You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
116 lines
3.3 KiB
116 lines
3.3 KiB
// This file is part of Eigen, a lightweight C++ template library
|
|
// for linear algebra.
|
|
//
|
|
// Copyright (C) 2008 Benoit Jacob <jacob.benoit.1@gmail.com>
|
|
//
|
|
// This Source Code Form is subject to the terms of the Mozilla
|
|
// Public License v. 2.0. If a copy of the MPL was not distributed
|
|
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
|
|
|
|
#include "main.h"
|
|
|
|
template<typename MatrixType> void matrixVisitor(const MatrixType& p)
|
|
{
|
|
typedef typename MatrixType::Scalar Scalar;
|
|
|
|
int rows = p.rows();
|
|
int cols = p.cols();
|
|
|
|
// construct a random matrix where all coefficients are different
|
|
MatrixType m;
|
|
m = MatrixType::Random(rows, cols);
|
|
for(int i = 0; i < m.size(); i++)
|
|
for(int i2 = 0; i2 < i; i2++)
|
|
while(m(i) == m(i2)) // yes, ==
|
|
m(i) = ei_random<Scalar>();
|
|
|
|
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
|
|
int minrow=0,mincol=0,maxrow=0,maxcol=0;
|
|
for(int j = 0; j < cols; j++)
|
|
for(int i = 0; i < rows; i++)
|
|
{
|
|
if(m(i,j) < minc)
|
|
{
|
|
minc = m(i,j);
|
|
minrow = i;
|
|
mincol = j;
|
|
}
|
|
if(m(i,j) > maxc)
|
|
{
|
|
maxc = m(i,j);
|
|
maxrow = i;
|
|
maxcol = j;
|
|
}
|
|
}
|
|
int eigen_minrow, eigen_mincol, eigen_maxrow, eigen_maxcol;
|
|
Scalar eigen_minc, eigen_maxc;
|
|
eigen_minc = m.minCoeff(&eigen_minrow,&eigen_mincol);
|
|
eigen_maxc = m.maxCoeff(&eigen_maxrow,&eigen_maxcol);
|
|
VERIFY(minrow == eigen_minrow);
|
|
VERIFY(maxrow == eigen_maxrow);
|
|
VERIFY(mincol == eigen_mincol);
|
|
VERIFY(maxcol == eigen_maxcol);
|
|
VERIFY_IS_APPROX(minc, eigen_minc);
|
|
VERIFY_IS_APPROX(maxc, eigen_maxc);
|
|
VERIFY_IS_APPROX(minc, m.minCoeff());
|
|
VERIFY_IS_APPROX(maxc, m.maxCoeff());
|
|
}
|
|
|
|
template<typename VectorType> void vectorVisitor(const VectorType& w)
|
|
{
|
|
typedef typename VectorType::Scalar Scalar;
|
|
|
|
int size = w.size();
|
|
|
|
// construct a random vector where all coefficients are different
|
|
VectorType v;
|
|
v = VectorType::Random(size);
|
|
for(int i = 0; i < size; i++)
|
|
for(int i2 = 0; i2 < i; i2++)
|
|
while(v(i) == v(i2)) // yes, ==
|
|
v(i) = ei_random<Scalar>();
|
|
|
|
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
|
|
int minidx=0,maxidx=0;
|
|
for(int i = 0; i < size; i++)
|
|
{
|
|
if(v(i) < minc)
|
|
{
|
|
minc = v(i);
|
|
minidx = i;
|
|
}
|
|
if(v(i) > maxc)
|
|
{
|
|
maxc = v(i);
|
|
maxidx = i;
|
|
}
|
|
}
|
|
int eigen_minidx, eigen_maxidx;
|
|
Scalar eigen_minc, eigen_maxc;
|
|
eigen_minc = v.minCoeff(&eigen_minidx);
|
|
eigen_maxc = v.maxCoeff(&eigen_maxidx);
|
|
VERIFY(minidx == eigen_minidx);
|
|
VERIFY(maxidx == eigen_maxidx);
|
|
VERIFY_IS_APPROX(minc, eigen_minc);
|
|
VERIFY_IS_APPROX(maxc, eigen_maxc);
|
|
VERIFY_IS_APPROX(minc, v.minCoeff());
|
|
VERIFY_IS_APPROX(maxc, v.maxCoeff());
|
|
}
|
|
|
|
void test_eigen2_visitor()
|
|
{
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST_1( matrixVisitor(Matrix<float, 1, 1>()) );
|
|
CALL_SUBTEST_2( matrixVisitor(Matrix2f()) );
|
|
CALL_SUBTEST_3( matrixVisitor(Matrix4d()) );
|
|
CALL_SUBTEST_4( matrixVisitor(MatrixXd(8, 12)) );
|
|
CALL_SUBTEST_5( matrixVisitor(Matrix<double,Dynamic,Dynamic,RowMajor>(20, 20)) );
|
|
CALL_SUBTEST_6( matrixVisitor(MatrixXi(8, 12)) );
|
|
}
|
|
for(int i = 0; i < g_repeat; i++) {
|
|
CALL_SUBTEST_7( vectorVisitor(Vector4f()) );
|
|
CALL_SUBTEST_4( vectorVisitor(VectorXd(10)) );
|
|
CALL_SUBTEST_4( vectorVisitor(RowVectorXd(10)) );
|
|
CALL_SUBTEST_8( vectorVisitor(VectorXf(33)) );
|
|
}
|
|
}
|