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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
// 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 <cstddef>
#include <vector>
#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<size_t> 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 <typename X_t>
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 <typename X_t, typename Y_t>
void SampleXY(const X_t& X, const Y_t& Y, X_t* X_rand, Y_t* Y_rand);
};
////////////////////////////////////////////////////////////////////////////////
// Implementation
////////////////////////////////////////////////////////////////////////////////
template <typename X_t>
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 <typename X_t, typename Y_t>
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_
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