From f6186a6590fe555c264f489cfedc8f28891a9a9b Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Sat, 29 Sep 2018 20:25:01 +0530 Subject: [PATCH 1/4] The ultimate test --- .../01.00-IPython-Beyond-Normal-Python.ipynb | 0 notebooks/01.01-Help-And-Documentation.ipynb | 350 --- notebooks/02.00-Introduction-to-NumPy.ipynb | 41 +- .../02.01-Understanding-Data-Types.ipynb | 341 +-- notebooks/03.09-Pivot-Tables.ipynb | 428 ++-- .../04.14-Visualization-With-Seaborn.ipynb | 313 +-- ...uction_to_artificial_neural_networks.ipynb | 2236 +++++++++++++++++ notebooks/helpers_05_08.py | 1 + 8 files changed, 2718 insertions(+), 992 deletions(-) rename {notebooks => nb3}/01.00-IPython-Beyond-Normal-Python.ipynb (100%) delete mode 100644 notebooks/01.01-Help-And-Documentation.ipynb create mode 100644 notebooks/10_introduction_to_artificial_neural_networks.ipynb diff --git a/notebooks/01.00-IPython-Beyond-Normal-Python.ipynb b/nb3/01.00-IPython-Beyond-Normal-Python.ipynb similarity index 100% rename from notebooks/01.00-IPython-Beyond-Normal-Python.ipynb rename to nb3/01.00-IPython-Beyond-Normal-Python.ipynb diff --git a/notebooks/01.01-Help-And-Documentation.ipynb b/notebooks/01.01-Help-And-Documentation.ipynb deleted file mode 100644 index 47af64c88..000000000 --- a/notebooks/01.01-Help-And-Documentation.ipynb +++ /dev/null @@ -1,350 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", - "\n", - "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Help and Documentation in IPython" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you read no other section in this chapter, read this one: I find the tools discussed here to be the most transformative contributions of IPython to my daily workflow.\n", - "\n", - "When a technologically-minded person is asked to help a friend, family member, or colleague with a computer problem, most of the time it's less a matter of knowing the answer as much as knowing how to quickly find an unknown answer.\n", - "In data science it's the same: searchable web resources such as online documentation, mailing-list threads, and StackOverflow answers contain a wealth of information, even (especially?) if it is a topic you've found yourself searching before.\n", - "Being an effective practitioner of data science is less about memorizing the tool or command you should use for every possible situation, and more about learning to effectively find the information you don't know, whether through a web search engine or another means.\n", - "\n", - "One of the most useful functions of IPython/Jupyter is to shorten the gap between the user and the type of documentation and search that will help them do their work effectively.\n", - "While web searches still play a role in answering complicated questions, an amazing amount of information can be found through IPython alone.\n", - "Some examples of the questions IPython can help answer in a few keystrokes:\n", - "\n", - "- How do I call this function? What arguments and options does it have?\n", - "- What does the source code of this Python object look like?\n", - "- What is in this package I imported? What attributes or methods does this object have?\n", - "\n", - "Here we'll discuss IPython's tools to quickly access this information, namely the ``?`` character to explore documentation, the ``??`` characters to explore source code, and the Tab key for auto-completion." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing Documentation with ``?``\n", - "\n", - "The Python language and its data science ecosystem is built with the user in mind, and one big part of that is access to documentation.\n", - "Every Python object contains the reference to a string, known as a *doc string*, which in most cases will contain a concise summary of the object and how to use it.\n", - "Python has a built-in ``help()`` function that can access this information and prints the results.\n", - "For example, to see the documentation of the built-in ``len`` function, you can do the following:\n", - "\n", - "```ipython\n", - "In [1]: help(len)\n", - "Help on built-in function len in module builtins:\n", - "\n", - "len(...)\n", - " len(object) -> integer\n", - " \n", - " Return the number of items of a sequence or mapping.\n", - "```\n", - "\n", - "Depending on your interpreter, this information may be displayed as inline text, or in some separate pop-up window." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because finding help on an object is so common and useful, IPython introduces the ``?`` character as a shorthand for accessing this documentation and other relevant information:\n", - "\n", - "```ipython\n", - "In [2]: len?\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Namespace: Python builtin\n", - "Docstring:\n", - "len(object) -> integer\n", - "\n", - "Return the number of items of a sequence or mapping.\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This notation works for just about anything, including object methods:\n", - "\n", - "```ipython\n", - "In [3]: L = [1, 2, 3]\n", - "In [4]: L.insert?\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Docstring: L.insert(index, object) -- insert object before index\n", - "```\n", - "\n", - "or even objects themselves, with the documentation from their type:\n", - "\n", - "```ipython\n", - "In [5]: L?\n", - "Type: list\n", - "String form: [1, 2, 3]\n", - "Length: 3\n", - "Docstring:\n", - "list() -> new empty list\n", - "list(iterable) -> new list initialized from iterable's items\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Importantly, this will even work for functions or other objects you create yourself!\n", - "Here we'll define a small function with a docstring:\n", - "\n", - "```ipython\n", - "In [6]: def square(a):\n", - " ....: \"\"\"Return the square of a.\"\"\"\n", - " ....: return a ** 2\n", - " ....:\n", - "```\n", - "\n", - "Note that to create a docstring for our function, we simply placed a string literal in the first line.\n", - "Because doc strings are usually multiple lines, by convention we used Python's triple-quote notation for multi-line strings." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we'll use the ``?`` mark to find this doc string:\n", - "\n", - "```ipython\n", - "In [7]: square?\n", - "Type: function\n", - "String form: \n", - "Definition: square(a)\n", - "Docstring: Return the square of a.\n", - "```\n", - "\n", - "This quick access to documentation via docstrings is one reason you should get in the habit of always adding such inline documentation to the code you write!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Accessing Source Code with ``??``\n", - "Because the Python language is so easily readable, another level of insight can usually be gained by reading the source code of the object you're curious about.\n", - "IPython provides a shortcut to the source code with the double question mark (``??``):\n", - "\n", - "```ipython\n", - "In [8]: square??\n", - "Type: function\n", - "String form: \n", - "Definition: square(a)\n", - "Source:\n", - "def square(a):\n", - " \"Return the square of a\"\n", - " return a ** 2\n", - "```\n", - "\n", - "For simple functions like this, the double question-mark can give quick insight into the under-the-hood details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you play with this much, you'll notice that sometimes the ``??`` suffix doesn't display any source code: this is generally because the object in question is not implemented in Python, but in C or some other compiled extension language.\n", - "If this is the case, the ``??`` suffix gives the same output as the ``?`` suffix.\n", - "You'll find this particularly with many of Python's built-in objects and types, for example ``len`` from above:\n", - "\n", - "```ipython\n", - "In [9]: len??\n", - "Type: builtin_function_or_method\n", - "String form: \n", - "Namespace: Python builtin\n", - "Docstring:\n", - "len(object) -> integer\n", - "\n", - "Return the number of items of a sequence or mapping.\n", - "```\n", - "\n", - "Using ``?`` and/or ``??`` gives a powerful and quick interface for finding information about what any Python function or module does." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Exploring Modules with Tab-Completion\n", - "\n", - "IPython's other useful interface is the use of the tab key for auto-completion and exploration of the contents of objects, modules, and name-spaces.\n", - "In the examples that follow, we'll use ```` to indicate when the Tab key should be pressed." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tab-completion of object contents\n", - "\n", - "Every Python object has various attributes and methods associated with it.\n", - "Like with the ``help`` function discussed before, Python has a built-in ``dir`` function that returns a list of these, but the tab-completion interface is much easier to use in practice.\n", - "To see a list of all available attributes of an object, you can type the name of the object followed by a period (\"``.``\") character and the Tab key:\n", - "\n", - "```ipython\n", - "In [10]: L.\n", - "L.append L.copy L.extend L.insert L.remove L.sort \n", - "L.clear L.count L.index L.pop L.reverse \n", - "```\n", - "\n", - "To narrow-down the list, you can type the first character or several characters of the name, and the Tab key will find the matching attributes and methods:\n", - "\n", - "```ipython\n", - "In [10]: L.c\n", - "L.clear L.copy L.count \n", - "\n", - "In [10]: L.co\n", - "L.copy L.count \n", - "```\n", - "\n", - "If there is only a single option, pressing the Tab key will complete the line for you.\n", - "For example, the following will instantly be replaced with ``L.count``:\n", - "\n", - "```ipython\n", - "In [10]: L.cou\n", - "\n", - "```\n", - "\n", - "Though Python has no strictly-enforced distinction between public/external attributes and private/internal attributes, by convention a preceding underscore is used to denote such methods.\n", - "For clarity, these private methods and special methods are omitted from the list by default, but it's possible to list them by explicitly typing the underscore:\n", - "\n", - "```ipython\n", - "In [10]: L._\n", - "L.__add__ L.__gt__ L.__reduce__\n", - "L.__class__ L.__hash__ L.__reduce_ex__\n", - "```\n", - "\n", - "For brevity, we've only shown the first couple lines of the output.\n", - "Most of these are Python's special double-underscore methods (often nicknamed \"dunder\" methods)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Tab completion when importing\n", - "\n", - "Tab completion is also useful when importing objects from packages.\n", - "Here we'll use it to find all possible imports in the ``itertools`` package that start with ``co``:\n", - "```\n", - "In [10]: from itertools import co\n", - "combinations compress\n", - "combinations_with_replacement count\n", - "```\n", - "Similarly, you can use tab-completion to see which imports are available on your system (this will change depending on which third-party scripts and modules are visible to your Python session):\n", - "```\n", - "In [10]: import \n", - "Display all 399 possibilities? (y or n)\n", - "Crypto dis py_compile\n", - "Cython distutils pyclbr\n", - "... ... ...\n", - "difflib pwd zmq\n", - "\n", - "In [10]: import h\n", - "hashlib hmac http \n", - "heapq html husl \n", - "```\n", - "(Note that for brevity, I did not print here all 399 importable packages and modules on my system.)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Beyond tab completion: wildcard matching\n", - "\n", - "Tab completion is useful if you know the first few characters of the object or attribute you're looking for, but is little help if you'd like to match characters at the middle or end of the word.\n", - "For this use-case, IPython provides a means of wildcard matching for names using the ``*`` character.\n", - "\n", - "For example, we can use this to list every object in the namespace that ends with ``Warning``:\n", - "\n", - "```ipython\n", - "In [10]: *Warning?\n", - "BytesWarning RuntimeWarning\n", - "DeprecationWarning SyntaxWarning\n", - "FutureWarning UnicodeWarning\n", - "ImportWarning UserWarning\n", - "PendingDeprecationWarning Warning\n", - "ResourceWarning\n", - "```\n", - "\n", - "Notice that the ``*`` character matches any string, including the empty string.\n", - "\n", - "Similarly, suppose we are looking for a string method that contains the word ``find`` somewhere in its name.\n", - "We can search for it this way:\n", - "\n", - "```ipython\n", - "In [10]: str.*find*?\n", - "str.find\n", - "str.rfind\n", - "```\n", - "\n", - "I find this type of flexible wildcard search can be very useful for finding a particular command when getting to know a new package or reacquainting myself with a familiar one." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "< [IPython: Beyond Normal Python](01.00-IPython-Beyond-Normal-Python.ipynb) | [Contents](Index.ipynb) | [Keyboard Shortcuts in the IPython Shell](01.02-Shell-Keyboard-Shortcuts.ipynb) >" - ] - } - ], - "metadata": { - "anaconda-cloud": {}, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.1" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/02.00-Introduction-to-NumPy.ipynb b/notebooks/02.00-Introduction-to-NumPy.ipynb index 3ab2a4cf8..813b85ab4 100644 --- a/notebooks/02.00-Introduction-to-NumPy.ipynb +++ b/notebooks/02.00-Introduction-to-NumPy.ipynb @@ -5,8 +5,8 @@ "metadata": {}, "source": [ "\n", - "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "\n", + "*This image is aligned to left notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" ] @@ -16,14 +16,14 @@ "metadata": {}, "source": [ "\n", - "< [More Jupyter Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](02.01-Understanding-Data-Types.ipynb) >" + "< [More Jupyter Resources](01.08-More-IPython-Resources.ipynb) | [Contents](Index.ipynb) | [Understanding Data Types in Python](https://app.reviewnb.com/amit1rrr/PythonDataScienceHandbook/commit/d620f7d85ece9d5a73bf23598524fb008316b0dc) > LAST LINK CHANGED TO REVIEWNB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Introduction to NumPy & Pandas" + "### Introduction to modifying header and it's size & Pandas" ] }, { @@ -34,7 +34,12 @@ "The topic is very broad: datasets can come from a wide range of sources and a wide range of formats, including be collections of documents, collections of images, collections of sound clips, collections of numerical measurements, or nearly anything else. Let us look at following data formats:\n", "\n", "* Illustration of main Pandas methods\n", - "* Plotting DataFrame\n", + "* Plotting \n", + "* DataFrame splitting bullets\n", + "* adding new bullets\n", + "\n", + "\n", + "\n", "\n", "For example, images–particularly digital images–can be thought of as simply two-dimensional arrays of numbers representing pixel brightness across the area.\n", "Sound clips can be thought of as one-dimensional arrays of intensity versus time.\n", @@ -42,6 +47,10 @@ "No matter what the data are, the first step in making it analyzable will be to transform them into arrays of numbers.\n", "(We will discuss some specific examples of this process later in [Feature Engineering](05.04-Feature-Engineering.ipynb))\n", "\n", + "1. Adding numbered bullet 1\n", + "\n", + "1. Adding numbered bullet 2\n", + "\n", "For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to the process of doing data science.\n", "We'll now take a look at the specialized tools that Python has for handling such numerical arrays: the NumPy package, and the Pandas package (discussed in Chapter 3).\n", "\n", @@ -56,23 +65,23 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "'1.11.1'" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "4\n" + ] } ], "source": [ - "import numpy\n", - "numpy.__version__" + "import numpy # modifying code cell and output\n", + "numpy.__version__\n", + "a = 7\n", + "b = 5\n", + "print(b-1)" ] }, { diff --git a/notebooks/02.01-Understanding-Data-Types.ipynb b/notebooks/02.01-Understanding-Data-Types.ipynb index 2f053aae2..c5765cbe8 100644 --- a/notebooks/02.01-Understanding-Data-Types.ipynb +++ b/notebooks/02.01-Understanding-Data-Types.ipynb @@ -137,14 +137,12 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]" ] }, "execution_count": 1, @@ -153,111 +151,43 @@ } ], "source": [ - "L = list(range(10))\n", + "L = list(range(20)) # modifying code\n", "L" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "int" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(L[0])" - ] - }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Or, similarly, a list of strings:" + "Deleted multiple code cells Because of Python's dynamic typing, we can even create heterogeneous lists:" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']" + "[bool, str, float, int]" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "L2 = [str(c) for c in L]\n", - "L2" + "L3 = [True, \"2\", 3.0, 4]" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "str" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(L2[0])" - ] - }, - { - "cell_type": "markdown", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "Because of Python's dynamic typing, we can even create heterogeneous lists:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[bool, str, float, int]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "L3 = [True, \"2\", 3.0, 4]\n", "[type(item) for item in L3]" ] }, @@ -266,7 +196,8 @@ "metadata": {}, "source": [ "But this flexibility comes at a cost: to allow these flexible types, each item in the list must contain its own type info, reference count, and other information–that is, each item is a complete Python object.\n", - "In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n", + "\n", + "Adding extra line In the special case that all variables are of the same type, much of this information is redundant: it can be much more efficient to store data in a fixed-type array.\n", "The difference between a dynamic-type list and a fixed-type (NumPy-style) array is illustrated in the following figure:" ] }, @@ -282,8 +213,14 @@ "metadata": {}, "source": [ "At the implementation level, the array essentially contains a single pointer to one contiguous block of data.\n", - "The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier.\n", - "Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\n", + "The Python list, on the other hand, contains a pointer to a block of pointers, each of which in turn points to a full Python object like the Python integer we saw earlier." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Splitting markdown cell Again, the advantage of the list is flexibility: because each list element is a full structure containing both data and type information, the list can be filled with data of any desired type.\n", "Fixed-type NumPy-style arrays lack this flexibility, but are much more efficient for storing and manipulating data." ] }, @@ -299,18 +236,16 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" + "array('i', [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39])" ] }, - "execution_count": 6, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } @@ -318,7 +253,10 @@ "source": [ "import array\n", "L = list(range(10))\n", - "A = array.array('i', L)\n", + "L2 = list(range(20)) \n", + "L3 = list(range(30)) \n", + "L4 = list(range(40)) \n", + "A = array.array('i', L4)\n", "A" ] }, @@ -329,6 +267,10 @@ "Here ``'i'`` is a type code indicating the contents are integers.