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// 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
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//
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// 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