# R/sgd.R In rcane: Different Numeric Optimizations to Estimate Parameter Coefficients

```StochasticGradientDescent <- function(X, Y, alpha = 0.1, max.iter = 1000, precision = 0.0001, AdaGrad=FALSE, ...){
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))
G <- matrix(rep(0,ncol(X)), ncol=1)
# 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 1:nrow(X)){
x <- X[i,, drop=FALSE]
y <- Y[i]
yhat <- x %*% B

g <- (t(x) %*% (y-yhat)) ^ 2
G <- G + g

B <- B + 1/(sqrt(G + 1e-8)) * alpha/n * (t(x) %*% (y - yhat))
} else {
B <- B + alpha/n * (t(x) %*% (y-yhat))
}
}

# Record loss vs iteration
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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rcane documentation built on June 4, 2018, 5:04 p.m.