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Enable saving intermidiate (Schur decomposition) but disable unstable specialization for matrix power-matrix product.

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This commit is contained in:
Chen-Pang He
2012-09-21 23:24:28 +08:00
parent d5d99dd1f0
commit 87afd99433
5 changed files with 463 additions and 555 deletions
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104
unsupported/test/matrix_power.cpp
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@@ -16,15 +16,17 @@ void test2dRotation(double tol)
T angle, c, s;
A << 0, 1, -1, 0;
for (int i = 0; i <= 20; i++) {
MatrixPower<Matrix<T,2,2> > Apow(A);
for (int i=0; i<=20; ++i) {
angle = pow(10, (i-10) / 5.);
c = std::cos(angle);
s = std::sin(angle);
B << c, s, -s, c;
C = A.pow(std::ldexp(angle, 1) / M_PI);
std::cout << "test2dRotation: i = " << i << " error powerm = " << relerr(C, B) << '\n';
VERIFY(C.isApprox(B, T(tol)));
C = Apow(std::ldexp(angle,1) / M_PI);
std::cout << "test2dRotation: i = " << i << " error powerm = " << relerr(C,B) << '\n';
VERIFY(C.isApprox(B, static_cast<T>(tol)));
}
}
@@ -36,15 +38,17 @@ void test2dHyperbolicRotation(double tol)
std::complex<T> ish(0, std::sinh(1));
A << ch, ish, -ish, ch;
for (int i = 0; i <= 20; i++) {
angle = std::ldexp(T(i-10), -1);
MatrixPower<Matrix<std::complex<T>,2,2> > Apow(A);
for (int i=0; i<=20; ++i) {
angle = std::ldexp(static_cast<T>(i-10), -1);
ch = std::cosh(angle);
ish = std::complex<T>(0, std::sinh(angle));
B << ch, ish, -ish, ch;
C = A.pow(angle);
std::cout << "test2dHyperbolicRotation: i = " << i << " error powerm = " << relerr(C, B) << '\n';
VERIFY(C.isApprox(B, T(tol)));
C = Apow(angle);
std::cout << "test2dHyperbolicRotation: i = " << i << " error powerm = " << relerr(C,B) << '\n';
VERIFY(C.isApprox(B, static_cast<T>(tol)));
}
}
@@ -55,71 +59,37 @@ void testExponentLaws(const MatrixType& m, double tol)
MatrixType m1, m2, m3, m4, m5;
RealScalar x, y;
for (int i = 0; i < g_repeat; i++) {
for (int i=0; i<g_repeat; ++i) {
generateTestMatrix<MatrixType>::run(m1, m.rows());
MatrixPower<MatrixType> mpow(m1);
x = internal::random<RealScalar>();
y = internal::random<RealScalar>();
m2 = m1.pow(x);
m3 = m1.pow(y);
m2 = mpow(x);
m3 = mpow(y);
m4 = m1.pow(x + y);
m4 = mpow(x+y);
m5.noalias() = m2 * m3;
std::cout << "testExponentLaws: error powerm = " << relerr(m4, m5);
VERIFY(m4.isApprox(m5, RealScalar(tol)));
if (!NumTraits<typename MatrixType::Scalar>::IsComplex) {
m4 = m1.pow(x * y);
m5 = m2.pow(y);
std::cout << " " << relerr(m4, m5);
VERIFY(m4.isApprox(m5, RealScalar(tol)));
}
std::cout << "testExponentLaws: error powerm = " << relerr(m4, m5);
VERIFY(m4.isApprox(m5, static_cast<RealScalar>(tol)));
m4 = mpow(x*y);
m5 = m2.pow(y);
std::cout << " " << relerr(m4, m5);
VERIFY(m4.isApprox(m5, static_cast<RealScalar>(tol)));
m4 = (std::abs(x) * m1).pow(y);
m5 = std::pow(std::abs(x), y) * m3;
std::cout << " " << relerr(m4, m5) << '\n';
VERIFY(m4.isApprox(m5, RealScalar(tol)));
}
}
template<typename MatrixType, typename VectorType>
void testMatrixVectorProduct(const MatrixType& m, const VectorType& v, double tol)
{
typedef typename MatrixType::RealScalar RealScalar;
MatrixType m1;
VectorType v1, v2, v3;
RealScalar p;
for (int i = 0; i < g_repeat; i++) {
generateTestMatrix<MatrixType>::run(m1, m.rows());
v1 = VectorType::Random(v.rows(), v.cols());
p = internal::random<RealScalar>();
v2.noalias() = m1.pow(p).eval() * v1;
v1 = m1.pow(p) * v1;
std::cout << "testMatrixVectorProduct: error powerm = " << relerr(v2, v1) << '\n';
VERIFY(v2.isApprox(v1, RealScalar(tol)));
}
}
template<typename MatrixType>
void testAliasing(const MatrixType& m)
{
typedef typename MatrixType::RealScalar RealScalar;
MatrixType m1, m2;
RealScalar p;
for (int i = 0; i < g_repeat; i++) {
generateTestMatrix<MatrixType>::run(m1, m.rows());
p = internal::random<RealScalar>();
m2 = m1.pow(p);
m1 = m1.pow(p);
VERIFY(m1 == m2);
VERIFY(m4.isApprox(m5, static_cast<RealScalar>(tol)));
}
}
void test_matrix_power()
{
typedef Matrix<long double,Dynamic,Dynamic> MatrixXe;
CALL_SUBTEST_2(test2dRotation<double>(1e-13));
CALL_SUBTEST_1(test2dRotation<float>(2e-5)); // was 1e-5, relaxed for clang 2.8 / linux / x86-64
CALL_SUBTEST_9(test2dRotation<long double>(1e-13));
@@ -135,20 +105,4 @@ void test_matrix_power()
CALL_SUBTEST_5(testExponentLaws(Matrix3cf(), 1e-4));
CALL_SUBTEST_8(testExponentLaws(Matrix4f(), 1e-4));
CALL_SUBTEST_6(testExponentLaws(MatrixXf(8,8), 1e-4));
CALL_SUBTEST_2(testMatrixVectorProduct(Matrix2d(), Vector2d(), 1e-13));
CALL_SUBTEST_7(testMatrixVectorProduct(Matrix<double,3,3,RowMajor>(), Vector3d(), 1e-13));
CALL_SUBTEST_3(testMatrixVectorProduct(Matrix4cd(), Vector4cd(), 1e-13));
CALL_SUBTEST_4(testMatrixVectorProduct(MatrixXd(8,8), MatrixXd(8,2), 1e-13));
CALL_SUBTEST_1(testMatrixVectorProduct(Matrix2f(), Vector2f(), 1e-4));
CALL_SUBTEST_5(testMatrixVectorProduct(Matrix3cf(), Vector3cf(), 1e-4));
CALL_SUBTEST_8(testMatrixVectorProduct(Matrix4f(), Vector4f(), 1e-4));
CALL_SUBTEST_6(testMatrixVectorProduct(MatrixXf(8,8), VectorXf(8), 1e-4));
CALL_SUBTEST_9(testMatrixVectorProduct(Matrix<long double,Dynamic,Dynamic>(7,7), Matrix<long double,7,9>(), 1e-13));
CALL_SUBTEST_7(testAliasing(Matrix<double,3,3,RowMajor>()));
CALL_SUBTEST_3(testAliasing(Matrix4cd()));
CALL_SUBTEST_4(testAliasing(MatrixXd(8,8)));
CALL_SUBTEST_5(testAliasing(Matrix3cf()));
CALL_SUBTEST_6(testAliasing(MatrixXf(8,8)));
}