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-rw-r--r--src/gensvm_init.c3
-rw-r--r--tests/aux/test_train.m52
-rw-r--r--tests/aux/test_train_kernel.m92
-rw-r--r--tests/src/test_gensvm_optimize.c2
-rw-r--r--tests/src/test_gensvm_train.c384
5 files changed, 530 insertions, 3 deletions
diff --git a/src/gensvm_init.c b/src/gensvm_init.c
index 8351529..90ee5cd 100644
--- a/src/gensvm_init.c
+++ b/src/gensvm_init.c
@@ -119,11 +119,12 @@ void gensvm_init_V(struct GenModel *from_model,
free(col_min);
free(col_max);
} else {
- for (i=0; i<m+1; i++)
+ for (i=0; i<m+1; i++) {
for (j=0; j<K-1; j++) {
value = matrix_get(from_model->V, K-1, i, j);
matrix_set(to_model->V, K-1, i, j, value);
}
+ }
}
}
diff --git a/tests/aux/test_train.m b/tests/aux/test_train.m
new file mode 100644
index 0000000..86d3074
--- /dev/null
+++ b/tests/aux/test_train.m
@@ -0,0 +1,52 @@
+function [V] = test_train()
+
+clear;
+more off;
+rand('state', 123456);
+
+n = 10;
+m = 3;
+K = 4;
+
+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;
+epsilon = 1e-15;
+rho = ones(n, 1);
+seedV = rand(m+1, K-1);
+set_matrix(seedV, "seed->V", "data->K-1");
+
+[W, t] = msvmmaj(X, y, rho, p, kappa, lambda, epsilon, 'show', 0, seedV);
+V = [t'; W];
+
+fprintf('\n');
+assert_matrix(V, "model->V", "model->K-1");
+
+end
+
+function set_matrix(A, name, cols)
+ for ii=1:size(A, 1)
+ for jj=1:size(A, 2)
+ fprintf("matrix_set(%s, %s, %i, %i, %.16f);\n", name, cols, ii-1, jj-1, A(ii, jj));
+ end
+ end
+ fprintf("\n");
+end
+
+function assert_matrix(A, name, cols)
+ for ii=1:size(A, 1)
+ for jj=1:size(A, 2)
+ fprintf(["mu_assert(fabs(matrix_get(%s, %s, %i, %i) -\n%.16f) <", ...
+ " eps,\n\"Incorrect %s at %i, %i\");\n"], name, cols, ...
+ ii-1, jj-1, A(ii, jj), name, ii-1, jj-1);
+ end
+ end
+ fprintf("\n");
+end \ No newline at end of file
diff --git a/tests/aux/test_train_kernel.m b/tests/aux/test_train_kernel.m
new file mode 100644
index 0000000..7bf14b8
--- /dev/null
+++ b/tests/aux/test_train_kernel.m
@@ -0,0 +1,92 @@
+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;
+ epsilon = 1e-15;
+ rho = ones(n, 1);
+
+ K = zeros(n, n);
+ # RBF kernel
+ # exp(-gamma * norm(x1 - x2)^2)
+ gamma = 0.348
+ for ii=1:n
+ for jj=1:n
+ K(ii, jj) = exp(-gamma * sum((X(ii, :) - X(jj, :)).^2));
+ end
+ end
+
+ [P, Sigma] = eig(K);
+
+ eigenvalues = diag(Sigma);
+ ratios = eigenvalues ./ eigenvalues(end, end);
+
+ realP = fliplr(P(:, ratios > cutoff));
+ realSigma = 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);
+ [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");
+
+end
+
+function set_matrix(A, name, cols)
+ for ii=1:size(A, 1)
+ for jj=1:size(A, 2)
+ fprintf("matrix_set(%s, %s, %i, %i, %.16f);\n", name, cols, ii-1, jj-1, A(ii, jj));
+ end
+ end
+ fprintf("\n");
+end
+
+function assert_matrix(A, name, cols)
+ for ii=1:size(A, 1)
+ for jj=1:size(A, 2)
+ fprintf(["mu_assert(fabs(matrix_get(%s, %s, %i, %i) -\n%.16f) <", ...
