template <typename Dtype>
class RandomNumberGeneratorTest : public ::testing::Test {
- public:
- virtual ~RandomNumberGeneratorTest() {}
+ protected:
+ RandomNumberGeneratorTest()
+ : sample_size_(10000),
+ seed_(1701),
+ data_(new SyncedMemory(sample_size_ * sizeof(Dtype))),
+ int_data_(new SyncedMemory(sample_size_ * sizeof(int))),
+ int_data_2_(new SyncedMemory(sample_size_ * sizeof(int))) {}
- Dtype sample_mean(const Dtype* const seqs, const size_t sample_size) {
- double sum = 0;
- for (int i = 0; i < sample_size; ++i) {
- sum += seqs[i];
+ virtual void SetUp() {
+ Caffe::set_random_seed(this->seed_);
+ }
+
+ Dtype sample_mean(const Dtype* const seqs, const int sample_size) {
+ Dtype sum = 0;
+ for (int i = 0; i < sample_size; ++i) {
+ sum += seqs[i];
+ }
+ return sum / sample_size;
+ }
+
+ Dtype sample_mean(const Dtype* const seqs) {
+ return sample_mean(seqs, sample_size_);
+ }
+
+ Dtype sample_mean(const int* const seqs, const int sample_size) {
+ Dtype sum = 0;
+ for (int i = 0; i < sample_size; ++i) {
+ sum += Dtype(seqs[i]);
+ }
+ return sum / sample_size;
+ }
+
+ Dtype sample_mean(const int* const seqs) {
+ return sample_mean(seqs, sample_size_);
+ }
+
+ Dtype mean_bound(const Dtype std, const int sample_size) {
+ return std / sqrt(static_cast<Dtype>(sample_size));
+ }
+
+ Dtype mean_bound(const Dtype std) {
+ return mean_bound(std, sample_size_);
+ }
+
+ void RngGaussianTest(const Dtype mu, const Dtype sigma, void* cpu_data) {
+ Dtype* rng_data = static_cast<Dtype*>(cpu_data);
+ caffe_vRngGaussian(sample_size_, rng_data, mu, sigma);
+ const Dtype true_mean = mu;
+ const Dtype true_std = sigma;
+ // Check that sample mean roughly matches true mean.
+ const Dtype bound = this->mean_bound(true_std);
+ const Dtype sample_mean = this->sample_mean(
+ static_cast<const Dtype*>(cpu_data));
+ EXPECT_NEAR(sample_mean, true_mean, bound);
+ // Check that roughly half the samples are above the true mean.
+ int num_above_mean = 0;
+ int num_below_mean = 0;
+ for (int i = 0; i < sample_size_; ++i) {
+ if (rng_data[i] > true_mean) {
+ ++num_above_mean;
+ } else if (rng_data[i] < true_mean) {
+ ++num_below_mean;
}
- return sum / sample_size;
+ }
+ EXPECT_EQ(sample_size_, num_above_mean + num_below_mean);
+ const Dtype sample_p_above_mean =
+ static_cast<Dtype>(num_above_mean) / sample_size_;
+ const Dtype bernoulli_p = 0.5;
+ const Dtype bernoulli_std = sqrt(bernoulli_p * (1 - bernoulli_p));
+ const Dtype bernoulli_bound = this->mean_bound(true_std);
+ EXPECT_NEAR(bernoulli_p, sample_p_above_mean, bernoulli_bound);
}
- Dtype sample_mean(const int* const seqs, const size_t sample_size) {
- Dtype sum = 0;
- for (int i = 0; i < sample_size; ++i) {
- sum += Dtype(seqs[i]);
+ void RngUniformTest(const Dtype lower, const Dtype upper, void* cpu_data) {
+ CHECK_GE(upper, lower);
+ Dtype* rng_data = static_cast<Dtype*>(cpu_data);
+ caffe_vRngUniform(sample_size_, rng_data, lower, upper);
+ const Dtype true_mean = (lower + upper) / 2;
+ const Dtype true_std = (upper - lower) / sqrt(12);
+ // Check that sample mean roughly matches true mean.
+ const Dtype bound = this->mean_bound(true_std);
+ const Dtype sample_mean = this->sample_mean(rng_data);
+ EXPECT_NEAR(sample_mean, true_mean, bound);
+ // Check that roughly half the samples are above the true mean, and none are
+ // above upper or below lower.
