/third_party/skia/third_party/externals/abseil-cpp/absl/random/ |
H A D | poisson_distribution_test.cc | 113 const double mean = std::min(kMax, m); in TYPED_TEST() local 114 const param_type param(mean); in TYPED_TEST() 117 absl::poisson_distribution<TypeParam> before(mean); in TYPED_TEST() 118 EXPECT_EQ(before.mean(), param.mean()); in TYPED_TEST() 137 ABSL_INTERNAL_LOG(INFO, absl::StrCat("Range {", param.mean(), "}: ", in TYPED_TEST() 146 EXPECT_NE(before.mean(), after.mean()); in TYPED_TEST() 152 EXPECT_EQ(before.mean(), after.mean()) // in TYPED_TEST() 165 PoissonModel(double mean) PoissonModel() argument 167 double mean() const { return mean_; } mean() function in __anon19313::PoissonModel 245 double mean; global() member [all...] |
H A D | poisson_distribution.h | 35 // p(n) = (mean^n / n!) exp(-mean) 45 // NOTE: param_type.mean() is a double, which permits values larger than 61 explicit param_type(double mean = 1.0); 63 double mean() const { return mean_; } in mean() function in absl::poisson_distribution::param_type 90 explicit poisson_distribution(double mean) : param_(mean) {} in poisson_distribution() argument 112 double mean() const { return param_.mean(); } in mean() function in absl::poisson_distribution 133 poisson_distribution<IntType>::param_type::param_type(double mean) in param_type() argument 248 double mean = random_internal::read_floating_point<double>(is); operator >>() local [all...] |
H A D | gaussian_distribution_test.cc | 94 const TypeParam mean = (mod & 0x1) ? -x : x; in TYPED_TEST() local 96 const param_type param(mean, stddev); in TYPED_TEST() 98 absl::gaussian_distribution<TypeParam> before(mean, stddev); in TYPED_TEST() 99 EXPECT_EQ(before.mean(), param.mean()); in TYPED_TEST() 120 INFO, absl::StrFormat("Range{%f, %f}: %f, %f", mean, stddev, in TYPED_TEST() 127 if (!std::isfinite(mean) || !std::isfinite(stddev)) { in TYPED_TEST() 135 EXPECT_NE(before.mean(), after.mean()); in TYPED_TEST() 142 EXPECT_EQ(before.mean(), afte in TYPED_TEST() 158 GaussianModel(double mean, double stddev) GaussianModel() argument 160 double mean() const { return mean_; } mean() function in __anon19271::GaussianModel [all...] |
H A D | gaussian_distribution.h | 67 // pdf = e ^ (-1/2 *x^2), assumes that the mean = 0 & stddev = 1. 95 explicit param_type(result_type mean = 0, result_type stddev = 1) in param_type() 96 : mean_(mean), stddev_(stddev) {} in param_type() 98 // Returns the mean distribution parameter. The mean specifies the location 100 result_type mean() const { return mean_; } in mean() function in absl::gaussian_distribution::param_type 125 explicit gaussian_distribution(result_type mean, result_type stddev = 1) in gaussian_distribution() argument 126 : param_(mean, stddev) {} in gaussian_distribution() 152 result_type mean() const { return param_.mean(); } in mean() function in absl::gaussian_distribution 199 auto mean = random_internal::read_floating_point<result_type>(is); operator >>() local [all...] |
H A D | distributions_test.cc | 297 EXPECT_NEAR(0.5, moments.mean, 0.02); in TEST_F() 316 EXPECT_NEAR(0.5, moments.mean, 0.02); in TEST_F() 345 EXPECT_NEAR(1.0, moments.mean, 0.02); in TEST_F() 361 EXPECT_NEAR(1.0, moments.mean, 0.02); in TEST_F() 378 EXPECT_NEAR(kMean, moments.mean, kMean * 0.015); in TEST_F() 395 EXPECT_NEAR(kP, moments.mean, 0.01); in TEST_F() 410 EXPECT_NEAR(0.4, moments.mean, 0.01); in TEST_F() 421 // The mean of a zipf distribution is: H(N, s-1) / H(N,s). in TEST_F() 426 EXPECT_NEAR(6.5944, moments.mean, 2000) << moments; in TEST_F() 439 EXPECT_NEAR(0.0, moments.mean, 0.0 in TEST_F() 456 const double mean = (0 + 1 + 1 + 2 + 3 + 4 + 7 + 8 + 15 + 16 + 31 + 32 + 63 + TEST_F() local [all...] |
/third_party/skia/third_party/externals/abseil-cpp/absl/random/internal/ |
H A D | distribution_test_util_test.cc | 161 m.mean = 5; in TEST() 166 m.mean = 0; in TEST() 171 m.mean = -5; in TEST() 179 m.mean = 5; in TEST() 184 m.mean = 0; in TEST() 189 m.mean = -5; in TEST()
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H A D | chi_square.cc | 93 // 0.5 * erfc((x - mean)/(sqrt(2) * sigma)) in normal_survival() 120 // chi^2 - mean in ChiSquareValue() 124 const double mean = 1 - 2.0 / (9 * dof); in ChiSquareValue() local 128 return std::pow(z * std::sqrt(variance) + mean, 3.0) * dof; in ChiSquareValue() 166 // sum of k independent random variables with finite mean and variance, it in ChiSquarePValue() 171 const double mean = 1 - 2.0 / (9 * dof); in ChiSquarePValue() local 175 const double z = (chi_square_scaled - mean) / std::sqrt(variance); in ChiSquarePValue()
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/third_party/skia/tools/ |
H A D | Stats.h | 27 min = max = mean = var = median = 0; in Stats() 42 mean = sum / n; in Stats() 46 err += (samples[i] - mean) * (samples[i] - mean); in Stats() 74 double mean; // Estimate of population mean.
