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// 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;
typedef typename MatrixType::Index Index;
Index rows = p.rows();
Index cols = p.cols();
// construct a random matrix where all coefficients are different
MatrixType m;
m = MatrixType::Random(rows, cols);
for(Index i = 0; i < m.size(); i++)
for(Index i2 = 0; i2 < i; i2++)
while(m(i) == m(i2)) // yes, ==
m(i) = internal::random<Scalar>();
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
Index minrow=0,mincol=0,maxrow=0,maxcol=0;
for(Index j = 0; j < cols; j++)
for(Index 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;
}
}
Index 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;
typedef typename VectorType::Index Index;
Index size = w.size();
// construct a random vector where all coefficients are different
VectorType v;
v = VectorType::Random(size);
for(Index i = 0; i < size; i++)
for(Index i2 = 0; i2 < i; i2++)
while(v(i) == v(i2)) // yes, ==
v(i) = internal::random<Scalar>();
Scalar minc = Scalar(1000), maxc = Scalar(-1000);
Index minidx=0,maxidx=0;
for(Index i = 0; i < size; i++)
{
if(v(i) < minc)
{
minc = v(i);
minidx = i;
}
if(v(i) > maxc)
{
maxc = v(i);
maxidx = i;
}
}
Index 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_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_8( vectorVisitor(VectorXd(10)) );
CALL_SUBTEST_9( vectorVisitor(RowVectorXd(10)) );
CALL_SUBTEST_10( vectorVisitor(VectorXf(33)) );
}
}
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