{ "metadata": { "language": "Julia", "name": "", "signature": "sha256:e555c308a4f434c7dbfd18eb7231c498456237ab8c824dfa0ee83c086d8a62cc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](files/img/julia_logo.png)\n", "# for Data Science\n", "\n", "@BenSadeghi\n", "\n", "[Twitter, GitHub, Linkedin]\n", "\n", "Based on presentations by John Myles White[[1]](http://nbviewer.ipython.org/github/johnmyleswhite/UCDavis.jl/blob/master/Julia.ipynb)[[2]](http://nbviewer.ipython.org/github/johnmyleswhite/DCStats.jl/blob/master/Base%20Julia.ipynb)[[3]](http://nbviewer.ipython.org/github/johnmyleswhite/DCStats.jl/blob/master/Statistical%20Programming%20in%20Julia.ipynb), Stefan Karpinski and others" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Contents\n", "* **Background**\n", "* **Why use Julia?**\n", "* **Language Basics**\n", " * Types\n", " * Linear Algebra\n", " * Functions, Multiple Dispatch\n", " * Programming Styles\n", "* **Package Manager**\n", "* **Statistics in Julia**\n", "* **Tabular Data**\n", "* **Data Visualization**\n", "* **Machine Learning Algorithms**\n", " * Unsupervised\n", " * Supervised\n", "* **Resources**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Background\n", "\n", "Julia is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general purpose programming.\n", "\n", "Julia's core is implemented in C and C++, its parser in Scheme, and the LLVM compiler framework is used for just-in-time generation of machine code for x86(-64).\n", "\n", "### Designed By\n", "* Jeff Bezanson\n", "* Stefan Karpinski\n", "* Viral B. Shah\n", "* Alan Edelman (MIT supervisor)\n", "\n", "Development began in 2009, open-sourced in February 2012\n", "\n", "Currently has 250+ contributors to the language, 400+ overall\n", "\n", "Stable release: v0.2.1 (2014/02/11), pre-release: v0.3 (nightly build)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# World of Julia\n", "![](files/img/julia_world.png)\n", "Source: GitHub, 2014/06/30 - [Source code](http://nbviewer.ipython.org/github/jiahao/ijulia-notebooks/blob/master/2014-06-30-world-of-julia.ipynb) by Jiahao Chen" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Why Use Julia?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Language Features\n", "* Multiple dispatch\n", "* Dynamic type system\n", "* Performance approaching that of statically-compiled languages like C\n", "* Built-in package manager\n", "* Lisp-like macros and other metaprogramming facilities\n", "* Call C functions directly: no wrappers or special APIs\n", "* Shell-like capabilities for managing other processes\n", "* Designed for parallelism and distributed computation\n", "* User-defined types are as fast and compact as built-ins\n", "* Efficient support for Unicode, including but not limited to UTF-8\n", "* Familiar Matlab/NumPy-like syntax" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![](files/img/benchmarks.svg)\n", "[Source code](http://nbviewer.ipython.org/url/julialang.org/benchmarks.ipynb)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Why You Shouldn't Use Julia\n", "\n", "* Julia is very young\n", "* The Julia package ecosystem is even younger\n", "* Breaking changes are still coming in core and will be quite frequent outside of core Julia\n", "* Language features are still being added: your favorite may not exist yet\n", "* Code quality for packages varies from reasonably well tested, to never tested, to broken" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# So... Should You Use Julia?\n", "That depends on your use case:\n", "\n", "* If you tend to build lots of tools from scratch, Julia is usable, but a little rough\n", "* If you tend to build upon lots of other packages, Julia isn't ready for you yet" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Hierarchical Built-In Types" ] }, { "cell_type": "code", "collapsed": false, "input": [ "subtypes(Number)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "2-element Array{Any,1}:\n", " Complex{T<:Real}\n", " Real " ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "subtypes(Real)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "4-element Array{Any,1}:\n", " FloatingPoint \n", " Integer \n", " MathConst{sym} \n", " Rational{T<:Integer}" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "subtypes(Integer)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "5-element Array{Any,1}:\n", " BigInt \n", " Bool \n", " Char \n", " Signed \n", " Unsigned" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Floating Point\n", "@show 5/3\n", "\n", "# Mathematical Constant\n", "@show pi\n", "\n", "# Rational\n", "@show 2//3 + 1\n", "\n", "# BigInt\n", "@show big(2) ^ 1000 ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "5 / 3 => 1.6666666666666667" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "pi => \u03c0 = 3.1415926535897...\n", "2 // 3 + 1 => 5//3\n", "big(2)^1000 => 10715086071862673209484250490600018105614048117055336074437503883703510511249361224931983788156958581275946729175531468251871452856923140435984577574698574803934567774824230985421074605062371141877954182153046474983581941267398767559165543946077062914571196477686542167660429831652624386837205668069376\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "subtypes(String)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "8-element Array{Any,1}:\n", " DirectIndexString \n", " GenericString \n", " RepString \n", " RevString{T<:String}\n", " RopeString \n", " SubString{T<:String}\n", " UTF16String \n", " UTF8String " ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "s = \"Hello World\"\n", "\n", "@show typeof(s)\n", "@show s[7] ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "typeof(s) => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "ASCIIString\n", "s[7] => 'W'\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Unicode Names and Values\n", "\n", "\u4f60\u597d = \"(\uff61\u25d5_\u25d5\uff61)\uff89 \"\n", "\n", "@show typeof(\u4f60\u597d)\n", "@show \u4f60\u597d ^ 3 ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "typeof(\u4f60\u597d) => UTF8String\n", "\u4f60\u597d^3 => \"(\uff61\u25d5_\u25d5\uff61)\uff89 (\uff61\u25d5_\u25d5\uff61)\uff89 (\uff61\u25d5_\u25d5\uff61)\uff89 \"\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# User-Defined Types" ] }, { "cell_type": "code", "collapsed": false, "input": [ "type NewType\n", " i::Integer\n", " s::String\n", "end\n", "\n", "new_t = NewType(33, \"this is a NewType\")\n", "\n", "@show new_t.i\n", "@show new_t.s ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "new_t.i => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "33\n", "new_t.s => \"this is a NewType\"\n" ] } ], "prompt_number": 8 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Linear Algebra" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Vectors\n", "\n", "v = [1, 1]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "2-element Array{Int64,1}:\n", " 1\n", " 1" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# Vector Operations\n", "\n", "@show v + [2, 0] # vector addition\n", "@show v + 1 # same as v + [1,1]\n", "@show 5*v # scalar multiplication" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "v + [2,0] => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[3,1]\n", "v + 1 => [2,2]\n", "5v => [5,5]\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 10, "text": [ "2-element Array{Int64,1}:\n", " 5\n", " 5" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "println( \"Dot Product : \", dot(v, v) )\n", "println( \"Norm : \", norm(v) )" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Dot Product : 2\n", "Norm : 1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ ".4142135623730951\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Matrices\n", "\n", "M = [1 1 ; 0 1]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 12, "text": [ "2x2 Array{Int64,2}:\n", " 1 1\n", " 0 1" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# Matrix Addition\n", "\n", "M + 1 ,\n", "M + [0 0 ; 5 5]" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "(\n", "2x2 Array{Int64,2}:\n", " 2 2\n", " 1 2,\n", "\n", "2x2 Array{Int64,2}:\n", " 1 1\n", " 5 6)" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "# Matrix Multiplication\n", "\n", "2M ,\n", "M ^ 2 ,\n", "M * v" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 14, "text": [ "(\n", "2x2 Array{Int64,2}:\n", " 2 2\n", " 0 2,\n", "\n", "2x2 Array{Int64,2}:\n", " 1 2\n", " 0 1,\n", "\n", "[2,1])" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# Gaussian Elimination\n", "\n", "b = M * v\n", "\n", "M \\ b # solve back for v" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "2-element Array{Float64,1}:\n", " 1.0\n", " 1.0" ] } ], "prompt_number": 15 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Functions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Named functions\n", "\n", "f(x) = 10x\n", "\n", "function g(x)\n", " return x * 10\n", "end\n", "\n", "@show f(5)\n", "@show g(5) ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "f(5) => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "50\n", "g(5) => 50\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "# Anonymous functions assigned to variables\n", "\n", "h = x -> x * 10\n", "\n", "i = function(x)\n", " x * 10\n", "end\n", "\n", "@show h(5)\n", "@show i(5) ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "h(5) => 50\n", "i(5) => 50\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "# Operators are functions\n", "\n", "+(4,5)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 18, "text": [ "9" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "p = +\n", "\n", "p(2,3)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ "5" ] } ], "prompt_number": 19 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Multiple Dispatch" ] }, { "cell_type": "code", "collapsed": false, "input": [ "bar(x::String) = println(\"You entered the string: $x\")\n", "bar(x::Integer) = x * 10\n", "bar(x::NewType) = println(x.s)\n", "\n", "methods(bar)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "html": [ "3 methods for generic function bar:" ], "metadata": {}, "output_type": "pyout", "prompt_number": 20, "text": [ "# 3 methods for generic function \"bar\":\n", "bar(x::String) at In[20]:1\n", "bar(x::Integer) at In[20]:2\n", "bar(x::NewType) at In[20]:3" ] } ], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "bar(\"Hello\")\n", "bar(new_t)\n", "bar(5)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "You entered the string: Hello" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "this is a NewType\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 21, "text": [ "50" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [ "# Adding strings\n", "\n", "\"Hello\" + \"World\"" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "ename": "LoadError", "evalue": "`+` has no method matching +(::ASCIIString, ::ASCIIString)\nwhile loading In[22], in expression starting on line 3", "output_type": "pyerr", "traceback": [ "`+` has no method matching +(::ASCIIString, ::ASCIIString)\nwhile loading In[22], in expression starting on line 3" ] } ], "prompt_number": 22 }, { "cell_type": "code", "collapsed": false, "input": [ "# But the addition operator is a function, so we can apply multi-dispatch\n", "\n", "+(a::String, b::String) = a * b\n", "\n", "\"Hello\" + \"World\"" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 23, "text": [ "\"HelloWorld\"" ] } ], "prompt_number": 23 }, { "cell_type": "code", "collapsed": false, "input": [ "+(a::Number, b::String) = string(a) + b\n", "+(a::String, b::Number) = a + string(b)\n", "\n", "99 + \"bottles\"" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 24, "text": [ "\"99bottles\"" ] } ], "prompt_number": 24 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Object-Oriented Programming" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Method Overloading\n", "\n", "type SimpleObject\n", " data::Union(Integer, String)\n", " set::Function\n", "\n", " function SimpleObject()\n", " this = new()\n", " this.data = \"\"\n", "\n", " function setter(x::Integer)\n", " println(\"Setting an integer\")\n", " this.data = x\n", " end\n", " function setter(x::String)\n", " println(\"Setting a string\")\n", " this.data = x\n", " end\n", " this.set = setter\n", "\n", " return this\n", " end\n", "end\n", "\n", "obj = SimpleObject()\n", "obj.set(99)\n", "obj.set(\"hello\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Setting an integer" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Setting a string\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 25, "text": [ "\"hello\"" ] } ], "prompt_number": 25 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Functional Programming" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Sum of odd integers between 1 and 5\n", "\n", "values = 1:5\n", "\n", "myMapper = x -> x\n", "myFilter = x -> x % 2 == 1\n", "myReducer = (x,y) -> x + y\n", "\n", "mapped = map( myMapper, values )\n", "filtered = filter( myFilter, mapped )\n", "reduced = reduce( myReducer, filtered )" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 26, "text": [ "9" ] } ], "prompt_number": 26 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Metaprogramming" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Code Generation\n", "# Functions for exponentiating to the powers of 1 to 5\n", "\n", "for n in 1:5\n", " s = \"power$n(x) = x ^ $n\"\n", " println(s)\n", " expression = parse(s)\n", " eval(expression) \n", "end\n", "\n", "power5( 2 )" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "power1(x) = x ^ 1" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "power2(x) = x ^ 2\n", "power3(x) = x ^ 3\n", "power4(x) = x ^ 4\n", "power5(x) = x ^ 5\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 27, "text": [ "32" ] } ], "prompt_number": 27 }, { "cell_type": "code", "collapsed": false, "input": [ "# Macros: Crude Timer Example\n", "\n", "macro timeit(expression)\n", " quote\n", " t = time()\n", " result = $expression # evaluation\n", " elapsed = time() - t\n", " println( \"elapsed time: \", elapsed )\n", " return result\n", " end\n", "end\n", "\n", "@timeit cos(2pi)\n", "@timeit cos(2pi)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "elapsed time: 0.005074977874755859\n", "elapsed time: 4.0531158447265625e-6\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 28, "text": [ "1.0" ] } ], "prompt_number": 28 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Package Manager\n", "Julia has a built-in package management system. All packages are git repositories, mostly hosted on GitHub.\n", "\n", "### Installing a new package\n", "Pkg.add(\"PackageName\")\n", "\n", "### Start using it\n", "using PackageName\n", "\n", "### Import a function to overload\n", "import PackageName.FunctionName\n", "\n", "### Update packages\n", "Pkg.update()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Basic Statistics" ] }, { "cell_type": "code", "collapsed": false, "input": [ "using StatsBase\n", "\n", "x = rand(100) # uniform distribution [0,1)\n", "\n", "println( \"mean: \", mean(x) )\n", "println( \"variance: \", var(x) )\n", "println( \"skewness: \", skewness(x) )\n", "println( \"kurtosis: \", kurtosis(x) )" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "mean: " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "0.5260291483830464\n", "variance: 0.09076375466564988\n", "skewness: 0.007890698485229702\n", "kurtosis: -1.3205549430417554\n" ] } ], "prompt_number": 29 }, { "cell_type": "code", "collapsed": false, "input": [ "describe(x)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Summary Stats:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Mean: 0.526029\n", "Minimum: 0.002137\n", "1st Quartile: 0.287252\n", "Median: 0.495243\n", "3rd Quartile: 0.809833\n", "Maximum: 0.995268\n" ] } ], "prompt_number": 30 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Probability Distributions" ] }, { "cell_type": "code", "collapsed": false, "input": [ "using Distributions\n", "\n", "distr = Normal(0, 2)\n", "\n", "println( \"pdf @ origin = \", pdf(distr, 0.0) )\n", "println( \"cdf @ origin = \", cdf(distr, 0.0) )" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "pdf @ origin = " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "0.19947114020071635\n", "cdf @ origin = 0.5\n" ] } ], "prompt_number": 31 }, { "cell_type": "code", "collapsed": false, "input": [ "x = rand(distr, 1000)\n", "\n", "fit_mle(Normal, x)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 32, "text": [ "Normal( \u03bc=-0.010365910392224809 \u03c3=2.0295200120189767 )" ] } ], "prompt_number": 32 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Tabular Data" ] }, { "cell_type": "code", "collapsed": false, "input": [ "using DataFrames\n", "\n", "df = DataFrame(\n", " A = [6, 3, 4],\n", " B = [\"a\", \"b\", \"c\"],\n", " C = [1//2, 3//4, 5//6],\n", " D = [true, true, false]\n", ")\n", "\n", "df[:C][2] = NA\n", "df" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "html": [ "
ABCD
16a1//2true
23bNAtrue
34c5//6false
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 33, "text": [ "3x4 DataFrame\n", "|-------|---|-----|------|-------|\n", "| Row # | A | B | C | D |\n", "| 1 | 6 | \"a\" | 1//2 | true |\n", "| 2 | 3 | \"b\" | NA | true |\n", "| 3 | 4 | \"c\" | 5//6 | false |" ] } ], "prompt_number": 33 }, { "cell_type": "code", "collapsed": false, "input": [ "# Joins\n", "\n", "names = DataFrame(ID = [5, 4], Name = [\"Jack\", \"Jill\"])\n", "jobs = DataFrame(ID = [5, 4], Job = [\"Lawyer\", \"Doctor\"])\n", "\n", "full = join(names, jobs, on = :ID)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "html": [ "
IDNameJob
14JillDoctor
25JackLawyer
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 34, "text": [ "2x3 DataFrame\n", "|-------|----|--------|----------|\n", "| Row # | ID | Name | Job |\n", "| 1 | 4 | \"Jill\" | \"Doctor\" |\n", "| 2 | 5 | \"Jack\" | \"Lawyer\" |" ] } ], "prompt_number": 34 }, { "cell_type": "code", "collapsed": false, "input": [ "using RDatasets\n", "\n", "iris = dataset(\"datasets\", \"iris\")\n", "head(iris)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "html": [ "
SepalLengthSepalWidthPetalLengthPetalWidthSpecies
15.13.51.40.2setosa
24.93.01.40.2setosa
34.73.21.30.2setosa
44.63.11.50.2setosa
55.03.61.40.2setosa
65.43.91.70.4setosa
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 35, "text": [ "6x5 DataFrame\n", "|-------|-------------|------------|-------------|------------|----------|\n", "| Row # | SepalLength | SepalWidth | PetalLength | PetalWidth | Species |\n", "| 1 | 5.1 | 3.5 | 1.4 | 0.2 | \"setosa\" |\n", "| 2 | 4.9 | 3.0 | 1.4 | 0.2 | \"setosa\" |\n", "| 3 | 4.7 | 3.2 | 1.3 | 0.2 | \"setosa\" |\n", "| 4 | 4.6 | 3.1 | 1.5 | 0.2 | \"setosa\" |\n", "| 5 | 5.0 | 3.6 | 1.4 | 0.2 | \"setosa\" |\n", "| 6 | 5.4 | 3.9 | 1.7 | 0.4 | \"setosa\" |" ] } ], "prompt_number": 35 }, { "cell_type": "code", "collapsed": false, "input": [ "# Group by Species, then compute mean of PetalLength per group\n", "\n", "by( iris, :Species, df -> mean(df[:PetalLength]) )" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "html": [ "
Speciesx1
1setosa1.462
2versicolor4.26
3virginica5.552