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/third_party/ffmpeg/libavutil/ |
H A D | pca.c | 34 double *mean; member 51 pca->mean= av_calloc(n, sizeof(double)); in ff_pca_init() 53 if (!pca->z || !pca->covariance || !pca->mean) { in ff_pca_init() 63 av_freep(&pca->mean); in ff_pca_free() 73 pca->mean[i] += v[i]; in ff_pca_add() 89 pca->mean[j] /= pca->count; in ff_pca() 93 pca->covariance[j + i*n] -= pca->mean[i] * pca->mean[j]; in ff_pca()
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/third_party/skia/third_party/externals/abseil-cpp/absl/base/internal/ |
H A D | exponential_biased.cc | 41 int64_t ExponentialBiased::GetSkipCount(int64_t mean) { in GetSkipCount() argument 56 double interval = bias_ + (std::log2(q) - 26) * (-std::log(2.0) * mean); in GetSkipCount() 59 // source of bias, but the mean would need to be such a large value that it's in GetSkipCount() 60 // not likely to come up. For example, with a mean of 1e18, the probability of in GetSkipCount() 61 // hitting this condition is about 1/1000. For a mean of 1e17, standard in GetSkipCount() 72 int64_t ExponentialBiased::GetStride(int64_t mean) { in GetStride() argument 73 return GetSkipCount(mean - 1) + 1; in GetStride()
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/third_party/ffmpeg/libavutil/tests/ |
H A D | lfg.c | 118 double mean = 1000; in main() local 133 PRN_arr[i ] = bmg_out[0] * stddev + mean; in main() 134 PRN_arr[i+1] = bmg_out[1] * stddev + mean; in main() 145 Z = (mean - samp_mean) / (stddev / sqrt(tot_samp)); in main() 179 printf("sample mean : %f\n" in main() 180 "true mean : %f\n" in main() 186 samp_mean, mean, samp_stddev, stddev, Z, p_value, QH); in main()
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/third_party/node/test/pummel/ |
H A D | test-crypto-timing-safe-equal-benchmarks.js | 57 const equalMean = mean(equalBenches); 58 const unequalMean = mean(unequalBenches); 69 // Returns the mean of an array 70 function mean(array) { function 76 const arrMean = mean(array); 89 // least 50 times larger than the mean. This prevents the tests from failing 93 const arrMean = mean(array);
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/third_party/skia/third_party/externals/libwebp/src/utils/ |
H A D | filters_utils.c | 38 int mean = p[0]; in WebPEstimateBestFilter() local 40 const int diff0 = SDIFF(p[i], mean); in WebPEstimateBestFilter() 50 mean = (3 * mean + p[i] + 2) >> 2; in WebPEstimateBestFilter()
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/third_party/node/lib/internal/ |
H A D | histogram.js | 73 mean: this.mean, 145 get mean() { 148 return this[kHandle]?.mean(); 261 mean: this.mean,
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/third_party/vk-gl-cts/modules/gles2/performance/ |
H A D | es2pDrawCallBatchingTests.cpp | 788 double mean; member 795 double mean = 0.0; in calculateStats() local 798 mean += (double)samples[i]; in calculateStats() 800 mean /= (double)samples.size(); in calculateStats() 807 standardDeviation += (x - mean) * (x - mean); in calculateStats() 817 stats.mean = mean; in calculateStats() 880 log << TestLog::Message << "Batched samples; Count: " << m_batchedSamplesUs.size() << ", Mean: " << batchedStats.mean << "us, Standard deviation: " << batchedStats.standardDeviation << "us, Standard error of mean in iterate() [all...] |
/third_party/skia/third_party/externals/dawn/scripts/ |
H A D | perf_test_runner.py | 40 def mean(data): function 41 """Return the sample arithmetic mean of data.""" 44 raise ValueError('mean requires at least one data point') 59 c = mean(data) 74 """Compute a truncated mean, n is truncation size""" 75 return mean(truncated_list(data, n)) 144 sys.stdout.write(", mean: %.2f" % mean(scores)) 150 sys.stdout.write(", truncated mean: %.2f" %
