Commit | Line | Data |
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1e20780e BA |
1 | #TODO: toy dataset, check that indices returned are correct + colors |
2 | ||
3 | context("Check that getParamsDirs behaves as expected") | |
4 | ||
5 | test_that("on input of sufficient size, beta is estimated accurately enough", { | |
6 | n = 100000 | |
7 | d = 2 | |
8 | K = 2 | |
9 | Pr = c(0.5, 0.5) | |
10 | ||
11 | betas_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(2,2,2) ) | |
12 | for (i in 1:(dim(betas_ref)[3])) | |
13 | { | |
14 | beta_ref = betas_ref[,,i] | |
15 | #all parameters are supposed to be of norm 1: thus, normalize beta_ref | |
16 | norm2 = sqrt(colSums(beta_ref^2)) | |
17 | beta_ref = beta_ref / norm2[col(beta_ref)] | |
18 | ||
19 | io = generateSampleIO(n, d, K, Pr, beta_ref) | |
20 | beta = getParamsDirs(io$X, io$Y, K) | |
21 | betas = .labelSwitchingAlign( | |
22 | array( c(beta_ref,beta), dim=c(d,K,2) ), compare_to="first", ls_mode="exact") | |
23 | ||
24 | #Some traces: 0 is not well estimated, but others are OK | |
25 | cat("\n\nReference parameter matrix:\n") | |
26 | print(beta_ref) | |
27 | cat("Estimated parameter matrix:\n") | |
28 | print(betas[,,2]) | |
29 | cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n") | |
30 | diff_norm = norm(beta_ref - betas[,,2]) | |
31 | cat(diff_norm,"\n") | |
32 | ||
33 | #NOTE: 0.5 is loose threshold, but values around 0.3 are expected... | |
34 | expect_that( diff_norm, is_less_than(0.5) ) | |
35 | } | |
36 | }) |