X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FoptimParams.R;h=a45f71ac4f64769f7da037324efef533ffa58ba0;hb=b357ac5457105ee87ad8650d61ad3c3845e0df95;hp=dc294f002fb42ec06233ef8c202c90bee62afcef;hpb=0a630686751ef8baa2c170067b981d2a219a9224;p=morpheus.git

diff --git a/pkg/R/optimParams.R b/pkg/R/optimParams.R
index dc294f0..a45f71a 100644
--- a/pkg/R/optimParams.R
+++ b/pkg/R/optimParams.R
@@ -117,11 +117,11 @@ setRefClass(
 
     computeW = function(θ)
     {
-      dim <- d + d^2 + d^3
-      W <<- solve( matrix( .C("Compute_Omega",
-        X=as.double(X), Y=as.double(Y), M=as.double(Moments(θ)),
-        pn=as.integer(n), pd=as.integer(d),
-        W=as.double(W), PACKAGE="morpheus")$W, nrow=dim, ncol=dim) )
+      #require(MASS)
+      dd <- d + d^2 + d^3
+      W <<- MASS::ginv( matrix( .C("Compute_Omega",
+        X=as.double(X), Y=Y, M=Moments(θ), pn=as.integer(n), pd=as.integer(d),
+        W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd ) )
       NULL #avoid returning W
     },
 
@@ -146,10 +146,10 @@ setRefClass(
 
     f = function(θ)
     {
-			"Product t(Mi - hat_Mi) W (Mi - hat_Mi) with Mi(theta)"
+			"Product t(hat_Mi - Mi) W (hat_Mi - Mi) with Mi(theta)"
 
       L <- expArgs(θ)
-			A <- as.matrix(Moments(L) - Mhat)
+			A <- as.matrix(Mhat - Moments(L))
       t(A) %*% W %*% A
     },
 
@@ -186,13 +186,11 @@ setRefClass(
 
       # Gradient on p: K-1 columns, dim rows
 			km1 = 1:(K-1)
-
 			res <- cbind(res, rbind(
         sweep(as.matrix(β [,km1]), 2, G1[km1], '*') - G1[K] * β [,K],
         sweep(as.matrix(β2[,km1]), 2, G2[km1], '*') - G2[K] * β2[,K],
         sweep(as.matrix(β3[,km1]), 2, G3[km1], '*') - G3[K] * β3[,K] ))
 
-      # TODO: understand derivatives order and match the one in optim init param
 			for (i in 1:d)
 			{
 				# i determines the derivated matrix dβ[2,3]
@@ -251,18 +249,21 @@ setRefClass(
       else if (any(is.na(θ0$b)))
         stop("θ0$b cannot have missing values")
 
-			op_res = constrOptim( linArgs(θ0), .self$f, .self$grad_f,
-				ui=cbind(
-					rbind( rep(-1,K-1), diag(K-1) ),
-					matrix(0, nrow=K, ncol=(d+1)*K) ),
-				ci=c(-1,rep(0,K-1)) )
-
-      # debug:
-      computeW(expArgs(op_res$par))
-      print(W)
-      # We get a first non-trivial estimation of W
-      # TODO: loop, this redefine f, so that we can call constrOptim again...
-      # Stopping condition? N iterations? Delta <= epsilon ?
+      # TODO: stopping condition? N iterations? Delta <= epsilon ?
+      for (loop in 1:10)
+      {
+        op_res = constrOptim( linArgs(θ0), .self$f, .self$grad_f,
+          ui=cbind(
+            rbind( rep(-1,K-1), diag(K-1) ),
+            matrix(0, nrow=K, ncol=(d+1)*K) ),
+          ci=c(-1,rep(0,K-1)) )
+
+        computeW(expArgs(op_res$par))
+        # debug:
+        #print(W)
+        print(op_res$value)
+        print(expArgs(op_res$par))
+      }
 
 			expArgs(op_res$par)
 		}