" ], "metadata": {}, "output_type": "pyout", "prompt_number": 36, "text": [ "3x2 DataFrame\n", "|-------|--------------|-------|\n", "| Row # | Species | x1 |\n", "| 1 | \"setosa\" | 1.462 |\n", "| 2 | \"versicolor\" | 4.26 |\n", "| 3 | \"virginica\" | 5.552 |" ] } ], "prompt_number": 36 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Data Visualization" ] }, { "cell_type": "code", "collapsed": false, "input": [ "using ASCIIPlots\n", "\n", "x = iris[:PetalLength]\n", "y = iris[:PetalWidth]\n", "\n", "scatterplot(x, y)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 37, "text": [ "\n", "\t-------------------------------------------------------------\n", "\t| ^ ^ | 2.50\n", "\t| ^ ^ |\n", "\t| ^ ^^^ ^ ^^ ^|\n", "\t| ^ ^ ^ |\n", "\t| ^^ ^ ^^ ^ ^ ^^ ^ |\n", "\t| ^ ^ ^ |\n", "\t| ^^^ ^ ^ ^ ^ |\n", "\t| ^ ^ |\n", "\t| ^ ^ ^ ^^ ^ |\n", "\t| ^ ^^ ^^ ^ |\n", "\t| ^ ^ ^^^^ |\n", "\t| ^ ^ ^ ^ ^ |\n", "\t| ^ ^ ^ ^^ ^ |\n", "\t| |\n", "\t| |\n", "\t| |\n", "\t| ^ |\n", "\t| ^ ^^ ^ |\n", "\t| ^ ^^ |\n", "\t|^^ ^ ^^ ^ | 0.10\n", "\t-------------------------------------------------------------\n", "\t1.00 6.90\n" ] } ], "prompt_number": 37 }, { "cell_type": "code", "collapsed": false, "input": [ "using Winston\n", "\n", "scatter(x, y, \".\")\n", "\n", "xlabel(\"PetalLength\")\n", "ylabel(\"PetalWidth\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "png": 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matrix() on Julia v0.2\n", "result = kmeans( features, 3 ) # onto 3 clusters\n", "\n", "plot(iris, x = \"PetalLength\", y = \"PetalWidth\", color = result.assignments, Geom.point)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " " ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Warning: using DataFrames.describe in module Main conflicts with an existing identifier.\n", "Warning: could not import StatsBase.bandwidth into Stat\n", "Warning: could not import StatsBase.kde into Stat\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " Iters objv objv-change | affected \n", "-------------------------------------------------------------\n", " 1 8.200215e+01 -6.279785e+01 | 2\n", " 2 8.108093e+01 -9.212131e-01 | 2\n", " 3 7.987358e+01 -1.207354e+00 | 2\n", " 4 7.934436e+01 -5.292157e-01 | 2\n", " 5 7.892131e+01 -4.230544e-01 | 2\n", " 6 7.885567e+01 -6.564390e-02 | 0\n", " 7 7.885567e+01 0.000000e+00 | 0\n", "K-means converged with 7 iterations (objv = 78.85566582597716)\n" ] }, { "html": [ "\n", "\n", "\n", " \n", " PetalLength\n", " \n", " \n", " -10\n", " -8\n", " -6\n", " -4\n", " -2\n", " 0\n", " 2\n", " 4\n", " 6\n", " 8\n", " 10\n", " 12\n", " 14\n", " 16\n", " 18\n", " -8.0\n", " -7.5\n", " -7.0\n", " -6.5\n", " -6.0\n", " -5.5\n", " -5.0\n", " -4.5\n", " -4.0\n", " -3.5\n", " -3.0\n", " -2.5\n", " -2.0\n", " -1.5\n", " -1.0\n", " -0.5\n", " 0.0\n", " 0.5\n", " 1.0\n", " 1.5\n", " 2.0\n", " 2.5\n", " 3.0\n", " 3.5\n", " 4.0\n", " 4.5\n", " 5.0\n", " 5.5\n", " 6.0\n", " 6.5\n", " 7.0\n", " 7.5\n", " 8.0\n", " 8.5\n", " 9.0\n", " 9.5\n", " 10.0\n", " 10.5\n", " 11.0\n", " 11.5\n", " 12.0\n", " 12.5\n", " 13.0\n", " 13.5\n", " 14.0\n", " 14.5\n", " 15.0\n", " 15.5\n", " 16.0\n", " -10\n", " 0\n", " 10\n", " 20\n", " -8.0\n", " -7.5\n", " -7.0\n", " -6.5\n", " -6.0\n", " -5.5\n", " -5.0\n", " -4.5\n", " -4.0\n", " -3.5\n", " 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"prompt_number": 40 }, { "cell_type": "code", "collapsed": false, "input": [ "# Principal Component Analysis\n", "\n", "using MultivariateStats\n", "\n", "pc = fit(PCA, features; maxoutdim = 2)\n", "reduced = transform(pc, features)\n", "@show size(reduced)\n", "\n", "plot(iris, x = reduced[1,:], y = reduced[2,:], color = \"Species\", Geom.point)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "size(reduced) => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "(2,150)\n" ] }, { "html": [ "\n", "\n", "\n", " \n", " x\n", " \n", " \n", " -14\n", " -12\n", " -10\n", " -8\n", " -6\n", " -4\n", " -2\n", " 0\n", " 2\n", " 4\n", " 6\n", " 8\n", " 10\n", " 12\n", " 14\n", " -12.0\n", " -11.5\n", " -11.0\n", " -10.5\n", " -10.0\n", " -9.5\n", " -9.0\n", " -8.5\n", " -8.0\n", " -7.5\n", " -7.0\n", " -6.5\n", " -6.0\n", " -5.5\n", " -5.0\n", " -4.5\n", " -4.0\n", " -3.5\n", " 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rand(3) # ground truth of weights\n", "targets = features * coeffs + 0.1 * randn(1000) # generate response\n", "\n", "# Linear Least Square Regression\n", "coeffs_llsq = llsq(features, targets; bias=false)\n", "\n", "# Ridge Regression\n", "coeffs_ridge = ridge(features, targets, 0.1; bias=false) # regularization coef = 0.1\n", "\n", "@show coeffs\n", "@show coeffs_llsq\n", "@show coeffs_ridge ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "coeffs => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[0.909725259136879,0.09457886909449087,0.5497737690044144]\n", "coeffs_llsq => [0.9136892428062334,0.09038032773513839,0.5584636569649853]\n", "coeffs_ridge => [0.9131715345035267,0.09081871314618502,0.5583822928501584]\n" ] } ], "prompt_number": 42 }, { "cell_type": "code", "collapsed": false, "input": [ "# Cross Validation: K-Fold Example\n", "\n", "using MLBase, MultivariateStats\n", "\n", "n = length(targets)\n", "\n", "# Define training and error evaluation functions\n", "function training(inds)\n", " coeffs = ridge(features[inds, :], targets[inds], 0.1; bias=false)\n", " return coeffs\n", "end\n", "\n", "function error_evaluation(coeffs, inds)\n", " y = features[inds, :] * coeffs \n", " rms_error = sqrt(mean(abs2(targets[inds] .- y)))\n", " return rms_error\n", "end\n", "\n", "# Cross validate\n", "scores = cross_validate(\n", " inds -> training(inds),\n", " (coeffs, inds) -> error_evaluation(coeffs, inds),\n", " n, # total number of samples\n", " Kfold(n, 3)) # cross validation plan: 3-fold\n", "\n", "# Get the mean and std of scores\n", "@show scores\n", "@show mean_and_std(scores) ;" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "scores => " ] }, { "output_type": "stream", "stream": "stderr", "text": [ "Warning: using MLBase.transform in module Main conflicts with an existing identifier.\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "[0.09242034479832754,0.10521231254886307,0.10191139455606114]\n", "mean_and_std(scores) => (0.0998480173010839,0.006640915148151889)\n" ] } ], "prompt_number": 43 }, { "cell_type": "code", "collapsed": false, "input": [ "# Model Tuning: Grid Search\n", "\n", "using MLBase, MultivariateStats\n", "\n", "# Hold out 20% of records for testing\n", "n_test = int(length(targets) * 0.2)\n", "train_rows = shuffle([1:length(targets)] .