MYNT-EYE-S-SDK/3rdparty/ceres-solver-1.11.0/internal/ceres/partitioned_matrix_view_test.cc
2019-01-03 16:25:18 +08:00

175 lines
6.0 KiB
C++

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2015 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
//
// * Redistributions of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
// * Neither the name of Google Inc. nor the names of its contributors may be
// used to endorse or promote products derived from this software without
// specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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//
// Author: sameeragarwal@google.com (Sameer Agarwal)
#include "ceres/partitioned_matrix_view.h"
#include <vector>
#include "ceres/block_structure.h"
#include "ceres/casts.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/random.h"
#include "ceres/sparse_matrix.h"
#include "glog/logging.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
const double kEpsilon = 1e-14;
class PartitionedMatrixViewTest : public ::testing::Test {
protected :
virtual void SetUp() {
srand(5);
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(2));
CHECK_NOTNULL(problem.get());
A_.reset(problem->A.release());
num_cols_ = A_->num_cols();
num_rows_ = A_->num_rows();
num_eliminate_blocks_ = problem->num_eliminate_blocks;
LinearSolver::Options options;
options.elimination_groups.push_back(num_eliminate_blocks_);
pmv_.reset(PartitionedMatrixViewBase::Create(
options,
*down_cast<BlockSparseMatrix*>(A_.get())));
}
int num_rows_;
int num_cols_;
int num_eliminate_blocks_;
scoped_ptr<SparseMatrix> A_;
scoped_ptr<PartitionedMatrixViewBase> pmv_;
};
TEST_F(PartitionedMatrixViewTest, DimensionsTest) {
EXPECT_EQ(pmv_->num_col_blocks_e(), num_eliminate_blocks_);
EXPECT_EQ(pmv_->num_col_blocks_f(), num_cols_ - num_eliminate_blocks_);
EXPECT_EQ(pmv_->num_cols_e(), num_eliminate_blocks_);
EXPECT_EQ(pmv_->num_cols_f(), num_cols_ - num_eliminate_blocks_);
EXPECT_EQ(pmv_->num_cols(), A_->num_cols());
EXPECT_EQ(pmv_->num_rows(), A_->num_rows());
}
TEST_F(PartitionedMatrixViewTest, RightMultiplyE) {
Vector x1(pmv_->num_cols_e());
Vector x2(pmv_->num_cols());
x2.setZero();
for (int i = 0; i < pmv_->num_cols_e(); ++i) {
x1(i) = x2(i) = RandDouble();
}
Vector y1 = Vector::Zero(pmv_->num_rows());
pmv_->RightMultiplyE(x1.data(), y1.data());
Vector y2 = Vector::Zero(pmv_->num_rows());
A_->RightMultiply(x2.data(), y2.data());
for (int i = 0; i < pmv_->num_rows(); ++i) {
EXPECT_NEAR(y1(i), y2(i), kEpsilon);
}
}
TEST_F(PartitionedMatrixViewTest, RightMultiplyF) {
Vector x1(pmv_->num_cols_f());
Vector x2 = Vector::Zero(pmv_->num_cols());
for (int i = 0; i < pmv_->num_cols_f(); ++i) {
x1(i) = RandDouble();
x2(i + pmv_->num_cols_e()) = x1(i);
}
Vector y1 = Vector::Zero(pmv_->num_rows());
pmv_->RightMultiplyF(x1.data(), y1.data());
Vector y2 = Vector::Zero(pmv_->num_rows());
A_->RightMultiply(x2.data(), y2.data());
for (int i = 0; i < pmv_->num_rows(); ++i) {
EXPECT_NEAR(y1(i), y2(i), kEpsilon);
}
}
TEST_F(PartitionedMatrixViewTest, LeftMultiply) {
Vector x = Vector::Zero(pmv_->num_rows());
for (int i = 0; i < pmv_->num_rows(); ++i) {
x(i) = RandDouble();
}
Vector y = Vector::Zero(pmv_->num_cols());
Vector y1 = Vector::Zero(pmv_->num_cols_e());
Vector y2 = Vector::Zero(pmv_->num_cols_f());
A_->LeftMultiply(x.data(), y.data());
pmv_->LeftMultiplyE(x.data(), y1.data());
pmv_->LeftMultiplyF(x.data(), y2.data());
for (int i = 0; i < pmv_->num_cols(); ++i) {
EXPECT_NEAR(y(i),
(i < pmv_->num_cols_e()) ? y1(i) : y2(i - pmv_->num_cols_e()),
kEpsilon);
}
}
TEST_F(PartitionedMatrixViewTest, BlockDiagonalEtE) {
scoped_ptr<BlockSparseMatrix>
block_diagonal_ee(pmv_->CreateBlockDiagonalEtE());
const CompressedRowBlockStructure* bs = block_diagonal_ee->block_structure();
EXPECT_EQ(block_diagonal_ee->num_rows(), 2);
EXPECT_EQ(block_diagonal_ee->num_cols(), 2);
EXPECT_EQ(bs->cols.size(), 2);
EXPECT_EQ(bs->rows.size(), 2);
EXPECT_NEAR(block_diagonal_ee->values()[0], 10.0, kEpsilon);
EXPECT_NEAR(block_diagonal_ee->values()[1], 155.0, kEpsilon);
}
TEST_F(PartitionedMatrixViewTest, BlockDiagonalFtF) {
scoped_ptr<BlockSparseMatrix>
block_diagonal_ff(pmv_->CreateBlockDiagonalFtF());
const CompressedRowBlockStructure* bs = block_diagonal_ff->block_structure();
EXPECT_EQ(block_diagonal_ff->num_rows(), 3);
EXPECT_EQ(block_diagonal_ff->num_cols(), 3);
EXPECT_EQ(bs->cols.size(), 3);
EXPECT_EQ(bs->rows.size(), 3);
EXPECT_NEAR(block_diagonal_ff->values()[0], 70.0, kEpsilon);
EXPECT_NEAR(block_diagonal_ff->values()[1], 17.0, kEpsilon);
EXPECT_NEAR(block_diagonal_ff->values()[2], 37.0, kEpsilon);
}
} // namespace internal
} // namespace ceres