).\n", " \"\"\"\n", " runtimes = []\n", " for i in range(no_inst):\n", " _, arr_input = gen_function(N)\n", " t0 = time()\n", " out = sort_function(arr_input)\n", " t1 = time()\n", " runtimes.append(t1-t0) # runtime in seconds\n", " return runtimes" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "source": [ "plt.rcParams.update({'font.size': 28})\n", "\n", "def make_runtimes_plot(gen_function = get_rnd_instance, N = 10, no_inst=100):\n", " \"\"\"creates a simple runtimes plot.\"\"\"\n", " runtimes = pd.DataFrame(data = {\n", " 'Bubble_sort' : get_runtimes(bubble_sort, gen_function, N, no_inst),\n", " 'Merge_sort' : get_runtimes(merge_sort, gen_function)\n", " }, columns = ['Bubble_sort', 'Merge_sort'])\n", "\n", " ax = sns.stripplot(y = runtimes[\"Bubble_sort\"]*1000., color='red', jitter=0.4)\n", " ax = sns.stripplot(y = runtimes['Merge_sort']*1000., color='blue', ax = ax, jitter = 0.4)\n", " ax.set(xlabel='red = bubble sort, blue = merge sort, N={}'.format(N), ylabel='Runtimes, msec')\n", " return ax" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "scrolled": true, "slideshow": { "slide_type": "slide" } }, "outputs": [ { "data": { "image/png": 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AAAAAACSgqAvvG264QaeccooOHDigrl276h//+Ie++OKLXNf54osv5PP51KFDh3wnmpycrO7du+v+++/XxIkTtWHDBpmZzExTp07N93YDde3a1b/NvGLMmDExaRMAAAAAULRF3dX8sssuk5npP//5j+bPnx/ROgsXLpQknXzyydE257dgwQI1bNgw3+sDAAAAAOCFqAvv5s2bS5K++uqriNfZtm2bJKlq1arRNucXeC35n3/+qdTU1Fy7uBfUwIEDlZqaGnb+zp07XWsbAAAAAFB0RF14V65cWZK0Z8+eiNcpVaqUJCk9PT3a5vyGDx+uVatWac6cOVq/fr2k0IO5xcqqVau0ZMkS17YPAAAAACgeoi68t2/fruOPP14nnniiFixYENE6TZs2lSRt3rw52ub8nnvuuXyvCwAAAACAV6IeXG3RokWSFNVAaf3795eZac6cOdE2BwAAAABAQou68J44caJ8Pp9uvvlmVa9ePc/lr732Wv3lL3+RJH344YfRZwgAAAAAQAKLuvAeNWqUVq5cqQoVKujbb79V69ats83Puu66Xr16ev755zV69GiZmRYuXKgJEybEJutC8Pjjj2vVqlU6dOiQduzYoYULF+o///mPTjvtNK9TAwAAAAAkGIs2mjdvbtu3b7eMjAxLT0+3FStW+P9evHixrVmzxtLT0y09Pd0yMjLszz//tIYNG0bdTl6RZerUqTHZXteuXS0SL774opUsWTLq7aempsb8NSAIgiAIgiAIgiDiI8LVfFEPriZJS5YsUbt27TR27Fh17NhRDRo08M879dRTsy37448/qn///lqzZk1+mip0mzZt0oQJEzRz5kytXLlSaWlpql27tnr27KlBgwapQoUKGjZsmJKTkzVw4ECv0wUAAAAAxDmfnAo838466yxdcsklateunWrWrKmkpCRt27ZNCxYs0CeffKLvvvsuRqnmlNWtfdq0aTr77LMLvL3y5cvryJEjYW971qRJE3377beqV6+eJOmCCy7QF198kes2hw4dqhtuuEGSVL16dTVs2LDAeQIAAAAA4k9qaqpSUlJyTC9w4e2lWBfekejSpYu+//57SdJXX32l8847L+J1w70JAAAAAIDEF67mi3pwteJu+vTpWrp0qSSnCPf5fB5nBAAAAACIZ64V3g0bNlRKSopq1KjhVhOeWbJkiSSpXLlyqlatmsfZAAAAAADiWdSFd7Vq1TR06FANHTpUlStXzjG/YcOGmjNnjv744w/Nnj1bGzdu1Pjx40Mum6iyurgDAAAAAJCXqEc179Onj/73v/9pxYoVGjlyZPaNlSypL774QieffLK/C7bP59Nll12matWqqXv37rHJ2mPNmzeXJB06dEjbt2/3OBsAAAAAQDyL+ox3z549JUkTJ07MMe+6665TkyZNJDkDj9177736+uuv5fP51K1bN1166aUFyzYOdOrUyV94z5w5k7PfAAAAAIBcRV14n3LKKTIz/fTTTznm9e/fX5I0Y8YMnX/++fr3v/+tXr16adq0adnme6F+/foyM5mZpk6dmmN+lSpV1K1bt1y30aRJE73zzjv+x6+88kqs0wQAAAAAFDFRdzXPGixtzZo12aaXLl1aZ511lsxMr732WrZ5r7/+urp166a2bdvmO9GWLVuqVatWIefVqlVLAwYMyDZt8uTJ2rx5c8TbT05O1tSpU/XLL79o4sSJmjdvnjZu3Ki0tDTVqVNHPXv21KBBg1ShQgVJ0nvvvRfyrD8AAAAAAIGiLryrVq0qSUpPT882PSUlRWXKlJGZ6Ztvvsk2b+XKlZKk448/Pr95qnfv3vrXv/4Vcl7Tpk31xhtvZJvWrVu3qArvLC1atFCLFi3Czj969KiGDx+uu+66K+ptAwAAAACKn6gL7/3796ty5cqqWbNmtuldunSRJP3+++85Bhw7fPiwpPgeDXzjxo26/PLL1b59e6WkpKhu3bqqXr26ypUrpz179mj58uWaOXOmRo0apd9++83rdAEAAAAACSLqwnv58uVq06aNzjnnHH399df+6b1795aZafr06TnWySrS83MGOssjjzyiRx55JN/rr1mzxj/SeihpaWn66KOP9NFHH+W7DQAAAAAAgkU9uFrWKOU33XSTevXqpQoVKujWW29Vu3btJEmffPJJjnVatmwpyTmrDAAAAABAcRL1Ge+XX35ZN998s5KTkzVp0qRs8xYvXqzJkyfnWOf888+XmWnBggX5zxQAAAAAgAQU9RnvLVu26KKLLtKmTZvk8/n8sXz5cvXt2zfH8o0aNVKnTp0kSd9++23BMwYAAAAAIIFEfcZbkmbNmqWGDRuqU6dOqlWrljZu3KgffvhBGRkZOZatVauWHnvsMUnKdk04AAAAAADFQb4Kb8m5ndi0adPyXO6HH37QDz/8kN9mAAAAAABIaFF3NQcAAAAAAJGj8AYAAAAAwEX57mouSaeffro6d+6sk046SZUqVVJSUlKuy5uZhgwZUpAmAQAAAABIKPkqvJs2bapRo0bpzDPPjHgdn89H4Q0AAAAAKHaiLrwbNmyomTNnqkqVKvL5fJKkvXv3ateuXTp69GjMEwQAAAAAIJFFXXg/+uijqlq1qo4ePap///vfevXVV7VmzRo3cgMAAAAAIOFFXXh3795dZqYXX3xR//jHP9zICQAAAACAIiPqUc0rV64sSfroo49ingwAAAAAAEVN1IX3unXrJEnp6ekxTwYAAAAAgKIm6sL7q6++kiSdccYZMU8GAAAAAICiJurC+7nnntPevXt19913q2rVqm7kBAAAAABAkRF14b127Vr16dNHxx13nGbNmqXu3bu7kRcAAAAAAEVC1KOaS9LUqVPVunVrzZo1S5MnT9bOnTu1fPlyHThwINf1zIxCHQAAAABQrOSr8O7QoYPeeustVa9eXT6fT8cdd1yu13ybmXw+n8ws34kCAAAAAJCIoi68Tz31VH311VcqX768fD6fDh06pD/++EO7du3S0aNH3cgRAAAAAICEFXXh/fDDD6tChQo6fPiw/v73v2v06NE6fPiwG7kBAAAAAJDwoi68O3bsKDPTE088oVdffdWNnAAAAAAAKDKiHtU86xZikydPjnkyAAAAAAAUNVEX3uvXr5ckJSUlxTwZAAAAAACKmqgL70mTJkmSunTpEvNkAAAAAAAoaqIuvJ999llt2bJFd999t+rXr+9GTgAAAAAAFBlRF95btmxRz549tWfPHv30008aMmSIkpOT3cgNAAAAAICEF/Wo5itWrJAklS9fXjVq1ND//vc/vfrqq9q2bZsOHDiQ67pmpsaNG+cvUwAAAAAAElDUhXeDBg2yPfb5fPL5fKpZs2ae65pZtM0BAAAAAJDQoi68x44d60YeAAAAAAAUSVEX3oMGDXIjDwAAAAAAiqSoB1cDAAAAAACRo/AGAAAAAMBFFN4AAAAAALiIwhsAAAAAABdReAMAAAAA4CIKbwAAAAAAXEThDQAAAACAiyi8AQAAAABwEYU3AAAAAAAuovAGAAAAAMBFFN4AAAAAALiIwhsAAAAAABeVdLuBzp07S5K2b9+upUuXut0cAAAAAABxxfUz3tOmTdPUqVO1aNEiTZ48WWeddZbbTQIAAAAAEDcKpau5z+eTz+dT9+7dNX36dH377bfq0qVLYTQNAAAAAICnXO9qPnbsWElSuXLl1KFDB9WrV09nn322unXrppIlXW8eAAAAAABPuV75Dho0KNvjBg0aqFu3bpzxBgAAAAAUCz5J5nUSxUVqaqpSUlK8TgMAAAAA4IJwNR+3EwMAAAAAwEUU3gAAAAAAuMiVa7wvuOAC9evXT9WrV9eqVas0YsQILVq0yI2mAAAAAACIa1Gf8e7atas2bNiglStXqkqVKjnmP/TQQ/rkk0909dVXq2fPnrrppps0Z84cXXHFFbHIFwAAAACAhBJ14X3++eerVq1aWrhwoXbt2pVtXrNmzfTQQw9Jcu7dffDgQfl8PpUqVUqvv/666tSpE5OkAQAAAABIFFEX3p06dZKZ6dtvv80x76abbpLP59OePXt01llnqVKlSurSpYt27dqlcuXK6aabbopJ0gAAAAAAJIqoC+8TTjhBkrR48eIc8y688EKZmV599VX99NNPkqQffvhB//3vf+Xz+dSjR48CpgsAAAAAQGKJuvCuUaOGJGnHjh3ZpterV0/169eXJE2YMCHbvO+//16SdPLJJ+crSQAAAAAAElXUhXdSUpIkqXLlytmmd+rUSZK0b98+zZs3L9u8rVu3SpIqVKiQryQBAAAAAEhUURfeW7ZskSQ1atQo2/Tu3btLkmbNmpVjnXLlyklSjsHYAAAAAAAo6qIuvOfOnSufz6fBgwfL5/NJko477jj16dNHZqYpU6bkWKdx48aSpM2bNxcwXQAAAAAAEkvUhfebb74pSerYsaN++OEHPfvss5o1a5YqV66stLQ0vffeeznW6dixoyRpxYoVBUwXAAAAAIDEUjLaFT799FO9//776tevn84880ydccYZ/jPfTz75pDZu3Jht+aSkJPXu3VtmppkzZ8YmawAAAABxoJOk3pJWSBoj6ZCn2QDxKurCW5L69++vGTNmqF+/fqpVq5Y2btyoN954w382PNCVV16pmjVrSpK++OKLgmULAAAAIE5cKulDHetEe5Gk871LB4hjPknmdRLFRWpqqlJSUrxOAwAAAIiBKZLOCZrWSNJKD3IB4kO4mi/qa7wBAAAAQDoQ9Pio6GoOhEbhDQAAACAfnlT24nuEpI1hlgWKtwIV3t26ddObb76pP/74Q3v37lVaWppOPfXUbMt07txZN998s66++uoCJQoAAAAgnsySdLKkIZK6SbrZ02yAeJavwdXKli2rMWPGqG/fvpLkH9XcLOfl4kePHtXw4cNlZvrpp5+0fPnyAqQLAAAAIH5slDTK6ySAuJevM97vvfee+vbtK5/Pp7lz5+q5554Lu+wPP/ygpUuXSpL69OmTvywBAAAAAEhQURfeF198sS6++GJJ0i233KL27dvrnnvuyXWdjz/+WD6fT127ds1flgAAAAAAJKioC+/rr79ekjRu3Di99tprEa2TmpoqSTmu/wYAAAAAoKiLuvA+44wzZGZ69913I15n06ZNkqSaNWtG2xwAAAAAAAkt6sK7evXqkqQNGzZEvE5GRobTWAnuXgYAAAAAKF6iroT37t0rSapVq1bE65x44omSpO3bt0fbHAAAAAAACS3qwjvrdmDNmzePeJ0LL7xQkrR48eJomwMAAAAAIKFFXXhPnjxZPp9Pt912m5KSkvJcvlWrVrrmmmtkZvr888/zlSQAAAAAAIkq6sJ7+PDh2rt3r+rVq6cxY8aodOnSYZe9+OKLNXnyZJUuXVrbtm3TqFGjCpQsAAAAAACJpmS0K2zfvl1Dhw7Ve++9p/79+6tHjx764osv/PPvuecelShRQh07dlSDBg3k8/mUkZGha6+9VgcPHoxp8gAAAAAAxLuoC29J+uCDD5SRkaHXX39dNWvW1IABA2RmkqRrr71WkuTz+SRJe/bs0XXXXadvvvkmRikDAAAAAJA48n1/rwkTJqhRo0Z66KGHNG/ePGVkZMjn8/kL7sWLF+vxxx9X48aNNWnSpJglDAAAAABAIvFJsphsyOfTcccdp6SkJG3fvt1/724ck5qaqpSUFK/TAAAAAAC4IFzNl6+u5qGYGffpBgAAAAAgSL67mgMAAAAAgLxReAMAAAAA4KJ8dzVv2rSpbrjhBnXu3FknnXSSKlWqpBIlcq/jzUylSpXKb5MAAAAAACScfBXe9957rx599FElJSX5RzEHAAAAAAA5RV14X3755XriiSckSUePHtWMGTO0cOFC7dq1S0ePHo15ggAAAAAAJLKoC+9hw4ZJkjZs2KDzzz9fixcvjnlSAAAAAAAUFVEPrnb66afLzPTggw9SdAMAAAAAkIeoC++0tDRJ0s8//xzrXHKVnJys7t276/7779fEiRO1YcMGmZnMTFOnTo15eykpKRozZoxWrlypgwcPavPmzfruu+80ePDgPAeRAwAAAAAgS9RdzZctW6YzzjhD1apVcyOfsBYsWKCGDRsWSlv33XefHnvsMSUlJfmnlS1bVjVr1tTZZ5+tgQMH6sILL9SuXbsKJR8AAAAAQOKK+tTt2LFj5fP51Lt3bxfSCS9w9PQ///xTkyZNcqWdgQMH6oknnlBSUpJWr16tG264QSkpKbrgggv06aefSpI6duyojz/+mBHdAQAAgCLnOEmnep0EiiCLJkqWLGnTpk2zQ4cO2YUXXhjVugWJv//979anTx+rW7euf1qWqVOnxqSN5ORk2759u5mZrVu3zmrWrJljmREjRvjbvfbaa6PafmpqaqG9XgRBEARBEARBRBt/N+mQSWbSApOOj4OciESKXGq+6DeWnJxsEydOtLS0NHv77bftoosuslNOOcXq1auXZ8TyScW68L7zzjv927z66qtDLlOhQgXbuXOnmZktWrQoVm8CQRAEQRAEQRCeRm2T0kyygHgpDvIiEinC1XxRX+MtSbt379bLL7+s9u3b68orr9SVV14Z0XpmplKlSuWnyULRp08fSdKePXv0wQcfhFxm//79+uCDDzR06FC1aNFCjRo10ooVKwozTQAAAAAx10A5h8Bq7EEeKIryNTz3Cy+8oK+//lrVq1eXz+eLKuJVyZIldcYZZ0iSfvzxRx05ciTssoGjqHfq1Mn13AAUtmqSbs+M6h7nAgAACkeqpHVB0yZ4kQiKoKjPeF999dW6/fbbJUl79+7Vxx9/rIULF2rXrl06evRozBMsLE2aNPGfjV+6dGmuy/7222/+v5s1a+ZqXgAKW1VJ8yWdmPn4HkktJW33LCMAAFAY0iR1l/QvSfUkvSdplJcJoQiJuvC+7bbbJDnF59lnn60tW7bEPCkv1K1b1//3+vXrc1123bpjR8Lq1avnWk4AvHCljhXdklRH0tWSXvYmHQAF0FrSU5LqSxon6VFJiXuSAEBhWCapv9dJoAiKuvBu2rSpzEz/+te/ikzRLUmVKlXy/71v375clw2cX7FiRddyAuAFCzGNH+pA4ikj6UtJx2c+fljSbkkveJYRAKD4ivoa77S0NEnSsmXLYp6Ml8qVK+f/O7fruyXp8OHDIdcLZejQoUpNTVVqaqqqV+daUSD+jZO0KuDxGknvepQLgPxro2NFd5ZeXiQCAED0hXfW9c21atWKeTJeOnjwoP/v0qVL57psmTJlQq4XysiRI5WSkqKUlBRt27atYEkCKAS75HRPvVHSzZJaSdrhYT4A8meZpENB037xIhEAAKIvvMeMGSOfz6errrrKjXw8s3fvXv/feXUfD5yfV7d0AIlot6QRkv4npxAHkHi2S7pJ0s7Mx1MlPe5dOgCAYi3qwnv06NH67LPPdM011+ivf/2rGzl5InBAtcCB1kIJHFAtcKA1AAAQT8ZKOkFSTUnniN4rAACvRD24WufOnfXyyy+rRo0aeumll9S/f3+NGzdOy5Yt04EDB/Jcf8aMGflK1G3Lli1TWlqaSpUqlectwpo2ber/O69bjwEAAC8dlrTV6yQAAMVc1IX3tGnTZHZs1N8zzzxTZ555ZkTrmpn/XtnxJj09XXPmzFHHjh3Vvn17lSpVyj+QXLBu3br5/545c2YhZQgAAAAASERRdzWXJJ/Pl++IZxMmTJAkVa5cWf369Qu5TIUKFfzzfvnlF61YsaLQ8gMAAAAAJJ6oz3ifffbZbuThuvr162v16tWSnLP2oZ7HqFGj9M9//lPHHXecnnzySX399dfaujV797Tnn39eVapUkSQ9++yzbqcNAAAAAEhwURfe06dPdyOPPLVs2VKtWrUKOa9WrVoaMGBAtmmTJ0/W5s2bo2pj9+7duvvuuzVq1CjVq1dPP/30k5544gn9/PPPqlGjhm688UZdcsklkpzi/e23387XcwEAAAAAFC+WCPHwww9bNLp27Zpt/fr16/vnTZ06Nde27r//fktPTw+77ZkzZ1rVqlWjfg6pqamev44EQRAEQRAEQRCEOxGu5svXNd5F3RNPPKEOHTpo7NixWr16tQ4dOqStW7dq2rRpGjJkiLp06aKdO3d6nSYAAAAAIAH45FTgKASpqalKSUnxOg0AAAAAgAvC1Xxhr/EeNWqUJMnMNGTIkBzT8yN4WwAAAAAAFHVhz3hnZGT479ddsmTJkNOjasjnk5ll21ZxwxlvAAAAACi6oj7jvXbt2pAFdrjpAAAAAAAgp7CFd8OGDaOaDgAAAAAAcmJUcwBF1O2SFkmaJqmrt6kAAACgWCu+F1wDKML6Snop4PHnkhpI2uZJNgAAACjeoj7jvXLlSi1fvlyNGjWKeJ169eppxYoVWr58ebTNAUA+9Ap6XEFSNw/yAAAAAPJxxrt+/foyM5UuXTridUqVKqUGDRowKBuAQrIkxLTFhZ4FAAAAIHGNN4Ai6RVJn2T+fUDSPyT95l06QFyrKKmpnDuMAgAANxRK4Z2cnCxJOnDgQGE0B6DYOySpt6QakmpKetrTbID41V/SRkm/yjk4FfllZAAAIHKFUnhfc801kqQ1a9YURnMAkGmbpP1eJwHEqXKS/iupUubjJpKe9C4dAACKsDyv8Z4yZUrI6WPGjNH+/bn/oC1TpoxOOukk1axZU2amr7/+On9ZAgCAGDteUnLQtCZeJAIAQJGXZ+HdrVs3mZl8vmPXfvl8PqWkpETV0MqVK/XkkxxJBwAgPqyWtFBSy4BpH3uTCgAARVyehff06dOzjUbetWtXmZnmzZuX6xlvM9OhQ4e0adMmzZo1S+PGjeMabwAA4sqFkh6XM7jaJNHVHAAAd+RZeJ999tnZHmdkZEiSrr/+ev3666/uZAUAAArBekkDvE4CAIAiL+r7eL/55psyM+3cudONfAAAAAAAKFKiLrwHDhzoRh4AAAAAABRJhXI7MQAAAAAAiquoz3gHq1ixoho2bKhKlSopKSkpz+VnzJhR0CYBAAAAAEgY+S68r7/+et16661q2bJltluN5cbMVKpUqfw2CQAAAABAwom68Pb5fHr33XfVt29f/2MAAAAAABBa1IX34MGD1a9fP0nS1q1bNXr0aM2dO1c7duzQ0aNHY54gAAAAAACJLN+jmv/+++/q1KmTduzYEfOkAAAAAAAoKqIe1bxZs2YyMz322GMU3QAAAAAA5CHqwjtr5PKlS5fGPBkAAABIUnNJqZJM0neS6nmbDgCgQKIuvFevXi1JSk5OjnUuAAAAkCS9I6ld5t9nS3rVw1wAAAUVdeH94YcfyufzqUePHm7kAwAAUMyVldQyaFp7LxIBAMRI1IX3Cy+8oNWrV+v2229Xs2bN3MgJAACgGDskaW7QtBleJAIAiJGoC++9e/fqvPPO06ZNmzRz5kz99a9/VdWqVd3IDQAAoJjqL2m6pAOSPpd0s7fpAAAKxCdn1I6IrVixQpJUvnx51axZU2YmM9O2bdt04MCBXNc1MzVu3DjfySa61NRUpaSkeJ0GAAAAAMAF4Wq+qO/j3aBBg2yPfT6ffD6fatasmee6ZlHV+AAAAAAAJLyoC++xY8e6kQcAAAAAAEVS1IX3oEGD3MgDAAAAAIAiKerB1QAAAAAAQOQovAEAAAAAcBGFNwAAAAAALor6Gu9rr722QA2+9dZbBVofAAAAAIBEEnXh/cYbb+T7tmBmRuENAAAAAChWoi68Jefe3QAAAAAAIG9RF94NGzbMc5kKFSqoadOmuuaaa9S7d2/NnDlTN9xwgw4ePJivJAEAiG/1JZ0o6UdJaR7nAgAA4k3UhffatWsjWu7XX3/Vxx9/rGuuuUZvvPGGXnrpJZ133nlRJwgAQHx7WNJDcsYrXSvpHEkrPM0IAADEF9dHNX/77bf1zjvvqHv37ho8eLDbzQEAUIhOkPSgjn2dnijpAe/SibnakoZK+oskLjMDACC/CuV2YuPHj5fP59P1119fGM0BHugo6SZJJ3udSBFRSlINr5MAIlBLUlLQtDpeJOKCdpKWSRoh6StJb3qbDgAACaxQCu8NGzZIkk499dTCaA4oZI9JminpVUlLJV3sbToJ70pJmyRtkfS9pOrepgPk6mdJS4KmveNBHm64W1KFgMfXSGrkUS4AACS2Qim8TzjhBElS2bJlC6M5oBCVl3RXwOOSkv7pUS5FQWVJr0uqlvm4i6R/eZYNkDeT1F3SS5I+kXS1pLGeZhQ7ob6zyxV6FgAAFAX5up1YtG677TZJ0urVqwujOaAQlVTO/0b8MM2/Rsp+hk2SWniRCBCFPyXd4XUSLnhF0gU61pX+O0mLvUsHAIAE5toZ7ypVqqhHjx6aPHmyzjvvPJmZPvroI7eaAzyyRzmve3zZi0SKiF8kbQia9pUXiQDQ15LaS3pa0s2SLvQ2HQAAEljUZ7zT09Pz1dBvv/2mZ599Nl/rAvHtBknT5JyZ/UrSFE+zSWzpks6X9IykhpI+lPOjH4A35mYGAAAoiKgLb58vutuJpKena9y4cbrzzju1b9++aJsDEkCGpLe8TqIIWSTpPK+TAAAAAGIm6sL7kUceyXOZo0ePau/evVq5cqVmzZql7du35ys5AACA2Dte0mavkwAAFCNRF96PPvqoG3kAAAC4rLWk9yU1lrRcUl85t4QDAMBdhXI7sSz9+vUrzOYAAAACvC6n6FbmvyM9zAUAUJy4Xnj7fD5dc801Wrp0qd555x23mwMAAAgj+PaE3K4QAFA4XCu8k5KSNGjQIC1btkxvvPGGTjnllKgHZgMKF/ffBoCiLfj2hNyuEABQOCIqvMuXL6877rhDn3/+uRYtWqR58+bpo48+Ur9+/UIW09dff72WL1+uESNGqGHDhvL5fDpy5IhGjqRLF+JRcznX+B2QNE/SKZ5mAwBwy0BJb0tamfnvYG/TKbaqSPpI0hFJC+XcLx4Aij7LLVq0aGHr1q2z9PT0kDF16lQrV66cSbLGjRvbnDlz/PMyMjJs