|  | // 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 | 
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|  | // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF | 
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|  | // | 
|  | // Author: sameeragarwal@google.com (Sameer Agarwal) | 
|  |  | 
|  | #include "ceres/block_sparse_matrix.h" | 
|  |  | 
|  | #include <string> | 
|  | #include "ceres/casts.h" | 
|  | #include "ceres/internal/eigen.h" | 
|  | #include "ceres/internal/scoped_ptr.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 : | 
|  | virtual void SetUp() { | 
|  | scoped_ptr<LinearLeastSquaresProblem> problem( | 
|  | CreateLinearLeastSquaresProblemFromId(2)); | 
|  | CHECK_NOTNULL(problem.get()); | 
|  | A_.reset(down_cast<BlockSparseMatrix*>(problem->A.release())); | 
|  |  | 
|  | problem.reset(CreateLinearLeastSquaresProblemFromId(1)); | 
|  | CHECK_NOTNULL(problem.get()); | 
|  | 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()); | 
|  | } | 
|  |  | 
|  | scoped_ptr<BlockSparseMatrix> A_; | 
|  | scoped_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); | 
|  | } | 
|  |  | 
|  | }  // namespace internal | 
|  | }  // namespace ceres |