summaryrefslogtreecommitdiffhomepage
path: root/eigen/test/redux.cpp
diff options
context:
space:
mode:
Diffstat (limited to 'eigen/test/redux.cpp')
-rw-r--r--eigen/test/redux.cpp167
1 files changed, 167 insertions, 0 deletions
diff --git a/eigen/test/redux.cpp b/eigen/test/redux.cpp
new file mode 100644
index 0000000..50b4738
--- /dev/null
+++ b/eigen/test/redux.cpp
@@ -0,0 +1,167 @@
+// 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 matrixRedux(const MatrixType& m)
+{
+ typedef typename MatrixType::Index Index;
+ typedef typename MatrixType::Scalar Scalar;
+ typedef typename MatrixType::RealScalar RealScalar;
+
+ Index rows = m.rows();
+ Index cols = m.cols();
+
+ MatrixType m1 = MatrixType::Random(rows, cols);
+
+ // The entries of m1 are uniformly distributed in [0,1], so m1.prod() is very small. This may lead to test
+ // failures if we underflow into denormals. Thus, we scale so that entires are close to 1.
+ MatrixType m1_for_prod = MatrixType::Ones(rows, cols) + RealScalar(0.2) * m1;
+
+ VERIFY_IS_MUCH_SMALLER_THAN(MatrixType::Zero(rows, cols).sum(), Scalar(1));
+ VERIFY_IS_APPROX(MatrixType::Ones(rows, cols).sum(), Scalar(float(rows*cols))); // the float() here to shut up excessive MSVC warning about int->complex conversion being lossy
+ Scalar s(0), p(1), minc(numext::real(m1.coeff(0))), maxc(numext::real(m1.coeff(0)));
+ for(int j = 0; j < cols; j++)
+ for(int i = 0; i < rows; i++)
+ {
+ s += m1(i,j);
+ p *= m1_for_prod(i,j);
+ minc = (std::min)(numext::real(minc), numext::real(m1(i,j)));
+ maxc = (std::max)(numext::real(maxc), numext::real(m1(i,j)));
+ }
+ const Scalar mean = s/Scalar(RealScalar(rows*cols));
+
+ VERIFY_IS_APPROX(m1.sum(), s);
+ VERIFY_IS_APPROX(m1.mean(), mean);
+ VERIFY_IS_APPROX(m1_for_prod.prod(), p);
+ VERIFY_IS_APPROX(m1.real().minCoeff(), numext::real(minc));
+ VERIFY_IS_APPROX(m1.real().maxCoeff(), numext::real(maxc));
+
+ // test slice vectorization assuming assign is ok
+ Index r0 = internal::random<Index>(0,rows-1);
+ Index c0 = internal::random<Index>(0,cols-1);
+ Index r1 = internal::random<Index>(r0+1,rows)-r0;
+ Index c1 = internal::random<Index>(c0+1,cols)-c0;
+ VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).sum(), m1.block(r0,c0,r1,c1).eval().sum());
+ VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).mean(), m1.block(r0,c0,r1,c1).eval().mean());
+ VERIFY_IS_APPROX(m1_for_prod.block(r0,c0,r1,c1).prod(), m1_for_prod.block(r0,c0,r1,c1).eval().prod());
+ VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().minCoeff(), m1.block(r0,c0,r1,c1).real().eval().minCoeff());
+ VERIFY_IS_APPROX(m1.block(r0,c0,r1,c1).real().maxCoeff(), m1.block(r0,c0,r1,c1).real().eval().maxCoeff());
+
+ // regression for bug 1090
+ const int R1 = MatrixType::RowsAtCompileTime>=2 ? MatrixType::RowsAtCompileTime/2 : 6;
+ const int C1 = MatrixType::ColsAtCompileTime>=2 ? MatrixType::ColsAtCompileTime/2 : 6;
+ if(R1<=rows-r0 && C1<=cols-c0)
+ {
+ VERIFY_IS_APPROX( (m1.template block<R1,C1>(r0,c0).sum()), m1.block(r0,c0,R1,C1).sum() );
+ }
+
+ // test empty objects
+ VERIFY_IS_APPROX(m1.block(r0,c0,0,0).sum(), Scalar(0));
+ VERIFY_IS_APPROX(m1.block(r0,c0,0,0).prod(), Scalar(1));
+}
+
+template<typename VectorType> void vectorRedux(const VectorType& w)
+{
+ using std::abs;
+ typedef typename VectorType::Index Index;
+ typedef typename VectorType::Scalar Scalar;
+ typedef typename NumTraits<Scalar>::Real RealScalar;
+ Index size = w.size();
+
+ VectorType v = VectorType::Random(size);
+ VectorType v_for_prod = VectorType::Ones(size) + Scalar(0.2) * v; // see comment above declaration of m1_for_prod
