cjbayesian

## Corey Chivers, 2012 ##
sim_bayes<-function(p=0.5,N=100,y_lim=20,a_a=2,a_b=10,b_a=8,b_b=3)
{
   ## Simulate outcomes in advance   
   outcomes<-sample(1:0,N,prob=c(p,1-p),replace=TRUE)
   success<-cumsum(outcomes)

   for(frame in 1:N)
   {
      png(paste("plots/",1000+frame,".png",sep=""))

cjbayesian

################## Plot training skies ###################
##
## corey.chivers@mail.mcgill.ca
##
##########################################################

## calculate a vector given
## x,y,e1,e2
gal_line<-function(g,scale=100)
{

cjbayesian

#########################################################
## Intro to Simulation -  R/Stats Intro Series
## Designed by: Corey Chivers, 2013
## Department of Biology, McGill University
#########################################################


##@  0.1  @##
rm(list=ls())					 # Housekeeping
#setwd('<my working directory>') # set working dir

cjbayesian

## Simulate Grime Dice ##

red<-c(4,4,4,4,4,9)
blue<-c(2,2,2,7,7,7)
olive<-c(0,5,5,5,5,5)
yellow<-c(3,3,3,3,8,8)
magenta<-c(1,1,6,6,6,6)


## Play n match-ups between d1 and d2

cjbayesian

###################################################
##
## Functions for calculating AUC and plotting ROC
## Corey Chivers, 2013
## corey.chivers@mail.mcgill.ca
##
###################################################


## Descrete integration for AUC calc

cjbayesian

## Functions for simulating Conway's Game of Life
## Modified from http://www.petrkeil.com/?p=236

neighbour_count <- function(X)
{
    side <- nrow(X)
    # make the shifted copies of the original array
    allW = cbind( rep(0,side) , X[,-side] )
    allNW = rbind(rep(0,side),cbind(rep(0,side-1),X[-side,-side]))
    allN = rbind(rep(0,side),X[-side,])

cjbayesian

cjbayesian

#!/usr/bin/python
"""
Parse BMC OA articles from XML to utf-8 text of the title, abstract, and body.
"""
import libxml2
from os import listdir
from time import gmtime, strftime

input_dir = './BMC_FTP/content/articles/'
files = listdir(input_dir)

cjbayesian

## Warren Buffet's 1B Basketball Challenge ##

expected_value <- function(p,ngames=63,prize=1000000000){
    p^ngames * prize
}

## What is the expected value of an entry 
## given a particular level of prediction accuracy
expected_value(p=0.80)
expected_value(p=0.85)

cjbayesian

def plot_correlogram(df,figsize=(20,20)):
    ''' Creat an n x n matrix of scatter plots for every
    combination of numeric columns in a dataframe'''

    cols = list(df.columns[df.dtypes=='float64'])
    n = len(cols)
    fig, ax = plt.subplots(n,n,figsize=figsize)
    for i,y in enumerate(cols):
        for j,x in enumerate(cols):
            if i != n-1: