From d83e911fe228571171f9ddc379708dc37c4bfddf Mon Sep 17 00:00:00 2001 From: Gertjan van den Burg Date: Tue, 27 Mar 2018 19:24:03 +0100 Subject: Major bugfix for nonlinear GenSVM Nonlinear GenSVM depends on the eigendecomposition of the kernel matrix. Mathematically, the Sigma vector in the code should contain the square root of the eigenvalues. Taking the square root was however neglected, which resulted in poor performance of nonlinear GenSVM. This is now fixed, which means that the performance of nonlinear GenSVM will be much better. --- CHANGELOG.md | 4 + Makefile | 2 +- src/gensvm_kernel.c | 5 +- tests/aux/test_eigendecomp.m | 2 +- tests/aux/test_kernel_pre.m | 46 +++++------ tests/aux/test_train_kernel.m | 30 +++---- tests/src/test_gensvm_io.c | 2 +- tests/src/test_gensvm_kernel.c | 183 +++++++++++++++++++++++------------------ tests/src/test_gensvm_train.c | 57 +++++++------ 9 files changed, 182 insertions(+), 149 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 81713d4..5f3bcc4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,9 @@ # Change Log +## Version 0.2.0 + +- Fix bug in nonlinear GenSVM, it should perform better now. + ## Version 0.1.4 - Keep track of elapsed iterations during training diff --git a/Makefile b/Makefile index 07855ae..15ff02a 100644 --- a/Makefile +++ b/Makefile @@ -1,4 +1,4 @@ -VERSION=0.1.4 +VERSION=0.2.0 CC=gcc CFLAGS=-Wall -Wno-unused-result -Wsign-compare -Wstrict-prototypes \ -DVERSION=$(VERSION) -g -O3 diff --git a/src/gensvm_kernel.c b/src/gensvm_kernel.c index 521bddb..190a120 100644 --- a/src/gensvm_kernel.c +++ b/src/gensvm_kernel.c @@ -264,9 +264,12 @@ long gensvm_kernel_eigendecomp(double *K, long n, double cutoff, double **P_ret, num_eigen = n - cutoff_idx; + // In the mathematical derivation (see paper), we state that the + // diagonal matrix Sigma contains the square root of the eigenvalues + // (i.e. the eigendecomposition is: K = P * Sigma^2 * P'). Sigma = Calloc(double, num_eigen); for (i=0; i cutoff)); -realSigma = flipud(eigenvalues(ratios > cutoff)); +realSigma = sqrt(flipud(eigenvalues(ratios > cutoff))); r = sum(ratios > cutoff); diff --git a/tests/aux/test_kernel_pre.m b/tests/aux/test_kernel_pre.m index a1c8337..69f6f1e 100644 --- a/tests/aux/test_kernel_pre.m +++ b/tests/aux/test_kernel_pre.m @@ -1,32 +1,32 @@ function test_kernel_pre() - + kerneltype = 'rbf'; rand('state', 123456); n = 10; m = 5; cutoff = 5e-3; - + X = rand(n, m); Z = [ones(n, 1), X]; - + set_matrix(Z, "data->Z", "data->m+1"); - + K = zeros(n, n); if strcmp(kerneltype, 'poly') - # Polynomial kernel - # (gamma * + c)^d + % Polynomial kernel + % (gamma * + c)^d gamma = 1.5; c = 3.0; d = 1.78; - + for ii=1:n for jj=1:n K(ii, jj) = (gamma * (X(ii, :) * X(jj, :)') + c)^d; end end elseif strcmp(kerneltype, 'rbf') - # RBF kernel - # exp(-gamma * norm(x1 - x2)^2) + % RBF kernel + % exp(-gamma * norm(x1 - x2)^2) gamma = 0.348 for ii=1:n for jj=1:n @@ -34,8 +34,8 @@ function test_kernel_pre() end end elseif strcmp(kerneltype, 'sigmoid') - # Sigmoid kernel - # tanh(gamma * + c) + % Sigmoid kernel + % tanh(gamma * + c) gamma = 1.23; c = 1.6; for ii=1:n @@ -44,29 +44,27 @@ function test_kernel_pre() end end end - - K(1, 2) - [P, Sigma] = eig(K); + [P, values] = eig(K); - eigenvalues = diag(Sigma); + eigenvalues = diag(values); ratios = eigenvalues ./ eigenvalues(end, end); - + realP = fliplr(P(:, ratios > cutoff)); - realSigma = flipud(eigenvalues(ratios > cutoff)); - + realSigma = sqrt(flipud(eigenvalues(ratios > cutoff))); + assert_matrix(realSigma, "data->Sigma", "1"); - + r = sum(ratios > cutoff); fprintf("mu_assert(data->r == %i);\n", r); - + M = realP * diag(realSigma); - + newZ = [ones(n, 1) M]; assert_matrix_abs(newZ, "data->Z", "data->r+1"); - + assert_matrix(Z, "data->RAW", "data->m+1"); - + end function set_matrix(A, name, cols) @@ -98,4 +96,4 @@ function assert_matrix_abs(A, name, cols) end end fprintf("\n"); -end \ No newline at end of file +end diff --git a/tests/aux/test_train_kernel.m b/tests/aux/test_train_kernel.m index 7bf14b8..b561bd4 100644 --- a/tests/aux/test_train_kernel.m +++ b/tests/aux/test_train_kernel.m @@ -1,21 +1,21 @@ function [V] = test_train_kernel() - + clear; more off; rand('state', 654321); - + n = 10; m = 5; classes = 4; cutoff = 5e-3; - + X = rand(n, m); Z = [ones(n, 1), X]; set_matrix(Z, "data->Z", "data->m+1"); - + y = [2 1 3 2 3 2 4 1 3 4]; set_matrix(y, "data->y", "1"); - + p = 1.2143; kappa = 0.90298; lambda = 0.00219038; @@ -36,24 +36,24 @@ function [V] = test_train_kernel() eigenvalues = diag(Sigma); ratios = eigenvalues ./ eigenvalues(end, end); - + realP = fliplr(P(:, ratios > cutoff)); - realSigma = flipud(eigenvalues(ratios > cutoff)); - + realSigma = sqrt(flipud(eigenvalues(ratios > cutoff))); + assert_matrix(realSigma, "data->Sigma", "1"); - + r = sum(ratios > cutoff); fprintf("mu_assert(data->r == %i);\n", r); - + M = realP * diag(realSigma); size(M) - + assert_matrix(Z, "data->RAW", "data->m+1"); - - seedV = zeros(size(M, 2) + 1, classes - 1); + + seedV = zeros(size(M, 2) + 1, classes - 1); [W, t] = msvmmaj(M, y, rho, p, kappa, lambda, epsilon, 'show', 0, seedV); V = [t'; W]; - + fprintf('\n'); assert_matrix_abs(V, "model->V", "model->K-1"); @@ -89,4 +89,4 @@ function assert_matrix_abs(A, name, cols) end end fprintf("\n"); -end \ No newline at end of file +end diff --git a/tests/src/test_gensvm_io.c b/tests/src/test_gensvm_io.c index bf9746f..b4210bb 100644 --- a/tests/src/test_gensvm_io.c +++ b/tests/src/test_gensvm_io.c @@ -654,7 +654,7 @@ char *test_gensvm_write_model() char buffer[GENSVM_MAX_LINE_LENGTH]; fgets(buffer, GENSVM_MAX_LINE_LENGTH, fid); - mu_assert(strcmp(buffer, "Output file for GenSVM (version 0.1.4)\n") + mu_assert(strcmp(buffer, "Output file for GenSVM (version 0.2.0)\n") == 0, "Line doesn't contain expected content (0).\n"); // skip the time line diff --git a/tests/src/test_gensvm_kernel.c b/tests/src/test_gensvm_kernel.c index cf1d4db..b075bc5 100644 --- a/tests/src/test_gensvm_kernel.c +++ b/tests/src/test_gensvm_kernel.c @@ -326,245 +326,269 @@ char *test_kernel_preprocess_kernel() mu_assert(data->r == 7, "Incorrect data->r"); double eps = 1e-14; + + mu_assert(fabs(matrix_get(data->Sigma, 1, 0, 0) - + 2.7638223432435374) < eps, + "Incorrect data->Sigma at 0, 0"); + mu_assert(fabs(matrix_get(data->Sigma, 1, 1, 0) - + 0.8989108618424078) < eps, + "Incorrect data->Sigma at 1, 0"); + mu_assert(fabs(matrix_get(data->Sigma, 1, 2, 0) - + 0.8492992522024180) < eps, + "Incorrect data->Sigma at 2, 0"); + mu_assert(fabs(matrix_get(data->Sigma, 1, 3, 0) - + 0.6551312398422764) < eps, + "Incorrect data->Sigma at 3, 0"); + mu_assert(fabs(matrix_get(data->Sigma, 1, 4, 0) - + 0.4151267289513675) < eps, + "Incorrect data->Sigma at 4, 0"); + mu_assert(fabs(matrix_get(data->Sigma, 1, 5, 0) - + 0.3219015071458272) < eps, + "Incorrect data->Sigma at 5, 0"); + mu_assert(fabs(matrix_get(data->Sigma, 1, 6, 0) - + 0.2206142024294812) < eps, + "Incorrect data->Sigma at 6, 0"); + + mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 0, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 1)) - - fabs(2.4632837902141640)) < eps, + fabs(0.8912598149573278)) < eps, "Incorrect