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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
// 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.
//
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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/compressed_row_sparse_matrix.h"
#include "ceres/casts.h"
#include "ceres/crs_matrix.h"
#include "ceres/internal/eigen.h"
#include "ceres/internal/scoped_ptr.h"
#include "ceres/linear_least_squares_problems.h"
#include "ceres/matrix_proto.h"
#include "ceres/triplet_sparse_matrix.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
void CompareMatrices(const SparseMatrix* a, const SparseMatrix* b) {
EXPECT_EQ(a->num_rows(), b->num_rows());
EXPECT_EQ(a->num_cols(), b->num_cols());
int num_rows = a->num_rows();
int num_cols = a->num_cols();
for (int i = 0; i < num_cols; ++i) {
Vector x = Vector::Zero(num_cols);
x(i) = 1.0;
Vector y_a = Vector::Zero(num_rows);
Vector y_b = Vector::Zero(num_rows);
a->RightMultiply(x.data(), y_a.data());
b->RightMultiply(x.data(), y_b.data());
EXPECT_EQ((y_a - y_b).norm(), 0);
}
}
class CompressedRowSparseMatrixTest : public ::testing::Test {
protected :
virtual void SetUp() {
scoped_ptr<LinearLeastSquaresProblem> problem(
CreateLinearLeastSquaresProblemFromId(1));
CHECK_NOTNULL(problem.get());
tsm.reset(down_cast<TripletSparseMatrix*>(problem->A.release()));
crsm.reset(new CompressedRowSparseMatrix(*tsm));
num_rows = tsm->num_rows();
num_cols = tsm->num_cols();
}
int num_rows;
int num_cols;
scoped_ptr<TripletSparseMatrix> tsm;
scoped_ptr<CompressedRowSparseMatrix> crsm;
};
TEST_F(CompressedRowSparseMatrixTest, RightMultiply) {
CompareMatrices(tsm.get(), crsm.get());
}
TEST_F(CompressedRowSparseMatrixTest, LeftMultiply) {
for (int i = 0; i < num_rows; ++i) {
Vector a = Vector::Zero(num_rows);
a(i) = 1.0;
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->LeftMultiply(a.data(), b1.data());
crsm->LeftMultiply(a.data(), b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
}
TEST_F(CompressedRowSparseMatrixTest, ColumnNorm) {
Vector b1 = Vector::Zero(num_cols);
Vector b2 = Vector::Zero(num_cols);
tsm->SquaredColumnNorm(b1.data());
crsm->SquaredColumnNorm(b2.data());
EXPECT_EQ((b1 - b2).norm(), 0);
}
TEST_F(CompressedRowSparseMatrixTest, Scale) {
Vector scale(num_cols);
for (int i = 0; i < num_cols; ++i) {
scale(i) = i + 1;
}
tsm->ScaleColumns(scale.data());
crsm->ScaleColumns(scale.data());
CompareMatrices(tsm.get(), crsm.get());
}
TEST_F(CompressedRowSparseMatrixTest, DeleteRows) {
for (int i = 0; i < num_rows; ++i) {
tsm->Resize(num_rows - i, num_cols);
crsm->DeleteRows(crsm->num_rows() - tsm->num_rows());
CompareMatrices(tsm.get(), crsm.get());
}
}
TEST_F(CompressedRowSparseMatrixTest, AppendRows) {
for (int i = 0; i < num_rows; ++i) {
TripletSparseMatrix tsm_appendage(*tsm);
tsm_appendage.Resize(i, num_cols);
tsm->AppendRows(tsm_appendage);
CompressedRowSparseMatrix crsm_appendage(tsm_appendage);
crsm->AppendRows(crsm_appendage);
CompareMatrices(tsm.get(), crsm.get());
}
}
#ifndef CERES_NO_PROTOCOL_BUFFERS
TEST_F(CompressedRowSparseMatrixTest, Serialization) {
SparseMatrixProto proto;
crsm->ToProto(&proto);
CompressedRowSparseMatrix n(proto);
ASSERT_EQ(n.num_rows(), crsm->num_rows());
ASSERT_EQ(n.num_cols(), crsm->num_cols());
ASSERT_EQ(n.num_nonzeros(), crsm->num_nonzeros());
for (int i = 0; i < n.num_rows() + 1; ++i) {
ASSERT_EQ(crsm->rows()[i], proto.compressed_row_matrix().rows(i));
ASSERT_EQ(crsm->rows()[i], n.rows()[i]);
}
for (int i = 0; i < crsm->num_nonzeros(); ++i) {
ASSERT_EQ(crsm->cols()[i], proto.compressed_row_matrix().cols(i));
ASSERT_EQ(crsm->cols()[i], n.cols()[i]);
ASSERT_EQ(crsm->values()[i], proto.compressed_row_matrix().values(i));
ASSERT_EQ(crsm->values()[i], n.values()[i]);
}
}
#endif
TEST_F(CompressedRowSparseMatrixTest, ToDenseMatrix) {
Matrix tsm_dense;
Matrix crsm_dense;
tsm->ToDenseMatrix(&tsm_dense);
crsm->ToDenseMatrix(&crsm_dense);
EXPECT_EQ((tsm_dense - crsm_dense).norm(), 0.0);
}
TEST_F(CompressedRowSparseMatrixTest, ToCRSMatrix) {
CRSMatrix crs_matrix;
crsm->ToCRSMatrix(&crs_matrix);
EXPECT_EQ(crsm->num_rows(), crs_matrix.num_rows);
EXPECT_EQ(crsm->num_cols(), crs_matrix.num_cols);
EXPECT_EQ(crsm->num_rows() + 1, crs_matrix.rows.size());
EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.cols.size());
EXPECT_EQ(crsm->num_nonzeros(), crs_matrix.values.size());
for (int i = 0; i < crsm->num_rows() + 1; ++i) {
EXPECT_EQ(crsm->rows()[i], crs_matrix.rows[i]);
}
for (int i = 0; i < crsm->num_nonzeros(); ++i) {
EXPECT_EQ(crsm->cols()[i], crs_matrix.cols[i]);
EXPECT_EQ(crsm->values()[i], crs_matrix.values[i]);
}
}
} // namespace internal
} // namespace ceres