Last updated: 2019-10-02
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Knit directory: ebpmf_demo/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 2851c1b | zihao12 | 2019-10-02 | start developing ebpmf rank k |
library(ebpm)
library(matrixStats)## Note: in rank1 case, what we need is just row and column sum of X
## TODO:
## 1. think about how to store qg. They include:
# qls_mean_log = matrix(replicate(n*K, 0), ncol = K)
# qfs_mean_log = matrix(replicate(p*K, 0), ncol = K)
# qls_mean = matrix(replicate(n*K, 0), ncol = K)
# qfs_mean = matrix(replicate(p*K, 0), ncol = K)
# # ... gls, gfs
## 2. think about the importance of initialization, and how to
ebpmf_rankk_exponential <- function(X, K, m, init, maxiter.out = 10, maxiter.int = 1, seed){
X = as(X, "dgTMatrix") ## triplet representation: i,j, x
set.seed(123)
qg = init
for(i in 1:maxiter.out){
for(k in 1:K){
## get row & column sum of <Z_ijk>
Ez = get_Ez(X, qg, k)
## update q, g
tmp = ebpmf_rank1_exponential_helper(Ez$rsum,Ez$csum,qg$qls_mean[,k],m, maxiter.int) ## need to deal with (manipulate) sparse matrix
qg = update_qg(tmp, qg, k)
}
}
return(qg)
}
## compute the row & col sum of <Z_ijk> for a given k
## since <Z_ijk> != 0 only if X_ij != 0, we only need to loop over nonzero elements of X
get_Ez <- function(X, qg, k){
rsum = replicate(nrow(x), 0)
csum = replicate(ncol(x), 0)
for(l in 1:length(X@i)){
i = X@i[l]
j = X@j[l]
current = X[i,j] * softmax1d(qg$qls_mean_log[i,] + qg$qfs_mean_log[j,])[k] ## <Z_ijk> = X_ij * psi_ijk
rsum[i] = rsum[i] + current
csum[j] = csum[j] + current
}
return(list(rsum = rsum, csum = csum))
}
softmax1d <- function(x){
return(exp(x - logSumExp(x)))
}
update_qg <- function(tmp, qg, k){
qg$qls_mean[,k] = tmp$ql$mean
qg$qls_mean_log[,k] = tmp$ql$mean_log
qg$qfs_mean[,k] = tmp$qf$mean
qg$qfs_mean_log[,k] = tmp$qf$mean_log
qg$gls[[k]] = tmp$gl
qg$gfs[[k]] = tmp$gf
return(qg)
}
# X: a matrix/array of shape n by p
# different in that only row and column sum of X is provided
ebpmf_rank1_exponential_helper <- function(X_rowsum,X_colsum, init, m = 2, maxiter = 1){
#El = init$ql$mean
ql = init
#E_f = get_exp_F(init)
for(i in 1:maxiter){
## update q(f), g(f)
sum_El = sum(ql$mean)
tmp = ebpm_exponential_mixture(x = X_colsum, s = replicate(p,sum_El), m = m)
qf = tmp$posterior
gf = tmp$fitted_g
ll_f = tmp$log_likelihood
## update q(l), g(l)
sum_Ef = sum(qf$mean)
tmp = ebpm_exponential_mixture(x = X_rowsum, s = replicate(n,sum_Ef), m = m)
ql = tmp$posterior
gl = tmp$fitted_g
ll_l = tmp$log_likelihood
qg = list(ql = ql, gl = gl, qf = qf, gf = gf, ll_f = ll_f, ll_l = ll_l)
# elbo = compute_elbo(X, qg)
# print(sprintf("ELBO: %f", elbo))
}
return(qg)
}
sessionInfo()R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.14
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] matrixStats_0.54.0 ebpm_0.0.0.9000
loaded via a namespace (and not attached):
[1] workflowr_1.4.0 Rcpp_1.0.2 digest_0.6.21 rprojroot_1.3-2
[5] backports_1.1.4 git2r_0.25.2 magrittr_1.5 evaluate_0.14
[9] stringi_1.4.3 fs_1.3.1 whisker_0.3-2 rmarkdown_1.13
[13] tools_3.5.1 stringr_1.4.0 glue_1.3.1 mixsqp_0.1-120
[17] xfun_0.8 yaml_2.2.0 compiler_3.5.1 htmltools_0.3.6
[21] knitr_1.25