data->Z at 0, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 2)) - - fabs(-0.3037489220604925)) < eps, + fabs(-0.3379077225053534)) < eps, "Incorrect data->Z at 0, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 3)) - - fabs(-0.0061287029147240)) < eps, + fabs(-0.0072161878146390)) < eps, "Incorrect data->Z at 0, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 4)) - - fabs(0.1822712619914593)) < eps, + fabs(0.2782209897902920)) < eps, "Incorrect data->Z at 0, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 5)) - - fabs(0.0252737053303148)) < eps, + fabs(0.0608819032061786)) < eps, "Incorrect data->Z at 0, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 6)) - - fabs(-0.0078753266252524)) < eps, + fabs(-0.0244650194249782)) < eps, "Incorrect data->Z at 0, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 0, 7)) - - fabs(-0.0012800124996018)) < eps, + fabs(-0.0058020403287988)) < eps, "Incorrect data->Z at 0, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 1, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 1)) - - fabs(2.2923640983040641)) < eps, + fabs(0.8294180354638190)) < eps, "Incorrect data->Z at 1, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 2)) - - fabs(-0.3048037728463330)) < eps, + fabs(-0.3390811990207864)) < eps, "Incorrect data->Z at 1, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 3)) - - fabs(-0.2586192720897897)) < eps, + fabs(-0.3045090071834334)) < eps, "Incorrect data->Z at 1, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 4)) - - fabs(0.1747912247100736)) < eps, + fabs(0.2668033732480146)) < eps, "Incorrect data->Z at 1, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 5)) - - fabs(-0.0623497873850738)) < eps, + fabs(-0.1501945864641692)) < eps, "Incorrect data->Z at 1, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 6)) - - fabs(-0.0199493291395259)) < eps, + fabs(-0.0619733946461100)) < eps, "Incorrect data->Z at 1, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 1, 7)) - - fabs(0.0068540206892510)) < eps, + fabs(0.0310679032164383)) < eps, "Incorrect data->Z at 1, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 2, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 1)) - - fabs(2.4167201742337761)) < eps, + fabs(0.8744122718819858)) < eps, "Incorrect data->Z at 2, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 2)) - - fabs(-0.1499385272847361)) < eps, + fabs(-0.1668002175181436)) < eps, "Incorrect data->Z at 2, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 3)) - - fabs(-0.1781619658696836)) < eps, + fabs(-0.2097752534311912)) < eps, "Incorrect data->Z at 2, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 4)) - - fabs(-0.2363293887681946)) < eps, + fabs(-0.3607359478462531)) < eps, "Incorrect data->Z at 2, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 5)) - - fabs(-0.0362117307160720)) < eps, + fabs(-0.0872305447725448)) < eps, "Incorrect data->Z at 2, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 6)) - - fabs(0.0366137533260933)) < eps, + fabs(0.1137420997209145)) < eps, "Incorrect data->Z at 2, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 2, 7)) - - fabs(0.0227049982868101)) < eps, + fabs(0.1029172103916005)) < eps, "Incorrect data->Z at 2, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 3, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 1)) - - fabs(2.3339314848494390)) < eps, + fabs(0.8444578540132961)) < eps, "Incorrect data->Z at 3, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 2)) - - fabs(0.4067278927345656)) < eps, + fabs(0.4524674358711562)) < eps, "Incorrect