MLprod <- function(y, experts, awake = NULL, loss.type = "square", loss.gradient = TRUE,
w0 = NULL, training = NULL, quiet = FALSE) {
experts <- as.matrix(experts)
N <- ncol(experts)
T <- nrow(experts)
# Uniform initial weight vector if unspecified
if (is.null(w0)) {
w0 <- rep(1, N)
}
awake <- as.matrix(awake)
idx.na <- which(is.na(experts))
awake[idx.na] <- 0
experts[idx.na] <- 0
weights <- matrix(0, ncol = N, nrow = T)
R <- log(w0)
L <- rep(1, N)
maxloss <- rep(0, N)
eta <- matrix(exp(700), ncol = N, nrow = T + 1)
prediction <- rep(0, T)
if (!is.null(training)) {
eta[1, ] <- training$eta
R <- training$R
L <- training$L
maxloss <- training$maxloss
}
if (! quiet) steps <- init_progress(T)
for (t in 1:T) {
if (! quiet) update_progress(t, steps)
# Update weights
idx <- awake[t,] > 0
R.max <- max(R[idx])
w <- numeric(N)
w[idx] <- exp(R[idx]-R.max)
w[idx] <- eta[t, idx] * w[idx]/sum(eta[t, idx] * w[idx])
p <- awake[t, ] * w/sum(awake[t, ] * w)
pred <- experts[t, ] %*% p # Predict
weights[t, ] <- p
prediction[t] <- pred
# Observe losses
lpred <- loss(pred, y[t], pred, loss.type = loss.type, loss.gradient = loss.gradient)
lexp <- loss(experts[t, ], y[t], pred, loss.type = loss.type, loss.gradient = loss.gradient)
r <- awake[t, ] * (c(c(lpred) - lexp))
L <- L + r^2
maxloss <- pmax(maxloss, abs(r))
neweta <- pmin(1/(2 * maxloss), sqrt(log(N)/L))
# Update regret and learning parameter
R <- neweta/eta[t, ] * R + log(1 + awake[t, ] * neweta * (c(c(lpred) - lexp)))
eta[t + 1, ] <- neweta
if (is.na(sum(R))) {
browser("Nan in R")
}
}
if (! quiet) end_progress()
R.max <- max(R)
w <- exp(R-R.max)
w <- eta[T + 1, ] * w/sum(eta[T + 1, ] * w)
object <- list(model = "MLprod", loss.type = loss.type, loss.gradient = loss.gradient,
coefficients = w)
object$parameters <- list(eta = eta[1:T, ])
object$weights <- weights
object$prediction <- prediction
object$training <- list(eta = eta[T + 1, ], R = R, L = L, maxloss = maxloss)
class(object) <- "mixture"
return(object)
}
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