+ int num_above_mean = 0;
+ int num_below_mean = 0;
+ int num_above_upper = 0;
+ int num_below_lower = 0;
+ for (int i = 0; i < sample_size_; ++i) {
+ if (rng_data[i] > true_mean) {
+ ++num_above_mean;
+ } else if (rng_data[i] < true_mean) {
+ ++num_below_mean;
+ }
+ if (rng_data[i] > upper) {
+ ++num_above_upper;
+ } else if (rng_data[i] < lower) {
+ ++num_below_lower;
}
- return sum / sample_size;
+ }
+ EXPECT_EQ(0, num_above_upper);
+ EXPECT_EQ(0, num_below_lower);
+ EXPECT_EQ(sample_size_, num_above_mean + num_below_mean);
+ const Dtype sample_p_above_mean =
+ static_cast<Dtype>(num_above_mean) / sample_size_;
+ const Dtype bernoulli_p = 0.5;
+ const Dtype bernoulli_std = sqrt(bernoulli_p * (1 - bernoulli_p));
+ const Dtype bernoulli_bound = this->mean_bound(true_std);
+ EXPECT_NEAR(bernoulli_p, sample_p_above_mean, bernoulli_bound);
}
- Dtype mean_bound(const Dtype std, const size_t sample_size) {
- return std/sqrt(static_cast<double>(sample_size));
+ void RngBernoulliTest(const Dtype p, void* cpu_data) {
+ int* rng_data = static_cast<int*>(cpu_data);
+ caffe_vRngBernoulli(sample_size_, rng_data, p);
+ const Dtype true_mean = p;
+ const Dtype true_std = sqrt(p * (1 - p));
+ const Dtype bound = this->mean_bound(true_std);
+ const Dtype sample_mean = this->sample_mean(rng_data);
+ EXPECT_NEAR(sample_mean, true_mean, bound);
}
+
+ int num_above_mean;
+ int num_below_mean;
+
+ size_t sample_size_;
+ uint32_t seed_;
+
+ shared_ptr<SyncedMemory> data_;
+ shared_ptr<SyncedMemory> int_data_;
+ shared_ptr<SyncedMemory> int_data_2_;
};
TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussian) {
- size_t sample_size = 10000;
- SyncedMemory data_a(sample_size * sizeof(TypeParam));
- Caffe::set_random_seed(1701);
- TypeParam mu = 0;
- TypeParam sigma = 1;
- caffe_vRngGaussian(sample_size,
- static_cast<TypeParam*>(data_a.mutable_cpu_data()), mu, sigma);
- TypeParam true_mean = mu;
- TypeParam true_std = sigma;
- TypeParam bound = this->mean_bound(true_std, sample_size);
- TypeParam empirical_mean =
- this->sample_mean(static_cast<const TypeParam*>(data_a.cpu_data()),
- sample_size);
- EXPECT_NEAR(empirical_mean, true_mean, bound);
+ const TypeParam mu = 0;
+ const TypeParam sigma = 1;
+ void* gaussian_data = this->data_->mutable_cpu_data();
+ this->RngGaussianTest(mu, sigma, gaussian_data);
+}
+
+
+TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussian2) {
+ const TypeParam mu = -2;
+ const TypeParam sigma = 3;
+ void* gaussian_data = this->data_->mutable_cpu_data();
+ this->RngGaussianTest(mu, sigma, gaussian_data);
}
TYPED_TEST(RandomNumberGeneratorTest, TestRngUniform) {
- size_t sample_size = 10000;
- SyncedMemory data_a(sample_size * sizeof(TypeParam));
- Caffe::set_random_seed(1701);
- TypeParam lower = 0;
- TypeParam upper = 1;
- caffe_vRngUniform(sample_size,
- static_cast<TypeParam*>(data_a.mutable_cpu_data()), lower, upper);
- TypeParam true_mean = (lower + upper) / 2;
- TypeParam true_std = (upper - lower) / sqrt(12);
- TypeParam bound = this->mean_bound(true_std, sample_size);
- TypeParam empirical_mean =
- this->sample_mean(static_cast<const TypeParam*>(data_a.cpu_data()),
- sample_size);