\n", "\n", "Much more useful, however, is the ``ndarray`` object of the NumPy package.\n", + "Adding line 1 md\n", + "Adding line 2 md\n", + "Adding line 3 md\n", + "Adding line 4 md\n", "While Python's ``array`` object provides efficient storage of array-based data, NumPy adds to this efficient *operations* on that data.\n", "We will explore these operations in later sections; here we'll demonstrate several ways of creating a NumPy array.\n", "\n", @@ -338,9 +280,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import numpy as np" @@ -358,9 +298,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -389,9 +327,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -418,9 +354,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -447,9 +381,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -489,9 +421,7 @@ { "cell_type": "code", "execution_count": 12, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -512,9 +442,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -537,9 +465,7 @@ { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -562,9 +488,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -587,9 +511,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -604,92 +526,82 @@ ], "source": [ "# Create an array of five values evenly spaced between 0 and 1\n", - "np.linspace(0, 1, 5)" + "np.linspace(0, 1, 5) # added new cells below" ] }, { "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 0.99844933, 0.52183819, 0.22421193],\n", - " [ 0.08007488, 0.45429293, 0.20941444],\n", - " [ 0.14360941, 0.96910973, 0.946117 ]])" + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" ] }, - "execution_count": 17, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a 3x3 array of uniformly distributed\n", - "# random values between 0 and 1\n", - "np.random.random((3, 3))" + "# Create a 3x3 identity matrix\n", + "np.eye(3)" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[ 1.51772646, 0.39614948, -0.10634696],\n", - " [ 0.25671348, 0.00732722, 0.37783601],\n", - " [ 0.68446945, 0.15926039, -0.70744073]])" + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a 3x3 array of normally distributed random values\n", - "# with mean 0 and standard deviation 1\n", - "np.random.normal(0, 1, (3, 3))" + "# Create a 3x3 identity matrix\n", + "np.eye(3)" ] }, { "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[2, 3, 4],\n", - " [5, 7, 8],\n", - " [0, 5, 0]])" + "array([[ 1., 0., 0.],\n", + " [ 0., 1., 0.],\n", + " [ 0., 0., 1.]])" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create a 3x3 array of random integers in the interval [0, 10)\n", - "np.random.randint(0, 10, (3, 3))" + "# Create a 3x3 identity matrix\n", + "np.eye(3)" ] }, { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -711,92 +623,73 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([ 1., 1., 1.])" + "array([[ 0.99844933, 0.52183819, 0.22421193],\n", + " [ 0.08007488, 0.45429293, 0.20941444],\n", + " [ 0.14360941, 0.96910973, 0.946117 ]])" ] }, - "execution_count": 21, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Create an uninitialized array of three integers\n", - "# The values will be whatever happens to already exist at that memory location\n", - "np.empty(3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## NumPy Standard Data Types\n", - "\n", - "NumPy arrays contain values of a single type, so it is important to have detailed knowledge of those types and their limitations.\n", - "Because NumPy is built in C, the types will be familiar to users of C, Fortran, and other related languages.\n", - "\n", - "The standard NumPy data types are listed in the following table.\n", - "Note that when constructing an array, they can be specified using a string:\n", - "\n", - "```python\n", - "np.zeros(10, dtype='int16')\n", - "```\n", - "\n", - "Or using the associated NumPy object:\n", - "\n", - "```python\n", - "np.zeros(10, dtype=np.int16)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "| Data type\t | Description |\n", - "|---------------|-------------|\n", - "| ``bool_`` | Boolean (True or False) stored as a byte |\n", - "| ``int_`` | Default integer type (same as C ``long``; normally either ``int64`` or ``int32``)| \n", - "| ``intc`` | Identical to C ``int`` (normally ``int32`` or ``int64``)| \n", - "| ``intp`` | Integer used for indexing (same as C ``ssize_t``; normally either ``int32`` or ``int64``)| \n", - "| ``int8`` | Byte (-128 to 127)| \n", - "| ``int16`` | Integer (-32768 to 32767)|\n", - "| ``int32`` | Integer (-2147483648 to 2147483647)|\n", - "| ``int64`` | Integer (-9223372036854775808 to 9223372036854775807)| \n", - "| ``uint8`` | Unsigned integer (0 to 255)| \n", - "| ``uint16`` | Unsigned integer (0 to 65535)| \n", - "| ``uint32`` | Unsigned integer (0 to 4294967295)| \n", - "| ``uint64`` | Unsigned integer (0 to 18446744073709551615)| \n", - "| ``float_`` | Shorthand for ``float64``.| \n", - "| ``float16`` | Half precision float: sign bit, 5 bits exponent, 10 bits mantissa| \n", - "| ``float32`` | Single precision float: sign bit, 8 bits exponent, 23 bits mantissa| \n", - "| ``float64`` | Double precision float: sign bit, 11 bits exponent, 52 bits mantissa| \n", - "| ``complex_`` | Shorthand for ``complex128``.| \n", - "| ``complex64`` | Complex number, represented by two 32-bit floats| \n", - "| ``complex128``| Complex number, represented by two 64-bit floats| " + "# Create a 3x3 array of uniformly distributed\n", + "# random values between 0 and 1\n", + "np.random.random((3, 3))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 18, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 1.51772646, 0.39614948, -0.10634696],\n", + " [ 0.25671348, 0.00732722, 0.37783601],\n", + " [ 0.68446945, 0.15926039, -0.70744073]])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "More advanced type specification is possible, such as specifying big or little endian numbers; for more information, refer to the [NumPy documentation](http://numpy.org/).\n", - "NumPy also supports compound data types, which will be covered in [Structured Data: NumPy's Structured Arrays](02.09-Structured-Data-NumPy.ipynb)." + "# Create a 3x3 array of normally distributed random values\n", + "# with mean 0 and standard deviation 1\n", + "np.random.normal(0, 1, (3, 3))" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 19, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 3, 4],\n", + " [5, 7, 8],\n", + " [0, 5, 0]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\n", - "< [Introduction to NumPy](02.00-Introduction-to-NumPy.ipynb) | [Contents](Index.ipynb) | [The Basics of NumPy Arrays](02.02-The-Basics-Of-NumPy-Arrays.ipynb) >" + "# Create a 3x3 array of random integers in the interval [0, 10)\n", + "np.random.randint(0, 10, (3, 3)) # deleted a bunch of cells at the end" ] } ], @@ -817,9 +710,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/03.09-Pivot-Tables.ipynb b/notebooks/03.09-Pivot-Tables.ipynb index 726d2019f..1f2d9e279 100644 --- a/notebooks/03.09-Pivot-Tables.ipynb +++ b/notebooks/03.09-Pivot-Tables.ipynb @@ -60,11 +60,165 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
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213female26.0007.9250SThirdwomanFalseNaNSouthamptonyesTrue
311female35.01053.1000SFirstwomanFalseCSouthamptonyesFalse
403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", + "
" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third \n", + "1 1 1 female 38.0 1 0 71.2833 C First \n", + "2 1 3 female 26.0 0 0 7.9250 S Third \n", + "3 1 1 female 35.0 1 0 53.1000 S First \n", + "4 0 3 male 35.0 0 0 8.0500 S Third \n", + "\n", + " who adult_male deck embark_town alive alone \n", + "0 man True NaN Southampton no False \n", + "1 woman False C Cherbourg yes False \n", + "2 woman False NaN Southampton yes True \n", + "3 woman False C Southampton yes False \n", + "4 man True NaN Southampton no True " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "titanic.head()" + "titanic.head() # added a table" ] }, { @@ -86,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -102,112 +256,73 @@ " vertical-align: top;\n", " }\n", "\n", - " .dataframe thead tr th {\n", - " text-align: left;\n", - " }\n", - "\n", - " .dataframe thead tr:last-of-type th {\n", + " .dataframe thead th {\n", " text-align: right;\n", " }\n", "\n", "\n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
fare(-0.001, 14.454](14.454, 512.329]
classFirstSecondThirdFirstSecondThirdsurvived
sexagefareclass
female(0, 18]NaN1.0000000.7142860.9090911.0000000.318182(-0.001, 14.454]First0.000000
(18, 80]NaN0.8800000.4444440.9729730.9142860.391304Second0.358696
male(0, 18]NaN0.0000000.2608700.8000000.8181820.178571Third0.226361
(18, 80]0.00.0980390.1250000.3913040.0303030.192308(14.454, 512.329]First0.647619
Second0.586957
Third0.281690
\n", "" ], "text/plain": [ - "fare (-0.001, 14.454] (14.454, 512.329] \\\n", - "class First Second Third First \n", - "sex age \n", - "female (0, 18] NaN 1.000000 0.714286 0.909091 \n", - " (18, 80] NaN 0.880000 0.444444 0.972973 \n", - "male (0, 18] NaN 0.000000 0.260870 0.800000 \n", - " (18, 80] 0.0 0.098039 0.125000 0.391304 \n", - "\n", - "fare \n", - "class Second Third \n", - "sex age \n", - "female (0, 18] 1.000000 0.318182 \n", - " (18, 80] 0.914286 0.391304 \n", - "male (0, 18] 0.818182 0.178571 \n", - " (18, 80] 0.030303 0.192308 " + " survived\n", + "fare class \n", + "(-0.001, 14.454] First 0.000000\n", + " Second 0.358696\n", + " Third 0.226361\n", + "(14.454, 512.329] First 0.647619\n", + " Second 0.586957\n", + " Third 0.281690" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fare = pd.qcut(titanic['fare'], 2)\n", - "titanic.pivot_table('survived', ['sex', age], [fare, 'class'])" + "titanic.pivot_table('survived', [fare, 'class']) # modified a table" ] }, { @@ -216,66 +331,110 @@ "source": [ "This immediately gives us some insight: overall, three of every four females on board survived, while only one in five males survived!\n", "\n", - "This is useful, but we might like to go one step deeper and look at survival by both sex and, say, class.\n", + "This is useful, \n", + "but \n", + "we\n", + "might\n", + "like\n", + "split into different lines to go one step deeper and look at survival by both sex and, say, class.\n", "Using the vocabulary of ``GroupBy``, we might proceed using something like this:\n", "we *group by* class and gender, *select* survival, *apply* a mean aggregate, *combine* the resulting groups, and then *unstack* the hierarchical index to reveal the hidden multidimensionality. In code:" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Multiple output with mixed types\n" + ] + }, { "data": { "text/html": [ "
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survived
fareclass
(-0.001, 14.454]First0.000000
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sex(14.454, 512.329]First0.647619
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" ], "text/plain": [ - "class First Second Third\n", - "sex \n", - "female 0.968085 0.921053 0.500000\n", - "male 0.368852 0.157407 0.135447" + " survived\n", + "fare class \n", + "(-0.001, 14.454] First 0.000000\n", + " Second 0.358696\n", + " Third 0.226361\n", + "(14.454, 512.329] First 0.647619\n", + " Second 0.586957\n", + " Third 0.281690" ] }, - "execution_count": 4, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "titanic.groupby(['sex', 'class'])['survived'].aggregate('mean').unstack()" + "titanic.pivot_table('survived', [fare, 'class'])\n", + "titanic.pivot_table('survived', [fare, 'class'])\n", + "print('Multiple output with mixed types')\n", + "titanic.pivot_table('survived', [fare, 'class']) ## multiple tables in one output\n" ] }, { @@ -910,74 +1069,25 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 7, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - "gender F M\n", - "decade \n", - "1960 1753634 1846572\n", - "1970 16263075 17121550\n", - "1980 18310351 19243452\n", - "1990 19479454 20420553\n", - "2000 18229309 19106428" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'births' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbirths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbirths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m//\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mbirths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'births'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mbirths\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpivot_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'births'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'decade'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gender'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maggfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'sum'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'births' is not defined" + ] } ], "source": [ "births['decade'] = 10 * (births['year'] // 10)\n", - "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')" + "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum')\n", + "births.pivot_table('births', index='decade', columns='gender', aggfunc='sum') # converted table to error" ] }, { diff --git a/notebooks/04.14-Visualization-With-Seaborn.ipynb b/notebooks/04.14-Visualization-With-Seaborn.ipynb index a16416ef1..652627b2a 100644 --- a/notebooks/04.14-Visualization-With-Seaborn.ipynb +++ b/notebooks/04.14-Visualization-With-Seaborn.ipynb @@ -57,9 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -79,9 +77,7 @@ { "cell_type": "code", "execution_count": 2, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Create some data\n", @@ -100,15 +96,13 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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H3PzFzWwq28SW8i2UmEoIVASSGCx5ESgVSqakTOFPa/5EdnW2X/pXairF7DLz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\nSKoIUuBLJBJJFeH/OyKinWS1qMsAAAAASUVORK5CYII=\n", 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4GLhTFEWL0At748MPP9z+PDs7m+zs7ECJJSNzSrPXam2vd7qjpYVf9LGC0us1NQwNCWFqD/3RX6iqotzlYoJez06rlbMjI/vUrz98osjSkhIeHTKk1/sSFU4nscHBaAZR2cCTxfr161m/fn3/GhFFsd8PIAhYC9zd4b0DQELb8wTgQDfXiv7o7v3BxDvvvCNOnjxZ1Ol0Ynx8vHjOOeeIGzduHGixTippaWmiRqMRdTpd+6OqquqY15wK3+1g4YWKCvF3BQXi69XVomHDBvHa/HzR5/P1up0xW7eK5+zZ0+Pzp2/fLm5obhbvLSoSr+tjn91RZLeLrFsn7rRYOr3v8/nEVQ0Nos/nE30+n+j2ertc+1lDg7hg9+6AyXIq0/Z/1Ctd3W+TjiBN5V8D8kVRfLbDodXA1W3PrwZW9bevwcSzzz7LnXfeyYMPPkhdXR3l5eXccsstrFr1s/qYPeLoEoeJiYkDLdLPAlEU2Wg2k6XXc21CArumTOEns5l1JlOv2vlNbi65djv7rNYene8VRfbbbIzX67k3JYUfzeZOicj6S57NBki1ZSvb/OgB/ltTw0X791PjdvNxQwMX7NvX5drvTKbTOr1xfwmEDX8WcCUwXxCE3W2P84C/AwsFQTgILGx7/bPAbDazdOlSli1bxiWXXIJOpyMoKIhFixbx9NNPD7R4MqcQPlFktdHo99i/q6vJs9u5uC1SdEhICIuio9naC1u82+fj/YYGACxeL3Vu93FdYtc0NhIbHIxBpSIuOJjrEhJY2Y2MfSHXZmO2wcDfy8tJ2bKFrRYLNq+XZVVVgLRZ/GZtLRvMZlw+H26fD2+bzN80NXUpJC7Tc/ptwxdFcRPQnSHuzP62PxjZvHkzTqeTiy++eKBFkTmFOGC3Y/J4mN7Bjp5ns3HR/v18OW4c50RFtb/v8vl4pqKCt0eN6pThcZRWyw+9mOFXulwA/ComBpUgMC4nh3tTUrgvNdXv+auMRn5/4AAft0WrAlwUHc2Zu3fz4ogR/Y4F+KKxkQdKSnht5EjOi4zkE6ORubt2ESQIqBUKro2P57K8PKKCgogOCuKS/fuJDApisl7PBVFRGFtbmRwa2i8ZTmdO7UjbQASi9CEAqLGxkejo6E5l/QaS9ev7fx+ys/tf6lFqJ5uVK1f2W56fI+/U1ZFns/FRB2W6u83Msryurl3hG91urj9wgIl6PWcYDJ3ayNRqebm6usd9ljqdzDMY+GDMGDaaTGy1WHi7ro4FERGkqNXEdIhErXQ6eay0lNcyM5nVod+RWi16pZIdLS093vTtjifLy3k0PZ0lMTHoVSpuSUri5sRE1jY1scNqRS0IBCkUFM+YwdqmJn6Zm0uwIFDscODw+bgkJmZQFwkf7AwOjdVXBihaMyoqCqPRiMfjGRRKvz/KOhDIJQ57RqnTyRaLhcdLS/lzWhqCILDLamVxdDQH7fb28/5VVUWQILDcTzGdTK2WXJuNzxsbsXu9XBoTQ0ePuLHbtnFXSgrXJyQAUOZ0tpf5mxMeTuH06YzZto3JO3bw59RUHu9QECRlyxYAzvZjMlkcHc2HDQ39UviVTie5NhvfZmV1KiIuCALnREVxTlQUm0wmroyLQ6dUcklMDLclJfF6TQ0FdjulTicfnYb1JgKJnC2zD5xxxhloNBp5JivTK8qcTqrcbv5aWsqXTU2UOhx8YjRyZVwchQ5Hu219tdHIHcnJ6Py4HkYGBfGvESNYkpvLkrw8Ch2O9mO7W1rItdvJbdsUBWmQSe9Q11UpCBRMn867o0ZxoO1as8dDndtNuEpF86xZqPyUALw+IYE3a2tx9CPy9tvmZs6MiOik7I9mdng4r44c2f56fng408LC+L+hQ0lSq5khpznuF7LC7wMGg4FHH32UW2+9lZUrV2K322ltbeXLL7/kvvvuG2jxZAYppU4nM8PCuDM5mYdLS3mwpITfxsZySUwMQYJAfWsreTYbtW43ZxxDsV2fkMCrI0eSpdOxsYM9/8OGBpLVamo7BEoV2O2M8BOkNEqrpcBux+rxkL5lC6/V1JCl0xEeFOS3zxFaLWN1Or7th7fOV01NLOjlhuvi6Gg+GzuW3ycm8tPEifQmvieQiKJIy66WAek7kMgKv4/cfffdPPvsszz++OPExMSQkpLCiy++yOLFcgYJma54fD5q3W7WTZjAM8OGISLVXD28eTpSq2VpSQlLcnO5NSnJ7yy7I5fHxXFLUhLrOyj8zxobuT0pidIOro5bLBa/xT8ytFqKHA7eqa/H4vHw55ISxnfIMumP+eHh/GAykWeztXvNHEYURXzHMLFWu1x83dzML2NijtnH0QiCgL7NbHq8e3IiMW80s2PSDuo/qu/xNXXv1tH4eePxTzyJyCUOZU46p+N3e3V+PgV2O1snTwakAaBVFAlpM9vkWCw8V1nJRdHRXBgV1aNI0lqXi1E5OZTPmMEuq5VrCwpYN2EC03fupGbmTGpdLkbn5NA4a5bfmfHobdtobG3lzuRkHiwpYc+UKcdU+ptMJubv2UOrKPLB6NH8qkPysn9XVbHOZOKDNhv7BpOJt+vq+Nfw4WiUSh4sLqbF622vO3uqkXdFHs5DTtQpasZ84H8foembJprWNuEsdTL8meFsGbqFkGEhTC+cfkJk6ku2zIHfcZSR+Zlj9Xj4xGikdubM9vdUCkWnf76pYWG8M3p0r9qNV6uZHx7Ou/X1bDCZuCclhWS1GpPHg8PrZWtLC9PDwro1gzySns5fS0r4U2oqD/QgzfBMg4G148dzwG7n/fr6Tgr/88ZGvmpqYrvFwpSwMFYajfynpgaDSsXD6en8p6aGLZMm9erzDRZEn0jTV02MeGEEVS9WdXte7eu1NHzUgOgR0Y7UEn1RNJbNFhzFDkKGDo7cP7JJR0bmBLPBbGZKaKjfTdj+8rvERF6trmZ7SwtzDQYUgkCqWt3uETT9GD7rv4qNZd/UqT32rVcIAr+IiODXsbH8YDa3R+56fD42mc3ckJDAh21BXkUOBy9nZPDvqipeqqpinsFwyma3tO23ERQRRNi0MNzVbqz7rV1WqO4GN41fNqLUS99x5bOVhGeHE/6LcMwbzQMhtl9khS8jc4JZXlvLhR2CqgLJwogIGltbqXC5yGyr5TpEo6HE6WSrxdIpyMsfx/KY6Y6IoCDuTUnhX22RsZstFtI1Gm5MTGRVW0Rurs1Gdng4Y3Q6Hiot5Q/Jyb3uZ7DQ+HkjEQsiCE4MxlnmZPu47exbtA+vQ/JYctW62H/RfhJ/l0j8tfHoxuvwOX3E/CoGzRANzjLncXo4ecgKX0bmBLK5LffNDW1+8YFGIQjcmJjIOJ2ufVMzXaPhgN3O9pYWvxu2gWCewcDOFslrZZXRyEXR0UzU66lyuylxOKh2uxmm0TDLYCAmKIjZRwWQnSqIXpGaV2uIvz4eZYgSVYSKyHMj8dl8NHwkrWYaPmggOCGYIf83hOHPDifjpQzir4lHnaBGk6rBVeEa4E9xBNmGLyNzghBFkesOHOCVjAxCT2CA3t3JySzp4P0yJCSEt+vqyNRqierGzbK/jNfrybPbKXE4+KqpiTcyM1EIAuN1Oq4pKOCy2FhUCgWXx8UxOTT0lC3P6Ch2IPpEwqZIA6c6SU3YGWEo9UosWyzEXxmPPd9OeHY4CpU04BpmGTDMkgY4dYqahk8bBkz+o5Fn+DIyJ4i9NhsOr5fFbcnPThQhSiUj2sw5IJl0dlqtnBvAHPZHo1Uq8YkiQ7dupcLlai8cPkGvZ7PFwtK2TeDJoaFcHhd3wuQ40TgOOtBmHrm3YTPCiDgzgrAZYdT8pwbrHiu2fBvaUVq/16tT1LjK5Rn+cUlrCz2X+fmRdpoUnn66vJwr4uJO+u/4rIgIXh85kl/10ue9t1SccQY3HDiA2+drNyddGB1NQnAw6afoBu1hjKuMhM8Px3HQQciII59l5KtSFLDP5SP8F+HsWbiH1oZWdO/o/LZz2KQjimKvfwetja0ERQV2hTZo/fBPFm/vfZsP8z7kh9IfKLitgHh9/IDJIvPzIc9mY+GePRROn35CvHMGC3k2G02trcz+GeWor3yxkqLbixjz0Ria1zWjzdCS/Af/m861b9diXGlkzIdjulXoW4ZvIfP1TMLnHvseiT4RQSHgKHZQ8UwF9e/WM2nbJLQj/K8e+uKHf9qbdLZWbmV2ymwuyryIv238G63e1oD3sbduLyXNJeTW5wa8bZnByTaLhezw8J+1sgcYrdP9rJR9y+4Wyh4vI2ZJDPZCO/Y8OyEZ3a9W4q+IZ+xHY485e09/KJ1D9xzC5/F1e44oivyg/IHGLxtp+qqJ6peq8Zg82HJt3V7TF057hb+pYhNz0uawdO5Sdtbu5HdrfhfwPu79+l5mvzGbeW/Ow+YO7BcoM/gostv5T00NU+S87acclc9WknJXChELI7BssdCyowXD7P55GMVdEYcqXEXVi1XYD9hxN3QtCu+uld479MdDWLZYCDsjDN04HfYCe5dz+0NAFL4gCK8LglAvCML+Du9FCoLwjSAIB9v+npAyNZsrNrOjegcAb+1567gKtaS5hKyXsyhuLsbislDUVMSkhEkMixzG2ivW8lHeR5icvSshdyxcHhebyjdR3SLlMH9v/3sBa1um91i2WtiVveuEpnZ4tKyMnywWObPjKUarqRXjKiMJNySgHamlcXUjEWdGoNL3b6tTEASGPTOM8r+XU3B9AQXXFHT5/dlybRjmGHCWOKl7q46MVzJIviOZ5q+baW0OnNUhUDP8N4FzjnrvfuA7URRHAN+1vQ44r+x4hX9t+xelplKuXnk1G8s3dnvuv7b+i6tWXsXeur3sq9vHrppdjIsdR7BSKgKhDdIyJXEK1626jkpLZUDk21a1jdExo7llyi08MPsBNpRvCEi7pyOBUNKWHAvmH8w0fdkUAIn8U+Fy8eHo0V2Kl8gMboyfGok4M4KgqCB0o6VN2KFPDT3OVT1DP16PbpQOy2YLrcZW9i/eT9VLVYhe6Tdtz7WjG6sj7c9pJN6SiG6sDv0kPaZ1Jsr/rzwgMkCAFL4oihuAo/+DLgKWtz1fDpyQNJIFxgLWlazj8Q2PE6wMZmfNzvZjN94I114LPh+4vW7u+OoONlds5rKxl1FrrWVX7S4mxk/s1F56eDqfFnzK23vf7rdsPtFHTnUOM5JnsOz8ZZw17Cx+qvip3+2eruy/cD/7L91P2d/K2t9zG7suj7uj1dSKu9qNNlPLvgv3sWfhHgBadrXgrj/STtXLVdR/0POsiB0RRZF9VuspG2h0uuA45CDv8jw8Zg8FNxSwbdQ2at+oJXy+tB8RFBVEtpiNdrj/DdO+EPubWEInhzJx40T0WXoO3nqw3WTTsqsF3XgdaQ+mkbEsA0EQCJ0YytiVY7Hu61nx+Z5wIm34caIo1gC0/Y09zvm9RhRF8o35VFgqyKnO4blznuPtvW9TYCzg7jVL+fBDyM2Ff7xxEPXjatLD07E9aGN45HBqrDXsqt3FhPgJndp8/BePc+vUW/k4/2M2lW/qs2xri9aifFTJX77/C5MTpAyJo2NGU2eto9nR95zipyueFg9NXzVh/NhIxdMVALjr3WxJ34LP7X8zzF3vxtfqQ/SJGD8z8mPEj1Q+V0nynclEnhNJ87fNNH3TxI5JO6j4R0X7dQdvPkjer/MQfb1fUdS43QiCQFyH0oEyJx5Piwfj6p4XWq98vpL6d+upe6eOhvcb8Jg9mDea0U84doro/pBwYwLj1oxDEaxgyKNDiFkSg3W3lJfH9J2JiPldrd76SXqsuzrn7rFst7D3/L19kmFQ+OE//PDD7c+zs7PJzs4+5vlGu5F7vr6Hs4aeRbAyGNuDNkJUIVS1VHHz5zez8K2F1Lc0csGCh5l1+Q88sOlmCIOzhp6FWqUmQZ/A7trd7KrZxe3Tbu/UdlJYErdPu51ly5ax5MMlrL9mPSlhKVy/+nreueSd9t349/a/x/rS9bx8wct+Zfx8/0aGa2ZQ5NxC9c6J7FFAVpaStPA0ys3lRISckC2Nny3mDWbCZoYRfXE0Na/WAFJSK5/Nhz3fjj6r8z+qrcDGrpm7CE4IRmVQ4SxxEvubWOrfq0eTrmHcZ+P4KfYnCq4uIPHWRExBjN6GAAAgAElEQVTrpX0b0SeiDFOi0Cho2d5C2LTe2eH32WyM0+nkGJKTTNMXTRTdWUTUoqjj3nuf20fdO3WEjAyh9OFShj41lKCoIPJ+nYd+/IlT+IJCIDj2yERAn6XHuttK6JRQUNDJ3/8w6mQ1+MBd42Zz4WbWr19P7fJanKV9y89zIhV+nSAICaIo1giCkAB0u0buqPB7wk8VP7Fizwq+KvqKCfET0AZpefVVCA5OZsv1W5jx2gwAZl+cx/Nl19Kqq+H1abu49lxpNp8QmsDKAys52HSQsbFju7SfEZXB1hu28nHex0z/73RumnwT/9v/P55e+DSbyjeRb8xn9YHVaIO6X+6t3Z9D0xcP8vT/1fDHM8dy4HJYsQKSQpOoaqkiKz6rV5/5dKfhkwaiFkWRcH0CJQ+WIIpiu8uadbe1i8JvXNNIxMIIXBUuvC1e4q6II/qSaOrfq0edokYQBHxOH16jl2H/GMZPcT/hqnHhtXpRRaiI+WUMTV839V7hW62M1/kPwpE5cZg3m3HXunFVuah9rRbHIQeZyzP9Kv/m75rRjtSin6in+qVqoi+ORlAJxF0Zhyrs5M2B9ZP0lD9Rjna0FsNsg19ZBUFAP1FPy84Wsi/IZt68eWx6dhPBI4NZfmC5n1aPzYn8dKuBq4G/t/1dFYhG3977Niv2rOCCjAtYU7iGc4efC8C770JQEHx+5UTUSg0up8Crzb9iTtocosveonY3/OsgfPwxPPVOAt8c+oaxsWPRqDRd+hAEgWlJ05iWNI3ZqbO58L0LAcipzuGmz2/C0epAo9IQGSKFrufkQGoqHI4gd3qclLi20bpvKiUfxqMOgsNlRpNCk6iydJ9T+3Sm7n91FD9QTOabmURkRyCKItbdVrSjtBg/MTI1dyqqUBUKrYLW+lZs+2yo09RYtlmIv7pzwJw9107EgggSb0zE6/Ci0CjwOXwoNApp1gRM/HEiQdFBKDVK4n4bR+lDpYg+kagLoog8K5KyJ8pI/0t6j2QXfSL2fDt7FTbm/Yz80k8VLD9ZCIoOoiWnhZrXa3CVu0j5Ywq6sUdWW6Iogghlj5eRcH0CMUtiGPLYEIIipWjWUSu6Fo0/kUT8IoL8K/Jp+LiBiAXdr/j1k/RYd1qJOi+K1sZWBJVA1PlRcKD3fQbKLfN/wGZgpCAIlYIgXI+k6BcKgnAQWNj2ut/8bdPf+Kb4G84dfi5jYsZwQcYFOJ2wfTts2QK+1mB+l/I8yY1XoVQoefn8l5k9GzZuhKVLYcMGiA0egojIqJjjf8GLRi4i58Ycrsq6itd3vc6YmDHMSp3Fg3MepMZagyiK3HADvPbakWv+u/M1KJ/DJWfF8/LL8MADkJ8vHUsKk2b4Ml0pfaiU8Hnh1L5ZC4A9386OSTtoyWkhOD4YdaKkqEOGhFD2f2UYPzWS8XIG9e/XY9lu6dSWLdeGbow001aGKBEEAaVWyYzSGe2zOP14fXubKX9KodXYirPUSdpf0jDMMWDdZcVj8fRIdvMmM3vO2tNu0vFHq6kVV+3gyasyGLFst/TaG6thZQMek4fUB1Ipe7wMr81L9OJods/dzdbhW9sDngpvLmSDdgM+h4/4a+NRharalf1AoFAriL8qnqbPmySzTjfoJ+gpfaiUkqUlOA45CBkWQup9qX3qMyAzfFEUL+vm0JmBaP8w+Q357e6SGVEZ7L9lPy6fjy0bYfRoiI+HJ56AUYm/wyI288w1PnTBOubNgyuvhMREGDIEjGWxlN5R2u6OeTymJE5hetJ0bv3iVu6beR+P/OIR1Eo1j294nF35Zvbu13HgvGHc5ytGpVDx5o53Cc1fymtfg1YLt94Kf/87tLZKM/zt1dsDeVt+FvhafTjLnAz/1/B2NzR7oeTBUPGPCtQp6vZzw84Io2pZFZlvZhJ1ThSZr2Wyd+Feku9Oxl3rZvjzw7Hl2dCO7mpyC47z/52HpIcw9pPO5r2wGWGY1pmIvuj4yc8sWy24q92EbGgldocFU6aP8HmdZ/rlfyuntaGVzNczj9ve6Yhpo4ndc3cz6t1R2PbbSPtrGkrN8SOVq16oYujfhxJ1YRT2AjvpD6XTsqMF40ojHpOHuhV1JFyXgGWLBdElknxXMoJicOyxDHliCD63j9DJ3Sv8iAURJPw+gfInyql6oYrIsyO7/R0fj0EVaSuKYLF0f3xr1VbOH3E+UxOnMjZ2LFvMZrJyclj/g0jwDaVk/72GF1+EQ4dgZFoEUVqp6ER0NFx8McyeDWPHwv79kBaeRkJoz3OUXz7ucuamzeXiURejUWkQBIGE0ATeXlXF3Ou/wqWpYF9ZJWanmVzjHiZFzyU8HN56S+o/Lg4qK6UZfmFT4WlX0/V4OEudqBPV6EbrcBxyAOA44CA4PpjGNY2oU48o/JhfxYAXIs+STGrRF0UzaeskSh8qpfqlaur/V492hJag8P7N3iLPi6RxTc+KUFu2Sj/c/7tHpPSWIoofLO5yTtPaJmz7/QcGHrrvEFsztuIodvRd4FOcqheriL08lsLfFVL1QhVFdxbhrnN3iUx1lDja3/OYPbRsayHy7EgUKgUjXx1J9IXR6EbrEFQC478eT9njZfg8PlxlLiasn0DcbwdP9k5liJKMFzNQhnQ/sAVHBzPy5ZEk/SEJr8XbXlWrLwwqhb96taSYu2NP7R6y4rLYduM24vXxfGcyccDh4N2WanaPqOTllnLi4kU++wyOTsi4bBk89RSMGSO5avYWg8bAD9f8wIzkGe3vmZwm/ukey4Ykycb/8D/L+KHsB5LEGUwc23nHPSUFysthbtpcTE4Tr+96vfdC/IxxHHQQkhGCOklNa2MrXocX+wE7Mb+OAZ+UdfAwhlkGstZldfJ40GZoCZsRhqAWKPlrCdG/7H9K4ujF0dT8t4bSR0rxWLs37biqXZjWm3BPDqFpqJI5ljnY9tkouL6A6lekCGu30Y2jyIE9397F3dNR7KDm9RqiL46m9JHSfst9qmLPt5NyTwqzmmYxeedknMVOtmVuY/uE7XjMR+5/6dJSKp+VVvpNXzVhmGtAqeusBMPOCCPu6jgiF0aiilRRdGcRqggV4fPCEZSDY3bfW0Y8P4K5zrmMeKnvheAHlcLftAn2HsO9dG/93k7eLRtMJsZpdRTOP8RtCUl4RZHxi+wUFnZV+OHhEBsLI0ZAUVFg5L1xzD1ov/835X+oYfGIJazfU8p7275FVb6Ao+s1p6RARQWEqcP425l/4y/r/sKXB78MjCA/A+yFdkJGhCAoBTRpGpwlTqx7rEQvjkZQC51m+G5E/jfC3mWVNH7teBKuS8BV5iLuiv7P4kKGhJD2UBqm9Saq/tX9vkvF0xUkXJtA1ZxgrNlalDolw/85HNteGyVLS8i/Jp+yx8rQjdahNCip/nc11f+tbr++9JFSEm9KJHpxNPb8wOZOCSR5l+Vx8A8HseUFPh+U6BNxFEmpiBVBCrTDtYxdNZbI8yLRjdNR93Zd+7mOIgfN65rxuX0YPzMStahr+UhNqobM/0qms2H/GIarzMWot0/upuyJQKFW9MjM1e31AZSl32zeDEYjNPgpEOP1edlVcyQydq/Vyi6rlb84x6K2qrkyJZaRWi3jrmvi4fcsTJvmv4/hwyWFX18PxcXw7beSbb0vpFXex0VJN5ESEc+Y+BFkTC/m60NrqfxhIeef3/ncwwofYN1rC6m11nLeu+f1reOfIY6DjvY0sPrxeurfq8dV6cIw20DopFBChh9ZMb1RU8OtBw/ydXPnADZVqArDHAP6iXpC0gOTj33Iw0OIvy6+3RRjy7ORf1V++3FRFDGuNhJ3RRwbrlHjXSqZCROuT2DS1kkoghUYPzZS9UIVmnQNhpkGDt13COMnUpCQ2+jGuNJI6p9S0WZosR/oOpANBkSf9DkRYc+CPRT/uRiv10ZJyUMAuN09D3ryh6vChSpC1SlvjTJEyeh3RhN1QRQNnzRQ8lAJtjwbjiIHLVtb2DZqG01fNhG96NiruYjsCMZ9Ng7DTDn6edAo/Nf+5qTmYCvjxkFeXtfjOdU5JIclE6eXZm6PlJbye20av54Xwm3bpjFGpyNNo2Fp7SEejtvJstqKro0Aw4ZJiv6qq2DGDFi4EO67r28yf/45XHCB9DzVkMp23aNYa5L5xaiJHJ0o8bBJB+DTj4K4rMiJWqnG4/NvKrB5vayorR2U//wngo6FJgxzDJQ/VU7cb+NQqBRkfZvVXjIO4O26Oq6Nj+eR0tIu9yfusjim7JwSUNl0o3XY8mwYPzNScE0BdW/VYd5sBiQzhNgqohuvo8TnIi3syEAjKAQm5Uxiwg8TQARNuobE3yfis/vaZ/KucheaIRrJYyQqCCFIoLU+8Cm6u6PhkwYs246xcdaGs8xJUGQQI14YwZS9UzCuMrLv4bcoK3sMu/0gW7cOxefruweS+Ucz2pH+41p0o3WYvjdR9lgZOWNyaDW2Mq1gGiHDQohdEos6Se33OpmuDBqFH/3oLv7b9BPTp4l+Ff7Xh77m7GFnA1DlcvG9ycSosniysuChpZJNLk0tffHLMzN5uLQUk5+pu14PDofkpglwzTWwbl3v5XU6pevOaUsZd9aws7gs5m+4XvuCcWO73tbUVEnhl5RAWRns3qEmMTSRUlOp3/a/a27m6oIC3q6r83v850ZHhR8+LxxESL5bKjqh1Co7BaUUORw8kp5OY2srP5rNJ1w2baYWR6GDkj+X0NrQyrB/DmPfBftwljtpXNNI1AVSdGep00m6pnNchzpeLZXIEySFHz4/nOlF03HXuvHavbgqXe1xAQDakVps+f0zmYiiKO2BFNpxG6V+/GHJsZD7y1yqlh3fTdiWa0M7RlLIwdHBTPxhIjbVj4BIY+NneL0tWCxbEEUvXm/vNp4tWy0U3VFE2lL/ldAOe1vFXhbbnpteO1Iy+Qx/YXiv+jrdGTQKv9UrIHhEJhus7T7rHdlTt4epSVMBuLOoiFsTE2msUDFzJu2z6bS2f7bzIiNZGBnJe/X+g3svvxxWrZJ88l94AQoLjwRGATS1tuI9zsx6/XoYPx4Olw1ND0/n9on3g1dNph+vuzFjYN8+yYR0ySWS0h9iGEFhY2GXc72iyM6WFvRKJZuP5bYUYFpauj+2eTOUlkqDpbvn+cp6hM/lw1XjQpMufX/6LD0zimegSekaFGf1eLB4vSSp1VwWG8uqxp550fQHpU5JcHwwtn02puZPJeXOFKLOj6LpyyaMKyUbssfno8btJlnddbap1CrRpGnQpEneXSHDQggZHoK9wI6rytVphhqeHd7rTJ6tza3tXkIgebtsy9zGtlHb2Ja5jfInu2Zb9Hl8VL1QRdiMMDxN0iqz5KESfoz50W8f9lw72nEqamreAKTkYkzYTpBjKI2NnwHQ3PwtlZXPsW/feb1amZY+VsrQJ4cSke0/+Cg4LpiIhRGMWDaCSVsmMX7teEAy+RwuHC7TMwbF3fK0eAhtdRN1dQLDLCa/M/wDxgNkRGXg9Hr5vLGRB9LSqKiQTCWHSddoiA8OJjo4mLuTk3m8rIyXq6rYfpTSfPttWLAAMjOlGf+4cbBjx5HjyZs3M2/XrmPKvGbNEXPOYVLbYiH8KfyhQ6VB5a234NxzYf582PNdJm+sycPq7pwN78YDB3ikrIyLo6OpcJ3YQB2HQzJz3XsvhIV1Vvoej7SnYrVK3lNnnQUZGTBq1LEHh95S/lQ5oRNDUQQd+Tl2t0wvdjoZotGgEATOiYzky5Og8AGyvssi883M9g2ziAURlD9djrvWTd5kgbfq6ogJCiJY4f9fatgzwzoV0jDMMdD0dVOXGX7MpTHU/LeG3fN39yhLoqvaReHvCtk9fze75+9m+6TtlPy1hOCEYKIvikY7Qkvz112T9ZX8uQTLVgvpj6XjKHLg8/goe6yMVmOr36RxtlwbiokHKSy8CZ/PjdNZgU9fj3rfLzGZ1qN2ZFGzaw1NTV9jNv+I2XwkTbkoirS07O72M9jz7ccsMiIIAllfZxEUHkRQRFC7O65M7xkUCr92i51KhZa48yMIO9TcReF7fV4ONR8iLmgE//e+DXVDCA0Vyi4Kf2poKKvGSsEzZxgM/Dktje9MJi7Pz6fV1315senTYetWaG5tZdiWLYjAjxYLFo9/+7ooSgr/6I3Z+HjJ537kyK7XCII0IGzcKO0bXH89WPLO4CPLHwn9W2eDf6lTSox0WWws5U7/SZIO2u2csXMnFo+HfJt/E8Ann8Dzz/v/zA88AJ99JkUnFxdLJqrwcGkVcpiPP4bFi6U2srPh8celAWvoUPjTn+D777u2+/TTnaOOj4fP5aPiHxWM+XhMj84vcjgY1lYge0poKEUOBw6vf5NFIAkZEtIpfUP0RdFEnh1J5huZPF9fzXUHDnBhdPebhzGXxKAyHNmQjPllDMZPjF0Uvn6CnlFvjUIIFmjZdvxRNfeXuXhtXjJeySDxpkQyXs5gZs1MJm6ayJiPx5C1Lgtbrq2LL7t5o5mRr47EMNOAo8QhpbDI1BKcGIyrouskw5Zrg7RDiKKb7dsnUVx8H2FBC/FtkpwolNsW4o7cTXPz1yQl3UZDwydHrrXlsmPHRMzmrqnBvU4vrippH0PmxDMoFH7lqmYqwg0YZhlo3WvB5RT5XYdKgxnTyolQxvLVjyoe0+3FlafnrrvoovBVCgXTOlQZujkpiQ9Gj0arVLLVj2lEFEVaPB4yz3Czdas0eyx2OtErlUwPDWWv1f8Mq75eChAbc5SOUiql4KruUqE//7zkepqUJCnSPavn+D2vxOnkwLRpTAsLo7zDDP/rpia+aJvRvlJdTYHdztQdOxidk9OljdZWuOsuqc/VqyUFfxiTCV56Ca67Dj76CO6/H158ES69FHZ3mIjt3SsNCP/8p6TslyyRFP/558N//gO/+Q38+KN0L77+Wrrmyy/hueekQfF4tDa10rimEd1YXXuKg+NxqIPCVykUpGo0lHQzKJ5IVAYVGcsyMMw1sNFs5tbERP5ytC/wMTDMNeAodmDeaG43ZYE0m406L4qwqWE4K479uewH7ThKHIxdPZb4K+KJXRJL2LSwdlOHIAgoNUpifhlD7eu17df5PD6se63oJ+hRapWok9QcuusQhlkGQkaEYD8obSh77V6av2uW8gQV2Gk1FCAIauz2Aurr3yMt8y6cGxIBcOYqYPUlpKc9Qnz8NRiNH+NwHGLv3vNoaHgftTqZ8vKnunwGR6GDkKEhnVZ3MieOQZEeufLTOnZcrsUdq0QZomT/pw7GnaPl/vsl+3yxdR+PvPwANt9uWOHh/evjuPZ5yeQw/Dh7NoIgMFqrpdjpZHaH90VRZPrOndi8XsLTgynfOoGyNsUxWqtllFbLbqvVb4HmggLJrOEvC6sfE247c+d2fj0qKYngse/jtn/PzYWFmD0eKlwuSp1Ohmo0KAUBt8+HxeMhTKXiPzU1aBQKzouK4vOmJj4eM4bL2pZDNq+3U8Hs3FwprYPdLin7Awfghhsk+ZYulTabIyKk2fiGtiJcEyZ0Vvj794NKJSn6jvf5qquk1x6PZOaZPVtaGVRVSdeLIhw8KJl/ukP0iuSMzwFf76oKHXI4GNshV82wkBAOORyM7mWGyt27pRXMY4/16rIuFDudBCsUvHisD+sHRZCC6Aujaf6+GcOcrjMEdYq6i/eMKIr4HD4aP2+k+ZtmbHk2Yn8Te1w7dvKdyexZsIfm75vRj9cTf0086kR1+4pj/Nrx1L9XT/RF0VS9UIWj0IE9xU7po6U0fNRA1tdZBMcFY3fnkp7+MFqtFPgTHjOdqLNyMf/zeXRMxfmZgpgbxqLVaYmMPJft2yfh89lpbv6eMWM+Ij//cnw+FwrFkX8SW54N7ajAFRmROTaDQuHrG+xsWOxgRV0d2TPCEHebuPRSLavf9zBumorkMzYz4tszSKh0sHRVGgvv1nL77aBQSLPl4zHEzyzw6+Zmyp1OnD4fdYIb5+xq3i03M0yj4azISOKCgvjYaMQjitzZcRmBpPD92emX19aSrtH0OFtipdOJOyoWwhdT57RT6/FR7nChEKXZK0j7EsUOB1l6PRtNJlI0GkRRpNTpZFpoKMUzZjBp+3bKnE5ybTYqXC7uTklh715JgU+fLs30r71WmtXn5EiroldflWS4/fYjK5UJE2B5W8ZVsxn27IG1a6U2OhIZeWT/4n//g4suarunX0sDyqxZUjK7uDhpNTTCT2Cgab2J4NjgHrtQlpZCXR0c0jhZ3MF0MrxN4feWTz6Bf/8bHnlE+h31lRKHgxEhffP5T30glfir4/0qbHWyGtcnnU0rNa/VUHhjIShAFa7C0+Rh+HPH91LRZ+mZtGUStnwb+ZfnE5wUjH7SkXTS2uHa9qyg+iw9Ff+ooOiOIoLjg4lYEEHur3NJ+kMiFbZ9jBv3OUFBRzZX0/6chmnDJSTckEDh7wox/2hGN1pHRsbLGAyz0GiG4vE0Ex19ATrdGJqbvycq6tz26607rMfMIyMTWAbFOqo8HW5JT+Gg3U7C7xOoeLKCySNaGfmXzfx+QQuemK1EeULZPgGyny+j+IFiHnoI/vrXnrU/NCSE4qOUwkaTiZsSE6meOZO3R43C9+tyPm6p46bERP6clsZv4+LYZ7Vy16FDfNHYiLB+PV5Rchm96SZp9uoTRdY2NbV7JFxTUED27t099lDYaDYzNzgSxQ9P8On8EN6KmsBTFTPwnTmPDz+Uzpmo17PTamWT2YwIFNjt1LrdhCqV6FUqdEolaRoNZU4n60wmvmpqYtMmuPpqyMqSFPo338Bf/iLl49+1S1L8BoP06GiWGj9eWhmYTJKSHz9eUt6aY5hXzz5b2rM4+2xpBXHBBTBlirRyyMiQ2ikp6Xpd4xeNxPwypkf3CeCWW6SN9txmB9GtIe3pMYZpNBzshcKvdDr5tKGBH36Apib8eoT1hkqXi6RjLeuOgXaEtkuCtcOok9VdbOn2AjsKjYKs77KYXjidCesn9FhZhgwLIfqCaCIWRlDyYEm310UsiMBZ4sQw18CIZSMY/vxwUu5NIfr3rahU4Z2UPUgDRPLtyShDlITPD6f5W2mDWBAUxMdfTXj4HKIiFnHo/kNE+n5LdfVLna5v2d4iK/yTyKBQ+NphBqaFhnLI6SRyQSRBcUGM+DCfEJ+Xl9jJeTuGEdys4L2LlQSlh9C4phHHIQfFDxQfM8fJYfzN8EucToaGhKBVKjknKoo/7pdCc31OySyiVSrZMWUKF0ZFcWmbdilyONo3QefPF7mzqIhz9u5tb3tk20wvz96z8Phvm5u5OCWCZJUPYvfz6rpP2bld4IwZAs88I9nGx6vDWGcycW1BAf/OyECvVLLJbO7k731Y4efb7ey32Xi9LU1PVpa0r7BggbTROnSotLHc3R5DaKi0Ylq6VMo+unq1ZNI5Fmq1pDTfe08aZJ56CqZNkzZ0ly2Dm2+WXF+PxrrTesyUsB05eFAaqJa94qPG7eLGCzVMmCB5GI3V6djXtmktitLA5q+/w/yjooLrDxxg2y4fl10mbULX1nZ//vGocrtJOgHlDNUpksLvOHmw7bUx5qMxRGRLhbbD54X3urJW4k2J+Bw+9BP9V3YKyQhBM1TDsKeHEX1hNNrhWlLvTcUp5qPTjT9m2xFnRtD8XTM+V2cHCftBOxVPVtDyj+k0N6+juvpV7PZCRJ9Iy84W9JNPXJUpmc4MCoU/e0FMuy0WIPmuZBQHzbwY5UURreS87edAUhDiQh0zC6cSPi+cHVN3UP73ciw/Hd9PfYxOR67N1sk9s7jNTn6Ye/6gYNwnoylbEcOmtlK2ccHBLIyIYIxOx6KoKPZYrKxcCZ/nWXlIvY9NZjPnR0ayziSVx6t2u7kyLo5VxuOHmbt8PlYajVwaE8PkkXFol9zMy2V3sG2bpHArKiRTyefPhPJ2XR3zIyK4JCaGUVotXzQ1MaSD7OkaDaVOJ/k2Gw2trdQ5WnnzzSNBYYdZsoRuU04cZupUaXZ+rCR2RyMIkofP449L+wK/+IWkRC+9FBYt6urNc7iwSXdKx2qV9gdeeQWamyVT0TnnwLhFNlKDQ9ido8DjkUxOk0ND2WmxMna8yP79UnrspUvhttugo/POGqORS/bv5536eiIIJnRhI088AU8+2XVvpTdUuVx+fe/7S1BEEAq1AnfNEe8a2z4bunH9q6YVcWYEiTcnEjbdfyUvQRCYmjuV0EmdB2OrdS96/bEVviZVQ+ikUOrfr2/PQX9Y7qDYIFrLlISGTqaw8CZKSv5MwbY7UJ65HVUkrF8vUFHxXL8+m8zxOeEKXxCEcwRBOCAIQpEgCPf7Oyf5xniGts3CVxas5lbxDyz602/5+PYF7HlzL1GWKCzZWkaHh6BQKYi/Nh6FWkHirYm0bPfvuiaKYvuPLjY4mL+kpbGs+kjCqhKHg6EdbK9hYXD7uFheeiKYOR2cZ25OSmJdVhbjdTo+z7eRnAwPWvOZazCwedIkLoqOZl1zM2aPBxH4fWIir1ZXc0thIY+UltLYTaKelUYj4/V6kjUa4nRxRKkTsHpMbDPcz/ipLVx4oRTcteHfYXDVVJYNlTYFR2m1fNHYKJmpiiWlN0mv56umJmw+H9NDQ8l1WZkwoeum8h13HLHd+9pmjnavt9Ms8q67pJnz/Pl+xe7CY6WlvH9UgJsgHKn+NWWKZH/vOAY6i50odIpO2S47Mm+eFKtw002S6+in2+zMX+hjk9nM/BhD++favh3CVUFgCuKAzc5DD0kDVWiotLrYsuVIm2saG/nUaCTcqWZWSzzqeY1Yoq1s3iytgvpKf0w6x0M3VtdextF+wA4CneoC9AVBEMh4KQNVaPdLN3/JuWy2veh0447bfuLvEyn/ezlb0rbgtUkjrm2/jcizI3FVuTAYZqFWp9JUu4468yv4rlpGc/O3AF3MPTKB54QqfEEQlMAy4FxgNHCZIAijjz5PZQgi2Aast2cAACAASURBVFbJXcEruTl3Mx+ppmOZtoL08HQ+rFnBN/d/w5Z79Axrm9XGLI5hav5UwueE0/x91yWkz+1jc9JmCm86EsU602Agx2JhtdGIzevF7PUSf9RSfHabG09Hk4dSENCrVIzT69lYaSP7Sjv1ra38MTUVtULBL8LD+d5kosLpJEWtZpbBwOLoaEIUCn4wmfhPh0HmMP+rq+OeoiL+0Lbj/MdZf+SDi9agaMrENeVJ8s3buOACiImBykqBrEgdW7ZIWi5Tq6W+tZWZYWHcc4+00TpKDGOvzcY5kZHMMhiojDT59V5SqUCng1VGI+oN/8/eecdFeWX///1MgZlhBhg6SJMiomDX2EuixpgYY3pPdtM2Pdkku0lM3/Sym56s6aabYkzviDUqgiJFiiDSOwPTgJm5vz+ugggoWBK/v83n9eKl88x97n3mmZkz557zOZ+zhgdKS/FZu5bXq6u7xuwz0ANJhgOktbSw7iDyBhqNDC3tr4La+G1jvy3dHA75I/bzz5K6unYtpM3Mo2NSAz80NTEn0I+4OJg6Vf4gXn896PPNLHyynpUrZd7h8cfl7uLLL7vn/aWlhSCVF8XvBJH2gi8Vo2oYnZFBQoJMBh8uKo+1wd8r2lb/aT3BZwX/Yc3RrdYdhwzpAAQsCMBZ6qSjqoOGLxvobJbU24CTA+io6SAk+AIitPchPl8MeSMwMoecnMUEB59DR0ctHR19KCcegIKCa9i165+0t//ZOW6wONYe/iSgWAhRIoToAD4CFvc1sHjnjcx3/gcfvyEEB43HS1Fx57S72FS5CefJRl5rruXs4O4kn9Zfi3m+GY/Tw46H3sbj6fakW9Ja6KjtwLKm2xCl+PiQZ7ezOCeHS/LzmW82ozrgy5OcLOl6TmdPBc3334fqjT6UqW1kTi3imogI1HvPjdfrqe7oIDUjgyCtbLjxbGIizyQkcEloKLkHxPM9QrCsupqFgYFdhTpx5jgmj4jivJPkb2F2bTannCLpkkOGSBrk/PmysUuywYACzPDzo7pa/ij8+qWWqb6+XBUeTlSjP+rpjdSr+k9kbrNaWRQYyPOVlUzx9eXrA6pVowfRPS3PZuu38GsfRozoKYjXsKqBoDP6LlLasUOygOLipKjd628IRKSdB+p3Ud7ezuKgIG66CZ5/Xnr/GzfCd3+JYmNYJdf+3cWJZ3dw4YUytPPGG1K/qLKtk+L6Dka/NpaTGqOo+KU7XBEQIKuGD0cuwuJyUehwMMJwbGiFPik+tP4mw5CW9RbM8/rve3os4XbbaW8vw2Doo6LwAKh91MTcG0PsA7FU/KeCypcq0cXoCD5XFp55dsbQ9vgkYqLvYeo1v5C66CU0Gn+MxrH4+p5Aa+tvh1yjtnY55eVPsnFj5NF4ef9TONYGfwiwv2xlxd5jPbD1Xz602bdhMIzgX+7b+C6qAJNazeS4Bdw6+Vb8I0/j1IAAUow9Y75as5ZR34yiefKtlH/1I9VvVLPj9B20pLcQfVc0LosLR6k0fD5qNcMNBi4MCcGgUrGsr3JYpDcaEdHNLHG54OKL4YEr9ChhTso8DpbuZxEVReGemBj+FRvL8gO4mik+PuzYr3hLCIE6PZ11FguPDB3a9aOxD9dPvJZrJ1xLdl02KlU39fPvf5eJ11degbatJpbGxGAQWnbsgPvvh2+/hdVjxjA/IID8FX7E+ngzLSuL2n6sWKnDwamBgTROm8Y3qan83NyM64BK5DN27OD8g3SKaXW5qO3ooKGzs1eSusLp7BEmGjFCNpnPz5d9Xds2txEwr+/y+MxMWdiVs9NDS2ITF/zDDl4eKtrbeXv4cPw0Gm66CcaPl/flpZdgQqiBE/39Kb40l5MaN1Lb0UF4nIvTr7Vz661w5eNWKDFibNPzzjI1ol1Nw7Rp6FQqFEUQHCzpo/vjIIXZAKysr2dVQwMz/PwwHiqzfZgIPi8YyxoLrVtapebOEYZzDhc2Wx56/TBUqoF1EIu5O4aYe2NQ1ArlT5YTemkoKq2KzoZOMidlYt1mJfLGaLwMAajVXgwd+hiBgafh6zuF1taNveYrKLianTuvAKC1dRNCCFJTv0at/pPdM1gcax5+X/vPXpzFtuk28Dgx+51HuP0ZDPZ1DDeMpw4jJ0+8h1Oys/kguXfzgqamn/HxSQFTK6XP/Qpf+IAi2+XF/zue9j3tNH3XxJDr5G/MD6mpROp0h9wWjx0rpQOCguD116WXXVGhYvQWA/PM5i6O/D78a+jQPucZ4eNDgcOBy+NBo1JRsbdqNtLbm+A+mB1ToqagUlT87Zu/dR0rbS5l+fblBA+7jH//J4YVK7SMGjWUjXfI/rznnivpqXU1KoKC4JPlGjb8LZW723P5vqmJy8LCeq2z2+nk0r33wazVEqTVUtbe3lXBCrDGYqHZ5eKZPkIW5U4nU7Oy8FEpBLobaekMYEdTGSnmaJpcLoZv3sytUVGcFRTEGJOJ5GTpiV91FXx6bRP+s/17dSgCybL55Rdp8O/fU8p/q6oIOEPLTG8/HoiNZewBmtOPPdb9/6fi43m2ogKb282XDQ1oFIW0eeXsfnginGNl/HAfvviie3ygVotOpaLJ5SI0VEttLUTu5zAmJcmq6H25iP1hdbk4Ly8PP42GR/p5748GtP5a4p6Io+j6ol4ia78nbLYdh0zYHghFpRBzTww7TtuB/wxJPfVJ8SHw9EAib4pEbeh+/yMirgSgo6OKsrLHsFqz8faORKsNwOPppKnpe9rby3G7rdTXr8DLK4KAgIWAQmdncy+q6J/oH8fa4FcA+1ctRQK9gtrvPGbC7eeA4FyiYnQsmrmeFPPdzM/OxndvRm1+QLdH6HY7aWz8kry8CwgK2hshGlpCwrMJVL9RjXOPE/8Z/nTWd5J/YT4ooA3QUvefcnyfTcBv8sEbIVx6qYwfK4rkle/j+98SGcnU/jiNfcBHrSZUq+XSnTtZGBCAWatlksnEo3H9V5aODhtNYWMh9k47Bq2BG767gW+LvsXk9zIi9j106nk0NcGFF8owT3CwNKTz5kkK5PDhsgr2hHJftra19TL4a1pa+K21tQetM8lgoMBu7zL4dR0dCGCe2UxmW1uXwV9vsfDo7lLi9ToC7UXsbMilvfwTiL+BU756mYumP86LlZU4PR4eLisjx2ZjZUoKs2fDhg1w002w4po6Jv2jN//eapW7qxK7g8ue6OCNqmpWjxnDoh07GGM0Mtt88C91lE7HMwkJrKir462aGk4wmdjtsfPgVy18p7WwOLz3jmKIlxcV7e2YzVquvx7+vrIeHy8Vzk5BcbOJ4mLvPg1++t6aCIvL1SPMeLTR3l5Jy6T7sGvG47GMRBt0ZD16DxcyYTs4gw8QcEoAY9LHSGVNYEL2hIM6WybTCVitW8nOXoDZPBdf3xMoKroBRfFi6tRaioquJynpDUJCzpeqo/o4HI5daLVHt//B8YrVq1ezevXqI5rjWBv8LUCioihDgUrgfODCAwfdN/MithZ9QNH5M6m3D8fj+YjHQhswaaJYVlWFc+bMLhVCm20nlZUvUFX1KkFBi2loWIlaZcRrQTWR8yNx7nbianWh8lYReGogkbdFUnR7NsxJQ9l2KtXLqvs1+B6XB5VGxcKF8PbbsjDnlFO6Qyt/CR940/N9iNfr+bS+nu1WK3l2OzcNGcJJBzFeOo2OlJAUtlRuISkoiTVla4jyjaK8tRwunc8Zk+/m8qhHGDmym2L52GOSvnjrrZLKCDDeaOTD2lraPR6899uRzNqrnRC1n9eeZDBQaLezMFC2itvU2spEk4kJJhOZViuL9uYa3qqu5pfaYr71CmJy/fcUnfIEOs3thHxyO5XaSF6srCDFx8hkX19idTo+29u6TKOBKVNg3TcdrI1p4ZWcZLo7A8tE7eLFssG8+ZxyziqXOY6xJhMVU6cO6n6fEhDAlQUF6FUqJppMfKAvxO7xcN2I3qXRQ7y9qWxvp73dyKZNgtsKd1HlbmeIxhsuNbN7dxLTpvU8J9dm4/E9e7gvJoZkg4EA7bExwh0d9WzalIDBMBzl8kK8Kp5EUf3+Cdu6uk+pqHiW1NTBt+NUVAr+M7sLyw61s9Zq/UlIeJ7a2neprX2XuroVABgMw/HyCmHkyE96jNfp4nE6S/D1/d8w+LNnz2b27Nldjx988MFBz3FMY/hCCBdwA/ADkA+sEEL0CgxHxM/g7Hdaafp+JVOjppGY+AoFOy/hr16/8kLC0B6Ss0VF11FV9TITJmQyYsTHAPgYU+k0FOFwlBD7QCxxz8TQ0LAKxaeDhKcTCH5vE9z+FOM2j6VhZQOdTd0ZWVeri4KrCyi6sYjfYn+j/ot6tFpZRHTrrX1LKIAsJnE73WSfmk3tBz1pHh6Xp0tiNl6vp1MICux2zgsO5vyQkEPet1kxszjtw9MIfyYcP28/FiQsYHyINJG7LDsZMULG9GfN2nstLitXXSUrYs8+Wx6bsDf8cX5eHlMzM3mpspISh4NQrZbsCRPQ7ndPk/R6CvarVv2puZkT/f2ZZDKR1tzM61VV3FBYyBcNDQQWPcLQnf9k5Wn/JsovimCfYJ6ffBmqiFOJUjlZlZLCQ0OHcllYGNttti76J4Aj345PipHv0zU9YuRffSVzJctXuHEOayFGp+O2yMNLyJk0Gmb5+7OyoYG7oqOJ0en4MiUF3z7i7DF7ZSsWL69g7BPltLUpeBZOJ+b5MTCrnryKnpTaDo+H6VlZzPb35+6YGM4ewHt5uLBY1uDvfyJjx25ARJShnP79MVvrYKir+wB//5Pw95/1u6wXHv4XUlJWoijyhzQgYCFJSa/1OdZkGktLy+rf5br+v4EQ4g/9A4RoaxOvTTeI5aMQFZYKIYQQxcX/EOvXh4mamvdFe3uNqK5eLlwuu1izxiQ6OprEPjgce4TTWS1KSu4VaWkIm61QVFcvF2vWGMW2bfOE290uNm0aKdLSEHZ7qci7LE8U/7NYCCGEs8Ip0kgT64LXidKHSkX+X/PFtpO3iUOho7lDpJEmfkv8TWyM3SjWh60X1e9WC4/bI4QQIue8HFH5WqUQQojHy8qEac0aUel0Co/Hc8i5hRDC0ekQXxV8JUKeChEL3lsgdjXtEkWNReLLnV+Khe8v7DG2oKFAhD4VKlxul2hq6jlPpdMpSEsTEzMyRODateLaggLxl/z8XutttlhE0m+/ic/r6oTH4xGJv/0mslpbRbvbLWI3bhRDN24UXqtXi4kZW4TuYZ1wdjp7nO/xeETSmu9E4qqHexyP2bBO3Ln+la7XXfNejVg5d6UIP+Ut8cYbQuy7HeeeK8SyZUKQliZISxOdbveA7lN/eLmiQpCWJnbabAcd91plpZiQkSE0q1fLtS8rETNnCgFCcFWxCP04s+vaC2w2cfK2bWL8li1HdG0DRWHhTaKs7HEhhBAl738v0r8MFx7Pkd2XgcDtdgq3u/v93bAhRthsBcd83QPR3l4jtm9fIMrKnuh3jMNRLtauNYvOzpbf8cqOH0jzPTh7e1xU2mI08tXpw1lcomWIlwwrxMc/QWLiK1RVLaOy8hVKSu6irW0zBsMItBi7eH46XRTe3mEMHfoQQUFLsFqzqKp6leHD38bttpGZOQWdLgqzeS52ez5xj8VR934dlvUWHCXSq419IJbYe2NJeC6BltUtvbRwNsZupPG7buqibYdNVokKqUToFeHFzkt2UreiDrfDTeNXjbIb0pcNJJUqJBkM+BZ0kjk5k/bKQzc00Wl0nDbsNJYMX0JqSCpx5jgSAhIIN4VTY5U6AOWWch5d+ygf7viQWlst22q2cWCkKMLbm2m+vtwdHc35ISHstNt5Kj6+13rjTSZa3W7OzM0le2+17iijES+Vim9SU9k4bhynBwUxW+8iISABb03P5KGiKKRPmEbdzmept3XzqCPc9Tye+wNvZEmB/KqiKla3r8Y9+x4efqyDZ/cWVq5fD8NnOwjQaMiZOLFXUnywOHlvvifqEPz4qX5+ZLS1cVtkJP4qDfM1oV1a/r6fxlHb3oFqjIXqaliSk8MPzc3MHqAw3pHCas3EZJIxu5hz56ELDqS+/hM6O3s3M+kLdXWf0Nq6udfxAz/b+8PjaSc7eyFFRTcBkhThdlvQ63//NoJeXqEkJLxAePhV/Y7R6SIJClpMWdlj/Y75Ez1xfBh84LPbNmFasLiHIpqf33Ss1m3U1i6ns7OOlpa1+PgkywD71Kky07cffHxSaGr6Dru9gMDA00lOfh+TaSLJyR9gMCRjt+fhHe5N1D+i2PPkHtpKywi+IIgh1w1BCIHGqEE57Xvys6+g6ott7Lh4I+XbPqC9rB3LOgutm1rxdHho/rkZ0wQT434bx5AbhjByxUiGvzucyhcqafm1BV2MjpbVsuly8pdO3kxKoum7Jto2t5E5OZNdd+wa0D15fO7jLJ2xtOtxmDGM6rZqdrfsZvFHi0kvS+eB9Afw9fbl55Kf+5wjbcwYzggO5sVhw/h1zBgC+4g5qxSFx+LiMGs0vFVdzQSTqatGYYSPD6FeXryfnMxwZz6jQ0f3uU6o3kRKUBJ59d2E+/aGDUTGnNFl8PcU7iE0PpRw3yCuujeL1aulKmdjTDMzKzdx4l4ZiyNFnF7P5nHjMByihHa4wcBpgYH8PSqKmulT+eF1Q1fBWsJQhSuHhBN4Vj2/bu2gsr0d16xZPNHHD+axgN1e2MV7V2lUDBv+Inl55/fbPtBm20l7e7coUEXFc9TUvNVjzO7dD7NhQ99hKCE8ZGSMQ6XSU1+/Aqs1h7y88xg+fDmK8seYCYMh4ZAMnKio26mv//R3uqL/+zhuDL5GpUF56SV4803Zggnw8gpCrdbj8bTj7z+L+vqP0WuHSnUsX1/4+OMec/j4jKSm5m2Cgk5HpdKi18eSlPQqWq0Zf/851NV9ghCCsMvC6KzvZFfnmYjxG2hvryQ9XUVr6xbE+ctpbPmCQt+JNM65mF0tF6EZZmHPE3vInJzJzst3UvZQGfpherSBWhS17FEafHYwth02Gr5oIOT8EPSJetoy2rBtbCPVaKT1t1aS30/G/0R/Kp6rGNA98df546frTjCH+oRSba1m+IvD2dW8i68u+AqzzsxtU27ju+Lv+LW0dwsq7QC95cvCwrgoNJS3amq64v/7w0ulIrtuO6NC+2drDAscRkFjAdYOK89seIay0pW06odS1FTCZ3mfsXvnHiISIxgWOAzvsBK2b5fcfOX6Yl5JTOS5QzU3GAQm+vatFbM/VIrCV6mphHh59Uhs//STlIm+dqIZy9Rq/m0rYazJhFpRetVOHAu4XBbcbhteXt0kAX//WUyf3orNlkN6ugq3u2ftw86dl5KZeQINDavweDpoa8uguTmt63mPp52ysn/R2dmAy9Vbf6qlJR1F0TJq1Nf4+IyioOAKwsIuJyho0bF7oUcBen0SHR3VuFyHbgf5J44jgw9ASIhsw7Rfjzwfn1TM5hPx85uOzZaD/ussyTv85z97iqUgaV1qtYnY2N7Z66Cg0+nsrMdqzULjqyHu5RCIL8Ge9AF79jwOQE7