fix for m==1

[valse.git] / pkg / R / EMGrank.R

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index db40948..b85a0fa 100644 (file)
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -46,10 +46,10 @@ matricize <- function(X)
 .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))
@@ -73,8 +73,8 @@ matricize <- function(X)
       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
@@ -92,7 +92,7 @@ matricize <- function(X)
       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)
         {