- EXPECT_NEAR(empirical_mean, true_mean, bound);
+ const TypeParam lower = 0;
+ const TypeParam upper = 1;
+ void* uniform_data = this->data_->mutable_cpu_data();
+ this->RngUniformTest(lower, upper, uniform_data);
+}
+
+
+TYPED_TEST(RandomNumberGeneratorTest, TestRngUniform2) {
+ const TypeParam lower = -7.3;
+ const TypeParam upper = -2.3;
+ void* uniform_data = this->data_->mutable_cpu_data();
+ this->RngUniformTest(lower, upper, uniform_data);
}
TYPED_TEST(RandomNumberGeneratorTest, TestRngBernoulli) {
- size_t sample_size = 10000;
- SyncedMemory data_a(sample_size * sizeof(int));
- Caffe::set_random_seed(1701);
- double p = 0.3;
- caffe_vRngBernoulli(sample_size,
- static_cast<int*>(data_a.mutable_cpu_data()), p);
- TypeParam true_mean = p;
- TypeParam true_std = sqrt(p * (1 - p));
- TypeParam bound = this->mean_bound(true_std, sample_size);
- TypeParam empirical_mean =
- this->sample_mean((const int *)data_a.cpu_data(), sample_size);
- EXPECT_NEAR(empirical_mean, true_mean, bound);
+ const TypeParam p = 0.3;
+ void* bernoulli_data = this->int_data_->mutable_cpu_data();
+ this->RngBernoulliTest(p, bernoulli_data);
+}
+
+
+TYPED_TEST(RandomNumberGeneratorTest, TestRngBernoulli2) {
+ const TypeParam p = 0.9;
+ void* bernoulli_data = this->int_data_->mutable_cpu_data();
+ this->RngBernoulliTest(p, bernoulli_data);
}
TYPED_TEST(RandomNumberGeneratorTest, TestRngGaussianTimesBernoulli) {
- size_t sample_size = 10000;
- SyncedMemory gaussian_data(sample_size * sizeof(TypeParam));
- SyncedMemory bernoulli_data(sample_size * sizeof(int));
- Caffe::set_random_seed(1701);
- // Sample from 0 mean Gaussian
- TypeParam mu = 0;
- TypeParam sigma = 1;
- caffe_vRngGaussian(sample_size, static_cast<TypeParam*>(
- gaussian_data.mutable_cpu_data()), mu, sigma);
- TypeParam true_mean = mu;
- TypeParam true_std = sigma;
- TypeParam bound = this->mean_bound(true_std, sample_size);
- TypeParam empirical_mean = this->sample_mean(
- static_cast<const TypeParam*>(gaussian_data.cpu_data()),
- sample_size);
- EXPECT_NEAR(empirical_mean, true_mean, bound);
+ // Sample from 0 mean Gaussian.
+ const TypeParam mu = 0;
+ const TypeParam sigma = 1;
+ TypeParam* gaussian_data =
+ static_cast<TypeParam*>(this->data_->mutable_cpu_data());
+ this->RngGaussianTest(mu, sigma, gaussian_data);
+
+ // Sample from Bernoulli with p = 0.3.
+ const TypeParam bernoulli_p = 0.3;
+ int* bernoulli_data =
+ static_cast<int*>(this->int_data_->mutable_cpu_data());
+ this->RngBernoulliTest(bernoulli_p, bernoulli_data);
+
+ // Multiply Gaussian by Bernoulli.
+ for (int i = 0; i < this->sample_size_; ++i) {
+ gaussian_data[i] *= bernoulli_data[i];
+ }
int num_pos = 0;
int num_neg = 0;
- int num_zeros = 0;
- TypeParam* samples =
- static_cast<TypeParam*>(gaussian_data.mutable_cpu_data());
- for (int i = 0; i < sample_size; ++i) {
- if (samples[i] == TypeParam(0)) {
- ++num_zeros;
- } else if (samples[i] > TypeParam(0)) {
- ++num_pos;
- } else if (samples[i] < TypeParam(0)) {
- ++num_neg;
- }
- }
- // Check that we have no zeros (possible to generate 0s, but highly
- // improbable), and roughly half positives and half negatives (with bound
- // computed from a Bernoulli with p = 0.5).
- EXPECT_EQ(0, num_zeros);
- double p = 0.5;
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- bound = this->mean_bound(true_std, sample_size);
- TypeParam expected_num_each_sign = sample_size * p;
- LOG(INFO) << "Gaussian: Expected " << expected_num_each_sign << " positives"
- << "; got " << num_pos;
- LOG(INFO) << "Gaussian: Expected " << expected_num_each_sign << " negatives"
- << "; got " << num_neg;
- EXPECT_NEAR(expected_num_each_sign, num_pos, sample_size * bound);
- EXPECT_NEAR(expected_num_each_sign, num_neg, sample_size * bound);
- // Sample from Bernoulli with p = 0.3
- p = 0.3;
- caffe_vRngBernoulli(sample_size,
- static_cast<int*>(bernoulli_data.mutable_cpu_data()), p);
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- bound = this->mean_bound(true_std, sample_size);
- empirical_mean =
- this->sample_mean((const int *)bernoulli_data.cpu_data(), sample_size);
- LOG(INFO) << "Bernoulli: Expected mean = " << true_mean
- << "; sample mean = " << empirical_mean;
- EXPECT_NEAR(empirical_mean, true_mean, bound);
- int bernoulli_num_zeros = 0;
- int num_ones = 0;
- int num_other = 0;
- const int* bernoulli_samples =
- static_cast<const int*>(bernoulli_data.cpu_data());
- for (int i = 0; i < sample_size; ++i) {
- if (bernoulli_samples[i] == 0) {
- ++bernoulli_num_zeros;
- } else if (bernoulli_samples[i] == 1) {
- ++num_ones;
+ for (int i = 0; i < this->sample_size_; ++i) {
+ if (gaussian_data[i] == TypeParam(0)) {
+ EXPECT_EQ(TypeParam(0), bernoulli_data[i]);
} else {
- ++num_other;
- }
- }
- LOG(INFO) << "Bernoulli: zeros: " << bernoulli_num_zeros
- << "; ones: " << num_ones << "; other: " << num_other;
- EXPECT_EQ(0, num_other);
- TypeParam epsilon = 1e-4;
- EXPECT_NEAR(sample_size * empirical_mean, num_ones, epsilon);
- EXPECT_NEAR(sample_size * (1.0 - empirical_mean), bernoulli_num_zeros,
- epsilon);
- // Multiply Gaussian by Bernoulli
- for (int i = 0; i < sample_size; ++i) {
- samples[i] *= bernoulli_samples[i];
- }
- num_pos = 0;
- num_neg = 0;
- num_zeros = 0;
- for (int i = 0; i < sample_size; ++i) {
- if (samples[i] == TypeParam(0)) {
- ++num_zeros;
- } else if (samples[i] > TypeParam(0)) {
- ++num_pos;
- } else if (samples[i] < TypeParam(0)) {
- ++num_neg;
+ EXPECT_EQ(TypeParam(1), bernoulli_data[i]);
+ if (gaussian_data[i] > TypeParam(0)) {
+ ++num_pos;
+ } else if (gaussian_data[i] < TypeParam(0)) {
+ ++num_neg;
+ }
}
}
- // Check that we have as many zeros as Bernoulli, and roughly half positives
- // and half negatives (with bound computed from a Bernoulli with p = 0.5).
- EXPECT_EQ(bernoulli_num_zeros, num_zeros);
- p = 0.5;
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- int sub_sample_size = sample_size - bernoulli_num_zeros;
- bound = this->mean_bound(true_std, sub_sample_size);
- expected_num_each_sign = sub_sample_size * p;
- LOG(INFO) << "Gaussian*Bernoulli: Expected " << expected_num_each_sign
- << " positives; got " << num_pos;
- LOG(INFO) << "Gaussian*Bernoulli: Expected " << expected_num_each_sign
- << " negatives; got " << num_neg;
- EXPECT_NEAR(expected_num_each_sign, num_pos, sample_size * bound);
- EXPECT_NEAR(expected_num_each_sign, num_neg, sample_size * bound);
+
+ // Check that Gaussian still has roughly half positives and half negatives
+ // (with bound computed from a Bernoulli with p = 0.5).
