+#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) )
+ }
+})