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[wpimath] Clean up Eigen usage

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* Replace Matrix<> with Vector<> where vectors are explicitly intended.
  I found these via `rg "Eigen::Matrix<double, \w+, 1>"`.
* Pass all Eigen matrices by const reference. I found these via `rg
  "\(Eigen"` on main (the initializer list constructors make more false
  positives).
* Replace MakeMatrix() and operator<< usage with initializer list
  constructors. I found these via `rg MakeMatrix` and `rg "<<"`
  respectively.
* Deprecate MakeMatrix()
This commit is contained in:
Tyler Veness
2021-08-19 00:23:48 -07:00
committed by Peter Johnson
parent 72716f51ce
commit 9359431bad
63 changed files with 821 additions and 955 deletions
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49
wpimath/src/test/native/cpp/system/NumericalJacobianTest.cpp
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@@ -6,60 +6,53 @@
#include "frc/system/NumericalJacobian.h"
Eigen::Matrix<double, 4, 4> A = (Eigen::Matrix<double, 4, 4>() << 1, 2, 4, 1, 5,
2, 3, 4, 5, 1, 3, 2, 1, 1, 3, 7)
.finished();
Eigen::Matrix<double, 4, 2> B =
(Eigen::Matrix<double, 4, 2>() << 1, 1, 2, 1, 3, 2, 3, 7).finished();
Eigen::Matrix<double, 4, 4> A{
{1, 2, 4, 1}, {5, 2, 3, 4}, {5, 1, 3, 2}, {1, 1, 3, 7}};
Eigen::Matrix<double, 4, 2> B{{1, 1}, {2, 1}, {3, 2}, {3, 7}};
// Function from which to recover A and B
Eigen::Matrix<double, 4, 1> AxBuFn(const Eigen::Matrix<double, 4, 1>& x,
const Eigen::Matrix<double, 2, 1>& u) {
Eigen::Vector<double, 4> AxBuFn(const Eigen::Vector<double, 4>& x,
const Eigen::Vector<double, 2>& u) {
return A * x + B * u;
}
// Test that we can recover A from AxBuFn() pretty accurately
TEST(NumericalJacobianTest, Ax) {
Eigen::Matrix<double, 4, 4> newA = frc::NumericalJacobianX<4, 4, 2>(
AxBuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
Eigen::Matrix<double, 4, 4> newA =
frc::NumericalJacobianX<4, 4, 2>(AxBuFn, Eigen::Vector<double, 4>::Zero(),
Eigen::Vector<double, 2>::Zero());
EXPECT_TRUE(newA.isApprox(A));
}
// Test that we can recover B from AxBuFn() pretty accurately
TEST(NumericalJacobianTest, Bu) {
Eigen::Matrix<double, 4, 2> newB = frc::NumericalJacobianU<4, 4, 2>(
AxBuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
Eigen::Matrix<double, 4, 2> newB =
frc::NumericalJacobianU<4, 4, 2>(AxBuFn, Eigen::Vector<double, 4>::Zero(),
Eigen::Vector<double, 2>::Zero());
EXPECT_TRUE(newB.isApprox(B));
}
Eigen::Matrix<double, 3, 4> C =
(Eigen::Matrix<double, 3, 4>() << 1, 2, 4, 1, 5, 2, 3, 4, 5, 1, 3, 2)
.finished();
Eigen::Matrix<double, 3, 2> D =
(Eigen::Matrix<double, 3, 2>() << 1, 1, 2, 1, 3, 2).finished();
Eigen::Matrix<double, 3, 4> C{{1, 2, 4, 1}, {5, 2, 3, 4}, {5, 1, 3, 2}};
Eigen::Matrix<double, 3, 2> D{{1, 1}, {2, 1}, {3, 2}};
// Function from which to recover C and D
Eigen::Matrix<double, 3, 1> CxDuFn(const Eigen::Matrix<double, 4, 1>& x,
const Eigen::Matrix<double, 2, 1>& u) {
Eigen::Vector<double, 3> CxDuFn(const Eigen::Vector<double, 4>& x,
const Eigen::Vector<double, 2>& u) {
return C * x + D * u;
}
// Test that we can recover C from CxDuFn() pretty accurately
TEST(NumericalJacobianTest, Cx) {
Eigen::Matrix<double, 3, 4> newC = frc::NumericalJacobianX<3, 4, 2>(
CxDuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
Eigen::Matrix<double, 3, 4> newC =
frc::NumericalJacobianX<3, 4, 2>(CxDuFn, Eigen::Vector<double, 4>::Zero(),
Eigen::Vector<double, 2>::Zero());
EXPECT_TRUE(newC.isApprox(C));
}
// Test that we can recover D from CxDuFn() pretty accurately
TEST(NumericalJacobianTest, Du) {
Eigen::Matrix<double, 3, 2> newD = frc::NumericalJacobianU<3, 4, 2>(
CxDuFn, Eigen::Matrix<double, 4, 1>::Zero(),
Eigen::Matrix<double, 2, 1>::Zero());
Eigen::Matrix<double, 3, 2> newD =
frc::NumericalJacobianU<3, 4, 2>(CxDuFn, Eigen::Vector<double, 4>::Zero(),
Eigen::Vector<double, 2>::Zero());
EXPECT_TRUE(newD.isApprox(D));
}