+ const int num_non_zero = num_pos + num_neg;
+ const TypeParam sample_p = num_pos / static_cast<TypeParam>(num_non_zero);
+ const TypeParam p = 0.5;
+ const TypeParam true_mean = p;
+ const TypeParam true_std = sqrt(p * (1 - p));
+ const TypeParam bound = this->mean_bound(true_std, num_non_zero);
+ EXPECT_NEAR(true_mean, sample_p, bound);
}
TYPED_TEST(RandomNumberGeneratorTest, TestRngUniformTimesBernoulli) {
- size_t sample_size = 10000;
- SyncedMemory uniform_data(sample_size * sizeof(TypeParam));
- SyncedMemory bernoulli_data(sample_size * sizeof(int));
- Caffe::set_random_seed(1701);
- // Sample from Uniform on [-1, 1]
- TypeParam a = -1;
- TypeParam b = 1;
- caffe_vRngUniform(sample_size, static_cast<TypeParam*>(
- uniform_data.mutable_cpu_data()), a, b);
- TypeParam true_mean = (a + b) / 2;
- TypeParam true_std = (b - a) / sqrt(12);
- TypeParam bound = this->mean_bound(true_std, sample_size);
- TypeParam empirical_mean = this->sample_mean(
- static_cast<const TypeParam*>(uniform_data.cpu_data()),
- sample_size);
- EXPECT_NEAR(empirical_mean, true_mean, bound);
+ // Sample from Uniform on [-1, 1].
+ const TypeParam lower = -1;
+ const TypeParam upper = 1;
+ TypeParam* uniform_data =
+ static_cast<TypeParam*>(this->data_->mutable_cpu_data());
+ this->RngUniformTest(lower, upper, uniform_data);
+
+ // Sample from Bernoulli with p = 0.3.
+ const TypeParam bernoulli_p = 0.3;
+ int* bernoulli_data =
+ static_cast<int*>(this->int_data_->mutable_cpu_data());
+ this->RngBernoulliTest(bernoulli_p, bernoulli_data);
+
+ // Multiply Uniform by Bernoulli.
+ for (int i = 0; i < this->sample_size_; ++i) {
+ uniform_data[i] *= bernoulli_data[i];
+ }
int num_pos = 0;
int num_neg = 0;
- int num_zeros = 0;
- TypeParam* samples =
- static_cast<TypeParam*>(uniform_data.mutable_cpu_data());
- for (int i = 0; i < sample_size; ++i) {
- if (samples[i] == TypeParam(0)) {
- ++num_zeros;
- } else if (samples[i] > TypeParam(0)) {
- ++num_pos;
- } else if (samples[i] < TypeParam(0)) {
- ++num_neg;
- }
- }
- // Check that we have no zeros (possible to generate 0s, but highly
- // improbable), and roughly half positives and half negatives (with bound
- // computed from a Bernoulli with p = 0.5).
- EXPECT_EQ(0, num_zeros);
- TypeParam p = 0.5;
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- bound = this->mean_bound(true_std, sample_size);
- TypeParam expected_num_each_sign = sample_size * p;
- LOG(INFO) << "Uniform: Expected " << expected_num_each_sign << " positives"
- << "; got " << num_pos;
- LOG(INFO) << "Uniform: Expected " << expected_num_each_sign << " negatives"
- << "; got " << num_neg;
- EXPECT_NEAR(expected_num_each_sign, num_pos, sample_size * bound);
- EXPECT_NEAR(expected_num_each_sign, num_neg, sample_size * bound);
- // Sample from Bernoulli with p = 0.3
- p = 0.3;
- caffe_vRngBernoulli(sample_size,
- static_cast<int*>(bernoulli_data.mutable_cpu_data()), p);
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- bound = this->mean_bound(true_std, sample_size);
- empirical_mean =
- this->sample_mean((const int *)bernoulli_data.cpu_data(), sample_size);
- LOG(INFO) << "Bernoulli: Expected mean = " << true_mean
- << "; sample mean = " << empirical_mean;
- EXPECT_NEAR(empirical_mean, true_mean, bound);
- int bernoulli_num_zeros = 0;
- int num_ones = 0;
- int num_other = 0;
- const int* bernoulli_samples =
- static_cast<const int*>(bernoulli_data.cpu_data());
- for (int i = 0; i < sample_size; ++i) {
- if (bernoulli_samples[i] == 0) {
- ++bernoulli_num_zeros;
- } else if (bernoulli_samples[i] == 1) {
- ++num_ones;
+ for (int i = 0; i < this->sample_size_; ++i) {
+ if (uniform_data[i] == TypeParam(0)) {
+ EXPECT_EQ(TypeParam(0), bernoulli_data[i]);
} else {
- ++num_other;
- }
- }
- LOG(INFO) << "Bernoulli: zeros: " << bernoulli_num_zeros
- << "; ones: " << num_ones << "; other: " << num_other;
- EXPECT_EQ(0, num_other);
- TypeParam epsilon = 1e-4;
- EXPECT_NEAR(sample_size * empirical_mean, num_ones, epsilon);
- EXPECT_NEAR(sample_size * (1.0 - empirical_mean), bernoulli_num_zeros,
- epsilon);
- // Multiply Uniform by Bernoulli
- for (int i = 0; i < sample_size; ++i) {
- samples[i] *= bernoulli_samples[i];
- }
- num_pos = 0;
- num_neg = 0;
- num_zeros = 0;
- for (int i = 0; i < sample_size; ++i) {
- if (samples[i] == TypeParam(0)) {
- ++num_zeros;
- } else if (samples[i] > TypeParam(0)) {
- ++num_pos;
- } else if (samples[i] < TypeParam(0)) {
- ++num_neg;
+ EXPECT_EQ(TypeParam(1), bernoulli_data[i]);
+ if (uniform_data[i] > TypeParam(0)) {
+ ++num_pos;
+ } else if (uniform_data[i] < TypeParam(0)) {
+ ++num_neg;
+ }
}
}
- // Check that we have as many zeros as Bernoulli, and roughly half positives
- // and half negatives (with bound computed from a Bernoulli with p = 0.5).