data->Z at 3, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 3)) - - fabs(0.0198947146890620)) < eps, + fabs(0.0234248583611390)) < eps, "Incorrect data->Z at 3, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 4)) - - fabs(0.1187106614859180)) < eps, + fabs(0.1812013445038858)) < eps, "Incorrect data->Z at 3, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 5)) - - fabs(0.0734848412140159)) < eps, + fabs(0.1770178504276099)) < eps, "Incorrect data->Z at 3, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 6)) - - fabs(-0.0166955533210990)) < eps, + fabs(-0.0518654089852882)) < eps, "Incorrect data->Z at 3, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 3, 7)) - - fabs(0.0229112619510384)) < eps, + fabs(0.1038521622757349)) < eps, "Incorrect data->Z at 3, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 4, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 1)) - - fabs(2.5061509421266424)) < eps, + fabs(0.9067699116960968)) < eps, "Incorrect data->Z at 4, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 2)) - - fabs(0.0574469229922174)) < eps, + fabs(0.0639072520210456)) < eps, "Incorrect data->Z at 4, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 3)) - - fabs(-0.2858649955147738)) < eps, + fabs(-0.3365892466918618)) < eps, "Incorrect data->Z at 4, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 4)) - - fabs(-0.0995031375002134)) < eps, + fabs(-0.1518827548571320)) < eps, "Incorrect data->Z at 4, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 5)) - - fabs(0.0223790101651578)) < eps, + fabs(0.0539088634973914)) < eps, "Incorrect data->Z at 4, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 6)) - - fabs(-0.0355571480867735)) < eps, + fabs(-0.1104597129788070)) < eps, "Incorrect data->Z at 4, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 4, 7)) - - fabs(-0.0219026472149696)) < eps, + fabs(-0.0992803136596373)) < eps, "Incorrect data->Z at 4, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 5, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 1)) - - fabs(2.4482858151168982)) < eps, + fabs(0.8858332812534048)) < eps, "Incorrect data->Z at 5, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 2)) - - fabs(-0.0670998214520230)) < eps, + fabs(-0.0746456899124524)) < eps, "Incorrect data->Z at 5, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 3)) - - fabs(0.3147064295566219)) < eps, + fabs(0.3705483417541217)) < eps, "Incorrect data->Z at 5, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 4)) - - fabs(0.1070535630418465)) < eps, + fabs(0.1634078128645188)) < eps, "Incorrect data->Z at 5, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 5)) - - fabs(-0.0052824396955993)) < eps, + fabs(-0.0127248845405431)) < eps, "Incorrect data->Z at 5, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 6)) - - fabs(0.0614363461130733)) < eps, + fabs(0.1908544842110400)) < eps, "Incorrect data->Z at 5, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 5, 7)) - - fabs(-0.0075355247061472)) < eps, + fabs(-0.0341570244488496)) < eps, "Incorrect data->Z at 5, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 6, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 1)) - - fabs(2.3638928644404329)) < eps, + fabs(0.8552984131629242)) < eps, "Incorrect data->Z at 6, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 2)) - - fabs(0.3482541374011597)) < eps, + fabs(0.3874178766595150)) < eps, "Incorrect data->Z at 6, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 