> n_test)\n", "features_train, features_test = features[train_rows, :], features[!train_rows, :]\n", "targets_train, targets_test = targets[train_rows], targets[!train_rows]\n", "\n", "# Define estimation function\n", "function estfun(regcoef, bias)\n", " coeffs = ridge(features_train, targets_train, regcoef; bias=bias)\n", " return bias ? (coeffs[1:end-1], coeffs[end]) : (coeffs, 0.0)\n", "end\n", "\n", "# Define error evaluation function as mean squared deviation\n", "evalfun(coeffs) = msd(features_test * coeffs[1] + coeffs[2], targets_test)\n", "\n", "result = gridtune(estfun, evalfun,\n", " (\"regcoef\", [0.01, 0.1, 1.0]),\n", " (\"bias\", [true, false]);\n", " ord=Reverse, # smaller msd value indicates better model\n", " verbose=true) # show progress information\n", "\n", "best_model, best_config, best_score = result\n", "\n", "# Print results\n", "coeffs, bias = best_model\n", "println(\"Best model:\")\n", "println(\" coeffs = $(coeffs')\"),\n", "println(\" bias = $bias\")\n", "println(\"Best config: regcoef = $(best_config[1]), bias = $(best_config[2])\")\n", "println(\"Best score: $(best_score)\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "regcoef=0.01, bias=true] => 0.011858694727414574\n", "[regcoef=0.1, bias=true] => 0.011850442117052462\n", "[regcoef=1.0, bias=true] => 0.011787552735182201\n", "[regcoef=0.01, bias=false] => 0.011804804972495204\n", "[regcoef=0.1, bias=false] => 0.011801611222973227\n", "[regcoef=1.0, bias=false] => 0.011777881905009186\n", "Best model:\n", " coeffs = [0.9133644779181795 0.09101416771441581 0.5528567682069362]" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " bias = 0.0\n", "Best config: regcoef = 1.0, bias = false\n", "Best score: 0.011777881905009186\n" ] } ], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "# Regression Tree\n", "\n", "using DecisionTree\n", "\n", "# Train model, make predictions on test records\n", "model = build_tree(targets_train, features_train)\n", "predictions = apply_tree(model, features_test)\n", "\n", "@show cor(targets_test, predictions)\n", "@show R2(targets_test, predictions)\n", "\n", "scatter(targets_test, predictions, \".\")\n", "xlabel(\"actual\"); ylabel(\"predicted\")" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "cor(targets_test,predictions) => " ] }, { "output_type": "stream", "stream": "stdout", "text": [ "0.9062575173707162\n", "R2(targets_test,predictions) => 0.8095402909108701\n" ] }, { "metadata": {}, "output_type": "pyout", "png": 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Xy+VysVhsaGhIygfGxcUJBIJRo0bJ5fLIyMjCwsIzZ86Q1YyMjLCEMkLMqqyszMvLA4AJEyYoKrYfOXLk888/B4Camhq8tlMFO0/qo6OjuVzurl274uLiuFzu6tWrye8nTpy4ceMGAOTk5Bw+fPivv/7q0KEDl8vlcrmkqjVCSEWFhYVubm5ubm7R0dHKzrn8/Pz8/f21PLC2B99iQqhtys/Pd3BwUHyVy+X6+k2cNuXm5ubm5urp6fXr10+xYXZ2NgD069evjV3UN6DbbzEhhDSNx+O9++67Dx48SElJoVitcUF5e3t7e3t7DY+uTdH5MJqTk3Po0CEACAoKwnlOSPu+++673NxcLy+vWbNmqb5VYWEheTUzJCRExZi1cuVKuVweFBSkYp7jDh06/Pzzzzk5OVlZWQDQ5Klou3XmzBmS4YmR022dD6NcLpf8LeVwOGyPBbUL1dXVQqEQ/pvBHh8f/++//4aGhrYojD579uy9994DgMuXL6sYRmNjY2UymYmJSYvSxTs6Ojo6Oqq+PiMKCwurqqqMjY0bnOe2HnZ2diKRSCaTFRUV0W9N5/9A2djYDB06dOjQoRSvliLEoG+++cbW1tbW1lYulwOAiYmJiYmJFv6Km5mZmZqa6sTpwpIlS9zc3EaMGMH2QJTq27fv0KFDAwICGKkorPNnowixi8wtaamAgICXL18CgOp3ouhkxkxPTyfF6VauXEmngBJqEoZRhFpm9OjRVlZWQO9uI4fD0WilowYKCgr27dsHAJGRkVoIowsXLhw9ejR5Obs9wDCKUMsEBAQEBAQoW3r+/Pny8nInJydSCV1tVVVVJJOIh4dHi0p91PfFF1+cP3/ezs5uxYoV5A5p47JL9RUXF5NzZD8/PzpXu4MHDybvwrYTGEYRYtKKFSsePXo0efJkmmH03r17pKD8lStX1L7J+OzZs7///tvZ2blfv35kKii1M2fOkNrx2dnZjR9MVVVVkTdlpkyZQnPv2hidD6PZ2dk///wzAAQHB3fu3Jnt4SCkvo8++uiXX34hnxVV4SZPnmxsbHzy5MlBgwa1tEFvb++goCCm/l1UV1dv27YNALp166brYfT06dNVVVV5eXmMzAPT+TDK4/FI4Rcul8v2WBCC3377raamRr3L8PLy8sLCQvLZy8vr3Llzjx49WrVqFQDU1NSo0eDy5cuXL1+u+vojR448d+4cKMlGqq+vT2536sRsAWoCgUAsFtfV1Sn+g9Oh82HUxsamXd2FQa2cGtXcKisrJRKJkZHRW2+9xeFwxGKxUCjkcDihoaFeXl4vXrwAAO1MwCSTTGtra4VCoVQqbZBHzsbGpqysTAvD0II+ffoAgJWVFSMZnnR+3ihCum7y5Mk2NjYhISGRkZE//PCDv7//8ePHnZ2dy8rK3Nzcfvjhhx9++EGbGZfv379vbm5ubm5evwQeooBhFCGEaNH5i3qEWJScnJySkqKvrz9z5ky1G1m0aNGYMWPs7OzI12HDhm3fvh0AeDweM6NsIYFAsGXLFgDo0aMHKwPQORhGEVLfyZMnv/zyS0NDQzphdNy4cfW/+vn5sVvw3dbW9uOPP2ZxADpH58Nodnb2gQMHAGDkyJE44Qk19uLFix9//BEA5syZ06VLF/Uaqaio+PrrrwFg4sSJvXv3ZnB4crl87dq1ADBy5EgyUVTTgoODa2pq3nnnnfnz52uhu9bp9OnTJPN/G6/FpCI+n09K2uGEJ9SkrKwsUm4oKCiIThgljbi4uPTu3Ts5ObmsrMzKymrFihXvv/8+nWRrcrmctMzj8bQTRm/cuCEWiwMDA7XQV6vl6OgoFov19PRwwhMAgLW1tRrTklH7oaenR8446AQ7RSNktvaHH3548+bNESNGXLlyhWZqsQYt11dQUFBRUWFsbKxi9M/OzibP1idNmmRra6tstWHDhtXU1HTt2pXGqHUeeZ3X0tJSh0vaqWLbtm3x8fEPHjyYM2fOrl272B4O0lVDhw5VFGtTm4ODA/1GiFu3blVUVNja2pIiSEZGRspaXrZs2ZEjR7p16/b06VMAqKysTEpKIov8/f0bBEqZTPbTTz/FxMQAQO/evSnC6IULFxjZEaTQesOoQCCIiYk5evQo2wNBqCFvb+/a2lpvb281tl20aFFycvKYMWPI+0KqS09PDwkJIZ9PnDgxadKk+kurq6tJDEXa13rDKDlKLl26xNRZAEJMefDgQVJSEp/PZ7DNwsJCkvhj7ty5ffv2BYCoqKhx48aZm5u3qJ2YmJhevXoxODDUrNYbRhHShOTk5OvXrwPAwoULW5oL7vr168nJyQBQUFBgYGCgXg33ffv2VVVVkYyl9ZWVlZF0JDU1NUlJSYsXLx4wYMCAAQMUK3h6et68eVPxucHmpLLIwIEDFyxYYGiI/661Suf/c9+5c4ek9vL29iY3+0NCQhQzmRFq4I8//lixYgUAzJkzp6VhNCEh4dtvvyWfZ82aRRIhN2vHjh1ZWVkeHh5z5swBAF9f3yZXMzQ0tLOzq6qqOnDgwIEDB95///0Gk0/4fH79qNqAVCoFAHNzc4q7oujkyZOVlZUAcPfuXbFYXFpa2rNnT/rNMhBGP/nkE2WLSN0CjbK2tiZZBrp06WJqagrsvfuBUJMOHjx469atkSNHkjCqjKura35+/qZNmyj+QVGYPXt2UFCQNl+910VdunQRi8UAUFdXJ5FI8vPzW0stJnLvsqys7ODBg8OHD3dzc3v9+vVvv/02fvx4+o03y83NrT3PIkYtFRUVRcIZ+aPbIhs2bCC3L6ElyeJMTEy4XC512nmFpUuXvv/++9DyedAffPBBi9ZvnxR3jcl5fVpaWmuZ8ERevx0zZszZs2dHjhxJfnz27NmyZcvoNCuTyWQymVwul8vlYrHY0NAQ7/gg+jgcjtrpMrlcrhpvefz555+qr0xneIgtjGV4SkxMrF/qwN3d/fXr13QajI6O5nK5u3btiouL43K5irMAhBgUFRXl4eHR4K124vTp0x4eHh4eHnl5eco2T0tLI+u0KFaqqH///h4eHuvXr//4449JL+pVIUWaxtj5naen5+bNm1esWEFeyTh58iTNk8cNGzZs2LCBodEh1LS8vLz09PQmTwDLy8vT09Phv6c3TRKLxWSdqqoqxseWkZFRUlLy+vXrkpIS0otQKGS8F0QfY2F03759YWFhW7ZscXJyKiws5PP5p0+fZqpxhDRk8ODBpNbxxIkTy8vLJ06cGBUVRRa5urpOmzYNKO+iWllZkXVMTEzI7PdJkyapNy2/sUmTJlVUVAQEBChiNE5BaZ306urqmGpLLpf/+++/+fn59vb2vXv3ZiR1CjWhUPjuu++OHTsWAEaPHo0HGWqpp0+fkvkkhw8frqmpWbRo0ffff0+9SUVFBblh5erqqjjI09PTyVPyQ4cOTZ8+nWLzL774guQi2bt377x58wDg8ePH2qxZjwDg+PHjlZWV+fn5xsbGK1eupNkakw9tLl68ePz48dLS0jNnzty5c6esrEzxxElzzM3Nu3fvDgDMvlKC2omCgoL9+/cDgK+vr4WFhSoJOw4fPrxgwQIAyM/PV+Mvt1QqFYlEACCTyciH2traljaCaHJ3dxeLxSYmJgUFBfRbYyyMxsbG7tmzZ9GiRV999RUAmJqaRkVFaSGMWllZ9e/fX9O9oLbKxMTEyckJAHbv3k3nQOrWrRuJhs3mkRo4cCCZ/+/t7U0+0MwRhdRA8sKYmZkVFRXRb43JMHrz5k1HR0cy/8nDw4PcFEeo9Vi+fPmtW7d69Oixe/du8kv//v2zsrJa1MiIESMOHjwIAJaWlvV/VzERX3BwcHBwMPnMbAZoxBbGwqhIJKpf27q6uhrzKKPWJi0t7ebNmxRP3lXRtWvXdp6sEzXA2LzRYcOGrV27Vi6XA0BdXV10dHT9aaQIqe7hw4cpKSn5+fmMt9yzZ88RI0aQ/ElqKygoSElJefDgAQD06dPH3d0dZ+a1c4ydjW7fvj0sLMza2rq6urpTp05ubm4JCQlMNY7alUGDBpWXly9ZsmTr1q3Mtvzll182/vHy5cskafzWrVtVuTCPjY3dvHkzl8utrq5+/vx5aWkpI4UokO5iLIza2dn9/fffqampWVlZjo6Ofn5+jYsiaMKLFy/i4uIA4O2337a3t9dCj0g7zp07l5mZGRUVRe4k1tbWkmud+rkkyI96enrU73rI5fLa2lplq92+fZsUNB4yZIivr6+7u7vqg5wyZUpVVZWPjw+ZJe3v7692uSekTUePHiUTnlTMdUCNsUh34MABPT09Pz+/cePG9ezZU19fnyRP1DQLCwtfX19fX181Mk2g1unQoUN79+7NzMw8d+6c4vnP+vXrjY2NjY2N6890joqKMjY2bnbW0axZs4yNjZVlP+JyueRZ+eTJk0+dOtXs8GbOnHnq1ClSl2H37t2HDh0aN25cWFhYWFjYxYsXVdxHxC5PT09fX99u3bq1rsqga9asmTFjRv1fVq5cSWYXa5SVlRXNW12otQkNDS0sLNy5cycA2NjYaLq75cuXL1q0SPX8ij169OjRo4dGh4Q0zc/PDwD4fD7N1B8EA2H07t27ACCRSMgHIiMjw8zMjH7jqH2ytbUleeYVhg8fTi7J69++HDdunL29fbNvXkyZMsXT07PB/KT6TExM7t+/DwDq3ReytrYmmwsEAjU2R7qOgTA6YcIEACgsLCQfAEBfX9/W1pbx5wOoPRs8ePDgwYMb/BgSEqKo8kZh3LhxTeZwUiD3o9Qem6Gh4fnz58+cOWNra3v27Fm121FbZmYmeQP1q6++euutt7Q/gHaOgTCam5sLAAsXLiRXYQixSCKRvHjxAgDs7e21eT30/Pnz27dvOzg4aK3H+qqrq2/fvg0AJSUlrAygnWPs3ijGUKSeqqqq0tJSABAIBCq+CEQhIyOD3Lg8evTo1KlTGRifary9vUNCQuq/gaJNZmZm5Ky8c+fOrAygnWMsjM6ePfu9995TXHZdv349Pj6eTEXSqBcvXpApAaGhoTjhSRf9+OOPS5cuBYA3b95Q3L5s5ZYuXUr2ghXOzs44SaBFjh49WlFRkZ+fz8jLloyF0TNnzuzatUvxtW/fvhMmTNBCGO3QoYMiy4Cm+0Ktn0Ag+OWXXwAgMDCQ7bGg1svLy6umpsbc3Jzck6SJsTBqaGgokUgUWcQlEgn9CzRVWFpaksqgSEcFBweTP7eNH7i/efNmzJgxALBy5UrFA0xq5ubmERERjX//5ptvTp48aWlpSV5YahKZkbpv377169cDQEpKirm5uWKRho5nzbWMKJAy1zwe79WrV/RbY/Kd+lWrVtXU1ACARCJZtWoVxRPDysrKadOm8fl8BweH+uew9T179iw4ONjCwsLOzu7jjz8mb7Cgtqd79+5z5syZM2eOsbFxg0USiSQpKSkpKYn+sZ6ZmZmUlHTnzh1lK1RXV+vr6+vr669evfrly5cvX748efIkWTRz5kx9fX3GX0/666+/goKC9PX1LSwsmG0ZaRljZ6Nbt24dP358p06dBAJBbm6uh4cHxTv1y5YtKy4uzs3NffLkSUhISI8ePRrPZZk5c6aPj8+5c+cKCgqGDx/etWtXLCHb3nA4HPImqKOjI82mvLy8goODVcns6ezszEgq32bFx8dfvXpVCx0hTWMsjNrb29++fTslJSUnJ8fR0dHf31/ZpYpUKj18+PDFixctLS0DAwPDw8Pj4+Mbh9G0tLSNGzeamJh06dIlKCgoLS2NqaEiVkRFRV28eLFbt24Ul9UNWFpaXr58WZU1vb29a2pq5s+fv3z58iZXWLx48eLFiylaMDY2jo6OBoC+ffuSk9aePXuSRWFhYa6urhp6/GVhYfHpp59qomWkNQyE0Vu3bnXp0oVM1gMAW1vbmpoaMoutyXTiL1++FAqF5N4EAPj5+R06dKjxamPHjj1w4EBAQEBeXt7Vq1d/+OEH+kNFLCooKMjIyNBQEfaMjIyampri4mK1WzA0NFy7dnJiWb8AACAASURBVC35PHr06PqLxo8fP378eFrja0psbOwXX3xhYGBga2vLeONImxgIo9OmTVu3bt2aNWsaL3r58mXjH0mZQ8WDdQsLi8rKysarbdq0KTg4mDx2WLhwobJ6JElJSe+++y4A+Pj4kEu2sWPH4synVmjIkCEcDof+5XkDdXV1Dx8+9Pb2tra2Vpw/NnD69OmLFy8aGRk1W65OmYqKCpIkxd3dnZGcQABgYWGBd0W17PDhwxUVFQDwzz//iMXi8vJyRjJyMBBGSaxskJeEAknFVFlZSR6DlpeXN56rJJVKhw8fPnPmTHIXNSIiYu3atevWrWvcmoODw6hRowDA2dmZZJfAmU+t04cffqiJZkUiEbmy2bRpU3h4eJPr3L59e8+ePRwOR+0weuTIkfnz5wNASkqK4kIK6RwfHx/yGNzS0lIikeTl5TEyTYLJyqAqcnFx4fF4qampgwYNAoCUlJTG+XJevnyZnp6+ZMkSExMTgUDwzjvv7N+/v8kw6uTkRM5GEVLG3Ny8c+fOdM4ijx8/zuB4EFu8vb3JB1IFKy0t7cqVK/SbZSCMfvLJJ8oWkQrgDRgZGUVERKxbt+7o0aNPnz49duzYr7/+ShbFxcUJBIJRo0Y5OTlZWlru2LHjo48+KikpOXz4MJljj9qw4uJiqVSqyP5ZX0xMTFJSUteuXRufTnI4nBMnTgCAj4+PspY/++yzzz77jP4IPTw8XFxc6LeD2hgG5o3KZDKZTFZcXLx169bU1FShUJiZmfndd99lZ2cr2yQ2NtbS0lIgEISFhW3cuFHxmP7EiRM3btwAAA6Hc/bs2dOnT1tZWfn6+jo7OzcZkVFbMmTIEHt7+/fff7/xouTk5EuXLt28ebPB73K5vKqqKigoaMKECd26dWuy2ZqamvLy8vLycjpjW7du3enTp7/55pu6ujqaFfFQG1THkNDQ0EuXLim+pqenjxkzhqnGlamqqlq6dKmme0HqEYlEI0aMGDFixLFjx1RZ38vLCwCmTJnSeNGKFSv69es3Y8aMBr///vvv5DC+fv26sma/+uorso5IJGrR+BvIzMwk7ezfv79FG5LyjvPmzaPTO9KEhw8fbt26lX47jN0bTUxMPHPmjOKru7s7I2mlkYb89NNP5P2xW7duaahqllwuJ9PLVUkJCgDR0dGlpaVubm6KX/755x+Sd2b9+vVff/11g/Xr6uqop4Kq7caNGwcOHACAr7/+WpUZ+9RSU1OvXr2K/xzaMMbCqKen5+bNm1esWEFqm5w8eZK6yhhTMjMzyZTScePGsZXtURfl5eX9888/8N+L5JpgYGAwcOBAUDknfOO8dhkZGSSMfvzxx506dWqwtK6u7unTpwAwatQoV1dXZc0GBQWRyar1a+FRe/LkCel3zZo1ijBqbW397bffAkBRUdHPP/8sEAhUTJDs5eU1cODA+n8eEOsOHTpUUVFRUFDASA03xiLdvn37wsLCtmzZ4uTkVFhYyOfzSa1ETbOysiKT/HEKXou4u7uTSeaay4thYmJC7nSrjcfjkXJ1Tf5J1tPTI7sQGRlJMVM4ICAgICBA9U6fP3/+/PlzfX39Tp061a93Zm5u/tFHH5EG//3339DQUBXDaIMq9o8ePSLPVN9//336p7pIPT179qypqXn+/DnFI5wWoH9fQEEmk92+ffv06dO3b9+WyWQMtqwM3htFjCPzQzt16qRsBTJXJjQ0VL32ye0CAMjIyFB3jIgZTN0bZfKm2MWLF3fu3Llv376+ffv++++/Kr4NjZB2PH361N7e3t7eXpWX+ouKiuzt7RX1xF6/fk22PXTo0JkzZzIyMrSQSxfpCsbCaGxs7PLlywMCAkh9UFNT0//9739MNY4QfVKptKCgoKCgQCQSUay2dOnSixcv1tbWFhQUKF5TlsvlZFuhUOjg4ODm5qZ2uY7p06cLhUKhUEhxPxfpFsbujcbGxt68edPR0XHLli0A4OHhkZ6ezlTjCNFnZWU1b948AKCOX56enh4eHmTNXr16kR/5fD75hczKosPAwIC8tYzaDMbCqEgkql/Pq7q6mpEiJwg1SyqVSiQSaCp/fn329vZ79uyhWEEkEtXW1hoZGRkbGzdY08LCgnpb1J4xmf1+7dq1JEd93X9TjplqnMLz58937NixY8cORmqqIApxcXHBwcFjx45lttnXr1+7ubm5ubkdPXpUvRY2btxoampqampKs0RCjx49TE1N586dS6cRpBN+/vnnHTt2HD58WCwW02+NsbPR7du3h4WFWVtbV1dXd+rUyc3NjSL7PYNsbGxIihOcO6Jp6enpV65cYfwiQyaTkReE9u7d+/DhQ1IHCSGN6tOnD5nw1GQyz5ZiLIza2dn9/fffqampWVlZjo6Ofn5+Gno3pgELCws/Pz8tdIRcXFwGDBjAVLZNBR6P9+677yYlJV29evXBgwcUYfS7774jye2LiooUxeYAICgoiIyK5iH36aeflpWVNc43plHl5eVk0uvXX3+9aNEibXbdnnl6egKAoaFhTk4O/daYCaMSiaRXr14PHz708/PDoNZWLVy4cOHChYw3a2lp+fPPP7/33nvPnz+nfhdAJpORe6B1//fNq8DAQEbKKZOHSFpWV1dXXV0NAJjxRHcxE0aNjY05HI5EImlc3BEhVRgYGDT7CLtv374kKyPjZ8QsMjExIafYLXrVCrUqjF3UT5o0adKkSR9++GHnzp0V5xSKJKkIUdu7dy95jZ2oqakhxR6srKwUb2QOHDiQvKRPraqqSiQS6evrW1tbt2gMEomE5NOzsLBQJNYzMDCwsrJqUTstYmJisnnzZs21j7SAyUdMABAZGVn/R/rlxVH7lJCQMG3aNAB49OhRS6dqfvLJJzt27LC2tm5phbtLly6NGzcOAH777bfQ0FDyo729fV5eXoM1ExMTybyCr7/+mnqWFWoPGAujbEXM58+fk4zoEyZMUDGTEEI0paam7ty5EwBiYmIwjOqiAwcOkAxP9Z9Vqo2FWkzMsrGxGTJkCOCEp7ald+/e5K+jGu9choeHe3l5KZuY9fnnn1+7ds3NzU2RIkTBx8eHdOrt7a2oVmJqanr58uW///6bw+EoCspzuVwbGxugPTcAsaVv374SieT58+eKyvC00M9uQojF4vXr13fv3r1Dhw7du3dfv359TU0NU40rgxmeUEuFhYUBgLe3t+qbLFu2DABMTU01NyrEilaX4enDDz+8e/dubGzstWvXvvjii3PnzkVFRSlbubKyctq0aXw+38HBgeRgb9L+/fs9PDw4HI67u/udO3eYGmr7dPfu3YSEBJKOXlfcv38/ISHh0qVLAPDHH38kJCSQVNPK5OXlJSQkJCQkVFVVKVvHz88vKChIxQlS2dnZCQkJz58/b+nIUftCPxITPj4+crlc8bW4uNjKykrZynPnzh0xYsSbN2/+/vtvMzOzxMTExuucOXPGzs7u/PnzJSUl9+/fz8rKarwOno2qjuSW9/T0ZHsgLUAmctrZ2dVRVmpSOHLkCDmqHz16xMgAFNnwsrOzGWkQtSqt7mzUzMysrt6k6NraWmXTTaRS6eHDh6Ojoy0tLQMDA8PDw+Pj4xuvtnbt2q+++mr06NFWVlZ+fn5OTk5MDRUxbtasWTY2NkOHDp0/f76NjQ2pRwAA69atGzBgQOPqIERaWtqqVatWrVpVUFCgxcEixDDGHjEFBwdPmDBh4cKFnTp1evHixebNmxcvXvzw4UOytP4E0pcvXwqFQl9fX/LVz8/v0KFDDVqrqalJSUnJyMhwcHCora2dNGnS5s2bMWUUHV9//fWnn36qoYnrFRUVJSUlpaWl5IPiL+izZ8+SkpKUJaZLT08nheqmT59OioU0sGbNmoULF5IaSqdOnRKLxZaWlhTDCAkJuXfvHjSXCk91EydOJLnu1c4uitoDxsIoucU5e/ZsxS9ffvnll19+ST7Xnw5F7luZmZmRrxYWForkuAp5eXl1dXXXrl1LTk6WSCTjxo3bsGHDF1980bjfGzduTJo0CQD8/PzIw/qwsDCc+dSYs7Ozs7Ozhhp/++237e3tHRwc7OzsrK2tFeXnfHx8goODmyyUNHz48D///FNfX9/Y2FjZ826BQKD4X0legqbWoUMHf39/dXeiCVZWVhqde4+0LD4+nrxYcefOHZFIVFFRMXjwYPrNsjBvlJTiq6ysJDO2ysvLFSFVgZx4Ll++nPyDXLp06datW5sMo66urhEREQDg4OBAtqI+YUEtUlJSsnr1agCYOXNm//798/PzSeqQ+fPn1w9Y9ZPLzZo1S/F55cqVK1eupGh/4MCBiYmJjA8boSYFBgaStAxubm5SqTQ7O7umpoZ+syzMG3VxceHxeKmpqSTBXUpKSuOcOnZ2djY2NqoUrbS3t58wYYJGBooAKioqyHVG//79+/fvX1JSQr4GBQXRPO+bNWvW0KFDKU6QCwoKpkyZAgCrV68eNWoUnb4QIrp160Y+kNuMaWlpV65cod8sC2HUyMgoIiJi3bp1R48effr06bFjx0i9WQCIi4sTCATk38zs2bO3bNkyYMAAqVS6ffv2MWPGaH+oyMDAwNbWtrCwcNu2bSdPnqysrOzYsaOent7evXvj4+M7duz4448/qtfyjBkzqFcQi8U3b94EgNevX6vXBULawc5bTLGxsXPnzhUIBBYWFhs3blTcnjhx4kRAQAAJo+vXr4+KinJzczMxMQkPD//8889ZGWo75+TklJOTw+Fw7t27R57eFBcXW1tbz5kz56efftLoPWgul0sKKFDUoC8rK0tJSQGAnj17MvJWH0JqYCeMmpmZNVkx4uLFi4rPHA5nz549WACHdfr6+j179lR8NTQ0BABnZ2dra+uSkpIFCxZQvEBBR+fOnZu94Lp7925wcDAAXL9+ndwjQkj7dP6deqRphoaGycnJDX5cs2ZNTk5OXFzcuXPnqMNoeXl5VlYWAJAX0jQ4UIRYovNhNCMjY+vWrQAwadIkR0dHtofDptTU1EePHhkYGJAnM5pmb2/v5eWlmNukzIULF8hUisePH6syaalFevfu/ccffwCAj48Psy2jtm3fvn3l5eWvXr1iZGKPzodRW1vbkSNHAkBLc/S2Pb/88sumTZs4HI52wmhMTExMTAydFvbs2VNWVubt7f3222+r14KlpeVbb71FZwyofRo8eLBEIsnIyMjIyKDfms6HUXNz8+7du7M9CqTUoEGDTp48CQCNn0dt3LjxxYsXkZGRaodRhNTTtWtXAKirq2MkUZ7Oh9H2rLq6euLEiQCwYMGCiRMnfvzxx7NmzWrwRtDZs2d37NhBPnfo0EGVWvD//PMPmXK/ffv2jIwMUtfgyJEj6l3+1H8TCaE2CcOoDpNKpZcvXwYAMkWsY8eOHTt2bLBOdnY2WYesoEqzRUVFZJPy8vKcnBzymZGXPRp49OhRbW0tefSPkO7CI1iHGRoaklxKFDMr7ezsFPmWVHw93NLSkmxiZmbWuXNn8lkTNV/bUoFP1J5hGNVhfD4/KSmJeh1SsbVFzQYGBiqa9fLyov+ubVFRUWZmJgD06tWLpGtCqC3R+TCakZERGxsLAJMnT27nE55arYSEhPfffx8AcnNzHRwc2B4OQvDTTz+RCU+MZPDS+TDaqVMncmcQJzwhhFQ0dOhQMuHp2bNn9FvT+TBqZmbW0jrmiCmrVq16+PChv79/kzkMFUaPHn3+/HkAINU0EWKdm5sbANTW1pLbTTTpfBhFyty5c4e8k758+XLGHxDNnj07MTExPz9fLBaLxWLqlVWc8zRx4sSbN28aGhpyudzt27eHhoYyNFiENAvDaJt148YNkhZr8eLFjIfR/Pz8zMxMPp8fEBDA1CuepaWlRUVF5HPjgggItVoYRpE6goKCbG1t3d3d16xZw1SbEyZM8PT0FAqFAODi4sJUswhpGobRVqe4uLisrMzAwKBLly502lm6dOnixYtBM1M+V6xYwXibS5cuZbxNhLRA58Pos2fPvvnmGwCYMmVK2yjCvGHDhm3btllYWJSVldFph1SLY2pUCLUlcXFxZMITI489dT6M2tnZkfoi+BQYIaSiESNGkAlPT58+pd+azodRU1NTDw8PtkfBpMjIyICAgNZwItmrV6+nT59OmTJl//79bI8FISaRO2YymYyRRHlN1wfXtMrKymnTpvH5fAcHB+rc6dnZ2aampu2qMGTv3r3ffffdqVOnsj0QqK6urq6ubnY+E0LtHDtno8uWLSsuLs7NzX3y5ElISEiPHj0UVe0aiIqKCggI0PLwEDFnzpzCwkKahZQRavNYCKNSqfTw4cMXL160tLQMDAwMDw+Pj49vMoyeOXOmtrZ2zJgxjNSSRi2licfxCLU9LFzUv3z5UigU+vr6kq9+fn5paWmNVxMKhatWrSI5g5EaamtrFyxYsGDBggsXLrA9FoTaMhbORquqqgDAzMyMfLWwsGjylZWYmJiIiIhm507+9ddf5Em9n59fhw4dACA8PLxtzHyiSS6X7969GwAcHR1Hjx4dHR2dl5fXu3fvBQsWsD00hNixd+9eMo/w9u3bYrG4srJy+PDh9JtlIYyampoCQGVlpbm5OQCUl5crQqpCWlra2bNn79+/32xrXl5es2bNAoDOnTuTNMAq5nhv8/T09EjZTvIf/MSJE2lpaaWlpdoPo2VlZdevXweA/v374/8dxKLg4GCpVAoAffr0kUqlWVlZNGdnEyyEURcXFx6Pl5qaOmjQIABISUnp0aNHg3WuX7+elZVF8lmIRCKpVCoQCHJzcxu3ZmtrGxwcrIVh6xxDQ8NXr14pvnbq1Km0tJSVdIJPnz4dN24cAJw/f3706NHaHwBChOIlY3d3dwBIS0tj5LkLC2HUyMgoIiJi3bp1R48effr06bFjx3799VeyKC4uTiAQjBo1KjIyklyqA8Du3btv3Lhx8OBB7Q+1Lbl69SrbQ0CobWJnwlNsbOzcuXMFAoGFhcXGjRsVj+lPnDgREBAwatQoPp/P5/PJjxYWFhwORxezpt+4cYO8qPr999/r4vjr/+kKDQ1VrzIoAPj4+Ny9exf+OwVAqI3Rq6urY3sM6hMKhatXryZFRFqhI0eOREREAMDjx4+ZyianTXp6eorP9+7dwwmkqI0hF/VLliyh2Y7OvwzamllaWpJ5XW2jBGZpaSkAcLnctrE7CDFF589Gp0yZMnToUACYNm2as7Mz2yNqFUpKSgCAz+erEu/KysrkcjmHwyEP9KuqqmpqagwNDS0sLOq/biwQCLhcLgDExMQwmGMUIVbs3r27rKzs9evXnTp1WrVqFc3WdP5sVCAQTJ48GQBsbW3ZHotWVVRUJCcnA4CBgYFcLjczM+vduzcAVFdXk2RXmzZtWrlyZbPt9OnTJyMjY/r06YcOHQKAWbNmnTx5skePHg8fPuzatatitdraWk3tCUJaN3r0aKlU+uzZs8ePH9NvTefDKJ/PJ9Wp2puHDx++9dZbAGBiYiIWi/39/e/du6e57vT19bdu3QoAgYGBmusFIe0gb+iIxWJMlIeaYGxsTN6gHThwoCrrr1mzpqysTJFscM6cOUOHDm1yein9O/EItUkYRrWqurqaTIkXCAQ0M4p6e3v/+eefUO+invxuaGgYFRWlejuRkZH1v+L0eIRaCsOoVv3+++8TJkwAgOTk5J49e6rXiEwmO336NAD4+/vjTEyEWMdO2mZER3V19dSpU6dOnXru3Dm2x4IQ0v2z0adPn27cuBEAIiIiWv+Ep169esXHxwMWEEaIVbt27SITnjp37ky/NZ0PowKBgNTbINmMWjlHR8cZM2bQbMTU1PThw4cAwMgRgFA7FBoaSiY8PXr0iH5rOh9G+Xy+q6sr26PQKn19/cY5sRBCqnN0dAQAkUj05MkT+q3hvVGEEKIFwyhCCNGCYRQhhGjR+XujSA0bN24sKSkJCAgIDw9XY/O4uLinT586OzsvXryY8bEhpHN0PsPT5MmTSdbnd955p/VPeNKQX3/99dtvvwWAffv2zZ49GwCioqImTpyobH1XV9cXL15ERkYeOHBAje5CQkIuX77cv3//pKQktceMEIt27txZVlZWWFjYuXPnTz75hGZrOn826uTkNH36dNCRCU8akpeXR14MraqqIh/Iu1LK8Hg8Ho/H4XDU687ExITH45G8eQjpovHjx0ul0vT09Caru7eUzodRHo+HU9nt7OzIKTmfzycfqGuWkGmnajtz5gydzRFiHfkHIhQKGUmUx84jpsrKymnTpvH5fAcHh127djVeQSKRfPDBB66urjwez9fXF//dUhs3blxiYmJiYqKLiwv5MGnSJLYHhVB7wc7Z6LJly4qLi3Nzc588eRISEtKjRw9FVTtCIpEYGxsfP37cxcXl1KlTU6dOffjwIabhQAi1QiycjUql0sOHD0dHR1taWgYGBoaHh5PXzOszNTXdtm1b7969ra2t582b5+TkRDK9I4RQa8PC2ejLly+FQiGp9QYAfn5+pHyFMq9evcrKylKsj9gVHR395s0b8nnIkCGkggtC7RkLYbSqqgoAFGmGLSwsKisrla0skUimT58+f/58Ly+vJlf4448/goKCAMDf379Dhw4A8O677+JDJ8356aefcnJyyGe5XI5hFOmQ77//vqysDABu374tEomEQuHIkSPpN8tCGCUVKCsrK83NzQGgvLxcEVIbkMlk4eHh1tbWpApQk/z9/T/44AMAsLGxIfnkqWc+vX79moyBz+fT2In2q0OHDuQPIQDgf0OkW8LCwmQyGQCEhITI5fLMzExSjYImFsKoi4sLj8dLTU0dNGgQAKSkpDSZr0gul0+fPl0mkx07dszAwEBZa1ZWVv3791e9d5Jcbu3atdHR0S0eOgJITU1lewgIqcne3p58ICXtzMzMioqK6DfLwiMmIyOjiIiIdevWlZaW3rp169ixYzNnziSL4uLiLl68CAByuTwyMrKwsPDgwYNyuVwsFsvlcu0PFSGEmsXOvNHY2FhLS0uBQBAWFrZx40bFbKcTJ07cuHEDAHJycg4fPvzXX3916NCBy+VyudzvvvuOka537ty5c+fOMWPGMNKahly6dMnHx8fHx+enn35KTExkezgIISrszBs1MzM7evRo49/JqSgAuLi4aOhlf3IjtZUrKysjLxrNmTPH19c3JSWF7REhhJTCRHmtkaOjY1hYmOI+DkKoNdP5d+ofP368bt06AJgxY0abmec0YMCAAQMGZGZmlpaW8ng8toeDUFvz3XfflZaWFhYWCgQC+q3pfBh1cXF57733AKBjx45sj4Vh7a3GFEJaM2XKFJlMlp6ezsgdM50Po1wul5G/J21MXl5eTEwMAHzwwQc9e/ZkezgItS5k4mN5ebm+PgM3NnU+jLY9d+7c+fHHHwFg/fr1tra2arTw22+/7d+//8SJEwAwatQoDKMIaRQ+Ymp1nj17tmfPnj179pSXl6vXwr///ktiqJ2dHSZXRkjT8Gy01eFyueSKw9BQzf87ZmZmpIW8vDw9PT0mB4cQagTPRludsLCwgoKCgoKCLl26qNfCRx99RFrAGIqQFuj82ejjx4/Js5QZM2aoHXcQQu3K9u3byYQnR0dH+q3pfBh1cXGZO3cuANjY2LA9FoSQbggPDycTnu7fv0+/NZ0Po1wul7p8G0IINUDSaZaVlTEy4QnvjSKEEC0YRhFCiBYMowghRAuGUYQQokXnHzE9evRo7dq1ADBr1iyc8IQQUsXWrVtLS0uLiopwwhMAgKur64IFCwDA2tqa7bHotp07dx49epTH4124cIHtsSCkWe+8845MJnv69Om9e/fot6bzYdTExMTOzo7tUbQFz58/T0xMJHVbEWrbSF7NN2/etPEJT5WVldOmTePz+Q4ODrt27WJ7OG1f165dhw0bNmTIELYHgpCOab1no8uWLSsuLs7NzX3y5ElISEiPHj0Ule+QJnzwwQc6UagKodamlZ6NSqXSw4cPR0dHW1paBgYGhoeHx8fHsz0ohBBqQisNoy9fvhQKhb6+vuSrn59fWloau0NCCKEmtdKL+qqqKgAwMzMjXy0sLCorK5tc8/fffycX+35+fpaWlgAwe/Zs1asYXb58eeTIkQyMuOVevHghk8nc3d1Z6Z3FHc/JyamqqvLy8mKldxZ3/NWrV4WFhYqTAy1jcceLi4uzs7N79erFSu/1d/zbb78tLS0FgNu3b4tEourq6tDQUPpdtNIwSp4XV1ZWmpubA0B5ebkipDbQr1+/jz76CAA6dOhgZGQEACSYqujEiRNsHVtPnjwRi8VshVEWdzwzMzM3N5etMMrijufk5Ny/f5+tMMrijr969erGjRtshdH6Oz5jxgy5XA4A06dPl8vlGRkZmZmZ9LtopWHUxcWFx+OlpqYOGjQIAFJSUnr06NHkmubm5t7e3todHUJIJynSaZIMT3p6ellZWfSbbaX3Ro2MjCIiItatW1daWnrr1q1jx47NnDmzyTVzcnJkMpmydoqKiqin1+bm5lIsvXfvXlFRkbKlUqn0zz//pNj88uXLFEtfvXpVUFCg9ubXr18XiUTKlpaWlv7zzz8Um1Pv+IMHD+iMjXppYWFhfn6+2psnJSUpu8MDAFVVVX///TfF5tQ7/vjx45ycHLXH9vvvv9fV1SlbWlJSQt07deP//PMPuSBtklgsTkxMpNicuutmz8uox3bt2jWpVKpsaWlpKZ0dv3//fmFhobKlcrn86tWrFJtTd82IVhpGASA2NtbS0lIgEISFhW3cuFHZbKfMzEyJRKKskfz8fOp/VC9evKBYevPmTYpoIpFIzp49S7E5qSunTG5ubnZ2ttqbX7hwQSgUKltaXFx87do1is2pd/zOnTsUY6urqzt58iTF5tQjLygooO6devMrV66UlZUpW1peXv77779TbE7d9f379zMyMtQe26lTp2pra5Utff36NZ0dT0xMpIgmdXQ+3gAAB9tJREFU1dXV58+fp9icuuu0tLTHjx+rPbZz586JxWJlS0tKSp4/f65240lJSXl5ecqWSqXSM2fOUGxOveOMaKUX9QBgZmZ29OhRtkeBEELN0KO4Bmn9pFJpaGiok5OTsje6qqurKysryX2QJt2+fbtfv37Klr5+/drMzIzH4zW5tLa2Nicnx9nZWdnmL168oMiWkpOTU1tbS2dzOzs7ZdVDxWJxaWkpxWuy1DteVFTE4/H4fL56Y6NeWlBQUF1d7ebmpt7meXl5HTt2NDY2bnKpRCIpKiqiKIhAveMlJSVGRkbkwaYaY3v58qWLi4uypUVFRaWlpd26dVOv8YKCAktLSxMTkyaXymSygoICikQb1DteWlqqp6fXoUMH9caWnZ3t4OBgYGCgrPFXr15RPFSkbpzmP0OKHReJRNOmTaP/sF63wyhCCLGu9d4bRQghnYBhFCGEaMEwihBCtGAYRQghWjCMIoQQLRhGEUKIFgyjCCFEC4ZRhBCipf2GUa3Vemq2I4lE8sEHH7i6uvJ4PF9fX+oXhBnvXSE7O9vU1HTUqFFa7nr//v0eHh4cDsfd3f3OnTva7P3Zs2fBwcEWFhZ2dnYff/wxSaGmCdu2bevVq5eRkRGpYqtRzfalueNN9d1k/GBTvXdNHG+t9516TdNaradmO5JIJMbGxsePH3dxcTl16tTUqVMfPnzIVB5S1XczKioqICCAkU5V7/rs2bOfffbZjz/+2K9fv5ycnBbliqXf+8yZM318fM6dO1dQUDB8+PCuXbtqqBqVQCCIiYnRTo6IZvvS3PGm+m4yfrCp2Lumjre6dkkikfD5/OvXr5Ovc+fOnTNnTivpqGvXrkeOHNFy7wkJCWPGjNm8eXNISIg2u/b399+/fz8jParRu7m5+V9//aVYZ9GiRYyPpL5FixbNnz9fo12o0ReDx5uKXTN+sKneu4aOt3Z6Ua+1Wk8t7ejVq1dZWVlMJUhXsXehULhq1art27cz0qnqXdfU1KSkpGRkZDg4ONjZ2S1evJgigyrjvQPA2LFjDxw4UF1d/ezZs6tXr44dO5aR3nUIs8ebKjRxsKlIc8dbOw2jqtd60mZHEolk+vTp8+fPZ6rAhoq9x8TEREREUKTY0VDXeXl5dXV1165dS05OvnPnzs2bNzds2KC13gFg06ZNf//9N5/P79at2+jRo9mqscEWxo83VWjiYFOR5o63dhpGFbWeyFeKWk9a60gmk4WHh1tbW2/dulWbvaelpZ09e3bVqlVMdap611wuFwCWL1/eqVMnR0fHpUuXUiceZrZ3qVQ6fPjwd999VyQS5eTkpKamrl27lpHedYImjrdmaehgU5Hmjrd2GkYVtZ7IV4paT9rpSC6XT58+XSaT/fLLL8qSNmqo9+vXr2dlZQkEAhsbm7Vr1/7xxx8CgUA7XdvZ2dnY2Ojp6dHvTo3eX758mZ6evmTJEhMTE4FA8M4771CXsmhLNHS8NUtDB5uKNHe8tdNHTHV1dXPmzAkODn7z5k1SUpK5uXliYqKWO9q7d++FCxfq6upkMllERMTQoUPLyspEIpFIJJLJZFrrvaqqKuc/q1evHjZsWG5urna6rqurW7FixeDBg4uKivLz83v16rV69WpGulald7FYbGlpuWnTJolEUlBQMGTIEM09/5FKpSKRaMGCBXPnzhWJRFKpVEMdUfSlheOt2a41d7Cp0nudxo639htGKyoqpk6dyuPx7Ozsdu7cqf2OQkJCPv/887q6usa1YmJjY7XWe33MPjxVpWuxWDxv3jxzc3NbW9uoqCiRSKTN3q9fv96/f39TU9OOHTtGRkaWlpYy1XsDn3/+ef3/v6tWrdJQRxR9aeF4a7br+hh/Uq9K7xo63jD7PUII0dJO740ihBBTMIwihBAtGEYRQogWDKMIIUQLhlGEEKIFwyhCCNGCYRS1QQKB4O7du+pt6+LicuvWLWbHg9o2DKMIIUQLhlGkM9asWePs7GxmZtarV6/r16+TH/Pz86dOndqpUycrK6v33nsPABYuXPjq1atx48a5uLgcOHAAAAwNDV+9ekXWX7p06SeffELRIEIt1X6z3yOd4+npefv27Y4dO8bHx0+dOvXFixfGxsbjx4/v06dPRkaGsbExqQmxc+fOs2fPJiQkNJtfvXGDJiYmWtkV1Kbg2SjSGdOnT+/cubOBgcF7773H4XCePn2akpLy7Nmz2NhYMzMzDofT0jIwjRvU0MhR24ZhFOmMAwcO9OnTx9HR0cXFJT8/v7i4OCcnRyAQcDgcphpkdsConcCLeqQbnj17tmTJksTERB8fHwBwdXWtq6tzdHTMzc0lNdrqr6yv/3/OD/h8fk1NDflcUlLi4OCgrEEt7QxqW/BsFOmGiooKHo9HClieO3eOZHvz9/d3d3f/+OOPq6qqampqFI+JbG1tMzIyFNv6+/tfunQJAJ4/f37u3DmKBhFSA4ZRpBt69+4dERHh7+8/ZMiQy5cvk2z2enp6CQkJ+fn5Li4u9vb28fHxZOXPPvtsxYoVlpaWe/fuBYCtW7f+8MMP/v7+y5YtCw0NpWgQITVgvlGEEKIFz0YRQogWDKMIIUQLhlGEEKIFwyhCCNGCYRQhhGjBMIoQQrRgGEUIIVowjCKEEC0YRhFCiBYMowghRAuGUYQQogXDKEII0YJhFCGEaMEwihBCtPw/yLveAbpU5JoAAAAASUVORK5CYII=", "prompt_number": 45, "text": [ "FramedPlot(...)" ] } ], "prompt_number": 45 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# ML Algorithms\n", "## Supervised Learning - Classification" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Support Vector Machine\n", "\n", "using LIBSVM\n", "\n", "features = array(iris[:, 1:4])\n", "labels = array(iris[:Species])\n", "\n", "# Hold out 20% of records for testing\n", "n_test = int(length(labels) * 0.2)\n", "train_rows = shuffle([1:length(labels)] .> n_test)\n", "features_train, features_test = features[train_rows, :], features[!train_rows, :]\n", "labels_train, labels_test = labels[train_rows], labels[!train_rows]\n", "\n", "model = svmtrain(labels_train, features_train')\n", "(predictions, decision_values) = svmpredict(model, features_test')\n", "\n", "confusion_matrix(labels_test, predictions)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 46, "text": [ "Classes: ASCIIString[\"setosa\",\"versicolor\",\"virginica\"]\n", "Matrix: 3x3 Array{Int64,2}:\n", " 15 0 0\n", " 0 9 1\n", " 0 0 5\n", "Accuracy: 0.9666666666666667\n", "Kappa: 0.9459459459459458" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "# Random Forest\n", "\n", "using DecisionTree\n", "\n", "# Train forest using 2 random features per split and 10 trees\n", "model = build_forest(labels_train, features_train, 2, 10)\n", "predictions = apply_forest(model, features_test)\n", "\n", "# Pretty print of one tree in forest\n", "print_tree(model.trees[1])\n", "\n", "confusion_matrix(labels_test, predictions)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Feature 4, Threshold 1.0" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "L-> setosa : 25/25\n", "R-> Feature 1, Threshold 6.3\n", " L-> Feature 3, Threshold 4.8\n", " L-> versicolor : 25/25\n", " R-> Feature 2, Threshold 2.7\n", " L-> virginica : 2/2\n", " R-> Feature 4, Threshold 1.8\n", " L-> versicolor : 2/2\n", " R-> Feature 3, Threshold 4.9\n", " L-> Feature 1, Threshold 6.2\n", " L-> versicolor : 1/1\n", " R-> virginica : 1/1\n", " R-> virginica : 3/3\n", " R-> Feature 1, Threshold 6.9\n", " L-> Feature 3, Threshold 5.0\n", " L-> versicolor : 6/6\n", " R-> virginica : 10/10\n", " R-> virginica : 9/9\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 47, "text": [ "Classes: {\"setosa\",\"versicolor\",\"virginica\"}\n", "Matrix: 3x3 Array{Int64,2}:\n", " 15 0 0\n", " 0 8 2\n", " 0 0 5\n", "Accuracy: 0.9333333333333333\n", "Kappa: 0.8928571428571429" ] } ], "prompt_number": 47 }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Other Statistics & ML Packages\n", "* [GLM](https://github.com/JuliaStats/GLM.jl) - Generalized Linear Models\n", "* [Orchestra](https://github.com/svs14/Orchestra.jl) - Heterogeneous ensemble learning package\n", "* [MCMC](https://github.com/JuliaStats/MCMC.jl) - Markov Chain Monte Carlo methods\n", "* [HypothesisTests](https://github.com/JuliaStats/HypothesisTests.jl) - Hypothesis testing toolkit" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Resources\n", "### Documentation\n", "* [Manual](http://docs.julialang.org/en/latest/#manual)\n", "* [Standard Library](http://docs.julialang.org/en/latest/stdlib/base/)\n", "\n", "### GitHub Groups\n", "* [Julia Statistics](https://github.com/JuliaStats)\n", "* [JuliaOpt: Optimization-related projects](http://www.juliaopt.org/)\n", "\n", "### Discussion Forums / Mailing Lists (Google Groups)\n", "* [Julia-Users](https://groups.google.com/forum/#!forum/julia-users)\n", "* [Julia-Dev](https://groups.google.com/forum/#!forum/julia-dev)\n", "* [Julia-Stats](https://groups.google.com/forum/#!forum/julia-stats)\n", "\n", "### Blogs / Curations\n", "* [JuliaBloggers](http://www.juliabloggers.com/)\n", "* [Curated decibans of Julia](http://svaksha.github.io/Julia.jl/)\n", "\n", "### Crash Courses\n", "* [Julia by Example](http://www.scolvin.com/juliabyexample/)\n", "* [Learn Julia in Y Minutes](http://learnxinyminutes.com/docs/julia/)\n", "* [The Julia Express](http://bogumilkaminski.pl/files/julia_express.pdf)\n", "\n", "### Cheat Sheets\n", "* [For MATLAB, Python NumPy, R, and Julia](http://sebastianraschka.com/Articles/2014_matrix_cheatsheet.html)\n", "* [Julia and IJulia](http://math.mit.edu/~stevenj/Julia-cheatsheet.pdf)" ] } ], "metadata": {} } ] }