- EXPECT_EQ(bernoulli_num_zeros, num_zeros);
- p = 0.5;
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- int sub_sample_size = sample_size - bernoulli_num_zeros;
- bound = this->mean_bound(true_std, sub_sample_size);
- expected_num_each_sign = sub_sample_size * p;
- LOG(INFO) << "Uniform*Bernoulli: Expected " << expected_num_each_sign
- << " positives; got " << num_pos;
- LOG(INFO) << "Uniform*Bernoulli: Expected " << expected_num_each_sign
- << " negatives; got " << num_neg;
- EXPECT_NEAR(expected_num_each_sign, num_pos, sample_size * bound);
- EXPECT_NEAR(expected_num_each_sign, num_neg, sample_size * bound);
+
+ // Check that Uniform still has roughly half positives and half negatives
+ // (with bound computed from a Bernoulli with p = 0.5).
+ const int num_non_zero = num_pos + num_neg;
+ const TypeParam sample_p = num_pos / static_cast<TypeParam>(num_non_zero);
+ const TypeParam p = 0.5;
+ const TypeParam true_mean = p;
+ const TypeParam true_std = sqrt(p * (1 - p));
+ const TypeParam bound = this->mean_bound(true_std, num_non_zero);
+ EXPECT_NEAR(true_mean, sample_p, bound);
}
TYPED_TEST(RandomNumberGeneratorTest, TestRngBernoulliTimesBernoulli) {
- size_t sample_size = 10000;
- SyncedMemory bernoulli1_data(sample_size * sizeof(int));
- SyncedMemory bernoulli2_data(sample_size * sizeof(int));
- Caffe::set_random_seed(1701);
- // Sample from Bernoulli with p = 0.5
- TypeParam p1 = 0.5;
- caffe_vRngBernoulli(sample_size, static_cast<int*>(
- bernoulli1_data.mutable_cpu_data()), p1);
- TypeParam empirical_mean = this->sample_mean(
- static_cast<const int*>(bernoulli1_data.cpu_data()),
- sample_size);
- int bernoulli1_num_zeros = 0;
- int num_ones = 0;
- int num_other = 0;
- int* bernoulli_samples =
- static_cast<int*>(bernoulli1_data.mutable_cpu_data());
- for (int i = 0; i < sample_size; ++i) {
- if (bernoulli_samples[i] == 0) {
- ++bernoulli1_num_zeros;
- } else if (bernoulli_samples[i] == 1) {
- ++num_ones;
- } else {
- ++num_other;
- }
- }
- TypeParam true_mean = p1;
- TypeParam true_std = sqrt(p1 * (1 - p1));
- TypeParam bound = this->mean_bound(true_std, sample_size);
- TypeParam expected_num_zeros = sample_size * (1 - true_mean);
- TypeParam expected_num_ones = sample_size * true_mean;
- LOG(INFO) << "Bernoulli1: Expected mean = " << true_mean
- << "; sample mean = " << empirical_mean;
- LOG(INFO) << "Bernoulli1: zeros: " << bernoulli1_num_zeros
- << "; ones: " << num_ones << "; other: " << num_other;
- empirical_mean = this->sample_mean(
- static_cast<const int*>(bernoulli1_data.cpu_data()), sample_size);
- EXPECT_NEAR(empirical_mean, true_mean, bound);
- EXPECT_EQ(num_other, 0);
- // Sample from Bernoulli with p = 0.3
- TypeParam p = 0.3;
- caffe_vRngBernoulli(sample_size, static_cast<int*>(
- bernoulli2_data.mutable_cpu_data()), p);
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- bound = this->mean_bound(true_std, sample_size);
