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// All rights reserved.
//
// 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 ETH Zurich and UNC Chapel Hill 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 HOLDERS 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
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// POSSIBILITY OF SUCH DAMAGE.
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// Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
#include "optim/sprt.h"
namespace colmap {
SPRT::SPRT(const Options& options) { Update(options); }
void SPRT::Update(const Options& options) {
options_ = options;
delta_epsilon_ = options.delta / options.epsilon;
delta_1_epsilon_1_ = (1 - options.delta) / (1 - options.epsilon);
UpdateDecisionThreshold();
}
bool SPRT::Evaluate(const std::vector<double>& residuals,
const double max_residual, size_t* num_inliers,
size_t* num_eval_samples) {
*num_inliers = 0;
double likelihood_ratio = 1;
for (size_t i = 0; i < residuals.size(); ++i) {
if (std::abs(residuals[i]) <= max_residual) {
*num_inliers += 1;
likelihood_ratio *= delta_epsilon_;
} else {
likelihood_ratio *= delta_1_epsilon_1_;
}
if (likelihood_ratio > decision_threshold_) {
*num_eval_samples = i + 1;
return false;
}
}
*num_eval_samples = residuals.size();
return true;
}
void SPRT::UpdateDecisionThreshold() {
// Equation 2
const double C = (1 - options_.delta) *
std::log((1 - options_.delta) / (1 - options_.epsilon)) +
options_.delta * std::log(options_.delta / options_.epsilon);
// Equation 6
const double A0 =
options_.eval_time_ratio * C / options_.num_models_per_sample + 1;
double A = A0;
const double kEps = 1.5e-8;
// Compute A using the recursive relation
// A* = lim(n->inf) A
// The series typically converges within 4 iterations
for (size_t i = 0; i < 100; ++i) {
const double A1 = A0 + std::log(A);
if (std::abs(A1 - A) < kEps) {
break;
}
A = A1;
}
decision_threshold_ = A;
}
} // namespace colmap
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