| \usepackage{wasysym} | |
| \usepackage{moresize} | |
| \usepackage{multicol} | |
| \usepackage[Sonny]{fncychap} | |
| \usepackage[margin=1in, paperwidth=6.69in, paperheight=9.61in]{geometry} | |
| \usepackage[protrusion=true,expansion=true]{microtype} | |
| \usepackage{xltxtra} | |
| \setmainfont[Mapping=tex-text]{Minion Pro} | |
| \setmonofont[Mapping=tex-text,Scale=0.85]{Inconsolata} | |
| %\setmonofont[Mapping=tex-text,Scale=0.75]{DejaVu Sans Mono} |
| class Person extends IdyllModel | |
| belongs_to :working_population | |
| define_attribute :stress, type: integer, min: 0, max: 100, init: random | |
| define_attribute :environment, type: enum, ['high income', 'low income', 'normal'] | |
| define_attribute :group, type: classification, |model| => { case model.stress; when 50..100: "high risk"; when 0..49: "low risk" } | |
| def next_step | |
| self.stress += 5 if self.environment == "high income" | |
| self.stress -= 5 if self.environment == "low income" |
| ##### Install a lot of stuff first ##### | |
| $sudo apt-get update | |
| ##install python | |
| $ wget http://09c8d0b2229f813c1b93-c95ac804525aac4b6dba79b00b39d1d3.r79.cf1.rackcdn.com/Anaconda-2.0.1-Linux-x86_64.sh | |
| $ sudo bash anaconda........sh | |
| ##install necessary libs | |
| $ sudo apt-get install -y python-matplotlib python-tornado ipython ipython-notebook python-setuptools python-pip |
| """Example to show how to plot histogram with accumulate ratio. | |
| Plot multiple histogram using seaborn.FacetGrid() | |
| """ | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| % matplotlib inline |
| ''' | |
| These functions take a dictionary of dependencies in the following way: | |
| depdict = { 'a' : [ 'b', 'c', 'd'], | |
| 'b' : [ 'c', 'd'], | |
| 'e' : [ 'f', 'g'] | |
| } |
| # import | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| # white background in plots | |
| sns.set(style="whitegrid") | |
| # df is a pandas dataframe | |
| # plot by column by variable 'var1', in 3 columns, coloring by variable 'var2' | |
| grid = sns.FacetGrid(df, col='var1', col_wrap=3, hue='var2', size=4, palette='Set2') |
| #include <map> | |
| #include <vector> | |
| /** | |
| * Provides a basic interpolation mechanism in C++ using the STL. | |
| * Maybe not the fastest or most elegant method, but it works (for | |
| * linear interpolation!!), and is fast enough for a great deal of | |
| * purposes. It's also super easy to use, so that's a bonus. | |
| */ | |
| class LinearInterpolator { |
| class bresenham: | |
| def __init__(self, start, end): | |
| self.start = list(start) | |
| self.end = list(end) | |
| self.path = [] | |
| self.steep = abs(self.end[1]-self.start[1]) > abs(self.end[0]-self.start[0]) | |
| if self.steep: | |
| print 'Steep' |
Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.
First, install https://www.docker.com/docker-toolbox
Since this is Windows, creating the Docker group "docker" is not necessary.
