X-Git-Url: https://git.auder.net/?p=morpheus.git;a=blobdiff_plain;f=pkg%2Ftests%2Ftestthat%2Ftest-computeMu.R;h=fccef55fbfa3f49841a69ae575642b050d2eedd4;hp=8234cd72dbb5f78f75fba1074c56f807ea492f2d;hb=6dd5c2acccd10635449230faa824b7e8906911bf;hpb=bbdcfe44da4011574dabb19b4f83e2ab199c667a

diff --git a/pkg/tests/testthat/test-computeMu.R b/pkg/tests/testthat/test-computeMu.R
index 8234cd7..fccef55 100644
--- a/pkg/tests/testthat/test-computeMu.R
+++ b/pkg/tests/testthat/test-computeMu.R
@@ -2,34 +2,34 @@ context("computeMu")
 
 test_that("on input of sufficient size, β/||β|| is estimated accurately enough",
 {
-	n = 100000
-	d = 2
-	K = 2
-	p = 1/2
+  n = 100000
+  d = 2
+  K = 2
+  p = 1/2
 
-	βs_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) )
-	for (i in 1:(dim(βs_ref)[3]))
-	{
-		μ_ref = normalize(βs_ref[,,i])
-		for (model in c("logit","probit"))
-		{
-			cat("\n\n",model," :\n",sep="")
+  βs_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(d,K,2) )
+  for (i in 1:(dim(βs_ref)[3]))
+  {
+    μ_ref = normalize(βs_ref[,,i])
+    for (model in c("logit","probit"))
+    {
+      cat("\n\n",model," :\n",sep="")
 
-			io = generateSampleIO(n, p, βs_ref[,,i], rep(0,K), model)
-			μ = computeMu(io$X, io$Y, list(K=K))
-			μ_aligned = alignMatrices(list(μ), ref=μ_ref, ls_mode="exact")[[1]]
+      io = generateSampleIO(n, p, βs_ref[,,i], rep(0,K), model)
+      μ = computeMu(io$X, io$Y, list(K=K))
+      μ_aligned = alignMatrices(list(μ), ref=μ_ref, ls_mode="exact")[[1]]
 
-			#Some traces: 0 is not well estimated, but others are OK
-			cat("Reference normalized matrix:\n")
-			print(μ_ref)
-			cat("Estimated normalized matrix:\n")
-			print(μ_aligned)
-			cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
-			diff_norm = norm(μ_ref - μ_aligned)
-			cat(diff_norm,"\n")
+      #Some traces: 0 is not well estimated, but others are OK
+      cat("Reference normalized matrix:\n")
+      print(μ_ref)
+      cat("Estimated normalized matrix:\n")
+      print(μ_aligned)
+      cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
+      diff_norm = norm(μ_ref - μ_aligned)
+      cat(diff_norm,"\n")
 
-			#NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
-			expect_that( diff_norm, is_less_than(0.5) )
-		}
-	}
+      #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
+      expect_that( diff_norm, is_less_than(0.5) )
+    }
+  }
 })