//799vLLL1udOnVybac4RGpqquc5EKHiJ5MsIGbEQU4EQRAEUVTjNVO27911JiXFQV4EQRAFj3A1X65nvMuXL69PPvlEtWvXls/nCxmdO3fWCy+8oFNPPVWzZ89WmzZt5PP5dODAAT3//PNq2LChbr/9dm3YsCG3pgAPtcvjMQAAiJ2zgh7XlXSiF4kAQKHJtfC+7rrrVL9+fZmZvvvuO3Xp0kUVK1ZUuXLl1LZtW40bN04+n0/XXXed3n33XR133HEyM/33v//VSSedpLvvvltbtmwprOcC5NPUoMffeZIFAADFww9Bj9dJWuNFIkAc6S/pM0mvigNRRVfY0+Sff/65ZWRk2NKlSy0pKXQXoKxlMjIy7ODBg9arVy/PT+/Ha9DVPF6jjkmfmrTVpI9NqhUHOREEQRBEUY1kkz4w6aBJ8006Iw5yIggv4ypTtssvlhuXXyRu5KureYsWLWRmev7555WRkRFymccff1ySZGYaPXq0vvzyy9w2CcShDZIullRD0qWS/vQ2HQAAirTdkvrKuZtIG0lzvE0H8NxVQY8bSTrTi0TgolwL72rVqkmSfv/997DL/Prrr/6/P/300xilBQAAAADFwfqgx0dDTEOiy7XwLlu2rCRp27ZtYZfZuXOn/++NGzfGKC0AAAAAKA6elPRH5t8Zkh6XtNa7dOCKkrHcWHp6eiw3BwAAAABF3DpJTSWdIWmjKLqLppgW3gAAAACAaB2V9KPXScBFERXet9xyS0S3BYtkucceeyyyzAAAAAAAKAJ8coY3DykjI0NmYWfnS8mSxfcke2pqqlJSUrxOAwAAAADggnA1X55VsM/ni1kSsS7iAQAAAACId7kW3meffXZh5QEAAAAAQJGUa+E9ffr0wsoDAAAAAIAiKdf7eAMAAAAAgIKh8AYAAAAAwEUU3gCAOHa3pF8lzZTUzdtUAAAA8qn43tsLABDnrpH0TMDjzyTVl7Tdm3QAAADyiTPeAIAg7eQUvLdKKu9hHn8JelxBUhcvEkFCuFzSPEkLJQ3wOJdEc4ak4ZL+Jammt6kAQBHFGW8AQIDukr7Usa+HfvKu2F0U9PiopF+8SARxr4WkcZKSMh+/IekPSbO8SiiBnCVpmqRSmY/7S2ouKc2rhAAXnCzncqV5kubnY/3Wcg5Kfy9pWezSQrHCGW8AUGdJ4yW9JefLtTi7RdmPyXaW1MqbVDRc0kdyCu69ku6UtNyjXCTJJ6mhpNIe5oDQeuhY0Z2lpxeJJKBBOlZ0S06Bco5HuQBu6CtnrJARcgrvu6Nc/y45xfoISUvlHJAGokfhDaCYayFpipwv0mskTZdUx9OMvHUgxLSDhZ6F45Cc7sPV5HR/fcmjPCSpqaTfJK2UtF7SeR7mgpyCe0eEm1aYkiTdIOfH+lUe55KbXRFOAxLVw8p+YO4BRd7pt6SkBwMeJ2VuD4gehTeAYq6vsp/tqSjpYo9yyctxkjpJKudiG8/JObuc5T1Jv7vYXiR2ySnCvfSCpCaZf9eQ9Lr4CvVaaUl/l9PFvLGkZyUdltNF+r+SJniXmiTpVUmvSRoq6V05P/bj0cuSNgQ8nijpJ29SASJSVdJ1cg6A+iJYPniskjLK2UMmnBKZyweqEOG6QHb8agBQzK2PcJrXrpTz43iGnPw6uNTOAjldTYfIGdysv0vtJJpTgx7XkZTsRSLwGyHp35KukFPkZsjpHVFN0l8lmXepqbRyDvB2oxeJRGCtnINKl0nqKulSb9MBcnWynGusx8oZj+SzCNZ5JejxKDkH6SJxRM6B1kDDI1wXyM4nb7+ZipXU1FSlpKR4nQaAbMpJmqxjA4hNlPMD9KhXCYWQJGmTnDOtWX6Qc/YbheM/ckZ5zzJLUkePcoHTS2W/svdW+VPSCd6kk0MJSVvl9FLJsljOpS0A8m+4nANrgdor714aF8kZu2C+pHcU3Xe8T9LVktpK+k7SpCjWRXEUruZjVHMAxdxBOWd52sg5sr3Y23RCKifnLF6gel4kUozdLecMyV/k3K7qHm/TKfbSJe2QdHzAtC0e5RLKUTnXhWadaUtT9utEAeRPqFtcRtL1e5LyXzCbpLczA8g/upoDgCTnKHg8Ft2StE/S50HT3vMikThwlpzn/racW7sUlkNyRrY9XdK1cnogwDsm5+BHeubjg5L+4V06If1XzqB8V0tqJKc3DYCCGSHnIHmWX+TcDg+If5zxBoCEcLWk++Xc7uxbOYN9FTdN5XTzyxro5lJJzSSt8SwjeOlNOftDK0k/StrmaTah/S7vBycEipIfJZ0hZ/yRLXKuv46nS8OA8Ci8ASAh7JV0n9dJeOxyZR9dtryc4vtFT7JBPFiv+BwMEYB7FmYGkFjoag4ASBAbQkyj6AIAAPGPwhsoMs6S9DcV7nWvRU1POV1Xp4tb6sSjd+W8P1k+F9fNAgCAREBXc6BIuEvSswGPb5QzAAkid4qcEU+zbk/UUdKZkuZ6lhGCHZZ0rpzr3DMkLfI2HQAAgAhxxhsoEu4PevxPT7JIbL2U/Z7AJeTc9xPxZ4EougF4xyfnIOD5yv69AQDhJWThXadOHT311FNasmSJ9u7dq507d2r+/Pl68MEHVaVKlQJtu2vXrjKziGLMmDGxeUIoBGdJGiVnJOgG3qbiiuDOK252Zqkl6U5Jf5VUxcV2CluokYd/K/QsAKDoOl/SQ5K6eJ1IAZSS9L2cu0t8LudAYLKnGQFIHJZI0bNnT9uxY4eFs27dOmvTpk2+t9+1a9ew2w42ZsyYqLadmprq+etXPOMMk46YZJmx0aSKcZBXLOPRgOdnJv3dpXbqmLQ5oJ0/TKoQB88/VvE/k9JNyjDpHZOS4iAngiCIohD/Z8r2PXVLHOSUn7gs6HmYSXfEQV4EQcRLhKv5Euoa7xYtWujDDz9UxYoVtX//fj399NOaMmWKSpYsqUsuuUS333676tatq88++0xt27bVpk2bCtTewIEDlZqaGnb+zp07C7R9FJZrlL0r2AlyuhV/4E06rnhI0hw51yRPl/SNS+0MkFQz4HFjSX0kveVSe4XtJjmvZQlJf3qcCwAUFUmS7giadrek/xZ+KgVWNcJpAJBdQhXeL774oipWrKj09HT16tVLM2bM8M+bPn265s+fr7ffflsnnHCC/u///k+DBw8uUHurVq3SkiVLCpo2PLc1xLQthZ6F+z7LDDcdjXBaIiuK+wYAeC34uyJRvzs+lvSEpBqZjw9Iese7dAAkjIS5xrtNmzY655xzJElvvPFGtqI7yzvvvKMpU6ZIkq677jrVqFEjxzIojl5V9ut3J8q5PgvRe0NSYE+S3+T8CAEAIJwMSf8OmvaUF4nEwHY5vcv+Lek/ktpLWuZpRgASQ8IU3n369PH/PWrUqLDLjR49WpJUsmRJXXzxxa7nhUSwTdJpkv4i58uS+zPn35+SWsgZWG2IpBQ5R/sBAMjNo3JGAr9XznfxSG/TKZBVcrrK3y7pF49zAZAoEqbw7tSpkyRp//79uV53PXXq1BzrAFK6nOue53idSBGwXc51eaMk7fM4FwBA4vhO0jPiuxhAcZQwhXezZs0kSX/88YcyMjLCLrdp0ybt2bMn2zr59fjjj2vVqlU6dOiQduzYoYULF+o///mPTjvttAJtFwAAAABQfCRE4V26dGn/9drr16/Pc/l169ZJkurVq1egdjt27KgGDRqoTJkyqlq1qk4//XTdeuut+uWXX/Tiiy+qZMmEGpsOAAAAAOCBhKgcK1Wq5P973768u7ZmLVOxYsV8tbdp0yZNmDBBM2fO1MqVK5WWlqbatWurZ8+eGjRokCpUqKBhw4YpOTlZAwcOzFcbAIB4crqklyWdIulTObc+OuhlQgAAoIjx/CbjeUXdunUty9ixY/Nc/vvvvzczs7S0tKjbKl++vJUsWTLs/CZNmtjatWv9+Zx//vm5bm/o0KGWmppqqamptmrVKs9fS4IgCCI4Spi0yiQLiOfiIC+CIAiCIBItUlNTQ05PiK7mBw8eO+tQunTpPJcvU6ZMjvUideDAAaWnp4edv2zZMl1zzTX+x7fffnuu2xs5cqRSUlKUkpKibdu2RZ0PAMBtJ0lqEDTtXA/yAAAARVVCFN579+71/x1J9/GsZSLplp4f06dP19KlSyVJXbp0kc/nc6UdAEBhWCtpa9C0eV4kAiBiSZIqe50EAEQsIQrvI0eOaOtW50dR3bp181w+a5msQdbcsGTJEklSuXLlVK1aNdfaQTyoImmwpGsklfM2FQAuOCLpaklrMh9/K+kf3qUDIA9XSNokabekLyUle5sOAEQgIQpvSf4zzCeffLKSkpLCLnfCCScoOTk52zpuMDPXto14UlPSL5Jel/SWpJ8klfU0IwBu+EZOd/MKknoo5xlwAPGhiqTRkmpkPj5P0oOeZQMAkUqYwnvmzJmSpAoVKiglJSXsct26dcuxjhuaN28uSTp06JC2b9/uWjvw2kBJgb0sWki61KNcALjvgNcJAMjVKZLKB01r5UEeABCdhCm8J0yY4P978ODBYZcbNGiQJCk9PV2ffvqpK7l06tTJX3jPnDmTs99FWqjB/EoVehYAAKCEpFvkDBAc6FsPcgGA6CRM4T1//nxNnTpVknT99derU6dOOZbp37+/unfvLkl68803/deFZ6lfv77MTGbm31agKlWqZDtjHkqTJk30zjvv+B+/8sor0T4VJJQ3JO0IeLxa0seeZAIAQPHWT9J1kgIHtX1f0rPepAMAUSjpdQLRGDZsmGbNmqWKFStq8uTJeuqppzRlyhSVLFlSl1xyiYYNGyZJ2rRpkx544IGot5+cnKypU6fql19+0cSJEzVv3jxt3LhRaWlpqlOnjnr27KlBgwapQoUKkqT33ntPEydOjOVTRNxZJ6mlpGslHZY0VtLeXNcAAABuaBZi2mxJGYWdCADki+c3GY8mevbsaTt27LBw1q1bZ23atAm5bv369f3LTZ06Ndf5ucnIyLCXXnrJSpUqFZObqRMEQRAEQRB5RXuTMkyyzEgzqWkc5EUQBHEswtV8CXXGW5K++uortWjRQrfffrsuvPBCnXjiicrIyNCqVav08ccf6+WXX9auXbvyte2NGzfq8ssvV/v27ZWSkqK6deuqevXqKleunPbs2aPly5dr5syZGjVqlH777bfYPjEAAADk4kc5t/77m6Q0SU9L4vdY4SghqY6kDZKOepwLkJh8cipwFILU1NRcR2QHAAAA4kuKpA8k1Ze0RlJfSameZgTEs3A1X8IMrgYAAACgsI2QU3Qr89/XPMwFSFwU3gAAAADCCB7UrrknWQCJjsIbAAAAQBhfBD3+3JMsgESXcIOrAQAAACgsgyRtl9RR0g+S7vY2HSBBUXgDiGPVJT0sp5vbF5JeEKOpAgBQmHZKGuLStmtJaiHpJ0l7XGoDiA90NQdcU1nSu5L2Spojqa236SSkiZJulXSOpH9LesDTbAAAQKxcL2mtpK8lrZPU2dNsALdReAOueUrSVZIqyrkVx0fiv1w0asvp1haonxeJAACAmCop54B6qczHleXclx0ouqgCANcEH7mtr2O34/BSKUmXSLpUUhmPc8nNDkm7g6at9iCPoqKkpDPkdN8HAMBLZSVVDZp2gheJAIWGwhtwzY9BjzfI6VLlpXJyur1PlDRB0gI5R5m91k7S45IGSyqdOe2QpDsy/5Wc1+8fhZ5Z0XCqpFVyrqFbL+kGb9MBABRz+yR9EjTtLS8SAQoNhTfgmnslTZIzGNivkq6QlOFpRlJfSa0CHp8q6RpvUvE7X85BivslvS7p44B5b0iqI+dMbQNJiws5t6LicUl1M/8uI+k5OZdAAADglWvkDKA6UdJfM/8Gii5GNQdcs0PSxZJ8kizMMi3kXAveQNIHkh6Vu6N2V4pwWmG6TVJSwOPzJTWWtDzz8Y7MQP4FX+JQUU6X830e5AIAgCQdkPO7BygeOOMNuC5c0V1K0pdyCs1mco703ulyLh9I2hrweJeckde9dCTCaci/94MezxHXywNAUVBZzgF+APGOwhvwTEs53agDne9ym1vkjLD+uJwz7e3k3MLDS89IOhjweKy8vxa+qHlG0jBJUyQNl3SRt+nEpRJyuuBvl/SbnN4qABJPTUn3yLl8Kfg7tiipLWmmnEFIV0o629t0AOSJruaAZ1bIKTjLBUxbUgjtrlF83Q/7B0lNJV0g58fDV96mUySZpJczA6HdpGM9To6T00vgRDkHqwAkhiqS5unYmBZ/k3OQe6NXCbnoOR275WYDSW/L+czyeiyZouxSSf3lDPb6bOa/QOQ44w14ZqecH/u7Mh/PlPSIZ9l4a62kV0XRDe90CXpcRtKZXiQC15WRVMvrJOCKvjpWdEvOWBZeDyDqltZBj2uL/dpNl8m5G8zlcnqQTVX28WmAvFF4A556U84XZW059/3e5m06QLEVfPu/NDlnzlC0XCPpT0mb5BzsrOFtOoix4jRmyLdBj38VZ2DdFHwA52RxcBbRovAGPHdYzo9AL5SQ08X7JhXta+FQ9NSV9LSkEZLax2B7wyWNlPP/cYOkASqa3VOLsyqSXsv8V3K66XL7oqLlQ0lLAx6vknOAuyi6V84tODdK+lpON2i4J/h32lE5B/GAyHGNN1CsjZfTbUpyrlfqKmm+d+kAESknabaOdSm9XlInOaO151e6pBsk3ajwdyJAYmskqXzQtNO8SASu2S9nANFL5fzEnSBpr6cZuWe/pKFeJ1GMPC2pl5zr6SXpeTnj0gCRo/AGiq1mOlZ0S869nf8u6Wpv0gEi1lPZr+MsJecMdUEK7ywU3UXXQknrlX3f+aIQ2k3O/Hd3IbQF597Q73idBIqcNZKayOkps0HSH96mg4REV3Og2CodYlqZQs8CiN6OENO2F3oWSDTpcs5YfS6nO/Kjkv7tcpuvyBm7Y2vm3/GmlKSBkv5Pzu0lAYSXJmmaKLqRXxTeQLH1s6TvAx6nKT5/GALBpkv6JODxSrHvIjKLJV0oqbmc67uPutjWxZJukdO5sFTm3xe52F5+fChptKR/yhlg8EJv0wGAIozCGyjWeskZWO1xOdfFTfU2HRQjPjkD+uX3a6i3nC5/F0g6VdLm2KQFxEzzENPi6ZryhnIODmRJknS7R7kAQNFH4Q0UawfljPL7gJzrH1E8VJVUycP2W0paJud625XK/y1ZZsm5Rreo3i4Iie0rZT+jflTSZI9yCSVNOc/4838JANxC4Q0AxUZJObfW2ZoZj3uUx/8kNc78u76cW+IAsdZB0ltybhPXzIP250u6UlJqZlwhaYEHeYSzXtLYgMeHJD3jUS4AUPQxqjkAFBvXZobkdCu9X85AU7MKOY/g7rbN5HQ9L04jivvk9DTpJ2e03Psk/eJpRkVLCzmDIGUNItlXzojEWyJcv4ykc+QcoJpbgDw+yIx4NUjObSUby+k9ssrbdOJWkqRWktbK2ScAIHqc8QaAuHeapEfk3Ge6XAG2Ey/XnH4Z9Pgr5V50XyWnKBitY2fKvXarpB/kFC1NQ8yvJam1nAI7lDvkjKp9mpzr1CfLGYALsXGlst+5IVnZr2fOTV05l0J8IedM9VuxTS3ufCVncEKK7tAaydkf5srpJXCrt+kASGhGFE6kpqZ6ngNRWFHFpLEmrTfpU5Pqx0FORGJGJ5MOm2SZMb0A2zo3YDuWud2TPHhOVUx63aRlJr1pUo1clr0sKOf1JpXx+D0ZEiKnUgHz7zcpLXPeUpNqh9jGt0HbMJPO8Ph5JVIkm3SxSRdk/ls5aP5tIV7fCyPc9nMh1m0dB8+5uMaJJj1j0qsmpbjYThuT2oWY/rZl3xcOmlQ1Dl4XgiDiNXKp+bxPrrgEhXdxirdM2b6oZxdCm61M+s6kNSa9YNkLASJx4z1TjiKgbQG2N8ik+Sb9YFKvOHh+ecX7IZ7/uR7n9FmInDplzqtrUnrQvOEhtvFS0DIHTaoWB693IkQ7k3YFvX47LHtxXNGkOQHzJ5lUIsLtjw3x/vaIg+ddHKOSSRsC3ofDFvuDIKVM+iqgjamW/eDezIB5WdEsDl4bwonmJv3VpDPjIBeCcCJczUdXc8AVPYIet5dU0cX2Skr6TNLZkk6U0431ARfbQ+FJi3BapEZLaiPnVlzBXb7j0doQ09YUehbZLQ96nC5ndHbJ+f+XFDS/QYhtPCZpZubfu+Xc43l7jPIris6Rc832PEmj5HQdD1RV0kMBj/dJOkPOft5Wzv2zI71n9xtBy67MbBuF73xJtQMel9axcSpipY+kvwQ87ibnUoUswdfoL5a0NMY5IH/6S1okabic+9D/w9t0gDxQeAOuWBD0+A85PwTd0kzOPZEDdXexPcReGYW+xvd5Zd93PpXzQyMaleQM4nV2/lLz1LNyfuhKUoakJ5Wz8C1sT8gZsVpyRoK+V9LGzMdzJK0OWv79ENvYJqmznP+3J0gaE/Msi47acgYB7CrnoNHpYZarHmLaLB17ryI1VdK5ct6TpyV1UsEOdiH/dkQ4rSBq5zHtJTnXdX8naYSk82LcPvLvQWUvZe5TzgOfxVUbSX+Vc/tOxBPPT8cXl6CreXGKRibNM8lMWm5S+yjWTTbpepP6mlQ6YHotC999vIJJuzPby4pX4uB1IPIOn0kvmnTIpL0m/TPEMnVMusWca1kj7S6bFQ1M2mTH9ouJcfCc8/MatTWpXhzkEhhNLPS1nieZNNqkb8zp2h/rdo/LfF+9fv6xiPYm9Tepei7LXGfK0dU3VETyWlcw6YnM9+YB45KceA+fSV8EvMfLLPd9JT/RwKR9AW0cNOnkOHju8RTHW/bfI/ESKwPet6z3jv/T0q1Br8vQOMipeAXXeMdBUHgnUpSy2HzJVIly+dqW/Xq2Oeb8KEjNfLzZpIvCrNvbpI2Zy31j4a8XLWnStSb9n3FNVDxE34D3Oys+NukTkzpGsZ0rTfrDnCL7oYDpL4TYPu97+DjFnANnXucR+P80cNC1f9mxwfammHMtcyzbrWjZi46S5lzffJYLz/E/mc/DTNpjoQe2kjkHXYL34RHmXP//rTnjIFwaYZvBYwaEuv7ey2hn0r0m/aUA26huzoBkXj+XWEYnk84394qqNuZc2/+WhR7ksKlJr2XOj+ZzOdGjph27xn27Od8zXucUGHdk5pYVL8RBTvEQW4Jel3VxkFPxCgrvOAgK70SJx0zab9IBk54q5LYfNuX4gfl90ONtFn5U5yTLObpvcIwL2FaGSX3i4DUvzvFUiPc8K46adEkE22hsOQf06pc5b1SI7TJQVM4obdLnAa/ROIu+d0EsI7BAzDDnwFqzEO/l/TFs8zpzel2YSb+Y1MKkJQFtfRnD16RO5vMKfC4f5bL8o+b0Cskw58DUp5l/b7Jj+3peUdJy/j/Z6uF7HOr1D8ztyXxs42mTjmSu/4VJ5eLgeeUnypj0N5PGWOTFXklzBi3cZtIii93nXDVzis6s9+WIFZ9R7l8NeN5mzudDpTjIKzB6mrPfR/o5UBwiuAfk9jjIqXgFhXccBIV3IkR3U44fthcUYvtvhGh/TYhpDfK5/RNCbKsgt6ciCh49QrwngbEkgm0MCLFe1qUGHezYD3Ez6VdzfqB6/bzjLUK9hhd7lEvdELlMNaf4Dp7+RozarGTHiu6smBuivfNi1N4pIbb9TR7rVDbn1nNPBK13wCK/vdPaEM/R630vKwIPcmQ9r2hundcuxGs6LA6eV34iuGfCnRGs8/egdfaac+lWQXMJ9dnwTBy8RoURoUZ0bxkHeRG5xwdB79kui89LBYpuMKo5EJG2Iaa1KaS2z5V0TdC0lZI+DJr2q3IO3hSpjMwIdCSf20JsfCPpNkkrdGyArkChBlwLlhpi2pzMf2dLOkvSy3IGoukkZxRuZNcgxLSGhZ1EpnTlHIE7Tc6gX7uDpn8SozbrKOedF0INOlUjRu39rpwjhb+Wxzp7JG1Vzs/kcpKaR9jubZL2Z/69U9KdEa5XGHwhHgdPy80pEU6Ld1UkXRY0bUgE63UJelxRUrsY5PNniGmbYrDdRBB854u1OjbYJRJHspzvfsQDz48KFJfgjHciRIopx9HdwrqeK9T9irubc5TyOZN+N2dgrMYFbCew69hho9txPIXPcp51vDnCdW82p9vsAXMGa/PFwfNJpDjdsvcMOGhSQw/zGRGQy2E7du/ydubck3qWSYNj3OavAW2aSS+b07076/Fmi37cityigjlnMoebdE4U690TlOd2k8pHsX6yOT1BolmnMGJg0PN6Nsr1a1r2QcLMnG64Xj+vaKOs5fwc/CGC9f4ZtM5Bi81AbD5zvnuztjvf8r6kq6hEkkmPm/SbOZfiNI+DnIi84zlTjt+TBf3tSEQTdDWPg6DwTpS43pwvmWVWuCNBjjHl+KBs5lJbfzHpNuODOB6jtjkHYWaZdHU+1vfyuuREj+7mXDs8weJjAKWeVrj/Txua9K45gzn+05x9KcWkkeYczDkpDl4TmXOpxL9NWm/S7Dh5r2IV7c0ZbT2/lzidZc613TNNuiYOnk9+406T/3twv0V2YKasOYOfHTFnMKnLY5xTG3MGeeOgJhHvcbxJi00yc8a1eCIOcipeEa7m82X+gUKQmpqqlJQUr9NA3GouaaacbnaS9J6k/p5lAwCAd06R1ELS93IuMYhUCeW8VAMobnxyLp/cLGmdx7kUP+FqvpIe5AIgpCWSGks6X9J6OddzAgBQHP2eGdGi6Aac86pzvU4CQSi8gbiyXdJbXicBAAAAIIYY1RwAAAAAABdReAMAAAAA4CIKbwAAAAAAXEThDQAAAACAiyi8AQAAAABwEYU3AAAAAAAuovAGAAAAAMBFFN4AAAAAALiIwhsAAAAAABdReAMAAAAA4CIKbwAAAAAAXEThDQAAAACAiyi8AQAAAABwEYU3AAAAAAAuovAGAAAAAMBFFN4AAABxoIKkByS9L2mQx7kAAGKrpNcJAAAAb5wk6WZJpSSNkLTU23SKvXGSLsz8u6+kmpKe8i4dAEAMccYbAIBiqKakOZLukjRM0k9yCnF44zgdK7qzDPAiEQCAKyi8AQAohvpIqhbwuKKk/h7lAumApL1B0zZ7kQgAwBUU3gAAFEO7QkzbWdhJwO+QpH9Iysh8vEfSP71LBwAQYxTeAIqkJEmNxUAWQDgTJM0OeLxY0pse5QLHf+V09+8lqZ6kH7xNBwAQQxTeAIqcFEmrJP0haa2kjt6mA8SlI5I6SfqLpAsktVbOrs4ofGslTZZzxhsAUHRwMghAkfOqnLNFknSCnNGam3uXDhC3jkr6xuskAAAoBjjjDaDIaRr0+BRPsgCQKK6XtF7SbnH7LgCAOyi8ARQ5k4Ief+ZJFgASQRNJoyTVkVRZ0r2SrgmY31zSnZLOK/zUAABFCF3NARQ5N0raLqmzpB/ljBQMAKG0V86zEGdJelvSxZI+0rEfS8Ml3VZ4qQEAihAKbwBFzh5Jt3qdBICEMFvOte6BxfeszH/vVfYfSjdJekjcdg0AED26mgMAgGLrD0mDdOwa76flnO2WnNsSBvKJH04AgPzh+wMAABRrY+XcCaGKsl+a8rycs+GBy20vvLQAAEUIXc0BAABCeF/SaknnS1oi53pvAADyg8IbAAAgjDmZAQBAQdDVHIgjTSUNldTK4zwAAAAAxA6FNxAn+svpyjhC0gJJd3iaDQAAAIBYofAG4sS/lP0/5ENyRtAFAAAAkNgovIE4UT7ocVnxHxQAAAAoCvhdD8SJ/wQ9fk1SRiG275NUrRDbAwAAAIoLCm8gTjwtqbekFyVdLenOQmy7i5xb5myTNF/SSYXYNgCptaSRkl6V1MzjXAAAQOxxOzEgjnySGYXJJ2mspBMzH7eW9JKkiwo5D6C4aixppo5dbtJf0qmSNnqWEQA3VJRz15IlknZ6m0qhKS3n82yZpIMe5wJ4jTPeQDFXRVKDoGktCz8NxJk6cgpCuO8KZR/jobKkPh7lAsAdXSWtkzRD0noVj//jZ0paI+lnSRsknedpNoD3KLyBYm6npNSgaV97kQjixquS1kr6Q9JXksp5m06RtyXCaQAS1/NyDnRLzoG2l7xLpdC8LKlW5t9V5Xy3ILZKeZ0AokLhDUD95HRxXyPnOtO/eZsOPNRF0k069uXwF0mDvEunWHhH0pyAx9MkfexNKgBcUi/o8Qkq+kVTo6DHJ6roP+fCUkHSB3K676+X9Jmk6ZIeENcRxzPeGwBaLWdgt0idIWlY5t8vS/opxvmEU1HSxZL2S/pcUnohtRtvqknaIclc2HbwD6Vw0xA7ByS1l3PQI13SD96mAw8NknSDnJ5Ij0qa7W06iKH3JN0e8PgjSWke5VJYPpKzP2f5TEX/OReWf0q6PPPvOpkhSZ0lVZJ0rxdJISJGFE6kpqZ6ngNBFDQaSXZAMsuMg5I1LoR2j5dsTUC7syQrGQevR25RX7LPJdsq2QeS1Sjg9ppK9kvm818u2RkxzLW2ZJUkO0GyfQGvc4ZknaPcVmXJzpOsXhy8BwSRKHGRjv2/M8n2SlYtDvIiYhMlJfuHZJMle0yyCi61U16yIZltNfT4OZeV7HHJfpDsRcmS4+B9KKxoKNlUyQ5J9p1kDWK8/W+U/fMiMFbGwfMv7pFLzed9csUlKLyJohB3K+eH/L2F0O4DIdq9yOPX4kTJrlD4L9SZQfl+VMD2pgZtb2kMnkNFOT8ETc4BlXslay/ZRDlf7JdGub2zJNudub10yW7x+D2SnB97p0nmi4Ncoo0qkj0l2WeF+Fr+Tc6+9YNk5+SxbFfJPs3Mr0ch5Vdasj5y/u+Vj4P3KFYxUjk/4/rFQV6xiCQ5nyt14iCX4Kgn2W1y9qmkOMinIFFCslRlP3hzWhzkVRxjmrL/X/4uxtv/p8IX3t/GwfMv7kHhHQdB4U0UheivnB/yV0ew3lDJ1ku2TbKHguZVlFNYv5vLtp4M0e6VBXwuV0n2iWT/kXPGN5p1r5AsTccKzGuD5pcKke+OzHmXS/aOZI/IKawibXNHiG2WijLvsyV7VrKBcs7ABH95Z0h2cgFe02lB29st56yHV/vrIMn2Z+ayTE6PDa9yyU9MCXo973e5vX5B7R2QrGaYZRvLOZuTtewRyVq4nF9ZyeYHtPmHZFXj4H2KJHzKvbC7Szn/f58eB3kXNBrKOQNncj4z74uDnLKijbL38JkUBzkVJM5Vzn3olTjIK16inpzfHw/K/YNAWb8PsuJwjLdfSs5vly2SrdCx77kNkrWKg9e6uAeFdxwEhXfRiaaSvS3nqOJ1BdhOSTk/rNzqctZLsp8lWyenyIvFNkvp2BlSk+xr5V38tVTOHwO9A+Z/GTRvWIhtnCLn6H3WMqujfN1ayOl+d0rm4/uD2vxV0Z0RXRm0/vrM6T45Re0IydYGLfONnEIwcNrMXF7nfpL9VU73b0k2LmjdqQHLl1feZ/8GBK2/SrIFId6b/PQkyNoHfg2xvePysb2CRg3J3pRzUCQwl/fysa1kyTrKOUBUmM/h+BCv5WKX2xwdos2+YZb9W4hlH3Q5v1AH/u4o5PdFcn7YviTnc7VWBMsPkXPJySHJXpfTS+YMye6UrEPmMuUl+0LHfqQ/6sHzijZ8mc8lt8t+gvepIxG+ZoURbyrn/pTIBzvODvF8/lNIbdeV7KQ4eA3CRS05RWrW6/Knor/8yyfZDXK+R/4up/dNuGW/D3ofprn8/CpL1lrxfwlecQkK7zgICu/EiL5yisnxcgrGR+QUrj9Ldr6cMy6bFNkP09yitZxizSTbJdklMX4eNeVcfx2Y58CAeZPk/AicrfBd0erJuT7recmaB81rqdyPqlaV88N0upyuqBYUz2YuVyvEvAVhttlEsifknKUNdxYuVPw1YNsZkj2T+W9wuylRbHNn0Lr7M6e/EDQ964v+JzlnW6eHaDfUGebvAubvkuzUzNd0rJxr3T/UsbP0z2a+l4cy2w+X89wQbQfHbkV3Hd7xcg5AmWS/SfZq0Pa8OoOUlVNwzAmx7EmSvSbZBOU86HCxjp0R2yXnh21hPYdyOnYWIyti3V0xMBrLORsU/JqNVegfc71DLBtJ75eCxJAQbf6zEN8TyfnsDjzTvzrzvQq3fCOF/rwJjDsClq+rxDiL31LOmTaTbKOcyw5CLRfqWtTWcZC/5PSyCs6tbRzklVv0kLO/nBpink/OJSJZz2WXnBMFbuc0Usf28c+Ue0HqVYQ6UHhrlNt4PGj9sbkse5Kc7/t0OUW419fbE4UbFN5xEBTe8R89lbOYCnx8UE43YQuK9/PR1tSgbWyUc31WrJ7LpSHyfCtz3vig6aHOolVR9gMM+3XsbHEk8VWI9gOjj5wvouuUfbA2k3PgI5bv67Y83leT8+VYN4ptPhO0/ouZ04O3vU3Zf5R/FDT/iGTVg7bdMUR+4boLnhdi2XBnrGfk8n4slNOT4cwInnsJOdeCfy+n+3bgdtbIKY7el3P2063eHLlF+Vye5wNBy5aV0zUvcJnzAuavCZq3IHN6KTlnGPKb46mZ7+kIhS9Cyip7N1iTc2mEG69XVnGULmfgvqNB7YbqHlxC2XthTJT7Z1uqyvmszGpzp5yxFgpz/3pZOfer3MZCCO6+Hyq2FvJziEUEf578EWa5wUHLLVX8jLdwlrIfRJkWBznlFi8E5Jom58Bg8DJl5PQMuV15X0J1npyD779IdnME7ZeR8xn6uZzB20pJ1l059+chcfBaBcfNIfKM9rfGxqD1jyhnj78TJHtDznfFU5mvmdvPrYSy/36sL+fyvOdUOAdeiJxRpArvOnXq2FNPPWVLliyxvXv32s6dO23+/Pn24IMPWpUqVWLWTkpKio0ZM8ZWrlxpBw8etM2bN9t3331ngwcPthIlSsTyTSDiJEYo7x9It4SY9lQ+2gruhmxyRpWO1XNpoJzdbO+Q8yN/d4i2qwStPyDEMv8XYduVQ6y7XU7Xrl1yRnQdHJBfuo4dLd8lZxCeWL0OPuUshg8pZ34vBKxTUdl/sFSTc0R/t5wfl70ztztITjfKoTr2pRfcGyJ4dNGWyn4g4F8hcu4aIr8RYZ7fvSGWDS4us+I8OV1Yg5ffp7y7qR8nZ2C9x+R0XQzeRmBEe718rMMn5yxkqNy+Clq2V4hl3sycV0I5r9PbLqer4U45++yECF674DhB2XtM7FfoXg+nhMhtrguvV6gzQcHxTZh1S8g5Sz5JzgBVhdHNsXZmm4/Jm7NIj4V4fXIbhK6unB/oub2+uzx4HgWN4AOapvBnOgfIKdZeUfwNsNZMzv40WN6OR5FXJCvnfjSrANs7UTm/Dy7IY50xQcu/KtmNyrkf5Oc3UWG8flm9DAPjwii2sTBo3c3KeRDph6BlXnD5ef1Tzm+TvXIuT6mWmVdW+3sV35cAFNUoMoV3z549bceOHRbOunXrrE2bNgVu57777rP09PSw7cycOTPqIp/C2924Sk530SHK/8ik/1DuP47S5RS0j+rYD/L5ynnGMpJ4KWjbsT7LKx27rvCInCOwfZSzGDeFHh37qhDL3R1hu0lyiuzAdacGLRPcDX6DnC/AZBdeh+eC2hqt7D84Pg5Y9u86dgb+68z3Kfg1O6TwXd0H6thBhPTM11FyziQ/JKdoryjnB064HgQ+ZR+Z9oDCd+tvq5zdWHM7a91Qztn6rIMRGXIKr9xev7LKfmY7t26zv7nw/uUnesjZ90PlGPi6nx5i/hMB898LmjdfOYvxaLs6h/qcCXVtdAnlHEvgSRdeq+Eh8gk+OBXY7omZr9FzynmN7H+Dtl1bzlnx5XIOXhWkl0C8xAnK3hMi+GBOqOitnJ+JgRE84GSkUVXOZ9Zjiq5HUizijaDnEMnrQOQ/jlPO76JQl85EGtcr5374csD84+WMPxB4Rje4UN8t5/MgsNdahiLrOeVFPBjiOYc7UB0qegU81zQ5B5QC51cLsf0VLj6fbiHaC/5taXJ/7A0iZxSJwrtFixa2d+9eMzPbt2+fPfjgg3bWWWdZly5d7LnnnrO0tDQzM9u4caOdcMIJ+W5n4MCB/gJ71apVNnToUGvXrp2df/759sknn/jnTZ061Xw+XyzeBKKAEfxhGvzjL9KorGO3gEqX8yX0hpzCdauyd5+qpYKN4FtaTgE/S85ZALcGoCqhY12dpgW9TkflHAEOHkymlHIe2V0VZY6X6dhgaBvljB6bNe9U5fxiOOjS85ecQra/nC+kPpnTTpHzg7W3jp2tPkl5X4uZFbmdGWgs51rXrLNxVwetG8lgNxXknEl4UHmPMn515vu1SJEN9nejjhWPe+R0bc9t+VCXLQTGYTkjrk9XzrEAvIzSyn6tfFbUD1ousPBcouwHVSrKOcud2/OP5lKT2nLObgZv48YwyzeXU9CsknM5gxvdFs8JymWvnANIG3TsrH7WwHLHKXsBGdwl/UDQtqcFzX8zRPuJGOXkfJaco8i7Tf83xPv+kfI+y5jb/h04mOF+Fe6toyrK+b+zWM73ZLQDVRHRxyhl33+uKsC22irn/jg0c95fdezs+no5vQKknAcCl2RO7yRnLJevFbr7e7xEO0V3oDpUVJdzOVeoS9OSlLPX2xcuPp/7QryH74eYNiwOXvviFkWi8J4yZYqZmaWlpVnnzp1zzL/66qv9RfGoUaPy1UZycrJt377df/a8Zs2aOZYZMWKEv51rr702Fm8CUcAI7j50SJF1ecwaPOw5HftiUebfgd3hyii21197FaGKkFC3j7owxHLP56O9SnIK7uBroFqE2L4XZ0qPk3MbsB5yfjyfHyKvUHFYztmASNtZELT+EcX2soJoooxyFn7f57FOqO7YWdvYL9k1Hj2XSCL4jHWaQveqOFnO2Z3g/+eh9tXgs05Dc2k/OIJH0zc5P2a9uBY+MPrKuXXZRDmjbUvO/4ngrrfBo/IHx5qAZcuGmL/F4+fpZXRV9h/921WwHj6hPqdfioPnSbgXJeTcyvJRHRsNvyDxoJyDZelyxoApKee7KfjyrI8yl784YN5eOePieP2aRBtZB6p/UcHuShMuLtSxg7V/KPQgeLlFNTnX5w9T7r0p/6rsd3rJinPlXLef9Xip3OlJSOQeCV94t2nTxl/sjhw5Muxy3377rb84r1GjRtTt3Hnnnf52rr766pDLVKhQwXbu3GlmZosWLYrFm0AUMJYq+wfPNuV9FqKKsh+Z3Cdn1Gyvn4ub0Uuhz+heFrTcBSGWeTrGuQSOOp0mp8tUYb4WTZT9GsWP5fzg2Bn0vIO7FW+SM8BeNG3NC9rGYRX+ramyomqI9/b3PNZJUvbr1jbKueSitaK7D7kXEepgUzSj1ycr54/QzzJfj+WKvgtfqG7mwfeAj+cINbhk1v+Rw3IGEgtcPnjwvSlx8By8jAvkHNwYq4L3Dgk1qFU8XltLxHeUV/bP8YbKuV+lBsyvKuf7OjkOco/XKCvnYG60gwhWVfbLWNYrdPHdUDl/y+3QsVHak+SM6XKx4nOE+eIQCV94/9///Z+/IG7fvn3Y5fr37+9fbvDgwVG3M3PmTDMz2717t5UuXTrscoFnvRs1alTQN4EoYPRR9gIpktE5Byjnl8tjcfBc3I57gp7zAeW8XrmksheL2xX7wTnKyOnO+rAKt3tkVgTf9srknJ1PkTO693w5R517yOku+5PyXyD1U/Yvyfz0HohlfBL0vCO5RrmknC7nA5RYP7iCu+L9qeh/iAyU0yXf5PReKMjgULWUfWTcXxX94GxeRik5l8dk5f+bnMs2Ltax+80HRkcd+yG5VNGf/SHCh0/Z7xX8p3JeRkEQ+YmflP1z8+9xkFNxiFAjr98eYrlLQiz3dhzkTxyLhC+8p02b5r+2OykpKexyJ5xwgr8gHjNmTFRtlCxZ0o4cOWJmZl999VWuy1511VX+dgYMGFDQN4GIQdSX04Uo0lsnhLpuNdLBwxI97pVzJupHOWdNQi1TTk6RdZucYsHrnGMdwYNCmWSdXWyvlZz96zwX24g0Ksg5U/uh4vO2L7GMknLOAq6Uc/Y7v/forSjnfsyxyKmanLsjDJZ3PR8KEiXk7MeXKLKDGCVUND9D4iFKyhmn4nolxr2/icSIGnLGlPhSkZ3IIGIToUaID3Wv8WrK2RNrQBzkTxyLhC+8t2zZYmZmCxYsyHPZ3bt3m5nZTz/9FFUbzZo18xfTL7zwQq7Ltm7d2r/s008/XdA3gfAgSurYQGompxB1a4AzIv6io7LfmmWB4ufesgRBEARBFK9IVvYB7FYr/O/Sc+Rcy/2HnLFDvM6dyB7har6SSgClS5dWjRo1JEnr16/Pc/l169apefPmqlevXlTt1K1b1/93Xu2sW7fO/3e07SA+pEvqIqmHpHKSvpR02NOMUJh+kNReUn9Jf0oaKedTEQAAoLDtltRa0hWSfJLGS9oVZtnvJHUonLQQQwlReFeqVMn/9759+/JcPmuZihUrutZO4Pxo20H8OCrpK6+TgGfmZwYAAIDXdksa4XUScE1CFN7lypXz/33kyJE8lz98+HCO9WLdTlYbebUzdOhQ3XDDDZKk6tWrR5UPAAAAACDxlfA6gUgcPHjQ/3fp0qXzXL5MmTI51ot1O1lt5NXOyJEjlZKSopSUFG3bti2qfAAAAAAAiS8hCu+9e/f6/46kW3fWMpF0S89vO4Hzo20HAAAAAFB8JEThfeTIEW3dulVS9gHQwslaJnAAtEgEDqiWVzuBA6pF2w4AAAAAoPhIiMJbkpYuXSpJOvnkk5WUlBR2uRNOOEHJycnZ1onUsmXLlJaWJklq1qxZrss2bdo0R24AAAAAAARLmMJ75syZkqQKFSooJSUl7HLdunXLsU6k0tPTNWfOHElS+/btVapUKVfaAQAAAAAUHwlTeE+YMMH/9+DBg8MuN2jQIElOEf3pp5/mu53KlSurX79+IZepUKGCf94vv/yiFStWRN0OAAAAAKD4sESJ7777zszM0tLSrFOnTjnm9+/f37KMGjUqx/z69ev750+dOjVkG8nJybZ9+3YzM1u7dq3VqFEjxzKvvfaafzvXXnttxPmnpqZ6/hoSBEEQBEEQBEEQ7kQuNZ/3yUUaLVq0sL1795qZ2b59++yBBx6wDh06WOfOne3f//63paWlmZnZxo0b7YQTTsixfiSFtyQbNGiQf7mVK1fakCFDrF27dtarVy+bOHFitm34fL5YvAkEQRAEQRAEQRBEgkeRKLwlWc+ePW3Hjh0Wzrp166xNmzYh14208JZk999/v6Wnp4dtZ+bMmVa1atVYvQkEQRAEQRAEQRBEgke4mi9hrvHO8tVXX6lFixZ65plntHTpUu3bt0+7d+/Wzz//rIcfflgtWrTQ/PnzC9zOE088oQ4dOmjs2LFavXq1Dh06pK1bt2ratGkaMmSIunTpop07d8bgGQEAAAAAijKfnAochSA1NTXXEdkBAAAAAIkrXM2XcGe8AQAAAABIJBTeAAAAAAC4iMIbAAAAAAAXUXgDAAAAAOAiCm8AAAAAAFxE4Q0AAAAAgIsovAEAAAAAcBGFNwAAAAAALqLwBgAAAADART5J5nUSxcWWLVu0Zs0ar9MAAKBQVK9eXdu2bfM6DQAACk39+vVVs2bNHNMpvAEAgCtSU1OVkpLidRoAAHiOruYAAAAAALiIwhsAAAAAABdReAMAAFeMGDHC6xQAAIgLXOMNAAAAAICLOOMNAAAAAICLKLwBAAAAAHARhTcAAAAAAC6i8AYAAAAAwEUU3gAAAAAAuIjCG0BUBgwYIDOTmal+/fpep5MvY8aMkZlp1apVXqei+vXr+1/PAQMG5Hs7sXpfHn74Yf92EJ9itc907drVv52uXbvGMEMAABCMwhsAAAAoJrIOPmfFpZdemuc6q1atkplp9uzZhZBhTsnJyerevbvuv/9+TZw4URs2bPDnP3Xq1Ki3d8opp2j48OFatmyZ9u/fr23btmn27Nm64447VKZMGReeAUDhDQBAgcRTDwoAiSUeepE98sgj8vl8nrQdqQULFuibb77R448/rksuuUS1a9fO97YGDBigBQsW6K9//atOPvlklS9fXtWqVVP79u31wgsvaP78+WrQoEHskgcyUXgDAAAAxVSLFi105ZVXep1GrgIPDPz555+aNGlSvrbTo0cPvf766ypXrpy2bt2qv/3tb2rfvr26d++uN954Q5LUrFkzff7556pQoUIsUgf8SnqdAAAAAIDCt3XrVtWoUUP/+te/NH78eB09etTrlEIaPny4Vq1apTlz5mj9+vWSFPVYJElJSRo+fLhKliypvXv3qlOnTlq2bJl//pQpU7R8+XL93//9n5o1a6Y777xTjz32WEyfB4o3zngDAAAAxdDTTz8tSWrSpEmBBmt023PPPacJEyb4i+78uOSSS9SkSRNJzvMOLLqzPPHEE/7pd9xxh5KSkvLdHhCMwhuIU8GjS1eqVEn333+/5s6dq+3bt8vMNGzYsBzrnX/++Xr33Xe1atUqHThwQLt27dKCBQv05JNP6vjjj8+z3SpVqujJJ5/Ur7/+qgMHDmjz5s365ptvdPnll8f8OcaLWrVq6ZlnntFvv/2m/fv3a+vWrfr666/Vp0+fXNfLen8efvjhXJeL9hrgPn366KuvvtKff/6pgwcPatmyZXr++edVs2bNiJ9T6dKl9be//U2pqanauXOn9uzZo7lz5+rOO+9UqVKlIt5OOKVKldKNN96oyZMna+PGjTp8+LC2bt2qadOm6bbbbovJ4DRdunTRW2+9peXLl2v//v06cOCA1qxZo9TUVL388su68MILc12/Z8+eGjdunNauXauDBw9qx44dmjt3rh555BFVq1Yt7HqhRvseMGCAvv32W23atEnp6en6+OOP/f9Hr7/+eklSgwYNsg1YVJijw8dinwk2derUiAYuivQa1cLYZ7wU6jP74Ycf1qJFi7R3715t3rxZn3/+uTp06JBtvRo1auixxx7T4sWLtW/fPm3btk0TJ05Uq1atImr3tNNO0/Dhw7VkyRLt3r1b+/fv1x9//KHXX39dLVu2DLtepPt5oKSkJN1222366aeftHv3bu3cuVOpqam64447VKpUqahG3C/od1VeGjVqpBdeeEELFy7Unj17dPjwYW3atEkLFy7U22+/rWuvvVYVK1YMu37Tpk31yiuv6LffftPevXu1b98+/f7773r11VfVrFmzXNsO/m7o2rWr3nvvPa1evVqHDh3Szp07/a9/VvdmSVq9enWOzw+37zgwYsQIrVmzRpL04IMPxuT7IV4FfqePHj065DJmprFjx0qSjjvuOHXr1q0wUkMxYgRBxF88/PDDlqVRo0a2fPlyCzZs2DD/8pUrV7Yvv/wyxzKBdu/ebb169QrbZtOmTW39+vVh1x81apQNGDDA/7h+/fqev075iTFjxpiZ2apVq6xNmzb2559/hn3Ob7zxhvl8vpDbyfLwww9H3F7wvPr16/u3M2DAABsxYkTYXLZt22YpKSkh2wh8X1q1amVz5swJu5358+dbtWrV8tzvwj2f5s2b2x9//BF2+2Zmv/76qzVq1Cjf79Gzzz6b6/azlClTJse6pUuXtnHjxuW63s6dO+3cc88N2XbXrl39y/Xs2dMmT56cY/2PP/4422uVm1jvv7HaZwKfZ9euXXPMnzp1qpmZTZ06Ndd8IvlMKIx9xusI3B/q1q1rv/32W8jnmZaWZpdffrlJshYtWti6detCLnfw4EHr1q1brm0++eSTlp6eHvY1zcjIsAceeKBA+3nW8pUqVbJZs2aFbevHH3+0li1b+h8PGDAgZLux+K7KK/r06WMHDx7MtY2s5x1q/b///e+WlpYWdr20tDS75557wraf5eGHH7ZHHnnEMjIysq2/c+fObK9/bkL93yxoZH0nmTmfoYMHD/Y/vvnmm0Ous2rVKjMzmz17tuf/14Jf57w+o7Ji9erVZmb2+++/57pchw4dsr2HXj9PokiF5wkQBBEiAn/E/fzzz3b48GEbPny49ejRw9q0aWOXXXaZ/0dZqVKl/IVWRkaGjRs3zvr372/t27e39u3b2x133GFr1641M+fHXOvWrXO0V6lSJVuzZo2/zfHjx1uvXr2sTZs2dsUVV9hPP/1kZub/1yzxC+/NmzfbypUr7dChQ/bMM89Yly5drF27djZ06FBbsWKF/3k++eSTIbcT6RdzpIV31ms7d+5c69+/v7Vp08Z69Ohho0aN8v9w2759u51wwgk5thNY/GRt5/3337fzzz/fv79kFVJmZjNnzgx5QCGvwrthw4a2Y8cOMzPbu3evPfvss3bppZda27Zt7eyzz7annnrK9u/fb2Zmy5Yts8qVK0f9/px//vn+HBYtWmS33HKLdevWzVq2bGmdO3e2G264wcaNG2f79u0LWXi/8847/vUXL15s119/vT+/l156yY4cOWJmZocOHbJWrVrlWD/wB/HPP/9sZmaTJk2yPn36+N+Ta665xmrUqGHNmze3jz/+2MzM1q9fb82bN88Rsd5/Y7XPFFbhXRj7TDxE4P+d2bNn2759++zxxx+3zp07W9u2bW3YsGG2a9cuM3MKywYNGtiaNWts27Ztdt9999lZZ51lKSkp9uCDD9qhQ4fMzGz16tVWqlSpkO299NJL2dobMmSIdevWzdq0aWPXXHONzZ492z//lltuyfd+nrX8559/nq29K664wtq0aWM9e/a0N9980z89S6jCOxbfVXlFzZo1be/evWbmfMY//PDD1qNHD2vVqpW1b9/e+vfvb6+88opt2LAhZOE9dOhQ/3PYvn273Xvvvf787r77btu2bZt/frgiNfh1/eWXX2zQoEGWkpJinTp1sjvvvNPKly9vzZs3t/vvv9+/fI8ePXJ8fpQvXz7m+2pw4Z2UlGTLli0zM+dzLNTnaqIX3hUqVPB/JgYeUAoVVatW9W97/Pjxnj9PokiF5wkQBBEiAn/Epaen23nnnRd22UcffdTMzPbs2WMdOnQIuUzVqlVtyZIlZmY2ffr0HPOfeeYZf3uhCsmkpKQcZynyW3hHeqYwL/l9bQN/dBw5csTOPvvsHMskJyfb4sWLzcw5u9G0adMcy+T2eoVqL6/C28xs8uTJVrJkyRzLBf4YfPvtt3PMDyx+zMwefPDBkLm89dZb/mWGDBmS63sTav3p06ebmfND8vjjjw+5TNu2bW3fvn1mZvboo49G/f6MHTvW/3pVqFAh7HJVqlTJMe28887z5z9z5kwrW7ZsjmUuuOAC/1nCuXPn5pgffCbqiSeeyPf760bEap8prMK7MPaZUP8H8isWn2sHDx60M844I8cygQeVNm/ebFu2bLGTTjopx3I333yzf7nevXvnmH/uuef65996660h8ylRooT/INTu3bstOTk53/v5xRdf7F9u0qRJVqJEiRzL3Hnnndm2F6rwjsV3VV4xcOBAfw6nnXZa2OVKlixpFStWzDatWrVq2Yr2UD0wGjZsaJs2bTIzs/3791vNmjVzLBPou+++C1nIRvr/x40ILrwl2dVXX+2f9re//S3HOnkV3l58r2eJpPBu0qSJf/n//Oc/eS6f9Xk0a9asQnlPiGITnidAEESICPwSGz16dNjlKlSoYDt37jQzs7vuuivXbfbq1cu/zcaNG/unlypVyrZv325mZkuXLg35o0qS1alTxw4fPuzfRlEovIcPHx52uc6dO/uXe/HFF3PMzxKrwvvQoUNWu3btsNuZMmWKmZkdPnzYatSokW1e4I+3hQsXhu0eX7lyZf97/fPPP+f63gTP69ixo39eu3btcn3OTz/9tJk5Z0+ifX+++uorMzP76KOPol73s88+MzPnYFVu3ZZff/11/3M566yzss0LLEiWLVtmSUlJ+X5/3YhY7TOFUXgX1j4TnEdBxOJzLVwvGelYAWNmduONN4ZcpmzZsnbgwAEzM3vuuedyzP/mm2/MzOyzzz7LNacqVar4u1wHH2iLZj//4osvzMzZ32rVqhV2ublz5/q3GVx4x+K7KpK47777zMw5Wx3te3jXXXf5273++uvDLnfNNdf4l7v//vtzzM+Snp5uDRo0yPf/H7ciVOHt8/n8B5s3b96c40x7ohfebdu29S+f2//PrMi6BG3RokWF8p4QxSO4nRiQAN5+++2w87p27aoqVapIkj744INctzN9+nT/3x06dNDy5cslSW3bttVxxx0nSXrrrbfC3k5kw4YN+vrrr/Mc1Cov//3vf/Xhhx8WaBuxEm6AFUmaMWOGli9frsaNG6tHjx6u5/L1119r48aNYeePHj1a55xzjkqXLq1u3bqFfb/Hjh0bdlCvPXv2aMKECRoyZIhatmypGjVqaOvWrRHld8kll0hyBgCaO3durstOnz5d99xzj+rUqaN69epp3bp1EbUhyf8adOnSRSeddJJWrlwZ0XpJSUn+gXCmTZumFStWhF12xIgRGjx4sCTnvq6zZs0Kudy4ceOUkZERce6FLVb7jFsKa5+RpIkTJ+q0007Ld65ZNmzYUOBtjBs3Luy8RYsWqUGDBjp69KjGjx8fcplDhw7pjz/+0Omnn66TTjop27xKlSr59/O83s9du3Zp8eLFateunTp06KDXX389bL7h9vOkpCT/AF9TpkzRn3/+Gba9t956S23btg05LxbfVZHI+v9w3HHH6eKLL9ann34a8bpZn/P79u3Te++9F3a58ePHa/jw4UpOTlaPHj30xBNPhFxu1qxZWr16dcTte8nM9NBDD+mjjz5SzZo1NWzYMD355JMRrx9P3+uhlCtXzv/3kSNH8lz+8OHDOdYDCorCG0gACxcuDDuvXbt2/r+j+YKvVauW/+8WLVr4/05NTc11vTlz5hS48N66dWvExZ6bDh8+nOtrKznPt3HjxmratKlKlSqltLQ01/KZM2dOxPNPP/30sD9eI9nOkCFD/NuZMmVKRPll7WtZo3dHqlatWlEVUWPHjtX111+v6tWra/Hixfr000/11Vdf6Ycffgh5+5csJ510kipUqCBJ+vHHH3NtY/78+Tpy5IhKly6dbf8Pltf+4bVY7TNuKax9RpJ2796t3bt3R7WOW3LbT3ft2iVJ2rZtm//v3JarVKlStumtW7dWyZLOz7c33ngj26jYuQn8zA+W237eqFEjlS9fXpI0b968XNvI7eBKLL6rIvHpp59qx44dOu644/Txxx/r+++/16RJkzRjxgwtWLAg1wNpWQduFi5c6C+8QklLS9P8+fN19tlnJ/TnR7AJEyZo3rx5atu2re666y698sor2rNnT0Trxsv3ejgHDx70/126dOk8l8+6y0LgekBBcTsxIAHs3Lkz7Lz83i4o64eUJP/ZbknasmVLrutt3rw5X+3Fox07duR5NjPr+ZYoUUJVq1Z1NZ9oXvvcbocVq+0Ei8W+Folp06bpxhtv1L59+1SuXDldccUVGj16tH7//Xdt2rRJo0ePznFLJim6/Tg9PV3bt2/PsV6w3P7vxQO33utYKax9Jt7k9mM9q0fRgQMHct1G1nLB9xF24zXNbT8P/NzLq7DKbX5h7Qs7d+7UhRdeqDVr1qhEiRI6++yz9fzzzys1NVW7du3SpEmT1LdvX/l8vhzrZn0W5PX/SpL/zH/WWfxwuSSahx56SJLzWtx1110eZxM7e/fu9f+d223kgpfZt2+fazmh+OGMN5AAwnX9lrL/KGvfvn3EXxLhflhEc1Yqv2rUqFGg+wtnWbJkSYHWL4znGo1Y5ePW88ra13799Vf17ds34vUivX95oBEjRuijjz7SlVdeqR49eqhTp06qVq2aatWqpYEDB2rgwIF6/fXXdcMNN4R8vrF6DeK5m7kUf/twsMLcZ5KTk1W3bt2o1wv2+++/Kz09vcDbcUvgZ/5tt92W533Ws+zfvz/svMLYz2P9XZWb2bNnq0mTJurdu7cuuOACdenSRQ0aNFDFihV14YUX6sILL9RPP/2kCy+8UNu2bcuxfnH5/Ajliy++0KxZs3TWWWdp2LBheumll/wHKXMTL9/r4WzYsEFHjx5ViRIl8vycqFq1qr/3VLQ9b4DcUHgDCS7wR8O2bdtyva41nMCj8scff7x++eWXsMsef/zxUW8/2C233KJ//etfBd5OqDMW0ahWrZqSkpJy/XGU9XyPHj2a4+xF1pd4iRK5dx7K+gLPS16vbeD83H4IHX/88frjjz8KvJ1gWftapUqVXPtxFGj79u165ZVX9Morr0hyuoFecskluvXWW1WrVi0NGTJEixYt0n/+8x9JTg+GLHm9liVLlvSfAQ5cL9HEap8JJeuAX0H278LcZ3r37h1xt+vcNGjQQGvWrCl4Qi4J/Mw/ePCg669r4OdejRo1cl02t/mx+K6KxpEjR/T+++/r/ffflyTVrVtXvXr10i233KJWrVrpzDPP1GuvvabLLrvMv86OHTtUu3btiL7nsrrA53a5QKJ68MEHNWXKFFWuXFn33nuv7rnnnjzXiZfv9XD279+vdevWqX79+mrWrFmuyzZt2tT/99KlS13JB8UTXc2BBLdgwQL/3506dcrXNgIL7ZSUlFyXzWt+IilTpoxatmyZ6zJZz/f333/PcX13Vte1vLqgB36J5+aMM86IKBdJuR4cidV2gmXta3Xr1lX9+vUjXi9WFi9erMcff1wdOnTQoUOHJEn9+vXzz1+5cqX/rN6ZZ56Z67Zat27tv84vmtcgFC/POrv1Xkux2b+93meKooULF/oPiuT3Mz8aK1as8HedDzdwWpbA67iDxeK7qiDWr1+vkSNH6owzzvD/X7joootUtmxZ/zKLFy+WJLVs2TLX64BLlSql1q1bS0rsz49wvvvuO39Pir/+9a8xOeAeD2bOnClJatKkiU444YSwy2UNXhi4DhALFN5AgpsyZYq/y95tt92W59mpUObNm+c/63fNNdeE3Ubt2rX1l7/8Jf/JZnrkkUfk8/kKHLFw/fXXh53XqVMnnXzyyZKkb775Jsf8rNG2czsY0aJFi1wH3wn0l7/8JdcfA4MGDZLkDOwzbdq0sMtdd911YV+fSpUq+c/wLFq0KKpunJ988on/77/97W8Rrxdrq1ev9r/21atX90/PyMjwvy7dunVTgwYNwm5j6NCh/r9DvbfRyDoIkDUYT2GK1T4TStZr3KRJkxwDfGUpXbp0tjOGwQpznxk7dmxMPlfi+Wy35JwtzhqFv2/fvqpdu7ar7WVkZPhHGT/33HNzHezs2muvDTsvFt9VsZCWlqYZM2ZIcgro5ORk/7ysz4KKFSvqiiuuCLuNvn37+q/tjtXnh+TNZ0g4DzzwgCTnGvt//vOfeS4fT9/r4UyYMMH/d9ZnYzCfz6cBAwZIcnpARPu5CeSGwhtIcLt37/Z3tW3btq1effXVHIPxBKpcubJuvfXWbNOOHDmiMWPGSJKaNWum+++/P8d6SUlJGjlyZFz9MIiFG2+80X+rnECVK1fWf//7X0nOQFz/+9//cizz/fffS3KuV+zcuXOO+cnJyf7XNRJlypTR66+/HvL9GzRokM4991xJ0ocffphrwdyyZUvdd999IecNHz7cP4BQVhfuSE2ZMkWzZ8+W5PxwzvpxEk7Dhg111VVXRdWG5JzFzu0WLg0aNFDjxo0l5bwWePjw4ZKcruRjxowJedaqV69e/h9d8+bNC3srsUht2rRJkjN4VF6D9tSvX19mJjOL+Nrc3MRqnwkla/8uU6aM7rjjjpDLDB8+PNfCr7D2meLmsccek+R0858wYUKuA+eVKFFCV199terUqZPv9l577TVJzr4wYsSIkEXznXfemesZ8Vh8V0Uir4NRZcqU8X9e79mzJ1sX+DFjxvgPDjz99NMhe2nUr19f//73vyU5A+SFu0VbpLI+PyRnBPm8rFq1yv8Z4qZZs2bpyy+/lCTdcMMNrg8uWhg++eQT/x0H7r33XjVp0iTHMvfdd59OOeUUSdKLL76YkNfpI755fjNxgiByxsMPP2xZ8lq2VKlS9sMPP/iXX7x4sQ0bNsw6depkLVu2tC5dutiNN95o7733nu3bt8+2bt2aYxuVK1e2tWvX+rfx3nvvWc+ePa1169bWr18/mz17tpmZzZkzx79M/fr1PX+d8hNjxowxM7PNmzfbqlWr7ODBg/bUU09Z586drW3btjZkyBBbvny5/3k+88wzIbfTtGlTO3z4sJmZ7dy50+68805r166dnXnmmXbbbbfZypUrbf/+/TZ37lwzM1u1alWObdSvX9/fzk8//eR/ja+66ipr3bq1de/e3UaOHGkZGRlmZrZjxw6rXbt2ju0MGDAgx3bGjx9vvXr1statW1ufPn3su+++8y8ze/ZsK1GiRNT7XYMGDWzLli3+ZSZPnmzXXXednXHGGda6dWvr0aOH3XXXXTZlyhRLT0+3Dz74IOr3Z9WqVbZz504bO3asDR482L8fn3POOXbvvffa+vXr/e1fdNFFOdZ/5513/PMXLVpk1113nbVp08a6detmL7zwgh05csTMzA4dOmStWrXKsX7Xrl3963ft2jXPfM8991z/8m+//badeeaZ1qhRI3+Ee7+nTp2ar/03VvtMXs+zZMmStmLFCjMzy8jIsBdffNE6depkbdq0sauuusqmT59uZmYzZszwbyfUZ0Jh7DPxEJF+Zmd9/oT6PAiMqVOn5rqfPPvss/72tmzZYo899ph1797dWrZsaR06dLD+/fvb8OHDbePGjWZm1rx58wLt55MnT/YvP3v2bOvXr5+1bt3a/vKXv9ibb75pZmY//vijf5nrrrsuxzZi8V2VV4wZM8YOHz5sX3zxhQ0bNszOPfdca9WqlXXs2NEGDx7s/zw2M/v3v/+dY/2hQ4f652/dutXuuusuO/PMM+3MM8+0v//977Z161b//JtvvjlkDlkefvjhPPOtWLGiHThwwMzM5s6da927d7eTTz7Z//lRtmzZbMuvWrUqov0skn3QzKxMmTJhl2vTpo0Fmz17tif/v1q2bGkDBgzIFll+/fXXHPOOP/74kNvp0aOHpaWl+f/fDBs2zM444ww755xzbPTo0f5tLlmyxCpUqODJcyWKdHieAEEQISKawluSVahQwd57770cX5KhrFixIuQ2mjVr5v+RFsro0aOzfdkleuG9atUqa9u2rW3evDnsc37rrbdCFqhZceutt4Zdd9++fda7d+9cf2gHFlEDBgyw119/Pez2tm/fbu3btw+ZR+D70qpVq2w/LoP9/PPPVqNGjXzvd40aNbIFCxaE3X6gUaNGRf3+BP6wDCctLc3uvvvukOuXLl3axo0bl+v6O3futHPPPTfk+tEWJD6fz2bNmhW2rcBlmzZt6p/+4Ycf5mv/jdU+E8nzbN++ve3Zsyfs9p944omIPhPc3mfiIQq78JZk//jHP+zQoUN5vqaHDh3KcRAo2v08OTk5W2EdbN68edkKtX79+oXcTiy+qyJ5ffMyfvx4K126dMht3HXXXf7iLJS0tDS79957w+aQJZLCW5I99dRTYdsKfm/+/PNPMzPbtm1bvvfVSAtvSTZhwoRs+XhVeAf+/4pEbvv0gAED/Ac7QlmyZIk1aNDAk+dJFPnwPAGCIEJEtIV3Vpx55pn26quv2pIlS2zXrl2WlpZmO3bssPnz59vIkSOtT58+YX9sSLKqVavaU089Zb///rsdPHjQtmzZYlOmTLErr7zSJBW5wluS1a5d255//nn7/fffbf/+/bZ9+3b79ttv7fLLL49oe+ecc4599tlntnXrVjt06JCtXr3aRo4caaecckrI9gIjuIiSZH379rVvvvnGNm/ebAcPHrQ//vjDXnjhhbBH8EO9L2XKlLG77rrL5s6da7t27bK9e/fa/Pnz7a677sr1/Y90v/P5fHb55ZfbuHHj/Gf2Dx8+bH/++afNmDHDnnnmGevUqVO+3p8GDRrYrbfeauPHj7eff/7ZNm3aZEeOHLHdu3fbzz//bC+99JKdeuqpeW6nZ8+eNn78eFu7dq0dOnTIdu7cafPmzbNHH33UqlWrFna9aAsSyTlr9eijj9qCBQtsz549/rPNwa/jwIED/dM7duyYr9cnVvtMpM+zcePGNnr0aFu7dq0dPvz/7d2/S2pxHMbxz42gSYigoSEkHBId+hOayqCmCIfOKLhGk4ObtIdDOTRFDtIYrUG1VASuGS5tQpaknIIbwnOHi4fbvV27/Tieur5f8CxyPHzAr8rjke/5rlqtpr29Pc3MzDy79oJYM58hQRRvM9P4+LjW1tZ0enqqer2ux8dHtVotVSoV7e7uKp1OP7ve37LOBwcHtbKyovPzc7VaLTWbTZXLZWUyGQ0NDWlqaso7ZyKR6Hqu935X/S3Dw8NaXl7W1taWzs7OvPf//f29qtWqisWiZmdnXzxPNBrV5uamKpWKXNeV67q6vLxUoVBQLBbr+tyOfy3eZqZUKqWjoyPd3Nw8Kf2/vjYTExPe49ls9s1r9TXFOx6Pq91ue8f/D8XbzDQ5OamNjQ1Vq1U9PDzo9vZWJycnWl1d/eNfBoR8YAIfgBBCCOmLbG9vS5IODg4Cn4WQj47jOF7x+f0KO3l/Oj/cNRoNhUKhwOchhLwubK4GAECPdDbyy+VyAU8CfLzOxnj1et33+3T3o87nRz6f9273B+DroHgDANAD4XDYwuGwHR8fezuGA1/F2NhY17sNpFIpm5+fNzOznZ2dXo3VV6anp63ZbFo+nw96FABv8M1+XvoGAAAAnuU4jq2vr1upVLLDw0O7urqygYEBi0QilkwmbXFx0czMrq+vLR6PP7lNFwCA4g0AAIAXOI5jxWKx6zG1Ws0WFhasXC73aCoA+Doo3gAAAOhqZGTElpaWbG5uzmKxmI2OjlooFLK7uzu7uLiw/f19KxQK5rpu0KMCwKdE8QYAAAAAwEdsrgYAAAAAgI8o3gAAAAAA+IjiDQAAAACAjyjeAAAAAAD4iOINAAAAAICPKN4AAAAAAPjoB7KhtLWdhRsBAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (16,10)); _ = make_runtimes_plot()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "So, *bubble sort is faster*. Right? Or do you see any problems with this approach?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Problem 1: scaling\n", "First, let us consider what happens as we change the length of the input. It is what usually matters -- how does our algorithm *scale* (no one is really interested in sorting arrays with `N=5`...)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (50,10))\n", "plt.subplot(1,4,1); make_runtimes_plot(N=10)\n", "plt.subplot(1,4,2); make_runtimes_plot(N=25)\n", "plt.subplot(1,4,3); make_runtimes_plot(N=50)\n", "plt.subplot(1,4,4); _ = make_runtimes_plot(N=100)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "So okay, to understand what's going on here, we'd need to run the experiment for several different `N`-s. Say, let us plot mean runtimes instead..." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def make_mean_runtimes(sort_function, gen_function = get_rnd_instance, N1 = 10, N2 = 100, no_inst=50):\n", " \"\"\"Creates a list of **mean** runtimes for different values of N.\"\"\"\n", " runtimes = []\n", " for i in range(N2-N1):\n", " runtimes.append(np.mean(\n", " get_runtimes(sort_function, gen_function, \n", " N = N1 + i, no_inst = no_inst))*1000)\n", " \n", " return runtimes\n", "\n", "N1 = 5; N2 = 100\n", "\n", "rt_bsort = make_mean_runtimes(bubble_sort, N1=N1, N2 = N2)\n", "rt_msort = make_mean_runtimes(merge_sort, N1=N1, N2 = N2)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams.update({'font.size': 28})\n", "plt.figure(figsize = (20,10))\n", "plt.subplot(1,2,1)\n", "plt.plot([N1 + i for i in range(N2-N1)], rt_bsort, 'r-')\n", "plt.plot([N1 + i for i in range(N2-N1)], rt_msort, 'b-')\n", "plt.gca().set(xlabel='Input array size(N). red = bubble sort, blue = merge sort', ylabel='mean runtimes, msec')\n", "plt.subplot(1,2,2)\n", "plt.plot([N1 + i for i in range(N2-N1)], [x*x/5000 for x in range(N1, N2)], 'r-')\n", "plt.plot([N1 + i for i in range(N2-N1)], [x*log(x)/800 for x in range(N1,N2)], 'b-')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### So, what to do?\n", "Quite often you can see this $O(\\cdot)$ notation. This is an *upper bound* on the respective runtime. What is meant, usually, is this is how the runtime behaves as the *size* of the problem increases infinitely. For our case, the problem size is, naturally, $N$ -- how many numbers are there in the array.\n", "\n", "People usually do not care about multiplicative constants (since $Ax^3$ will grow more than $Bx^2$, eventually, for any $A,B>0$ and $x>0$)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Hey, asymptotics is a big deal, actually:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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/MT969KgSExM1atSoGi9o/PjxeuWVVyRJ+/bt0yuvvKJ169bJZrPp3nvv1ejRo9W/f3999dVXGjJkCJP9oEalpqY6lvPz87V58+Yq7efuu++WJO3cuVOXXXaZsrKyyuwXFhZWpf0DAAAAtc0WGaXM9DM6czzZ7FKqzeng++6772rv3r1avXq1Dh06JEk1HjpDQ0M1depUSdKhQ4fUt2/fEhMUffPNN/rnP/+pu+++W4MGDdItt9yiTz75pEZrgHfbu3evTp06pcaNG2vAgAFV3k/Xrl0lSQsWLCg39EpSr169qjwGAAAAUJvC7dEecZmzJDn9OKN//OMfmjt3riP01oYJEyY4zoA9+eSTZc7K++ijjzoucf7zn/9ca7XAOxUUFGjRokWSpMsvv1w9e/as0n6s1sL/pxQUFFRunzFjxjjukwcAAADcjc0e5RGXOUsuBN+6MG7cOEnSmTNn9MUXX5TZJyMjw7EuJiZGHTp0qLP64B1effVV5ebmysfHR//9738rvbd95MiRiomJKfHazp07JUmjRo1SkyZNSm3Tvn17vffeezVXNAAAAFCDGoXZFBgcopQDO80upUa4TfC1Wq3q06ePJGnVqlWlJgsqLiEhwbFcnctRgbJs2bJFjz76qCQpKipKv/32m9544w2NGDFCPXr0UN++fXXttdfq73//u/bs2aOFCxcqMjKyxD5mzZolSWrdurV+/vlnjR8/XrGxsYqLi9Nzzz2nX3/9VWFhYfr111/r/P0BAAAAlSma2Cp5n2ec8XWb5/h27NhRfn5+kgqDR0W2bdvmWO7SpUut1gXv9N577yk9PV3vvvuuGjVqpIkTJ2rixIll9s3Pz1dGRkaJ19566y1deeWVGjZsmDp16qQZM2aUWH/27Fnddttt+t3vfqfevXvX1tsAAAAAqiTcfu5RRgc9I/i6zRnfiIgIx3Jl9xEfPHjQsdymTZtaqwne7eOPP1bbtm01adIkLV++XCkpKcrNzVVGRoZ2796tBQsWaOLEiWrbtq2WLVtWYtu8vDz97ne/00MPPaTExERlZGTo7Nmz2rlzpz744AP16tVLc+bMMeeNAQAAAJWwRUbpzIkUZaadNruUGmNUtRVJSEio8j6K2rXXXuvY37333lth3wYNGjj6LliwoMK+d999t5GYmGgkJiYae/furVJts2bNqvb7o9G8oXGs0Gg0Go1Go3lGu+cfnxm3Pv+B6XW42hITE8t83W3O+AYGBjqWK7q/V5Kys7PL3K4s06ZNU2xsrGJjY0s8oxUAAAAAUJrFx0fN27T3mEcZSW50qXNmZqZj2d/fv8K+AQEBZW4HAAAAAKieJuER8g8IVLKHPMpIcqPgm5aW5lgODg6usG/x9enp6bVWEwAAAAB4m6IZnTnjWwuKT2hVfKKrshSf0Kr4RFcAAAAAgOoJj4xSQUGBjh3cY3YpNcZtgu+OHTuUm5srqfJHFHXu3NmxXNmjjwAAAAAAzrPZo3Qy+ZBysz3ntlK3Cb55eXlavXq1JOnSSy91PNO3LIMGDXIsr1y5srZLAwAAAACvYbNHe9RlzpIbBV9Jmjt3riQpJCREv//978vsExQU5Fi3ceNG7d69u87qAwAAAABP5mv1U9NWdqV40MRWUh0GX7vdLsMwZBiGEhISyuwzffp0nThxQpL06quvqnnz5qX6vP7662rcuLEk6e9//3ut1QsAAAAA3qZZ67by9bUqef9Os0upUVZnO3bv3l09evQoc12LFi10++23l3ht8eLFSk5OdqmY06dP689//rOmT5+uNm3a6JdfftErr7yi9evXq3nz5rr33ns1ZswYSdKyZcv06aefurR/AAAAAED5bPZoSZ41o7PkQvAdO3asnn/++TLXde7cWf/6179KvDZo0CCXg68kzZgxQy1atNDf/vY3tWvXTtOmTSvV58cff9S4ceNkGIbL+wcAAAAAlC3cHqW83FydSNpvdik1yq3u8S3yyiuvqF+/fvr444+1b98+ZWVl6dixY1q2bJnuuusuXX755Tp58qTZZQIAAACAR7FFRuv44X3Kz8szu5Qa5fQZ3xdeeEEvvPBClQfav3+/LBaL0/0TExN1xx13VHk8AAAAAIBrbPYoHdz2m9ll1Di3POMLAAAAAKhb/oEN1SS8tcfN6CwRfAEAAAAAkmxtoiSJ4AsAAAAA8Ew2+7ng62GPMpIIvgAAAAAASbbIKGVnntWplCNml1LjCL4AAAAAANnsUTp2cLdHPjaW4AsAAAAAULg92iMvc5YIvgAAAADg9YJCwxTcuKlHTmwlEXwBSdLMmTNlGIb27t1rdikAAABAnWse2UGSlMwZX6D2DRw4UIZhONr8+fMr3ea5555z9O/UqVMdVOne+AwBAADgqnB7tCQpZT9nfIE6N3r0aMXGxppdRr3GZwgAAIDK2CKjlHHmpNJPHTe7lFpB8IXbe+mll8wuod7jMwQAAEBFbPYojz3bKxF84caOHTsmSbrqqqsUFxdncjX1E58hAAAAnGGLjPLYGZ0lgi/c2LvvvquMjAxJ0osvvmhyNfUTnyEAAAAqE9q8pRo0DPbYGZ0lgi/cWHJyst555x1JhRM2XXnllSZXJHXu3Fnvvfeetm3bprS0NKWnp2v79u364IMP1KVLF6f2MXLkSH377bdKSUlRRkaGtm/frtdee03h4eGSpL1798owDM2cObPa9brjZwgAAAD3YouMkiQlc6kzYI7XXntNp0+flmT+GcvHHntMGzdu1AMPPKBOnTopODhYQUFB6tixo+677z799ttv+stf/lLhPt59910tXLhQw4cPV/PmzdWwYUN17NhRf/7zn7Vu3Tr16NGjxut2p88QAAAA7ifcXhh8jx3cbXIltYfgC7d28uRJvfHGG5Kkvn37atSoUabUcffdd2vq1KmyWq06ceKEnnzySfXr10/9+vXTX/7yFx0/flxWq1VTpkzR/fffX+Y+/vznP+uPf/yjJOnw4cN68MEH1adPH8XFxemll15SaGio5syZo4YNG9Zo7e7yGQIAAMA92SKjdOpYkrIy0swupdYQfOH2Xn/9dR0/Xjit+t/+9rc6H79p06Z6/fXXJUkpKSnq06ePpkyZolWrVmnVqlX6+9//rtjYWB09elSSNHXqVNlsthL7CA8P1wsvvCBJ2rdvn3r37q333ntPiYmJWrlypZ555hkNHTpUERERpbatCWZ/hgAAAHBfNnu0R8/oLBF8UQ+kpaXp73//uySpR48euv766+t0/PHjxys4OFiS9MQTT2j37tKXgOzdu1d//vOfJUkNGzbUXXfdVWL97bffrsDAQEmFl0wnJyeX2sfPP/+s9957r6bLl2T+ZwgAAAD35OPjq+Zt2nv0xFaSZDW7AE/yxht3qXuP9maXUat+W79Hjz76UZ2P+/bbb2vixIlq0aKFXnjhBX355ZcqKCiok7GLJoRKT0/XZ599Vm6/zz//XO+++65CQ0N15ZVX6pVXXnGsGzp0qCTp9OnTWrBgQbn7mDVrlv70pz/VUOUlmfkZAgAAwD2FtWwjq5+/xwdfzviiXsjMzNSrr74qSbrooot0yy231NnY3bp1kyT99ttvys7OLrdfbm6u1q5dK0mKiYkpcx8bNmxQXl5eufvYuHFjhWNUh5mfIQAAANyTzR4tSUre57nP8JU441ujzDgT6k0+/PBDPf7442rTpo2effZZ/fvf/64wRNaUsLAwSYX391am6D7fxo0bl3i9SZMmkqRjx45VuH1BQYFOnDihli1bVqHSypn1GQIAAMA9hdujVZCfr9TDe80upVZxxhf1Rk5Ojl566SVJUocOHXTnnXfW6fiGYdTpeLXB7M8QAAAA7sUWGaUTRw8qL6d2rjp0FwRf1CszZsxwTC719NNPy9/fv9bHPHHihKTCmZkr06JFC0nSqVOnSrx+8uRJSVLz5s0r3N7Hx8dxdri2mPEZAgAAwD3Z7FEeP6OzRPBFPZOXl+d4HE+bNm1033331fqYmzZtkiR17969wpDo5+ennj17Siq8V7e4zZs3S5IuvvhiWa3l32EQExOjBg0aVLfkCpnxGQIAAMD9WP0DFNaijZL3e/b9vRLBF/XQp59+qq1bt0qSnnrqKTVs2LBWx/vuu+8kScHBwbrhhhvK7Xf99dc77u0t2qZIfHy8JCk0NFSjR48udx+33XZbNat1Tl1/hgAAAHA/zSPay8fX1+NndJYIvqiHCgoK9Pzzz0sqvLT4jjvuqNXxZs6cqfT0dEnSlClTZLfbS/Wx2+2aOnWqJOns2bP66KOSE519/PHHysrKkiRNnTpVNput1D4uvfRS/fGPf6zp8stU158hAAAA3I/NHiVJSuGML+Ce/vvf/+q3336TpDJDZE06fvy449m6LVu21Jo1a/T444+rb9++6tu3rx577DGtWbPGMRPz448/XmoG6KSkJL3wwguSpHbt2unXX3/VAw88oEsuuUT9+/fX3/72N8XHx+vIkSOObWt7Mq26/AwBAADgfmyRUcrNydaJpINml1LreJwR6q1nn31W8+fPr5Oxpk2bptDQUL366qtq1qyZ/v73v5fqk5eXp6effloffPBBmfuYPHmy7Ha77rvvPkVEROi9994rsf7YsWO6/vrrNXfuXElynCGuTXX5GQIAAMC92CKjlHpojwoK8s0updZxxhf11oIFC7R69eo6G2/q1KmKiYnRBx98oO3btysjI0MZGRnasWOHPvzwQ3Xv3l1TpkypcB/333+/Ro8erSVLluj48ePKzMzUzp079dZbb6lnz5769ddfFRISIkk6ffp0rb+nuv4MAQAA4D7C20Z7xYzOEmd84WaWL18ui8XidP++ffvWyLjjx4/X+PHjK+23bds2PfDAA9Uaa+HChVq4cGGZ61q3bu2YIGvXrqr9R8iszxAAAAD1R4OgRgpt1sIrJraSOOMLuJU//OEPjuVVq1aZWAkAAAA8WfM2HSRJyV5yxpfgC9SRBg0aOCbAKkuPHj30zDPPSJLWrl3rePYvAAAAUNPCvWhGZ4lLnYE607RpU+3atUvz5s3Tt99+q+3btys7O1utWrXS8OHDNWHCBDVs2FAFBQV67LHHzC4XAAAAHsxmj1bW2XSdTj1qdil1guAL1KEGDRroxhtv1I033ljm+pycHN17771atmxZ3RYGAAAAr2KLjPKa+3slgi9QZ44eParrrrtOI0aMUJ8+fdS8eXOFhYXp7Nmz2r9/v+Lj4/XOO+9o3759ZpcKAAAAD2ezR2nrT0vNLqPOEHyBOpKfn68vv/xSX375pdmlAAAAwIsFN26qoJAmXnXGl8mtAAAAAMCL2OzRkqRkgi8AAAAAwBOdn9GZ4AsAAAAA8EC2yCilnUzV2TMnzS6lzhB8AQAAAMCL2OzRXnV/r0TwBQAAAACvYbFYCh9l5EWXOUsEXwAAAADwGo1treTfIJAzvgAAAAAAz+SY0Xn/TpMrqVsEXwAAAADwEuHngu+xg7tNrqRuEXwBAAAAwEvYIqN0MvmwcjLPml1KnSL4AgAAAICX8MaJrSSCLwAAAAB4BV+rVc0i2nrd/b0SwRdeolWrVnrooYf07bffat++fcrKylJKSooWLVqkK664wuzyAAAAgFrXtFVb+Vr9vG5GZ4ngCy/x0EMP6e2331ZUVJS+++47vf7661q+fLmGDRumpUuXauLEiWaXCAAAANQqmz1KkpTihWd8rWYXANSF1atXKy4uTitXrizx+oABAxQfH68pU6boP//5j44ePWpShQAAAEDtskVGKT8/T6mH95ldSp3jjC+8wldffVUq9ErSypUrlZCQIH9/f1122WUmVAYAAADUDVtklI4f3qf8vFyzS6lzBF+4rZtvvlmGYTjatGnTKt3mgw8+cPS32WxOjZOXlydJys/Pr1a9AAAAgDsLt0d75f29EsEXbqxnz54lfh41apQsFotT2yQlJSklJaXSMdq0aaMrrrhCZ8+e1YoVK6peLAAAAODG/AICFdayjVc+ykgi+MKNXRh8w8PDK7wc2cfHRzExMZKkdevWVbp/q9WqTz75RA0aNNBLL72kEydOVK9gAAAAwE01b9NekpTMGV/AvXTv3l2S9MMPP+jkyZOSpLFjx5bbv1OnTmrYsKEkaf369RXu22KxaMaMGRo4cKC++uorTZ48uUZqBgAAANyRY0bnfd43o7NE8IWbioyMVNOmTSVJa9as0ddffy1JGjNmTLnbFD9DXFnwnTZtmm699VbNnz9fN9xwgwzDqH7RAAAAgJsKt0crJztTJ1MOm12KKQi+cEs9evRwLK9fv17z58+XJEVHR6tr165lblM8+JZ3qbPFYtH06dM1YcIEzZ8/X9dff71yc71vVjsAAAB4F1tklI4d3COjoMDsUkxB8IVbuvDs7bfffqusrCxJ5V/uXBSW09LStGtX6XsXLBaLPvroI91555366quvdN111xF6AQAA4BVs9iivndhKIvjCTRWF2KysLG3ZskUZGRn6/vvvJZUffIvC8m+//VZqXdGZ3jvvvFNffvmlfv/73zseYwQAAAB4ssBGoQoJsyllv3fe3ytJVrMLAMpSFGI3b97seL7uvHnzdPXVV+uSSy5R69atdfjw+fsT2rRp47gnuKz7e5999lmNHz9eaWlp2rp1qyZNmlSqz7Jly7R8+fJaeDcAAACAeWyRhRNbeeuMzhLBF26oSZMmstvtkkqG2AULFujDDz+Uj4+Pxo4dq/fee8+x7sJ7gi/Utm1bSVKjRo309NNPlznu888/T/AFAACAxwkvmtHZiy91JvjWoD/e/1dFdehsdhm1atfubXrvg1dqdYzyQmxycrJ++eUX9evXr1TwrWxiq/Hjx2v8+PG1Ui8AAADgzmyR0cpMP6O0Eylml2Ia7vGF26nosURFszsPHDhQoaGhpbbJzc3Vpk2bar9IAAAAoJ6wRUYp2Yvv75U441ujavtMqLcoOuNbUFBQaqKqefPmafLkyfLz89PIkSM1e/bsEtts27ZNOTk5dVkuAAAA4NZs9iht/OFbs8swFWd84XaKzt7u3btXaWlpJdZt375d27dvl3R+dufGjRs77uEt7/m9AAAAgDcKaRquwOAQpXjxxFYSwRduJiAgQJ06dZJUfoidN2+eJGnYsGEKCAio8NJoAAAAwJsVzejszRNbSQRfuJmYmBj5+flJKj/EFt3n26hRIw0dOrTEZFic8QUAAADOC28bLUlKOcA9voDbcObs7apVq3T06FG1aNFCY8eOVUBAQKXbAAAAAN7IFhmlM8eTlZl+xuxSTMUZX7iVyp7HK0mGYWjhwoWSpFGjRql3796SpP379+vUqVO1XCEAAABQf9gio7z+/l6J4As3U3TG99ixYzp8+HC5/Yru8w0PD1eXLl0kcZkzAAAAUJzFx0fN27T3+vt7JYIv3IjFYlFMTIwklXqM0YXi4+NLzfjMZc4AAADAeWEt2sgvoIHXP8NXIvjCjXTs2FHBwcGSKg+x2dnZWrJkSYnXCL4AAADAeY4ZnbnUuWqTW7Vu3VoPPfSQRo0apcjISOXl5Wnv3r366quv9M4779TIfZZNmzbVvffeqxEjRuiiiy5SSEiIMjMztXfvXi1fvlwffvihtm7dWu1x4D62b98ui8XidP/rr7++FqsBAAAA6jebPUoFBQU6dnCv2aWYzuXgO2zYMH322Wdq0qRJidd79uypnj176p577tGYMWO0du3aKhc1ZMgQff7552rWrFmJ1/38/NS9e3d1795d999/v5566in94x//qPI4AAAAAOCpbJFROpl8SLnZmWaXYjqXLnWOiYnRnDlz1KRJE2VkZOjZZ59V//79NXDgQL3++uvKy8tTRESEFi1apJYtW1apoLZt22rBggWO0Lto0SJdd911io2N1ciRI/Xuu+8qNzdXfn5+mjp1Kmf9AAAAAKAM4fZopXB/r4PhbIuPjzcMwzByc3ONuLi4Uutvvvlmo8j06dOd3m/x9s477zj2MXXq1DL7jB071tFnw4YNTu87MTGxSjXNmjWrStvRaN7WOFZoNBqNRqPR3KNZ/fyNZ+euNYbc9EfTa6nLVl7mc/qMb69evTRkyBBJ0r/+9S+tWLGiVJ/Zs2crPj5eknTbbbepefPmzu7e4bLLLpMkFRQU6IUXXiizz7x58xyPromJiXFMiAQAAAAAkJq2bitfX6uSmdhKkguXOo8bN86xPH369HL7zZgxQ5JktVo1evRolwvy9/eXJB0/frzU42qK27Xr/BdYtA0AAAAAoHBiK0lc6nyO08F3wIABkqSMjAwlJiaW2y8hIaHUNq7Yvn27pMJZnRs1alRuvw4dOkiSUlNTdeLECZfHAQAAAABPFW6PVl5uro4fOWB2KW7B6eDbpUsXSdLOnTuVn59fbr+kpCSdOXOmxDau+PDDDwsL8/HRM888U2afUaNGqVevXpKk9957z+UxAAAAAMCT2SKjlHp4rwry88wuxS04FXz9/f0d9+seOnSo0v4HDx6UJLVp08blgpYuXaoXX3xRkvTnP/9ZX331lcaNG6dLLrlEV199td566y3NmTNHkvT1119rypQpLo8BAAAAAJ7MFhmllP3c31vEqef4Fr/kOD09vdL+RX2qOunUs88+q4SEBD355JMaO3asxo4dW2L9jh079Morr+iTTz5RQUFBhfu6++67dc8990hSqecCAwAAAICnCQgMUpPw1vp1yRyzS3EbTp3xDQwMdCzn5ORU2j87O7vUdq4IDw/X+PHjy71HOCoqSrfddpv69u1b6b6mTZum2NhYxcbGKjU1tUr1AAAAAEB90TyycD4kZnQ+z6ngm5mZ6Vh2ZgblgICAUts5q3PnzkpMTNStt96qrKwsPfjgg4qMjJSfn5+aNWuma6+9Vtu2bdOQIUOUkJCg66+/3uUxAAAAAMBThUdGSxKXOhfjVPAt/lghZy5fLurjzGXRF5o1a5batGmjs2fPKi4uTu+9954OHjyovLw8HT9+XHPnztWll16qrVu3KiAgQDNnzpTNZnN5HAAAAADwRDZ7lLIzz+r0sSNml+I2nAq+OTk5OnbsmCQpIiKi0v5FfYomuXLWxRdfrNjYWEnS7NmztWXLljL7paWl6eWXX5YkBQUF6cYbb3RpHAAAAADwVLbIKKUc2CXDMMwuxW04/TijohAaHR0tX1/fcvu1bNlSoaGhJbZx1kUXXeRY/vXXXyvsW3x9586dXRoHAAAAADyVzR6tFO7vLcHp4Lty5UpJhWdYi87KlmXQoEGltnFWXt75Z0z5+flV2Lf4+uLbAQAAAIC3CgoNU3DjMO7vvYDTwXfu3LmO5QkTJpTb784775RUGEYXLFjgUjF79uxxLMfFxVXYd+DAgWVuBwAAAADeyhYZJUlKObDT5Erci9PBd+3atUpISJAk3XHHHWU+auimm27S0KFDJRVOUlV0X3ARu90uwzBkGIZjX8WtX7/ecV/wuHHjdOWVV5ZZS9u2bTVp0iRJUn5+vr7++mtn3wYAAAAAeCybvTD4Ju8j+BZndaXzI488op9++knBwcFavHixJk+erPj4eFmtVo0ZM0aPPPKIJCkpKUlPP/20y8UYhqEnn3xSs2fPltVq1ddff61p06Zp4cKFSkpKUmhoqAYNGqRHHnlEYWFhkqTp06dr506+VAAAAAAIt0cr4/QJZZw+YXYpbsdwpQ0bNsw4ceKEUZ6DBw8avXr1KnNbu93u6JeQkFDuGBMnTjSys7PLHaPIJ598Yvj5+Tlde2JiokvvtajNmjWrStvR3Kc1atTIeOONN4yVK1caR44cMbKysowjR44YK1asMO644w7DarWaXqMnNI4VGo1Go9FoNHPbXVM+Me546SPT6zCrlZf5nL7UuciSJUsUExOj1157TVu2bFF6erpOnz6t9evX67nnnlNMTIzWrl3r6m5LePPNN9WlSxe99tprSkxM1IkTJ5SXl6e0tDRt2bJFM2bM0OWXX65bb71Vubm51RoL3iEsLEx33323cnJytHDhQv3jH//Q/PnzFRERoZkzZ+rrr7+WxWIxu0wAAACgWmz2KCa2KoNLlzoXOXz4sJ544gk98cQTLm23f/9+p8PF7t27Xd4/UJ6DBw+qcePGpWYAt1qtWrJkia666iqNGjXK5QnZAAAAAHfR2NZKAYFBSt7PraAXcvmML1AfFRQUlPnYq7y8PM2bN0+SFBUVVcdVAQAAADXn/IzOnPG9EMEXbuvmm292zAJuGIamTZtW6TYffPCBo7/NZqu0v8Vi0dVXXy1J2rBhQ7VrBgAAAMxSNKNzyoHdJlfifqp0qTNQF3r27Fni51GjRsliscgwjEq3SUpKUkpKSqn1gYGB+stf/iKLxaKmTZvqyiuvVKdOnTRz5kwtXbq0Zt8AAAAAUIdskVE6dSxJ2WfTzS7F7RB84bYuDL7h4eG67LLL9OOPP5bZ38fHRzExMZKkdevWldmnYcOGev755x0/FxQUaOrUqXrqqadqpmgAAADAJOH2aKVwf2+ZuNQZbqt79+6SpB9++EEnT56UJI0dO7bc/p06dVLDhg0lSevXry+zz/Hjx2WxWOTj46M2bdrooYce0l133aXly5crNDS0RusHAAAA6oqPr1XNItoxo3M5CL5wS5GRkWratKkkac2aNfr6668lSWPGjCl3m+JniMsLvkUMw9ChQ4f0/vvv695779Vll12mZ555pvqFAwAAACYIa9lGVj9/JTOxVZkIvnBLPXr0cCyvX79e8+fPlyRFR0era9euZW5TPPiWd6lzWRYvXixJGjRokOuFAgAAAG7AMaMzZ3zLRPCFW7rw7O23336rrKwsSeVf7lwUltPS0rRrl/MHfOvWrSVJubm5VSsWAAAAMFl422gV5Ocr9dAes0txSwRfuKWiEJuVlaUtW7YoIyND33//vaTyg29RWP7tt99KrevWrZv8/f1Lvd6wYUO98cYbkqRvvvmmBioHAAAA6p4tMkrHkw4oLzfH7FLcErM6wy0VhdjNmzcrPz9fkjRv3jxdffXVuuSSS9S6dWsdPnzY0b9NmzaOe4LLur93woQJuuOOO7Ry5Urt379faWlpioiI0NVXX62wsDD9+OOPmjp1au2/MQAAAKAW2CKjlMyMzuXijC/cTpMmTWS32yWVDLELFixQQUGBpNJnfS+8J/hCX3zxhb788ku1b99eN998sx5//HENGzZM69ev17333quBAwcqMzOzpt8KAAAAUOus/gEKaxnJ/b0V4IxvDZo6fLC6t7CZXUat+u1oih5fnFCrY5QXYpOTk/XLL7+oX79+Gjt2rN577z3Husomtvrpp5/0008/1Uq9AAAAgJmat2kvHx8fzvhWgDO+cDsVPZaoaHbngQMHlnjubtE2ubm52rRpU+0XCQAAALgJW2S0JCmFRxmVizO+Nai2z4R6i6IzvgUFBaUmqpo3b54mT54sPz8/jRw5UrNnzy6xzbZt25STww39AAAA8B7h9ijl5mTrZNJBs0txW5zxhdspOnu7d+9epaWllVi3fft2bd++XdL5+3wbN26stm3bSnLt+b0AAACAJ7BFRin10B4VFOSbXYrbIvjCrQQEBKhTp06Syg+x8+bNkyQNGzZMAQEBFV4aDQAAAHg6mz1ayUxsVSGCL9xKTEyM/Pz8JJUfYovu823UqJGGDh1aYjIszvgCAADAmzQIaqTQZuHM6FwJ7vGFW3Hm7O2qVat09OhRtWjRQmPHjlVAQECl2wAAAACeyBYZJUlKOcCMzhXhjC/cSmXP45UkwzC0cOFCSdKoUaPUu3dvSdL+/ft16tSpWq4QAAAAcB82+7ngyxnfChF84VaKzvgeO3ZMhw8fLrdf0X2+4eHh6tKliyQucwYAAID3CbdHKysjTadTj5pdilsj+MJtWCwWxcTESFKpxxhdKD4+vtSMz1zmDAAAAG9ji4zi+b1OIPjCbXTs2FHBwcGSKg+x2dnZWrJkSYnXCL4AAADwNrbIKC5zdgKTW8FtbN++XRaLxen+119/fS1WAwAAALi34CbN1DCkMWd8ncAZXwAAAACoh8Lt0ZKk5P3M6FwZgi8AAAAA1EPnH2W02+RK3B/BFwAAAADqIZs9SmknU3X2zEmzS3F7BF8AAAAAqIfC7dFK4TJnpxB8AQAAAKCesVgsah7ZgYmtnETwBQAAAIB6pnF4a/kHBPIoIycRfAEAAACgnima2CqZM75OIfgCAAAAQD1T9CijYwRfpxB8AQAAAKCesdmjdOLoIeVkZZpdSr1A8AUAAACAesYWGc3EVi4g+AIAAABAPeJrtapZazsTW7mA4AsAAAAA9UjT1u3ka/VTMs/wdRrBFwAAAADqkaIZnbnU2XkEXwAAAACoR8LtUcrPy9Xxw3vNLqXeIPgCAAAAQD1ii4zS8SP7lZ+XZ3Yp9QbBFwAAAADqEZs9mvt7XUTwBQAAAIB6wr9BoMJaRDCjs4sIvgAAAABQTzRv00ESE1u5iuALj7Zq1SoZhqG8vDyFh4eX2WfTpk3Kz89XSEhIHVcHAAAAuMYxozNnfF1C8IXH8vX11cUXX+xYHjduXKk+DRs2VOfOnbVr1y6dOXOmrksEAAAAXBLeNlo52Zk6mXzI7FLqFYIvPFbXrl0VGBio+Ph4ZWRk6LrrrivVp0ePHvL19dWvv/5qQoUAAACAa2yRUTp2YLcMwzC7lHqF4AuP1bt3b0nSDz/8oMWLF+vyyy9X8+bNy+xD8AUAAEB9YIuM4v7eKiD4wi316tVLkyZN0rfffqsDBw4oKytL6enp2rVrl2bPnq1hw4ZVuo+iULt27VrNmTNHVqtV11xzTZl9CL4AAABwdw0bNVajsObc31sFVrMLAC60bNkyDRw4sNTrAQEB6tChgzp06KCbbrpJixYt0s0331zuvbnFQ21aWpqysrJ03XXX6Z///GeJPgUFBVq7dm3tvBkAAACghtjshRNb8Qxf13HGF26ndevWkqSjR4/q3Xff1e9//3v17dtXffr00QMPPKAdO3ZIkkaOHKkFCxbIYrGU2oePj48uvvhiJSUlKSkpSenp6frf//6nQYMGKSwsTJIUGBiozp07a/fu3UxsBQAAALfnmNGZS51dRvCF29m2bZtuvPFGRURE6KGHHtIXX3yh1atXKzExUR988IF69uypH3/8UZI0cOBA3XTTTaX20aVLFzVs2LDEmdwvv/xSfn5+jsude/ToIavVymXOAAAAqBds9iidTTuttBPHzC6l3iH4wu2MGjVKn3/+ufLz88tcf/bsWd13332On6+//vpSfcq6d3f+/PnKyclxzO5c1GfNmjU1VjsAAABQW5jYquoIvqiXNm3apNTUVElSVFRUqfVlBd/Tp0/r+++/15AhQ9S4cWMmtgIAAEC9YouMUgr391YJwRf1lp+fnySVeWa4+IzOxc2ZM0f+/v4aO3YsE1sBAACg3ghpFq7A4BBmdK4igi/qpR49eig0NFSStHXr1hLrfHx81L17d6WkpOjQoUMl1s2bN095eXm67bbbdNFFFzGxFQAAAOoFW2S0JCmZS52rhOCLeunpp592LH/++ecl1l100UUKCgoq8xLm48ePa/ny5Ro8eDATWwEAAKDeCD/3KCMuda4agi/qnRtuuEHXXnutJCkxMVFfffVVifXlXeZc5Msvv3QsE3wBAABQH9js0TqdmqysjDSzS6mXrGYX4EmGT/iLWrTrZHYZtero3u1aPP0108aPiYnRRx99JEnKyMjQrbfeWqrPrFmzNGvWrHL38cEHH+iDDz6otRoBAACAmsaMztXDGV/UG3a7Xd98842Cg4OVn5+v22+/Xdu3bze7LAAAAKBWWXx81LxNeya2qgbO+NYgM8+EeroWLVrou+++U0REhCTp3nvvLXHJMgAAAOCpwlpGys8/QCkHuL+3qjjjC7fXtGlTfffdd4qOLpzJbuLEiZo+fbrJVQEAAAB1wxZZOLFVMhNbVRnBF24tJCRE//vf/9StWzdJhbM5v/XWWyZXBQAAANSd8MgoFRQUKPXgXrNLqbcIvnBbQUFBWrx4sXr16iVJmjx5sl5++WWTqwIAAADqls0epZNHDyo3J8vsUuotgi/cUoMGDbRw4UL169dPkvT222/rqaeeMrkqAAAAoO7Z7NHM6FxNBF+4HT8/P3355ZcaPHiwJOmjjz7SI488YnJVAAAAQN2z+vmractI7u+tJmZ1htv597//rauvvlqS9NNPP+ntt99W165dK9xm8+bNdVEaAAAAUKeaRbSTj68vjzKqJoIv3M51113nWL7sssu0YcOGSrexWCy1WRIAAABgiqIZnbnUuXq41BkAAAAA3FS4PVp5ubk6fuSA2aXUa5zxhdvh7C0AAABQyGaPUuqhPSrIzzO7lHqNM74AAAAA4KZskVFc5lwDCL4AAAAA4IYCAoPU2NaKia1qAMEXAAAAANyQzV44sRWPMqo+gi8AAAAAuCFmdK45BF8AAAAAcEM2e5SyMzN0+liS2aXUe1UKvq1bt9bkyZO1efNmpaWl6eTJk1q7dq2eeeYZNW7cuEYLjIqK0iuvvKJ169YpNTVVmZmZ2r9/v5YvX64XXnhBXbt2rdHxAAAAAMAd2CKjlXJgtwzDMLuUes/lxxkNGzZMn332mZo0aVLi9Z49e6pnz5665557NGbMGK1du7baxT3zzDOaNGmSAgICSrweGRmpyMhIXX755QoJCdGjjz5a7bEAAAAAwJ2E26O07ZcEs8vwCC4F35iYGM2ZM0fBwcHKyMjQlClTFB8fL6vVqjFjxujhhx9WRESEFi1apN69eyspqeqn5N977z098MADkqT169dr5syZWrdunc6cOaNmzZqpZ8+euuaaa1RQUFDlMQAAAADAHQWFhikoNIwZnWuQ4WyLj483DMMwcnNzjbi4uFLrb775ZqPI9OnTnd7vhe22225z7GfKlCmGxWIpt6+fn5/T+01MTKxSPbNmzarye6HRvKlxrNBoNBqNRqPVTGt3cV/jhfkbjHYX9zW9lvrUyst8Tt/j26tXLw0ZMkSS9K9//UsrVqwo1Wf27NmKj4+XJN12221q3ry5s7t3CAoK0uuvvy5J+vbbb/XEE09UeE17bm6uy2MAAAAAgDsLL5rRmUcZ1Qing++4ceMcy9OnTy+334wZMyRJVqtVo0ePdrmgm266SU2bNpUkvfjiiy5vDwAAAAD1na1ttNJPnVDG6RNml+IRnA6+AwYMkCRlZGQoMTGx3H4JCQmltnHFDTfcIElKTU3Vzz//7Hi9adOm6tChg0JDQ13eZ02wWCymjAvUFxaLhRkHAQAAaogtMorn99Ygp4Nvly5dJEk7d+5Ufn5+uf2SkpJ05syZEts4y2KxKDY2VpK0YcMGSdIDDzygHTt2KDU1Vbt27dKpU6e0efNmPfLII/Lz83Np/1WVkZGhkJCQOhkLqK/CwsKUnp5udhkAAAD1nsVikS2yg1IOcJlzTXEq+Pr7+zvu1z106FCl/Q8ePChJatOmjUvFtGnTxhEwT5w4oTlz5ui9995TdHR0iX5dunTRm2++qaVLl9ZJIN26dau6d+9e6+MA9Vm/fv20bt06s8sAAACo90Kbt1RAYBAzOtcgp4Jvo0aNHMvOnNEp6hMcHOxSMWFhYY7l3/3ud7r22mu1Z88eXXvttQoJCVHDhg01ZMgQrV69WpJ0+eWX66OPPqpwn3fffbcSExOVmJioZs2auVRPkZ9++knDhw9XUFBQlbYHPJ3dbtfw4cMdk9sBAACg6mz2whN/yUxsVWOceo5vYGCgYzknJ6fS/tnZ2aW2c0bxYBkYGKiUlBT1799fR48edbyekJCgQYMGadWqVbr44ot1/fXX65JLLtGaNWvK3Oe0adM0bdo0Sarw3uSKrFmzRp06ddKkSZO0ZMkSrV+/XmfOnOF+Rngti8WiwMBAtWvXTjExMYqLi9PHH3+svXv3ml0aAABAvVc0o/OxA7tNrsRzOBV8MzMzHcv+/v6V9g8ICCi1nTOysrJK/Pzaa6+VCL3F65k0aZIWLlwoSbrxxhvLDb41Zfbs2brkkkt02WWX6cYbb+TsL7xeVlaWDh06pI0bN+qll15SUlKS2SUBAAB4BJs9SqdSjig7M8PsUjyGU8E3LS3NsezM5ctFfVyd6Kb4OFLhc3zLs3TpUuXm5srPz88xIVZtW7NmTa0HbAAAAADezWaPZkbnGubUPb45OTk6duyYJCkiIqLS/kV9iia5ctahQ4dUUFDg+Lmi7bOyspSamipJjom3AAAAAKA+8/G1qlnrdtzfW8OcfpzRli1bJEnR0dHy9fUtt1/Lli0dz9ot2sZZZ8+e1b59+xw/VzRO8fUVPV4JAAAAAOqLpq0iZfXzY0bnGuZ08F25cqWkwgmoKrq0eNCgQaW2ccUPP/zgWO7QoUO5/UJCQhyzNB8+fNjlcQAAAADA3djOTWzFpc41y+ngO3fuXMfyhAkTyu135513SpLy8vK0YMEClwv64osvHMvXXnttuf2uueYa+fgUll88LAMAAABAfRVuj1Z+fp5SD/G0jJrkdPBdu3atEhISJEl33HGHBgwYUKrPTTfdpKFDh0qSZs2a5bgvuIjdbpdhGDIMw7GvC3377bdav369JOmRRx5Rjx49SvVp1aqVXn75ZUmF9/rOnDnT2bcBAAAAAG7LZo/SiSMHlJdb+WNk4Tyng69UGETT09NltVq1ePFiPf300+rXr5/i4uI0depUffzxx5KkpKQkPf3001UqyDAM3X///crKylLDhg21fPlyPfPMM+rXr59iY2P14IMPavXq1WrdurUkadKkSTxGBQAAAIBHsEVGcZlzLTFcacOGDTNOnDhhlOfgwYNGr169ytzWbrc7+iUkJFQ4zqhRo4yTJ0+WO05+fr7x7LPPulR7YmKiS/1pNBqNRqPRaDQara6an38D47mv1huDbrjP9Frqaysv8zn1HN/ilixZopiYGD388MMaOXKkIiMjlZ+fr7179+qrr77S22+/rVOnTrm621IWLlyorl276qGHHnKMY7VadeTIESUkJOidd97Rxo0bqz0OAAAAALiD5pHt5ePjo2TO+NY4iwoTsFdITEyscEZqAAAAADBLjyGjdc0jL+nt+0fp+JH9ZpdTL5WX+Vy6xxcAAAAAUDtskdHKzcnWiaMHzS7F4xB8AQAAAMAN2OxROnZwj4yCArNL8TgEXwAAAABwA+F2ZnSuLQRfAAAAADBZYHCIQpqGK2X/TrNL8UgEXwAAAAAwWfPIKElS8n7O+NYGgi8AAAAAmCz8XPBNOcAZ39pA8AUAAAAAk9ns0cpMP6Mzqclml+KRCL4AAAAAYDIbE1vVKoIvAAAAAJjMFknwrU0EXwAAAAAwUaOw5mrYKFQpTGxVawi+AAAAAGAimz1akjjjW4sIvgAAAABgIseMzpzxrTUEXwAAAAAwkc0epbQTx3Q27ZTZpXgsgi8AAAAAmIiJrWofwRcAAAAATGLx8VHzyA4E31pG8AUAAAAAkzSxtZZ/QKCS9+00uxSPRvAFAAAAAJPY7OcmtuKMb60i+AIAAACAScLPPcro2MHdJlfi2Qi+AAAAAGASmz1KJ5IOKicr0+xSPBrBFwAAAABMwozOdYPgCwAAAAAm8LX6qWkrO8G3DhB8AQAAAMAEzVq3la/VT8n7Cb61jeALAAAAACawtS2c2CplP48yqm0EXwAAAAAwQcfecTp75pRSD+8zuxSPR/AFAAAAgDrm599AnfoM1pafl6ogP8/scjwewRcAAAAA6ljH2MsVENhQG1d8a3YpXoHgCwAAAAB1rFvccJ05kaL9m381uxSvQPAFAAAAgDoU0DBY0b3jtHnl/2QUFJhdjlcg+AIAAABAHercd4j8/AO0acVis0vxGgRfAAAAAKhDMZeP0Mnkwzq0Y4PZpXgNgi8AAAAA1JGGIU3UvntfbWJSqzpF8AUAAACAOtL1sivl62vVxh8IvnWJ4AsAAAAAdaRb3HClHNyt5P07zS7FqxB8AQAAAKAOhDQNV2SXXtrE2d46R/AFAAAAgDrQtf9V8vHx0UZmc65zBF8AAAAAqAMxl4/Q4V2bdSLpgNmleB2CLwAAAADUsrAWbdQ6uhvP7jUJwRcAAAAAalm3uOGSpE0rCb5mIPgCAAAAQC3rFjdC+zb/qjOpyWaX4pUIvgAAAABQi2z2aIXbo7jM2UQEXwAAAACoRTFxw5Wfn6ctP31ndilei+ALAAAAALWo24Dh2rvhF2WcPmF2KV6L4AsAAAAAtaR1dDeFtWyjjT98a3YpXo3gCwAAAAC1JObyEcrLzdG2XxLMLsWrEXwBAAAAoBZYfHzUdcAw7fx1hbIy0swux6sRfAEAAACgFti79FJImE0bmc3ZdARfAAAAAKgF3eKGKzvzrHYk/mB2KV6P4AsAAAAANczH16oul12p7auXKTc70+xyvB7BFwAAAABqWIcelyoopIk2rmA2Z3dA8AUAAACAGtYtboQy089o97qfzC4FIvgCAAAAQI2y+gfookuHaMvPS5Wfl2t2ORDBFwAAAABqVHTvOAUEBmkTlzm7DYIvAAAAANSgmMtHKO1kqvZuTDS7FJxD8AUAAACAGhIQGKSOveO05cf/ySgoMLscnEPwBQAAAIAa0qnvYPkFNGA2ZzdD8AUAAACAGhITN1ynUo7o0PYNZpeCYgi+AAAAAFADAhuFqkOPftq0YrEMwzC7HBRD8AUAAACAGtCl31D5Wv20ccVis0vBBQi+AAAAAFADYi4foWOH9uro3m1ml4ILEHwBAAAAoJoahTWXvesl2sTZXrdE8AUAAACAauraf5h8fHy0idmc3RLBFwAAAACqqVvccCXt2arUw/vMLgVlIPgCAAAAQDU0CW+tNp0u1sYfuMzZXRF8AQAAAKAaug0YLknatJLg664IvgAAAABQDd0uH6EDW9fp9LEks0tBOQi+AAAAAFBFzdt0UIu2HbXxBya1cmcEXwAAAACoopi44SrIz9fmH/9ndimoAMEXAAAAAKqoW9xw7d24WhmnT5hdCipA8AUAAACAKmgV1UVNW9m1aQWTWrk7gi8AAAAAVEG3ASOUl5urLT8vNbsUVILgCwAAAAAuslgs6hY3TLvW/aisjDSzy0ElCL4AAAAA4KI2F/VUaLMWzOZcTxB8AQAAAMBFMXEjlJOdqR2Jy8wuBU6oUvBt3bq1Jk+erM2bNystLU0nT57U2rVr9cwzz6hx48Y1XGIhi8Win376SYZhOBoAAAAA1DUfH1917X+ltq9eppysTLPLgROsrm4wbNgwffbZZ2rSpEmJ13v27KmePXvqnnvu0ZgxY7R27doaK1KS/vjHP6pfv341uk8AAAAAcFW77n0VFBrGbM71iEtnfGNiYjRnzhw1adJEGRkZevbZZ9W/f38NHDhQr7/+uvLy8hQREaFFixapZcuWNVZkRESEXn75ZRUUFCglJaXG9gsAAAAAroqJG67M9DPa+etKs0uBk1w64/vmm28qODhYeXl5GjFihFasWOFY98MPP2jt2rX69NNP1bJlS7300kuaMGFCjRT5/vvvKyQkRNOmTVN0dLRsNluN7BcAAAAAXGH189dFl16hravilZ+Xa3Y5cJLTZ3x79eqlIUOGSJL+9a9/lQi9RWbPnq34+HhJ0m233abmzZtXu8Df//73GjVqlFJSUvTEE09Ue38AAAAAUFVRvfqrQVAjZnOuZ5wOvuPGjXMsT58+vdx+M2bMkCRZrVaNHj26GqVJjRs31ltvvSVJeuyxx3Ty5Mlq7Q8AAAAAqiPm8hFKP3VCezesNrsUuMDp4DtgwABJUkZGhhITE8vtl5CQUGqbqpo6dapatGih77//Xp9++mm19gUAAAAA1eHfIFAdYwdqy0//U0FBvtnlwAVOB98uXbpIknbu3Kn8/PK/5KSkJJ05c6bENlUxaNAgTZgwQVlZWbrvvvuqvB8AAAAAqAmd+gyWf0CgNjKbc73jVPD19/d33K976NChSvsfPHhQktSmTZsqFRUQEKB//vOfkqRXX31VO3furNJ+AAAAAKCmxMSN0OnUozq4dZ3ZpcBFTgXfRo0aOZbT09Mr7V/UJzg4uEpFPffcc4qOjtb27ds1efLkKu2jyN13363ExEQlJiaqWbNm1doXAAAAAO8UGByiDj0v06YVS2QYhtnlwEVOBd/AwEDHck5OTqX9s7OzS23nrJiYGD3++OOSpPvvv9+p8Soybdo0xcbGKjY2VqmpqdXaFwAAAADvdFG/obL6+Wnjim/MLgVV4FTwzczMdCz7+/tX2j8gIKDUds6wWCz66KOP5Ofnp1mzZpWYKAsAAAAAzNItbriOH9mvpN1bzS4FVeBU8E1LS3MsO3P5clEfZy6LLu6RRx5Rnz59dPz4cT322GMubQsAAAAAtSG4cVO16xarjSt4dm99ZXWmU05Ojo4dO6bmzZsrIiKi0v5FfYomuXLWk08+KanwkUhXXHFFmX1sNptj+YYbbnDU99VXX7k0FgAAAAA4o2v/q+Tj66tNzOZcbzkVfCVpy5YtGjhwoKKjo+Xr61vuI41atmyp0NBQxzauKLpE+rrrrtN1111Xaf///Oc/kqRTp04RfAEAAADUim6Xj9DRvdt17OAes0tBFTn9HN+VK1dKkoKCghQbG1tuv0GDBpXaBgAAAADqo8a2Vors3IPLnOs5p4Pv3LlzHcsTJkwot9+dd94pScrLy9OCBQtcKqZJkyayWCwVtmXLljn6F73WpEkTl8YBAAAAAGd0GzBMkrRpxRKTK0F1OB18165d65hl+Y477tCAAQNK9bnppps0dOhQSdKsWbN07NixEuvtdrsMw5BhGMzYDAAAAMDtdYsbroPbftOplMNml4JqcDr4SoWzLqenp8tqtWrx4sV6+umn1a9fP8XFxWnq1Kn6+OOPJUlJSUl6+umna6VgAAAAAKgLzVq3Vcv2FzGplQdwenIrSdq4caOuu+46ffbZZ2rSpIlefPFFvfjiiyX6HDp0SGPGjFFSUlKNFgoAAAAAdalb3AgVFBRo049c5lzfuXTGV5KWLFmimJgYvfbaa9qyZYvS09N1+vRprV+/Xs8995xiYmK0du3a2qgVAAAAAOpMzOUjtG/TGqWfTDW7FFSTS2d8ixw+fFhPPPGEnnjiCZe2279/vywWS1WGdBg8eHC1tgcAAACAyrRo11nNWrfVT199bHYpqAEun/EFAAAAAE8Xc/kI5eflasuqpWaXghpA8AUAAACAYiwWi7oNGKZd635SZtpps8tBDSD4AgAAAEAxEZ26q7GtFbM5exCCLwAAAAAUExM3XLnZWdq2OsHsUlBDCL4AAAAAcI6Pj6+6DhimHWt+UE7mWbPLQQ0h+AIAAADAOW1jLlFw46ba+MO3ZpeCGkTwBQAAAIBzYuJGKOtsunauXWl2KahBBF8AAAAAkORr9dNF/YZq26rvlZeTbXY5qEEEXwAAAACQFNWrvwKDQ7SR2Zw9DsEXAAAAACR1GzBcGWdOas9vq8wuBTWM4AsAAADA6/kFBKpz38Ha8tN3KsjPM7sc1DCCLwAAAACv1zH2cvk3CGQ2Zw9F8AUAAADg9WLiRujM8WQd2LrO7FJQCwi+AAAAALxag6BGiu49QJtWLpFRUGB2OagFBF8AAAAAXq3zpUNk9fPXJmZz9lgEXwAAAABeLSZuuE4kHdThnZvMLgW1hOALAAAAwGsFhYap3cV9tWklZ3s9GcEXAAAAgNfq2v9K+fpamc3ZwxF8AQAAAHitbgNGKHn/LqUc2GV2KahFBF8AAAAAXim0WQvZu/bSphWc7fV0BF8AAAAAXqnrgGGSpI0EX49H8AUAAADglWLiRujQjo06efSQ2aWglhF8AQAAAHidpq3sahXVhcucvQTBFwAAAIDX6RY3XAUFBdq08n9ml4I6QPAFAAAA4HVi4kZo/+ZflXYixexSUAcIvgAAAAC8Snjbjmrepr02rVxsdimoIwRfAAAAAF4lJm648vPztOXH78wuBXWE4AsAAADAq3SLG6E961fpbNops0tBHSH4AgAAAPAaER0vVpPw1jy718sQfAEAAAB4jZjLRyg3J1vbVn1vdimoQwRfAAAAAF7B4uOjrv2v0s41K5SdmWF2OahDBF8AAAAAXqFt10vUKKw5lzl7IYIvAAAAAK/QLW64sjMztHPNCrNLQR0j+AIAAADweL5Wq7pcdqW2/ZKg3Jwss8tBHSP4AgAAAPB47bv3U8NGodr4A5c5eyOCLwAAAACPF3P5CJ1NO609v/1sdikwAcEXAAAAgEfz82+gzn0Ha8tP3yk/L8/scmACgi8AAAAAjxZ9SZwCAoO0acVis0uBSQi+AAAAADxaTNwIpZ04pn2b15hdCkxC8AUAAADgsQIaBiv6kjht/vF/MgoKzC4HJiH4AgAAAPBYnfsOlp9/ALM5ezmCLwAAAACPFRM3QieTD+vQjg1mlwITEXwBAAAAeKSGjRqrfY9LtWkFZ3u9HcEXAAAAgEfq0v9K+fpatZHZnL0ewRcAAACAR+o2YLhSDu5W8r4dZpcCkxF8AQAAAHicRmE22bv21iYmtYIIvgAAAAA8ULcBV8nHx4fLnCGJ4AsAAADAA3WLG6HDuzbrRNIBs0uBGyD4AgAAAPAoTVpEKKJjjDZxthfnEHwBAAAAeJSYuBGSpE0rCb4oRPAFAAAA4FG6xY3Q/s1rdSY12exS4CYIvgAAAAA8RlTP/gq3R2njCmZzxnkEXwAAAAAeIbBRqMY8/IJSDuzSuqVfmV0O3AjBFwAAAIBHGHnfJAWFNNGXr/9Vebk5ZpcDN0LwBQAAAFDvxVx+tboNGK5l//lQR/duM7scuBmCLwAAAIB6LaRpuH537191cNtvWvnlDLPLgRsi+AIAAACotywWi8Y+/Df5Wv00942/qqAg3+yS4IYIvgAAAADqrT5X36gOPfppyYy/68TRg2aXAzdF8AUAAABQLzVr3VZX3v6odqz5QWuWzDG7HLgxgi8AAACAesfH16pxj76inOxMzX/3ebPLgZsj+AIAAACody6//m61ju6mRR+8pPSTqWaXAzdH8AUAAABQr7SO7qbLf3+3fktYqC0/fWd2OagHCL4AAAAA6g0//wYa9+grSj+Zqm+mTTa7HNQTBF8AAAAA9caVt09Us9Zt9dVbzygrI83sclBPEHwBAAAA1Avtu1+qviNv0s8LPtXeDb+YXQ7qEYIvAAAAALfXIKiRxj78olIO7tbST94yuxzUMwRfAAAAAG7vd/dOUnDjMM1946/Ky8k2uxzUMwRfAAAAAG6t64Bhunjg1Vr++f8pafdWs8tBPUTwBQAAAOC2GoXZNPK+p3Vw+watmDPd7HJQTxF8AQAAALitMQ+9IKu/v756c5IKCvLNLgf1FMEXAAAAgFuKHXGDonv11/9mvq7jR/abXQ7qMYIvAAAAALfTtJVdV43/k3au/VGJ335udjmo56oUfFu3bq3Jkydr8+bNSktL08mTJ7V27Vo988wzaty4cbUKslqtuuqqq/T3v/9dK1asUEpKinJycnTq1Cn99ttvevvtt9W1a9dqjQEAAADAffn4+Grco68oLydH89951uxy4CEMV9qwYcOMEydOGOU5ePCg0atXL5f2WdSaNWtmHDt2rNx9F8nLyzNeeukll/efmJhYpbpoNBqNRqPRaDRa3bWBv7/HeGH+BqPrgGGm10KrX628zGeVC2JiYjRnzhwFBwcrIyNDU6ZMUXx8vKxWq8aMGaOHH35YERERWrRokXr37q2kpCRXdq+AgAA1a9ZMkrRx40bNnz9fP//8s44ePaqgoCANGTJEEydOVOPGjTVp0iQVFBTo2Wf5P0AAAACAp2jZ4SINvOFebVj+jTavXGJ2OfAgTqfn+Ph4wzAMIzc314iLiyu1/uabb3aclZ0+fbrL6bxVq1bG//73P6Nfv37l9omKijJSUlIMwzCMnJwco23bttVO/zQajUaj0Wg0Gs38ZvUPMB58d57xp+nfGQ2CGpleD63+tQoyn3M76NWrlyPUTps2rdx+S5cudYTj5s2b18qbefDBBx21TJw4sSY+BBqNRqPRaDQajWZyGz7hL8YL8zcY7btfanottPrZyst8Tk9uNW7cOMfy9OnlPzh6xowZkgonqRo9erSzu3dJQkKCYzkqKqpWxgAAAABQd9pd3Ff9Rt+iXxb9W3t+W2V2OfAwTgffAQMGSJIyMjKUmJhYbr/iobRom5rm7+/vWM7P5yHWAAAAQH3WIKiRrnnkRR07tFffffym2eXAAzkdfLt06SJJ2rlzZ4VhMykpSWfOnCmxTU0bOHCgY3nr1q21MgYAAACAunH13U8quEkzzX3jr8rNyTK7HHggp4Kvv7+/mjdvLkk6dOhQpf0PHjwoSWrTpk01Sitbw4YNNXHiRElSVlaW5s+fX+NjAAAAAKgbXS67Ut0Hj9IP/52mI7s2m10OPJRTjzNq1KiRYzk9Pb3S/kV9goODq1hW+aZOnSq73S5Jevfddyt9ZNLdd9+te+65R5Icj0oCAAAAYL7gJs006v5ndHjnJv3wxTSzy4EHc+qMb2BgoGM5Jyen0v7Z2dmltqsJ48eP1/333y9J2rRpk5555plKt5k2bZpiY2MVGxur1NTUGq0HAAAAQNWNefAF+QU00Nw3/qqC/Dyzy4EHcyr4ZmZmOpaLTyxVnoCAgFLbVdfw4cP14YcfSpKOHTumcePGKSuL6/8BAACA+uiS4der4yVx+u7jN5R6eJ/Z5cDDORV809LSHMvOXL5c1MeZy6KdERcXpy+//FL+/v46deqUhg0bpp07d9bIvgEAAADUrbCWkRo2/nHtXv+zVn/zH7PLgRdwKvjm5OTo2LFjkqSIiIhK+xf1KZrkqjpiY2O1aNEiNWzYUOnp6frd736ndevWVXu/AAAAAOqej4+vxk18Wfl5uZr39rMyDMPskuAFnH6c0ZYtWyRJ0dHR8vX1Lbdfy5YtFRoaWmKbqrr44ou1ePFihYSEKCsrS2PHjtVPP/1UrX0CAAAAMM+Aa+9Um87dtejDl3XmeLLZ5cBLOB18V65cKUkKCgpSbGxsuf0GDRpUapuq6Ny5s7777juFhYUpJydH119/veLj46u8PwAAAADmatn+Ig268T5tXPGtNq341uxy4EWcDr5z5851LE+YMKHcfnfeeackKS8vTwsWLKhSUe3bt9fSpUtls9mUl5enW265RYsWLarSvgAAAACYz+rnr3GPvqyM0yf09Ycvm10OvIzTwXft2rVKSEiQJN1xxx0aMGBAqT433XSThg4dKkmaNWuW477gIna7XYZhyDAMx74uFBERofj4eLVu3VoFBQWaMGGCvvjiC6ffEAAAAAD3c8WtD8sWGaV57zynzPQzZpcDL2N1pfMjjzyin376ScHBwVq8eLEmT56s+Ph4Wa1WjRkzRo888ogkKSkpSU8//bTLxYSFhWnp0qVq27atJOm9997Tr7/+qq5du5a7TUZGhvbt2+fyWAAAAADqRtuYWF025jat/uY/2r2OOXtgDsOVNmzYMOPEiRNGeQ4ePGj06tWrzG3tdrujX0JCQqn1AwcOLHe/5SlrP+W1xMREl94rjUaj0Wg0Go1Gq14LaBhsPPrREuOh9xcYfgGBptdD8+xWXuZz+lLnIkuWLFFMTIxee+01bdmyRenp6Tp9+rTWr1+v5557TjExMVq7dq2ruwUAAADgga6++0k1CmuuuW9OUm52ptnlwEtZVJiAvUJiYmKFM1IDAAAAqDkXXXqFbnzqDS37/EMl/Pt9s8uBFygv87l8xhcAAAAAKhPcuKlG/fFZHd61Wcs//6fZ5cDLEXwBAAAA1LjRDz4v/4BAzX3jryrIzzO7HHg5gi8AAACAGtX7ymvVKXagls56S6mH9ppdDkDwBQAAAFBzmrSI0LAJf9bu31bpl6//bXY5gCSCLwAAAIAaYvHx0TWPvKSC/DzNe/sZGYbXzKMLN0fwBQAAAFAj+l9zh+xdeumbf76qM6nJZpcDOBB8AQAAAFRbi3adNPgPf9TmH/+nDcu/NrscoASCLwAAAIBqsfr5a9zEV5SZdkoLP3jR7HKAUgi+AAAAAKplyM0PKrxttOa985wy006bXQ5QCsEXAAAAQJXZu/ZWvzG3KXHxf7Vr7UqzywHKRPAFAAAAUCUBgUG65pGXdDL5kP438x9mlwOUy2p2AQAAAADqp+F3/UWhzVpo+lO3Kycr0+xygHJxxhcAAACAyzr1GaReQ6/RyrkzdGj7BrPLASpE8AUAAADgkqDQMI3+4/M6snurlv3nA7PLASpF8AUAAADgktF/fE4BDYM0942/Kj8vz+xygEoRfAEAAAA4recVY9W572DFf/K2jh3cbXY5gFMIvgAAAACc0tjWWiPufkJ7N67WqoWfml0O4DSCLwAAAIBKWXx8dM3El2QUFOirt56RYRhmlwQ4jeALAAAAoFIDxt2ptl1765tpU3T6WJLZ5QAu4Tm+AAAAAMrlFxCoEXc/od5XjtPmH/+n3xIWmF0S4DKCLwAAAIAytWjXWdc9PkVNW9n1wxcfKeGz980uCagSgi8AAACAUi4ddYuuvH2izp45qVnP3qO9G1ebXRJQZQRfAAAAAA5BoWEa+/CL6nhJnLb9kqD57zyns2mnzC4LqBaCLwAAAABJUoce/XTNxJfVIKiRFn34shK//dzskoAaQfAFAAAAvJyv1aorbnlE/a+5Xcn7d2nWc/cqZf9Os8sCagzBFwAAAPBiTVvZdd1jU9QqqotWf/O5lsycqrycbLPLAmoUwRcAAADwUj2GjNbV9/xV+bk5+uyVR7TtlwSzSwJqBcEXAAAA8DIBDYM16v5nFHP5CO3duFpz35ikM8eTzS4LqDUEXwAAAMCLRHS6WNc9NkUhzcK19JO3tXLuDBkFBWaXBdQqgi8AAADgBSw+Poq7doIG/eF+nT52VDOevEOHdmwwuyygThB8AQAAAA8X0ixc4ya+onYxsdqw/Bst+vAlZZ9NN7ssoM4QfAEAAAAP1vnSIRrz4Avytfpp7ptP67eEBWaXBNQ5gi8AAADggfz8G2jYhMcVO/z3Orxrs+ZMfUInkg6YXRZgCoIvAAAA4GHC7dG67vEpskVGaeXcmfp+9jvKz8szuyzANARfAAAAwIP0/d0fdOUdf1JW+hl9/Ow92vPbKrNLAkxH8AUAAAA8QMOQJhr70N/Uqc9A7Vjzg+a9/awyTp8wuyzALRB8AQAAgHqu3cV9Ne7Rl9WwUWN9O22KVi2abXZJgFsh+AIAAAD1lI+vVUNu/qP6XzNexw/v0+y//VFH9243uyzA7RB8AQAAgHqoSYsIXffYFEV0jNGaJXO0ePrflZudaXZZgFsi+AIAAAD1zMWDRmrkfZNUkJ+vz6c8pi0/fWd2SYBbI/gCAAAA9URAYJB+d98kdR80Uvs2/6q5rz+l06lHzS4LcHsEXwAAAKAeaN0xRtf9abIa21rp+3+/px++mCajoMDssoB6geALAAAAuDGLj48GXDNeg296QGknjmnGX8fr4Lb1ZpcF1CsEXwAAAMBNNQqzadyjr6j9xX20aeViLXz/RWVlpJldFlDvEHwBAAAAN9SpzyCNeegF+fk30Ly3n9W6+HlmlwTUWwRfAAAAwI1Y/QN01R1/Ut/f/UFJe7bqi6lP6PjhfWaXBdRrBF8AAADATTRv00HXP/6awttG66f5s7R01lvKz8s1uyyg3iP4AgAAAG4gdvjvNezOx5WdmaFPnr9fu9b9aHZJgMcg+AIAAAAmCmwUqjEPPq+LLr1CO9f+qK/enKSM0yfMLgvwKARfAAAAwAR+AYG66NIhGnrbRAWFhmnx9L9r1cJPZRiG2aUBHofgCwAAANQRi4+P2sX0UffBo3TRpVcoILChUg7u1mcvP6ykPVvNLg/wWARfAAAAoJaFt+2o7oNGKWbgCIWE2ZSZfkabVnyr3xIW6cDWtZzlBWoZwRcAAACoBSFNwxVz+dXqPmikwttGKz8vVzt/Xalvl03RjsTlysvNMbtEwGsQfAEAAIAaEhAYpIsuG6ruA0eqbUysfHx8dGDbei368GVtXrlEZ9NOmV0i4JUIvgAAAEA1+Pha1aFHP3UfNFKd+w6WX0ADHU86oOX/+VC/LV+kk0cPmV0i4PUIvgAAAEAVtIrqqu6DR6rbgBEKbhymjDMntS5+nn5LWKRDOzaYXR6AYgi+AAAAgJMa21rr4oFX6+JBI9U8op1yc7K1ffUybVj+tXatXan8vDyzSwRQBoIvAAAAUIEGQY3Utf9V6j5olOxde0mS9m5M1E/zPtaWn75TVkaayRUCqAzBFwAAALiAr9VP0ZfEqfvAkeoYe7msfv5KObhbS2e9pQ0/fKPTx5LMLhGACwi+AAAAwDltOvdQ98Ej1bX/MDVsFKq0k6lK/PZz/ZawSEl7tppdHoAqIvgCAADAqzVtZdfFg36niweOVFiLCOVkZWrrqnhtWPa19vy2SgUF+WaXCKCaCL4AAADwOg1DmigmbrguHjRSER1jVJCfrz0bflHCZ+9r2y/fKyfzrNklAqhBBF8AAAB4Bat/gDr1GaTuA0cqqtdl8rX6KWnPNi2ZMVUbV3yrtBPHzC4RQC0h+AIAAMBjWSwW2btdou6DRqrLZVeqQcNgnU5N1s/zP9Fvy79Wyv6dZpcIoA4QfAEAAOBxbPbowuftDvydQpu1UNbZdG39eal+S1ikfZvXyCgoMLtEAHWI4AsAAIB6y79BoGz2aLVo21HhbTsq3N5R4W2j1SCokfLz87Rr7Y9aMvMf2rF6uXJzsswuF4BJCL4AAABwexaLRY3DWyu8bUe1aNtJ4W2jFd62o5q2jHT0ycpIU/K+ndqw/Gsl7dmm7auXKeP0CROrBuAuCL4AAABwKwGBQYVnb9t2dATccHu0AgKDJEkFBQU6kXRAR/ds0/r4+Urev1PJ+3boVMoRkysH4K4IvgAAADCFxcdHYS3anDuL29ERdpuEt3b0yUw/o6N7t2td/Dwl79upo3u369iB3Vy2DMAlBF8AAADUugZBjUoFXFtklPwbBEqSCvLzlXpknw5t36Bfl8zR0X07lLx/h86kJptcOQBPQPAFAABAjfHx8VVYq8hS9+I2bt7S0SfjzEkl792hX/83R0f3blfyvp06dnC38nJzTKwcgCcj+AIAAKBKGjZqXOJe3BZtO6l5ZAf5+QdIkvLzcnXs0F7t3/yrVu8rvA/36L7tSj+ZanLlALxNlYJv69at9dBDD2nUqFGKjIxUXl6e9u7dq6+++krvvPOOTp06VSPFxcbG6oEHHtDAgQPVsmVLnTlzRps3b9bs2bM1c+ZMFfD8NQAAgFrj4+OrhiGNFdykmYIaN1Vw42aytWnvCLohTcMdfdNOpip53w6t/vqzwsuU9+1Q6qG9ys/LNfEdAEAhiyTDlQ2GDRumzz77TE2aNClz/aFDhzRmzBitXbu2WoU99dRTevHFF+Xr61vm+h9//FEjR450KWQnJiYqNja2WnUBAADUZz6+VgWFNlFQaFMFN256LtA2VXCTYj+HFv67YUhj+fj4lNg+LzdHxw7uVnKxM7jJ+3by2CAAbqG8zOdS8I2JidFPP/2k4OBgZWRkaMqUKYqPj5fVatWYMWP08MMPy2q1KikpSb1791ZSUlKVih0/frxmzJghSdq3b59eeeUVrVu3TjabTffee69Gjx4tSVq2bJmGDBkiw3DuLRB8AQCAJ/K1WhUU2lRBjcNKhtnGTR0BN7hJ4etBIWWfvMjJylT6qePKOHX8/L9PH1f6yeMlXj+VckQF+Xl1/A4BwDk1Enzj4+M1ZMgQ5eXlaciQIVqxYkWJ9TfffLM+/fRTSdKMGTM0YcIElwsNDQ3Vnj17FBYWpkOHDql3795KSUkp0eef//yn7r77bknSbbfdpk8++cSpfRN8AQBAfeFr9TsfZB3htdm5YBtW4uxsw5DGZe4jO/NssQCbqoxTJ0qE2PPLqcrJyqzbNwgAtaDawbdXr1769ddfJUkfffSRI3heaOnSpbriiiuUl5enVq1a6dixYy4V+qc//Un/+Mc/JEm33HKLZs+eXapPUFCQDh06pMaNG2vjxo26+OKLndo3wRcAANQFi8UivwaB8g8IlH+DhoXLDRrKv+jfgQ0L1wU2lF9AoAIbhZwPuOfCbWBwSJn7zjqbXuys7IkLzs6mFguzJ5SbTZgFPIHFIll9fORr8ZGvj0W+Fkvhzz4+8rVYzr3mI6uPpdhrhT/7FNum8LWKtynat49P8T6F+0jNOKv/btpu9sdRofIyn9OTW40bN86xPH369HL7zZgxQ1dccYWsVqtGjx5dYd+Kxjlz5oy++OKLMvtkZGToiy++0N13362YmBh16NBBu3fvdmkcAAAAi49PiQBaPJwWhdWAwED5BTSUf2AZQbYowJYKt4Eu1ZGVkeYIsikHdmnPb6suOCt7PuDm5mTV0qcBuMbnXHjysVgcgaxw2eJY52vxuaBfYbgq3s/H0U8l1hXf9/n9Fe27dD8fi498fFQsHJ7fZ/FQV7SueBi0FNvXhesufL3U+yhnvxWuq6CW86H2XOj09an8y6gjaw4fdfvgWx6ng++AAQMkFYbOxMTEcvslJCSU2MaV4Gu1WtWnTx9J0qpVq5STU/6z3BISEhxnnQcMGEDwBQCgnrD4+MjX1ypfq19h8/OTT/Gfz71WUR+r1U++flb5+BZtYy3cxnp+O6t/wLkwez6knv/5XFgNaOBS7dmZZ5WTdVa5WZnKyTqrnOxMZWdmFF4qnFn4c05m4brc7Mxir53/t+P1c9vnZmfJ8JAnVVgsOhdAzjeLLPIp9rqlxHoV61NsmzL3U/I1OdGn7OZMn3N1+pTf19eJfVw4lqWCfkUhqqx9nw92hessZbx+4Xbn96cS4bFoO4uTY5W1ztdSeDawvisoMJRvFCi/wFC+YajAMJRfUKB8wzj32vl1Ra8XFBSowFCxdaX75OYXKMsoOLe/yvdbYBjKK7Y+r9hYJV87v11eQUGpfeblGyooVldeQelxC7crWW9eQUGJzyKvjFqLXsvJzzf7a6syp4Nvly5dJEk7d+5UfgVvOCkpSWfOnFFISIhjG2d17NhRfn5+kqQtW7ZU2Hfbtm2lagMAoLjC340Lf4mWzi8X/npc0fqi7Qt7WiwWWSw+534R9jn3S3fhL34+Fh9ZfHzO/RJeuM7i41O4L18f+ejcz5aivuf3VbQ/x359fWXx8T131sKncNnXt3CdT+G/LSV+9pXFxyIfH1/Jp7AOR79S/z6/vvRy4ZjyOTeuxUcWXx9ZLOf7FvUr3M4qH6tVFt/C5uNb8mdLGT+XaD61d/bCyMuVCvKl/DwpP09GbraUmyPlZku52bLknJEyUqW8bFlyc2TJzZbychzLlrycwpabLZ+8HPmc+9knL0c++Xny0flwV/Rno+xlycfHIkuQRZZgyccSKIsaymJpWu62xX8uHv6KQmHxcS8MUpVt48o4ZY114TYX9vOtxe+0vio4F3gqb5Kh8yGjwJAjKBnF+uSfC1LFw5Tj52L/zskvKPG6UbxfQfFtChz7Lhrvwv0V38a4cKyC8/twhLti+ynVr0AqKNa/eNA8v23J2kr3O/c5FFxYS0GJ8Yr2UVmghXdxKvj6+/urefPmkgofV1SZgwcPqmvXrmrTpo1LxURERDiWKxvn4MGDjmVXx3FHU156Sa2i+ppdhrlq4b8/Ri39z0ind+vCe6ru2y+rptr7T3ot/mVR1q5d+R6NMhedcOEgxrl/WspZU3WVb39+RKOsmRiKFWQU/cNS9odU1lhGJR9oyW3OFWCxOFYYJcYvuS+jWIHF92NYqtqv2GdRfJsS3Swl96HzydUotg/DYimxXNTXqOhni/f8Mm8xCmQxDPnIkMUo/CR8zv27+Os+MuRTUCBfo7D5GPnFls+/7mtky8fIlG9ugXyzy+uTLx/DkO+5ffgYBfItKJCvCoqNkX/BNhfuJ1++BYU/+1T6p/s8QxbJKhlWSY4/M1YZskqWoPN9LLpg+dyfN8efVUuxP49Fy0X/J8Vy/s+tRSWWL9zP+eXz+yj+mlT8z+WFYxftv5x9l9W/+OvF6jQsFhWcey2/2Pt2fLIWyZDP+XEs57e7cAyjqHZHbWXVVXatpbYt8V7Pb1Picyv1WrExin+2Rf8tuKBO48LPv8Tnc64Ww1LyZ5X9s4rVUBd8z7WS6m78OlNbv9iZro7elxO/wFTWJTvvtIZdObBGyqlrTgXfRo0aOZbT09Mr7V/UJzg42KViXBmn+PqKxrn77rt1zz33SJKaNWvmUj11KTSksU76NDS7DJfUn//01FZIq41PoIxaXRqmdGdLeY/7KnO/Lgx24X6d/22zCsNaSkQbV75RVz6+6n6j52ssGdYsTlZctJVRTi3l1ndu9xZLOeMUf7no97Fy/qgZ534XLDGWYZz/Xb14lcXebvH+JfZ97ofifw6L54LitRX+jlgsuFxQd6n3b5T8vdJS/JO7YDyLDMcvTMWiSBn/rmhdUX2WknUWq81iFBZ1PkbL0b/k+1SJ/RRV7nOuvvN9irdzY1uMkq+X6HtuneX8MXO+77nXLOeXi/qe/38nxXbmCBIXfAIlfpkv+mXfV7L4lvwULeVto/PhpFg/QxYVWKQCWZR74XaWonEu+MAvCE3nQ0vx2kq+p9oOI9Xae5U39q4zV659ha5/NqWP+zpS4wO6+t5d+7uq5jubzcnPy4X35JZvv7zfFZzdPMfDL3UODDw/QUNF990Wyc7OLrVdTY9TNEZl40ybNk3Tpk2TpArvTTbbfQ8/aHYJAAAAAOCRnLqGKzPz/FT4/v7+lfYPCAgotV1Nj1M0RlXGAQAAAAB4D6eCb1pammPZmcuXi/o4c1l0Vccpvt7VcQAAAAAA3sOp4JuTk6Njx45JKjkBVXmK+hSfgMoZxSe0qmyc4hNauToOAAAAAMB7OD1dZdHjhaKjo+XrW3reuCItW7ZUaGhoiW2ctWPHDuXmFk5pUdkjijp37lyqNgAAAAAALuR08F25cqUkKSgoSLGxseX2GzRoUKltnJWXl6fVq1dLki699FLHM31rehwAAAAAgPdwOvjOnTvXsTxhwoRy+915552SCkPsggULXC6oaJyQkBD9/ve/L7NPUFCQY93GjRu1e/dul8cBAAAAAHgPw9n2/fffG4ZhGLm5ucaAAQNKrb/pppuMItOnTy+13m63O9YnJCSUOUZoaKhx/PhxwzAM48CBA0bz5s1L9fm///s/x35uvfVWp+tPTEx0ui+NRqPRaDQajUaj0epXqyDzOb+TmJgYIy0tzTAMw0hPTzeefvppo1+/fkZcXJwxdepUIzc31zAMwzhy5IjRsmXLUts7E3wlGXfeeaej3549e4y77rrLuOSSS4wRI0YY8+bNK7EPi8VSEx8CjUaj0Wg0Go1Go9HqeauR4CvJGDZsmHHixAmjPAcPHjR69epV5rbOBl9Jxl//+lcjLy+v3HFWrlxpNGnSpKY+BBqNRqPRaDQajUaj1fNWXuZz+h7fIkuWLFFMTIxee+01bdmyRenp6Tp9+rTWr1+v5557TjExMVq7dq2ruy3llVdeUb9+/fTxxx9r3759ysrK0rFjx7Rs2TLddddduvzyy3Xy5MlqjwMAAAAA8GwWFSZgr5CYmFjhjNQAAAAAgPqrvMzn8hlfAAAAAADqE4IvAAAAAMCjEXwBAAAAAB6N4AsAAAAA8GgEXwAAAACARyP4AgAAAAA8GsEXAAAAAODRCL4AAAAAAI9G8AUAAAAAeDSCLwAAAADAoxF8AQAAAAAejeALAAAAAPBoFkmG2UXUlZSUFO3fv9/sMsrVrFkzpaamml0GnMB3VX/wXdUffFf1B99V/cD3VH/wXdUffFfuz263y2azlbnOoLlHS0xMNL0GGt+VpzW+q/rT+K7qT+O7qh+N76n+NL6r+tP4rupv41JnAAAAAIBHI/gCAAAAADwawdeN/POf/zS7BDiJ76r+4LuqP/iu6g++q/qB76n+4LuqP/iu6i+vmtwKAAAAAOB9OOMLAAAAAPBoBF8AAAAAgEcj+AIAAAAAPBrBtwa1bt1akydP1ubNm5WWlqaTJ09q7dq1euaZZ9S4ceMaGyc2NlYzZ87Unj17lJmZqeTkZH3//feaMGGCfHz4SsvTq1cvTZo0Sd9++60OHDigrKwspaena9euXZo9e7aGDRtWI+PcfvvtMgzDqfbcc8/VyJiextnPb+/evTUyHsdU1SQkJDj9XRW122+/3eVxOKYqFhoaqqFDh+qvf/2r5s2bp8OHDzs+j4SEBJf21alTJ7377rvasWOHMjIylJqaqp9//lkTJ05UQEBAjdY9dOhQ/fe//3X89/jw4cP6+uuvdd1119XoOO6kut+VxWLRgAED9OKLL+r777/XkSNHlJ2drTNnzmjr1q366KOPdOmll9ZIrc8991ytHtfurrrfld1ud/rzc/U4rQjHlevf1d69e13+u2zgwIFVqtXbjyt3YPrDhD2hDRs2zDhx4oRRnoMHDxq9evWq9jhPPfWUkZeXV+44K1euNBo3bmz65+FubdmyZeV+ZsUtXLjQCAkJqdZYt99+u1NjGYZhPPfcc6Z/Nu7YnLV3795qj8UxVfWWkJDg9HdVpG/fvi6PwzFVcduzZ0+5n0dCQoJLn/PZs2fL3dfmzZuNtm3b1kjN7733XoXf41dffWX4+/ub/tm623e1b9++yg8CwzCmT59u+Pn5VavW5557zqmxDMMwbr/9dtM/W3f7rux2u9OfnyvHaUWN46pqn+3evXud/q4MwzDy8vKMVq1aValWbz+uzG5WodpiYmI0Z84cBQcHKyMjQ1OmTFF8fLysVqvGjBmjhx9+WBEREVq0aJF69+6tpKSkKo0zfvx4vfLKK5Kkffv26ZVXXtG6detks9l07733avTo0erfv7+++uorDRkyRIZh1OTbrNdat24tSTp69KjmzJmjH374Qfv375dhGLrkkks0ceJEdezYUSNHjtSCBQs0ePDgGvn8rrrqKh05cqTc9SkpKdUew5O9//77ev/998tdn5OTU639c0xVz/jx4xUUFFRhn/DwcMXHx0uStm/frl9++aVaY3JMlWaxWBzLR48eVWJiokaNGuXSPq688kp99NFHslqtOnbsmF555RX9/PPPCg4O1i233KI77rhDXbp00ddff60+ffooIyOjyvX+7W9/0wMPPCBJ2rBhg6ZMmaLt27fLbrdr4sSJiouL09ixYzVt2jSPO+NR3e+q6O+yPXv26Msvv9SPP/6oQ4cOyd/fX/3799ejjz6qVq1a6c4775Sfn59uu+22Gqm7W7duFa4/dOhQjYzjTmriuCoyadIkzZ8/v9z11TmeinBcFarKd3XVVVfJ39+/wj49e/bUJ598IklaunRphX8POcsbjyt3YHr6ru8tPj7eMAzDyM3NNeLi4kqtv/nmmx3/92b69OlVGiM0NNQ4fvy4YRiFZ49tNlupPv/85z8d49x6662mfy7u1BYuXGjccMMNhq+vb5nrGzZsaKxcudLx+d18881VHqv42Sm73W76e6+PrUhtnr3jmKqb9uijjzo+w6eeeqpK++CYqrg99thjxrhx44yIiAjHa0WcOdvh6+trbN++3TAMwzhz5ozRsWPHUn0mTZrk2OczzzxT5Vrbt29vZGdnG4ZhGGvXrjUaNmxYqpZvvvnGMdaAAQNM/3zd6bv66aefjOHDh5e7vnnz5o7v0jAMo3///lWutfiZKbM/t/r4XRU/41vbZ+44rqr3XTnT3nrrLcc+//CHP1R5P95+XLlBM72Aet169erl+AM8bdq0cvstXbrUMIzCcNy8eXOXx/nTn/7kGKe8UBYUFGScPHnSMAzD2LBhg+mfTX1r3bp1c3zG8+bNq/J++CW9+q1IbQZfjqm6aevXrzcMo/DSsOK/lLjSOKZcb6780jdu3DhH/0mTJpXZx2KxOALV8ePHy/2fiJW1t99+2zFWeaEsMjLScfvBggULTP8s3em7cqaNHDnSsc8333yzyvvhF/TqfVd1GXw5rqr3XVXWrFarkZKSYhiGYZw6dcpo0KBBlffFcWVuY9aWaho3bpxjefr06eX2mzFjhiTJarVq9OjRVR7nzJkz+uKLL8rsk5GR4VgXExOjDh06uDyON9u0aZNSU1MlSVFRUSZXg9rGMVX7Lr74YnXv3l2S9P3333PZlpsq/vdY0d9VFzIMQx9//LEkKSwsTIMGDarSWGPHjpVUeNn7jz/+WGafAwcO6Pvvv5dUOFFPZZfTo6Tik/nwd5l34LiqXVdffbWaN28uSfrvf/+rrKwskytCVRF8q2nAgAGSCn9BTkxMLLdf8b+IirZxltVqVZ8+fSRJq1atqvC+xuqMA8nPz0+SlJ+fb3IlqE0cU3Wj+H1kRaEJ7qfoz/WOHTsqnIOiusdC27Zt1aZNG0nS8uXLK+xbNFZgYKAuueQSl8fyZsXvVeTvMs/HcVX7+LvMcxB8q6lLly6SpJ07d1b4F0xSUpLOnDlTYhtndezY0RHItmzZUmHfbdu2laoNzunRo4dCQ0MlSVu3bq2Rfc6cOVOHDh1Sdna2UlNTtXr1ak2ZMkVt27atkf17uuuvv16bNm1Senq60tLSHI+euvrqq6u1X46p2ufr66ubb75ZUuFZ9blz59bIfjmmalZQUJDjl+baPhaKb8NxV3uKP2alpv4uW7x4sY4ePars7GwlJydrxYoVevbZZ2Wz2Wpk/57uoYce0o4dO5SZmanTp087Hj3Vv3//au+b46p2hYWF6Xe/+50kadeuXeWeUa8Kjqu6R/CtBn9/f8elD85cwnfw4EFJcvyS4ayIiAjHcmXjFI1RlXG83dNPP+1Y/vzzz2tkn4MHD1br1q3l7++vpk2bKjY2Vn/5y1+0Y8cOPfnkkzUyhifr2rWrunbtqqCgIAUHB6tDhw666aab9PXXXys+Pr7KfzlwTNW+4cOHKzw8XJI0Z84cZWZm1sh+OaZqVuvWrR3Pqq7sWDh58qRj9tmqHAscd7XPYrHoqaeecvz83//+t0b2O2zYMIWHh8vf3182m00DBgzQCy+8oD179njcDMG1oXfv3oqOjlaDBg0UEhKizp07a8KECVq5cqX+85//KDg4uMr75riqXX/4wx8czzCfNWtWje6b46ru8TijamjUqJFjOT09vdL+RX1c/Q+cK+MUX1+d/5B6mxtuuEHXXnutJCkxMVFfffVVtfa3Z88ezZ07Vz///LMOHDiggoIC2e12jR49WjfddJP8/f316quvKiAgQC+88EJNvAWPkpGRoYULFyo+Pl7btm1TWlqawsLCdNlll+m+++5TRESEhgwZou+++079+/d36vgrjmOq9hV/jMq//vWvau+PY6p2VOXvsaL/EVWbY3HcVc2f//xnx20cc+bM0dq1a6u1v40bN2r+/PlavXq1Dh8+LF9fX3Xo0EHXXXedrrnmGgUFBTmOby4BLe3kyZOaN2+eli1bpp07d+rs2bMKDw/XoEGDdM8996hp06a64YYbFBYWphEjRlTp0nSOq9pV9HdZQUFBjQVfjitzmT7DVn1tERERjpnZPv7440r7L1++3DCMwpmdXRnnlltucYwzfvz4CvtaLBZH3++++870z6g+tJiYGCMtLc0wDMNIT083OnXqVK39hYSEVLj+0ksvNU6dOmUYRuFMt926dTP9M3C3FhoaWu66Ro0aGd99953jz/lrr73m8v45pmq3NW7c2MjMzDQMwzB2795d7f1xTLnenJ3RdMCAAY6+L7zwQqX73b9/v2EYhrFz506Xa3r66acdYw0ePLjCvu3atXP0reiJCZ7Qamr22aFDhxq5ubmGYRjGkSNHjGbNmlVrfxX9d1iSMXr0aMcjdM6cOVOlJ1bUt+bKd+Xn52cEBgaWu75FixaOWe8NwzAeeOCBKtXEcVX976q81rlzZ8d+vv/++xqpi+PK3MalztVQ/NK9yh58LclxqYSrl/y5Mk7RGFUZxxvZ7XZ98803Cg4OVn5+vm6//XZt3769Wvssupe7PKtWrdLDDz8sqfA+yAcffLBa43mi06dPl7suLS1N119/vY4fPy5Juu+++xz36zqLY6p23XDDDWrQoIGkmrk0jGOq9tTV32OujsVx55qePXtqzpw5slqtOnv2rK677jrHUwqqqqL/DkvSggUL9PLLL0sqPOs4YcKEao3naXJzcyv8s3v06FFde+21jskVi/4b5iqOq9pTG5NacVyZi+BbDWlpaY5lZy4ZKerj6mWZroxTfL2r43ibFi1a6LvvvnPcH3Pvvffqyy+/rJOxZ8+e7fhlvqqPBfFmp06dctyH3ahRI/Xu3dul7TmmalfRLws1eWlYZTimqqau/h5zdSyOO+d17txZS5YsUWhoqHJycnTttdfqp59+qpOxP/zwQxUUFEjiuKuK3bt3a+nSpZKkTp06qUWLFi7vg+OqdlgsFt1yyy2SCj+rOXPm1NnYHFe1h+BbDTk5OTp27JikkpMLlKeoT/HJBZxRfLKCysYpPlmBq+N4k6ZNm+q7775TdHS0JGnixIkVPoe5puXn5zvOLDvzZwelbd682bHs6mfIMVV7oqOj1a9fP0nSihUrtHfv3joZl2Oqag4fPuz4Bauyz61JkyaOZ39W5VjguKt57du319KlS9W8eXPl5eXppptu0uLFi+ts/JSUFMfVNxx3VVOdv8skjqvacsUVVzg+zy+//NIxsV9d4LiqPQTfaiqaOj46Olq+vr7l9mvZsqXjUTmVTTd/oR07dig3N1dS5dPPd+7cuVRtKCkkJET/+9//1K1bN0mFszm/9dZbdV6HYRh1PqYnqc7nxzFVe4pPalXXk3JwTLkuIyPD8QtwbR8LxbfhuKu+iIgIxcfHq3Xr1iooKNCdd95ZZ1ctFcdxVz3V/fw4rmqH2c/u5biqHQTfalq5cqWkwmchxsbGltuv+KUKRds4Ky8vT6tXr5YkXXrppRXez1idcbxBUFCQFi9erF69ekmSJk+e7LiXoi75+vqqU6dOkqQjR47U+fieoGvXro5lVz9Djqnac+utt0oqDFRffPFFnY3LMVV1RX+uO3bsqJYtW5bbr7rHwr59+xxnp4o/a7aisbKysrRmzRqXx/J04eHhio+Pdzy/+oEHHtAnn3xS53U0b95czZo1k8RxV1XV+btM4riqDcHBwbrmmmskSfv371dCQkKdjs9xVbtMn2GrPrdevXo5NUNe0Sy0ubm5VZqh7U9/+pNjnJtvvrnMPkFBQcbJkycNwzCMDRs2mP7ZuFtr0KCB8f333zs+x7feesu0WorPKuzpMyvWRgsNDTVSU1MNwyicidvf39/lfXBM1XwbPHiw4zP95JNP6nRsjqmSzZUZTceNG+foP2nSpDL7WCwWY9u2bYZhGMbx48cNX1/fKtX19ttvO8bq379/mX0iIyMdsxMvWLDA9M/Snb4rSUbTpk2NjRs3OrZ79NFHTau9+IzC5f3Z8aTm6ndVWWvfvr1jBt+qzJRe1Diuava7Gj9+vGP7F198sc5r97bjqo6b6QXU+1YUpnJzc40BAwaUWn/TTTc5/gBPnz691Hq73V7pARoaGmocP37cMAzDOHDgQJnh+f