OEhSPgaiMNag3n4w2yo2v51SCH9sDbskjrvuwjui7owm7pCfdUa1T4zfTj+rXq/Gb6UfkzZH4TffDut2Ky+rCssGC3zQpOauolUE1ed4HrVp65+3udqZFTcNL7UXTP5u4+YSbSS9L56TlJ+H29N3ur7W9lQnLJtDibCF9d3qf66f4+NDqdnNyPyyi7bXb+/XwAZICk3gt8zWe2fAMt/90O6/Mf5RonZ6JI6/jnJWXo3WmYkxMJN4cj9VrFw0N8PM6Nx0hDv4aHk7EMZIoGCzmzpWMoTFGIy6Nh8zQGtrKtPQji3TUYbcXodcn9GK1aDQmfH2nAGC1dneqcTh24XSWMWzYfyksvJaSkrsxGBLp7GzA6ZQNzFtbN2E0jsLffzYWS+9G5Q0NXxAScj4gv3NtbZsxmwcoqPQHQqXSYDAkY7Pl/NGX8n8Cx5fBB6l+tXJll5cfEnIBoaGXEBJyAQC6bzNh6VJZHbV9e49T9fpYZsxoRaeL6jWtoqjx95/TFdd0R+aAxRe7MZ3KyhcZOvQROjvr0NXOYvftDbifuYwRKR8QNnwRzkBZ/Rf3ZBzhV4cT+2AsXqG9C6cSn0vEONaI7wm+hF4Qyui00ZhPlPrjGl8NuhgdGqMGlV5WHR4uls5Yyq2Tb+167Kfz45JRlwBQ2tKH+DyQV5/H1uqtXPnllcx+ZzbvZr/ba8zFoaHkTpzYJ+fdIzxsr9nO6LD+DX6cOY6MqgweSH+Ah+c8zDkjz+H0wEC+N8xCOeFjIivAOTya+IB4Si27uOAC+Nf7VmJVhn6bf/+RUCkKexJmsfDzCZQtje/RDexYwm7f2dVV6kCMHPkZ4eFX09aW0XWspWUtZvNcAgMXkJr6FbW17zJ06KMEBp5KQ8OXe8ek4e8/B1/fqX3KF7S1ZeDrK5lgPj6yL7TROO5ov7RjAh+fZLKyptDRUXfowf/jOP6+ZSecIOUOTzkFhCA8/C8EBMzHYBhGvHkpPrkOaexTUmS3jkE0sDaZxmG1ZgJgaV1HkNflpCT8zLBhrxIW9lciIv6GrmQhACNemoU5YipG41jatbJLhmmiiaT/JvXbUk4fr2dC5gTUehk7VmlUDLlpCJUvVOJ/Uneyb5/Wed4FeVS+OjgBKM99Hh4+8WHmxc/rcXz5kuUsSFjQI4a+P/Lq8zhhyAl8nv85c+PmcufPd+Lo7Km346NW99sqcGvVVsKMYYQZe1fu7sPCxIW8cbrcnU0aIhOOj8TF4Zk1ix1BE/DyVbPN0I7ZN46N7Tpue9bOkyutzIo4fvXQoyIVvnneyPzROvZXmnA6Dy2/cLhobv4Jf//ZfT4nvfwTKC6+mczMKTid5Xt/IGQlusk0nqlTawgKWkRw8JnU1LyxN8STia/vCRiNo7DZdvSYUwg3Vut2TCbZkNxoTMXLKwxv78HXnfwRiI9/Gm/vKOz2nX/0pRz3OP4MvlotQzV2u+wluB+ido1DPW6y7Enn6ytFbw6i93IgjMZxNDf/TFHRTVRXv8aQyYsJijyJiIhr8PYOIzHxebxapYBZyLkyuWUwjMDpKiDmoSi8ggbf6CJgXgCjfhxF9D+79Xe8h3hT9kgZdR/V0fJry6DmO1jxyoigEeTX993CKb8+n8VJi3nkxEd4fsHzpIam8lXhVwNas8nRxMqdK1mYuPCg43y8fPjr2L/y0OyHmBI1pcc1B+zwoJlgJL2lhetqvSgxTeVv2enktlsZazx+Df4+jBzZ86MWGgqLFxdgtxcPap7Gxu/IzT2fvLwLaGvb2ut5Idw0NX1LYGD/sfPQ0IuYNKmQoKAlbNs2h5aWXySZYS/2fUYCAxeh1YZSVbUMmy0HH58UfHx6G3ybLR9v73A0Gpkv8vWdwujRvwzqdf2R8PIKxd9/Fk5n37vbP9GN48/gg9Q0OOMMGdoB2YF7xgzZrWL/BqkLFkjlsAHCZJpARMS1dHTU0tlZh69v7yrOxBcSmVrffVyjMeLlFUbobQPfSRyIgHkB6Id269R4R3pjWWsh5PyQLs3wo4HJkZP5ubQnW2ePZQ8uj4vttdsZETyCu2bcRXJwMjOjZ5JRldHPTN3wCA+BTwbyRtYbXD3+6gFdx7Vt15I3Jo+MsRl01EsBt7atbcRMMaNRFC4ICeWhUC+22trJslp7aeQcj9jf4Le2SoXN1NTHqKxcNqh5iotvobn5Z+rqPuqqJN0fTuduVCoDOl3/kqUqlTcGQyLR0f8gJORc2toyMBh6Vwgqiorw8Cuor/+Ujo5qdLp49PpE2tsreyQ5LZZ0/Pxm9DjPx6eXivlxDZ1uKA5HSdfjzs4Wams//MOuRwhBRcVzvZLrfzSOT4MP0uAvXSoTtHPmyG/Y5s09Df5pp8GqVQOeUqXSEB19ByNGfMikSQWo1b2btaoN6l6evNE4uk9vbH94PAPX1x3232FMq5tGzL0xOIoH34C7PyxKWkRWdRa3fn8rLU65c1jy8RIeXfsoW6u3MjdubtfY8RHjB2TwG+2N+Ov8Kb+1nGGBwwZ0HYVXFRL/VDy6eB0NK2XnE1uOjYDRJjaMG8eziYksiR6LTfEmo60Nle338cwa7A10uAevgyyEIDFxMzk5MtGdmwvjxkFq6m80NlYihBshDv3D7XbbaG8vJzHxRYKDz6Gp6VvcbjvZ2afQ3PwrbrcTmy13QDLE+xATsxRf3yno9X3H/M3mE7FY0veqXWpQqTT4+U2nsXHV3usWNDfL+P7/Zeh0Q3t4+MXFt5Cf30vF5XdDR0cNxcW3sGvXHX/YNfSF49fgz5wJ8fFSBF6n66Zg7m/wTzwRiou7ErwDhaKoMBgGZrwA/P1n09LSm/K4Dx5PJxs2RNDRcejWhnJ9ueXWDdXhLHMi3INn7PQFnUbHJ+d8QpW1iqW/LMXpcpJdm82D6Q9y5dgr8fHqjs+PDx/PhvIN3Jd230HnrLZWM8Q0BC/1wMJZwi3obOjEfLKZkHNDqPuoDiEEthwbPind6w8LTERX8SFYd/GXz88/vBc8SMQ/H8+J7wyeebJnz6PU1Z1AQEAGd94pFT2nTcsjPLyAtrZKSkvvZePGGKqr3+5xns2WT1tbZtdjq3UHBkMyoaHnk5z8AQ5HCa2tv9HU9D2lpfexbp0fOTmL0esH/tlUq30YN24DKlXf749WG8iIER8xcmQ3hTk09FLy8y9mx47FZGZOoqUlDbP5pMHdlOMMBsMwmpt/oa1tK0J4aGyU4UqPp7vntcWygZKSpWzcGEtZ2SO43Qfv43AksNlyUKtNWCxrj9kah4Pj1+BrtdKYjxsH06bBsGHS2x8xoueYc8+FFb23xkcTZvNcqqtfJy/v4q5jNls+VqukgrW3l+NyNdLSMri4p1qvxjDMwO4Hdndp7xwp5gydwzXjryGnPodtNdsY6j+UC1Mv5L5ZPQ17sE8wRAcdCQAAIABJREFU3130HW9te+ugFNHqtmrCTQNP3nU2daL2U6PSqAg8PZCOmg6q36jG4/T0kPdVFIXXxp7MsN1PYvQ69jH8dlc7re2tZNVkDWhnsz8slo14eYUzZ846Xn65Cb1+HqefPofKyivo7Kykqek7oqPvpLj4pi6Ou8fTTkXFc+zefT8gC6mysqZ0MWBUKg1G4yjq6j7EaBxLa+v6Li/d23uA7cYGiJCQ83rsGkJDLyAh4Tmamr6hrS1jb9vBiKO65u8NX9+pDBlyIyUlS7Fas9FqA/HyiqCjQ3ZzE0JQVHQDzc0/EhV1G9XVb2Kx9NbePxzYbJIo0d5e2VX7YLPlEBR0Jg5H0YB2f78Xjl+Dvw/nny+lFBUFiorgQMXKE0+kq+v4gWhogGuuOeJLMBhGMGzYMurrP8HlasPttrFlywh27rwcoCt22NQ0eIGr0T+NpunHpgEXYw0EwwKHUdhYyKqdq5gfP593l7zbw7vfh9mxs1FQKGws7GMWiWprNeHGQRj8us6uXrVqnZqYpTGU/KME30m+vRLOF426iC1XbWFr9VZW5q8c8BqDxc6GnZz/2flMiJjAE3Of4KkNTw3qfIejgPDwKzjhhC949NF7GTPOzNSZuwgJeRaDoRiHYxcREdeg1yewYUMoNTXvsXnzCGpr36Ox8WuysmZTUfEcQ4bcQELCf7rmNRrHU1v7HoGBp6EoXpjNJ2E0jsFkmni0b0EPKIqaiIhrUKl8GDZsGfHx/z6m6/0eUBSFyMibaWvLoLr6Nczmeeh00V3Mnba2rbjdbYwbt4nIyBsxm0/E4Sg64nU7OmrZsmUkbW1baWhYRXGxlKWwWNbj6zsZrTYYp7PsiNc5Wjj+Df4dd3RLQPaFqVNld42+OHI5ObKIy+nsedxqlfMOEIqiEBFxFWbzXJqavsVuL8TLKwyncxdCCJzOEszm+TQ0fInb7Tz0hPvBK9SL2Adjafh8YOGggWCIaQg11hqe2fgMd8+4u99xiqKwaNgiPtjxASC9oAMLt6rbBmfwO+o70IZ0yzcELAzA1eoi9OI+hOUBX29ZEXvmijNpdx1aZ+hwcOWXVzIyeCRvL36bBQkL2FSxacDnejwdOJ17CAu/Dv/wQMaMWc0zewoY+fIopk+XOxMfn9mUt1ZhNs/DxyeVwsJr6OysAwRBQWdhtWZRU/MW0dF3otV26/IHBp6Cx+PEYBiBwTAco3EsEyZkERAwl0ce6dkH+GhDpfJmwoQswsOvxGD4/bVyjgXUaj1BQYuoqnqF4OAzUav9yM6eT1vbNpqbfyYg4JQumQi9PvGoGHybTWbyKyqexeHYhc2WQ1XVa9hs2Xt3VsOPK7ro8W/wD4XwcNl1PD29d5+6sjLJ0887gJuemwvPPis1eQcBs/kkWlrSsdt34uc3E5XKh7a2DGpqluPvPwujcTRNTd8MOjboP8sf63YrnS1Hp5Rznyd9YeqFRJgOvlW/ftL1LMtchsvjYtnWZWj+1VNGuKqtanAhnbpOvIK748las5aRn44k+Jz+G4UU3VhESkgKqwpW4REeLv784oPuOgaDWmstOXU53DvzXkaGjCTOHEeTo4lmx8BEyHJzz0VRNKwsSuPs9T+SOOZXdls7KW0p5ZrvL6WychJVVQ8x460ZFHtmMHbsekaP/oXRo39l7Nh1pKR8SmTkrQQHn9UrVBMYeCqjR6eh1S4iKOgpgoJOB2Tnr+efh1/7TxsdFRgMiX9YU/RjhaCgM/HyCsPffzYqlQwhtrVtoanp2x55iqNp8AMCTqWp6XscjkI0mkBKSv5JRMTf0GrNmEwTerRt3L/39h+B//sGH6SXf/rpcNttPY/vkWXlZGfLbib7UFwsjX3F4MIofn4zqar6L1VVL2MwDMfXdyJZWTOw23diMk0iNPRicnPPZv36vht09we1Xo1xjBHr1qOnB1J8YzHLFh2aMjgieAQRpgjWlq2lsk0WgRU3dXPLyyxlRPn2rlzuDx11PT18gOAzgruK0fpCQkAC48LHcd6n53HXz3fxUc5HZFZn9hjj6HQM2Ejvj68Lv+bkhJO7FD5ViopRoaPYXrv9EGdCZ2czjY2rSE5+l5U7V+Lr7ctb296ixlrDe0ve493sdykrX8cXPw6hvLWcT/K/QKXS4uc3GV/fiV2FTLGx95GU1FPI7OkNT7OmbA0Wy2wCA3248sr5Xd5/QYH0XQ70U/7EoREYeBoTJmxHUdSMHLmCuLinKCy8GiE6MZu7ixV9fEbS0rKG/PzLcLksh72ezZZDYOApaLVBNDZ+TUzM3bhczZjNkhFnNs+lrOxhduxYTH7+5ezYceoRv8Yjwf8fBn/aNBmm+eqrnuGbsjLJ9Pn4YwgMhKefhs8+k8wfGDS7x2gcQ3j4X7FY1uHjk8Lw4cuZPHk306c3EBAwl+DgswAOK0ljHGukLbNt0Of1h/iA+AEza85IOoNlmcvYY9mDWlHzVUF3QVZ+Qz4jggfGyfa4PLRtauuK4Q8Gj5/0OKsvW82yzGW4hZtdTd2KorYOG+YnzCz6cPBCXl8UfMHipMU9jo0OHc22mm6dBKezHJer971va9uMv/9sgoPPZPXu1Vw74Vp+Kf0FjUrDRaMuIjkomdhJufy4I4sYvxh+2PVDn9egKCpUqp47pzt+uoNZb8/invvc3HADbN0KnZ3SL/n733sXev2JgUFRFLy85G5SpfLG11fmQ0aO/Ay12tA1zmAYxtix67Hb8/rUFhooHI4i9Pokhgy5AYDw8CtJTHwZH59UAPz8pu39dwZNTd9isaz/Q7n5R2TwFUU5R1GUXEVRPIqiTDjgubsURSlWFKVAUZSTj+wyD4G5cyUnf/x4+PLL7uNlZbBoEfzwA8TEwJ13yiTwjh2S6vnOO7Kit7MT3n1X7qUPApVKQ1LSa8yc6SQ4+Gw0GhPe3t1SAxqNH1On1qJWD76QyDTOhDXrj1H8u27idRQ1FvHO9ne4bPRlbKjYAIDT5aTcUk5CwMBivNXLqmnLbCNgYe/+sYdCuCmcWbGzWHH2Cs5MPpPiZrnLEEKw6MNFRPtFD5pDb+uwkb47nYWJC3tQZseEjelh8PPyzuft9JN7SU1YLBtQ61KwddiwdliZFTuLtNI0xoZLz318xHi2dCynfPpi5kYuweK00Ghv5FBwe9x4qb2IM8fxc04WCy/PJXDyt2zZApdcArGx8OOP8kfg6acl9+D3gP0gdkgImDwZFi485NfkuIKf30wmT97TJwvJaEzBZJrYo2CrL1it2dTX900qcDrL0OliiIi4jvHjt6LR+DJkyLVdoTKVypvZswXR0bczdWoNRuMYLBb5/erLyTjWOFIPPwc4E1iz/0FFUUYg+9eOBBYALyuKcnBx8iNBYqL07q+8El5/HaqrpRHPzobzzpOf0CuvlEbfywu++UbSPZcvl1LLn30mO5e/9RbYbPL8g0Cl8u439qnVBuN2WwedvPUZ7YM1+48x+IGGQO6cficAl4y+hPV71iOEIPmlZDo9nV0qnf1BeATt1e1UvlDJsFeG4Tvx0NLE/WFe/DxumHgDu5pkQnzlzpW0OFtY99d1FDYWDlhlNLM6k2lvTuOEyBPwUcPGjRFdwnn7G/zNuz/F0vob7baNrC/v9vTW595FRuFDnPXNi/xU8hPRftEkBSbhFm5mRMuq1ItTL+a5Tc+S3Hw7Yy339QoVVbRWkF3bO/Na0VpBiE8IJ8cuosn8I5vbPqNl3D3k58tmMA8+KFVDPv4YnnwSoqJ68w6OBUaPlr7QgRACPvwQGhtllPQI+2j/rlAUpU8xxX3Q6eJwOg/en6K09F5yc89ECEkM6ehooLHxGzyedtrbK/D2jkJRFEymg4vNKYoKf/85tLSkIYRg06Y42tsPbmv6gstlobHxm0GfB0do8IUQ+UKIgj6eWgx8JIRoF0KUAsXApCNZa0BYsgSysuQPwFlnQXS0FGMLDpaf5u3b4eqrJZ//66/lbuDOO+GCC+Txp5+W37Zzzz3sS5BbynA6Oqp6HM/NPZfNm1Noaem7EMMwzIBzlxOP6xgpch0CCxMX4qX2Ynr0dOyddoqbitndsptbTrjlkOfWf1LPxoiNuCwu/Gb03SB+MEgKSiK3Ppc578zh5u9v5rYptxHiE4JGpaHWVnvoCYAfin9ge+12lgxfQmvrRoTwUF7+NJs2JRLl1UhRUxGf5X3GG2vOYU2TmaE+CuW776OhYRWNjd9jq3+F3Z3xRAefxIrcFcT4xRDiE4Kvty/To6cDcHLCyWy5ags3jbqfDb+aGR06mqzqrK5reHrD04x+tWf4yGqF0y4twcsaR5h9Ht4jf6SoqZAm7yxW/lJBQID8uILcnAohjf2bb/bmJBxNtLTI1NbaPj6emzfD9dfDI49I9fIP/zjFgqMOvT6emprltLSk9ztmXyiotvZ9ams/ICtrGoWF11NUdCMajbnPiv3+YDZLg+9yNdPZ2dClaySEm/r6z+nsPPgO0e22s3XrRMrKHhnwmvvjWMXwhwDl+z2u2Hvs2EKngyuukOEdjQb+8Q/J33/hBZg9G4xGScf87DMwm+U3qqkJKivhlVfkruCpp+QPQ87h62t7e0fQ3i4NvtWaQ3X1mzQ3/0RMzFLy8s5l8+bkLtnafVAb1HiFeeHc3e3KeTo8FN1YhGPX0ZNf6A8GrYH2e9rRqDTEmeNIL0snNSSVZ05+5pDn7mvbaJ5vPiqsjwhTBENMQ9hYsZEWZwuLh8sY/PCg4f2qgR6I1vZW/j7571w55iJqa98nMPAUWlrScDiKqSp/mPfPfJ9zPz2HeaHwZnEjHt9L8XdnsHPnFRSX3M2PTTGERz/AgoQFfJb/GTF+MSiKwoqzVzAtalrXOuMjxnPyPA0//wyLk87gzW1v4tnrCVa2VRLlG8Xn+Z93jS8shNzqIipzh3L/ZbNwmjPIqsnCXx3ON9kbmLSfW6RSwY03wvDh0uB+9NER39p+sS9fsLGPWqS0NLjsMukHzZoFGzYcu+v4vaHXx+FyNVFc3O3YVFW9TmnpA12P7fYi4uOfYdeu26mtfZ+hQx9hzJjV1NS8g04XM6j1fH2nYrPtwGqVjsE+Hf/S0vvZtesOtm+fe9BdbEXF8xiNYxk37vDehEMafEVRflYUJaePv8UHO62PY79P5O/RR6UL8vnncM458th550nJZZB75f3lGcxmeUylkmGhxx+XXv6tt/aee4Dw8orA6SzF43GRkZFKQcEVGI1j91Y4yjesubk3506fpGfPY3twtUm6aPlT5TR81UDehXlHTX5hIIgzx7F692qi/AbGzrHvtBO4KJCwG336FAQ7HJwz4hwuGXUJe27Z01WJOy58HJnVmcx/dz7fF3+PvaP/oHOVtYqUkBQaGz6ltfU3oqPvpLOzYa/IVgFnDD+DW8adjU7rw7DwuSyc8CZ3FUTT4T0BqzWLV/OymRgxkZkxM+lwdxDtJ8XMTk44uVeIa+hQ+fEKtc1Frah5I/MNvB/2Jq00jVsm38I3Rd3b7927wTzrPTSlC7njZiOz46eRV5/HtOBTIaCIeT1Vr7nvvu6P8ZYtg7+Pdjt96vg/8YTcNQC8+KIMH82cKXMHrQc0xFq9WspZAYwaJVNjLYMTeT1u4eMziri4J1Cru3em9fUfd0kiCCFwOAoJC/sL06bVMmrUN4SEnI1eH8uQIdeh1w+u5aVarSck5Hx27bodoMvDt1jSSUpaRmdnE1Zr/wyyurqPuhLEhwPNoQYIIeYeakwfqAD2txaRQFU/Y3nggQe6/j979mxmz559GEvuxZE00hg+XP65XDK8s3OnfDxIhIVdRlHRDej18ej1sqTdaJSJvpCQc1CpvKmqerXXebooHdWvV9P0QxMJ/06g8etGkl5PouzBMqrfrCbiqgg6O1uorHye2NiDa+AcCeLMcXyw4wMWDRsYK8a+007sg7E4I1ZRXHATQUGLuzjQhwMh3CyduRSXx9WDaTQhYgLfFn3LTyU/8VPJT6ycbmLi8CcZMuRvveaoaqsiwhSBzfY9ERHX4Oc3Da02GD+/6TQ0rKKzs5Hbxy+kuVnN97PfQ6WoePjEx7nxh/O5PjmO7Ot/Is4cB0DhDYUE+/RfRwCSN/DLLwrzRs7j+m+vp9PTSaOjkcXx5/PPH+/G5XHxZtabPJv3JZ2GMl69ZQlz50AtTzL5jTXMiJvMN4FrOOWU3nNPmyYLyjf37kneL265RW5ya2rg/fdlbL6sTG6CTzpJisyazXLje++9kJoqm8h9/LH8Abh7v3q97GyZ8gKpZjJlCrz3nkzgxsUN/JqORyiKQnDwWVRVvQKAy2XFYlmHRiObALW0pKPVBvbZWzcu7inc7sHn3SIjb2HLlhT8/U+ksfEbKitfxWrdjtE4lpCQC6iv/wSTaUyv83788SM++qiIqKifUJTDlK8WQhzxH7AamLDf45HAdsAbGAqUAOp+zhXHJf7+dyHuvfewT8/NvVCsXx8u8vIuFlVVb4jm5rVdz9lsO0VaGmLXrjuFx+PpOu7ucAuPyyOafm0S6YZ0kUaacNldwrLJIjZEbRBup1vU1q4QaWlq4XJZj+jlHQyvbHlF8ADi0TWPHnJsw9cNYo3fGtFe2y7y8/8i0tIQaWmI9vbag57ndrcLj8fV45jTWSWys08TmzaNFC0tG0Vj4w/C4SgXHR1NQgghcutyRdxzcSLl3wjDv+Q6a9f695rH4/GIES+NEDtqd4ht2+aKhoZvhRBCbN06WZSWPigyMiaKlpb1Ijf3QlFe/kKPc0ubS3u8JwPFp58KMX++ECvzVwrlAUW8u/1dYX7cLL7+WghuiRGZldki5KkQYbo3Vlz+5Ipe56eXrhFJT03pd36XSwh/fyGqq4UYyOVFRgoBQhgMQpx5phBDhwqRkiJEWJgQv/wihNEoRGCgEDfeKMRNN3Wfl5YmxJgxQuzZI8R//iPEF18IodcL4XZ3j1m2TM590klCrFwpxLZtg7hRxyHc7naxerWXcLs7RX39lyIra7ZYs8YoqqvfFmvXBor6+q+O+pppaYjs7MXCZisQaWmKSEuTdrC5ebXYsmV8n+fU138ltm9f0PV4r+0clK0+UlrmEkVRKoApwDeKovyw14LnAiuAPOB74HpxPCkIDQSzZ0PG4ES29kdc3KN0dFRjMk0gPPyv+PtP73pOp5NuUXn5M1RXv9GltaHSqlDUCuY5ZkZ8NIKEFxJQ69X4TvJFF6Mj95xcCv/7CeCmtfUw9vfIH/iWlvQuxkFfmBcn4woH498L4WHLT7PZ+eAvhH+xBU2QQktLGomJLwJgsfSjbwQ4HKX89ttQtm49oUdVcnPzjzQ2fo3dnktd3Ufk5Czmt9+iyMlZAsieubXWWu5IUpN98RPYXQoa7wSqq9/sMf/4ZePJq88j3Bje1fgDwM9vFibTJAyGEdTXf0Zz80+Ehl7c49xY/9jDykPMny8LpbTlJ/Hsgme5IOUCVp2/irQ0oCGJZ1YvIykwiWkZuzhj2Dm9zk8KGka9p6Df+K1aDfPmSRJaXFzPOsID4XLJ599+W7aMuPxymfhdswb+9S+ZFwgNlTqEX3wB99zTfe706TIEFB0to5pnnCHX23/jfM45slZgyxYpVXX77f1fS34+fD94ianfFSqVF1ptMB0dVTQ1fU9AwCn4+KRQWPg3UlO/JijotKO+ZnLye8TG3o/BMIyJE/O6CvN8fafgcBT1qbzrdO5CpxtcCKkXBvsLcbT/OF49/Px8IeLjhdiwYWAuVR9wu529vM99qKp6S9TXr+ryiA+FsifKRBppYs3Hw8W6D8eIzMyZwuHY3fV87Ypa0dHccdA5WlrWi4yMSWL1am9RXHz7gF6Dx+MWGzfGC4ejrOdczRtEWhpiwy+JIi0NUVr6kNi4MV54PB5RWvqQKC6+o8/5amo+FBs3DhUlJfeKrVun7PXiy0R5+XNi27aTRWnpA6Ks7AmRlobYufNqUVBwbQ+PZ85bU8XPvyIyMiaJ938IEB9kPCTWrQsRDmedaHG0CI/HI5QHFDH+v+NFZeXrYvPm0b089srKV8Xq1RpRWHjzgO7BQPHuu0LMm9fz2LhxQujPvFHwAGLMzQ+J2Fgh6up6n+vxeETIUyGi3FLe7/wffSSESiWERiPEJZcI8eyz0vOurBTi88+7x+XkCJGQsP/cQrS2yv83Ngqh1Urv3eMRorOz9zolJXI3ERkpRGqqEIsX9309q1fLnYDJJERenhBbtvQec/PNQixc2O9LOm6QlTVbNDR8JzZvHi0slk2io6NBOBx7/pBr2bbtZFFXt7LX8cLCG8SePf/peszv7eH/f424OJlhmzpVxvEzMw95yoGQfP2+yw/Cwy8nMPA0QkMvBehXUU/s9fiCzgjCb6YfSngt7vvuwOQ1ma1p8yi5X+qBlPyzBEv6wUvEW1rS8HjsjBr1XRdLqK0tk87O/jNwDkcxTucutr1/Hbsf3r13njWUbXwHgHZVEaGhF7N7932EhV2Coij4+U2lquoV6uo+7jVfdfUydLoYoqJux2SagNW6jW3bZtHa+hsqlZawsCsIDb0UtdqP+PgniYt7DLt9J0J4cLnamB0RjYKsgjUax3Hh1/extdWPT9aewBkfn0FVWxWBhkC2XLWF2tp3iI9/opfH7uc3HSFcBAQchXrADz6A0lKwWDjrLFksVbb3rWxulqycU+POBFswIU2nk5HRTbvcH4qiMC58HFurtrJuT9+7o3PPlR/DZctkneC//y2TqKecAn/7W3d756ws2fa5e+5uzkJAgMwF3HijPK7pI4s3dKhkM48eLXcI/aWxZs2SNQJLlsiax1mz5C5if/z4o7wHxzv8/GZgsazB6dyFXp+IVht4UP7+sYS//4xeOvpCCOz2gkEniQ/Enwa/P3h5yUKtiy+GoKC+CcpHCEVRkZz8DsHB51Jb+36v591uJxkZo2QXpGEGUn9JxCOcaDpiqZpyKp0lehraPsHtcOPc7cReePCSbaezjIiI6/D3n0lHRy0dHXXk519CTc3b/Z7T1rYVo3E8zoCN7H7/O3J3XMS2bbNoUpZjFhcCCnFxT5Kc/D7R0XcB4O9/ItHRd9PY+F2PuTweF21tGYwY8QkajS8+Pqns2fMkWm0wI0Z8QGrqV+h0kXh7hzFjejMatS+aWisej4OdO//C1q3jmWXcTKe3LOqemPwUOo2OQs8czJRSVJ/BPWn3MCFCPm+1ZmM09i6GMRiSCQ4+F3//WQe9X4fErl2yNDYuDm69Fb1elnS89ZZkAn/6qaxOffxvs3klro7PXxlNYGD/040LG8dn+Z8x460Z1Nl6k+4VRRrhv/xFisNed51UDM/Olg3hvv1WEs0yM3sa/AMxZowMER0MM2bIa3/wQckUOhhefFFyHO64Q5a3tLfDXXdJzkNVFZSXS8bz8Qx//1nU13+Comj7TND+vtcyh4aGVbS0rCUv7yKEEOTmnkVz809H3HrykCyd/2nMmSO/0Dt2HBEv/1CIibmXHTtOwc9vWpcRstuL2bJlJEK4aGhYhY/PSNrby/H2jsQwxkTjl42w4kwcZ3+Bo/gmEGAvOLTBDwo6A0VR4+8/m927H8Juz6O5+WeiovousLJaMwkOXoK16hlUf/2U+sZf0TkmYvh+KaF323CUbsLbO5zQ0O52coqiYDbPo67uYzyejq5uTDZbDt7eQ/DykuJyRuMoXK5GYmPfkbURLS2yjgIkTeStt8Dfn4Qz4qk+Swpi+Xj5Mjl1FRqNP2q1AcdSB0II1m3J5NV5p5BeVc1tMy7Hbs9HrdZ36arsD0VR9egAddh4911pdUNDu6Qtr7pKeroWi2S0vPCClHOKH4BjNiVqCk99LLX6I56J4NbJt/LU/L61+xUFbr5Ztop49llpwO+6q5tPP4hWz31i6dKBjzWZ5G1Yv15+XUJDJbv53Xclu6esTG6Chg28kdfvDl/fqTgcxeh0Q//oS8HXdwpG4yh27DgVj8dJSMi5OBzFTJ/ehkZzZM2C/vTwD4bXX5ff3uRkmX06RjAaU4iJuZfy8u5GFBbLOkymcSQnv9dVRi0NfhS+k3yJuCaCEX+/HBGfS/OWPWgCNDgKDl6gtU/3AyAu7nHq6z8lOnopFsvaLtlWl8vaJf7W2dm418MfB/nD8Uz8FaUhDE3ODALix+PvP4eYmHv6XMvHZyQ223Y2bx7eFZay2/MxGEZ2jTGZJjFxYi6BgadKDYGlS6V7CNJtra2FxYuJ/G89Y4LfY1TiKiZMyMDbO6KHEJaiKEQEnkSKn4arE/Q0lJzPli0ju6h1xwx5eTLkd+WVslhPCEaPlknKH3+U4q1XXTXw6U4ceiIalYZgQzBu4ebljJdpbW/td7xOJ8Mtr74qvfbcXFkJC900yt8TkyfLDfHtt8vwU2Wl3GmMGDE4SukfAbVaD9CjL+4fBUVRSE5+n9jY+9Hp4igpuYvw8CuO2NgDfyZtB4TKSiGCgoRwOISYMkVy5I4y2tvrxZo1vsLjkfw3maB5Wrjd7WL9+jBhteaKqqrXRX7+5cLj8gh3pxy39uVZYv2l94vCmwpFuk+6yL8iv8/5nc4qkZ6uF52dbb2e27JlvGhuXiOEECIn52xRXb1cuFx2kZ6uF+npemGtqhBrhn8sKipeEjlXrRVp3t+J5rXNh3xNpaUPiPXrhwiLRWbzSkruF7t2Le09cM8eeX/HjZOZQCFkxrCoSP7/2mslh/Dss/tdq6VlvVi3Llhs2TJepKUhdu9+RNjtJYe8xiPC6NHdmcqQECHK+0+4DhS3/3C72Fq1VXS4OsToV0aLjMqMAZ23dq2kSq5YIYTTecSXcdjweORX5JdfhEhMFGL5ciFWrRJifN9Mw+MKdXWfiaqqN//oy+iBvLyLRVoawums6vUcfyZtjxHCw0Gvl9W7Gzd26+wfRXh5BaHVBtLY+BVCeGht3YLROB6VyovQ0Eupq/sIu70AnS4ORa2g0si3LizuHDqSfiU6aWLoAAAgAElEQVTm3him1U6jaWs+WS/dyY4dZ7BhQyQVFS8AkJd3Hv7+s/v0EgIC5tHc/BMgGzq0tW3GZsvF43Gg1QbhqfZF5z2UIUOuI+mpyQSfHoVp7KEVQWNj7ycy8iYKC6+mqeknHI6CHr1Vu7B9u8z6pabKoHRtrRSx2xcH+ec/ZXbyxx9lrKQP+PlNJTLyZqzWrYwb9xsxMXej1x/l7XlurgxMFxXJDGlxcXfV9ujR8voWLJDxi8PEU/OfYlz4OLRqLYmBiQNuBLMvsZqcDN6HX/N2xFAU2XH0xBNlwfuSJVLpJC9PvqXHM4KDzyQ8/C9/9GX0QGLiS0yfbsHbe+BNiA6GPw3+QKAokmj95JPycUFfenFHDq02kJycM9i+/SRcrkZ8fScDUoffbt9JU9MPPbr2AETPORf19G1oA9XY3Jl4nr+a1tbf8FctIj7+aaqr3wBko+WkpDd7rQkQFHQWNTVv43bbcDiKaWvLwmbbjl4/jKCgJThKHOhipUCUxk/DyBUjUfsMTPw0KuoOhgy5iby8c2lry+rb4OflSQH4xERpRHfskAZ0H7smJkbGyMeP71vsZS9CQi5EUby6tMiPGvLyJP3k0kulRU1JkUR0g0F2WwMZU7nuOinF/e67R2XZYQHDBmzwg4Lk8oeMk197rSTZH0wL+Qixj7M/fryUr1KppOTzEfwO/s9Co/FFozl89dkD8afBHyjOOkt6onDMDH5i4iuMHv0LgYGLSEn5skuFT69PwGJZT3t7Bb6+J/Q4x8srGK1XCHZ7ARUVzxI79D5C8l5BtXYBwcFn4XSWYrcXIoQLL69++sr6Tvh/7Z13VFTXFsa/AyJFQUBAERFBbCjYuybYe0ueL0aj0VSfGtNMotEoGhO7KWpMorHEGBNLjMaSWCIWFOyIIiqiVBFRQQGps98fe4YZYIAZZmBUzm8tFjN37j33zOWy7zn77P1t2Nq2x5kzLSFEVaSnh+LevX2oU+ctNGz4DdLOp6F6y7L5D4UQcHUdh+rVWwKgfIkJABy68d//8gjex4dVTK9f5wXyZs2KNubuXqJ0tbW1Jzp2vFXAv28UZs7ktZy4OHbKr18PrF5d0FHeogWvP6xezQvQRsDH2QenEnR3fq9cycFlJXLoEOstBAcb1jk98fJS1x2SmA5p8HWlf39g7lxg8mSe1pcDdnZt4eDQA+7uH6Batab5262tGyI7OwGOjn20xvXb2bXHgweHcP/+Xri4/Bf2Pe2ReiwVZmYWcHDojcjId2Fj06TEDFIfn9+gUGRCoUiHu/tHyMy8CWfnlwAAaefSdHLhlISn53w0bfoLzMw0hMeCgtRhJX5+PMK/dIkfrM2bF23E1bXUWgXGmvoW4NIlXn1MSmKN4P79OXV12DD1Ph068JD21Vd5lvLI8OIWw5sOx9mEszgVb6QVz4QEFrWfOBEI0b2Qez7FZAHrgpcXXy7VJFlXcnJKL0x3+LCsDqYz+jr9jf2Dp2HRVpNDh4i6dq3w0x4/7kS3b2/Q+tnt2xspMLAKXb36PyIiSj2VSqdb8mLi48fRdPgw6PLlUaWeIycnldLTrxXc9jCHjjsdp8fRjw38Blr48kui99/nxXDuAAu0mJkRBQcX3f/rr4kmTzZ+P0oiJYWoWjWib78lcnRUZ11Pnao9ZZaIqGNH9eKzgWwP307Oi5xpc9hmwxv75Rei4cM5ZRfg66krH39MNGmS+r2eC9TvvsuntLYmysjQ/bjNm3k9PK0Y6ajwcCIrK6IhQ/TqzjMByrBoKw2+viQncy65pppUBRAf/yPl5KQU+3lGRlS+jEPOoxwKtAiku3/eJSIihSKX8vJKll0gInoQ+ICuvcMGP+tuFl0cfJHOPX+OIt6MMMI30MLgwRxWokluLtHdu9r3//13ohdfLJ++FMe+ffyAj4wsaPBKYvJkonnz1HoGBnLk1hFyX+ZOeYoy3HOxsay7oFAQvfYa0fLlRI8esQbE6NG6tXHqFFGtWmx5r19n2RGANRh0JD6e6ORJlmo4c4Zfl8a9e6xhWLUq0fz5BT/bsoWobVsiLy9+mNjbEyUm6tydZwJp8CsKIfjSlUN4prE4jMN0GIcp46buw6nYb2LpiM0Ryk3PpYg3Iuhsx7MU2j80PwTU6Li6Et28qfv+R48Sde5cPn0pjtdfJ1qyRL9jQkN5lmLEe9v3O186cuuI/gf268f9uHGDqH59osuXefvp0xxWqgv+/kTr1rEc6N69HGcJ8CxHT0aN4uhaoORJQkYGUePGvN/ChRy1q4rSffCA1T43b2YV0GvXuM316/XuzlNNWQy+9OGXBdViXUKxEv8mp8GyBrDvYY+wAWG48UnB1bJH5x4heWdRNb6s+CwoMhS4/899pBxNQaPVjeC31y8/BNRoDBvGSqTp6RyBoys6+PCNwp49LEijUHAZzBdf1O94Pz9e2LeyYp+5PhABW7aoE9CUjPIdhV/DftWvrYcPeZ2kdWt2dD9+zFFGAC+SX7tWuuaBQsGymC+8wNc/MZHXKOrUKd25roWGDVlyokEDXj8ujq1b1eGl//kPL58NG8bRsAcOsPTDyJG8ENywIWvz796td3cqHdLgl4XTpznD8gmOM3N/3x1NNzaF5xeeiF8ej9w0rqKlyFbgyugrSFhd9GGVFZ8F2/a2SFybiKz4LNg0MXK0C8CCdDt3Al99xXH3+kgR1+OqU8aKgtGKQsHKYitW8GJtjRocU6gv3t680KuvJEdoKFdoe/tt9bbHj/Fy85exLXwb8hScBf3WX29hecjyktvauZPLWPn6skxF9+7q621jw31s0wb45pvi24iOZsU1Ozugdm0e5Fy/zoH2sbHFH1cM48cDO3ZwYNaBA8Xvd/Agry2fOsVibhMm8APAzo5vnUFKxWLV1xkyhB8gFTEeeJqRBr8sCMF34RNs8AHAso4lnIc7w7qRNTIiMpCVkIVrb18D5RGyE7KL7J+dkA23yW64t/sebBrbGH9kD7DIS/XqrDLpq2e8fNWqLNKyfj2/v3uXi8/Pnm1QBEkB9u/nCJwDB9iC9OhR9rZ8fdng37yp+2h4/34eTe/cyaP81FTAxgYel2Jha2mLGw94trbm3BpM+XtKidIL+PFHln3w8OCRfs+CORwYMYJzHgIDi2/j8mWeDQA8wp85k7UcuncvUwKihweP1Hv1YqOeXHSiCSL+rFcvoF07/ncTgp/zY8aw6saoUQWPqVmTJ2KaBdb37uV2tBEaynUDKhvS4JeVp8Dgq6jWtBqiPorChe4XQHkEn9988guPa5IVnwXbtrZoc64NmqzXv7SjTgQFcf29GjWAKVP0P75tW3a35OWxAUtP5wD0Zs3YfTJsGCcVnT6tfjDoSl4eMG8exw7WrMkykWPH6t9HFc2bszH18lKLwpVEbi5brPHj+fv8/be6IO0nn6Bl7Za4kHgBClLAxsIGfRr0wcZQTvIiIozYOgKZuZm8f3IyW8aBAwE3N942cGDB8732Gmdq2ZWQ2HPwoDpEtnZt9XEjR3KVlayi95EueHpyQrWzM//u0EE90QgPZ29YYcG5+vU5Kvb33wF7+6Jt+vkVfK5u2cIeOW0MHJivd1epMLTi1WIhRIQQ4qIQYocQwl7js+lCiEghxFUhhBGEx58wvLxKFvom4unypqKyxxWNeQ1zpASmwPNzTzTZ0ATVW1RH7oNcKLLVVa+ICFnxWbB0s4RtK1tU9zWCUJM2zpxhl0VKitqfrA+enqzKVaUKUK0aF1etUYPF7ebM4ZHxhg3ApEk8wtWVhAQu92Rlxcbs4EG2Fl266N9HFc2bc0kpPz/1kHLqVJbp0MaGDVxoduBA9mcsXcoPtzfeAK5dg7/CAxcSLyDuYRxqWNXAjG4zsOL0ChARktKTsC18GyKSI/g869bx97GwULvCXAvlKLi5sX8kqaAUs4IUiLwfiYRzR5C2YbW6pJXK4M+axS4hV1f+W5SR337jyz1vHi8L/PILb1eN7rVRs2bR55aKevUKTjoiI9W1CVT8+it7pOLjy5aK8NSj7yqv5g+APgCqKF8vBLBQ+doHBWva3sDTVtO2NBISiBwciO4UU7v13385xEDXUL5yJCMqg+4ful9g24m6J+jxLXVsfVp4Gp1wP1E+Hbh3j2jZMnVMu7YyS/rg4cH1hu8rv9PRo0SLFvH17tqVC7l6eXFoh66sX8+lmYypPJaUxH365BOOiDl2jKhGDd6mLYawXz91mGpODheXrV2b6IcfiCZMoPAPxlKvn3vRvuv7qOeGnqRQKMj3O1/6N+pfOnrrKCEAtOniJqLt2/kcS5eq2y6uatvp0yxap8Evob+QxVwL+vR/jWlHY1ByejJ/cPkyt6tq6/nnifbvN+gSTZrETS5ezKGVCgVR+/ZcS1dfzp3jsE9VNI+LC19CTerUIZowgcjSkmjgQIO6bnJQ0VE6RLSfiFSesGAAdZWvhwL4jYiyiOgmgEgA7Q051xOHqyuvFKmchvv3Az/8oP78xg0WOImMNE3/NLD2tIbDuikFfLVV3aoiK0E9Hb+35x5qDiyhOochnD7N+vaqEbO2Mkv6cPMmh204KOWPu3XjAqx16rAo/Lvv8gg3K6vk4q+aXL3K+r7GVB5zdgZcXHjxtk8f9lk4OfFCauHF3IwMVh3r14/fV6nC12vGDGD0aGD0aDT64whsDh/HgRsH0KluJwgh0N+7P07GncTVeyz3EX43nGcFH3/M10FFcYvjLi7sU9FgxbGlWGM2HD2y6iCjgTsO3zrMH/j48MxM1VbLlmqXUxlRTfLefZfXyxctAtLSgMGD9W+rXj1ekujdmwOU7t1Tj/Czs/l2SEjgSffYscCJE7oVZomNZQ9Z/frcx6cZY/rwXwOgKnHkBkBzCT9Oue3ZYuBAFsu6dIn/KT/9VC0Ycvs2z0vDwyu+X6q7WLUilpHBDk0NUS8rDytk3szMf39/7304DnAsn/7cusULoePHc0VtQ9FmvKpU4Xn6gAHAl1+yUW3UiOfvuhARATTWIuxmKHPm8FpD374cj9i5M7t6fv214KrhjRusFWSrIWHh7s5SHtWqAZ07w3zceKzdnoukH5ahT4M+wKZN8KnZBElhIRA7/kTL2i1xKekS++7btSu9rBXABj8pif0rq1fj5oObqHshCmNnbkHPx7VRo0UHBMUEqfdXicUB/CArQ+lPTSZOZANvYcFGedo0Xpw1K4NlclTevrdu8YKtnx/fdirv4c6d/PmjR+wl8/LiJaWSOH2aHyT9+/PD49Yt/fv1RFHaFADAQQCXtPwM1dhnBoAdAITy/UoAr2h8/hOAF4tpn2bPnp3/c/jw4fKdBxmT+/eJ7Ow4lX7lSqI33+QUfCKeN377LbswUorPkC2WhASimTN123fqVKLu3fn15ctEzs6cXalyn/z9Nxdkd3IiysoiSk+nqPcvUNRMzpTMSc2ho9WPUm5aOSWSffIJz9vd3CpWrP2VV4jWrCl9v5gYzuC5cKH8+pKdza6ZxET2V1SpUrDy+I4dRIMGldrM6b/XUoaVOWVfvEAE0LW1S+hhVRABtDNiJ9VZWocUHh5E167RnbQ7tCtiV34R973X9tKOKzvoRMwJSkrTkIWws+O/TbVq9OuGj+ib6T347wXQ8d+XUO+fe2vvzMWLRI0aGXBRCtKqleHKGe+8Q+Tjw91ftYqzcV9/nd93785lFry8OFF+1iy+NUti/Hj+V1ZeDtpRtLZ4hXH48OECthKmyLQF8CqAkwBsNLZNBzBd4/0/ADoVc3x5XqPy57PP2FGYk8N+5EGD+E4bMoT/oTt1ItL2EMvLIzpyhI2NNpYs4ZzyzEzOS08oWgAhn7Zt+U+Zns53aM2afCzAWZ99+vADqXt39gk//zzd8X6DwoaHERFR0o4kutC7HI3dSy9xJYzi/MjlxbffsmH18yt5v5Ejibp1U2v6VAQLFrB1ysri94sXE733nm7Hdu1K9OGHRAApLC3puDso16IKKXJyqMmsmpRnx9If7+x9h+wX2JPPSh+KuBtB1vOsadCvg6jD6g7kstiFrt9TOrsHDyby9ib67DPaPaAhnfxsfL6Fu5kcSW5L3bT3IzeX7zUjFH4hMp5ayfr1RGPG8J/z6FEuxDJxIn+ll15SJ8j/9Rf/axSH6uvFxPByRbt2RHPm6KUoUa5UuMEH0A9AOADnQtuboeCibRSetUVbFQqFetSqWixTVW8KDuZ/6jfeIHruOdYwUTF9Ov+TOTgQjRjBDwvNNtu04ZWlgABus1cv/mz27KILfoMG8T5DhxJ16MB3ZHAwtxsQwCP+7GwWfuvYkQigR8KLjtocoqyh4+jGhLN0c87N8rtGHToQBQWVX/vFERKiHpqVRKtWrBdTkZw8yf0aMYLfT5jAOje6oBKPmTqVKCaGgqOOkaJePaLISJrzUQeKbtuIUh6nUI35NajJiiZkPc+aBv86mFwWu+Q3Me3ANPpo/0f8RnUPh4VRlFMVuvvJO6xZFBZGeYo8qvZFNUp5XMwsdeRI3WZRJiYri9fkf/pJvS06miWCiiMkhKhZM36tUPD6tGrNPbt0aapyxxQGPxLsq7+g/Ple47MZ4OicqwD6l9BG+V6ViuT8ebWBAfiO2rCBdVVsbdUuA4WC3S0JCawKZWPDSoyqiJ/vv+eojqFDuR13d/V039mZaNu2guft1IlH7h06FJQVXLaM57ADBqi3xccT1a1LeaNepWCzjXTRYhGdF8souZv+uihaycoqGIWTlMTG6UHpJRGNTlYWh22oImUyMlgETVMbR/W3qOj+5ebyUNTBgSgujmcY//yj27GbNvF32r1bva1fP6ING+j+4N70RTdQva/qkeNCR7pw+wL9G/UvIQDUaU2n/N33Xd9H/uv9CzR7IeY0ZVQBKcaPL/DwaftjWzoZW4za2aZNRP37s8ZOYcOflUW0YkXFz+x0RKEoWXRt7lwWb9Nk9my+9KpIIFNiEpeOoT/PlMFPTeVLumQJ0bhxbPjS04lOnGCju3Mn75eYyHNFIp53njpF9MILbAAOHWIjHRrKypx37/KDolkzniEA7EbSFG5zd9cuQhYezvvPKFRHNjeX6PffKQO1KMjxMB2rcYSybOvx+QxBoeDv2bgxzzDS0lg18vXXDWvXUOrW5eswZQrPuABWwSTiB6CLS8nHlydjx7IVsbfXXTc4N7fo3+qXX/h7DRpESZdOEQJAbX9sS0RECoWCai6sSaO3q9Ux76bfJbv5dvkKnGlZaeS8yJnu16/NPpCtW9Vd3DGW1pwtZhSfns4Prdat+aGjydat3Kfz53X7XiagpMjSLl20P4N79VLfPqakLAZfZtoaEzs7Dr177z0OC6xShRNUOnXinHJVjNiNG+o0QisrjqgYMIAjWCZN4tBCPz/OMnFy4nCCqCh1iMC2bUCtWlx2Ly+PBa3q1Cnan6ZNudipZmUmgKM3evSAlUMOFGQOKy8bVB3QiRWrFAoWOsnL0/4dSSnupS2eLSSEk9H+9z8OcZw1C1i1qmwZtcakUSNOpgoNBZYv5ygZVVhkeHj5ROfoSr9+HMkzeHDxCVmFMTfne0OT0aNZIG3nTjj5tIWjtSO8HLwAcNWxjnU7ooGDOnXVycYJtavXxpmEM7iafBU1F9VEZ/fOcGjViSObVElWAHycfHD57mXcuH9DNUjDo6xHSM5I5vt7xgy+tleu5B+zOWwzbi7/nO+/334r48Upf1q04Nv57NmC2+/e5WCnbt2KHuPtrU4UmzCBo36ekqR7OcKvMBYs4IU2le925MiCnz94wH78OXO066ir3ESNG3PEzejRvEBbmiMyOrr4RKesLLrQ9wLFrYzjFawuXTgRByC6dEn7MT/+yPLQX3xR9LPvv1eP5o8c4TUIf/+i+1U0R46w2yQlhd1kc+ao5+rvvcfvTUVyMo/uS1qULwNd13alaQem5b8PigmiiLsF6xrMDZxLE/6aQCtPraRW37ei2NRYLkpjZVVgBvHX1b8IASAEgI5FHyMiojF/jCEEgDKyM3hmFx/P1U2Sk6n3V60JAaBbDmYcmaSlYNDXJ7+mzj91pi2XthT5rCL56Se+3QsnYX30ES+raGPJEp4o9upF1KQJLxCboCaSdOk80WzezAthqkXYvn31O151ZzZtyu/j49lQLF2qeyELLWQ/yCZFroJXoZycuEgGUHB1S5MhQ9gouLgUfZC8+67aP56VxWsTmqGHTwobNxK9/DIbqnr1iMLCTN0jozM3cC7tuFJyDGHU/ShyWexCL297mX46V8zfm4gSHiaQ6xJXGrltJK08tZLWnV9HtRbXoqqfV6VtlzXWk3x9iVq3pj0+FrTvkxcps4pgl2T16gVckNEp0VRzYU3aHr6d7BfYU/zDeIO/b1k5c4YD2uzs1Auxu3bxbVFc8NH587yU5ujIzzhnZ/6KRqp3ozPS4D/JREfzwqqbGxvokBD92zh/nujsWfX7rl35T6haGzCUiRN5gXn0aKK33tK+T7NmvKbQujXRwYMFP+vdmxfvVISHV3hlMJ04cIBDVM+cYX/1E7qoWBE0/LYhWc+zpqvJV0vd95vgb6jD6g5kPsecEACad2QevbP3HfUO27dTrneD/NlohJPgHAAvL66SpeSncz/RyG08w311x6s078i8/FyB0kh5nEIHbxwsfUcdUSj4X6pzZ5Z5yMvj4KfCFbYKM3Agj4natOHZQM+eXDly40aOC6gIpMF/0gkP52iMe/eM015gINE33xjPqEZG8h0bHKy9GpJCwaP2hw95pqKZtZKXx66lW7eM05fy5NIlnotPn1565s0zzof/fEhv7npTp30PRR0iBIDWn19PsamxFBwbTH6rCuY4nIw9SbFOVYlq1KC9TS3oTtodohdeoLQNa2hz2Gb6/vT3VGtxrfwZxam4U1RrcS36JfSXUs8f/zCe5gbOpepfVqcHj40bVfXgAQe5ffQRe/0KV94sjOr5dPIke7927+aCYgBPdI8cMbqXrgjS4EuMQ2YmG/bClaNv3+b5KxFHE3XooPb1Hj6se8k8U3PvHodi1qnD2aKVmNy8XJ1H1w8zH9KnBz/Nj+zJzMkky88tKTNHnT297vw6mhfQkygkhN76oBEFxwZTxIQRFPA88tcBVp1eRdm56kD2NWfX0MvbXi7Qp6YrmhaI/VcoFNTux3aEAJD7MndaEbLC0K9ehKQkHgcABSfSuqBQcAT2pEk86ndw0D2PrqyUxeDLKB1JUSwtOZLljz8KioZHRrI8McAC5iEhLBAGcKjDyy9XfF/LgoMD6+g3aaJ/EZZnDHMzcwgdq47ZWtrii55fwEyw2bCsYglPB09cu6eWCT9/+zwse/cD2rdHznNdcDrhNH7NuwCfu0D1qtXxbb9v8Vabt2BhbpF/TE+vnjh08xDSstMwac8khCWF4UryFQTHBefvczzmOFKzUvFljy8xt/tc7I/ab6QroMbZmaX+gaJa/KUhBAuyBQRwxE+fPqyrmJZm9G4ahDT4Eu28+SbfwWPHqiUCd+xQV4CqVo3lBqtX54dCYCDLFD4NCMFyiv/8Y+qePPU0c26Gy3cv578/c/sM2ri2AQD08+6HPdf34B/LWDyX4YznPZ7HOx3eyX9gqKhvXx81rWti6G9D8d2Z7/JLN56IPZG/z9bwrRjjNwbTu03HgIYDcOTWEeTkFQwNVpBC5TUoM32VlTs0NeL0wcmJQzR/+43LMKpKCTwpSIMv0c4bb7BksKMj8N13nAPw888FKzd16sT5Aj17cgx2ixYm667e2NoaLtMsQTPnZgi7EwYAyFPkITQxFK1dOe+jt1dv/B35NyKdzeGcWxW/B9Xh2aMWjeEXmr6AU/GnMLndZKy9sBYd63ZEcLx6hL/r6i4MazIMAOBSzQWtXFth86XNBdqYfnA6Fp9YbND38fMrcxGvfFQlkOfMKT5lxVRIgy8pnkaNOHFq7lxODPrgA8460eSPP7j+6+zZusnxSp4p+nn3w9bwrSAiXEq6hLp2dVHDiofHDtYO6F6/O6xtHWAWcgrVomJZAzkuTt2Asur4pHaTsM93EaY3eQPTukzD/J7zcfHORQBAUnoSUjJT0My5Wf5hM7vNxMKghSAiKIgfINuvbMf6C+sN/k5VqxrcBAAuKObpyfXoV640TpuGIoc4kpLp0oXrxK5bx2mFhXFxYd3/4mrSSZ5pOtbtiKrmVREUG4SwO2Ho7N65wOf7x+xHRk4GYGkH7NvHxc+vX2eR+cePOUN8xQq4TpgA1ymLgcaNMX/vXhCAxzmPkZyRjLA7YfCr5VdgraGHZw/kKfIQFBuEPyP+RMKjBKTnpCM7LxuXk9jFNHnfZPw79t+S1yjOnOHZXjllW69axVUkP/mEtf91XC4pN6TBl5TOSy9x/riqwpREokQIgRebvoidETuRmJ6I5z2eL/B5FbMqsLPUKJLeqBHLb/TsyQVrhODZYVYWO8CvXweCgyG+/x4tu/jgUtIlXLxzEX61/Iqcd7TvaPwZ8Sd2Xd2F6/ev46u+XyEmNQZbw7fCztIOgbcCYTbXDBGTItDYqZBBv3eP16fOnOFZx/DhXH2lUSOjXp/27bnWjZsbq6NoWwzOzjberKI0pEtHUjo9e6pLOUokhRjceDD+uvYXTsSeKDLCL0LDhlylPDKSXTvduvHK5ocf8s///gfMnw/8/DO659VD2J0wnE44jVa1WxVpyr++P9ZdWIeUzBRcmXQFUzpMwQifEdgavhXHYo7l92XpyaVYdnIZsvOyuZaitzewYgXrKQUFccTZunXlpvkjBC8Gt2xZtADeDz+wBFdEBFfkMnT9oFT0jeM09g9kHL5E8lSTp8gj1yWuZL/APj9Gv1hu3GBl2OrVuVDQqFEswzFnDmsbhIYSmZsTAbRn0Zv02p+vkeNCR9b5KURmTiYhADTr31kF+lJ3WV2y/dKWEh4m0IYLGwgBIOdFznQg5DeWKV+wgIPtly3jg06e5Iyrdu2MeTlot4QAABODSURBVFkKoFDw6V58seD2Tp04nWXcOJao0kfWCWWIw1eVJDQZQggydR8kEolhvPXXW4h7GIe9o/fqdkCHDqz46esLLFyo3p6VxSG/eXmImvY2Glj9gFa1W+Hc29pr50bej4SXg1eBUM85gXMAALP9ZyM5IxkrTq1AniIPvgcu4r/h4ECDfv1YObVJEz4oJ4fXo8LDAVfXslyCUomO5sC2hATWnpg2jYu2h4ayoG2TJsD9+2pPV2kIIUBEeq0KSJeORCIxmKmdp+Kz5z7T/YB27XgRt27dgtstLXkB1dkZbgmctTShrZZgASXejt5F4vpn+8/G7HZTgU2b4GTpgID4hnhv5m7U3XecfSvm5hxZpjL2AFdR79sX2LOH3ycnA5/p8X10oF49fp4lJrILZ9Ei3u7nxwrin3zCatNhYUY9bQEMGuELIT4HMBSAAkASgHFElCB4WfwbAAMAZCi3a31EyxG+RFIJ+f13rlEQHMyjfU3Gj2ejfOMGvls0Aq+3eh2WVSz1a3/5cq4r4ezMI/dTpwAA2efOoGqrNtqPWb2aEwjbtOEHwt9/s4U24opqr14c3Xz5Mqe5BAQUfOZNnswPho8/Lr0tU4zwFxORHxG1BLAbwCzl9v4AGip/3gKwysDzSCSSZ4mXXgJyc4saewD46Se2irdvY2K7ifobewBYv55XQbt0YWO/ZAke2pjjjOPj4o/x8QH+/JMXj9u25aghVdEhI9G1Kyemb9zISiSFJzh9+5ZvArhBBp+IHmq8rQZANVQfCuBn5dpCMAB7IUT5OMYkEsnTSXGJemZm7EdXJmXpxLVrwLff8uvcXB5Cd+7MCYP29sCUKfjpi/9gXeiG4uUXmjYFMjI4y/zzzzms5sYN/b5TKQweDCxdyqfp3r3o59278/MpMVFZdE4VzWQkDPbhCyG+EELEAhgN9QjfDVzcXEWccptEIpGUjr09B6inp+u2/3/+wyGXeXlATAy7cayt2cJu2ABYWGD0m9/i4M2DOBV/Snsbjo58XBuly6dBAw6eNyKtW7PHassWfq4Vpnp1due0bs3KH2kfzOIQUiNRauKVEOIggNpaPppBRDuJaAaAGUKI6QAmA5gNQJtfqVhHfUBAQP5rf39/+Pv7l9YtiUTyLCOEepRfWM6jMDk5PML38OCR/e3b6gQqGxtgyBAArMHjX98foXdC0aGuFlcSAIwZo84ab9CAR9hGRAhg7dpiPpw/H3j/fXz2mRWGDePMXJw8AZhlAAACAwMRGBho0PlLNfhEpGvO/K8A9oANfhwAd43P6gJIKO5ATYMvkUgkAFh2ISGhdIMfFcWprM89xwquOTmc4KUFXxfffLE3rSxZon7drBkv3lYEKSnAp59yIlrXrvD1Bcb0iIf18VjkXbaCOYoOhufMmaP3aQxy6QghNK/qEAARyte7AIwVTEcAqUSkh0NOIpFUeqpUAZ5/nqWsS+LqVQ7lbNUKuHABOH6cX2vB18UXYUk6xj36+XGQfEVw9iz/Pn2afy9fjpf/ehm/4yUobt5i95YRMNSHv0AIcUkIcRFAHwDvKrfvBRAFIBLAagATDTyPRCKpbCxfzi6Zq1dL3u/qVXbh+PpyWOX+/RwFpIV2bu0QeicUtx/pMP50c+PZQmIiLwSXJ6dO8frB0aPAgwdAQABsM5LgumIGEqy8jBacb2iUzotE1FwZmjmYiOKV24mIJhFRAyLyJaIzRumtRCKpPPj68qLrtWvKkBVwimpMDOs7qUImVSN8X19+/eKLxVYwsbeyx6jmo7D63GrkKfKQmZtZ/PmF4LDODh14Iff+feN+P02OHwdmzeLv2qcPx29GRKDh0GbYp+gL2ve3UU4jM20lEsmTS6NGrGppbc3y3Bs38uJsSIhakkFl8J2cWIBem4y3Bv28+yEoNgiv73odsw7PKnFf/Porh2g2acLRPuVBTg4b/JdeAr75hhU8laW33NyAw5b98XDLPgQO/wYZJw10MekrvmPsH0jxNIlEUhyrVrHQWWQkkYcHkZsb0dKlRMeOEdWrx/s4OxPFxfHrzMxim1Jx+9Ftsp5nTWZzzGjgpoG69WPPHqIuXcr2HUpj/36iFi34dXY2UZ06/H2V7P39IaXBhgigDAtbOv37DSKFQoqnSSSSZ4xHj4Dz5zkCJzSUFzXHj2d3i4MDj+xPn+ayiXpUF/H42gP9vftj59Wd6NugL9YPW1/yAVlZQO3awMWLLO88frxh30uFQsELzDNmAP/9L2/LyWFtH83dmvjgbro1dsW1xmtYC/OliyE+/FBvaQVp8CUSydOJvz9w5Ai/1tOGRKdEw9HaEXYLuDhL6rTUgoVaCqEgBczGvgokJfGicGoqC9kbyh9/AAsWsIuqpAfW2LFIN7NFu+Dl6FonCj+ebweRkiLVMiUSSSXhhRe4MM/jEvRxisHD3gO2lraoY1sHABCaWLJvvP+m/jg1tB0bZqBoJZOy8v33rN1T2uxk2jRUm/k+Nv9uhpN3vUvPTSgGafAlEsnTyZQprLhpZVXmJqZ2morhTYbjfOL5YvchIoTEhWCnwx1OBBszhoXtg4LUO23fzro7jx7pfvJ79/gBMmhQ6fv6+ADe3vDw4OAkGjxE9/NoIA2+RCKptLzf6X0MajQIIfEhxe4TnRqN1KxUnIw7iQ3XtuKOUuoAu3apd5oxg5XPnnuO/fIlkZzM0T+BgRz2Wa2azv21t+d8tPsTZ+p8jCbS4EskkkpN13pdcTzmeP77JSeW4IN/PsCWy1sAAOdvn0dn984IvROKqQemopP7ftCyZZwkBfCo//p1IDaWcwRK0zdevpxDTd9/nxU59aRNG+DzeXq57vMpVUtHIpFInmUaOjbE45zHiEmNgZ2lHb489iVSMlNAIHR274ylJ5diXItx6OzeGSmZKRjy2xDcfWU4XD77DEhL44XjAQNYQ79qVa7k1b+/9pMRsVb/Rx8BCxYg0csFFhn3UNOmps79ffNNYPTosn1XafAlEkmlRgiBnl49sfb8WjjZOMGvlh9cqrkgIjkCo7aPQnZeNl5r9RrMzVi/36OGB2JykuHSogUnZFlYAO+9x/LMZ85w+cbBg4HevYueLCICeSBs9HfAuAXApNjv4X70Fr7u97XO/R0xgmV+fHz0/67SpSORSCo9gxsNxpwjc/DOvnfQtk5bbBmxBRuHb0St6rWwZsiafGMPAPVq1ENMagz76xMSeBVVpWLZqhWP9N9+W/uJDh/GjZb1sChyPb6Y0AyBVon448ofxRdl0YKZGddqKQvS4EskkkrPoEaD8HHnj1G9anW0ceUCKC1qt8DWEVvhV8uvwL4eNTwQnRKN7FdfQcra7xAzehDr+ABcxevECeDuXRZBK8y//+JYAwtMbDcRH684j4uTLiOP8hCdGo3sPOMoYpaENPgSiaTSY2dph4W9F+LnYT+jf8Ni/O9K6tWoh+jUaMyM3QDH6InwaLgbN1JuqnewsAA6dmR9HE0UCjw++DdmiUB0rNsRFuYWcLNzg6e9J6JTotHg2wZITEssh2+nRhp8iUQiUTK86XDYW9mXuE/bOm2x5/oerDm3BiFvhOAVv1fgu8oXEckR6p3atGFtfg1C929EQtVMvNLvY7So1SJ/u4e9B4LjghH3MA437hu3hm5hpMGXSCQSPejm0Q3Zedl4tcWraOfWDlPaT8Hj3Mc4GHVQvZOfH/DDD8Dbb0NxIgiZ772DUz/ORlbP7ljYeyEszNVaOfVr1MfeyL0AwGsD5YiM0pFIJBI9MBNm+Ovlv+Bp7wmAi6os7LUQNx9ouHV8fYH4eGDfPkQlXIb37iAMsq8Cl21rirTnYe+BL49/CYCTvMq178ZoRAgxVQhBQggn5XshhPhWCBEphLgohGhtjPNIJBLJk4BfLT/YWtrmv2/g0AA3Hmi4Yxo3Zt3+2Fh47w7CX42Bqs18Yf68f5G2vB1ZF6dbvW6ITnnCDb4Qwh1AbwCac5H+ABoqf94CsMrQ80gkEsmTipeDF6IeRKk3VK0Kuskj/qSa1sjd8QdqHj/HugiF6F6/O5I/SsbUzlOfihH+VwA+BqAZSDoUwM9K/f5gAPZCCFcjnEsikUieOFQGPyOHdXbSs9NRZ1kdPDz8NwZOsi8S2qmJEAI1bWrC29Eb1+9fL9d+GmTwhRBDAMQTUWFtUTcAsRrv45TbJBKJ5JmjhlUNDG0yNL9k4rGYY0hMS8SbyWuRWbsmPB08S22joWNDxD2Mw+Mc/eWedaXURVshxEEAtbV8NAPApwD6aDtMy7ZiU8kCAgLyX/v7+8NflbUmkUgkTwlf9f0KTVc2xbsd3sX+G/vhaO2ILZe3IOSNEJiJ0sfWFuYW8Hb0xpXkK2jtWnTZMzAwEIGBgQb1scwVr4QQvgAOAVBqhaIugAQA7QHMARBIRJuV+14F4E9Et7W0IyteSSSSZ4I3d70JLwcvfBX8FXaP2g0FKdCxbkedjx+5bST6NuiL8a1KL6EohKi4ildEFEZELkRUn4jqg902rYkoEcAuAGOV0TodAaRqM/YSiUTyLNHJvRPmH5+Pzu6d0d6tvV7GHgBG+47GvGPzkJmbWS79K6/Eq70AogBEAlgNYGI5nUcikUieGNq7tcej7EcY13JcmY4f3HgwalrXREhc8QVZDMFoiVfKUb7qNQGYZKy2JRKJ5GmgqVNTjG0xFv29S9bjKYmu9boiKDYIztWc0dSpKURp9W71QEorSCQSiZEwNzPHhmEbYFnFssxtdHHvgj8j/kTL71viVPwpI/ZOGnyJRCJ5oujn3Q/3Ht9DjiIHQbFBpR+gB9LgSyQSyRNEtarVcHDMQSzts7SIwX+Y9RBp2WllblsafIlEInnC8HTwxNDGQ/NdOmnZaUjPTsenhz7NT+4qC1ItUyKRSJ5APB088TDrIZIzkjF+53hcSrqEPEUealjVwMLjC8vUphzhSyQSyROImTBDy9ot8ePZHxF2JwzDmwzHo+xHiHsYhyPRR8rWppH7KJFIJBIj0cGtA7449gXebvM25vWYh10jd6G1a+uCxVb0QBp8iUQieUKZ+dxMDGk8BONajoONhQ26eXRDc+fmyFHklKk96cOXSCSSJxQ7SztsfnFzgW3NXZrDXJgjD3l6tydH+BKJRPIU4VvLF/Xt65fp2DKrZRoLqZYpkUgkukNEiEmNQX2H+nqrZUqDL5FIJE8hFSqPLJFIJJKnC2nwJRKJpJIgDb5EIpFUEgwtYh4ghIgXQlxQ/gzQ+Gy6ECJSCHFVCNHX8K5KJBKJxBCMMcL/iohaKn/2AoAQwgfASADNAPQD8J0QwtwI53qmMbRA8bOEvBZq5LVQI6+FYZSXS2cogN+IKIuIboJLHbYvp3M9M8ibWY28FmrktVAjr4VhGMPgTxZCXBRCrBVCOCi3uQGI1dgnTrlNIpFIJCaiVIMvhDgohLik5WcogFUAGgBoCeA2gKWqw7Q0JYPtJRKJxIQYLfFKCFEfwG4iai6EmA4ARDRf+dk/AAKI6KSW4+SDQCKRSMqAvolXBomnCSFciei28u1wAJeUr3cB+FUIsQxAHQANAWitxqtvhyUSiURSNgxVy1wkhGgJdtfcAvA2ABDRZSHEFgDhAHIBTCIi/aXdJBKJRGI0TK6lI5FIJJKKwaSZtkKIfsrErEghxDRT9sWUCCHchRCHhRBXhBCXhRDvmrpPpkQIYS6EOC+E2G3qvpgaIYS9EGKbECJCeX90MnWfTIUQ4n3l/8clIcRmIYSVqftUUSijIJOEEJc0tjkKIQ4IIa4rfzuU1AZgQoOvTMRaCaA/AB8ALysTtiojuQA+JKKmADoCmFSJrwUAvAvgiqk78YTwDYC/iagJgBaopNdFCOEGYAqAtkTUHIA5OLmzsrAenMSqyTQAh4ioIYBDyvclYsoRfnsAkUQURUTZAH4DJ2xVOojoNhGdU75+BP6nrpR5C0KIugAGAlhj6r6YGiGEHYDnAPwEAESUTUQppu2VSakCwFoIUQWADYAEE/enwiCiowDuF9o8FMAG5esNAIaV1o4pDb5MztKCMry1FYAQ0/bEZHwN4GMAClN35AnAC8BdAOuULq41Qohqpu6UKSCieABLAMSAc35SiWi/aXtlcmqpoiSVv11KO8CUBl8mZxVCCFEdwHYA7xHRQ1P3p6IRQgwCkEREZ03dlyeEKgBaA1hFRK0ApEOHafuziNI/PRSAJzjUu5oQ4hXT9urpw5QGPw6Au8b7uqhEU7TCCCEswMZ+ExH9Yer+mIguAIYIIW6BXXw9hBC/mLZLJiUOQBwRqWZ728APgMpILwA3ieguEeUA+ANAZxP3ydTcEUK4ApwTBSCptANMafBPA2gohPAUQlQFL8DsMmF/TIYQQoD9tFeIaJmp+2MqiGg6EdUlovrg++FfIqq0ozgiSgQQK4RorNzUE5zbUhmJAdBRCGGj/H/piUq6gK3BLgCvKl+/CmBnaQcYmnhVZogoVwgxGcA/4BX3tUR02VT9MTFdAIwBECaEuKDc9qlKblpSqXkHwCbloCgKwHgT98ckEFGIEGIbgHPgqLbzAH40ba8qDiHEZgD+AJyEEHEAZgNYAGCLEOJ18ANxRKntyMQriUQiqRzIEocSiURSSZAGXyKRSCoJ0uBLJBJJJUEafIlEIqkkSIMvkUgklQRp8CUSiaSSIA2+RCKRVBKkwZdIJJJKwv8B7z+Cb5hjfgUAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -118,7 +112,7 @@ "source": [ "# Plot the data with Matplotlib defaults\n", "plt.plot(x, y)\n", - "plt.legend('ABCDEF', ncol=2, loc='upper left');" + "plt.legend('ABCDEF', ncol=2, loc='upper left'); # recreated same plot" ] }, { @@ -136,9 +130,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", @@ -154,16 +146,14 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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hJGWVk9eYtpea8HujHDvQm7fdUT692tTOSitqjYq25kGe+fcPee2FY+OOa28e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KqqJJ3z+NReVWDndvyvvYL/1Zen3Cc+NDjn2rlBqeCra2tZadEzGI3jDsdIi2JOXjHbdgLkGJop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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -173,7 +163,7 @@ "source": [ "# same plotting code as above!\n", "plt.plot(x, y)\n", - "plt.legend('ABCDEF', ncol=2, loc='upper left');" + "plt.legend('TUVXYZ', ncol=2, loc='upper left'); ## legend changes" ] }, { @@ -206,28 +196,22 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [ { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "I am image destroyer\n" + ] } ], "source": [ "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", "data = pd.DataFrame(data, columns=['x', 'y'])\n", "\n", - "for col in 'xy':\n", - " plt.hist(data[col], normed=True, alpha=0.5)" + "print('I am image destroyer')" ] }, { @@ -240,9 +224,7 @@ { "cell_type": "code", "execution_count": 7, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -257,6 +239,11 @@ ], "source": [ "for col in 'xy':\n", + " a = List(range(10))\n", + " b = List(range(10))\n", + " # Going to pretend like I am writing some serious code\n", + " data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n", + " data = pd.DataFrame(data, columns=['x', 'y'])\n", " sns.kdeplot(data[col], shade=True)" ] }, @@ -270,9 +257,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -300,9 +285,7 @@ { "cell_type": "code", "execution_count": 9, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -330,9 +313,7 @@ { "cell_type": "code", "execution_count": 10, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -360,9 +341,7 @@ { "cell_type": "code", "execution_count": 11, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -383,128 +362,26 @@ { "cell_type": "markdown", "metadata": {}, - "source": [ - "### Pair plots\n", - "\n", - "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n", - "\n", - "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
sepal_lengthsepal_widthpetal_lengthpetal_widthspecies
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\n", - "
" - ], - "text/plain": [ - " sepal_length sepal_width petal_length petal_width species\n", - "0 5.1 3.5 1.4 0.2 setosa\n", - "1 4.9 3.0 1.4 0.2 setosa\n", - "2 4.7 3.2 1.3 0.2 setosa\n", - "3 4.6 3.1 1.5 0.2 setosa\n", - "4 5.0 3.6 1.4 0.2 setosa" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "iris = sns.load_dataset(\"iris\")\n", - "iris.head()" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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VAW1dnR5yXyjixd90ovtLdSEBDBcMpWiFW+gw3LZYuIUMwy98KGrrYc6niUeS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\nqkIzN7UlPwmtkMybNw933nkn1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f+kx2Mh3fuE9zERUWwFmqd6nnlFyB4Pls2UbFghiaFA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\nU1M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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "sns.pairplot(iris, hue='species', size=2.5);" - ] + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -512,16 +389,14 @@ "source": [ "### Faceted histograms\n", "\n", - "Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n", + "Emptied above cells Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n", "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:" ] }, { "cell_type": "code", "execution_count": 14, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -617,9 +492,7 @@ { "cell_type": "code", "execution_count": 15, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -651,9 +524,7 @@ { "cell_type": "code", "execution_count": 16, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -684,9 +555,7 @@ { "cell_type": "code", "execution_count": 17, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -714,9 +583,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -745,9 +612,7 @@ { "cell_type": "code", "execution_count": 19, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -837,9 +702,7 @@ { "cell_type": "code", "execution_count": 20, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -869,9 +732,7 @@ { "cell_type": "code", "execution_count": 21, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -915,9 +776,7 @@ { "cell_type": "code", "execution_count": 22, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [], "source": [ "# !curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv" @@ -926,9 +785,7 @@ { "cell_type": "code", "execution_count": 23, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1013,9 +870,7 @@ { "cell_type": "code", "execution_count": 24, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1046,9 +901,7 @@ { "cell_type": "code", "execution_count": 25, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1131,9 +984,7 @@ { "cell_type": "code", "execution_count": 26, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1164,9 +1015,7 @@ { "cell_type": "code", "execution_count": 27, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1264,9 +1113,7 @@ { "cell_type": "code", "execution_count": 28, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1299,9 +1146,7 @@ { "cell_type": "code", "execution_count": 29, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1405,9 +1250,7 @@ { "cell_type": "code", "execution_count": 30, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1428,9 +1271,7 @@ { "cell_type": "code", "execution_count": 31, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1459,9 +1300,7 @@ { "cell_type": "code", "execution_count": 32, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1494,9 +1333,7 @@ { "cell_type": "code", "execution_count": 33, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1528,9 +1365,7 @@ { "cell_type": "code", "execution_count": 34, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1560,9 +1395,7 @@ { "cell_type": "code", "execution_count": 35, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1664,9 +1497,7 @@ { "cell_type": "code", "execution_count": 36, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1693,17 +1524,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n", + "Removed 2 lines and adde crap 1\n", "\n", - "Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:" + "and here is crap 2" ] }, { "cell_type": "code", "execution_count": 38, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1730,9 +1559,7 @@ { "cell_type": "code", "execution_count": 37, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1784,9 +1611,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.1" + "version": "3.6.4" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/notebooks/10_introduction_to_artificial_neural_networks.ipynb b/notebooks/10_introduction_to_artificial_neural_networks.ipynb new file mode 100644 index 000000000..d017def96 --- /dev/null +++ b/notebooks/10_introduction_to_artificial_neural_networks.ipynb @@ -0,0 +1,2236 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Chapter 10 – Introduction to Artificial Neural Networks**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_This notebook contains all the sample code and solutions to the exercises in chapter 10._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# To support both python 2 and python 3\n", + "from __future__ import division, print_function, unicode_literals\n", + "\n", + "# Common imports\n", + "import numpy as np\n", + "import os\n", + "\n", + "# to make this notebook's output stable across runs\n", + "def reset_graph(seed=42):\n", + " tf.reset_default_graph()\n", + " tf.set_random_seed(seed)\n", + " np.random.seed(seed)\n", + "\n", + "# To plot pretty figures\n", + "%matplotlib inline\n", + "import matplotlib\n", + "import matplotlib.pyplot as plt\n", + "plt.rcParams['axes.labelsize'] = 14\n", + "plt.rcParams['xtick.labelsize'] = 12\n", + "plt.rcParams['ytick.labelsize'] = 12\n", + "\n", + "# Where to save the figures\n", + "PROJECT_ROOT_DIR = \".