| 123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218 | // 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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE// POSSIBILITY OF SUCH DAMAGE.//// Author: sameeragarwal@google.com (Sameer Agarwal)#include "ceres/block_sparse_matrix.h"#include <memory>#include <string>#include "ceres/casts.h"#include "ceres/internal/eigen.h"#include "ceres/linear_least_squares_problems.h"#include "ceres/triplet_sparse_matrix.h"#include "glog/logging.h"#include "gtest/gtest.h"namespace ceres {namespace internal {class BlockSparseMatrixTest : public ::testing::Test { protected :  void SetUp() final {    std::unique_ptr<LinearLeastSquaresProblem> problem(        CreateLinearLeastSquaresProblemFromId(2));    CHECK(problem != nullptr);    A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release()));    problem.reset(CreateLinearLeastSquaresProblemFromId(1));    CHECK(problem != nullptr);    B_.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));    CHECK_EQ(A_->num_rows(), B_->num_rows());    CHECK_EQ(A_->num_cols(), B_->num_cols());    CHECK_EQ(A_->num_nonzeros(), B_->num_nonzeros());  }  std::unique_ptr<BlockSparseMatrix> A_;  std::unique_ptr<TripletSparseMatrix> B_;};TEST_F(BlockSparseMatrixTest, SetZeroTest) {  A_->SetZero();  EXPECT_EQ(13, A_->num_nonzeros());}TEST_F(BlockSparseMatrixTest, RightMultiplyTest) {  Vector y_a = Vector::Zero(A_->num_rows());  Vector y_b = Vector::Zero(A_->num_rows());  for (int i = 0; i < A_->num_cols(); ++i) {    Vector x = Vector::Zero(A_->num_cols());    x[i] = 1.0;    A_->RightMultiply(x.data(), y_a.data());    B_->RightMultiply(x.data(), y_b.data());    EXPECT_LT((y_a - y_b).norm(), 1e-12);  }}TEST_F(BlockSparseMatrixTest, LeftMultiplyTest) {  Vector y_a = Vector::Zero(A_->num_cols());  Vector y_b = Vector::Zero(A_->num_cols());  for (int i = 0; i < A_->num_rows(); ++i) {    Vector x = Vector::Zero(A_->num_rows());    x[i] = 1.0;    A_->LeftMultiply(x.data(), y_a.data());    B_->LeftMultiply(x.data(), y_b.data());    EXPECT_LT((y_a - y_b).norm(), 1e-12);  }}TEST_F(BlockSparseMatrixTest, SquaredColumnNormTest) {  Vector y_a = Vector::Zero(A_->num_cols());  Vector y_b = Vector::Zero(A_->num_cols());  A_->SquaredColumnNorm(y_a.data());  B_->SquaredColumnNorm(y_b.data());  EXPECT_LT((y_a - y_b).norm(), 1e-12);}TEST_F(BlockSparseMatrixTest, ToDenseMatrixTest) {  Matrix m_a;  Matrix m_b;  A_->ToDenseMatrix(&m_a);  B_->ToDenseMatrix(&m_b);  EXPECT_LT((m_a - m_b).norm(), 1e-12);}TEST_F(BlockSparseMatrixTest, AppendRows) {  std::unique_ptr<LinearLeastSquaresProblem> problem(      CreateLinearLeastSquaresProblemFromId(2));  std::unique_ptr<BlockSparseMatrix> m(      down_cast<BlockSparseMatrix*>(problem->A.release()));  A_->AppendRows(*m);  EXPECT_EQ(A_->num_rows(), 2 * m->num_rows());  EXPECT_EQ(A_->num_cols(), m->num_cols());  problem.reset(CreateLinearLeastSquaresProblemFromId(1));  std::unique_ptr<TripletSparseMatrix> m2(      down_cast<TripletSparseMatrix*>(problem->A.release()));  