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MiniBatchGradientDescent <- function(X, Y, alpha=0.1, max.iter=1000, precision=0.0001, batchRate=0.5, seed=1){
if (is.null(n <- nrow(X))) stop("'X' must be a matrix")
if(n == 0L) stop("0 (non-NA) cases")
p <- ncol(X)
if(p == 0L) {
return(list(
x = X,
y = Y,
coefficients = numeric(),
residuals = Y,
fitted.values = 0 * Y
))
}
if(NROW(Y) != n) {
stop("incompatible dimensions")
}
# Initial value of coefficients
B <- rep(0, ncol(X))
# batch size
batchSize <- ceiling(n * batchRate)
# Recorded for loss vs iteration
loss_iter <- data.frame(
loss = numeric(),
iter = integer()
)
for(iter in 1:max.iter){
B.prev <- B
for(i in seq(1, n, batchSize)){
indexes <- i:min((i+batchSize), n)
Xtemp <- X[indexes,,drop=FALSE]
Ytemp <- Y[indexes,drop=FALSE]
yhat <- Xtemp %*% B
B <- B + (alpha / length(indexes)) * t(Xtemp) %*% (Ytemp - yhat)
}
loss <- Y - X %*% B
loss_iter <- rbind(loss_iter, c(sqrt(mean(loss^2)), iter))
if(any(is.na(B)) ||
!any(abs(B.prev - B) > precision * B)){
break
}
}
names(B) <- colnames(X)
fv <- X %*% B
rs <- Y - fv
coef <- as.vector(B)
names(coef) <- rownames(B)
colnames(loss_iter) <- c('loss', 'iter')
z <- structure(list(
x=X,
y=Y,
coefficients = coef,
fitted.values = fv,
residuals = rs,
loss_iter = loss_iter
),
class = c("rlm", "rlmmodel"))
z
}
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