\"\n", + "CHAPTER_ID = \"ann\"\n", + "\n", + "def save_fig(fig_id, tight_layout=True):\n", + " path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n", + " print(\"Saving figure\", fig_id)\n", + " if tight_layout:\n", + " plt.tight_layout()\n", + " plt.savefig(path, format='png', dpi=300)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perceptrons" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from sklearn.datasets import load_iris\n", + "from sklearn.linear_model import Perceptron\n", + "\n", + "iris = load_iris()\n", + "X = iris.data[:, (2, 3)] # petal length, petal width\n", + "y = (iris.target == 0).astype(np.int)\n", + "\n", + "per_clf = Perceptron(max_iter=100, random_state=42)\n", + "per_clf.fit(X, y)\n", + "\n", + "y_pred = per_clf.predict([[2, 0.5]])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure perceptron_iris_plot\n" + ] + }, + { + "data": { + "image/png": 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z64E+wP3xuw8BiSvdcOAgkquZGW3b3sqqVVFUq1bOH1+37kMiImry229rPMxOREREsossWSAnwQGnnzjcAFQ0s4R3jGvEx0W44oqyrFw5hA4dbvPH9uz5naioBixYMIS4uDgPsxMRkZwoJ7VaTs9nCdTnt2TWn0gunt4cAr3MWwgQAvQGygAdgFjnXGyicU2Ar51zO8ysKjADmO6cey1+/2pgOdALaIJvybdLnXO7Urq+lnnLfWbMWEHnzqPYv//fVS2uuOI22rR5n/Dwkh5mJiIiIoGWVVtN98K3tnEPoGX8615mVs7MDpnZ6d+L3wx8b2aHgc+AmUD/BOd5GKgL7AMGAvenVhxL7nT//Q1Yu3YYV11VxR/74Yf5RETU4KefvvAwMxEREcmqAnoH2Wu6g5x7nTwZS+/ek4mMnOmPmRm33fYid931GsHBISkcLSIiIjlBVr2DLOKJ0NAQ+vdvzdy5vSlZshAAzjnmzetPVFRD9u7d6nGGIiIiklXoDrLkOtu376Nt2+F88cV3/ljhwvl56KGJ1Kp1r4eZiYhISrJyO+dASU+r5XOVnq/z+beATtt1zpfuIIsko3TpIsyd25u+fVsSHOz7K/DPP4d58837mDKlCydPHvM4QxERSUpOauecXulptXyu0vN1zsgW0Fnh+6kCWXKloKAgXnjhfhYv7k+5ciX88aVLRzNw4NVs3/6Th9mJiIiIl1QgS652zTVViY4eRrNm9f2xv/76nv7967By5btqUy0iIpILqUCWXK9IkQJMm/YCI0c+TlhYKAAnThzh/ffbMmFCK44dU5NGERGR3EQFsgi+Jd8ef7wJK1YM4bLLyvjja9d+QL9+tdmyZZ2H2YmIiEggqUAWSeA//6nA6tWRtGlzsz+2a9cmBg++hi++GK4pFyIiHspJ7ZzTKz2tls9Ver7OGdkCOit8P7XMm0gypkxZSpcuYzh06N9VLapXb8qjj06gQIHiHmYmIiIi6aFl3kTO0yOPNGTt2mHUrl3JH1u//lMiImqyceNSDzMTERGRzKQCWSQFlStfyFdfDeTpp+/2x/755y+GDbuJTz99jbi4Ux5mJyIiIplBBbJIKvLkCWXIkHbMmtWTYsUKAuBcHJ9++irDht3Mvn1/epyhiIiIZKQ0F8hmdoGZXWtmzczsvoRbZiYoklXceWc9YmKGc8MN1fyxX35ZSkRETb7//lMPMxMREZGMFJKWQWZ2CzAFKJbEbgcEZ2RSIlnVxRcXY/78PgwYMIOIiGnExcVx+PAeRo++i5tueoZ77x1IaGiY12mKiJyX7t2TbvcbHg6DBwc+n/PRqVPy+8aOTTreuXPSLZLNYMwYb49Jz/fmXI/JSd//9ErrHeTXgblAGedcUKJNxbHkKsHBwfTq9RALFvTh4ov//Tfj4sXDGTKkATt3bvIwOxGR85dUcZRSPKdJboGvlBb+CtQx6fnenOsxuf37D2kvkCsAfZ1z2zIxF5Fs5YYbriQ6ehh33PHvajFbt66jX79arF072cPMRERE5HyktUBeAVyWmYmIZEfFi4cza1ZPoqLaExrqm7F0/Pghxo9vwfvvt+P48cMeZygiIiLnKtk5yGZWO8HbsUCkmV0ErAdOJhzrnPs6c9ITyfrMjCefvIsGDS6nZcsoNm36G4CVKyfw668r6dBhGmXK1PA4SxEREUmrlO4gxwDR8f+dAVQFxgGr4mMxCcakiZl1NbMYMztuZu+mMO5RM1tnZgfM7E8zG2xmIQn2f2lmx8zsUPz2c1pzEMkstWtXZs2aoTzySEN/bMeOnxk48Gq+/HK02lSLiIhkEykVyJcAFeP/m9JW8Ryutw2IAManMu4C4BmgOHA1cDPwfKIxXZ1zBeI3Tf+QLKFgwXy8++4zvP32U1xwgW81i9jY40yd2oU332zO4cP7PM5QRCR14eHnFs9pzM4tHshj0vO9Oddjcvv3H8DSclfLzG4AVjrnYhPFQ4BrnXNfndNFzSLwrYjRJo3jnwVudM7dFf/+S2CSc+7tc7lunTqV3erVUedyiEi6/fTTn7RsGcn33//ujxUtWo727adQqdK13iUmIiKSS3XqZOucc3VTG5fWh/SWAEWTiBeK35fZbgA2JIoNMLPdZrbCzBold6CZdYyf1hGze3cuWp9EPFe1ahmWLx9M5853+GN7924lKuoGPv+8v9pUi4iIZFFpLZANX0OQxIoBmfqYvpm1A+oCkQnCL+Cb2nExvnnRc8ysUlLHO+fGOefqOufqFi+ei343IFlC3rx5eP31jnz4YQ+KFCkAQFzcKT75pCcjRtzG/v3bPc5QREREEkuxQDaz2WY2G19xPOn0+/htLrAQWJlZyZlZM2AA0MQ5t/t03Dm3xjl30Dl33Dn3Hr5l6O5I7jwiXmvWrD7R0cO49trL/bGffvqCiIgabNgw38PMREREJLHUWk3vif+vAfuAown2nQCWA29lQl6Y2e3x577TObc+leEuPkeRLKtcuRIsWhRBnz5TGTRoBs45Dh7cyciRt9O4cXfuuSeC4OBQr9MUEcmyAtFmOZC5ZeXrnKusmld6pVggO+faApjZ70Ckc+68plPEP9QXAgQDwWaWF4hN4uG/m4APgHudc2sT7SuMb2WLpUAs8BC+OcpPn09uIoEQEhJMnz4taNToStq0Gc727b5VLRYsGMwvvyylffspFC9+icdZiohkTYFos5xeOe065yqr5pVeaZqD7Jx77XyL43i98N2F7gG0jH/dy8zKxa9nXC5+3Mv4HgD8LMFax5/H7wvFt1TcLmA38CTQzDm3MQPyEwmIm26qQUzMMBo3ruWP/fbbGiIiarJu3XQPMxMREZGUOun9RtIP5p3FOZemtZCdc68Cryazu0CCcTemcI5dQL20XE8kKytZsjCzZ7/M8OGz6dVrIrGxpzh27ABvvfUgP/3UkQceGEaePBd4naaIiEiuk9Id5DeAUfHbe/hWrPgVmBS//RofezdzUxTJuYKCgnj22WYsXTqASy4p5Y8vWzaOgQOvYtu2xKsbioiISGZLtkB2zkWd3vB1zBvknLvVOfdK/HYrMBCoEqhkRXKqevWqsHbtUJo3/7eByLZtGxgwoB7Ll7+tNtUiIiIBlNZ1kO8DPkwiPh24O+PSEcm9ChXKz+TJ3Rgz5gny5s0DwMmTR5k0qQPvvPMIR4/u9zhDERFvBaLNcnrltOucq6yaV3qltdX038DLiVs7m9ljQIRzrnQm5Zeh1GpasosNG7bSokUkP/yw1R8rXvwS2refyiWXXOVhZiIiItlXRreaHgaMMrOxZtYmfhsLjIzfJyIZqFq1cqxcOYTHHmvsj+3e/RtRUdeyYEEkcXFxHmYnIiKSs6V1mbfBQCugOjA0fqsOPOqcG5R56YnkXhdcEMbo0U/wwQfPEx7uW80iNvYUM2d2Y9SoOzlwYKfHGYqIiORMab2DjHPuQ+dcA+dc0fitgXMuqXnJIpKBHnjgOqKjh1Gv3qX+2IYN8+jXryY//bTYw8xERERyptRaTYtIFnDJJaVYsqQ/r7zyAUOHfgzA/v1/8/rrt3D77S/RtOmrBAfrr7NIUnJaC9ycIiu3jRZJ9g6ymR0ws+Lxrw/Gv09yC1y6IrlXnjyhDBzYhjlzXqFEiUIAOOf4/PN+DB3aiL17t6ZyBpHcKae1wM0psnLbaJGUbjk9CRxM8FoLsYpkAbfdVpuYmGG0bTucxYu/B+DXX1cQEVGT1q3HU7NmM48zFBERyd6SLZCdc+8leP1uQLIRkTS58MKizJ3bmyFDZvLaa1M4dSqOI0f2MXbsvTRq1JXmzYcQGprX6zRFRESypTQ9pGdmL5nZNWamSY4iWURwcDA9ejzAF1/0o2zZ4v74l1++waBB9dm+/WcPsxMREcm+0rqKRRNgCbDPzBbEF8zXqmAW8d61115OdPQw7rmnvj/255/fMWBAHVatei+FI0VERCQpaV0H+XqgCHAvsAZfwfwFvoJ5fualJyJpUbRoQT788AVGjOhIWFgoAMePH+a999owYUIrjh07mMoZRHKunNYCN6fIym2jRdLUavqMA8xKATcBdwIPArHOuQsyIbcMp1bTkht8991vtGgRycaNf/ljJUpUpkOHaZQrV9vDzERERLyVoa2mzexBMxttZj8Cm4EOwC/ArfjuLItIFlGjxiWsWRNF69Y3+WO7dm1i8OBrWLx4BOf6j2IREZHcJq1zkKcCzYHxQAnn3E3Oudecc0udc8czLz0RSY/8+fPy9ttPMWHCMxQo4FvNIjb2BB9++DRjxtzDoUN7PM5QREQk60prgdwRWIBvPeRtZjbHzJ4zs9pmZpmXnoicjxYtGrFmzVBq1aroj33//RwiImrwyy9feZiZiIhI1pWeOciVgEb4plfcCxxyzhVL47FdgTZAdWCKc65NCmP/D3gBuACYAXQ+fbfazCoAE4Crga1AV+fcotSurznIklsdP36Snj3fZ8SIOf6YWRBNm/amSZOeBAUFe5idSO7UuTMk9b9gMxgzJvtdJ6u2gVZLa0koQ+cgA5hZkJldDdyP7+G8poABG88hr21ABL6pGild6zagB3AzUB6oCLyWYMgU4BugGNATmGFmJc4hD5FcJSwslMjI9syc+RLFihUEwLk45szpzbBhN7Nv31+pnEFEMlpy96cy+jGBQF0nq7aBVktrSY+0PqT3ObAPWAY0A77GNye5iHPumrRezDk30zn3MZDaBMhHgXeccxucc/uAvvjuPGNmVYDaQG/n3FHn3EfA+vh8RCQFTZteRXT0MK6/vpo/9ssvS4mIqMH69XM9zExERCTrSOsd5G/x3TUu4py7xjn3onNuvnPucCblVQ34LsH774BSZlYsft9m59zBRPurkQQz62hmMWYWs3u3/uknUqZMcRYs6EOvXg8RFOT7EXD48B5GjWrK9OnPEht7wuMMRUREvJXWRiGZXRAnVgDYn+D96dcFk9h3en/BpE7knBvnnKvrnKtbvLhWEhcBX5vqV155hPnzX+Oii4r64198MYzBg69l585NHmYnIiLirTTPQQ6wQ0DCavb064NJ7Du9X63CRM5Rw4bViYkZzh13/Pu8wtat6+jfvzbR0VM8zExERMQ7WbVA3gDUSPC+BrDDObcnfl9FMyuYaP+GAOYnkmMULx7OrFk9iYxsR2hoCADHjh3knXf+y/vvt+f48UD94kgkd0lukdSMXjw1UNfJqm2g1dJa0uOcl3k7r4uZhQAhQG+gDL6OfLHOudhE424H3sXX0nobMBNY65zrEb9/NbAc6AU0wbfk26XOuV0pXV/LvImk7OuvN9GiRSS//rrdHytduiqPPTaNMmX+42FmIiIi5y/Dl3nLIL2Ao/iWcGsZ/7qXmZUzs0NmVg7AOTcPGAwswbfO8RZ8RfVpDwN18a2sMRC4P7XiWERSV7t2ZdasGcrDD9/gj23f/hMDB17F0qVj1KZaRERyhYDeQfaa7iCLpI1zjokTF/PUU+M4cuTfbvK1ajWnZcu3yJ+/iIfZiYiIpM9530E2s4NmdiAtW8amLiJeMzNat76Z1aujqF69gj/+zTcf0a9fLTZvXuVdciIiIpksJIV9XQOWhYhkSVWrlmH58kG88MK7jB37OQB7924hMvJ67r67L40bv+BfS1lERCSn0BQLEUmTWbNW8fjjb/DPP/+ualG16i20bTuRQoVKe5iZiIhI2mTVh/REJJu6995riI4eRv36l/ljP/20iIiIGvzwwwIPMxMREclYaSqQzSyPmb1mZhvN7JiZnUq4ZXaSIpI1lC9fki++6McLL9yPxS+ievDgTkaMuI1Zs3pw6tRJjzMUERE5f2m9g9wXeBSIAuKAbsAoYA/wROakJiJZUWhoCH37tuSzz16lVKnC/vj8+YOIjLyB3bt/8zA7ERGR85fWAvlBoJNz7k3gFPCJc+4pfGsT35pZyYlI1nXzzTWIiRlO48a1/LHffltNv361WLduhoeZiYiInJ+0FsilgB/iXx8CTt82mgc0zuikRCR7KFWqMLNnv8yAAY8SEhIMwNGj+3nrrQf44INOnDhx1OMMRUREzl1aC+StwEXxrzcBt8W/vgZfNzwRyaWCgoJ47rl7WbKkPxUqlPTHly17k4EDr2Lbth9SOFpERCRkH9FdAAAgAElEQVTrSWuBPAu4Of7168BrZvYb8C7wdibkJSLZzNVXX8batUNp3vxaf2zbtv8xYEBdVqx4R22qRUQk20hTgeyce9E51y/+9QzgOmAkcJ9zrmcm5ici2UjhwgWYPLkbo0d3Jm/ePACcPHmUiRMf4513/svRo/s9zlBERCR1aV3m7QYz83fdc86tcc4NBeaZ2Q2Zlp2IZDtmxmOP3cbKlUO4/PKy/nhMzFT69avN779He5idiIhI6tI6xWIJUDSJeKH4fSIiZ7jyyvKsWhVJ+/b/LnSze/dmBg++loULo4iLi/MwOxERkeSltUA2IKkJhMWAw0nERUS44IIwxozpwqRJzxMefgEAcXGxfPTR84wa1ZSDB3d5nKGIiMjZUiyQzWy2mc3GVxxPOv0+fpsLLARWBiJREcm+HnzwOtauHUrdupf6Yxs2fE5ERA1+/lm/hBIRkawltTvIe+I3A/YleL8H+BMYC7TMzARFJGeoWLE0X37Zn2efbeaP7d//N8OH38zs2a9w6lSsh9mJiIj8KySlnc65tgBm9jsQ6ZzTdAoRSbc8eUIZOLANjRpVp337EezatR/nHJ991peNG5fQrt1kihYtm/qJREREMlFal3l7zTl32MzqmtlDZpYfwMzyJ1zdQkQkLW6/vQ7R0cO48cbq/timTcuJiKjBt99+4mFmIiIiaV/mrZSZrQbWApPxtZ4GGApEpfViZlbUzGaZ2WEz22Jm/01m3OdmdijBdsLM1ifY/7uZHU2wf0FacxCRrOGii4ry2Wev8tprLQgK8v0oOnJkH2PHNmPatKc4efKYxxmKiEhuldZVLIYBO/CtWnEkQXw60PgcrjcKOIGvwG4BjDGzaokHOeeaOOcKnN7wPQg4PdGwuxKMOZccRCSLCA4O5sUXH+CLLyIoW7a4P75kyUgGD76GHTs2epidiIjkVmktkG8Gejrn9iWK/wqUS8sJ4qdlNAdeds4dcs4tB2YDrVI5rgJwPfB+GnMVkWymQYMriI4ext13X+2P/fHHt/TvX5vVq/VXX0REAiutBXI+fHd+EysBpPX3oFWAWOdcwltC3wFn3UFOpDWwzDn3e6L4B2a2y8wWmFmN5A42s45mFmNmMbt3H0hjqiISaEWLFmT69B68/npH8uTxPdpw/Phh3n33USZMaM2xY4c8zlBERHKLtBbIXwFtErx3ZhYMvAB8kcZzFAASV6j7gYKpHNcaeDdRrAVQASiPr5PffDMrnNTBzrlxzrm6zrm6xYuHpzFVEfGCmdG58x0sXz6YSy+9yB9fs2Yi/fvXZuvWbzzMTkREcou0FsjdgQ5mthAIw/dg3g9AA+DFNJ7jEJC4Qg0HDiZ3gJldB5QGZiSMO+dWOOeOOueOOOcGAP/gm4YhIjlAzZoVWbMmilatbvTHdu78hcGD67N48QicS6qxp4iISMZI6zJvPwD/AVYBC4C8+B6aq+Wc+zWN19oIhJjZpQliNYANKRzzKDDTOZfa71YdvmYmIpJDFCiQj3feeZrx458mf/68AMTGnuDDD59mzJhmHDq0x+MMRUQkp0rrHWScc387515xzjV1zt3hnOvlnPv7HI4/DMwE+sSvn9wAuAeYmNR4M8sHPEii6RVmVs7MGphZHjPLa2bdgOLAirTmIiLZR8uWN7JmTRQ1a1b0x77/fjb9+tXkl1+WeZiZiIjkVCkWyGZ2gZmNMrO/zGynmU02s+IpHZOKJ/A98LcTmAJ0ds5tMLPrzSzxXeJm+KZOLEkULwiMwdf6+i/gdqCJc063k0RyqCpVLmbZskE8+WRTf2zfvj8ZOrQRc+f2JS7ulIfZiYhITmMpzeUzsyH4itoP8K1W8QjwpXPugcCkl7Hq1KnsVq9Oc18TEcmC5sxZS4cOI9m799/HF6pUaUS7dh9QuPBFKRwpIiK5XadOts45Vze1calNsbgPaO+c6+icewq4E2gWv4KFiEjA3XXXVcTEDOO6667wxzZu/JKIiBqsX/+Zh5mJiEhOkVqBXBbwT/Jzzq0FYgHdphERz5QpU5wFC/rSs+dDmPmezz10aDejRt3JjBnPExub1LLtIiIiaZNagRzM2Q1CYoGQzElHRCRtQkKC6d37ERYs6MNFFxX1xxctimLIkAbs2pXWBXZERETOlFqBbMAkM5t9esO3xNtbiWIiIp5o2LA60dHDaNKkjj+2ZUsM/frVIjp6qoeZiYhIdpVagfwesA3Yk2CbBPyRKCYi4pkSJQoxa1ZPhgxpR2io7xdcx44d5J13HmHixMc4fvywxxmKiEh2kuIqFjmNVrEQyfnWrdtEy5aR/Prrdn+sdOnL6dBhGhdfXN3DzERExGsZtYqFiEi2UqdOZdasGcpDD/3bfX779h8ZOPAqvvpqrNpUi4hIqlQgi0iSdu5cSkxMB1asuJeYmA7s3LnU65TSLDz8At5//1nGjetKvnx5ADh58hiTJ3dm3LgHOHLkH48zFBGRrEwFsoicZefOpfz662iOH98FOI4f38Wvv47OVkWymdGmzS2sXh3FlVeW98e/+eYjIiJqsnnzag+zExGRrEwFsoicZevWScTFHT8jFhd3nK1bJ3mUUfpdfnlZVqwYzOOP3+6P7d27haioBsyfP4i4uDgPsxMRkaxIBbKInOX48d3nFM/q8uULY+TITkyd2p1ChS4A4NSpOGbN6sHIkbdz4MAOjzMUEZGsRAWyiJwlLKz4OcWzi/vuu5bo6GHUr3+ZP/bjjwuJiKjBDz8s9DAzERHJSlQgi8hZypVrSVBQ2BmxoKAwypVr6VFGGadChVJ88UU/undv7m9TfeDADkaOvI1Zs17k1KmTHmcoIiJeU4EsImcpWbIhlSo9QVhYCcAICytBpUpPULJkQ69TyxChoSFERLRi7tzelCxZCADnHPPnDyQy8gZ27/7d2wRFRMRTahQiIrna9u37aNduOIsWfeeP5ctXiFat3qF27eYeZiYiIhlNjUJERNKgdOkifPppb/r1a01wsO9H4tGj+xk37n4mT+7MiRNHPc5QREQCTQWyiOR6QUFBdOt2H0uW9Kd8+RL++FdfjWXQoKv5++8fPcxOREQCTQWyiEi8+vWrEh09jHvvvcYf++uv9QwYUJcVK8arTbWISC4R0ALZzIqa2SwzO2xmW8zsv8mMe9XMTprZoQRbxQT7a5rZOjM7Ev/fmoH7FCKSlOzcmjqhwoULMHVqd0aN6kzevL421SdOHGHixPaMH9+Co0cPeJyhiIhktkDfQR4FnABKAS2AMWZWLZmx05xzBRJsmwHMLA/wCTAJKAK8B3wSHxcRD+SE1tQJmRkdOtzGihWDqVq1jD8eHT2F/v1rs2VLjIfZiYhIZgtYgWxm+YHmwMvOuUPOueXAbKDVOZ6qERACDHfOHXfOjQAMuCkj8xWRtMtJrakTql69AqtWRdK27S3+2K5dvzJ48LUsWjRUbapFRHKoQN5BrgLEOuc2Joh9ByR3B/kuM9trZhvMrHOCeDXge3fmZMDvkzuPmXU0sxgzi9m9W78aFckMOa01dUL58+flzTe7MnHicxQsmA+AU6dOMmPGc4wefRcHD+7yOEMREclogSyQCwCJK9T9QMEkxn4IXA6UADoAr5jZIwnOsz+N58E5N845V9c5V7d48fD05i4iKciprakTeuih61m7dih16lT2x/73v8+IiKjJzz9/6V1iIiKS4QJZIB8CEleo4cDBxAOdcz8457Y5504551YCrwP3n+t5RCQwcnJr6oQqVbqQpUsH8H//d48/tn//NoYPv4k5c3pz6lSsh9mJiEhGCQngtTYCIWZ2qXPul/hYDWBDGo51+OYZEz/+OTOzBNMs/oPvAUAR8cDpFtRbt07i+PHdhIUVp1y5ljmmNXVCefKEMmhQWxo1qk779iPYvfsAzjnmzu3Dzz8voV27DyhatKzXaYpkWWZxlCy5m1Kl/iE4+JTX6UgOcupUMDt2FGbnzuI4d373gAPaatrMpuIrdh8DagKfAdc65zYkGncP8BXwD1APmAW85Jx7L361il+AocBYfFMwugGXOudOpHR9tZoWkYy0bdte2rQZxpdfrvfH8ucvSuvWE6hR424PMxPJuipV2sqFFxpFi5YiODgUM0v9IJFUOOc4deoke/fu4O+/Hb/+Wi7JcVm11fQTQD5gJzAF6Oyc22Bm15vZoQTjHgY24Zs28T4wyDn3HkB8EdwMaI2vgG4HNEutOBYRyWgXXVSUzz9/lVdf/S9BQb4fp4cP72XMmHuYNu1pTp48nsoZRHKf8PDDlChxMSEheVQcS4YxM0JC8lCixMWEhx8+7/MFcooFzrm9+IrbxPFl+B6+O/3+kcRjEo3/BqiT4QmKiJyj4OBgXnrpQW64oRqtWw/lzz/3ALBkyQg2bVrGY49NpVSpKh5nKZK1mKmRr2SOjPqzpT+hIiIZ4LrrqhEdPYymTa/yx/744xv696/N6tUTPcxMRETOVUDvIIvImXbuXBqQB9vWr3+FAwe+978PD/8P1av3ydDcAvVZAnWd9ChWLJyPPnqR0aPn8sIL73LiRCzHjx/m3Xdb89NPi3j44VHkzVsg9ROJiIindAdZxCOBas+cuDgGOHDge9avfyXDcgvUZ8kOLa3NjC5dmrJs2WAqV77IH1+9+n0GDKjDH39862F2IpITNWvWiB49unqdRo6iAlnEI4Fqz5y4OE4tnp7cAvVZslNL61q1KrJmTRQtW97oj+3YsZFBg65myZI3COQKQiJy/p58sg0lSxpRUX3PiK9Y8SUlSxp79qS9c2haC9onn2xDixZNUx03YcJMevUakObrJ3bkyBH69XuJq66qTNmyealatTh33tmAmTOnpPkcW7f+TsmSxrffxqQ7j6xEBbKIR7Jye+ZzzS1QnyUrf82SUrBgPsaPf5p33nma/PnzAhAbe4Jp055k7Nh7OXx4r8cZimQ/1apByZJnb9WqZf618+bNy6hRQ9i9O2u0mD9xwreAV5EiRSlQIMmGwmnSrVsnPv54GhERw1mx4iemT1/I/fe3ZN++3PszSgWyiEeycnvmc80tUJ8lK3/NUtKq1Y2sWRNFjRqX+GPfffcJERE12bRpuYeZiWQ/u5KpTZOLZ6QGDW6kbNkKDB3aN8Vxq1Z9xe23X03Zsnm54opSvPzy//mL2SefbMPKlUsZP34UJUsaJUsaW7f+nqbrn76jPGLEIGrUKEPNmmWAs+9If/rpTBo2/A/lyuWjSpWi3HNPQ3bu3JHseefPn83TT79I48ZNKVeuAtWr16Jt2860b9/FP8Y5x8iRg6lXrxLlyuWjYcPqTJ/+72/v6tb1/Xxr3LgeJUsazZo1AiAuLo6oqL7UrFmWMmXCaNiwOp9//skZ14+M7EPt2uUpUyaMatVK06VLa/++xYvncddd13PppUWoUqUoDz54Gxs3/pimr9f5UIEs4pFAtWcOD//POcXTk1ugPkt2bmldpcrFLFs2iC5d7vTH9u37g6iohnz2WQRxceooJpLVBQUF8fLLA3nvvbH89tuvSY75+++/eOSRJlx5ZS2++OIbhg9/h5kzpxAR8SIA/fq9Tt261/DII21Zv/5v1q//m4svTnv3zZUrl/LDD98zdeo8Zsz44qz9O3Zs5/HHH+ahhx5l+fIf+eSTr3jggVYpnrNkydIsXjyPAwf2JztmwIBeTJ78DoMGjWLZsh946qkX6dbtcRYunAvA/PlrAZg6dR7r1//NhAkzARg37nVGjRrCyy8PYunS9TRpci9t297H+vW+5zHmzPmI0aMjGTRoNKtX/8IHH3xK7dr/rgZ0+PBhOnZ8hvnz1zJr1peEhxeiZcu7/P/gyCxaxULEI4Fqz1y9ep9zXsXiXHML1GfJ7i2t8+bNw7BhHbjxxv/QocNI9u07hHNxzJ79Mj//vJi2bSdRuPBFqZ9IRDxzyy13cNVVDRgwoCfjxk09a/+ECaMpVeoiBg8eTVBQEFWqXM7LLw/k+ecfp0ePvoSHFyJPnjzky3cBpUqVPufr582bl9dfH09YWFiS+3fs2MbJkye56677KVu2PACXX35liueMihpH584tqFq1OJdfXp169a7l9tvvoVGjWwFfkTp27FA+/HAB9etfD0D58pfwzTdrGT9+FLfeeifFipUAoGjRYmd8rtGjI3niiedp3vy/APTo0YfVq79i9OhIxoyZxJ9/bqFUqQtp1KgxoaGhlClTjpo1/210d9ddzc/I9fXXJ1CpUjhff72W+vWvO5cv3TlRgSzioZIlGwakuEttSbeknGtugfosgbpOZrr77qupVasirVsPZcUK368Kf/55Cf361eTRR9/jyiubeJyhiKTk5ZcHcccd19ClS7ez9m3c+CN16tT3d9cEuOqq6zhx4gS//baJatWS/+1dWlStemWyxTFAtWo1uOGGW7jhhitp1KgxN9xwC3fddT/Fi5fgzz+3ct11V/jHPvPMSzzzzEtcc80NREdvZt261axdu4Jlyxbz4IONadWqI1FRb7Jx4w8cO3aMhx++Hfi3+2Fs7EnKlq2QbC4HDx5g+/ZtXHVVgzPiV199HYsWfQbA3Xc/wFtvvU7dupdw4423cdNNt3PbbXf7P+Nvv/3KoEEvs27dGvbs2UVcXBxxcXH89dfWdHz10k5TLEREPFC2bAkWLozgpZce9LfbPXhwF2+8cQcffdSN2NjM/fWhiKRf7dpX0bRpc/r06X5Ox2VEa+0LLsif4v7g4GCmT1/Ahx8u4Ior/sPkye9Qv/6l/O9/31G69EUsXvytf3v00U7+40JDQ6lf/3qeeqoH06cvoEePvkycOI6tW38nLi4OgIkT55xx/FdfbeDDDxek63Oc/lpcfHFZVq78mcjINylYMJzevZ/j1lvrcPiwr110y5ZN2b17F5GRbzJv3hoWL/6GkJAQTp7M3J+RKpBFRDwSEhLMq6/+l/nz+3DhhUX88YULIxky5Dp27drsYXYiWVOJEucWzywvvdSf1auXsXjxvDPiVapczrp1q/1FJcDatcvJkycPFSpUAiA0NA+nTmXecwdmRr1619CtW28WLIimdOmL+OSTaYSEhFCxYmX/VqRI0WTPUaWK707z4cOHuOyyKwgLC+PPP7eccXzFipX90zjy5MkDcMbnKlgwnNKlL2Lt2hVnnHvNmuX+84Nv2sitt95J377DmD8/mp9+2sDatSvYu3cPv/zyE8888xING95ClSqXc+jQQWJjYzPsa5UcTbEQEfFYo0bViYkZTvv2rzNv3tcAbNkSTb9+tWjZchx16z7kcYYiWceGDV5n4FOxYmVaterIW2+9fka8bdsnGDduON27P0HHjk+zZctm+vbtQbt2XbngggsAKFeuAt98s5atW38nf/4CFClS9IwpGecjJmY1X321iBtvvI0SJUqxfv03/PXXH2cUpIk1a9aIe+99hJo161KkSDE2bvyB/v1f4tJLq1KlyuUEBwfzxBPP8+qrz+Oco379Gzh8+BDr1q0mKCiI1q07Urx4SfLly8eSJfMpW7YCefPmJTy8EF26dGPQoFeoWPFSatSow/Tpk1i9ehmLFvl+1k2d+i6xsbHUrn01+fMX4JNPphEaGkrFipdSuHARihUrzqRJb3HRRWXZvv0vXnutGyEhmV++qkAW8dCmTWPZsWMBEAcEUapUYypX7pTiMYFoG50eWbkFdHZQokQhPv64FyNGzKFnz4mcPBnLsWMHePvth/nxx0U89NDr5MlzgddpikgCzz33CtOmvXdG7MILL2bKlM957bVu3HRTTcLDC9O8+X/p2bO/f8wTTzxP166Pcv31V3D06FFiYn6jXLkKGZJTeHgh1q5dwdtvj+TAgX+46KKyPPvsyzzwQPKr/dx4421Mnz6RAQN6cvjwIUqWLE3Dhrfy3HOvEBwcDECPHn0pUaIUo0dH0r17ZwoWDKdatZp07eqbZhISEkK/fiOIiupDZORr1K9/PR9//CUdOjzFoUMH6dOnO7t27aBy5csYP/4jrryyRny+hRk5chCvvvo8sbEnqVLlCiZMmEn58r5l48aNm0bPnk/RsOGVXHJJZV59NYp27Zon/UEykOWmbk516lR2q1dHeZ2GCHC6OJ53VrxUqduTLZKTahsNKRfJp9szJ+xAFxQURqVKT2RYARuIa+QmMTG/0LJlJJs3/7tu6YUXXsFjj03j4otTfhpdJKurVetHLrnkcq/TkBzst99+5Jtvkv4z1qmTrXPO1U1yZwKagyziEd+d47THITBto9MjO7WAzg7q1r2UtWuH8eCD1/tjf//9AwMH1uOrr95Um2oRkUymAlnEM3HnGE+fQLRnzm4toLOD8PALmDjxWd58swv58vkefjl58hiTJ3firbce4siRfzzOUEQk51KBLOKZ5P76Zexfy0C0Z86uLaCzOjOjbdtbWbUqimrVyvnjX389nX79arF582oPsxMRyblUIIt4pFSpxucUh8C0jU6P7NwCOju44oqyrFw5hI4db/fH9uz5naFDr2P+/MFnLCclIiLnL6AFspkVNbNZZnbYzLaY2X+TGdfNzP5nZgfN7Dcz65Zo/+9mdtTMDsVv6VulWsRDlSt3olSp2/n3r2FQig/oga8jXuJiOC1toytVeoKwsBKAERZWIsMfngvENXK7fPnCeOONTkyZ0p1ChXyrWcTGnmLWrBd4440mHDiwI5UziIhIWgV0FQszm4KvGmgP1ATmAtc65zYkGtcdWAR8D1QCFgAvOOemxu//HXjMObfoXK6vVSxEJCf4/fcdtGo1lDVrfvbHwsNL0bbtJC6//BYPMxNJnVaxkMyWrVaxMLP8QHPgZefcIefccmA20CrxWOfcYOfc1865WOfcz8AnQIPE40REcqMKFUqxeHE/nn/+Pn/swIEdjBjRmI8/folTp056mJ2ISPYXyCkWVYBY59zGBLHvgGopHWS+Zt3XA4l753xgZrvMbIGZ1Ujh+I5mFmNmMbt3H0hv7iIiWUpoaAj9+7dm7tzelCxZCADnHPPmDSAqqiF79mzxOEMRkewrkAVyASBxhbofKJjKca/iy3NCglgLoAJQHlgCzDezwkkd7Jwb55yr65yrW7x4eDrSFhHJum69tRYxMcO5+eZ/7xNs3ryKfv1q8s03Mz3MTEQk+wpkq+lDQOIKNRw4mNwBZtYVaA1c75zzdyFwzq1IMGyAmT2K7y7znIxLV3KKQLVATk/b6HXrnuTYsT/87/PmLUudOiNTPGbFiubAqQSRYBo0+CjFY1aubIFzh/3vzfJz7bUfpHjMmjXtiI3d638fElKUq68en+z4QH2d1dL6bKVLF2Hu3N5ERs6id+8POHUqjiNH/uHNN5vTsOET3H9/FKGheb1OUyTHa9asEVWrXsnAgW94nYqcp0DeQd4IhJjZpQliNTh76gQAZtYO6AHc7Jz7M5VzO8AyJEvJUU63QD5+fBfgOH58F7/+OpqdO5dm6HX+bRt9ermtOHbsmMemTWOTPSZxcQxw7NgfrFv3ZLLHnF0cA5yKjyctcXEM4NxhVq5skewxiYtjgNjYvaxZ0y7J8YH6OgfqOtlRUFAQ3bs3Z8mS/pQvX8IfX7p0NAMHXs3ff//oYXYi2d+TT7ahRYumKY6ZMGEmvXoNSPc1jhw5Qr9+L3HVVZUpWzYvVasW5847GzBz5pQ0n2Pr1t8pWdL49tuYdOchASyQne//0DOBPmaW38waAPcAExOPNbMWQH/gVufc5kT7yplZAzPLY2Z545eAKw6sSHwekUC1QE5P2+jExXFqcZ/ExXFqcc4qjlOLA2cVx6nFA/V1Vkvr1NWvX5W1a4fRrFl9f+yvv75nwIC6rFw5QW2qJUf4558P2LixAhs2BLFxYwX++Sfl34hlthMnTgBQpEhRChRIbeZo8rp168THH08jImI4K1b8xPTpC7n//pbs25f0z17JPIFuFPIEkA/YCUwBOjvnNpjZ9WZ2KMG4CKAYEJ1grePTt+IKAmOAfcBfwO1AE+fcnoB9Csk2AtcCOTBto7OqQH2d1dI6bYoUKcC0aS8wcuTjhIWFAnDixBHef78d48e35OhRPbAs2dc//3zAtm0dOXlyC+A4eXIL27Z1DGiRfPpu8ogRg6hRoww1a5YBfFMsevTo6h/36aczadjwP5Qrl48qVYpyzz0N2bkz+TXL58+fzdNPv0jjxk0pV64C1avXom3bzrRv38U/xjnHyJGDqVevEuXK5aNhw+pMn/7vTYK6dS8BoHHjepQsaTRr1giAuLg4oqL6UrNmWcqUCaNhw+p8/vknZ1w/MrIPtWuXp0yZMKpVK02XLq39+xYvnsddd13PpZcWoUqVojz44G1s3JhzfzMVyDnIOOf2As2SiC/D9xDf6feXpHCODUDybcNEEggLKx7/6/iz4xkriKSL4dzRrDJQX+fAfT+zPzPj8cebcM01l9OiRSQ//+ybqRYdPZnff1/DY49NpXz5VJcCFclydu7siXNHzog5d4SdO3tSuHDyU8cy2sqVSylYsBBTp85L8jczO3Zs5/HHH6ZnzwE0bdqcw4cPsW5dyu3hS5YszeLF87j77gcIDy+U5JgBA3oxZ84MBg0aRaVKlxETs4rnnutA4cJFuPXWO5k/fy233XYVU6fOo1q1GuTJkweAceNeZ9