PX4-Autopilot/test/pseudoInverse.cpp
Julien Lecoeur a172c3cdac Add implementation of pseudo-inverse (#102)
* Fix compilation error

* Add implementation of pseudo-inverse

The implementation is based on this publication:
Courrieu, P. (2008). Fast Computation of Moore-Penrose Inverse Matrices, 8(2), 25–29. http://arxiv.org/abs/0804.4809
It is a fully templated implementation to guaranty type correctness.

* Add tests for pseudoinverse

* Apply suggestions from code review

Co-Authored-By: Mathieu Bresciani <brescianimathieu@gmail.com>

* Adapt fullRankCholesky tolerance to type

* Add pseudoinverse test with effectiveness matrix

* Fix coverage

* Fix rebase issue

* Fix SquareMatrix test, add null Matrix test
2019-11-18 14:36:30 -08:00

140 lines
4.9 KiB
C++

#include "test_macros.hpp"
#include <matrix/PseudoInverse.hpp>
using namespace matrix;
static const size_t n_large = 20;
int main()
{
// 3x4 Matrix test
float data0[12] = {
0.f, 1.f, 2.f, 3.f,
4.f, 5.f, 6.f, 7.f,
8.f, 9.f, 10.f, 11.f
};
float data0_check[12] = {
-0.3375f, -0.1f, 0.1375f,
-0.13333333f, -0.03333333f, 0.06666667f,
0.07083333f, 0.03333333f, -0.00416667f,
0.275f, 0.1f, -0.075f
};
Matrix<float, 3, 4> A0(data0);
Matrix<float, 4, 3> A0_I = geninv(A0);
Matrix<float, 4, 3> A0_I_check(data0_check);
TEST((A0_I - A0_I_check).abs().max() < 1e-5);
// 4x3 Matrix test
float data1[12] = {
0.f, 4.f, 8.f,
1.f, 5.f, 9.f,
2.f, 6.f, 10.f,
3.f, 7.f, 11.f
};
float data1_check[12] = {
-0.3375f, -0.13333333f, 0.07083333f, 0.275f,
-0.1f, -0.03333333f, 0.03333333f, 0.1f,
0.1375f, 0.06666667f, -0.00416667f, -0.075f
};
Matrix<float, 4, 3> A1(data1);
Matrix<float, 3, 4> A1_I = geninv(A1);
Matrix<float, 3, 4> A1_I_check(data1_check);
TEST((A1_I - A1_I_check).abs().max() < 1e-5);
// Stess test
Matrix<float, n_large, n_large - 1> A_large;
A_large.setIdentity();
Matrix<float, n_large - 1, n_large> A_large_I;
for (size_t i = 0; i < n_large; i++) {
A_large_I = geninv(A_large);
TEST(isEqual(A_large, A_large_I.T()));
}
// Square matrix test
float data2[9] = {0, 2, 3,
4, 5, 6,
7, 8, 10
};
float data2_check[9] = {
-0.4f, -0.8f, 0.6f,
-0.4f, 4.2f, -2.4f,
0.6f, -2.8f, 1.6f
};
SquareMatrix<float, 3> A2(data2);
SquareMatrix<float, 3> A2_I = geninv(A2);
SquareMatrix<float, 3> A2_I_check(data2_check);
TEST((A2_I - A2_I_check).abs().max() < 1e-3);
// Null matrix test
Matrix<float, 6, 16> A3;
Matrix<float, 16, 6> A3_I = geninv(A3);
Matrix<float, 16, 6> A3_I_check;
TEST((A3_I - A3_I_check).abs().max() < 1e-5);
// Mock-up effectiveness matrix
const float B_quad_w[6][16] = {
{-0.5717536f, 0.43756646f, 0.5717536f, -0.43756646f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f},
{ 0.35355328f, -0.35355328f, 0.35355328f, -0.35355328f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f},
{ 0.28323701f, 0.28323701f, -0.28323701f, -0.28323701f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f},
{ 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f},
{ 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f},
{-0.25f, -0.25f, -0.25f, -0.25f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f}
};
Matrix<float, 6, 16> B = Matrix<float, 6, 16>(B_quad_w);
const float A_quad_w[16][6] = {
{ -0.495383f, 0.707107f, 0.765306f, 0.0f, 0.0f, -1.000000f },
{ 0.495383f, -0.707107f, 1.000000f, 0.0f, 0.0f, -1.000000f },
{ 0.495383f, 0.707107f, -0.765306f, 0.0f, 0.0f, -1.000000f },
{ -0.495383f, -0.707107f, -1.000000f, 0.0f, 0.0f, -1.000000f },
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f},
{ 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f}
};
Matrix<float, 16, 6> A_check = Matrix<float, 16, 6>(A_quad_w);
Matrix<float, 16, 6> A = geninv(B);
TEST((A - A_check).abs().max() < 1e-5);
// Test error case with erroneous rank in internal impl functions
Matrix<float, 2, 2> L;
Matrix<float, 2, 3> GM;
Matrix<float, 3, 2> retM_check;
Matrix<float, 3, 2> retM0 = GeninvImpl<float, 2, 3, 0>::genInvUnderdetermined(GM, L, 5);
Matrix<float, 3, 2> GN;
Matrix<float, 2, 3> retN_check;
Matrix<float, 2, 3> retN0 = GeninvImpl<float, 3, 2, 0>::genInvOverdetermined(GN, L, 5);
TEST((retM0 - retM_check).abs().max() < 1e-5);
TEST((retN0 - retN_check).abs().max() < 1e-5);
Matrix<float, 3, 2> retM1 = GeninvImpl<float, 2, 3, 1>::genInvUnderdetermined(GM, L, 5);
Matrix<float, 2, 3> retN1 = GeninvImpl<float, 3, 2, 1>::genInvOverdetermined(GN, L, 5);
TEST((retM1 - retM_check).abs().max() < 1e-5);
TEST((retN1 - retN_check).abs().max() < 1e-5);
float float_scale = 1.f;
fullRankCholeskyTolerance(float_scale);
double double_scale = 1.;
fullRankCholeskyTolerance(double_scale);
TEST(static_cast<double>(float_scale) > double_scale);
return 0;
}
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