3)) - - fabs(-0.2422541976251498)) < eps, + fabs(-0.2852400929318269)) < eps, "Incorrect data->Z at 6, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 4)) - - fabs(0.0251886519764033)) < eps, + fabs(0.0384482534865339)) < eps, "Incorrect data->Z at 6, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 5)) - - fabs(-0.0079397861684362)) < eps, + fabs(-0.0191261742853618)) < eps, "Incorrect data->Z at 6, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 6)) - - fabs(0.0424975213407462)) < eps, + fabs(0.1320202620905840)) < eps, "Incorrect data->Z at 6, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 6, 7)) - - fabs(-0.0204279932276333)) < eps, + fabs(-0.0925960024453241)) < eps, "Incorrect data->Z at 6, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 7, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 1)) - - fabs(2.3607306299135344)) < eps, + fabs(0.8541542605604135)) < eps, "Incorrect data->Z at 7, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 2)) - - fabs(-0.0220102589508912)) < eps, + fabs(-0.0244854744615944)) < eps, "Incorrect data->Z at 7, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 3)) - - fabs(0.3913398731540265)) < eps, + fabs(0.4607797217991149)) < eps, "Incorrect data->Z at 7, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 4)) - - fabs(-0.0941469673695446)) < eps, + fabs(-0.1437070340168947)) < eps, "Incorrect data->Z at 7, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 5)) - - fabs(-0.0477595489009114)) < eps, + fabs(-0.1150481180085769)) < eps, "Incorrect data->Z at 7, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 6)) - - fabs(-0.0367688438245860)) < eps, + fabs(-0.1142238946024231)) < eps, "Incorrect data->Z at 7, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 7, 7)) - - fabs(-0.0133498576642393)) < eps, + fabs(-0.0605122313850435)) < eps, "Incorrect data->Z at 7, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 8, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 1)) - - fabs(2.5023932475093376)) < eps, + fabs(0.9054103110595034)) < eps, "Incorrect data->Z at 8, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 2)) - - fabs(0.2929602950386334)) < eps, + fabs(0.3259058350214859)) < eps, "Incorrect data->Z at 8, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 3)) - - fabs(0.1371647327912284)) < eps, + fabs(0.1615034187720415)) < eps, "Incorrect data->Z at 8, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 4)) - - fabs(-0.0270505649533715)) < eps, + fabs(-0.0412902992687150)) < eps, "Incorrect data->Z at 8, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 5)) - - fabs(-0.0685258491091892)) < eps, + fabs(-0.1650721197410958)) < eps, "Incorrect data->Z at 8, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 6)) - - fabs(-0.0213385621647371)) < eps, + fabs(-0.0662891030052566)) < eps, "Incorrect data->Z at 8, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 8, 7)) - - fabs(0.0121305554343051)) < eps, + fabs(0.0549853785509707)) < eps, "Incorrect data->Z at 8, 7"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 0)) - fabs(1.0000000000000000)) < eps, "Incorrect data->Z at 9, 0"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 1)) - - fabs(2.4579608302226870)) < eps, + fabs(0.8893338735144964)) < eps, "Incorrect data->Z at 9, 1"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 2)) - - fabs(-0.2538863526247282)) < eps, + fabs(-0.2824377403832496)) < eps, "Incorrect data->Z at 9, 2"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 3)) - - fabs(0.0991005899665861)) < eps, + fabs(0.1166851256604740)) < eps, "Incorrect data->Z at 9, 