44 lines
1.6 KiB
R
44 lines
1.6 KiB
R
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library(CVEpureR)
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# Setup histories.
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(epochs <- 50)
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(attempts <- 10)
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loss.history <- matrix(NA, epochs + 1, attempts)
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error.history <- matrix(NA, epochs + 1, attempts)
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tau.history <- matrix(NA, epochs + 1, attempts)
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true.error.history <- matrix(NA, epochs + 1, attempts)
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# Create a dataset
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ds <- dataset("M1")
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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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# Define the logger for the `cve()` method.
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logger <- function(env) {
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# Note the `<<-` assignement!
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loss.history[env$epoch + 1, env$attempt] <<- env$loss
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error.history[env$epoch + 1, env$attempt] <<- env$error
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tau.history[env$epoch + 1, env$attempt] <<- env$tau
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# Compute true error by comparing to the true `B`
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B.est <- null(env$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[env$epoch + 1, env$attempt] <<- true.error
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}
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# Performe SDR for ONE `k`.
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dr <- cve(Y ~ X, k = k, logger = logger, epochs = epochs, attempts = attempts)
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# Plot history's
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par(mfrow = c(2, 2))
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matplot(loss.history, type = 'l', log = 'y', xlab = 'iter',
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main = 'loss', ylab = expression(L(V[iter])))
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matplot(error.history, type = 'l', log = 'y', xlab = 'iter',
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main = 'error', ylab = 'error')
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matplot(tau.history, type = 'l', log = 'y', xlab = 'iter',
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main = 'tau', ylab = 'tau')
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matplot(true.error.history, type = 'l', log = 'y', xlab = 'iter',
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main = 'true error', ylab = 'true error')
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