B_->AppendRows(*m2);  Vector y_a = Vector::Zero(A_->num_rows());  Vector y_b = Vector::Zero(A_->num_rows());  for (int i = 0; i < A_->num_cols(); ++i) {    Vector x = Vector::Zero(A_->num_cols());    x[i] = 1.0;    y_a.setZero();    y_b.setZero();    A_->RightMultiply(x.data(), y_a.data());    B_->RightMultiply(x.data(), y_b.data());    EXPECT_LT((y_a - y_b).norm(), 1e-12);  }}TEST_F(BlockSparseMatrixTest, AppendAndDeleteBlockDiagonalMatrix) {  const std::vector<Block>& column_blocks = A_->block_structure()->cols;  const int num_cols =      column_blocks.back().size + column_blocks.back().position;  Vector diagonal(num_cols);  for (int i = 0; i < num_cols; ++i) {    diagonal(i) = 2 * i * i + 1;  }  std::unique_ptr<BlockSparseMatrix> appendage(      BlockSparseMatrix::CreateDiagonalMatrix(diagonal.data(), column_blocks));  A_->AppendRows(*appendage);  Vector y_a, y_b;  y_a.resize(A_->num_rows());  y_b.resize(A_->num_rows());  for (int i = 0; i < A_->num_cols(); ++i) {    Vector x = Vector::Zero(A_->num_cols());    x[i] = 1.0;    y_a.setZero();    y_b.setZero();    A_->RightMultiply(x.data(), y_a.data());    B_->RightMultiply(x.data(), y_b.data());    EXPECT_LT((y_a.head(B_->num_rows()) - y_b.head(B_->num_rows())).norm(), 1e-12);    Vector expected_tail = Vector::Zero(A_->num_cols());    expected_tail(i) = diagonal(i);    EXPECT_LT((y_a.tail(A_->num_cols()) - expected_tail).norm(), 1e-12);  }  A_->DeleteRowBlocks(column_blocks.size());  EXPECT_EQ(A_->num_rows(), B_->num_rows());  EXPECT_EQ(A_->num_cols(), B_->num_cols());  y_a.resize(A_->num_rows());  y_b.resize(A_->num_rows());  for (int i = 0; i < A_->num_cols(); ++i) {    Vector x = Vector::Zero(A_->num_cols());    x[i] = 1.0;    y_a.setZero();    y_b.setZero();    A_->RightMultiply(x.data(), y_a.data());    B_->RightMultiply(x.data(), y_b.data());    EXPECT_LT((y_a - y_b).norm(), 1e-12);  }}TEST(BlockSparseMatrix, CreateDiagonalMatrix) {  std::vector<Block> column_blocks;  column_blocks.push_back(Block(2, 0));  column_blocks.push_back(Block(1, 2));  column_blocks.push_back(Block(3, 3));  const int num_cols =      column_blocks.back().size + column_blocks.back().position;  Vector diagonal(num_cols);  for (int i = 0; i < num_cols; ++i) {    diagonal(i) = 2 * i * i + 1;  }  std::unique_ptr<BlockSparseMatrix> m(      BlockSparseMatrix::CreateDiagonalMatrix(diagonal.data(), column_blocks));  const CompressedRowBlockStructure* bs = m->block_structure();  EXPECT_EQ(bs->cols.size(), column_blocks.size());  for (int i = 0; i < column_blocks.size(); ++i) {    EXPECT_EQ(bs->cols[i].size, column_blocks[i].size);    EXPECT_EQ(bs->cols[i].position, column_blocks[i].position);  }  EXPECT_EQ(m->num_rows(), m->num_cols());  Vector x = Vector::Ones(num_cols);  Vector y = Vector::Zero(num_cols);  m->RightMultiply(x.data(), y.data());  for (int i = 0; i < num_cols; ++i) {    EXPECT_NEAR(y[i], diagonal[i], std::numeric_limits<double>::epsilon());  }}}  // namespace internal}  // namespace ceres
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