joelkr

notebook:
  image: my-data-science-docker:0.1
  ports:
    - "80:8888"
  environment:
    - WISEDS_CODE_DIR=${PWD}
    - WISEDS_DATA_DIR=${PWD}/data
#    - IPYTHON_PASSWORD=WhoMe321
    - PASSWORD=WhoMe321
    - KERAS_BACKEND=theano

joelkr

FROM ubuntu:16.04
#FROM ubuntu:14.04
# Extended and moved to python 3 based on image:
#MAINTAINER Wise.io, Inc. <help@wise.io>

ENV DEBIAN_FRONTEND noninteractive

ENV PATH /anaconda/bin:$PATH
# For image inheritance.
ONBUILD ENV PATH /anaconda/bin:$PATH

joelkr

library(deSolve)

handyMod <- function(Time, State, Pars) {
  with(as.list(c(State, Pars)), {
    # First calculate threshold below which famine begins
    # rho = minimum required consumption
    wThresh = rho * popC + rho * kappa * popE
    # Check to avoid dividing by zero then estimate consumption of
    # commons = cC and elites = cE
    if(wThresh > 0) {

joelkr

# BayesProbability.R

#set.seed(49)

# Build a dummy dictionary
a <- letters
b <- letters
tokens <- apply(expand.grid(a, b), 1, function(x) paste(x, collapse=""))
# Number of tokens to use 1-676
tl <- 100

joelkr

# Try setting up a smaller test set

# Variables to set how large a square of data to test
npatients <- 4
nreadings <- 8
# We are still not converging in 500 trials. Perhaps we have too many unknowns.
#nleads <- 10
nleads <- 12
load("cardiacSetup.rda")

joelkr

# Cost and gradient functions for optim(). These are a port of Coursera
# Machine Learning course topic Collaborative Filtering from MATLAB
# to R.

# Cost Function
coFilterCost <- function (params, npatients, nleads, nreadings,Y, R, lambda) 
{
  # params: X and Theta flattened to vector
  # npatients, nleads, nreadings: number of patients, leads, and numeric 
  # fields in data

joelkr

  cardiac <- read.csv("arrhythmia.data", header=F, na.strings="?")
  colnames(cardiac)[1:280] <- c("Age","Gender_Nom","Height","Weight","QRS_Dur",
"P-R_Int","Q-T_Int","T_Int","P_Int","QRS","T","P","QRST","J","Heart_Rate",
"Q_Wave","R_Wave","S_Wave","R_Prime","S_Prime","Int_Def","Rag_R_Nom",
"Diph_R_Nom","Rag_P_Nom","Diph_P_Nom","Rag_T_Nom","Diph_T_Nom", 
"DII00", "DII01","DII02", "DII03", "DII04","DII05","DII06","DII07","DII08","DII09","DII10","DII11",
"DIII00","DIII01","DIII02", "DIII03", "DIII04","DIII05","DIII06","DIII07","DIII08","DIII09","DIII10","DIII11",
"AVR00","AVR01","AVR02","AVR03","AVR04","AVR05","AVR06","AVR07","AVR08","AVR09","AVR10","AVR11",
"AVL00","AVRL1","AVL02","AVL03","AVL04","AVL05","AVL06","AVL07","AVL08","AVL09","AVL10","AVL11",
"AVF00","AVF01","AVF02","AVF03","AVF04","AVF05","AVF06","AVF07","AVF08","AVF09","AVF10","AVF11",