Fix OptimParams + clean test file

diff --git a/pkg/R/optimParams.R b/pkg/R/optimParams.R
index b7d901c..c42e6c5 100644 (file)
--- a/pkg/R/optimParams.R
+++ b/pkg/R/optimParams.R
@@ -104,7 +104,7 @@ setRefClass(
       list(
         # p: dimension K-1, need to be completed
         "p" = c(v[1:(K-1)], 1-sum(v[1:(K-1)])),
-        "β" = matrix(v[K:(K+d*K-1)], ncol=K),
+        "β" = t(matrix(v[K:(K+d*K-1)], ncol=d)),
         "b" = v[(K+d*K):(K+(d+1)*K-1)])
     },
 
@@ -112,12 +112,12 @@ setRefClass(
     {
       "Linearize vectors+matrices from list L into a vector"
 
-      c(L$p[1:(K-1)], as.double(L$β), L$b)
+      # β linearized row by row, to match derivatives order
+      c(L$p[1:(K-1)], as.double(t(L$β)), L$b)
     },
 
     computeW = function(θ)
     {
-      #return (diag(c(rep(6,d), rep(3, d^2), rep(1,d^3))))
       require(MASS)
       dd <- d + d^2 + d^3
       M <- Moments(θ)

diff --git a/pkg/tests/testthat/test-optimParams.R b/pkg/tests/testthat/test-optimParams.R
index 993015f..305c36f 100644 (file)
--- a/pkg/tests/testthat/test-optimParams.R
+++ b/pkg/tests/testthat/test-optimParams.R
@@ -86,130 +86,38 @@ test_that("naive computation provides the same result as vectorized computations
   }
 })
 
