|  | // Ceres Solver - A fast non-linear least squares minimizer | 
|  | // Copyright 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. | 
|  | // | 
|  | // 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 <algorithm> | 
|  | #include "ceres/internal/port.h" | 
|  | #include "ceres/suitesparse.h" | 
|  | #include "ceres/triplet_sparse_matrix.h" | 
|  | #include "glog/logging.h" | 
|  | #include "gtest/gtest.h" | 
|  |  | 
|  | namespace ceres { | 
|  | namespace internal { | 
|  |  | 
|  | TEST(SuiteSparse, BlockPermutationToScalarPermutation) { | 
|  | vector<int> blocks; | 
|  | //  Block structure | 
|  | //  0  --1-  ---2---  ---3---  4 | 
|  | // [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] | 
|  | blocks.push_back(1); | 
|  | blocks.push_back(2); | 
|  | blocks.push_back(3); | 
|  | blocks.push_back(3); | 
|  | blocks.push_back(1); | 
|  |  | 
|  | // Block ordering | 
|  | // [1, 0, 2, 4, 5] | 
|  | vector<int> block_ordering; | 
|  | block_ordering.push_back(1); | 
|  | block_ordering.push_back(0); | 
|  | block_ordering.push_back(2); | 
|  | block_ordering.push_back(4); | 
|  | block_ordering.push_back(3); | 
|  |  | 
|  | // Expected ordering | 
|  | // [1, 2, 0, 3, 4, 5, 9, 6, 7, 8] | 
|  | vector<int> expected_scalar_ordering; | 
|  | expected_scalar_ordering.push_back(1); | 
|  | expected_scalar_ordering.push_back(2); | 
|  | expected_scalar_ordering.push_back(0); | 
|  | expected_scalar_ordering.push_back(3); | 
|  | expected_scalar_ordering.push_back(4); | 
|  | expected_scalar_ordering.push_back(5); | 
|  | expected_scalar_ordering.push_back(9); | 
|  | expected_scalar_ordering.push_back(6); | 
|  | expected_scalar_ordering.push_back(7); | 
|  | expected_scalar_ordering.push_back(8); | 
|  |  | 
|  | vector<int> scalar_ordering; | 
|  | SuiteSparse::BlockOrderingToScalarOrdering(blocks, | 
|  | block_ordering, | 
|  | &scalar_ordering); | 
|  | EXPECT_EQ(scalar_ordering.size(), expected_scalar_ordering.size()); | 
|  | for (int i = 0; i < expected_scalar_ordering.size(); ++i) { | 
|  | EXPECT_EQ(scalar_ordering[i], expected_scalar_ordering[i]); | 
|  | } | 
|  | } | 
|  |  | 
|  | // Helper function to fill the sparsity pattern of a TripletSparseMatrix. | 
|  | int FillBlock(const vector<int>& row_blocks, | 
|  | const vector<int>& col_blocks, | 
|  | const int row_block_id, | 
|  | const int col_block_id, | 
|  | int* rows, | 
|  | int* cols) { | 
|  | int row_pos = 0; | 
|  | for (int i = 0; i < row_block_id; ++i) { | 
|  | row_pos += row_blocks[i]; | 
|  | } | 
|  |  | 
|  | int col_pos = 0; | 
|  | for (int i = 0; i < col_block_id; ++i) { | 
|  | col_pos += col_blocks[i]; | 
|  | } | 
|  |  | 
|  | int offset = 0; | 
|  | for (int r = 0; r < row_blocks[row_block_id]; ++r) { | 
|  | for (int c = 0; c < col_blocks[col_block_id]; ++c, ++offset) { | 
