+context("computeMedoidsIndices")
+
+test_that("serialization + getDataInFile retrieve original data / from matrix",
+{
+ data_bin_file = "/tmp/epclust_test_m.bin"
+ unlink(data_bin_file)
+
+ #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"
+
+ #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)
+})
+
+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()
+