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()