| // Ceres Solver - A fast non-linear least squares minimizer | 
 | // Copyright 2013 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/compressed_col_sparse_matrix_utils.h" | 
 | #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(_, 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; | 
 |   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(_, 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; | 
 |   CompressedColumnScalarMatrixToBlockMatrix(reinterpret_cast<const int*>(ccsm->i), | 
 |                                             reinterpret_cast<const int*>(ccsm->p), | 
 |                                             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 |