SoIQwZMpaaNesyffok2ra9j4UL11G9ek3mzPmI0aMjefPNKVx+eXV27955Rr6HDx+mY8dnqFbtPxw9epRhwyL+v727j7Oxzv84/vrMjXE7iMZdBuMmdz/3Zao1bqrVVqifrSyySmtLKltZU1sroWgnsv1SPwkVrfJLKW1tSZL24WbSVkuaUppFRbUbMxiD7++P6zKOY8bccS7mvJ+Px/XA9/pe1/lc51zO+Zzv+d4wdGg/Vq3aWPAYFUl0fHpL1IrUEshlWTa6cuXGpSr3xJay3BuQV5py8AbklaY8Us+zlrQuvQ4dmrJ6dQbDh19YULZz52Yeeuh83n77Ea1o6ZMAABIjSURBVHW5kNNOfn52qcpPlsqVKzNjxhzatGlP27b/dcz+777bTn5+Pv36/ZLk5Ka0adOeoUNvICmpXpHnfPjhWaxfv4bWrety4YVdSE8fzYoVbxXsz83N5YknpjF9+mz69LmEJk2aMXDgYIYO/Q1z5jwGQJ063hiEM86oQ7169ald23vfnjkzg1Gj7mTgwME0b96K9PT7SU3twcyZGQBs3fo19eo1oFevn3PWWcl06tSNESOOtIb36zeQfv0GkpLSknbtOjBjxlyys79i/fq15X8yT0FKkKVCi9QSyGVZNrpr10ePSYaLm8XCm60iPBk+/iwW55+/4JhkuLhZLLp3n3NMMny8WSwi9TxrSeuyqVatMrNm3cIzz9xOjRpVADh4MJ9Fi37HzJn9yclRFxU5fcTHJ5eq/GRp3bo9CQkJRe5v164jaWkXkZbWnuuuG8jcuY/z/ffeL2Bbt2bTtGn1gu2RRx4A4Lzz0li37ksWL17OgAFXs3lzFldf/XPuuOO3AGRlbWTfvn0MGnTJUcfPm/c4W7ZsLjKW3bt38e232zn33KPXXOve/WdkZW0EoH//q8jL20e3bs0YM2YEr7yyiLy8I2M+vvpqMzfeOJhzzmlOSkoi7drV49ChQ2zbFtkvJpES0S4WIkFISuoZkQSqRYsbi53WLVxxU7oVprgp3QpT3JRuhTnelG6FidTzHKnHqYgGDUqjW7eWDB2awfr13ofpJ58sZeLEjowY8RytWul5lVNfUtJktm8feVQ3C7OqJCVNjmgcVasW/SscQGxsLIsWvUlm5mpWrHiT5557ismT7+Lll9+ldet2LF/+j4K6h1t5AeLj40lN7UFqag9uvTWdadMmMWXKvdx2210cOuR15Xv22Vdp1OjoLwTx8fFlug5vPTZo1Kgxf//7Z7z33tusXLmM8ePvICNjAq+/voZq1aoxdOjlNGhwFhkZ/0uDBo2Ii4vjZz9rS37+/jI97qlOLcgiIlGkRYsGrFw5hTFj+heU/fTTdqZP78Orr97HwYMHAoxOpHi1ag2hYcNZxMc3AYz4+CY0bDgrov2PS8rMOOec8xg7djxvvrmO+vUbsmTJ88TFxZGS0qJgC02Qw7Vq1RaA3Nwczj67LQkJCWzd+vVRx6ektKBx4yYABf2BDx48MrtRjRqJ1K/fkLVrj57wa82aVQXnB6/byMUXX8bEidP529/WsWnTBtaufZ8ff/yBzz/fxJgxd9Oz50W0atWGnJzdHDhQcd8v1IIsIhJlKlWK56GHrqdXrw6MGDGDH37YjXOHeO21CWRlvcP11y+gdu2zgg5TpEi1ag05JRPiUJmZq1m5chm9e/flzDPr8cknH7Jt27+OSkjDXXFFL6688ld06tSN2rXrkJW1kQceuJuWLVvTqlUbYmNjGTXqTu67706cc6SmphUM/ouJiWHYsJHUrZtElSpVeOedv9G4cVMqV65MYmJNbr55LFOn/pGUlJZ07NiVRYvms3r1eyxbth6AhQvnceDAAbp06U61atVZsuR54uPjSUlpSa1atalTpy7z5z9Jw4aN+fbbbUyYMJa4uIqbRqoFWUQkSl16aTcyMx+hZ8/2BWWff76SSZM68vHHWphUpDwSE2uydu37DBlyOampLRk//g5uv/1errqq6EHFvXv3ZdGiZ7nmmr5ccEFrxo0bRWpqD1544U1iY73xJ+npExk79j5mzswgLa0dV199MUuXvkhysjcBWFxcHJMn/5kFC2bToUNDhg0bAMBvfnMrN988lvvv/z1pae15/fWXmDPnRdq37+jHW4sFC56if/8e9OzZnqVLX2Tu3MU0adKMmJgYZs16no0bP6Znz/akp9/MuHETqVSp6D7YpzuLphHMXbu2cKtXPxx0GCIip5SDBw8yZcr/MXHi8wV9HAH69LmNK6+cSnx8xf0QlMjr3PlTmjVrE3QYUoF99dWnfPhh4ffYjTfaB865Yue4rLht4yLlsGPHu2Rnzycv73sSEuqSnDz0lBkYVpbYwpe1Lm62DIkusbGx/OEP15CW1p5hw6axbZu37tLy5TP44ov3GDFiIfXqtQw4ShGRyFEXC5EwO3a8y+bNM/0FKRx5eTvZvHkmO3a8G3RoZYotPDkGbznrDz645SRHK6ebHj3akZk5ncsuO6egLDt7PQ880IU1a7Sct4hEDyXIImGys+dz6FDeUWWHDuWRnR18glCW2MKT4+LKJbrVqZPI4sV3M23aDVSq5P3ImJeXw9y51zJv3nD27csJOEIRkZNPCbJImLy8whdNKKo8kk7l2KTiMDNGj76c996bSosWDQvKV69+mgcf7MbWrR8FGJ2IyMmnBFkkTEJC3VKVR9KpHJtUPJ07N2fNmocZPPhIH/fvvvuMKVO6s2LFY1qmWspM946cLCfq3lKCLBImOXkoMTFHj9qPiUkgObnoqXkipSyxhS9nXVy5SKgaNaowb97vmD37VqpW9e69AwfyWLhwNE888d/k5v4YcIRyusnPjyc/f2/QYUgFlZ+/l/z8sq0qGEoJskiYpKSeNG8+ioSEMwEjIeFMmjcfdUrMYlGW2Lp2ffSYZFizWEhpDRvWhzVrptGhQ9OCso8+eplJkzrxxRfvF32gSJjs7CS++WYb+/fvUUuynDDOOfbv38M332wjOzup3OfTPMgiIlJi+/btJz19HjNn/rWgLCYmlssvn8All6QTExMbYHRyukhM3EVy8g7i4/ODDkUqkPz8eLKzk9i1K7HIOiWdB1kJsoiIlNqSJasZOfJ/+Pe/j8xq0br1hVx33bPUrNkgwMhERIpW0gRZXSxERKTUBgxIZd266Zx//pHVqjZteptJkzqyYcMbAUYmIlJ+SpBFRKRMkpPPZNmySdx111WYGQC7d+/k0Ud/wYsv/p4DB/YHHKGISNlENEE2szPM7CUzyzWzr81scBH1zMymmtkP/jbVDr/7evs7mdkHZrbH/7NT5K5CREQOi4uLZcKEIbzxxgTq169dUP7WW38iI6MHO3d+GWB0IiJlE+kW5MeA/UA9YAjwuJm1K6TeSOAKoCPQAegH/BbAzCoBS4D5QG3gaWCJXy4iIgHo3bsDmZnT6du3S0HZli1rmTy5M5mZLwQYmYhI6UUsQTazasBA4F7nXI5zbhXwCnBtIdV/DTzsnNvqnNsGPAwM9/f1AuKAR5xzec65PwMG9DnJlyAiIseRlFSLJUvuYcqU4cTFebNZ7Nu3i9mzr2H+/JHs378n4AhFREomLoKP1Qo44JzLCin7CChsAtd2/r7Qeu1C9n3sjp5+42O//JiRIWY2Eq9FGiCvUqUr/lm28KUCqAtoTebopnsgIKtWPcmqVU8GHQboHoh2ev3l7JJUimSCXB3YFVb2E1CjiLo/hdWr7vdDDt93vPPgnJsFzAIws8ySTO0hFZNef9E9ILoHoptefzGzzJLUi2Qf5BwgfObmRGB3CeomAjl+q3FpziMiIiIiUiqRTJCzgDgzaxlS1hHYUEjdDf6+wuptADqEzmqBN5CvsPOIiIiIiJRKxBJk51wusBi438yqmdkFwADg2UKqPwPcbmaNzKwhcAcwz9+3AjgI3GpmCWY22i9fXoIwZpXjEuT0p9dfdA+I7oHoptdfSnQPRHSpaTM7A5gDXAz8AKQ7554zsx7A68656n49A6YCN/iHzgbGHR6YZ2ad/bK2wKfACOfchxG7EBERERGpsCKaIIuIiIiInOq01LSIiIiISAglyCIiIiIiIaIiQTazM8zsJTPLNbOvzWxw0DFJ5JjZaDPLNLM8M5sXdDwSWf5g3qf8//u7zewfZvaLoOOSyDKz+Wb2jZntMrMsM7uh+KOkojGzlma2z8zmBx2LRJaZrfBf+xx/++x49aMiQQYeA/YD9YAhwONm1u74h0gFsh2YhDdAVKJPHPAvvFU7awL3AC+YWdMAY5LIexBo6pxLBPoDk8ysa8AxSeQ9BqwLOggJzGjnXHV/O+6KehU+QTazasBA4F7nXI5zbhXwCnBtsJFJpDjnFjvnXsabOUWijHMu1zl3n3Nui3PukHNuKfAVoOQoijjnNjjn8g7/09+aBxiSRJiZDQL+A7wddCxy6qvwCTLQCjjgnMsKKfsIUAuySBQys3p47wtaXCjKmNlMM9sDbAK+Af4acEgSIWaWCNwP3B50LBKoB83sezN738x6Ha9iNCTI1YFdYWU/ATUCiEVEAmRm8cAC4Gnn3Kag45HIcs6Nwnvv74G3cFXe8Y+QCmQi8JRzbmvQgUhgxgEpQCO8xUJeNbMif0WKhgQ5B0gMK0sEdgcQi4gExMxi8Fbu3A+MLqa6VFDOuYN+V7uzgJuCjkdOPjPrBFwETA86FgmOc26Nc263cy7POfc08D5waVH14yIXWmCygDgza+mc+9wv64h+XhWJGv7qnE/hDdS91DmXH3BIErw41Ac5WvQCmgLZ3lsB1YFYM2vrnOsSYFwSLAdYUTsrfAuycy4X76e0+82smpldAAzAa0mSKGBmcWZWGYjFe1OsbGbR8OVQjngcaAP0c87tDToYiSwzSzKzQWZW3cxizawv8Cs0WCtazML7MtTJ354AXgP6BhmURI6Z1TKzvoc//81sCJAGvFHUMRU+QfaNAqoAO4C/ADc559SCHD3uAfYC6cBQ/+/3BBqRRIyZNQF+i/fB+G3IHJhDAg5NIsfhdafYCvwbyADGOOdeCTQqiQjn3B7n3LeHN7yul/ucczuDjk0iJh5vutedwPfALcAVYRM4HMWccxGKTURERETk1BctLcgiIiIiIiWiBFlEREREJIQSZBERERGREEqQRURERERCKEEWEREREQmhBFlEREREJIQSZBGR05yZDTeznGLqbDGzOyMV0/GYWVMzc2bWLehYREQKowRZROQEMLN5ftLnzCzfzL40swwzq1bKcyw9mXFGWkW8JhGp+LTcrojIibMMuBZv1aYewGygGt4qbiIicppQC7KIyImT5y9n+y/n3HPAAuCKwzvNrK2ZvWZmu81sh5n9xczq+/vuA34NXBbSEt3L3zfFzD4zs71+V4mHzKxyeQI1s5pmNsuPY7eZvRva5eFwtw0zu9DM/mlmuWb2jpk1CzvPXWb2nV/3GTMbb2ZbirsmXxMze8vM9pjZRjO7uDzXJCJyoihBFhE5efbitSZjZg2AlcA/gXOBi4DqwBIziwEygBfwWqEb+Nvf/fPkAtcDbYBRwCDgD2UNyswMeA1oBFwOdPZjW+7HeVgCcJf/2OcBtYAnQs4zCBjvx9IF+BS4PeT4410TwGTgz0BHYB2w0Myql/W6REROFHWxEBE5CczsXGAw8LZfdBPwkXNuXEidYcCPQDfn3Foz24vfCh16LufcxJB/bjGzB4A7gXvLGF5voBNwpnNur192r5n1w+si8pBfFgfc7Jz7zI83A5hjZuacc8BtwDzn3Gy//oNm1hto5cedU9g1efk5ANOdc6/6ZXcDw/y4VpXxukRETgglyCIiJ84l/mwScXgtx0uAW/x9XYG0ImabaA6sLeqkZvZLYAzQAq/VOdbfyqorUBXYGZKsAlT2Yzks73By7NsOVAJq4yX2rYEnw869Bj9BLoGPw84NkFTCY0VETholyCIiJ85KYCSQD2x3zuWH7IvB69ZQ2FRr3xV1QjNLBRYCE4DfAf8B+uN1XyirGP8xexSyb1fI3w+E7XMhx58IBc+Pc875ybq6/olI4JQgi4icOHucc18UsW89cDXwdVjiHGo/x7YMXwBsC+1mYWZNyhnneqAecMg592U5zrMJOAeYE1J2blidwq5JROSUpm/qIiKR8RhQE3jezLqbWYqZXeTPJFHDr7MFaG9mZ5tZXTOLB7KARmY2xD/mJuBX5YxlGfA+3gDBX5hZMzM7z8wmmFlhrcpFmQEMN7Przaylmf0e6M6RluairklE5JSmBFlEJAKcc9vxWoMPAW8AG/CS5jx/A68/76dAJrATuMAfxPYn4BG8PrsXA38sZywOuBRY7j/mZ3izTZzNkb7AJTnPQmAiMAX4EGiPN8vFvpBqx1xTeWIXEYkE894nRUREys/MXgLinHP9go5FRKSs1AdZRETKxMyq4k1f9wbegL6BwAD/TxGR05ZakEVEpEzMrArwKt5CI1WAz4Gp/iqCIiKnLSXIIiIiIiIhNEhPRERERCSEEmQRERERkRBKkEVEREREQihBFhEREREJoQRZRERERCTE/wPJ+NxdpaYohAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = -per_clf.coef_[0][0] / per_clf.coef_[0][1]\n", + "b = -per_clf.intercept_ / per_clf.coef_[0][1]\n", + "\n", + "axes = [0, 5, 0, 2]\n", + "\n", + "x0, x1 = np.meshgrid(\n", + " np.linspace(axes[0], axes[1], 500).reshape(-1, 1),\n", + " np.linspace(axes[2], axes[3], 200).reshape(-1, 1),\n", + " )\n", + "X_new = np.c_[x0.ravel(), x1.ravel()]\n", + "y_predict = per_clf.predict(X_new)\n", + "zz = y_predict.reshape(x0.shape)\n", + "\n", + "plt.figure(figsize=(10, 4))\n", + "plt.plot(X[y==0, 0], X[y==0, 1], \"bs\", label=\"Not Iris-Setosa\")\n", + "plt.plot(X[y==1, 0], X[y==1, 1], \"yo\", label=\"Iris-Setosa\")\n", + "\n", + "plt.plot([axes[0], axes[1]], [a * axes[0] + b, a * axes[1] + b], \"k-\", linewidth=3)\n", + "from matplotlib.colors import ListedColormap\n", + "custom_cmap = ListedColormap(['#9898ff', '#fafab0'])\n", + "\n", + "plt.contourf(x0, x1, zz, cmap=custom_cmap)\n", + "plt.xlabel(\"Petal length\", fontsize=14)\n", + "plt.ylabel(\"Petal width\", fontsize=14)\n", + "plt.legend(loc=\"lower right\", fontsize=14)\n", + "plt.axis(axes)\n", + "\n", + "save_fig(\"perceptron_iris_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Activation functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def logit(z):\n", + " return 1 / (1 + np.exp(-z))\n", + "\n", + "def relu(z):\n", + " return np.maximum(0, z)\n", + "\n", + "def derivative(f, z, eps=0.000001):\n", + " return (f(z + eps) - f(z - eps))/(2 * eps)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saving figure activation_functions_plot\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "z = np.linspace(-5, 5, 200)\n", + "\n", + "plt.figure(figsize=(11,4))\n", + "\n", + "plt.subplot(121)\n", + "plt.plot(z, np.sign(z), \"r-\", linewidth=2, label=\"Step\")\n", + "plt.plot(z, logit(z), \"g--\", linewidth=2, label=\"Logit\")\n", + "plt.plot(z, np.tanh(z), \"b-\", linewidth=2, label=\"Tanh\")\n", + "plt.plot(z, relu(z), \"m-.\", linewidth=2, label=\"ReLU\")\n", + "plt.grid(True)\n", + "plt.legend(loc=\"center right\", fontsize=14)\n", + "plt.title(\"Activation functions\", fontsize=14)\n", + "plt.axis([-5, 5, -1.2, 1.2])\n", + "\n", + "plt.subplot(122)\n", + "plt.plot(z, derivative(np.sign, z), \"r-\", linewidth=2, label=\"Step\")\n", + "plt.plot(0, 0, \"ro\", markersize=5)\n", + "plt.plot(0, 0, \"rx\", markersize=10)\n", + "plt.plot(z, derivative(logit, z), \"g--\", linewidth=2, label=\"Logit\")\n", + "plt.plot(z, derivative(np.tanh, z), \"b-\", linewidth=2, label=\"Tanh\")\n", + "plt.plot(z, derivative(relu, z), \"m-.\", linewidth=2, label=\"ReLU\")\n", + "plt.grid(True)\n", + "#plt.legend(loc=\"center right\", fontsize=14)\n", + "plt.title(\"Derivatives\", fontsize=14)\n", + "plt.axis([-5, 5, -0.2, 1.2])\n", + "\n", + "save_fig(\"activation_functions_plot\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def heaviside(z):\n", + " return (z >= 0).astype(z.dtype)\n", + "\n", + "def sigmoid(z):\n", + " return 1/(1+np.exp(-z))\n", + "\n", + "def mlp_xor(x1, x2, activation=heaviside):\n", + " return activation(-activation(x1 + x2 - 1.5) + activation(x1 + x2 - 0.5) - 0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x1s = np.linspace(-0.2, 1.2, 100)\n", + "x2s = np.linspace(-0.2, 1.2, 100)\n", + "x1, x2 = np.meshgrid(x1s, x2s)\n", + "\n", + "z1 = mlp_xor(x1, x2, activation=heaviside)\n", + "z2 = mlp_xor(x1, x2, activation=sigmoid)\n", + "\n", + "plt.figure(figsize=(10,4))\n", + "\n", + "plt.subplot(121)\n", + "plt.contourf(x1, x2, z1)\n", + "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n", + "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n", + "plt.title(\"Activation function: heaviside\", fontsize=14)\n", + "plt.grid(True)\n", + "\n", + "plt.subplot(122)\n", + "plt.contourf(x1, x2, z2)\n", + "plt.plot([0, 1], [0, 1], \"gs\", markersize=20)\n", + "plt.plot([0, 1], [1, 0], \"y^\", markersize=20)\n", + "plt.title(\"Activation function: sigmoid\", fontsize=14)\n", + "plt.grid(True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# FNN for MNIST" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using the Estimator API (formerly `tf.contrib.learn`)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Warning**: `tf.examples.tutorials.mnist` is deprecated. We will use `tf.keras.datasets.mnist` instead. Moreover, the `tf.contrib.learn` API was promoted to `tf.estimators` and `tf.feature_columns`, and it has changed considerably. In particular, there is no `infer_real_valued_columns_from_input()` function or `SKCompat` class." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n", + "X_train = X_train.astype(np.float32).reshape(-1, 28*28) / 255.0\n", + "X_test = X_test.astype(np.float32).reshape(-1, 28*28) / 255.0\n", + "y_train = y_train.astype(np.int32)\n", + "y_test = y_test.astype(np.int32)\n", + "X_valid, X_train = X_train[:5000], X_train[5000:]\n", + "y_valid, y_train = y_train[:5000], y_train[5000:]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Using default config.\n", + "WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpuflzeb_h\n", + "INFO:tensorflow:Using config: {'_evaluation_master': '', '_session_config': None, '_model_dir': '/tmp/tmpuflzeb_h', '_task_type': 'worker', '_cluster_spec': , '_save_summary_steps': 100, '_is_chief': True, '_save_checkpoints_steps': None, '_log_step_count_steps': 100, '_master': '', '_service': None, '_keep_checkpoint_every_n_hours': 10000, '_task_id': 0, '_tf_random_seed': None, '_num_ps_replicas': 0, '_global_id_in_cluster': 0, '_train_distribute': None, '_num_worker_replicas': 1, '_save_checkpoints_secs': 600, '_keep_checkpoint_max': 5}\n", + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Create CheckpointSaverHook.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpuflzeb_h/model.ckpt.\n", + "INFO:tensorflow:loss = 122.883514, step = 0\n", + "INFO:tensorflow:global_step/sec: 480.267\n", + "INFO:tensorflow:loss = 9.599711, step = 100 (0.209 sec)\n", + 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0.015441139, step = 41200 (0.141 sec)\n", + "INFO:tensorflow:global_step/sec: 797.458\n", + "INFO:tensorflow:loss = 0.010338774, step = 41300 (0.125 sec)\n", + "INFO:tensorflow:global_step/sec: 747.132\n", + "INFO:tensorflow:loss = 0.021103038, step = 41400 (0.134 sec)\n", + "INFO:tensorflow:global_step/sec: 750.845\n", + "INFO:tensorflow:loss = 0.025617313, step = 41500 (0.133 sec)\n", + "INFO:tensorflow:global_step/sec: 754.583\n", + "INFO:tensorflow:loss = 0.013572226, step = 41600 (0.133 sec)\n", + "INFO:tensorflow:global_step/sec: 756.386\n", + "INFO:tensorflow:loss = 0.0033336552, step = 41700 (0.132 sec)\n", + "INFO:tensorflow:global_step/sec: 782.906\n", + "INFO:tensorflow:loss = 0.053168457, step = 41800 (0.128 sec)\n", + "INFO:tensorflow:global_step/sec: 719.6\n", + "INFO:tensorflow:loss = 0.029688315, step = 41900 (0.139 sec)\n", + "INFO:tensorflow:global_step/sec: 788.136\n", + "INFO:tensorflow:loss = 0.01654516, step = 42000 (0.127 sec)\n", + "INFO:tensorflow:global_step/sec: 738.526\n", + "INFO:tensorflow:loss = 0.015659308, step = 42100 (0.135 sec)\n", + "INFO:tensorflow:global_step/sec: 777.578\n", + "INFO:tensorflow:loss = 0.01840079, step = 42200 (0.129 sec)\n", + "INFO:tensorflow:global_step/sec: 786.416\n", + "INFO:tensorflow:loss = 0.021663003, step = 42300 (0.127 sec)\n", + "INFO:tensorflow:global_step/sec: 763.347\n", + "INFO:tensorflow:loss = 0.011599571, step = 42400 (0.131 sec)\n", + "INFO:tensorflow:global_step/sec: 762.321\n", + "INFO:tensorflow:loss = 0.0044469903, step = 42500 (0.131 sec)\n", + "INFO:tensorflow:global_step/sec: 768.549\n", + "INFO:tensorflow:loss = 0.0019147585, step = 42600 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 771.429\n", + "INFO:tensorflow:loss = 0.0054854164, step = 42700 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 793.871\n", + "INFO:tensorflow:loss = 0.0017117725, step = 42800 (0.126 sec)\n", + "INFO:tensorflow:global_step/sec: 770.1\n", + "INFO:tensorflow:loss = 0.012048513, step = 42900 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 744.636\n", + "INFO:tensorflow:loss = 0.06634566, step = 43000 (0.134 sec)\n", + "INFO:tensorflow:global_step/sec: 696.882\n", + "INFO:tensorflow:loss = 0.0003919307, step = 43100 (0.144 sec)\n", + "INFO:tensorflow:global_step/sec: 705.516\n", + "INFO:tensorflow:loss = 0.06582007, step = 43200 (0.141 sec)\n", + "INFO:tensorflow:global_step/sec: 699.244\n", + "INFO:tensorflow:loss = 0.0038124803, step = 43300 (0.143 sec)\n", + "INFO:tensorflow:global_step/sec: 792.079\n", + "INFO:tensorflow:loss = 0.003364585, step = 43400 (0.126 sec)\n", + "INFO:tensorflow:global_step/sec: 753.586\n", + "INFO:tensorflow:loss = 0.00725976, step = 43500 (0.133 sec)\n", + "INFO:tensorflow:global_step/sec: 720.951\n", + "INFO:tensorflow:loss = 0.024148291, step = 43600 (0.139 sec)\n", + "INFO:tensorflow:global_step/sec: 770.384\n", + "INFO:tensorflow:loss = 0.013779048, step = 43700 (0.130 sec)\n", + "INFO:tensorflow:global_step/sec: 799.363\n", + "INFO:tensorflow:loss = 0.014951154, step = 43800 (0.125 sec)\n", + "INFO:tensorflow:global_step/sec: 791.774\n", + "INFO:tensorflow:loss = 0.0015594304, step = 43900 (0.126 sec)\n", + "INFO:tensorflow:Saving checkpoints for 44000 into /tmp/tmpuflzeb_h/model.ckpt.\n", + "INFO:tensorflow:Loss for final step: 0.0012097486.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feature_cols = [tf.feature_column.numeric_column(\"X\", shape=[28 * 28])]\n", + "dnn_clf = tf.estimator.DNNClassifier(hidden_units=[300,100], n_classes=10,\n", + " feature_columns=feature_cols)\n", + "\n", + "input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"X\": X_train}, y=y_train, num_epochs=40, batch_size=50, shuffle=True)\n", + "dnn_clf.train(input_fn=input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Starting evaluation at 2018-05-18-19:12:49\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpuflzeb_h/model.ckpt-44000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n", + "INFO:tensorflow:Finished evaluation at 2018-05-18-19:12:50\n", + "INFO:tensorflow:Saving dict for global step 44000: accuracy = 0.9798, average_loss = 0.10096103, global_step = 44000, loss = 12.779877\n" + ] + } + ], + "source": [ + "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n", + " x={\"X\": X_test}, y=y_test, shuffle=False)\n", + "eval_results = dnn_clf.evaluate(input_fn=test_input_fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'accuracy': 0.9798,\n", + " 'average_loss': 0.10096103,\n", + " 'global_step': 44000,\n", + " 'loss': 12.779877}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "eval_results" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Calling model_fn.\n", + "INFO:tensorflow:Done calling model_fn.\n", + "INFO:tensorflow:Graph was finalized.\n", + "INFO:tensorflow:Restoring parameters from /tmp/tmpuflzeb_h/model.ckpt-44000\n", + "INFO:tensorflow:Running local_init_op.\n", + "INFO:tensorflow:Done running local_init_op.