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// Copyright (c) 2017-2021 Advanced Micro Devices, Inc. All rights reserved.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to deal
// in the Software without restriction, including without limitation the rights
// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
// copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
// THE SOFTWARE.
#include <stdio.h>
#include <gtest/gtest.h>
#include <random>
#include <vector>
#include <rng/generator_type.hpp>
#include <rng/generators.hpp>
#include <rng/distribution/poisson.hpp>
template<typename T>
double get_mean(std::vector<T> values)
{
double mean = 0.0f;
for (auto v : values)
{
mean += static_cast<double>(v);
}
return mean / values.size();
}
template<typename T>
double get_variance(std::vector<T> values, double mean)
{
double variance = 0.0f;
for (auto v : values)
{
const double x = static_cast<double>(v) - mean;
variance += x * x;
}
return variance / values.size();
}
class poisson_distribution_tests : public ::testing::TestWithParam<double> { };
TEST_P(poisson_distribution_tests, mean_var)
{
const double lambda = GetParam();
std::random_device rd;
std::mt19937 gen(rd());
rocrand_poisson_distribution<ROCRAND_DISCRETE_METHOD_ALIAS, true> dis;
dis.set_lambda(lambda);
const size_t samples_count = static_cast<size_t>(std::max(2.0, sqrt(lambda))) * 100000;
std::vector<unsigned int> values(samples_count);
for (size_t si = 0; si < samples_count; si++)
{
const unsigned int v = dis(gen());
values[si] = v;
}
dis.deallocate();
const double mean = get_mean(values);
const double variance = get_variance(values, mean);
EXPECT_NEAR(mean, lambda, std::max(1.0, lambda * 1e-2));
EXPECT_NEAR(variance, lambda, std::max(1.0, lambda * 1e-2));
}
TEST_P(poisson_distribution_tests, histogram_compare)
{
const double lambda = GetParam();
std::random_device rd;
std::mt19937 gen(rd());
std::poisson_distribution<unsigned int> host_dis(lambda);
rocrand_poisson_distribution<ROCRAND_DISCRETE_METHOD_ALIAS, true> dis;
dis.set_lambda(lambda);
const size_t samples_count = static_cast<size_t>(std::max(2.0, sqrt(lambda))) * 100000;
const size_t bin_size = static_cast<size_t>(std::max(2.0, sqrt(lambda)));
const size_t bins_count = static_cast<size_t>((2.0 * lambda + 10.0) / bin_size);
std::vector<unsigned int> historgram0(bins_count);
std::vector<unsigned int> historgram1(bins_count);
for (size_t si = 0; si < samples_count; si++)
{
const unsigned int v = host_dis(gen);
const size_t bin = v / bin_size;
if (bin < bins_count)
{
historgram0[bin]++;
}
}
for (size_t si = 0; si < samples_count; si++)
{
const unsigned int v = dis(gen());
const size_t bin = v / bin_size;
if (bin < bins_count)
{
historgram1[bin]++;
}
}
dis.deallocate();
// Very loose comparison
for (size_t bi = 0; bi < bins_count; bi++)
{
const unsigned int h0 = historgram0[bi];
const unsigned int h1 = historgram1[bi];
EXPECT_NEAR(h0, h1, std::max(samples_count * 1e-3, std::max(h0, h1) * 1e-1));
}
}
const double lambdas[] = { 1.0, 5.5, 20.0, 100.0, 1234.5, 5000.0 };
INSTANTIATE_TEST_SUITE_P(poisson_distribution_tests,
poisson_distribution_tests,
::testing::ValuesIn(lambdas));
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