|  | rows[offset] = row_pos + r; | 
|  | cols[offset] = col_pos + c; | 
|  | } | 
|  | } | 
|  | return offset; | 
|  | } | 
|  |  | 
|  | TEST(SuiteSparse, ScalarMatrixToBlockMatrix) { | 
|  | // Block sparsity. | 
|  | // | 
|  | //     [1 2 3 2] | 
|  | // [1]  x   x | 
|  | // [2]    x   x | 
|  | // [2]  x x | 
|  | // num_nonzeros = 1 + 3 + 4 + 4 + 1 + 2 = 15 | 
|  |  | 
|  | vector<int> col_blocks; | 
|  | col_blocks.push_back(1); | 
|  | col_blocks.push_back(2); | 
|  | col_blocks.push_back(3); | 
|  | col_blocks.push_back(2); | 
|  |  | 
|  | vector<int> row_blocks; | 
|  | row_blocks.push_back(1); | 
|  | row_blocks.push_back(2); | 
|  | row_blocks.push_back(2); | 
|  |  | 
|  | TripletSparseMatrix tsm(5, 8, 18); | 
|  | int* rows = tsm.mutable_rows(); | 
|  | int* cols = tsm.mutable_cols(); | 
|  | fill(tsm.mutable_values(), tsm.mutable_values() + 18, 1.0); | 
|  | int offset = 0; | 
|  |  | 
|  | #define CERES_TEST_FILL_BLOCK(row_block_id, col_block_id) \ | 
|  | offset += FillBlock(row_blocks, col_blocks, \ | 
|  | row_block_id, col_block_id, \ | 
|  | rows + offset, cols + offset); | 
|  |  | 
|  | CERES_TEST_FILL_BLOCK(0, 0); | 
|  | CERES_TEST_FILL_BLOCK(2, 0); | 
|  | CERES_TEST_FILL_BLOCK(1, 1); | 
|  | CERES_TEST_FILL_BLOCK(2, 1); | 
|  | CERES_TEST_FILL_BLOCK(0, 2); | 
|  | CERES_TEST_FILL_BLOCK(1, 3); | 
|  | #undef CERES_TEST_FILL_BLOCK | 
|  |  | 
|  | tsm.set_num_nonzeros(offset); | 
|  |  | 
|  | SuiteSparse ss; | 
|  | scoped_ptr<cholmod_sparse> ccsm(ss.CreateSparseMatrix(&tsm)); | 
|  |  | 
|  | vector<int> expected_block_rows; | 
|  | expected_block_rows.push_back(0); | 
|  | expected_block_rows.push_back(2); | 
|  | expected_block_rows.push_back(1); | 
|  | expected_block_rows.push_back(2); | 
|  | expected_block_rows.push_back(0); | 
|  | expected_block_rows.push_back(1); | 
|  |  | 
|  | vector<int> expected_block_cols; | 
|  | expected_block_cols.push_back(0); | 
|  | expected_block_cols.push_back(2); | 
|  | expected_block_cols.push_back(4); | 
|  | expected_block_cols.push_back(5); | 
|  | expected_block_cols.push_back(6); | 
|  |  | 
|  | vector<int> block_rows; | 
|  | vector<int> block_cols; | 
|  | SuiteSparse::ScalarMatrixToBlockMatrix(ccsm.get(), | 
|  | row_blocks, | 
|  | col_blocks, | 
|  | &block_rows, | 
|  | &block_cols); | 
|  |  | 
|  | EXPECT_EQ(block_cols.size(), expected_block_cols.size()); | 
|  | EXPECT_EQ(block_rows.size(), expected_block_rows.size()); | 
|  |  | 
|  | for (int i = 0; i < expected_block_cols.size(); ++i) { | 
|  | EXPECT_EQ(block_cols[i], expected_block_cols[i]); | 
|  | } | 
|  |  | 
|  | for (int i = 0; i < expected_block_rows.size(); ++i) { | 
|  | EXPECT_EQ(block_rows[i], expected_block_rows[i]); | 
|  | } | 
|  |  | 
|  | ss.Free(ccsm.release()); | 
|  | } | 
|  |  | 
|  | }  // namespace internal | 
|  | }  // namespace ceres |