Initial commit of Ceres Solver.
diff --git a/internal/ceres/dense_sparse_matrix_test.cc b/internal/ceres/dense_sparse_matrix_test.cc
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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.
+//
+// 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: keir@google.com (Keir Mierle)
+//
+// TODO(keir): Implement a generic "compare sparse matrix implementations" test
+// suite that can compare all the implementations. Then this file would shrink
+// in size.
+
+#include "ceres/dense_sparse_matrix.h"
+
+#include "gtest/gtest.h"
+#include "ceres/casts.h"
+#include "ceres/linear_least_squares_problems.h"
+#include "ceres/matrix_proto.h"
+#include "ceres/triplet_sparse_matrix.h"
+#include "ceres/internal/eigen.h"
+#include "ceres/internal/scoped_ptr.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 DenseSparseMatrixTest : 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()));
+ dsm.reset(new DenseSparseMatrix(*tsm));
+
+ num_rows = tsm->num_rows();
+ num_cols = tsm->num_cols();
+ }
+
+ int num_rows;
+ int num_cols;
+
+ scoped_ptr<TripletSparseMatrix> tsm;
+ scoped_ptr<DenseSparseMatrix> dsm;
+};
+
+TEST_F(DenseSparseMatrixTest, RightMultiply) {
+ CompareMatrices(tsm.get(), dsm.get());
+
+ // Try with a not entirely zero vector to verify column interactions, which
+ // could be masked by a subtle bug when using the elementary vectors.
+ Vector a(num_cols);
+ for (int i = 0; i < num_cols; i++) {
+ a(i) = i;
+ }
+ Vector b1 = Vector::Zero(num_rows);
+ Vector b2 = Vector::Zero(num_rows);
+
+ tsm->RightMultiply(a.data(), b1.data());
+ dsm->RightMultiply(a.data(), b2.data());
+
+ EXPECT_EQ((b1 - b2).norm(), 0);
+}
+
+TEST_F(DenseSparseMatrixTest, 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());
+ dsm->LeftMultiply(a.data(), b2.data());
+
+ EXPECT_EQ((b1 - b2).norm(), 0);
+ }
+
+ // Try with a not entirely zero vector to verify column interactions, which
+ // could be masked by a subtle bug when using the elementary vectors.
+ Vector a(num_rows);
+ for (int i = 0; i < num_rows; i++) {
+ a(i) = i;
+ }
+ Vector b1 = Vector::Zero(num_cols);
+ Vector b2 = Vector::Zero(num_cols);
+
+ tsm->LeftMultiply(a.data(), b1.data());
+ dsm->LeftMultiply(a.data(), b2.data());
+
+ EXPECT_EQ((b1 - b2).norm(), 0);
+}
+
+TEST_F(DenseSparseMatrixTest, ColumnNorm) {
+ Vector b1 = Vector::Zero(num_cols);
+ Vector b2 = Vector::Zero(num_cols);
+
+ tsm->SquaredColumnNorm(b1.data());
+ dsm->SquaredColumnNorm(b2.data());
+
+ EXPECT_EQ((b1 - b2).norm(), 0);
+}
+
+TEST_F(DenseSparseMatrixTest, Scale) {
+ Vector scale(num_cols);
+ for (int i = 0; i < num_cols; ++i) {
+ scale(i) = i + 1;
+ }
+ tsm->ScaleColumns(scale.data());
+ dsm->ScaleColumns(scale.data());
+ CompareMatrices(tsm.get(), dsm.get());
+}
+
+#ifndef CERES_DONT_HAVE_PROTOCOL_BUFFERS
+TEST_F(DenseSparseMatrixTest, Serialization) {
+ SparseMatrixProto proto;
+ dsm->ToProto(&proto);
+
+ DenseSparseMatrix n(proto);
+ ASSERT_EQ(dsm->num_rows(), n.num_rows());
+ ASSERT_EQ(dsm->num_cols(), n.num_cols());
+ ASSERT_EQ(dsm->num_nonzeros(), n.num_nonzeros());
+
+ for (int i = 0; i < n.num_rows() + 1; ++i) {
+ ASSERT_EQ(dsm->values()[i], proto.dense_matrix().values(i));
+ }
+}
+#endif
+
+TEST_F(DenseSparseMatrixTest, ToDenseMatrix) {
+ Matrix tsm_dense;
+ Matrix dsm_dense;
+
+ tsm->ToDenseMatrix(&tsm_dense);
+ dsm->ToDenseMatrix(&dsm_dense);
+
+ EXPECT_EQ((tsm_dense - dsm_dense).norm(), 0.0);
+}
+
+// TODO(keir): Make this work without protocol buffers.
+#ifndef CERES_DONT_HAVE_PROTOCOL_BUFFERS
+TEST_F(DenseSparseMatrixTest, AppendDiagonal) {
+ DenseSparseMatrixProto proto;
+ proto.set_num_rows(3);
+ proto.set_num_cols(3);
+ for (int i = 0; i < 9; ++i) {
+ proto.add_values(i);
+ }
+ SparseMatrixProto outer_proto;
+ *outer_proto.mutable_dense_matrix() = proto;
+
+ DenseSparseMatrix dsm(outer_proto);
+
+ double diagonal[] = { 10, 11, 12 };
+ dsm.AppendDiagonal(diagonal);
+
+ // Verify the diagonal got added.
+ Matrix m = dsm.matrix();
+ EXPECT_EQ(6, m.rows());
+ EXPECT_EQ(3, m.cols());
+ for (int i = 0; i < 3; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ EXPECT_EQ(3 * i + j, m(i, j));
+ if (i == j) {
+ EXPECT_EQ(10 + i, m(i + 3, j));
+ } else {
+ EXPECT_EQ(0, m(i + 3, j));
+ }
+ }
+ }
+
+ // Verify the diagonal gets removed.
+ dsm.RemoveDiagonal();
+ m = dsm.matrix();
+
+ EXPECT_EQ(3, m.rows());
+ EXPECT_EQ(3, m.cols());
+
+ for (int i = 0; i < 3; ++i) {
+ for (int j = 0; j < 3; ++j) {
+ EXPECT_EQ(3 * i + j, m(i, j));
+ }
+ }
+}
+#endif
+
+} // namespace internal
+} // namespace ceres
