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Fully ported MLPPTests::test_linear_algebra().

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Relintai 2023-12-27 11:00:59 +01:00
parent 2ee4d3043a
commit 1d28a33074
1 changed files with 45 additions and 37 deletions
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82
test/mlpp_tests.cpp
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@ -141,53 +141,61 @@ void MLPPTests::test_linear_algebra() {
Ref<MLPPMatrix> square_rot(memnew(MLPPMatrix(square_rot_res_arr, 4, 2)));
is_approx_equals_mat(square->rotaten(Math_PI / 4), square_rot, "square->rotaten(Math_PI / 4)");
/*
std::vector<std::vector<real_t>> A = {
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 },
{ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 },
};
std::vector<real_t> a = { 4, 3, 1, 3 };
std::vector<real_t> b = { 3, 5, 6, 1 };
std::vector<std::vector<real_t>> mmtr_res = {
{ 2, 4, 6, 8, 10, 12, 14, 16, 18, 20 },
{ 4, 8, 12, 16, 20, 24, 28, 32, 36, 40 },
{ 6, 12, 18, 24, 30, 36, 42, 48, 54, 60 },
{ 8, 16, 24, 32, 40, 48, 56, 64, 72, 80 },
{ 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 },
{ 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 },
{ 14, 28, 42, 56, 70, 84, 98, 112, 126, 140 },
{ 16, 32, 48, 64, 80, 96, 112, 128, 144, 160 },
{ 18, 36, 54, 72, 90, 108, 126, 144, 162, 180 },
{ 20, 40, 60, 80, 100, 120, 140, 160, 180, 200 }
const real_t A_arr[] = {
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, //
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, //
};
const real_t a_arr[] = { 4, 3, 1, 3 };
const real_t b_arr[] = { 3, 5, 6, 1 };
const real_t mmtr_res_arr[] = {
2, 4, 6, 8, 10, 12, 14, 16, 18, 20, //
4, 8, 12, 16, 20, 24, 28, 32, 36, 40, //
6, 12, 18, 24, 30, 36, 42, 48, 54, 60, //
8, 16, 24, 32, 40, 48, 56, 64, 72, 80, //
10, 20, 30, 40, 50, 60, 70, 80, 90, 100, //
12, 24, 36, 48, 60, 72, 84, 96, 108, 120, //
14, 28, 42, 56, 70, 84, 98, 112, 126, 140, //
16, 32, 48, 64, 80, 96, 112, 128, 144, 160, //
18, 36, 54, 72, 90, 108, 126, 144, 162, 180, //
20, 40, 60, 80, 100, 120, 140, 160, 180, 200 //
};
is_approx_equals_dmat(dstd_mat_to_mat_old(alg.matmult(alg.transpose(A), A)), dstd_mat_to_mat_old(mmtr_res), "alg.matmult(alg.transpose(A), A)");
Ref<MLPPMatrix> A(memnew(MLPPMatrix(A_arr, 2, 10)));
Ref<MLPPVector> a(memnew(MLPPVector(a_arr, 4)));
Ref<MLPPVector> b(memnew(MLPPVector(b_arr, 4)));
Ref<MLPPMatrix> mmtr_res(memnew(MLPPMatrix(mmtr_res_arr, 10, 10)));
is_approx_equalsd(alg.dot(a, b), 36, "alg.dot(a, b)");
is_approx_equals_mat(alg.matmultnm(alg.transposenm(A), A), mmtr_res, "alg.matmultnm(alg.transposenm(A), A)");
std::vector<std::vector<real_t>> had_prod_res = {
{ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100 },
{ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100 }
is_approx_equalsd(alg.dotnv(a, b), 36, "alg.dotnv(a, b)");
const real_t had_prod_res_arr[] = {
1, 4, 9, 16, 25, 36, 49, 64, 81, 100, //
1, 4, 9, 16, 25, 36, 49, 64, 81, 100 //
};
is_approx_equals_dmat(dstd_mat_to_mat_old(alg.hadamard_product(A, A)), dstd_mat_to_mat_old(had_prod_res), "alg.hadamard_product(A, A)");
Ref<MLPPMatrix> had_prod_res(memnew(MLPPMatrix(had_prod_res_arr, 2, 10)));
std::vector<std::vector<real_t>> id_10_res = {
{ 1, 0, 0, 0, 0, 0, 0, 0, 0, 0 },
{ 0, 1, 0, 0, 0, 0, 0, 0, 0, 0 },
{ 0, 0, 1, 0, 0, 0, 0, 0, 0, 0 },
{ 0, 0, 0, 1, 0, 0, 0, 0, 0, 0 },
{ 0, 0, 0, 0, 1, 0, 0, 0, 0, 0 },
{ 0, 0, 0, 0, 0, 1, 0, 0, 0, 0 },
{ 0, 0, 0, 0, 0, 0, 1, 0, 0, 0 },
{ 0, 0, 0, 0, 0, 0, 0, 1, 0, 0 },
{ 0, 0, 0, 0, 0, 0, 0, 0, 1, 0 },
{ 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 },
is_approx_equals_mat(alg.hadamard_productnm(A, A), had_prod_res, "alg.hadamard_productnm(A, A)");
const real_t id_10_res_arr[] = {
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, //
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, //
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, //
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, //
};
is_approx_equals_dmat(dstd_mat_to_mat_old(alg.identity(10)), dstd_mat_to_mat_old(id_10_res), "alg.identity(10)");
*/
Ref<MLPPMatrix> id_10_res(memnew(MLPPMatrix(id_10_res_arr, 10, 10)));
is_approx_equals_mat(alg.identitym(10), id_10_res, "alg.identitym(10)");
}
void MLPPTests::test_univariate_linear_regression() {