dirkschumacher

# Build sparse models with filter guards
# this problem arises in network models where you have a variable for each
# pair of nodes. However if your graph is not fully connected, you end up
# creating a lot of useless variables if you have all combinations in your MIP Model
# and then set the invalid edges to 0.
# Below is a toy example with 1 millionen edges, but only 36 are actually being used.
library(rmpk)
is_adjacent <- function(i, j) {
  i < j & j < 10 # just a dummy function indicating when two nodes are adjacent

dirkschumacher

# an example of the TSP solved through solver callbacks
# follows the formulation of the Gurobi example
# http://examples.gurobi.com/traveling-salesman-problem/
# and from the TSP vignette for the MTZ formulation

# all experimental

library(ggplot2)
suppressPackageStartupMessages(library(dplyr))

dirkschumacher

`?` <- function(lhs, rhs) {
  if (missing(rhs)) {
    return(eval(bquote(utils::`?`(.(substitute(lhs))))))
  }
  rhs <- substitute(rhs)
  envir <- parent.frame()
  split_colon <- strsplit(deparse(rhs), ":")
  stopifnot(length(split_colon) == 1L, length(split_colon[[1L]]) == 2L)
  rhs_chr <- split_colon[[1L]]

dirkschumacher

library(armacmp)
# code from https://nextjournal.com/wolfv/how-fast-is-r-with-fastr-pythran
# which in turn comes in part from http://www.tylermw.com/throwing-shade/
# Author: Tyler Morgan-Wall

# first the R version

faster_bilinear <- function (Z, x0, y0){
  i = floor(x0)

dirkschumacher

library(armacmp)

# some of julia's microbenchmarks translated to C++
# https://github.com/JuliaLang/Microbenchmarks/blob/master/perf.R

fib_cpp <- armacmp(function(n = type_scalar_int()) {
  fib_rec <-  function(nr = type_scalar_int()) {
    if (nr < 2) {
      return(nr, type = type_scalar_int())

dirkschumacher

library(armacmp)
# Arnold, T., Kane, M., & Lewis, B. W. (2019). A Computational Approach to Statistical Learning. CRC Press.
# logistic regression using the Newton-Raphson
log_reg <- armacmp(function(X, y) {
  beta <- rep.int(0, ncol(X))
  for (i in seq_len(25)) {
    b_old <- beta
    alpha <- X %*% beta
    p <- 1 / (1 + exp(-alpha))

dirkschumacher

library(armacmp)

# taken from https://gallery.rcpp.org/articles/black-scholes-three-ways/
put_option_pricer_arma <- armacmp(function(s = type_colvec(),
                                           k = type_scalar_numeric(),
                                           r = type_scalar_numeric(),
                                           y = type_scalar_numeric(),
                                           t = type_scalar_numeric(),
                                           sigma = type_scalar_numeric()) {

dirkschumacher

a <- Matrix::sparseVector(1:2, i = 1:2, length = 2)
b <- Matrix::sparseVector(1:2, i = 1:2, length = 2)
class(a * b)
#> [1] "dsparseVector"
#> attr(,"package")
#> [1] "Matrix"
class(a / b) # bug? numeric instead of sparseVector
#> [1] "numeric"

dirkschumacher

splice_df <- function(x, ...) {
  expr <- rlang::enquo(x)
  cols <- lapply(rlang::ensyms(..., .named = TRUE), as.character)
  lapply(cols, function(col_name) {
    rlang::quo(`[[`(!!expr, !!col_name))
  })
}

dirkschumacher

# this is just a script to test the rhxl package, I just quickly looked at the data

# Ethiopia Who is doing What Where - 3W December 2017 
# source: https://data.humdata.org/dataset/3w-december-2017
url <- "https://data.humdata.org/dataset/615416d2-457b-461a-8155-090f0ced0bf8/resource/f71bf111-8706-42f4-ba46-4ce3c8c949dc/download/3w_hxl.xlsx"

# load the rhxl package
# https://github.com/dirkschumacher/rhxl
library(rhxl)
download.file(url, "file.xlsx")