library(morpheus)
-testMultistart <- function(N, n, K, p, beta, b, link, nstart, ncores)
+testMultistart <- function(N, n, p, beta, b, link, nstart, ncores)
{
- ms <- multiRun(
- list(n=n,p=p,beta=beta,b=b,optargs=list(K=K,link=link,nstart=nstart)),
+ res <- multiRun(
+ list(n=n, p=p, beta=beta, b=b, link=link, nstart=nstart),
list(
function(fargs) {
# 1 start
library(morpheus)
- K <- fargs$optargs$K
- op <- optimParams(K, fargs$optargs$link, fargs$optargs)
+ K <- ncol(fargs$beta)
+ op <- optimParams(fargs$X, fargs$Y, K, fargs$link, fargs$M)
x_init <- list(p=rep(1/K,K-1), beta=fargs$mu, b=rep(0,K))
- res <- NULL
+ res2 <- NULL
tryCatch({
- res <- do.call(rbind, op$run(x_init))
- }, error = function(e) {
- res <- NA
- })
- res
+ res2 <- do.call(rbind, op$run(x_init))
+ }, error = function(e) {})
+ res2
},
function(fargs) {
# B starts
library(morpheus)
- K <- fargs$optargs$K
- op <- optimParams(K, fargs$optargs$link, fargs$optargs)
+ K <- ncol(fargs$beta)
+ d <- nrow(fargs$beta)
+ op <- optimParams(fargs$X, fargs$Y, K, fargs$link, fargs$M)
best_val <- Inf
best_par <- list()
- for (i in 1:fargs$optargs$nstart)
+ for (i in 1:fargs$nstart)
{
#x_init <- list(p=rep(1/K,K-1), beta=i*fargs$mu, b=rep(0,K))
M <- matrix(rnorm(d*K), nrow=d, ncol=K)
M <- t(t(M) / sqrt(colSums(M^2)))
x_init <- list(p=rep(1/K,K-1), beta=M, b=rep(0,K))
- tryCatch({
+ par <- NULL
+ tryCatch({
par <- op$run(x_init)
- }, error = function(e) {
- par <- NA
- })
- if (!is.na(par[0]))
+ }, error = function(e) {})
+ if (!is.null(par))
{
val <- op$f( op$linArgs(par) )
if (val < best_val)
),
prepareArgs = function(fargs, index) {
library(morpheus)
- io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$optargs$link)
- fargs$optargs$M <- computeMoments(io$X, io$Y)
- mu <- computeMu(io$X, io$Y, fargs$optargs)
+ io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$link)
+ fargs$M <- computeMoments(io$X, io$Y)
+ mu <- computeMu(io$X, io$Y, list(M=fargs$M))
fargs$mu <- mu
- fargs
+ fargs$X <- io$X
+ fargs$Y <- io$Y
+ fargs
}, N=N, ncores=ncores, verbose=TRUE)
for (i in 1:2)
- ms[[i]] <- alignMatrices(ms[[i]], ref=rbind(p,beta,b), ls_mode="exact")
- ms
+ res[[i]] <- alignMatrices(res[[i]], ref=rbind(p,beta,b), ls_mode="exact")
+ res
}
-#model = binomial
-K <- 2
-p <- .5
-b <- c(-.2, .5)
# Default values:
link = "logit"
N <- 10
-d <- 2
n <- 1e4
ncores <- 1
nstart <- 3 #nstart-1 random starting points for each MC run
}
}
}
-betas <- list(
- matrix( c(1,-2, 3,1), ncol=K ), #d=2
- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K ), #d=5
- matrix( c(1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0), ncol=K ) ) #d=10
-beta <- betas[[ ifelse( d==2, 1, ifelse(d==5,2,3) ) ]]
-ms <- testMultistart(N, n, K, p, beta, b, link, nstart, ncores)
-ms_params <- list("N"=N, "nc"=ncores, "n"=n, "K"=K, "d"=d, "link"=link,
+if (d == 2) {
+ p <- .5
+ b <- c(-.2, .5)
+ beta <- matrix( c(1,-2, 3,1), ncol=2 )
+} else if (d == 5) {
+ p <- .5
+ b <- c(-.2, .5)
+ beta <- matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=2 )
+} else if (d == 10) {
+ p <- c(.3, .3)
+ b <- c(-.2, 0, .5)
+ beta <- matrix( c(1,2,-1,0,3,4,-1,-3,0,2, 2,-3,0,1,0,-1,-4,3,2,0, -1,1,3,-1,0,0,2,0,1,-2), ncol=3 )
+}
+
+mr <- testMultistart(N, n, p, beta, b, link, nstart, ncores)
+mr_params <- list("N"=N, "nc"=ncores, "n"=n, "link"=link,
"p"=c(p,1-sum(p)), "beta"=beta, "b"=b, "nstart"=nstart)
-save("ms", "ms_params", file=paste("res_",n,"_",d,"_",link,"_",nstart,".RData",sep=""))
+save("mr", "mr_params", file=paste("res_",n,"_",d,"_",link,"_",nstart,".RData",sep=""))