.EMGrank_R <- function(Pi, Rho, mini, maxi, X, Y, tau, rank)
{
# matrix dimensions
- n <- dim(X)[1]
- p <- dim(X)[2]
- m <- dim(Rho)[2]
- k <- dim(Rho)[3]
+ n <- nrow(X)
+ p <- ncol(X)
+ m <- ncol(Y)
+ k <- length(Pi)
# init outputs
phi <- array(0, dim = c(p, m, k))
if (length(Z_indice) == 0)
next
# U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
- s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ])))
- %*% crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
+ s <- svd(MASS::ginv(crossprod(matricize(X[Z_indice, ]))) %*%
+ crossprod(matricize(X[Z_indice, ]), matricize(Y[Z_indice, ])))
S <- s$d
# Set m-rank(r) singular values to zero, and recompose best rank(r) approximation
# of the initial product
for (r in seq_len(k))
{
dotProduct <- tcrossprod(Y[i, ] %*% Rho[, , r] - X[i, ] %*% phi[, , r])
- logGamIR <- log(Pi[r]) + log(det(Rho[, , r])) - 0.5 * dotProduct
+ logGamIR <- log(Pi[r]) + log(gdet(Rho[, , r])) - 0.5 * dotProduct
# Z[i] = index of max (gam[i,])
if (logGamIR > maxLogGamIR)
{