3"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 4)) - - fabs(-0.1374781282330359)) < eps, + fabs(-0.2098482256259584)) < eps, "Incorrect data->Z at 9, 4"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 5)) - - fabs(0.1043628273525485)) < eps, + fabs(0.2513999221784036)) < eps, "Incorrect data->Z at 9, 5"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 6)) - - fabs(-0.0024213407513349)) < eps, + fabs(-0.0075219925896089)) < eps, "Incorrect data->Z at 9, 6"); mu_assert(fabs(fabs(matrix_get(data->Z, data->r+1, 9, 7)) - - fabs(0.0007673936590348)) < eps, + fabs(0.0034784417801946)) < eps, "Incorrect data->Z at 9, 7"); mu_assert(fabs(matrix_get(data->RAW, data->m+1, 0, 0) - @@ -748,9 +772,9 @@ char *test_kernel_preprocess_kernel() 0.1028774221216107) < eps, "Incorrect data->RAW at 9, 5"); - // end test code // + // end test code // gensvm_free_model(model); gensvm_free_data(data); @@ -2317,7 +2341,7 @@ char *test_kernel_eigendecomp() mu_assert(r == 7, "Incorrect number of eigenvalues kept"); - // Note: to overcome sign variability in the eigenvectors, we take the + // Note: to overcome sign variability in the eigenvectors, we take the // absolute value of the elements of P and the expected outcome. mu_assert(fabs(fabs(matrix_get(P, r, 0, 0)) - @@ -2532,19 +2556,20 @@ char *test_kernel_eigendecomp() "Incorrect P at 9, 6"); eps = 1e-13; - mu_assert(fabs(Sigma[0] - 22.9663721202447704) < eps, + + mu_assert(fabs(Sigma[0] - 4.7923242920575353) < eps, "Incorrect Sigma at 0"); - mu_assert(fabs(Sigma[1] - 2.2569857335186856) < eps, + mu_assert(fabs(Sigma[1] - 1.5023267732150303) < eps, "Incorrect Sigma at 1"); - mu_assert(fabs(Sigma[2] - 1.4177404640754938) < eps, + mu_assert(fabs(Sigma[2] - 1.1906890711161726) < eps, "Incorrect Sigma at 2"); - mu_assert(fabs(Sigma[3] - 1.0075496644459232) < eps, + mu_assert(fabs(Sigma[3] - 1.0037677343120393) < eps, "Incorrect Sigma at 3"); - mu_assert(fabs(Sigma[4] - 0.7919296491505164) < eps, + mu_assert(fabs(Sigma[4] - 0.8899042921295091) < eps, "Incorrect Sigma at 4"); - mu_assert(fabs(Sigma[5] - 0.6808726728950240) < eps, + mu_assert(fabs(Sigma[5] - 0.8251500911319253) < eps, "Incorrect Sigma at 5"); - mu_assert(fabs(Sigma[6] - 0.2909718164349679) < eps, + mu_assert(fabs(Sigma[6] - 0.5394180349552358) < eps, "Incorrect Sigma at 6"); // end test code // diff --git a/tests/src/test_gensvm_train.c b/tests/src/test_gensvm_train.c index b95de28..f7033f0 100644 --- a/tests/src/test_gensvm_train.c +++ b/tests/src/test_gensvm_train.c @@ -275,23 +275,24 @@ char *test_gensvm_train_seed_kernel() mu_assert(model->K == data->K, "Incorrect model K"); double eps = 1e-13; + mu_assert(fabs(matrix_get(data->Sigma, 1, 0, 0) - - 7.8302939172918506) < eps, + 2.7982662341692670) < eps, "Incorrect data->Sigma at 0, 0"); mu_assert(fabs(matrix_get(data->Sigma, 1, 1, 0) - - 0.7947913383766066) < eps, + 0.8915107056993801) < eps, "Incorrect data->Sigma at 1, 0"); mu_assert(fabs(matrix_get(data->Sigma, 1, 2, 0) - - 0.5288740088908547) < eps, + 0.7272372438832145) < eps, "Incorrect data->Sigma at 2, 0"); mu_assert(fabs(matrix_get(data->Sigma, 1, 3, 0) - - 0.4537982052555444) < eps, + 0.6736454596117636) < eps, "Incorrect data->Sigma at 3, 0"); mu_assert(fabs(matrix_get(data->Sigma, 1, 4, 0) - - 0.2226012271232192) < eps, + 0.4718063449374322) < eps, "Incorrect data->Sigma at 4, 0"); mu_assert(fabs(matrix_get(data->Sigma, 1, 5, 0) - - 0.0743004417495061) < eps, + 0.2725810737184557) < eps, "Incorrect data->Sigma at 5, 0"); // we need a large eps here because there are numerical precision @@ -299,69 +300,71 @@ char *test_gensvm_train_seed_kernel() // compare with absolute values because of variability in the // eigendecomposition. eps = 1e-7; + mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 0, 0)) - - fabs(5.0555413160638665)) < eps, + fabs(1.3968329665264863)) < eps, "Incorrect model->V at 0, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 0, 1)) - - fabs(-2.2586632211763198)) < eps, + fabs(-0.4491223112772532)) < eps, "Incorrect model->V at 0, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 0, 2)) - - fabs(-4.5572671806963143)) < eps, + fabs(-1.2044427235549637)) < eps, "Incorrect model->V at 0, 2"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 1, 0)) - - fabs(-1.9627432869558412)) < eps, + fabs(-1.2834234211019704)) < eps, "Incorrect model->V at 1, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 1, 1)) - - fabs(0.9934555242449399)) < eps, + fabs(0.6330939040375793)) < eps, "Incorrect model->V at 1, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 1, 2)) - - fabs(1.7855287218670219)) < eps, + fabs(1.2876548429115076)) < eps, "Incorrect model->V at 1, 2"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 2, 0)) - - fabs(1.9393083227054353)) < eps, + fabs(2.0023377286211428)) < eps, "Incorrect model->V at 2, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 2, 1)) - - fabs(-1.1958487809502740)) < eps, + fabs(-1.5454495147993872)) < eps, "Incorrect model->V at 2, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 2, 2)) - - fabs(2.1140967864804359)) < eps, + fabs(1.8380262406111434)) < eps, "Incorrect model->V at 2, 2"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 3, 0)) - - fabs(2.3909204618652535)) < eps, + fabs(1.8873525552961188)) < eps, "Incorrect model->V at 3, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 3, 1)) - - fabs(-0.2834554569573399)) < eps, + fabs(-0.5671111794102348)) < eps, "Incorrect model->V at 3, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 3, 2)) - - fabs(1.0926232371314393)) < eps, + fabs(1.3530484176263944)) < eps, "Incorrect model->V at 3, 2"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 4, 0)) - - fabs(3.3374545494113272)) < eps, + fabs(2.9991675684385952)) < eps, "Incorrect model->V at 4, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 4, 1)) - - fabs(1.6699291195221897)) < eps, + fabs(1.6232323178615611)) < eps, "Incorrect model->V at 4, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 4, 2)) - - fabs(-1.4345249893609275)) < eps, + fabs(-1.0853101351516645)) < eps, "Incorrect model->V at 4, 2"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 5, 0)) - - fabs(-0.0221825925355533)) < eps, + fabs(-0.2735156994082831)) < eps, "Incorrect model->V at 5, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 5, 1)) - - fabs(-0.1216077739550210)) < eps, + fabs(-0.2154874773946488)) < eps, "Incorrect model->V at 5, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 5, 2)) - - fabs(-0.7900947982642630)) < eps, + fabs(-0.9036193937904904)) < eps, "Incorrect model->V at 5, 2"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 6, 0)) - - fabs(-0.0076471781062262)) < eps, + fabs(-0.1010202110238350)) < eps, "Incorrect model->V at 6, 0"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 6, 1)) - - fabs(-0.8781872510019056)) < eps, + fabs(-1.7921615999242961)) < eps, "Incorrect model->V at 6, 1"); mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 6, 2)) - - fabs(-0.2782284589344380)) < eps, + fabs(-0.6850178130530472)) < eps, "Incorrect model->V at 6, 2"); + // end test code // gensvm_free_model(model); -- cgit v1.2.3