cobalt / cobalt / 08336faa599332e3e3bf29b7ad38ef023018b55e / . / third_party / chromium / media / learning / impl / random_number_generator_unittest.cc

// Copyright 2018 The Chromium Authors. All rights reserved. | |

// Use of this source code is governed by a BSD-style license that can be | |

// found in the LICENSE file. | |

#include "base/bind.h" | |

#include "base/callback.h" | |

#include "media/learning/impl/test_random_number_generator.h" | |

#include "testing/gtest/include/gtest/gtest.h" | |

namespace media { | |

class RandomNumberGeneratorTest : public testing::Test { | |

public: | |

RandomNumberGeneratorTest() : rng_(0) {} | |

void GenerateAndVerify(base::RepeatingCallback<int64_t()> generate, | |

int64_t lower_inclusive, | |

int64_t upper_inclusive) { | |

const size_t n = 10000; | |

std::map<int64_t, size_t> counts; | |

for (size_t i = 0; i < n; i++) | |

counts[generate.Run()]++; | |

// Verify that it's uniform over |lower_inclusive| and | |

// |upper_inclusive|, at least approximately. | |

size_t min_counts = counts[lower_inclusive]; | |

size_t max_counts = min_counts; | |

size_t total_counts = min_counts; | |

for (int64_t i = lower_inclusive + 1; i <= upper_inclusive; i++) { | |

size_t c = counts[i]; | |

if (c < min_counts) | |

min_counts = c; | |

if (c > max_counts) | |

max_counts = c; | |

total_counts += c; | |

} | |

// See if the min and the max are too far from the expected counts. | |

// Note that this will catch only egregious problems. Also note that | |

// random variation might actually exceed these limits fairly often. | |

// It's only because the test rng has no variation that we know it | |

// won't happen. However, there might be reasonable implementation | |

// changes that trip these tests (deterministically!); manual | |

// verification of the result is needed in those cases. | |

// | |

// These just catch things like "rng range is off by one", etc. | |

size_t expected_counts = n / (upper_inclusive - lower_inclusive + 1); | |

EXPECT_LT(max_counts, expected_counts * 1.05); | |

EXPECT_GT(max_counts, expected_counts * 0.95); | |

EXPECT_LT(min_counts, expected_counts * 1.05); | |

EXPECT_GT(min_counts, expected_counts * 0.95); | |

// Verify that the counts between the limits accounts for all of them. | |

// Otherwise, some rng values were out of range. | |

EXPECT_EQ(total_counts, n); | |

} | |

// We use TestRandomNumberGenerator, since we really want to test the base | |

// class method implementations with a predictable random source. | |

TestRandomNumberGenerator rng_; | |

}; | |

TEST_F(RandomNumberGeneratorTest, ExclusiveUpTo) { | |

// Try Generate with something that's not a divisor of max_int, to try to | |

// catch any bias. I.e., an implementation like "rng % range" should fail | |

// this test. | |

// | |

// Unfortunately, it won't. | |

// | |

// With uint64_t random values, it's unlikely that we would ever notice such a | |

// problem. For example, a range of size three would just remove ~three from | |

// the upper range of the rng, and it's unlikely that we'd ever pick the three | |

// highest values anyway. If, instead, we make |range| really big, then we're | |

// not going to sample enough points to notice the deviation from uniform. | |

// | |

// However, we still look for issues like "off by one". | |

const uint64_t range = 5; | |

GenerateAndVerify(base::BindRepeating( | |

[](RandomNumberGenerator* rng, uint64_t range) { | |

return static_cast<int64_t>(rng->Generate(range)); | |

}, | |

&rng_, range), | |

0, range - 1); | |

} | |

TEST_F(RandomNumberGeneratorTest, DoublesStayInRange) { | |

const double limit = 1000.5; | |

int num_non_integer = 0; | |

for (int i = 0; i < 1000; i++) { | |

double v = rng_.GenerateDouble(limit); | |

EXPECT_GE(v, 0.); | |

EXPECT_LT(v, limit); | |

// Also count how many non-integers we get. | |

num_non_integer += (v != static_cast<int>(v)); | |

} | |

// Expect a lot of non-integers. | |

EXPECT_GE(num_non_integer, 900); | |

} | |

} // namespace media |