+
+ for(int i = 1; i < size; i++)
+ {
+ Scalar s(0), p(1);
+ RealScalar minc(numext::real(v.coeff(0))), maxc(numext::real(v.coeff(0)));
+ for(int j = 0; j < i; j++)
+ {
+ s += v[j];
+ p *= v_for_prod[j];
+ minc = (std::min)(minc, numext::real(v[j]));
+ maxc = (std::max)(maxc, numext::real(v[j]));
+ }
+ VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.head(i).sum()), Scalar(1));
+ VERIFY_IS_APPROX(p, v_for_prod.head(i).prod());
+ VERIFY_IS_APPROX(minc, v.real().head(i).minCoeff());
+ VERIFY_IS_APPROX(maxc, v.real().head(i).maxCoeff());
+ }
+
+ for(int i = 0; i < size-1; i++)
+ {
+ Scalar s(0), p(1);
+ RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
+ for(int j = i; j < size; j++)
+ {
+ s += v[j];
+ p *= v_for_prod[j];
+ minc = (std::min)(minc, numext::real(v[j]));
+ maxc = (std::max)(maxc, numext::real(v[j]));
+ }
+ VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.tail(size-i).sum()), Scalar(1));
+ VERIFY_IS_APPROX(p, v_for_prod.tail(size-i).prod());
+ VERIFY_IS_APPROX(minc, v.real().tail(size-i).minCoeff());
+ VERIFY_IS_APPROX(maxc, v.real().tail(size-i).maxCoeff());
+ }
+
+ for(int i = 0; i < size/2; i++)
+ {
+ Scalar s(0), p(1);
+ RealScalar minc(numext::real(v.coeff(i))), maxc(numext::real(v.coeff(i)));
+ for(int j = i; j < size-i; j++)
+ {
+ s += v[j];
+ p *= v_for_prod[j];
+ minc = (std::min)(minc, numext::real(v[j]));
+ maxc = (std::max)(maxc, numext::real(v[j]));
+ }
+ VERIFY_IS_MUCH_SMALLER_THAN(abs(s - v.segment(i, size-2*i).sum()), Scalar(1));
+ VERIFY_IS_APPROX(p, v_for_prod.segment(i, size-2*i).prod());
+ VERIFY_IS_APPROX(minc, v.real().segment(i, size-2*i).minCoeff());
+ VERIFY_IS_APPROX(maxc, v.real().segment(i, size-2*i).maxCoeff());
+ }
+
+ // test empty objects
+ VERIFY_IS_APPROX(v.head(0).sum(), Scalar(0));
+ VERIFY_IS_APPROX(v.tail(0).prod(), Scalar(1));
+ VERIFY_RAISES_ASSERT(v.head(0).mean());
+ VERIFY_RAISES_ASSERT(v.head(0).minCoeff());
+ VERIFY_RAISES_ASSERT(v.head(0).maxCoeff());
+}
+
+void test_redux()
+{
+ // the max size cannot be too large, otherwise reduxion operations obviously generate large errors.
+ int maxsize = (std::min)(100,EIGEN_TEST_MAX_SIZE);
+ TEST_SET_BUT_UNUSED_VARIABLE(maxsize);
+ for(int i = 0; i < g_repeat; i++) {
+ CALL_SUBTEST_1( matrixRedux(Matrix<float, 1, 1>()) );
+ CALL_SUBTEST_1( matrixRedux(Array<float, 1, 1>()) );
+ CALL_SUBTEST_2( matrixRedux(Matrix2f()) );
+ CALL_SUBTEST_2( matrixRedux(Array2f()) );
+ CALL_SUBTEST_3( matrixRedux(Matrix4d()) );
+ CALL_SUBTEST_3( matrixRedux(Array4d()) );
+ CALL_SUBTEST_4( matrixRedux(MatrixXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_4( matrixRedux(ArrayXXcf(internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_5( matrixRedux(MatrixXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_5( matrixRedux(ArrayXXd (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_6( matrixRedux(MatrixXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_6( matrixRedux(ArrayXXi (internal::random<int>(1,maxsize), internal::random<int>(1,maxsize))) );
+ }
+ for(int i = 0; i < g_repeat; i++) {
+ CALL_SUBTEST_7( vectorRedux(Vector4f()) );
+ CALL_SUBTEST_7( vectorRedux(Array4f()) );
+ CALL_SUBTEST_5( vectorRedux(VectorXd(internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_5( vectorRedux(ArrayXd(internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_8( vectorRedux(VectorXf(internal::random<int>(1,maxsize))) );
+ CALL_SUBTEST_8( vectorRedux(ArrayXf(internal::random<int>(1,maxsize))) );
+ }
+}
generated by cgit v1.2.3 (git 2.39.1) at 2025-04-14 22:25:21 +0000