+ " eps,\n\"Incorrect %s at %i, %i\");\n"], name, cols, ...
+ ii-1, jj-1, A(ii, jj), name, ii-1, jj-1);
+ end
+ end
+ fprintf("\n");
+end
+
+
+function assert_matrix_abs(A, name, cols)
+ for ii=1:size(A, 1)
+ for jj=1:size(A, 2)
+ fprintf(["mu_assert(fabs(fabs(matrix_get(%s, %s, %i, %i)) -\nfabs(%.16f)) <", ...
+ " eps,\n\"Incorrect %s at %i, %i\");\n"], name, cols, ...
+ ii-1, jj-1, A(ii, jj), name, ii-1, jj-1);
+ end
+ end
+ fprintf("\n");
+end \ No newline at end of file
diff --git a/tests/src/test_gensvm_optimize.c b/tests/src/test_gensvm_optimize.c
index 6263441..d7c71bc 100644
--- a/tests/src/test_gensvm_optimize.c
+++ b/tests/src/test_gensvm_optimize.c
@@ -28,8 +28,6 @@
#include "gensvm_optimize.h"
#include "gensvm_init.h"
-#include "gensvm_debug.h"
-
char *test_gensvm_optimize()
{
struct GenModel *model = gensvm_init_model();
diff --git a/tests/src/test_gensvm_train.c b/tests/src/test_gensvm_train.c
new file mode 100644
index 0000000..4e3c0b4
--- /dev/null
+++ b/tests/src/test_gensvm_train.c
@@ -0,0 +1,384 @@
+/**
+ * @file test_gensvm_optimize.c
+ * @author G.J.J. van den Burg
+ * @date 2016-09-01
+ * @brief Unit tests for gensvm_optimize.c functions
+ *
+ * @copyright
+ Copyright 2016, G.J.J. van den Burg.
+
+ This file is part of GenSVM.
+
+ GenSVM is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ GenSVM is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with GenSVM. If not, see <http://www.gnu.org/licenses/>.
+
+ */
+
+#include "minunit.h"
+#include "gensvm_train.h"
+
+char *test_gensvm_train_seed_linear()
+{
+ struct GenModel *model = gensvm_init_model();
+ struct GenModel *seed = gensvm_init_model();
+ struct GenData *data = gensvm_init_data();
+
+ // note that model n, m, K are set by the train function
+ model->p = 1.2143;
+ model->kappa = 0.90298;
+ model->lambda = 0.00219038;
+ model->epsilon = 1e-15;
+ model->weight_idx = 1;
+ model->kerneltype = K_LINEAR;
+
+ data->n = 10;
+ data->m = 3;
+ data->K = 4;
+ data->RAW = Calloc(double, data->n * (data->m+1));
+ data->Z = data->RAW;
+ data->y = Calloc(long, data->n);
+
+ matrix_set(data->Z, data->m+1, 0, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 0, 1, 0.8056271362589000);
+ matrix_set(data->Z, data->m+1, 0, 2, 0.4874175854113872);
+ matrix_set(data->Z, data->m+1, 0, 3, 0.4453015882771756);
+ matrix_set(data->Z, data->m+1, 1, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 1, 1, 0.7940590105180981);
+ matrix_set(data->Z, data->m+1, 1, 2, 0.1861049005485224);
+ matrix_set(data->Z, data->m+1, 1, 3, 0.8469394287449229);
+ matrix_set(data->Z, data->m+1, 2, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 2, 1, 0.0294257611061681);
+ matrix_set(data->Z, data->m+1, 2, 2, 0.0242717976065267);
+ matrix_set(data->Z, data->m+1, 2, 3, 0.5039128672814752);
+ matrix_set(data->Z, data->m+1, 3, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 3, 1, 0.1746563833537603);
+ matrix_set(data->Z, data->m+1, 3, 2, 0.9135736087631979);
+ matrix_set(data->Z, data->m+1, 3, 3, 0.5270258081021366);
