computeW = function(θ)
{
- dim <- d + d^2 + d^3
- W <<- solve( matrix( .C("Compute_Omega",
+ #require(MASS)
+ dd <- d + d^2 + d^3
+ W <<- MASS::ginv( 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")$W, nrow=dim, ncol=dim) )
+ W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd ) )
NULL #avoid returning W
},
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)
}