#TODO: toy dataset, check that indices returned are correct + colors

context("Check that getParamsDirs behaves as expected")

test_that("on input of sufficient size, beta is estimated accurately enough", {
	n = 100000
	d = 2
	K = 2
	Pr = c(0.5, 0.5)

	betas_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(2,2,2) )
	for (i in 1:(dim(betas_ref)[3]))
	{
		beta_ref = betas_ref[,,i]
		#all parameters are supposed to be of norm 1: thus, normalize beta_ref
		norm2 = sqrt(colSums(beta_ref^2))
		beta_ref = beta_ref / norm2[col(beta_ref)]

		io = generateSampleIO(n, d, K, Pr, beta_ref)
		beta = getParamsDirs(io$X, io$Y, K)
		betas = .labelSwitchingAlign(
			array( c(beta_ref,beta), dim=c(d,K,2) ), compare_to="first", ls_mode="exact")

		#Some traces: 0 is not well estimated, but others are OK
		cat("\n\nReference parameter matrix:\n")
		print(beta_ref)
		cat("Estimated parameter matrix:\n")
		print(betas[,,2])
		cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
		diff_norm = norm(beta_ref - betas[,,2])
		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) )
	}
})