X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=epclust%2Ftests%2Ftestthat%2Ftest.computeMedoidsIndices.R;h=8347fb6fbf501a01d4e631fdc58f62bbec195658;hb=0fe757f750f51e580d2c5a7b7f7df87cc405d12d;hp=be5b2b480688fbe322b1f216094ef133860b2c71;hpb=6ad3f3fd0ec4f3cd1fd5de4a287c1893293e5bcc;p=epclust.git

diff --git a/epclust/tests/testthat/test.computeMedoidsIndices.R b/epclust/tests/testthat/test.computeMedoidsIndices.R
index be5b2b4..8347fb6 100644
--- a/epclust/tests/testthat/test.computeMedoidsIndices.R
+++ b/epclust/tests/testthat/test.computeMedoidsIndices.R
@@ -1,42 +1,25 @@
 context("computeMedoidsIndices")
 
-test_that("serialization + getDataInFile retrieve original data / from matrix",
+test_that("computeMedoidsIndices behave as expected",
 {
-	data_bin_file = "/tmp/epclust_test_m.bin"
-	unlink(data_bin_file)
+	# Generate a gaussian mixture
+	n = 999
+	L = 7
+	medoids = cbind( rep(0,L), rep(-5,L), rep(5,L) )
+	# short series...
+	series = t( rbind( MASS::mvrnorm(n/3, medoids[,1], diag(L)),
+		MASS::mvrnorm(n/3, medoids[,2], diag(L)),
+		MASS::mvrnorm(n/3, medoids[,3], diag(L)) ) )
 
-	#dataset 200 lignes / 30 columns
-	data_ascii = matrix(runif(200*30,-10,10),ncol=30)
-	nbytes = 4 #lead to a precision of 1e-7 / 1e-8
-	endian = "little"
+	# With high probability, medoids indices should resemble 1,1,1,...,2,2,2,...,3,3,3,...
+	require("bigmemory", quietly=TRUE)
+	mi = epclust:::computeMedoidsIndices(bigmemory::as.big.matrix(medoids)@address, series)
+	mi_ref = rep(1:3, each=n/3)
+	expect_lt( mean(mi != mi_ref), 0.01 )
 
-	#Simulate serialization in one single call
-	binarize(data_ascii, data_bin_file, 500, ",", nbytes, endian)
-	expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8)
-	for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
-	{
-		data_lines = getDataInFile(indices, data_bin_file, nbytes, endian)
-		expect_equal(data_lines, data_ascii[indices,], tolerance=1e-6)
-	}
-	unlink(data_bin_file)
-
-	#...in several calls (last call complete, next call NULL)
-	for (i in 1:20)
-		binarize(data_ascii[((i-1)*10+1):(i*10),], data_bin_file, 20, ",", nbytes, endian)
-	expect_equal(file.info(data_bin_file)$size, length(data_ascii)*nbytes+8)
-	for (indices in list(c(1,3,5), 3:13, c(5,20,50), c(75,130:135), 196:200))
-	{
-		data_lines = getDataInFile(indices, data_bin_file, nbytes, endian)
-		expect_equal(data_lines, data_ascii[indices,], tolerance=1e-6)
-	}
-	unlink(data_bin_file)
+	# Now with a random matrix, compare with (trusted) R version
+	series = matrix(runif(n*L, min=-7, max=7), nrow=L)
+	mi = epclust:::computeMedoidsIndices(bigmemory::as.big.matrix(medoids)@address, series)
+	mi_ref = R_computeMedoidsIndices(medoids, series)
+	expect_equal(mi, mi_ref)
 })
-
-TODO: test computeMedoids + filter
-#		#R-equivalent, requiring a matrix (thus potentially breaking "fit-in-memory" hope)
-#		mat_meds = medoids[,]
-#		mi = rep(NA,nb_series)
-#		for (i in 1:nb_series)
-#			mi[i] <- which.min( rowSums( sweep(mat_meds, 2, ref_series[i,], '-')^2 ) )
-#		rm(mat_meds); gc()
-