mike-lawrence

#define a function to generate a random vector
makeNewVector = function(vectorLength){
	#random normal
	x = rnorm(vectorLength)
	#normalize to length 1
	x/(sqrt(sum(x^2)))
}

#define a function to compute cosine similarity between two vectors
cosine <- function( x, y) {

mike-lawrence

Version: 1.0

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SaveWorkspace: No
AlwaysSaveHistory: No

EnableCodeIndexing: Yes
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Encoding: UTF-8

mike-lawrence

functions{
	// GP: computes noiseless Gaussian Process
	vector GP(real volatility, real amplitude, vector normal01, int n_x, real[] x ) {
		matrix[n_x,n_x] cov_mat ;
		real amplitude_sq_plus_jitter ;
		amplitude_sq_plus_jitter = amplitude^2 + 1e-6 ;
		cov_mat = cov_exp_quad(x, amplitude, 1/volatility) ;
		for(i in 1:n_x){
			cov_mat[i,i] = amplitude_sq_plus_jitter ;
		}

mike-lawrence

fname = 'temp.txt'
#wait for it to exist
while(!file.exists(fname)){}
old = 0
new = old
while(T){
	while(new==old){
		new = file.size(fname)
	}
	f = file(

mike-lawrence

f = file(description='bigFile.txt',open='rb')
n = 0
temp = 1
while(length(temp)<2){
  n = n + 1
  seek(con=f,origin='end',where=-(2^n))
  temp <- scan(f,what='character',quiet=T)
}
last_line = temp[2]

mike-lawrence

functions{

	# GP: computes noiseless Gaussian Process given pre-computed unique distances
	vector GP(real volatility, real amplitude, vector normal01, int n_x, int n_dx, vector dx_unique, int [,] dx_index) {

		# covars: unique entries in covariance matrix
		vector[n_dx] covars ;

		# covMat: covariance matrix
		matrix[n_x,n_x] covMat ;

mike-lawrence

library(tidyverse)
library(rstan)
rstan_options(auto_write = TRUE)

curve(dweibull(x,shape=1.75,scale=1e3),from=0,to=3e3)
one_response = dweibull(x=1:3e3,shape=1.75,scale=1e3)
one_response = one_response/max(one_response)

# create a signal with pulses at t=2e3, t=4e3 & t=5e3
obs = rep(0,8e3)

mike-lawrence

# Code below may not be up to date, see ezStan (https://github.com/mike-lawrence/ezStan/blob/master/R/bigStan.R) for latest version



# todo:
#  during-sampling: effective sample size and rhat for each parameter
#  during-sampling: diagnostics?

#usage:
# #compile the model using rstan::stan_model:

mike-lawrence

data{
	# n_subj: number of subjects
	int n_subj ;
	# n_y: number of outcomes
	int n_y ;
	# y: matrix of outcomes
	matrix[n_subj,n_y] y ;
	# n_fac: number of latent factors
	int n_fac ;
	# y_fac: list of which factor is associated with each outcome

mike-lawrence

#load packages
library(tidyverse)
library(rstan)
rstan_options(auto_write = TRUE)

# Make some fake data ----

#set random seed for reproducibility
set.seed(1)