# library(CVEpureR) # path <- '~/Projects/CVE/tmp/logger.R.pdf' library(CVE) path <- '~/Projects/CVE/tmp/seeded_test.pdf' epochs <- 100 attempts <- 25 # Define the logger for the `cve()` method. logger <- function(env) { # Note the `<<-` assignement! loss.history[env$epoch + 1, env$attempt] <<- env$loss error.history[env$epoch + 1, env$attempt] <<- env$error tau.history[env$epoch + 1, env$attempt] <<- env$tau # Compute true error by comparing to the true `B` B.est <- null(env$V) # Function provided by CVE P.est <- B.est %*% solve(t(B.est) %*% B.est) %*% t(B.est) true.error <- norm(P - P.est, 'F') / sqrt(2 * k) true.error.history[env$epoch + 1, env$attempt] <<- true.error } pdf(path) par(mfrow = c(2, 2)) for (name in paste0("M", seq(5))) { # Seed random number generator set.seed(42) # Create a dataset ds <- dataset(name) X <- ds$X Y <- ds$Y B <- ds$B # the true `B` k <- ncol(ds$B) # True projection matrix. P <- B %*% solve(t(B) %*% B) %*% t(B) # Setup histories. loss.history <- matrix(NA, epochs + 1, attempts) error.history <- matrix(NA, epochs + 1, attempts) tau.history <- matrix(NA, epochs + 1, attempts) true.error.history <- matrix(NA, epochs + 1, attempts) dr <- cve(Y ~ X, k = k, logger = logger, epochs = epochs, attempts = attempts) # Plot history's matplot(loss.history, type = 'l', log = 'y', xlab = 'i (iteration)', main = paste('loss', name), ylab = expression(L(V[i]))) matplot(true.error.history, type = 'l', log = 'y', xlab = 'i (iteration)', main = paste('true error', name), ylab = expression(group('|', B * B^T - B[i] * B[i]^T, '|')[F] / sqrt(2 * k))) matplot(error.history, type = 'l', log = 'y', xlab = 'i (iteration)', main = paste('error', name), ylab = expression(group('|', V[i-1] * V[i-1]^T - V[i] * V[i]^T, '|')[F])) matplot(tau.history, type = 'l', log = 'y', xlab = 'i (iteration)', main = paste('learning rate', name), ylab = expression(tau[i])) }