grosscol

Pathways-attributes matrix
   A B C
p0 0 0 0
p1 0 0 1
p2 0 1 0
p3 1 0 0
p4 0 1 1
p5 1 0 1
p6 1 1 0
p7 1 1 1

grosscol

---

#
# This template example assumes a UserPool and UserPoolDomain exist.
# The function of this is to produce a custom resource with an attribute 
#   that can be referenced for DNSName of an Route53::RecordSet AliasTarget.
#   
#   AliasTarget:
#     HostedZone: Z2FDTNDATAQYW2
#     DNSNAME: !GetAtt UPDomain.CloudFrontDistribution

grosscol

# Generate single round of meetings.
#  from a data frame with `agentNo` and `state` cols.
generate_meetings <- function(agent_df){
  # 1 Make a list of Buyer agents
  is_a_buyer <- agent_df$state == "Buyer"
  buyers_ids <- agent_df[is_a_buyer, c('agentNo')]
  
  # 2 Make list of Seller agents
  is_a_seller <- agent_df$state == "Seller"
  sellers_ids <- agent_df[is_a_seller, c('agentNo')]

grosscol

# Author: Colin Gross
# Date: 2022-02-22

#' Simple group assignments.
#'   Randomly order equally distributed group ids
#' @param num_groups Number of study groups
#' @param num_per_group Number of individuals per group.
#' @return named vector of randomly selected group assignments. 
simple_grp_assignments <- function(num_groups, num_per_group){
  num_indv   <- num_groups * num_per_group

grosscol

structure(list(StandID = c("BEF 15", "NUL 10", "BEF 16", "NUL 2", 
"BEF 17", "NUL 8", "BEF 17", "BEF 17", "BEF 14", "NUL 14", "NUL 1", 
"NUL 13", "NUL 15", "NUL 6", "NUL 13", "BEF 11", "BEF 14", "NUL 15", 
"BEF 16", "BEF 3", "NUL 14", "BEF 3", "BEF 14", "NUL 3", "BEF 11", 
"BEF 2", "NUL 2", "BEF 16", "BEF 8", "BEF 3", "BEF 8", "NUL 4", 
"BEF 9", "BEF 17", "BEF 7", "NUL 14", "BEF 17", "NUL 10", "NUL 4", 
"NUL 13", "NUL 9", "NUL 10", "BEF 3", "BEF 2", "BEF 3", "NUL 13", 
"NUL 15", "NUL 6", "NUL 4", "NUL 7", "BEF 11", "NUL 10", "NUL 5", 
"NUL 9", "BEF 1", "NUL 12", "NUL 5", "BEF 8", "NUL 7", "NUL 8", 
"BEF 9", "BEF 1", "NUL 2", "NUL 8", "BEF 7", "BEF 8", "BEF 9", 

grosscol

rosen_height <- function(x,y) {
  100 * (y-x^2)^2 + (1-x)^2
}

gradient <- function(x,y) {
  val_x <- -400*x*(y-x^2)-2*(1-x)
  val_y <- 200*(y-x^2)
  return( c(val_x, val_y) )
}

grosscol

options(
  repos = c(CRAN = "https://cran.rstudio.com/"),
  browserNLdisabled = TRUE,
  deparse.max.lines = 2)

# Set lib path
.libPaths( "~/R/x86_64-pc-linux-gnu-library/dev/" )

if (interactive()) {
  suppressMessages(require(devtools))

grosscol

# regarding: https://www.reddit.com/r/Rlanguage/comments/d0ytw7/issues_of_bias_with_rnorm/

# set the RNG seed so the results are reporducible
set.seed(12345)
# Generate 100 target means beteen 0 and 100 from the uniform distribution
generated_means <- runif(100, 0, 100)

# Generate 100 standard deviation values also from uniform distribution
generated_sdev <- runif(100, 1, 20)

grosscol

#!/bin/bash

# Consider the following structure where this script is target.sh
# /tmp/
# ├── target.sh
# └── l1
#     ├── one.sh -> ../target.sh
#     └── l2
#         └── two.sh -> ../one.sh
# From your home directory run:

grosscol

# I either end up with a rubocop error or an rspec error.  In each case, the travis build will not pass.

# Rubocop error:
# RSpec/DescribedClass: Use described_class instead of Hydra::Works::GenericFile::Characterization
describe Hydra::Works::GenericFile::Characterization do
  let(:demo_class) do
    Class.new(Hydra::Works::GenericFile::Base) do
      include Hydra::Works::GenericFile::Characterization
    end
  end