Initial commit of Ceres Solver.

internal/ceres/loss_function.cc

diff --git a/internal/ceres/loss_function.cc b/internal/ceres/loss_function.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)
+//
+// Purpose: See .h file.
+
+#include "ceres/loss_function.h"
+
+#include <cmath>
+#include <cstddef>
+
+namespace ceres {
+
+void TrivialLoss::Evaluate(double s, double rho[3]) const {
+  rho[0] = s;
+  rho[1] = 1;
+  rho[2] = 0;
+}
+
+void HuberLoss::Evaluate(double s, double rho[3]) const {
+  if (s > b_) {
+    // Outlier region.
+    // 'r' is always positive.
+    const double r = sqrt(s);
+    rho[0] = 2 * a_ * r - b_;
+    rho[1] = a_ / r;
+    rho[2] = - rho[1] / (2 * s);
+  } else {
+    // Inlier region.
+    rho[0] = s;
+    rho[1] = 1;
+    rho[2] = 0;
+  }
+}
+
+void SoftLOneLoss::Evaluate(double s, double rho[3]) const {
+  const double sum = 1 + s * c_;
+  const double tmp = sqrt(sum);
+  // 'sum' and 'tmp' are always positive, assuming that 's' is.
+  rho[0] = 2 * b_ * (tmp - 1);
+  rho[1] = 1 / tmp;
+  rho[2] = - (c_ * rho[1]) / (2 * sum);
+}
+
+void CauchyLoss::Evaluate(double s, double rho[3]) const {
+  const double sum = 1 + s * c_;
+  const double inv = 1 / sum;
+  // 'sum' and 'inv' are always positive, assuming that 's' is.
+  rho[0] = b_ * log(sum);
+  rho[1] = inv;
+  rho[2] = - c_ * (inv * inv);
+}
+
+void ScaledLoss::Evaluate(double s, double rho[3]) const {
+  if (rho_.get() == NULL) {
+    rho[0] = a_ * s;
+    rho[1] = a_;
+    rho[2] = 0.0;
+  } else {
+    rho_->Evaluate(s, rho);
+    rho[0] *= a_;
+    rho[1] *= a_;
+    rho[2] *= a_;
+  }
+}
+
+}  // namespace ceres