| 1 | library(morpheus) |
| 2 | |
| 3 | testMultistart <- function(N, n, K, p, beta, b, link, nstart, ncores) |
| 4 | { |
| 5 | ms <- multiRun( |
| 6 | list(n=n,p=p,beta=beta,b=b,optargs=list(K=K,link=link,nstart=nstart)), |
| 7 | list( |
| 8 | function(fargs) { |
| 9 | # 1 start |
| 10 | library(morpheus) |
| 11 | K <- fargs$optargs$K |
| 12 | op <- optimParams(K, fargs$optargs$link, fargs$optargs) |
| 13 | x_init <- list(p=rep(1/K,K-1), beta=fargs$mu, b=rep(0,K)) |
| 14 | res <- NULL |
| 15 | tryCatch({ |
| 16 | res <- do.call(rbind, op$run(x_init)) |
| 17 | }, error = function(e) { |
| 18 | res <- NA |
| 19 | }) |
| 20 | res |
| 21 | }, |
| 22 | function(fargs) { |
| 23 | # B starts |
| 24 | library(morpheus) |
| 25 | K <- fargs$optargs$K |
| 26 | op <- optimParams(K, fargs$optargs$link, fargs$optargs) |
| 27 | best_val <- Inf |
| 28 | best_par <- list() |
| 29 | for (i in 1:fargs$optargs$nstart) |
| 30 | { |
| 31 | #x_init <- list(p=rep(1/K,K-1), beta=i*fargs$mu, b=rep(0,K)) |
| 32 | M <- matrix(rnorm(d*K), nrow=d, ncol=K) |
| 33 | M <- t(t(M) / sqrt(colSums(M^2))) |
| 34 | x_init <- list(p=rep(1/K,K-1), beta=M, b=rep(0,K)) |
| 35 | tryCatch({ |
| 36 | par <- op$run(x_init) |
| 37 | }, error = function(e) { |
| 38 | par <- NA |
| 39 | }) |
| 40 | if (!is.na(par[0])) |
| 41 | { |
| 42 | val <- op$f( op$linArgs(par) ) |
| 43 | if (val < best_val) |
| 44 | { |
| 45 | best_par <- par |
| 46 | best_val <- val |
| 47 | } |
| 48 | } |
| 49 | } |
| 50 | # Bet that at least one run succeded: |
| 51 | do.call(rbind,best_par) |
| 52 | } |
| 53 | ), |
| 54 | prepareArgs = function(fargs, index) { |
| 55 | library(morpheus) |
| 56 | io = generateSampleIO(fargs$n, fargs$p, fargs$beta, fargs$b, fargs$optargs$link) |
| 57 | fargs$optargs$M <- computeMoments(io$X, io$Y) |
| 58 | mu <- computeMu(io$X, io$Y, fargs$optargs) |
| 59 | fargs$mu <- mu |
| 60 | fargs |
| 61 | }, N=N, ncores=ncores, verbose=TRUE) |
| 62 | for (i in 1:2) |
| 63 | ms[[i]] <- alignMatrices(ms[[i]], ref=rbind(p,beta,b), ls_mode="exact") |
| 64 | ms |
| 65 | } |
| 66 | |
| 67 | #model = binomial |
| 68 | K <- 2 |
| 69 | p <- .5 |
| 70 | b <- c(-.2, .5) |
| 71 | # Default values: |
| 72 | link = "logit" |
| 73 | N <- 10 |
| 74 | d <- 2 |
| 75 | n <- 1e4 |
| 76 | ncores <- 1 |
| 77 | nstart <- 3 #nstart-1 random starting points for each MC run |
| 78 | |
| 79 | cmd_args <- commandArgs() |
| 80 | for (arg in cmd_args) |
| 81 | { |
| 82 | if (substr(arg,1,1)!='-') |
| 83 | { |
| 84 | spl <- strsplit(arg,'=')[[1]] |
| 85 | if (spl[1] == "nc") { |
| 86 | ncores <- as.integer(spl[2]) |
| 87 | } else if (spl[1] == "N") { |
| 88 | N <- as.integer(spl[2]) |
| 89 | } else if (spl[1] == "n") { |
| 90 | n <- as.integer(spl[2]) |
| 91 | } else if (spl[1] == "d") { |
| 92 | d <- as.integer(spl[2]) |
| 93 | } else if (spl[1] == "link") { |
| 94 | link <- spl[2] |
| 95 | } else if (spl[1] == "nstart") { |
| 96 | nstart <- spl[2] |
| 97 | } |
| 98 | } |
| 99 | } |
| 100 | betas <- list( |
| 101 | matrix( c(1,-2, 3,1), ncol=K ), #d=2 |
| 102 | matrix( c(1,2,-1,0,3, 2,-3,0,1,0), ncol=K ), #d=5 |
| 103 | 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 |
| 104 | beta <- betas[[ ifelse( d==2, 1, ifelse(d==5,2,3) ) ]] |
| 105 | |
| 106 | ms <- testMultistart(N, n, K, p, beta, b, link, nstart, ncores) |
| 107 | ms_params <- list("N"=N, "nc"=ncores, "n"=n, "K"=K, "d"=d, "link"=link, |
| 108 | "p"=c(p,1-sum(p)), "beta"=beta, "b"=b, "nstart"=nstart) |
| 109 | |
| 110 | save("ms", "ms_params", file="multistart.RData") |