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// 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 "ceres/numeric_diff_test_utils.h"
#include <algorithm>
#include <cmath>
#include "ceres/cost_function.h"
#include "ceres/internal/macros.h"
#include "ceres/test_util.h"
#include "ceres/types.h"
#include "gtest/gtest.h"
namespace ceres {
namespace internal {
bool EasyFunctor::operator()(const double* x1,
const double* x2,
double* residuals) const {
residuals[0] = residuals[1] = residuals[2] = 0;
for (int i = 0; i < 5; ++i) {
residuals[0] += x1[i] * x2[i];
residuals[2] += x2[i] * x2[i];
}
residuals[1] = residuals[0] * residuals[0];
return true;
}
void EasyFunctor::ExpectCostFunctionEvaluationIsNearlyCorrect(
const CostFunction& cost_function,
NumericDiffMethod method) const {
double x1[] = { 1.0, 2.0, 3.0, 4.0, 5.0 };
double x2[] = { 9.0, 9.0, 5.0, 5.0, 1.0 };
double *parameters[] = { &x1[0], &x2[0] };
double dydx1[15]; // 3 x 5, row major.
double dydx2[15]; // 3 x 5, row major.
double *jacobians[2] = { &dydx1[0], &dydx2[0] };
double residuals[3] = {-1e-100, -2e-100, -3e-100 };
ASSERT_TRUE(cost_function.Evaluate(¶meters[0],
&residuals[0],
&jacobians[0]));
EXPECT_EQ(residuals[0], 67);
EXPECT_EQ(residuals[1], 4489);
EXPECT_EQ(residuals[2], 213);
const double tolerance = (method == CENTRAL)? 3e-9 : 2e-5;
for (int i = 0; i < 5; ++i) {
ExpectClose(x2[i], dydx1[5 * 0 + i], tolerance); // y1
ExpectClose(x1[i], dydx2[5 * 0 + i], tolerance);
ExpectClose(2 * x2[i] * residuals[0], dydx1[5 * 1 + i], tolerance); // y2
ExpectClose(2 * x1[i] * residuals[0], dydx2[5 * 1 + i], tolerance);
ExpectClose(0.0, dydx1[5 * 2 + i], tolerance); // y3
ExpectClose(2 * x2[i], dydx2[5 * 2 + i], tolerance);
}
}
bool TranscendentalFunctor::operator()(const double* x1,
const double* x2,
double* residuals) const {
double x1x2 = 0;
for (int i = 0; i < 5; ++i) {
x1x2 += x1[i] * x2[i];
}
residuals[0] = sin(x1x2);
residuals[1] = exp(-x1x2 / 10);
return true;
}
void TranscendentalFunctor::ExpectCostFunctionEvaluationIsNearlyCorrect(
const CostFunction& cost_function,
NumericDiffMethod method) const {
struct {
double x1[5];
double x2[5];
} kTests[] = {
{ { 1.0, 2.0, 3.0, 4.0, 5.0 }, // No zeros.
{ 9.0, 9.0, 5.0, 5.0, 1.0 },
},
{ { 0.0, 2.0, 3.0, 0.0, 5.0 }, // Some zeros x1.
{ 9.0, 9.0, 5.0, 5.0, 1.0 },
},
{ { 1.0, 2.0, 3.0, 1.0, 5.0 }, // Some zeros x2.
{ 0.0, 9.0, 0.0, 5.0, 0.0 },
},
{ { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros x1.
{ 9.0, 9.0, 5.0, 5.0, 1.0 },
},
{ { 1.0, 2.0, 3.0, 4.0, 5.0 }, // All zeros x2.
{ 0.0, 0.0, 0.0, 0.0, 0.0 },
},
{ { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros.
{ 0.0, 0.0, 0.0, 0.0, 0.0 },
},
};
for (int k = 0; k < CERES_ARRAYSIZE(kTests); ++k) {
double *x1 = &(kTests[k].x1[0]);
double *x2 = &(kTests[k].x2[0]);
double *parameters[] = { x1, x2 };
double dydx1[10];
double dydx2[10];
double *jacobians[2] = { &dydx1[0], &dydx2[0] };
double residuals[2];
ASSERT_TRUE(cost_function.Evaluate(¶meters[0],
&residuals[0],
&jacobians[0]));
double x1x2 = 0;
for (int i = 0; i < 5; ++i) {
x1x2 += x1[i] * x2[i];
}
const double tolerance = (method == CENTRAL)? 3e-9 : 2e-5;
for (int i = 0; i < 5; ++i) {
ExpectClose( x2[i] * cos(x1x2), dydx1[5 * 0 + i], tolerance);
ExpectClose( x1[i] * cos(x1x2), dydx2[5 * 0 + i], tolerance);
ExpectClose(-x2[i] * exp(-x1x2 / 10.) / 10., dydx1[5 * 1 + i], tolerance);
ExpectClose(-x1[i] * exp(-x1x2 / 10.) / 10., dydx2[5 * 1 + i], tolerance);
}
}
}
} // namespace internal
} // namespace ceres
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