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diff --git a/internal/ceres/numeric_diff_test_utils.h b/internal/ceres/numeric_diff_test_utils.h
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+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2015 Google Inc. All rights reserved.
+// http://ceres-solver.org/
+//
+// 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)
+
+#ifndef CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_
+#define CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_
+
+#include "ceres/cost_function.h"
+#include "ceres/sized_cost_function.h"
+#include "ceres/types.h"
+
+namespace ceres {
+namespace internal {
+
+// Noise factor for randomized cost function.
+static const double kNoiseFactor = 0.01;
+
+// Default random seed for randomized cost function.
+static const unsigned int kRandomSeed = 1234;
+
+// y1 = x1'x2 -> dy1/dx1 = x2, dy1/dx2 = x1
+// y2 = (x1'x2)^2 -> dy2/dx1 = 2 * x2 * (x1'x2), dy2/dx2 = 2 * x1 * (x1'x2)
+// y3 = x2'x2 -> dy3/dx1 = 0, dy3/dx2 = 2 * x2
+class EasyFunctor {
+ public:
+ bool operator()(const double* x1, const double* x2, double* residuals) const;
+ void ExpectCostFunctionEvaluationIsNearlyCorrect(
+ const CostFunction& cost_function,
+ NumericDiffMethodType method) const;
+};
+
+class EasyCostFunction : public SizedCostFunction<3, 5, 5> {
+ public:
+ virtual bool Evaluate(double const* const* parameters,
+ double* residuals,
+ double** /* not used */) const {
+ return functor_(parameters[0], parameters[1], residuals);
+ }
+
+ private:
+ EasyFunctor functor_;
+};
+
+// y1 = sin(x1'x2)
+// y2 = exp(-x1'x2 / 10)
+//
+// dy1/dx1 = x2 * cos(x1'x2), dy1/dx2 = x1 * cos(x1'x2)
+// dy2/dx1 = -x2 * exp(-x1'x2 / 10) / 10, dy2/dx2 = -x2 * exp(-x1'x2 / 10) / 10
+class TranscendentalFunctor {
+ public:
+ bool operator()(const double* x1, const double* x2, double* residuals) const;
+ void ExpectCostFunctionEvaluationIsNearlyCorrect(
+ const CostFunction& cost_function,
+ NumericDiffMethodType method) const;
+};
+
+class TranscendentalCostFunction : public SizedCostFunction<2, 5, 5> {
+ public:
+ virtual bool Evaluate(double const* const* parameters,
+ double* residuals,
+ double** /* not used */) const {
+ return functor_(parameters[0], parameters[1], residuals);
+ }
+ private:
+ TranscendentalFunctor functor_;
+};
+
+// y = exp(x), dy/dx = exp(x)
+class ExponentialFunctor {
+ public:
+ bool operator()(const double* x1, double* residuals) const;
+ void ExpectCostFunctionEvaluationIsNearlyCorrect(
+ const CostFunction& cost_function) const;
+};
+
+class ExponentialCostFunction : public SizedCostFunction<1, 1> {
+ public:
+ virtual bool Evaluate(double const* const* parameters,
+ double* residuals,
+ double** /* not used */) const {
+ return functor_(parameters[0], residuals);
+ }
+
+ private:
+ ExponentialFunctor functor_;
+};
+
+// Test adaptive numeric differentiation by synthetically adding random noise
+// to a functor.
+// y = x^2 + [random noise], dy/dx ~ 2x
+class RandomizedFunctor {
+ public:
+ RandomizedFunctor(double noise_factor, unsigned int random_seed)
+ : noise_factor_(noise_factor), random_seed_(random_seed) {
+ }
+
+ bool operator()(const double* x1, double* residuals) const;
+ void ExpectCostFunctionEvaluationIsNearlyCorrect(
+ const CostFunction& cost_function) const;
+
+ private:
+ double noise_factor_;
+ unsigned int random_seed_;
+};
+
+class RandomizedCostFunction : public SizedCostFunction<1, 1> {
+ public:
+ RandomizedCostFunction(double noise_factor, unsigned int random_seed)
+ : functor_(noise_factor, random_seed) {
+ }
+
+ virtual bool Evaluate(double const* const* parameters,
+ double* residuals,
+ double** /* not used */) const {
+ return functor_(parameters[0], residuals);
+ }
+
+ private:
+ RandomizedFunctor functor_;
+};
+
+
+} // namespace internal
+} // namespace ceres
+
+#endif // CERES_INTERNAL_NUMERIC_DIFF_TEST_UTILS_H_