+ matrix_set(data->Z, data->m+1, 4, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 4, 1, 0.0022298761599785);
+ matrix_set(data->Z, data->m+1, 4, 2, 0.3773482059713607);
+ matrix_set(data->Z, data->m+1, 4, 3, 0.8009654729622842);
+ matrix_set(data->Z, data->m+1, 5, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 5, 1, 0.6638830667081945);
+ matrix_set(data->Z, data->m+1, 5, 2, 0.6467607601353914);
+ matrix_set(data->Z, data->m+1, 5, 3, 0.0434948735457108);
+ matrix_set(data->Z, data->m+1, 6, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 6, 1, 0.0770493004546461);
+ matrix_set(data->Z, data->m+1, 6, 2, 0.3699566427075194);
+ matrix_set(data->Z, data->m+1, 6, 3, 0.7863539761080217);
+ matrix_set(data->Z, data->m+1, 7, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 7, 1, 0.2685233952731509);
+ matrix_set(data->Z, data->m+1, 7, 2, 0.8539966432782011);
+ matrix_set(data->Z, data->m+1, 7, 3, 0.0967159557826836);
+ matrix_set(data->Z, data->m+1, 8, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 8, 1, 0.1163951898554611);
+ matrix_set(data->Z, data->m+1, 8, 2, 0.7667861436369238);
+ matrix_set(data->Z, data->m+1, 8, 3, 0.5031912600213351);
+ matrix_set(data->Z, data->m+1, 9, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 9, 1, 0.2290251898688216);
+ matrix_set(data->Z, data->m+1, 9, 2, 0.4401981048538806);
+ matrix_set(data->Z, data->m+1, 9, 3, 0.0884616753393881);
+
+ matrix_set(data->y, 1, 0, 0, 2);
+ matrix_set(data->y, 1, 0, 1, 1);
+ matrix_set(data->y, 1, 0, 2, 3);
+ matrix_set(data->y, 1, 0, 3, 2);
+ matrix_set(data->y, 1, 0, 4, 3);
+ matrix_set(data->y, 1, 0, 5, 2);
+ matrix_set(data->y, 1, 0, 6, 4);
+ matrix_set(data->y, 1, 0, 7, 1);
+ matrix_set(data->y, 1, 0, 8, 3);
+ matrix_set(data->y, 1, 0, 9, 4);
+
+ seed->V = Calloc(double, (data->m+1)*(data->K-1));
+ matrix_set(seed->V, data->K-1, 0, 0, 0.8233234072519983);
+ matrix_set(seed->V, data->K-1, 0, 1, 0.7701104553132680);
+ matrix_set(seed->V, data->K-1, 0, 2, 0.1102697774064020);
+ matrix_set(seed->V, data->K-1, 1, 0, 0.7956168453294307);
+ matrix_set(seed->V, data->K-1, 1, 1, 0.3267543833513200);
+ matrix_set(seed->V, data->K-1, 1, 2, 0.8659836346403005);
+ matrix_set(seed->V, data->K-1, 2, 0, 0.5777227081256917);
+ matrix_set(seed->V, data->K-1, 2, 1, 0.3693175185473680);
+ matrix_set(seed->V, data->K-1, 2, 2, 0.2728942849022845);
+ matrix_set(seed->V, data->K-1, 3, 0, 0.4426030703804438);
+ matrix_set(seed->V, data->K-1, 3, 1, 0.2456426390463990);
+ matrix_set(seed->V, data->K-1, 3, 2, 0.2665038412777220);
+
+ // start test code //
+ gensvm_train(model, data, seed);
+
+ mu_assert(model->n == data->n, "Incorrect model n");
+ mu_assert(model->m == data->m, "Incorrect model m");
+ mu_assert(model->K == data->K, "Incorrect model K");
+
+ double eps = 1e-13;
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 0) -
+ -1.1907736868272805) < eps,
+ "Incorrect model->V at 0, 0");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 1) -
+ 1.8651287814979396) < eps,
+ "Incorrect model->V at 0, 1");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 0, 2) -
+ 1.7250030581662932) < eps,