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/third_party/alsa-utils/bat/ |
H A D | analyze.c | 67 float mean, float p, int channel, int start) in check_peak() 76 10.0 * log10f(a->mag[peak] / mean)); in check_peak() 78 10.0 * log10f(p / mean), start * hz, end * hz); in check_peak() 108 float mean = 0.0, t, sigma = 0.0, p = 0.0; in check() local 112 /* calculate mean */ in check() 114 mean += a->mag[i]; in check() 115 mean /= (float) N; in check() 119 t = a->mag[i] - mean; in check() 126 /* clip any data less than k sigma + mean */ in check() 128 if (a->mag[i] > mean in check() 66 check_peak(struct bat *bat, struct analyze *a, int end, int peak, float hz, float mean, float p, int channel, int start) check_peak() argument [all...] |
/third_party/ffmpeg/libavfilter/ |
H A D | af_aspectralstats.c | 33 float mean; member 169 set_meta(metadata, ch + 1, "mean", "%g", stats->mean); in set_metadata() 200 static float spectral_variance(const float *const spectral, int size, int max_freq, float mean) in spectral_variance() argument 205 sum += sqrf(spectral[n] - mean); in spectral_variance() 311 float max = 0.f, mean = 0.f; in spectral_crest() local 315 mean += spectral[n]; in spectral_crest() 318 mean /= size; in spectral_crest() 319 if (mean <= FLT_EPSILON) in spectral_crest() 321 return max / mean; in spectral_crest() [all...] |
/third_party/astc-encoder/Test/testlib/ |
H A D | resultset.py | 134 dat = (np.mean(self.tTimesRel), np.std(self.tTimesRel)) 137 dat = (np.mean(self.cTimesRel), np.std(self.cTimesRel)) 140 dat = (np.mean(self.psnrRel), np.std(self.psnrRel)) 143 dat = (np.mean(self.cTime), np.std(self.cTime)) 146 dat = (np.mean(self.psnr), np.std(self.psnr))
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/third_party/benchmark/src/ |
H A D | statistics.cc | 68 const auto mean = StatisticsMean(v); in StatisticsStdDev() local 69 if (v.empty()) return mean; in StatisticsStdDev() 75 return Sqrt(v.size() / (v.size() - 1.0) * (avg_squares - Sqr(mean))); in StatisticsStdDev() 82 const auto mean = StatisticsMean(v); in StatisticsCV() local 84 return stddev / mean; in StatisticsCV()
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/third_party/skia/third_party/externals/angle2/scripts/ |
H A D | perf_test_runner.py | 9 # tests in an infinite batch, printing out the mean and coefficient of 42 def mean(data): function 43 """Return the sample arithmetic mean of data.""" 46 raise ValueError('mean requires at least one data point') 61 c = mean(data) 76 """Compute a truncated mean, n is truncation size""" 77 return mean(truncated_list(data, n)) 168 sys.stdout.write(", mean: %.2f" % mean(scores)) 174 sys.stdout.write(", truncated mean [all...] |
/third_party/skia/third_party/externals/angle2/src/tests/perf_tests/ |
H A D | ANGLEPerfTest.cpp | 220 double mean = 0; in ComputeMean() local 223 mean += value; in ComputeMean() 225 mean /= static_cast<double>(values.size()); in ComputeMean() 226 return mean; in ComputeMean() 315 double mean = ComputeMean(mTestTrialResults); in run() local 320 double difference = trialResult - mean; in run() 326 double coefficientOfVariation = standardDeviation / mean; in run() 328 if (mean < 0.001) in run() 330 printf("Mean result time: %.4lf ns.\n", mean * 1000.0); in run() 334 printf("Mean result time: %.4lf ms.\n", mean); in run() [all...] |
/third_party/typescript/tests/baselines/reference/tsc/incremental/ |
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