/+7/8c+7n11ltN/1zcqfn5+Rlff/11tX8x/v/27i+kqTaOA/j3VfNCtkKauQqboHRVmHrhBHVEUBAjhejKYiTUIEVD6GY49EYZXUgY2Y26AjHwYruIlmXlIttWJI5giKaMkBAhhjH8kzSeLsTzztc55zbdOO/3A78LOec8z+Pz46d7drbz6HQ6qQ2r1Roxl6WlpVHb0Gq10mIqFArtev7/LfR6fdQX1UqlUrx+/VrKw4MHDyLmgTV18GG1WqX5unDhQkzXsKb2J/byoi8zM1NMT08LITa2zzh9+vS2c0wmk9Sm2WyOK5cARFFRkVhfXxdCCDExMSFycnK2jcXhcEjtVFdXp3wu0ylXhw8fFl++fJGuaWtri6tPg8EgtdHe3r7t+JkzZ0RxcXHUNsK3XVleXhbHjx9P+VymU67q6uqiHler1WJyclJq8+7duxHPY13tf67+G2NjY9L1u9VBeLCu0j+yQAlraWmBy+WCQqHAyMgILBYL3r59i6ysLNTW1qKlpQUAsLCwgLa2trj6+PXrF+7du4f+/n4UFBTg06dP6OrqgtfrRV5eHoxGI2prawEATqcTg4ODSfv95GBoaAiXL18GALhcLvT09Gz5eFEk4Q+ciFVhYSGcTic8Hg+eP38Or9eLxcVFCCGg0Whw5coV1NfXSx+tvX//PiYnJ/f+C8nYw4cPkZ2dDZvNBrfbDb/fj5WVFeTm5qKqqgpGo1F62MPU1BQ6Ojri6oc1lVw5OTm4evUqgI2Hprx79y4p7bKmdldSUoJz585FPKZWq7d8Vw0ARkZGsLi4KP0cCoXQ1NQEh8MBpVKJ8fFxdHZ2wu12Q6FQ4Pr167h58yaAje8Fdnd3xz3Wubk5WCwWmM1mlJWVwe12w2KxYGZmBgUFBWhtbUV1dTWAja2wPnz4EHdf6SiRXGVnZ+PFixfSU+xtNhvsdnvU/2Xr6+v49u3bnsdZXl6O/v5+OJ1OvHz5El+/fsXPnz+RmZmJ4uJiXLt2DXV1dcjI2PjGXGtrKxYWFvbcTzpLtK7sdjvm5uZgs9nw+fNnzM/PY21tDceOHcP58+dx+/ZtHD16FADw/v17PHr0KO6xsq4Sy1U4jUaDmpoaAMDHjx8xOzubtHGyrtJDylffcohLly6JQCAgdjI/Py/KysoiXhvL3anNMJlM4s+fPzv2Mz4+LnJzc1M+H+kW8YjUzm7vvIYfj+b379/CZDKlfF7SMfx+f0xz+ObNmx3fCWVNHXzcuHFDmrPOzs6Yr2NNJR7t7e0xzdEmnU4XsR2DwSBWVlZ2vM7n84nCwsK4cxkevb29Ucdot9vj+gpDukciuQr/uxYrv9+/Y643RbozFX48mmAwKBoaGlI+r+mWKyD21x3Pnj0TSqVyx3GwrvY/V+FhNpul827durWncbCu0j94xzdJXr16hbNnz6K5uRl6vR6nTp1CKBSC3++H3W5HT08PlpaWEu6nq6sLo6OjaGxshE6ng1qtRjAYhM/nw+DgIKxWq7Q1BR28iYkJ1NfXQ6vVory8HCdOnIBKpcKhQ4ewtLSE6elpjI2Noa+vb8sWBPQvg8EAnU6HiooKFBUVQaVS4ciRI1heXsaPHz/g8XgwNDSUtDuKrKnk2K8nYLKmDs7Tp0/h8XjQ3NyMixcv4uTJk1hdXcXMzAyGh4fx+PFjrK2tJaWvO3fuwGazwWg0QqvVIi8vD4FAAF6vFwMDAwe6ZyZt53A40NDQAK1Wi9LSUuTn50OlUiEjIwOBQAA+nw+jo6MYGBiQtl2hrfR6PSorK1FRUQGNRgOVSgWFQoFgMIjv37/D5XLhyZMnSX3IFOsqcZs7E6yurmJ4eDipbbOuUu8fbKyAiYiIiIiIiGSJ2xkRERERERGRrHHhS0RERERERLLGhS8RERERERHJGhe+REREREREJGtc+BIREREREZGsceFLREREREREssaFLxEREREREckaF75EREREREQka1z4EhERERERkaxx4UtERERERESyxoUvERERERERydpfXdTS2kKIpdkAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "ns = [n for n in range(1,21)]\n", "\n", "plt.figure(figsize = (16,10))\n", "plt.plot(ns, label=\"linear\")\n", "plt.plot([n * log(n) for n in ns], label=\"N log N\")\n", "plt.plot([n**2 for n in ns], label=\"$N^2$\")\n", "plt.plot([n**3 for n in ns], label=\"$N^3$\")\n", "plt.plot([2**n for n in ns], label=\"$2^N$\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "More **formally**: runtime $T(n) \\in O(g(n))$ if and only if there exists some $n_0 > 0$ and $C > 0$ such that for all $n>n_0:~T(n)\\leq Cg(n)$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "This allows to disregard details and just count operations that do not depend on the input size.\n", "\n", "For example, take **bubble sort**:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "- In the worst case, we will have to flip every one of the $N$ numbers with every other (if the input array is sorted, but in the wrong way -- e.g., in descending instead of ascending order). \n", "- Then, we will have to perform $N(N-1)$ `swap` operations -- at most, regardless of anything. \n", "- This is $N^2-N$, which still grows as fast as $N^2$, if $N$ increases infinitely. \n", " \n", "So, we'd write it as \n", "> bubble sort worst-case runtime is in $O(N^2)$. \n", "\n", "(There is no way it could run slower, right?)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**Now, for the merge sort**:" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "- Every `merge` operation takes $O(N)$ time, since it performs at most $N$ \"ticks\" while merging. We'd just need to count how many times this function could be invoked. \n", "- Well, this is easy: this is the number of times we can divide $N$ into two, since each time we split it in two halves. \n", "- If you think about it, this is the largest number $k$ such that $2^k \\leq N$. We can use a slight overestimate, a number $k$ such that $2^k=N$, which mathematicians call $\\log_2 N$. \n", "- Therefore, we can say that we invoke the `merge` function (which takes $O(N)$ time), $\\log_2 N$ times.\n", " \n", "So, the asymptotic worst-case runtime of the merge sort is $O(N \\log N)$ (computer science people sometimes omit this subscript of $2$, if most of the logarithms are base-two. And also it doesn't matter in $O(\\cdot)$ notation if you recall properties of the logarithm...)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**So:**\n", "- $O(\\cdot)$ is asymptotic runtime, when the problem size skyrockets. Up to a (multiplicative) const.\n", "- bubble sort \"runs is\" $O(N^2)$, merge sort --- in $O(N\\textrm{log}N)$.\n", "- we have calculated this by counting operations that do not depend on the problem size." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "**Any questions so far?**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Problem 2: special inputs\n", "Then, let us feed in some pretty special inputs:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "def gen_magic_sequence(N):\n", " \"\"\"Generates a \"special\" input sequence (with only two elements swapped)\"\"\"\n", " \n", " input_sorted = [i+1 for i in range(N)] # generate a sequence of numbers\n", " input_unsorted = [i+1 for i in range(N)]\n", " a = input_unsorted[N // 2]\n", " b = input_unsorted[N // 2 + 1]\n", " input_unsorted[N // 2] = b; input_unsorted[N // 2 + 1] = a # how 'bout this?!\n", " \n", " return input_sorted, input_unsorted" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (30,10))\n", "plt.subplot(1,2,1); make_runtimes_plot(N=50)\n", "plt.subplot(1,2,2); _ = make_runtimes_plot(gen_magic_sequence, N=50)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Huh?.. 😕" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Ah, okay, maybe this is scaling again?" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [], "source": [ "rt_bsort_m = make_mean_runtimes(bubble_sort, gen_function=gen_magic_sequence, N1=N1, N2 = N2)\n", "rt_msort_m = make_mean_runtimes(merge_sort, gen_function=gen_magic_sequence, N1=N1, N2 = N2)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (20,10)); plt.subplot(1,2,1)\n", "plt.plot([N1 + i for i in range(N2-N1)], rt_bsort_m, 'r-')\n", "plt.plot([N1 + i for i in range(N2-N1)], rt_msort_m, 'b-')\n", "plt.gca().set(xlabel='Input array size(N). red = bubble sort, blue = merge sort', ylabel='mean runtimes, msec')\n", "plt.subplot(1,2,2); plt.plot([N1 + i for i in range(N2-N1)], [x*x/5000 for x in range(N1, N2)], 'r-')\n", "plt.plot([N1 + i for i in range(N2-N1)], [x*log(x)/800 for x in range(N1,N2)], 'b-')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "What happened?! 🤯🤯🤯 Why is this so?.." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "**And the answer is:** well, it depends on input! (surprise!). So, intuitively, we'd want to differentiate between the *worst-case*, *best-case*, and *average* performance (whatever it all would mean, precisely).\n", "\n", "Actually, what we see, say, on Wiki [page](https://en.wikipedia.org/wiki/Merge_sort) describing an alorithm, is exactly this.\n", "\n", "Therefore, people quite often talk about the *asymptotic worst-case runtime*. Now you have seen what (almost) each word means :)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## A word on other properties\n", "### Space requirements\n", "You can use this $O(\\cdot)$ notation to analyze other requirements as well: for example, how much space does your (or someone else's) algorithm requires." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "For example, note that bubble sort does not require additional space (it can run over the same input array, *in-place*), while our implementation of merge sort required $O(N)$ units of additional memory." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "### Can we run it in parallel?\n", "Also, it might be interesting how difficult it is to build a *parallel* version of an algorithm? E.g., compare selection sort and merge sort." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### A final remark on this\n", "In practice, please do sorting like this, unless there are good reasons to do otherwise:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 2, 3, 4, 5, 7])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "np.sort([1,5,2,7,3,4,1])" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 2, 3, 4, 5, 7])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# or, if you want to look fancy:\n", "np.sort([1,5,2,7,3,4,1], kind='mergesort')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "No need to reinvent the bicycle. There are many algorithms already implemented for you in several great libraries. For example, lots of useful things are in [scikit-learn](https://scikit-learn.org/stable/) and (as you can tell even from this notebook) [numpy](https://numpy.org/), and so on. So if you are thinking to implement something -- a good first step is\n", "\n", "**❗❗ to look around, maybe something is already implemented ❗❗**\n", "\n", "(and chances are, with high-quality code, with support for special hardware, if needed, etc.)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Finally: NP-hard problems\n", "\n", "It seems that not all problems are solvable in polynomial time (in general case). There is a class of problems such that:\n", "- a (given) solution can be *verified* in polynomial time, (class \"NP\")\n", "- but no known algorithm exists to solve it (i.e., find a solution in first place) in polynomial time. (actually, *solving it in polynomial time would allow to solve some more really hard problems in polynomial time*)\n", "\n", "Is is a little more complicated than that, actually -- you can check out the concepts of [NP-hardness](https://en.wikipedia.org/wiki/NP-hardness), and [P vs NP](https://en.wikipedia.org/wiki/P_versus_NP_problem) problem, if you are interested. But if so, picking up a Computer Science textbook (or a course) might be a good idea." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "In practice, it means that there is a big class of (relevant) problems, that do not allow for efficient exact solution. In this case people sometimes try to devise:\n", "- *approximate* algorithms (and sometimes, pretty successfully) -- give solution + *certificate*.\n", "- heuristics (just gives \"kinda okay\" solutions, without any guarantees... pretty good in practice sometimes, though).\n", "\n", "Selecting optimal phylogenetic trees / multiple sequence alignment (see, e.g., [wiki](https://en.wikipedia.org/wiki/Sequence_alignment)), [k-means](https://en.wikipedia.org/wiki/K-means_clustering) clustering, and many others are \"computationally difficult\". Also, see the [List](https://en.wikipedia.org/wiki/List_of_NP-complete_problems) of NP-complete problems for some more examples." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Take-aways from Topics 1 and 2\n", "- (Some) algorithms are cool\n", "- They can (and should) be analyzed in a systematic, scientific way,\n", "- ...and are characterized by runtime, space requirements, and correctness, among other things.\n", "- Asymptotic runtime: $O(\\cdot)$ (and Co.) notations describe how does the runtime grow with the problem size. \n", "- Yes, usually that's a big deal.\n", "- Sometimes we discuss best-case, worst-case, and average performance.\n", "- Cool algorithms are numerous; existing libraries are your friends (`numpy`, `sklearn`, etc. -- if we talk Python)\n", "- There are theoretical limits, though. We cannot do sorting (unless you know something about the array) in less than $O(N\\log N)$ worst-case. It *seems* we cannot really solve many problems in polynomial time (and this is actually a serios research direction).\n", "\n", "**Any questions?**" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.5" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 1, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "name": "1_Algorithms.ipynb", "rise": { "enable_chalkboard": true, "scroll": true, "theme": "moon" }, "toc": { "base_numbering": 1, "nav_menu": { "height": "110px", "width": "437px" }, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": null, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }