303 lines
11 KiB
C++
303 lines
11 KiB
C++
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// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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#include "ceres/small_blas.h"
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#include <limits>
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#include "gtest/gtest.h"
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#include "ceres/internal/eigen.h"
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namespace ceres {
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namespace internal {
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const double kTolerance = 3.0 * std::numeric_limits<double>::epsilon();
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TEST(BLAS, MatrixMatrixMultiply) {
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const int kRowA = 3;
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const int kColA = 5;
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Matrix A(kRowA, kColA);
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A.setOnes();
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const int kRowB = 5;
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const int kColB = 7;
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Matrix B(kRowB, kColB);
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B.setOnes();
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for (int row_stride_c = kRowA; row_stride_c < 3 * kRowA; ++row_stride_c) {
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for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
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Matrix C(row_stride_c, col_stride_c);
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C.setOnes();
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Matrix C_plus = C;
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Matrix C_minus = C;
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Matrix C_assign = C;
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Matrix C_plus_ref = C;
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Matrix C_minus_ref = C;
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Matrix C_assign_ref = C;
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for (int start_row_c = 0; start_row_c + kRowA < row_stride_c; ++start_row_c) {
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for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
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C_plus_ref.block(start_row_c, start_col_c, kRowA, kColB) +=
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A * B;
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MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(
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A.data(), kRowA, kColA,
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B.data(), kRowB, kColB,
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C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
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EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
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<< "C += A * B \n"
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<< "row_stride_c : " << row_stride_c << "\n"
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<< "col_stride_c : " << col_stride_c << "\n"
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<< "start_row_c : " << start_row_c << "\n"
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<< "start_col_c : " << start_col_c << "\n"
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<< "Cref : \n" << C_plus_ref << "\n"
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<< "C: \n" << C_plus;
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C_minus_ref.block(start_row_c, start_col_c, kRowA, kColB) -=
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A * B;
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MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(
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A.data(), kRowA, kColA,
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B.data(), kRowB, kColB,
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C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
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EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
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<< "C -= A * B \n"
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<< "row_stride_c : " << row_stride_c << "\n"
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<< "col_stride_c : " << col_stride_c << "\n"
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<< "start_row_c : " << start_row_c << "\n"
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<< "start_col_c : " << start_col_c << "\n"
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<< "Cref : \n" << C_minus_ref << "\n"
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<< "C: \n" << C_minus;
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C_assign_ref.block(start_row_c, start_col_c, kRowA, kColB) =
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A * B;
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MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(
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A.data(), kRowA, kColA,
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B.data(), kRowB, kColB,
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C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
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EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
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<< "C = A * B \n"
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<< "row_stride_c : " << row_stride_c << "\n"
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<< "col_stride_c : " << col_stride_c << "\n"
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<< "start_row_c : " << start_row_c << "\n"
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<< "start_col_c : " << start_col_c << "\n"
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<< "Cref : \n" << C_assign_ref << "\n"
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<< "C: \n" << C_assign;
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}
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}
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}
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}
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}
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TEST(BLAS, MatrixTransposeMatrixMultiply) {
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const int kRowA = 5;
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const int kColA = 3;
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Matrix A(kRowA, kColA);
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A.setOnes();
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const int kRowB = 5;
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const int kColB = 7;
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Matrix B(kRowB, kColB);
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B.setOnes();
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for (int row_stride_c = kColA; row_stride_c < 3 * kColA; ++row_stride_c) {
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for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
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Matrix C(row_stride_c, col_stride_c);
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C.setOnes();
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Matrix C_plus = C;
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Matrix C_minus = C;
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Matrix C_assign = C;
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Matrix C_plus_ref = C;
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Matrix C_minus_ref = C;
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Matrix C_assign_ref = C;
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for (int start_row_c = 0; start_row_c + kColA < row_stride_c; ++start_row_c) {
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for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
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C_plus_ref.block(start_row_c, start_col_c, kColA, kColB) +=
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A.transpose() * B;
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MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(
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A.data(), kRowA, kColA,
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B.data(), kRowB, kColB,
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C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
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EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
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<< "C += A' * B \n"
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<< "row_stride_c : " << row_stride_c << "\n"
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<< "col_stride_c : " << col_stride_c << "\n"
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<< "start_row_c : " << start_row_c << "\n"
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<< "start_col_c : " << start_col_c << "\n"
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<< "Cref : \n" << C_plus_ref << "\n"
