84 lines
2.6 KiB
R
84 lines
2.6 KiB
R
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args <- commandArgs(TRUE)
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if (length(args) > 0L) {
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method <- args[1]
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} else {
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method <- "simple"
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}
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if (length(args) > 1L) {
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momentum <- as.double(args[2])
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} else {
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momentum <- 0.0
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}
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max.iter <- 50L
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attempts <- 25L
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# library(CVEpureR)
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# path <- paste0('~/Projects/CVE/tmp/logger_', method, '.R.pdf')
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library(CVE)
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path <- paste0('~/Projects/CVE/tmp/logger_', method, '.C.pdf')
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# Define logger for `cve()` method.
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logger <- function(epoch, attempt, L, V, tau) {
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# Note the `<<-` assignement!
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loss.history[epoch + 1, attempt] <<- mean(L)
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if (epoch == 0) {
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error <- NA
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} else {
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error <- norm(V %*% t(V) - V_last %*% t(V_last), type = 'F')
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}
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V_last <<- V
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error.history[epoch + 1, attempt] <<- error
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tau.history[epoch + 1, attempt] <<- tau
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# Compute true error by comparing to the true `B`
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B.est <- null(V) # Function provided by CVE
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P.est <- B.est %*% solve(t(B.est) %*% B.est) %*% t(B.est)
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true.error <- norm(P - P.est, 'F') / sqrt(2 * k)
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true.error.history[epoch + 1, attempt] <<- true.error
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}
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pdf(path)
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par(mfrow = c(2, 2))
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for (name in paste0("M", seq(5))) {
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# Seed random number generator
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set.seed(42)
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# Create a dataset
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ds <- dataset(name)
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X <- ds$X
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Y <- ds$Y
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B <- ds$B # the true `B`
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k <- ncol(ds$B)
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# True projection matrix.
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P <- B %*% solve(t(B) %*% B) %*% t(B)
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# Setup histories.
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V_last <- NULL
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loss.history <- matrix(NA, max.iter + 1, attempts)
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error.history <- matrix(NA, max.iter + 1, attempts)
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tau.history <- matrix(NA, max.iter + 1, attempts)
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true.error.history <- matrix(NA, max.iter + 1, attempts)
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dr <- cve(Y ~ X, k = k, method = method,
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momentum = momentum,
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max.iter = max.iter, attempts = attempts,
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logger = logger)
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# Plot history's
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matplot(loss.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('loss', name),
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ylab = expression(L(V[i])))
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matplot(true.error.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('true error', name),
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ylab = expression(group('|', B*B^T - B[i]*B[i]^T, '|')[F] / sqrt(2*k)))
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matplot(error.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('error', name),
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ylab = expression(group('|', V[i-1]*V[i-1]^T - V[i]*V[i]^T, '|')[F]))
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matplot(tau.history, type = 'l', log = 'y', xlab = 'i (iteration)',
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main = paste('learning rate', name),
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ylab = expression(tau[i]))
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}
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cat("Created plot:", path, "\n")
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