+ "Incorrect model->V at 0, 2");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 0) -
+ 0.7925100058806183) < eps,
+ "Incorrect model->V at 1, 0");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 1) -
+ -3.6093428916761665) < eps,
+ "Incorrect model->V at 1, 1");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 1, 2) -
+ -1.3394018960329377) < eps,
+ "Incorrect model->V at 1, 2");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 0) -
+ 1.5203132433193016) < eps,
+ "Incorrect model->V at 2, 0");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 1) -
+ -1.9118604362643852) < eps,
+ "Incorrect model->V at 2, 1");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 2, 2) -
+ -1.7939246097629342) < eps,
+ "Incorrect model->V at 2, 2");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 0) -
+ 0.0658817457370326) < eps,
+ "Incorrect model->V at 3, 0");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 1) -
+ 0.6547924025329720) < eps,
+ "Incorrect model->V at 3, 1");
+ mu_assert(fabs(matrix_get(model->V, model->K-1, 3, 2) -
+ -0.6773346708737853) < eps,
+ "Incorrect model->V at 3, 2");
+
+ // end test code //
+
+ gensvm_free_model(model);
+ gensvm_free_model(seed);
+ gensvm_free_data(data);
+
+ return NULL;
+}
+
+char *test_gensvm_train_seed_kernel()
+{
+ struct GenModel *model = gensvm_init_model();
+ struct GenData *data = gensvm_init_data();
+
+ // note that model n, m, K are set by the train function
+ model->p = 1.2143;
+ model->kappa = 0.90298;
+ model->lambda = 0.00219038;
+ model->epsilon = 1e-15;
+ model->weight_idx = 1;
+ model->kerneltype = K_RBF;
+ model->kernelparam = Calloc(double, 1);
+ model->kernelparam[0] = 0.348;
+ model->kernel_eigen_cutoff = 5e-3;
+
+ data->n = 10;
+ data->m = 5;
+ data->K = 4;
+ data->RAW = Calloc(double, data->n * (data->m+1));
+ data->Z = data->RAW;
+ matrix_set(data->Z, data->m+1, 0, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 0, 1, 0.0657799204744603);
+ matrix_set(data->Z, data->m+1, 0, 2, 0.2576653302581353);
+ matrix_set(data->Z, data->m+1, 0, 3, 0.0221000752651170);
+ matrix_set(data->Z, data->m+1, 0, 4, 0.6666929354133441);
+ matrix_set(data->Z, data->m+1, 0, 5, 0.6178892590244618);
+ matrix_set(data->Z, data->m+1, 1, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 1, 1, 0.9797668012781366);
+ matrix_set(data->Z, data->m+1, 1, 2, 0.7636361573939686);
+ matrix_set(data->Z, data->m+1, 1, 3, 0.3195806959299131);
+ matrix_set(data->Z, data->m+1, 1, 4, 0.2947771273705799);
+ matrix_set(data->Z, data->m+1, 1, 5, 0.8358899802514324);
+ matrix_set(data->Z, data->m+1, 2, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 2, 1, 0.9473849700145257);
+ matrix_set(data->Z, data->m+1, 2, 2, 0.8682867844262768);
+ matrix_set(data->Z, data->m+1, 2, 3, 0.7116177283612393);
+ matrix_set(data->Z, data->m+1, 2, 4, 0.5092752476335579);
+ matrix_set(data->Z, data->m+1, 2, 5, 0.1046097156193449);
+ matrix_set(data->Z, data->m+1, 3, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 3, 1, 0.5846585351601830);
+ matrix_set(data->Z, data->m+1, 3, 2, 0.4076887966131124);
