Initial commit of Ceres Solver.
diff --git a/internal/ceres/loss_function_test.cc b/internal/ceres/loss_function_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: sameeragarwal@google.com (Sameer Agarwal)
+
+#include "ceres/loss_function.h"
+
+#include <cstddef>
+
+#include <glog/logging.h>
+#include "gtest/gtest.h"
+
+namespace ceres {
+namespace internal {
+namespace {
+
+// Helper function for testing a LossFunction callback.
+//
+// Compares the values of rho'(s) and rho''(s) computed by the
+// callback with estimates obtained by symmetric finite differencing
+// of rho(s).
+void AssertLossFunctionIsValid(const LossFunction& loss, double s) {
+ CHECK_GT(s, 0);
+
+ // Evaluate rho(s), rho'(s) and rho''(s).
+ double rho[3];
+ loss.Evaluate(s, rho);
+
+ // Use symmetric finite differencing to estimate rho'(s) and
+ // rho''(s).
+ const double kH = 1e-4;
+ // Values at s + kH.
+ double fwd[3];
+ // Values at s - kH.
+ double bwd[3];
+ loss.Evaluate(s + kH, fwd);
+ loss.Evaluate(s - kH, bwd);
+
+ // First derivative.
+ const double fd_1 = (fwd[0] - bwd[0]) / (2 * kH);
+ ASSERT_NEAR(fd_1, rho[1], 1e-6);
+
+ // Second derivative.
+ const double fd_2 = (fwd[0] - 2*rho[0] + bwd[0]) / (kH * kH);
+ ASSERT_NEAR(fd_2, rho[2], 1e-6);
+}
+} // namespace
+
+// Try two values of the scaling a = 0.7 and 1.3
+// (where scaling makes sense) and of the squared norm
+// s = 0.357 and 1.792
+//
+// Note that for the Huber loss the test exercises both code paths
+// (i.e. both small and large values of s).
+
+TEST(LossFunction, TrivialLoss) {
+ AssertLossFunctionIsValid(TrivialLoss(), 0.357);
+ AssertLossFunctionIsValid(TrivialLoss(), 1.792);
+}
+
+TEST(LossFunction, HuberLoss) {
+ AssertLossFunctionIsValid(HuberLoss(0.7), 0.357);
+ AssertLossFunctionIsValid(HuberLoss(0.7), 1.792);
+ AssertLossFunctionIsValid(HuberLoss(1.3), 0.357);
+ AssertLossFunctionIsValid(HuberLoss(1.3), 1.792);
+}
+
+TEST(LossFunction, SoftLOneLoss) {
+ AssertLossFunctionIsValid(SoftLOneLoss(0.7), 0.357);
+ AssertLossFunctionIsValid(SoftLOneLoss(0.7), 1.792);
+ AssertLossFunctionIsValid(SoftLOneLoss(1.3), 0.357);
+ AssertLossFunctionIsValid(SoftLOneLoss(1.3), 1.792);
+}
+
+TEST(LossFunction, CauchyLoss) {
+ AssertLossFunctionIsValid(CauchyLoss(0.7), 0.357);
+ AssertLossFunctionIsValid(CauchyLoss(0.7), 1.792);
+ AssertLossFunctionIsValid(CauchyLoss(1.3), 0.357);
+ AssertLossFunctionIsValid(CauchyLoss(1.3), 1.792);
+}
+
+TEST(LossFunction, ScaledLoss) {
+ // Wrap a few loss functions, and a few scale factors. This can't combine
+ // construction with the call to AssertLossFunctionIsValid() because Apple's
+ // GCC is unable to eliminate the copy of ScaledLoss, which is not copyable.
+ {
+ ScaledLoss scaled_loss(NULL, 6, TAKE_OWNERSHIP);
+ AssertLossFunctionIsValid(scaled_loss, 0.323);
+ }
+ {
+ ScaledLoss scaled_loss(new TrivialLoss(), 10, TAKE_OWNERSHIP);
+ AssertLossFunctionIsValid(scaled_loss, 0.357);
+ }
+ {
+ ScaledLoss scaled_loss(new HuberLoss(0.7), 0.1, TAKE_OWNERSHIP);
+ AssertLossFunctionIsValid(scaled_loss, 1.792);
+ }
+ {
+ ScaledLoss scaled_loss(new SoftLOneLoss(1.3), 0.1, TAKE_OWNERSHIP);
+ AssertLossFunctionIsValid(scaled_loss, 1.792);
+ }
+ {
+ ScaledLoss scaled_loss(new CauchyLoss(1.3), 10, TAKE_OWNERSHIP);
+ AssertLossFunctionIsValid(scaled_loss, 1.792);
+ }
+}
+
+TEST(LossFunction, LossFunctionWrapper) {
+ // Initialization
+ HuberLoss loss_function1(1.0);
+ LossFunctionWrapper loss_function_wrapper(new HuberLoss(1.0),
+ TAKE_OWNERSHIP);
+
+ double s = 0.862;
+ double rho_gold[3];
+ double rho[3];
+ loss_function1.Evaluate(s, rho_gold);
+ loss_function_wrapper.Evaluate(s, rho);
+ for (int i = 0; i < 3; ++i) {
+ EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
+ }
+
+ // Resetting
+ HuberLoss loss_function2(0.5);
+ loss_function_wrapper.Reset(new HuberLoss(0.5), TAKE_OWNERSHIP);
+ loss_function_wrapper.Evaluate(s, rho);
+ loss_function2.Evaluate(s, rho_gold);
+ for (int i = 0; i < 3; ++i) {
+ EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
+ }
+
+ // Not taking ownership.
+ HuberLoss loss_function3(0.3);
+ loss_function_wrapper.Reset(&loss_function3, DO_NOT_TAKE_OWNERSHIP);
+ loss_function_wrapper.Evaluate(s, rho);
+ loss_function3.Evaluate(s, rho_gold);
+ for (int i = 0; i < 3; ++i) {
+ EXPECT_NEAR(rho[i], rho_gold[i], 1e-12);
+ }
+}
+
+} // namespace internal
+} // namespace ceres