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'class_ids': array([7]),\n", + " 'classes': array([b'7'], dtype=object),\n", + " 'logits': array([ -3.809414 , -4.1564407, -0.426081 , 3.2636993, -11.065331 ,\n", + " -8.790985 , -10.436305 , 19.935707 , -6.9282775, 2.2807484],\n", + " dtype=float32),\n", + " 'probabilities': array([4.8710768e-11, 3.4428106e-11, 1.4354495e-09, 5.7469666e-08,\n", + " 3.4389070e-14, 3.3431518e-13, 6.4506329e-14, 1.0000000e+00,\n", + " 2.1533745e-12, 2.1505466e-08], dtype=float32)}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_pred_iter = dnn_clf.predict(input_fn=test_input_fn)\n", + "y_pred = list(y_pred_iter)\n", + "y_pred[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "## Using plain TensorFlow" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "\n", + "n_inputs = 28*28 # MNIST\n", + "n_hidden1 = 300\n", + "n_hidden2 = 100\n", + "n_outputs = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", + "y = tf.placeholder(tf.int32, shape=(None), name=\"y\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "def neuron_layer(X, n_neurons, name, activation=None):\n", + " with tf.name_scope(name):\n", + " n_inputs = int(X.get_shape()[1])\n", + " stddev = 2 / np.sqrt(n_inputs)\n", + " init = tf.truncated_normal((n_inputs, n_neurons), stddev=stddev)\n", + " W = tf.Variable(init, name=\"kernel\")\n", + " b = tf.Variable(tf.zeros([n_neurons]), name=\"bias\")\n", + " Z = tf.matmul(X, W) + b\n", + " if activation is not None:\n", + " return activation(Z)\n", + " else:\n", + " return Z" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"dnn\"):\n", + " hidden1 = neuron_layer(X, n_hidden1, name=\"hidden1\",\n", + " activation=tf.nn.relu)\n", + " hidden2 = neuron_layer(hidden1, n_hidden2, name=\"hidden2\",\n", + " activation=tf.nn.relu)\n", + " logits = neuron_layer(hidden2, n_outputs, name=\"outputs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"loss\"):\n", + " xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,\n", + " logits=logits)\n", + " loss = tf.reduce_mean(xentropy, name=\"loss\")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "with tf.name_scope(\"train\"):\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"eval\"):\n", + " correct = tf.nn.in_top_k(logits, y, 1)\n", + " accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "n_epochs = 40\n", + "batch_size = 50" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "def shuffle_batch(X, y, batch_size):\n", + " rnd_idx = np.random.permutation(len(X))\n", + " n_batches = len(X) // batch_size\n", + " for batch_idx in np.array_split(rnd_idx, n_batches):\n", + " X_batch, y_batch = X[batch_idx], y[batch_idx]\n", + " yield X_batch, y_batch" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Batch accuracy: 0.9 Val accuracy: 0.9146\n", + "1 Batch accuracy: 0.92 Val accuracy: 0.936\n", + "2 Batch accuracy: 0.96 Val accuracy: 0.945\n", + "3 Batch accuracy: 0.92 Val accuracy: 0.9512\n", + "4 Batch accuracy: 0.98 Val accuracy: 0.9558\n", + "5 Batch accuracy: 0.96 Val accuracy: 0.9566\n", + "6 Batch accuracy: 1.0 Val accuracy: 0.9612\n", + "7 Batch accuracy: 0.94 Val accuracy: 0.963\n", + "8 Batch accuracy: 0.98 Val accuracy: 0.9652\n", + "9 Batch accuracy: 0.96 Val accuracy: 0.966\n", + "10 Batch accuracy: 0.92 Val accuracy: 0.9688\n", + "11 Batch accuracy: 0.98 Val accuracy: 0.969\n", + "12 Batch accuracy: 0.98 Val accuracy: 0.967\n", + "13 Batch accuracy: 0.98 Val accuracy: 0.9706\n", + "14 Batch accuracy: 1.0 Val accuracy: 0.9714\n", + "15 Batch accuracy: 0.94 Val accuracy: 0.9732\n", + "16 Batch accuracy: 1.0 Val accuracy: 0.9736\n", + "17 Batch accuracy: 1.0 Val accuracy: 0.9742\n", + "18 Batch accuracy: 1.0 Val accuracy: 0.9746\n", + "19 Batch accuracy: 0.98 Val accuracy: 0.9748\n", + "20 Batch accuracy: 1.0 Val accuracy: 0.9752\n", + "21 Batch accuracy: 1.0 Val accuracy: 0.9752\n", + "22 Batch accuracy: 0.98 Val accuracy: 0.9764\n", + "23 Batch accuracy: 0.98 Val accuracy: 0.9752\n", + "24 Batch accuracy: 0.98 Val accuracy: 0.9772\n", + "25 Batch accuracy: 1.0 Val accuracy: 0.977\n", + "26 Batch accuracy: 0.98 Val accuracy: 0.9778\n", + "27 Batch accuracy: 1.0 Val accuracy: 0.9774\n", + "28 Batch accuracy: 0.96 Val accuracy: 0.9754\n", + "29 Batch accuracy: 0.98 Val accuracy: 0.9776\n", + "30 Batch accuracy: 1.0 Val accuracy: 0.9756\n", + "31 Batch accuracy: 0.98 Val accuracy: 0.9772\n", + "32 Batch accuracy: 0.98 Val accuracy: 0.9772\n", + "33 Batch accuracy: 0.98 Val accuracy: 0.979\n", + "34 Batch accuracy: 1.0 Val accuracy: 0.9784\n", + "35 Batch accuracy: 1.0 Val accuracy: 0.9778\n", + "36 Batch accuracy: 0.98 Val accuracy: 0.978\n", + "37 Batch accuracy: 1.0 Val accuracy: 0.9776\n", + "38 Batch accuracy: 1.0 Val accuracy: 0.9792\n", + "39 Batch accuracy: 1.0 Val accuracy: 0.9776\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " init.run()\n", + " for epoch in range(n_epochs):\n", + " for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n", + " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", + " acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n", + " acc_val = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n", + " print(epoch, \"Batch accuracy:\", acc_batch, \"Val accuracy:\", acc_val)\n", + "\n", + " save_path = saver.save(sess, \"./my_model_final.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_model_final.ckpt\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " saver.restore(sess, \"./my_model_final.ckpt\") # or better, use save_path\n", + " X_new_scaled = X_test[:20]\n", + " Z = logits.eval(feed_dict={X: X_new_scaled})\n", + " y_pred = np.argmax(Z, axis=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted classes: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n", + "Actual classes: [7 2 1 0 4 1 4 9 5 9 0 6 9 0 1 5 9 7 3 4]\n" + ] + } + ], + "source": [ + "print(\"Predicted classes:\", y_pred)\n", + "print(\"Actual classes: \", y_test[:20])" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow_graph_in_jupyter import show_graph" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_graph(tf.get_default_graph())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using `dense()` instead of `neuron_layer()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note: previous releases of the book used `tensorflow.contrib.layers.fully_connected()` rather than `tf.layers.dense()` (which did not exist when this chapter was written). It is now preferable to use `tf.layers.dense()`, because anything in the contrib module may change or be deleted without notice. The `dense()` function is almost identical to the `fully_connected()` function, except for a few minor differences:\n", + "* several parameters are renamed: `scope` becomes `name`, `activation_fn` becomes `activation` (and similarly the `_fn` suffix is removed from other parameters such as `normalizer_fn`), `weights_initializer` becomes `kernel_initializer`, etc.\n", + "* the default `activation` is now `None` rather than `tf.nn.relu`.\n", + "* a few more differences are presented in chapter 11." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "n_inputs = 28*28 # MNIST\n", + "n_hidden1 = 300\n", + "n_hidden2 = 100\n", + "n_outputs = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", + "y = tf.placeholder(tf.int32, shape=(None), name=\"y\") " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"dnn\"):\n", + " hidden1 = tf.layers.dense(X, n_hidden1, name=\"hidden1\",\n", + " activation=tf.nn.relu)\n", + " hidden2 = tf.layers.dense(hidden1, n_hidden2, name=\"hidden2\",\n", + " activation=tf.nn.relu)\n", + " logits = tf.layers.dense(hidden2, n_outputs, name=\"outputs\")\n", + " y_proba = tf.nn.softmax(logits)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"loss\"):\n", + " xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n", + " loss = tf.reduce_mean(xentropy, name=\"loss\")" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "with tf.name_scope(\"train\"):\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"eval\"):\n", + " correct = tf.nn.in_top_k(logits, y, 1)\n", + " accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 Batch accuracy: 0.9 Validation accuracy: 0.9024\n", + "1 Batch accuracy: 0.92 Validation accuracy: 0.9254\n", + "2 Batch accuracy: 0.94 Validation accuracy: 0.9372\n", + "3 Batch accuracy: 0.9 Validation accuracy: 0.9416\n", + "4 Batch accuracy: 0.94 Validation accuracy: 0.9472\n", + "5 Batch accuracy: 0.94 Validation accuracy: 0.9512\n", + "6 Batch accuracy: 1.0 Validation accuracy: 0.9548\n", + "7 Batch accuracy: 0.94 Validation accuracy: 0.961\n", + "8 Batch accuracy: 0.96 Validation accuracy: 0.962\n", + "9 Batch accuracy: 0.94 Validation accuracy: 0.9648\n", + "10 Batch accuracy: 0.92 Validation accuracy: 0.9656\n", + "11 Batch accuracy: 0.98 Validation accuracy: 0.9668\n", + "12 Batch accuracy: 0.98 Validation accuracy: 0.9684\n", + "13 Batch accuracy: 0.98 Validation accuracy: 0.9702\n", + "14 Batch accuracy: 1.0 Validation accuracy: 0.9696\n", + "15 Batch accuracy: 0.94 Validation accuracy: 0.9718\n", + "16 Batch accuracy: 0.98 Validation accuracy: 0.9728\n", + "17 Batch accuracy: 1.0 Validation accuracy: 0.973\n", + "18 Batch accuracy: 0.98 Validation accuracy: 0.9748\n", + "19 Batch accuracy: 0.98 Validation accuracy: 0.9756\n" + ] + } + ], + "source": [ + "n_epochs = 20\n", + "n_batches = 50\n", + "\n", + "with tf.Session() as sess:\n", + " init.run()\n", + " for epoch in range(n_epochs):\n", + " for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n", + " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", + " acc_batch = accuracy.eval(feed_dict={X: X_batch, y: y_batch})\n", + " acc_valid = accuracy.eval(feed_dict={X: X_valid, y: y_valid})\n", + " print(epoch, \"Batch accuracy:\", acc_batch, \"Validation accuracy:\", acc_valid)\n", + "\n", + " save_path = saver.save(sess, \"./my_model_final.ckpt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "show_graph(tf.get_default_graph())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "# Exercise solutions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. to 8." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "See appendix A." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 9." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Train a deep MLP on the MNIST dataset and see if you can get over 98% precision. Just like in the last exercise of chapter 9, try adding all the bells and whistles (i.e., save checkpoints, restore the last checkpoint in case of an interruption, add summaries, plot learning curves using TensorBoard, and so on)._" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First let's create the deep net. It's exactly the same as earlier, with just one addition: we add a `tf.summary.scalar()` to track the loss and the accuracy during training, so we can view nice learning curves using TensorBoard." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "n_inputs = 28*28 # MNIST\n", + "n_hidden1 = 300\n", + "n_hidden2 = 100\n", + "n_outputs = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "reset_graph()\n", + "\n", + "X = tf.placeholder(tf.float32, shape=(None, n_inputs), name=\"X\")\n", + "y = tf.placeholder(tf.int32, shape=(None), name=\"y\") " + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"dnn\"):\n", + " hidden1 = tf.layers.dense(X, n_hidden1, name=\"hidden1\",\n", + " activation=tf.nn.relu)\n", + " hidden2 = tf.layers.dense(hidden1, n_hidden2, name=\"hidden2\",\n", + " activation=tf.nn.relu)\n", + " logits = tf.layers.dense(hidden2, n_outputs, name=\"outputs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"loss\"):\n", + " xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)\n", + " loss = tf.reduce_mean(xentropy, name=\"loss\")\n", + " loss_summary = tf.summary.scalar('log_loss', loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "with tf.name_scope(\"train\"):\n", + " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", + " training_op = optimizer.minimize(loss)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "with tf.name_scope(\"eval\"):\n", + " correct = tf.nn.in_top_k(logits, y, 1)\n", + " accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))\n", + " accuracy_summary = tf.summary.scalar('accuracy', accuracy)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "init = tf.global_variables_initializer()\n", + "saver = tf.train.Saver()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we need to define the directory to write the TensorBoard logs to:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "\n", + "def log_dir(prefix=\"\"):\n", + " now = datetime.utcnow().strftime(\"%Y%m%d%H%M%S\")\n", + " root_logdir = \"tf_logs\"\n", + " if prefix:\n", + " prefix += \"-\"\n", + " name = prefix + \"run-\" + now\n", + " return \"{}/{}/\".format(root_logdir, name)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "logdir = log_dir(\"mnist_dnn\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create the `FileWriter` that we will use to write the TensorBoard logs:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "file_writer = tf.summary.FileWriter(logdir, tf.get_default_graph())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Hey! Why don't we implement early stopping? For this, we are going to need to use the validation set." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "m, n = X_train.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch: 0 \tValidation accuracy: 92.180% \tLoss: 0.30208\n", + "Epoch: 5 \tValidation accuracy: 95.980% \tLoss: 0.15037\n", + "Epoch: 10 \tValidation accuracy: 97.100% \tLoss: 0.11160\n", + "Epoch: 15 \tValidation accuracy: 97.700% \tLoss: 0.09562\n", + "Epoch: 20 \tValidation accuracy: 97.840% \tLoss: 0.08309\n", + "Epoch: 25 \tValidation accuracy: 98.040% \tLoss: 0.07706\n", + "Epoch: 30 \tValidation accuracy: 98.140% \tLoss: 0.07287\n", + "Epoch: 35 \tValidation accuracy: 98.280% \tLoss: 0.07133\n", + "Epoch: 40 \tValidation accuracy: 98.220% \tLoss: 0.06968\n", + "Epoch: 45 \tValidation accuracy: 98.220% \tLoss: 0.06993\n", + "Epoch: 50 \tValidation accuracy: 98.160% \tLoss: 0.07093\n", + "Epoch: 55 \tValidation accuracy: 98.280% \tLoss: 0.06994\n", + "Epoch: 60 \tValidation accuracy: 98.200% \tLoss: 0.06894\n", + "Epoch: 65 \tValidation accuracy: 98.260% \tLoss: 0.06906\n", + "Epoch: 70 \tValidation accuracy: 98.220% \tLoss: 0.07057\n", + "Epoch: 75 \tValidation accuracy: 98.280% \tLoss: 0.06963\n", + "Epoch: 80 \tValidation accuracy: 98.320% \tLoss: 0.07264\n", + "Epoch: 85 \tValidation accuracy: 98.200% \tLoss: 0.07403\n", + "Epoch: 90 \tValidation accuracy: 98.300% \tLoss: 0.07332\n", + "Epoch: 95 \tValidation accuracy: 98.180% \tLoss: 0.07535\n", + "Epoch: 100 \tValidation accuracy: 98.260% \tLoss: 0.07542\n", + "Early stopping\n" + ] + } + ], + "source": [ + "n_epochs = 10001\n", + "batch_size = 50\n", + "n_batches = int(np.ceil(m / batch_size))\n", + "\n", + "checkpoint_path = \"/tmp/my_deep_mnist_model.ckpt\"\n", + "checkpoint_epoch_path = checkpoint_path + \".epoch\"\n", + "final_model_path = \"./my_deep_mnist_model\"\n", + "\n", + "best_loss = np.infty\n", + "epochs_without_progress = 0\n", + "max_epochs_without_progress = 50\n", + "\n", + "with tf.Session() as sess:\n", + " if os.path.isfile(checkpoint_epoch_path):\n", + " # if the checkpoint file exists, restore the model and load the epoch number\n", + " with open(checkpoint_epoch_path, \"rb\") as f:\n", + " start_epoch = int(f.read())\n", + " print(\"Training was interrupted. Continuing at epoch\", start_epoch)\n", + " saver.restore(sess, checkpoint_path)\n", + " else:\n", + " start_epoch = 0\n", + " sess.run(init)\n", + "\n", + " for epoch in range(start_epoch, n_epochs):\n", + " for X_batch, y_batch in shuffle_batch(X_train, y_train, batch_size):\n", + " sess.run(training_op, feed_dict={X: X_batch, y: y_batch})\n", + " accuracy_val, loss_val, accuracy_summary_str, loss_summary_str = sess.run([accuracy, loss, accuracy_summary, loss_summary], feed_dict={X: X_valid, y: y_valid})\n", + " file_writer.add_summary(accuracy_summary_str, epoch)\n", + " file_writer.add_summary(loss_summary_str, epoch)\n", + " if epoch % 5 == 0:\n", + " print(\"Epoch:\", epoch,\n", + " \"\\tValidation accuracy: {:.3f}%\".format(accuracy_val * 100),\n", + " \"\\tLoss: {:.5f}\".format(loss_val))\n", + " saver.save(sess, checkpoint_path)\n", + " with open(checkpoint_epoch_path, \"wb\") as f:\n", + " f.write(b\"%d\" % (epoch + 1))\n", + " if loss_val < best_loss:\n", + " saver.save(sess, final_model_path)\n", + " best_loss = loss_val\n", + " else:\n", + " epochs_without_progress += 5\n", + " if epochs_without_progress > max_epochs_without_progress:\n", + " print(\"Early stopping\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "os.remove(checkpoint_epoch_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:tensorflow:Restoring parameters from ./my_deep_mnist_model\n" + ] + } + ], + "source": [ + "with tf.Session() as sess:\n", + " saver.restore(sess, final_model_path)\n", + " accuracy_val = accuracy.eval(feed_dict={X: X_test, y: y_test})" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9796" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_val" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + }, + "nav_menu": { + "height": "264px", + "width": "369px" + }, + "toc": { + "navigate_menu": true, + "number_sections": true, + "sideBar": true, + "threshold": 6, + "toc_cell": false, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/helpers_05_08.py b/notebooks/helpers_05_08.py index 0f3b15aa9..72cc3df4e 100644 --- a/notebooks/helpers_05_08.py +++ b/notebooks/helpers_05_08.py @@ -2,6 +2,7 @@ import numpy as np import matplotlib.pyplot as plt from sklearn.tree import DecisionTreeClassifier +# random change to see this is last from ipywidgets import interact From e023582ef79dd9cbdc01f2a6811c9917c5e93cd2 Mon Sep 17 00:00:00 2001 From: Administrator Date: Fri, 9 Jul 2021 10:34:41 +0000 Subject: [PATCH 2/4] Test --- notebooks/01.03-Magic-Commands.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/01.03-Magic-Commands.ipynb b/notebooks/01.03-Magic-Commands.ipynb index 4a47b373a..59387436d 100644 --- a/notebooks/01.03-Magic-Commands.ipynb +++ b/notebooks/01.03-Magic-Commands.ipynb @@ -6,7 +6,7 @@ "source": [ "\n", "\n", - "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "*This notebo ok contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" ] @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The previous two sections showed how IPython lets you use and explore Python efficiently and interactively.\n", + "The previous two secti ons showed how IPython lets you use and explore Python efficiently and interactively.\n", "Here we'll begin discussing some of the enhancements that IPython adds on top of the normal Python syntax.\n", "These are known in IPython as *magic commands*, and are prefixed by the ``%`` character.\n", "These magic commands are designed to succinctly solve various common problems in standard data analysis.\n", @@ -211,7 +211,7 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -229,5 +229,5 @@ } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } From 331c21e0fe238441f1f3faea86f5d35da8706802 Mon Sep 17 00:00:00 2001 From: Administrator Date: Fri, 9 Jul 2021 11:12:13 +0000 Subject: [PATCH 3/4] t3 --- notebooks/01.03-Magic-Commands.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/01.03-Magic-Commands.ipynb b/notebooks/01.03-Magic-Commands.ipynb index 59387436d..38412a4e9 100644 --- a/notebooks/01.03-Magic-Commands.ipynb +++ b/notebooks/01.03-Magic-Commands.ipynb @@ -6,7 +6,7 @@ "source": [ "\n", "\n", - "*This notebo ok contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", + "*This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*\n", "\n", "*The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*" ] From 223269d30b58320bca60a5e70373d70025181e9c Mon Sep 17 00:00:00 2001 From: Amit Rathi Date: Mon, 17 Jun 2024 16:58:17 +0530 Subject: [PATCH 4/4] Large table of type pandas.io.formats.style.Styler --- local11.ipynb | 3423 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 3423 insertions(+) create mode 100644 local11.ipynb diff --git a/local11.ipynb b/local11.ipynb new file mode 100644 index 000000000..2b79aa100 --- /dev/null +++ b/local11.ipynb @@ -0,0 +1,3423 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "dfa2d018-7f32-4e0f-b8ef-b738a52cfc7e", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "# Create a large DataFrame with random data\n", + "np.random.seed(0)\n", + "data = np.random.randn(100, 30) # 100 rows and 10 columns\n", + "columns = [f'Column_{i}' for i in range(30)]\n", + "df = pd.DataFrame(data, columns=columns)\n", + "\n", + "# Function to color negative numbers red and positive numbers green\n", + "def color_negative_red(value):\n", + " color = 'red' if value < 0 else 'green'\n", + " return f'color: {color}'\n", + "\n", + "# Apply Styling\n", + "styled_df = df.style.applymap(color_negative_red)\n", + "\n", + "# Display the styled DataFrame in a Jupyter Notebook\n", + "styled_df\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "357c38a3-5142-40ed-af9f-c1ca5ede7a5c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}