-# TODO: test computeW
-#    computeW = function(θ)
-#    {
-#      require(MASS)
-#      dd <- d + d^2 + d^3
-#      M <- Moments(θ)
-#      Id <- as.double(diag(d))
-#      E <- diag(d)
-#      v1 <- Y * X
-#      v2 <- Y * t( apply(X, 1, function(Xi) Xi %o% Xi - Id) )
-#      v3 <- Y * t( apply(X, 1, function(Xi) { return (Xi %o% Xi %o% Xi
-#        - Reduce('+', lapply(1:d, function(j) as.double(Xi %o% E[j,] %o% E[j,])), rep(0, d*d*d))
-#        - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% Xi %o% E[j,])), rep(0, d*d*d))
-#        - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% E[j,] %o% Xi)), rep(0, d*d*d))) } ) )
-#      Wtmp <- matrix(0, nrow=dd, ncol=dd)
-#      
-#
-#g <- matrix(nrow=n, ncol=dd); for (i in 1:n) g[i,] = c(v1[i,], v2[i,], v3[i,]) - M
-#
-#
-#
-#
-#
-#
-#  p <- θ$p
-#  β <- θ$β
-#  b <- θ$b
-#
-#
-#
-#
-##  # Random generation of the size of each population in X~Y (unordered)
-##  classes <- rmultinom(1, n, p)
-##
-##  #d <- nrow(β)
-##  zero_mean <- rep(0,d)
-##  id_sigma <- diag(rep(1,d))
-##  X <- matrix(nrow=0, ncol=d)
-##  Y <- c()
-##  for (i in 1:ncol(β)) #K = ncol(β)
-##  {
-##    newXblock <- MASS::mvrnorm(classes[i], zero_mean, id_sigma)
-##    arg_link <- newXblock %*% β[,i] + b[i]
-##    probas <-
-##      if (li == "logit")
-##      {
-##        e_arg_link = exp(arg_link)
-##        e_arg_link / (1 + e_arg_link)
-##      }
-##      else #"probit"
-##        pnorm(arg_link)
-##    probas[is.nan(probas)] <- 1 #overflow of exp(x)
-##    X <- rbind(X, newXblock)
-##    Y <- c( Y, vapply(probas, function(p) (rbinom(1,1,p)), 1) )
-##  }
-#
-#
-#
-#
-#
-#
-#
-#
-#  Mhatt <- c(
-#    colMeans(Y * X),
-#    colMeans(Y * t( apply(X, 1, function(Xi) Xi %o% Xi - Id) )),
-#    colMeans(Y * t( apply(X, 1, function(Xi) { return (Xi %o% Xi %o% Xi
-#      - Reduce('+', lapply(1:d, function(j) as.double(Xi %o% E[j,] %o% E[j,])), rep(0, d*d*d))
-#      - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% Xi %o% E[j,])), rep(0, d*d*d))
-#      - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% E[j,] %o% Xi)), rep(0, d*d*d))) } ) ) ))
-#  λ <- sqrt(colSums(β^2))
-#  β2 <- apply(β, 2, function(col) col %o% col)
-#  β3 <- apply(β, 2, function(col) col %o% col %o% col)
-#  M <- c(
-#    β  %*% (p * .G(li,1,λ,b)),
-#    β2 %*% (p * .G(li,2,λ,b)),
-#    β3 %*% (p * .G(li,3,λ,b)) )
-#  print(sum(abs(Mhatt - M)))
-#
-#save(list=c("X", "Y"), file="v2.RData")
-#
-#
-#
-#
-#browser()
-#      for (i in 1:n)
-#      {
-#        gi <- t(as.matrix(c(v1[i,], v2[i,], v3[i,]) - M))
-#        Wtmp <- Wtmp + t(gi) %*% gi / n
-#      }
-#      Wtmp
-#      #MASS::ginv(Wtmp)
-#    },
-#
-#    #TODO: compare with R version?
-#    computeW_orig = function(θ)
-#    {
-#      require(MASS)
-#      dd <- d + d^2 + d^3
-#      M <- Moments(θ)
-#      Omega <- matrix( .C("Compute_Omega",
-#        X=as.double(X), Y=as.double(Y), M=as.double(M),
-#        pn=as.integer(n), pd=as.integer(d),
-#        W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd )
-#      Omega
-#      #MASS::ginv(Omega) #, tol=1e-4)
-#    },
-#
-#    Moments = function(θ)
-#    {
-#      "Vector of moments, of size d+d^2+d^3"
-#
-#      p <- θ$p
-#      β <- θ$β
-#      λ <- sqrt(colSums(β^2))
-#      b <- θ$b
-#
-#      # Tensorial products β^2 = β2 and β^3 = β3 must be computed from current β1
-#      β2 <- apply(β, 2, function(col) col %o% col)
-#      β3 <- apply(β, 2, function(col) col %o% col %o% col)
-#
-#      c(
-#        β  %*% (p * .G(li,1,λ,b)),
-#        β2 %*% (p * .G(li,2,λ,b)),
-#        β3 %*% (p * .G(li,3,λ,b)))
-#    },
-#
+test_that("W computed in C and in R are the same",
+{
+  # TODO: provide data X,Y + parameters theta
+  dd <- d + d^2 + d^3
+  p <- θ$p
+  β <- θ$β
+  λ <- sqrt(colSums(β^2))
+  b <- θ$b
+  β2 <- apply(β, 2, function(col) col %o% col)
+  β3 <- apply(β, 2, function(col) col %o% col %o% col)
+  M <- c(
+    β  %*% (p * .G(li,1,λ,b)),
+    β2 %*% (p * .G(li,2,λ,b)),
+    β3 %*% (p * .G(li,3,λ,b)))
+  Id <- as.double(diag(d))
+  E <- diag(d)
+  v1 <- Y * X
+  v2 <- Y * t( apply(X, 1, function(Xi) Xi %o% Xi - Id) )
+  v3 <- Y * t( apply(X, 1, function(Xi) { return (Xi %o% Xi %o% Xi
+    - Reduce('+', lapply(1:d, function(j) as.double(Xi %o% E[j,] %o% E[j,])), rep(0, d*d*d))
+    - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% Xi %o% E[j,])), rep(0, d*d*d))
+    - Reduce('+', lapply(1:d, function(j) as.double(E[j,] %o% E[j,] %o% Xi)), rep(0, d*d*d))) } ) )
+  Omega1 <- matrix(0, nrow=dd, ncol=dd)
+  for (i in 1:n)
+  {
+    gi <- t(as.matrix(c(v1[i,], v2[i,], v3[i,]) - M))
+    Omega1 <- Omega1 + t(gi) %*% gi / n
+  }
+  Omega2 <- matrix( .C("Compute_Omega",
+    X=as.double(X), Y=as.double(Y), M=as.double(M),
+    pn=as.integer(n), pd=as.integer(d),
+    W=as.double(W), PACKAGE="morpheus")$W, nrow=dd, ncol=dd )
+  rg <- range(Omega1 - Omega2)
+  expect_that(rg[2] - rg[1] <= 1e8)
+})