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#include "tensorflow/core/lib/random/distribution_sampler.h"
#include <string.h>
#include <memory>
#include <vector>
#include "tensorflow/core/platform/port.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/platform/test_benchmark.h"
#include "tensorflow/core/lib/random/simple_philox.h"
#include <gtest/gtest.h>
namespace tensorflow {
namespace random {
class DistributionSamplerTest : public ::testing::Test {
protected:
// Returns the Chi-Squared statistic for the two distributions.
float TestWeights(const std::vector<float>& weights, int trials_per_bin) {
int iters = weights.size() * trials_per_bin;
std::unique_ptr<float[]> counts(new float[weights.size()]);
memset(counts.get(), 0, sizeof(float) * weights.size());
DistributionSampler sampler(weights);
PhiloxRandom philox(testing::RandomSeed(), 17);
SimplePhilox random(&philox);
for (int i = 0; i < iters; i++) {
int r = sampler.Sample(&random);
EXPECT_LT(r, weights.size());
EXPECT_GE(r, 0);
counts[r] += 1.0;
}
float chi2 = 0.0;
for (size_t i = 0; i < weights.size(); i++) {
counts[i] /= iters;
float err = (counts[i] - weights[i]);
chi2 += (err * err) / weights[i];
}
return chi2;
}
void TestDistribution(float* arr, int n) {
std::vector<float> w;
w.reserve(n);
for (int i = 0; i < n; i++) {
w.push_back(arr[i]);
}
float var = TestWeights(w, 1000);
if (var < 0.001) return;
// Maybe a statistical skew. Let's try more iterations.
var = TestWeights(w, 100000);
if (var < 0.001) return;
EXPECT_TRUE(false) << "Chi2 is " << var << " in " << n * 100000
<< "iterations";
}
};
TEST_F(DistributionSamplerTest, KnownDistribution) {
float kEven2[] = {0.5, 0.5};
float kEven3[] = {0.33333333, 0.33333333, 0.33333333};
float kEven4[] = {0.25, 0.25, 0.25, 0.25};
float kDist1[] = {0.8, 0.15, 0.05};
TestDistribution(kEven2, TF_ARRAYSIZE(kEven2));
TestDistribution(kEven3, TF_ARRAYSIZE(kEven3));
TestDistribution(kEven4, TF_ARRAYSIZE(kEven4));
TestDistribution(kDist1, TF_ARRAYSIZE(kDist1));
}
static void BM_DistributionSampler(int iters, int n) {
testing::StopTiming();
PhiloxRandom philox(173, 371);
SimplePhilox rand(&philox);
std::vector<float> weights(n, 0);
for (int i = 0; i < n; i++) {
weights[i] = rand.Uniform(100);
}
DistributionSampler picker(weights);
testing::StartTiming();
int r = 0;
for (int i = 0; i < iters; i++) {
r |= picker.Sample(&rand);
}
CHECK_NE(r, kint32max);
}
BENCHMARK(BM_DistributionSampler)->Arg(10)->Arg(100)->Arg(1000);
} // namespace random
} // namespace tensorflow
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