// Copyright (c) 2022, ETH Zurich and UNC Chapel Hill. // 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 // 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: Johannes L. Schoenberger (jsch-at-demuc-dot-de) #ifndef COLMAP_SRC_OPTIM_SAMPLER_H_ #define COLMAP_SRC_OPTIM_SAMPLER_H_ #include #include #include "util/logging.h" namespace colmap { // Abstract base class for sampling methods. class Sampler { public: Sampler(){}; explicit Sampler(const size_t num_samples); // Initialize the sampler, before calling the `Sample` method. virtual void Initialize(const size_t total_num_samples) = 0; // Maximum number of unique samples that can be generated. virtual size_t MaxNumSamples() = 0; // Sample `num_samples` elements from all samples. virtual std::vector Sample() = 0; // Sample elements from `X` into `X_rand`. // // Note that `X.size()` should equal `num_total_samples` and `X_rand.size()` // should equal `num_samples`. template void SampleX(const X_t& X, X_t* X_rand); // Sample elements from `X` and `Y` into `X_rand` and `Y_rand`. // // Note that `X.size()` should equal `num_total_samples` and `X_rand.size()` // should equal `num_samples`. The same applies for `Y` and `Y_rand`. template void SampleXY(const X_t& X, const Y_t& Y, X_t* X_rand, Y_t* Y_rand); }; //////////////////////////////////////////////////////////////////////////////// // Implementation //////////////////////////////////////////////////////////////////////////////// template void Sampler::SampleX(const X_t& X, X_t* X_rand) { const auto sample_idxs = Sample(); for (size_t i = 0; i < X_rand->size(); ++i) { (*X_rand)[i] = X[sample_idxs[i]]; } } template void Sampler::SampleXY(const X_t& X, const Y_t& Y, X_t* X_rand, Y_t* Y_rand) { CHECK_EQ(X.size(), Y.size()); CHECK_EQ(X_rand->size(), Y_rand->size()); const auto sample_idxs = Sample(); for (size_t i = 0; i < X_rand->size(); ++i) { (*X_rand)[i] = X[sample_idxs[i]]; (*Y_rand)[i] = Y[sample_idxs[i]]; } } } // namespace colmap #endif // COLMAP_SRC_OPTIM_SAMPLER_H_