bnp

commit fc7f6f60687fddc60226bf597fee63222246f016
parent dd80190895cd11da9b0a0d7ea4a836e7d4c89772
Author: Jared Tobin <jared@jtobin.ca>
Date:   Fri, 18 Mar 2016 08:55:48 +1300

Misc fixes.

Diffstat:
M
finite-gaussian-mixture/src/fmm_multivariate_conditional_collapsed.r
 | 
16
+++++++++++-----
M
finite-gaussian-mixture/src/fmm_multivariate_generative.r
 | 
1
+
M
finite-gaussian-mixture/src/sim_fmm_1d_conditional_collapsed.r
 | 
5
++---
M
finite-gaussian-mixture/src/sim_fmm_2d_conditional.r
 | 
6
+++---
M
finite-gaussian-mixture/src/sim_fmm_2d_conditional_collapsed.r
 | 
19
+++++++++----------

5 files changed, 26 insertions(+), 21 deletions(-)
diff --git a/finite-gaussian-mixture/src/fmm_multivariate_conditional_collapsed.r b/finite-gaussian-mixture/src/fmm_multivariate_conditional_collapsed.r
@@ -1,11 +1,17 @@
 require(mvtnorm)
 
 cluster_statistics = function(cluster, l, b, w) {
-  mclust   = as.matrix(cluster)
+  mclust   =
+    # R, seriously?
+    if (is.null(dim(cluster))) {
+      matrix(cluster, ncol = ncol(w))
+    } else {
+      as.matrix(cluster)
+    }
   m        = ncol(mclust)
   n        = nrow(mclust)
   ybar     = colMeans(mclust)
-  centered = as.matrix(mclust) - ybar
+  centered = mclust - ybar
   ss       = t(centered) %*% centered
   ln       = (l + n * ybar) / (1 + n)
   tn       =
@@ -25,7 +31,7 @@ cluster_statistics = function(cluster, l, b, w) {
     )
 }
 
-conditional_label_model = function(y, k, z, a, l, r, b, w) {
+conditional_label_model = function(y, k, z, a, l, b, w) {
   cluster_labels = seq(k)
   rows           = seq(nrow(y))
   m              = ncol(y)
@@ -79,9 +85,9 @@ conditional_label_model = function(y, k, z, a, l, r, b, w) {
   sapply(rows, relabel)
 }
 
-inverse_model = function(n, k, y, a, l, r, b, w) {
+inverse_model = function(n, k, y, a, l, b, w) {
   gibbs = function(z0) {
-    list(z = conditional_label_model(y, k, z0, a, l, r, b, w))
+    list(z = conditional_label_model(y, k, z0, a, l, b, w))
   }
   params = list(z = sample(seq(k), size = nrow(y), replace = T))
   acc    = params
diff --git a/finite-gaussian-mixture/src/fmm_multivariate_generative.r b/finite-gaussian-mixture/src/fmm_multivariate_generative.r
@@ -1,4 +1,5 @@
 require(mvtnorm)
+require(plyr)
 
 source('fmm_utils.r')
 
diff --git a/finite-gaussian-mixture/src/sim_fmm_1d_conditional_collapsed.r b/finite-gaussian-mixture/src/sim_fmm_1d_conditional_collapsed.r
@@ -11,8 +11,7 @@ config = list(
   , m = dimension
   , a = 1
   , l = rep(0, dimension)
-  , r = diag(0.05, dimension)
-  , b = 2
+  , b = dimension
   , w = diag(1, dimension)
   , n = 50
   )
@@ -34,7 +33,7 @@ set.seed(990909)
 params = inverse_model(
     config$n, config$k, as.matrix(m[, c('x')])
   , config$a
-  , config$l, config$r
+  , config$l
   , config$b, config$w
   )
 
diff --git a/finite-gaussian-mixture/src/sim_fmm_2d_conditional.r b/finite-gaussian-mixture/src/sim_fmm_2d_conditional.r
@@ -20,9 +20,9 @@ config = list(
 set.seed(222)
 
 d = list(
-    t(replicate(250, rnorm(2, c(5, 5))))
-  , t(replicate(250, rnorm(2, c(-5, -5))))
-  , t(replicate(500, rnorm(2))))
+    t(replicate(250, rnorm(dimension, c(5, 5))))
+  , t(replicate(250, rnorm(dimension, c(-5, -5))))
+  , t(replicate(500, rnorm(dimension))))
 dn = lapply(d, function(j) { data.frame(x = j[,1], y = j[,2]) })
 m  = melt(dn, id.vars = c('x', 'y'))
 
diff --git a/finite-gaussian-mixture/src/sim_fmm_2d_conditional_collapsed.r b/finite-gaussian-mixture/src/sim_fmm_2d_conditional_collapsed.r
@@ -11,27 +11,26 @@ config = list(
   , m = dimension
   , a = 1
   , l = rep(0, dimension)
-  , r = diag(0.05, dimension)
-  , b = 2
-  , w = diag(1, dimension)
-  , n = 50
+  , b = dimension
+  , w = diag(0.05, dimension)
+  , n = 25
   )
 
 set.seed(222)
 
 d = list(
-    t(replicate(100, rnorm(config$m, c(5, 5))))
-  , t(replicate(100, rnorm(config$m, c(-5, -5))))
-  , t(replicate(200, rnorm(config$m))))
+    t(replicate(250, rnorm(config$m, c(5, 5))))
+  , t(replicate(250, rnorm(config$m, c(-5, -5))))
+  , t(replicate(500, rnorm(config$m))))
 dn = lapply(d, function(j) { data.frame(x = j[,1], y = j[,2]) })
 m  = melt(dn, id.vars = c('x', 'y'))
 
-set.seed(990909)
+set.seed(222)
 
 params = inverse_model(
-    config$n, config$k, m[, c('x', 'y')]
+    config$n, config$k, as.matrix(m[, c('x', 'y')])
   , config$a
-  , config$l, config$r
+  , config$l
   , config$b, config$w
   )