+ matrix_set(data->Z, data->m+1, 3, 3, 0.8661556045821296);
+ matrix_set(data->Z, data->m+1, 3, 4, 0.0904082115920005);
+ matrix_set(data->Z, data->m+1, 3, 5, 0.0799888711622944);
+ matrix_set(data->Z, data->m+1, 4, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 4, 1, 0.8112201081242789);
+ matrix_set(data->Z, data->m+1, 4, 2, 0.3112642417912803);
+ matrix_set(data->Z, data->m+1, 4, 3, 0.7902557587124555);
+ matrix_set(data->Z, data->m+1, 4, 4, 0.3001992968661185);
+ matrix_set(data->Z, data->m+1, 4, 5, 0.6030590437920392);
+ matrix_set(data->Z, data->m+1, 5, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 5, 1, 0.0098576324913424);
+ matrix_set(data->Z, data->m+1, 5, 2, 0.5686603332895077);
+ matrix_set(data->Z, data->m+1, 5, 3, 0.9933970661175713);
+ matrix_set(data->Z, data->m+1, 5, 4, 0.5215400841900655);
+ matrix_set(data->Z, data->m+1, 5, 5, 0.4307310515440625);
+ matrix_set(data->Z, data->m+1, 6, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 6, 1, 0.2773296707204919);
+ matrix_set(data->Z, data->m+1, 6, 2, 0.5114254316901164);
+ matrix_set(data->Z, data->m+1, 6, 3, 0.5057613745592034);
+ matrix_set(data->Z, data->m+1, 6, 4, 0.6411421568717217);
+ matrix_set(data->Z, data->m+1, 6, 5, 0.3114658800558432);
+ matrix_set(data->Z, data->m+1, 7, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 7, 1, 0.7195909422652624);
+ matrix_set(data->Z, data->m+1, 7, 2, 0.7754155342547566);
+ matrix_set(data->Z, data->m+1, 7, 3, 0.5955643008534165);
+ matrix_set(data->Z, data->m+1, 7, 4, 0.5920949759391909);
+ matrix_set(data->Z, data->m+1, 7, 5, 0.7029537245575100);
+ matrix_set(data->Z, data->m+1, 8, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 8, 1, 0.3792168380438625);
+ matrix_set(data->Z, data->m+1, 8, 2, 0.1920178667928286);
+ matrix_set(data->Z, data->m+1, 8, 3, 0.2742847467912714);
+ matrix_set(data->Z, data->m+1, 8, 4, 0.2337979820454409);
+ matrix_set(data->Z, data->m+1, 8, 5, 0.3978991644742557);
+ matrix_set(data->Z, data->m+1, 9, 0, 1.0000000000000000);
+ matrix_set(data->Z, data->m+1, 9, 1, 0.0797813938980598);
+ matrix_set(data->Z, data->m+1, 9, 2, 0.5863311792537960);
+ matrix_set(data->Z, data->m+1, 9, 3, 0.8565105304166337);
+ matrix_set(data->Z, data->m+1, 9, 4, 0.8266471128109379);
+ matrix_set(data->Z, data->m+1, 9, 5, 0.8070610088865674);
+
+ data->y = Calloc(long, data->n);
+ matrix_set(data->y, 1, 0, 0, 2);
+ matrix_set(data->y, 1, 0, 1, 1);
+ matrix_set(data->y, 1, 0, 2, 3);
+ matrix_set(data->y, 1, 0, 3, 2);
+ matrix_set(data->y, 1, 0, 4, 3);
+ matrix_set(data->y, 1, 0, 5, 2);
+ matrix_set(data->y, 1, 0, 6, 4);
+ matrix_set(data->y, 1, 0, 7, 1);
+ matrix_set(data->y, 1, 0, 8, 3);
+ matrix_set(data->y, 1, 0, 9, 4);
+
+ // start test code //
+
+ // because the kernel eigendecomposition isn't known in advance,
+ // there's no way to seed the model when using kernels. We therefore
+ // use seed == NULL here. Note that due to the Memset in
+ // gensvm_reallocate_model(), V will be a matrix of zeros after
+ // reallocation, so we compare with the V = 0 result from Octave.
+ gensvm_train(model, data, NULL);
+
+ mu_assert(model->n == data->n, "Incorrect model n");
+ mu_assert(model->m == data->r, "Incorrect model m");
+ 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,
+ "Incorrect data->Sigma at 0, 0");
+ mu_assert(fabs(matrix_get(data->Sigma, 1, 1, 0) -
+ 0.7947913383766066) < eps,
+ "Incorrect data->Sigma at 1, 0");
+ mu_assert(fabs(matrix_get(data->Sigma, 1, 2, 0) -
+ 0.5288740088908547) < eps,
+ "Incorrect data->Sigma at 2, 0");
+ mu_assert(fabs(matrix_get(data->Sigma, 1, 3, 0) -
+ 0.4537982052555444) < eps,
+ "Incorrect data->Sigma at 3, 0");
+ mu_assert(fabs(matrix_get(data->Sigma, 1, 4, 0) -
+ 0.2226012271232192) < eps,
+ "Incorrect data->Sigma at 4, 0");
+ mu_assert(fabs(matrix_get(data->Sigma, 1, 5, 0) -
+ 0.0743004417495061) < eps,
+ "Incorrect data->Sigma at 5, 0");
+
+ // we need a large eps here because there are numerical precision
+ // differences between the C and Octave implementations. We also
+ // 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,
+ "Incorrect model->V at 0, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 0, 1)) -
+ fabs(-2.2586632211763198)) < eps,
+ "Incorrect model->V at 0, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 0, 2)) -
+ fabs(-4.5572671806963143)) < eps,
+ "Incorrect model->V at 0, 2");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 1, 0)) -
+ fabs(-1.9627432869558412)) < eps,
+ "Incorrect model->V at 1, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 1, 1)) -
+ fabs(0.9934555242449399)) < eps,
+ "Incorrect model->V at 1, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 1, 2)) -
+ fabs(1.7855287218670219)) < eps,
+ "Incorrect model->V at 1, 2");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 2, 0)) -
+ fabs(1.9393083227054353)) < eps,
+ "Incorrect model->V at 2, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 2, 1)) -
+ fabs(-1.1958487809502740)) < eps,
+ "Incorrect model->V at 2, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 2, 2)) -
+ fabs(2.1140967864804359)) < eps,
+ "Incorrect model->V at 2, 2");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 3, 0)) -
+ fabs(2.3909204618652535)) < eps,
+ "Incorrect model->V at 3, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 3, 1)) -
+ fabs(-0.2834554569573399)) < eps,
+ "Incorrect model->V at 3, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 3, 2)) -
+ fabs(1.0926232371314393)) < eps,
+ "Incorrect model->V at 3, 2");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 4, 0)) -
+ fabs(3.3374545494113272)) < eps,
+ "Incorrect model->V at 4, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 4, 1)) -
+ fabs(1.6699291195221897)) < eps,
+ "Incorrect model->V at 4, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 4, 2)) -
+ fabs(-1.4345249893609275)) < eps,
+ "Incorrect model->V at 4, 2");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 5, 0)) -
+ fabs(-0.0221825925355533)) < eps,
+ "Incorrect model->V at 5, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 5, 1)) -
+ fabs(-0.1216077739550210)) < eps,
+ "Incorrect model->V at 5, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 5, 2)) -
+ fabs(-0.7900947982642630)) < eps,
+ "Incorrect model->V at 5, 2");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 6, 0)) -
+ fabs(-0.0076471781062262)) < eps,
+ "Incorrect model->V at 6, 0");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 6, 1)) -
+ fabs(-0.8781872510019056)) < eps,
+ "Incorrect model->V at 6, 1");
+ mu_assert(fabs(fabs(matrix_get(model->V, model->K-1, 6, 2)) -
+ fabs(-0.2782284589344380)) < eps,
+ "Incorrect model->V at 6, 2");
+ // end test code //
+
+ gensvm_free_model(model);
+ gensvm_free_data(data);
+
+ return NULL;
+}
+
+char *all_tests()
+{
+ mu_suite_start();
+
+ mu_run_test(test_gensvm_train_seed_linear);
+ mu_run_test(test_gensvm_train_seed_kernel);
+
+ return NULL;
+}
+
+RUN_TESTS(all_tests);
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