DannyArends

# Unit-Variance Scaling or Autoscaling
autoscale <- function(X) {
  return(apply(X,2, function(x){ (x- mean(x)) / sd(x) }))
}

# Compute the covariance matrix
covariance <- function(X) {
  cM <- matrix(NA, ncol(X), ncol(X), dimnames = list(colnames(X), colnames(X)))
  for(x in colnames(X)) {

DannyArends

/*
The most atomic way to train and run inference for a GPT in pure, dependency-free D.
This file is the complete algorithm,
Everything else is just efficiency.
@karpathy

Translation by @DannyArends
*/

import std.algorithm  : countUntil, fold, joiner, map, maxElement, min, sort, sum, uniq;