- empirical_mean = this->sample_mean(
- static_cast<const int*>(bernoulli2_data.cpu_data()), sample_size);
- LOG(INFO) << "Bernoulli2: Expected mean = " << true_mean
- << "; sample mean = " << empirical_mean;
- EXPECT_NEAR(empirical_mean, true_mean, bound);
- int bernoulli2_num_zeros = 0;
- num_ones = 0;
- num_other = 0;
- const int* bernoulli2_samples =
- static_cast<const int*>(bernoulli2_data.cpu_data());
- for (int i = 0; i < sample_size; ++i) {
- if (bernoulli2_samples[i] == 0) {
- ++bernoulli2_num_zeros;
- } else if (bernoulli2_samples[i] == 1) {
- ++num_ones;
- } else {
- ++num_other;
- }
- }
- LOG(INFO) << "Bernoulli2: zeros: " << bernoulli2_num_zeros
- << "; ones: " << num_ones << "; other: " << num_other;
- EXPECT_EQ(0, num_other);
- TypeParam epsilon = 1e-4;
- EXPECT_NEAR(sample_size * empirical_mean, num_ones, epsilon);
- EXPECT_NEAR(sample_size * (1.0 - empirical_mean), bernoulli2_num_zeros,
- epsilon);
- // Multiply Bernoulli1 by Bernoulli2
- for (int i = 0; i < sample_size; ++i) {
- bernoulli_samples[i] *= bernoulli2_samples[i];
+ // Sample from Bernoulli with p = 0.5.
+ const TypeParam p_a = 0.5;
+ int* bernoulli_data_a =
+ static_cast<int*>(this->int_data_->mutable_cpu_data());
+ this->RngBernoulliTest(p_a, bernoulli_data_a);
+
+ // Sample from Bernoulli with p = 0.3.
+ const TypeParam p_b = 0.3;
+ int* bernoulli_data_b =
+ static_cast<int*>(this->int_data_2_->mutable_cpu_data());
+ this->RngBernoulliTest(p_b, bernoulli_data_b);
+
+ // Multiply Bernoullis.
+ for (int i = 0; i < this->sample_size_; ++i) {
+ bernoulli_data_a[i] *= bernoulli_data_b[i];
}
- bernoulli1_num_zeros = 0;
- num_ones = 0;
- num_other = 0;
- for (int i = 0; i < sample_size; ++i) {
- if (bernoulli_samples[i] == 0) {
- ++bernoulli1_num_zeros;
- } else if (bernoulli_samples[i] == 1) {
+ int num_ones = 0;
+ for (int i = 0; i < this->sample_size_; ++i) {
+ if (bernoulli_data_a[i] != TypeParam(0)) {
+ EXPECT_EQ(TypeParam(1), bernoulli_data_a[i]);
++num_ones;
- } else {
- ++num_other;
}
}
- // Check that we have as many zeros as Bernoulli, and roughly half positives
- // and half negatives (with bound computed from a Bernoulli with p = 0.5).
- p *= p1;
- true_mean = p;
- true_std = sqrt(p * (1 - p));
- empirical_mean = this->sample_mean(
- static_cast<const int *>(bernoulli1_data.cpu_data()), sample_size);
- bound = this->mean_bound(true_std, sample_size);
- LOG(INFO) << "Bernoulli1*Bernoulli2: Expected mean = " << true_mean
- << "; sample mean = " << empirical_mean;
- EXPECT_NEAR(empirical_mean, true_mean, bound);
+
+ // Check that resulting product has roughly p_a * p_b ones.
+ const TypeParam sample_p = this->sample_mean(bernoulli_data_a);
+ const TypeParam true_mean = p_a * p_b;
+ const TypeParam true_std = sqrt(true_mean * (1 - true_mean));
+ const TypeParam bound = this->mean_bound(true_std);
+ EXPECT_NEAR(true_mean, sample_p, bound);
}
projects / platform / upstream / caffeonacl.git / commitdiff