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// DistributionSampler allows generating a discrete random variable with a given
// distribution.
// The values taken by the variable are [0, N) and relative weights for each
// value are specified using a vector of size N.
//
// The Algorithm takes O(N) time to precompute data at construction time and
// takes O(1) time (2 random number generation, 2 lookups) for each sample.
// The data structure takes O(N) memory.
//
// In contrast, util/random/weighted-picker.h provides O(lg N) sampling.
// The advantage of that implementation is that weights can be adjusted
// dynamically, while DistributionSampler doesn't allow weight adjustment.
//
// The algorithm used is Walker's Aliasing algorithm, described in Knuth, Vol 2.
#ifndef TENSORFLOW_LIB_RANDOM_DISTRIBUTION_SAMPLER_H_
#define TENSORFLOW_LIB_RANDOM_DISTRIBUTION_SAMPLER_H_
#include <memory>
#include <utility>
#include <vector>
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/port.h"
namespace tensorflow {
namespace random {
class DistributionSampler {
public:
explicit DistributionSampler(const gtl::ArraySlice<float>& weights);
~DistributionSampler() {}
int Sample(SimplePhilox* rand) const {
float r = rand->RandFloat();
// Since n is typically low, we don't bother with UnbiasedUniform.
int idx = rand->Uniform(num_);
if (r < prob(idx)) return idx;
// else pick alt from that bucket.
DCHECK_NE(-1, alt(idx));
return alt(idx);
}
int num() const { return num_; }
private:
float prob(int idx) const {
DCHECK_LT(idx, num_);
return data_[idx].first;
}
int alt(int idx) const {
DCHECK_LT(idx, num_);
return data_[idx].second;
}
void set_prob(int idx, float f) {
DCHECK_LT(idx, num_);
data_[idx].first = f;
}
void set_alt(int idx, int val) {
DCHECK_LT(idx, num_);
data_[idx].second = val;
}
int num_;
std::unique_ptr<std::pair<float, int>[]> data_;
TF_DISALLOW_COPY_AND_ASSIGN(DistributionSampler);
};
} // namespace random
} // namespace tensorflow
#endif // TENSORFLOW_LIB_RANDOM_DISTRIBUTION_SAMPLER_H_
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