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<< "C: \n" << C_plus;
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C_minus_ref.block(start_row_c, start_col_c, kColA, kColB) -=
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A.transpose() * B;
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MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(
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A.data(), kRowA, kColA,
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B.data(), kRowB, kColB,
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C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
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EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
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<< "C -= A' * B \n"
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<< "row_stride_c : " << row_stride_c << "\n"
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<< "col_stride_c : " << col_stride_c << "\n"
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<< "start_row_c : " << start_row_c << "\n"
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<< "start_col_c : " << start_col_c << "\n"
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<< "Cref : \n" << C_minus_ref << "\n"
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<< "C: \n" << C_minus;
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C_assign_ref.block(start_row_c, start_col_c, kColA, kColB) =
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A.transpose() * B;
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MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(
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A.data(), kRowA, kColA,
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B.data(), kRowB, kColB,
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C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
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EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
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<< "C = A' * B \n"
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<< "row_stride_c : " << row_stride_c << "\n"
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<< "col_stride_c : " << col_stride_c << "\n"
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<< "start_row_c : " << start_row_c << "\n"
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<< "start_col_c : " << start_col_c << "\n"
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<< "Cref : \n" << C_assign_ref << "\n"
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<< "C: \n" << C_assign;
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}
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}
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}
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}
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}
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TEST(BLAS, MatrixVectorMultiply) {
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const int kRowA = 5;
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const int kColA = 3;
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Matrix A(kRowA, kColA);
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A.setOnes();
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Vector b(kColA);
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b.setOnes();
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Vector c(kRowA);
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c.setOnes();
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Vector c_plus = c;
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Vector c_minus = c;
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Vector c_assign = c;
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Vector c_plus_ref = c;
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Vector c_minus_ref = c;
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Vector c_assign_ref = c;
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c_plus_ref += A * b;
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MatrixVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
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b.data(),
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c_plus.data());
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EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
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<< "c += A * b \n"
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<< "c_ref : \n" << c_plus_ref << "\n"
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<< "c: \n" << c_plus;
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c_minus_ref -= A * b;
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MatrixVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
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b.data(),
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c_minus.data());
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EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
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<< "c += A * b \n"
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<< "c_ref : \n" << c_minus_ref << "\n"
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<< "c: \n" << c_minus;
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c_assign_ref = A * b;
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MatrixVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
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b.data(),
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c_assign.data());
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EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
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<< "c += A * b \n"
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<< "c_ref : \n" << c_assign_ref << "\n"
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<< "c: \n" << c_assign;
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}
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TEST(BLAS, MatrixTransposeVectorMultiply) {
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const int kRowA = 5;
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const int kColA = 3;
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Matrix A(kRowA, kColA);
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A.setRandom();
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Vector b(kRowA);
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b.setRandom();
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Vector c(kColA);
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c.setOnes();
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Vector c_plus = c;
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Vector c_minus = c;
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Vector c_assign = c;
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Vector c_plus_ref = c;
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Vector c_minus_ref = c;
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Vector c_assign_ref = c;
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c_plus_ref += A.transpose() * b;
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MatrixTransposeVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
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b.data(),
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c_plus.data());
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EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
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<< "c += A' * b \n"
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<< "c_ref : \n" << c_plus_ref << "\n"
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<< "c: \n" << c_plus;
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c_minus_ref -= A.transpose() * b;
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MatrixTransposeVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
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b.data(),
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c_minus.data());
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EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
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<< "c += A' * b \n"
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<< "c_ref : \n" << c_minus_ref << "\n"
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<< "c: \n" << c_minus;
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c_assign_ref = A.transpose() * b;
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MatrixTransposeVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
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b.data(),
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c_assign.data());
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EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
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<< "c += A' * b \n"
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<< "c_ref : \n" << c_assign_ref << "\n"
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<< "c: \n" << c_assign;
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}
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} // namespace internal
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} // namespace ceres
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