| /external/fio/lib/ |
| gauss.c | 14 if (!gs->stddev)
18 vr = gs->stddev * (r / (FRAND32_MAX + 1.0));
20 return vr - gs->stddev / 2;
33 if (gs->stddev) {
55 gs->stddev = ceil((double) (nranges * 100.0) / dev);
56 if (gs->stddev > nranges / 2)
57 gs->stddev = nranges / 2;
|
| gauss.h | 10 unsigned int stddev; member in struct:gauss_state
|
| /frameworks/av/media/libcpustats/ |
| CentralTendencyStatistics.cpp | 70 double CentralTendencyStatistics::stddev() const function in class:CentralTendencyStatistics
72 double stddev; local
74 stddev = sqrt(variance());
75 mStddev = stddev;
78 stddev = mStddev;
80 return stddev;
|
| /external/v8/tools/unittests/ |
| run_perf_test.py | 160 "stddev": trace["stddev"]} for trace in traces],
185 {"name": "Richards", "results": ["1.234"], "stddev": ""},
186 {"name": "DeltaBlue", "results": ["10657567.0"], "stddev": ""},
198 {"name": "Richards", "results": ["1.234"], "stddev": ""},
199 {"name": "DeltaBlue", "results": ["10657567.0"], "stddev": ""},
216 {"name": "Richards", "results": ["50.0", "100.0"], "stddev": ""},
217 {"name": "DeltaBlue", "results": ["300.0", "200.0"], "stddev": ""},
234 {"name": "Richards", "results": ["50.0", "100.0"], "stddev": ""},
235 {"name": "DeltaBlue", "results": ["300.0", "200.0"], "stddev": ""}
[all...] |
| /test/suite_harness/common/util/tests/src/com/android/compatibility/common/util/ |
| StatTest.java | 72 double stddev = Stat.getStat(values).mStddev; local
73 assertEquals(Math.sqrt(2.5), stddev, 0.00001);
76 stddev = Stat.getStat(values).mStddev;
77 assertEquals(Math.sqrt(2.5), stddev, 0.00001);
80 stddev = Stat.getStat(values).mStddev;
81 assertEquals(Math.sqrt(10.0), stddev, 0.00001);
|
| /external/tensorflow/tensorflow/contrib/kernel_methods/python/mappers/ |
| random_fourier_features_test.py | 49 def _compute_exact_rbf_kernel(x, y, stddev):
50 """Computes exact RBF kernel given input tensors x and y and stddev."""
53 return math_ops.exp(-diff_squared_norm / (2 * stddev * stddev))
94 stddev = 3.0
99 rffm1 = RandomFourierFeatureMapper(3, 100, stddev)
100 rffm2 = RandomFourierFeatureMapper(3, 100, stddev)
114 stddev = 3.0
119 rffm = RandomFourierFeatureMapper(3, 100, stddev, seed=0)
122 exact_kernel_value = _compute_exact_rbf_kernel(x, y, stddev)
[all...] |
| /external/autotest/frontend/migrations/ |
| 078_add_tko_iteration_perf_value.py | 7 stddev FLOAT DEFAULT NULL,
|
| /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.normal/ |
| ctor_double_double.pass.cpp | 15 // explicit normal_distribution(result_type mean = 0, result_type stddev = 1);
26 assert(d.stddev() == 1);
32 assert(d.stddev() == 1);
38 assert(d.stddev() == 5.25);
|
| param_ctor.pass.cpp | 28 assert(p.stddev() == 1);
35 assert(p.stddev() == 1);
42 assert(p.stddev() == 5);
|
| /prebuilts/ndk/r16/sources/cxx-stl/llvm-libc++/test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.normal/ |
| ctor_double_double.pass.cpp | 15 // explicit normal_distribution(result_type mean = 0, result_type stddev = 1);
26 assert(d.stddev() == 1);
32 assert(d.stddev() == 1);
38 assert(d.stddev() == 5.25);
|
| param_ctor.pass.cpp | 28 assert(p.stddev() == 1);
35 assert(p.stddev() == 1);
42 assert(p.stddev() == 5);
|
| /frameworks/av/services/audioflinger/ |
| FastMixerDumpState.cpp | 131 " mean=%.2f min=%.2f max=%.2f stddev=%.2f\n",
133 wall.stddev()*1e-6);
135 " mean=%.0f min=%.0f max=%.0f stddev=%.0f\n",
137 loadNs.stddev()*1e-3);
143 " mean=%.0f min=%.0f max=%.0f stddev=%.0f\n",
144 kHz.mean()*1e-3, kHz.minimum()*1e-3, kHz.maximum()*1e-3, kHz.stddev()*1e-3);
146 " mean=%.1f min=%.1f max=%.1f stddev=%.1f\n",
147 loadMHz.mean(), loadMHz.minimum(), loadMHz.maximum(), loadMHz.stddev());
159 "(> ~3 stddev outliers):\n"
160 " left tail: mean=%.2f min=%.2f max=%.2f stddev=%.2f\n
[all...] |
| /external/skia/tools/skpbench/ |
| _benchresult.py | 23 '(?P<stddev>' + FLOAT_REGEX + '%)'
47 self.stddev = float(match.group('stddev')[:-1]) # Drop '%' sign.
64 for name in ['accum', 'median', 'max', 'min', 'stddev',
|
| /external/skqp/tools/skpbench/ |
| _benchresult.py | 23 '(?P<stddev>' + FLOAT_REGEX + '%)'
47 self.stddev = float(match.group('stddev')[:-1]) # Drop '%' sign.
64 for name in ['accum', 'median', 'max', 'min', 'stddev',
|
| /external/tensorflow/tensorflow/tools/api/golden/ |
| tensorflow.initializers.random_normal.pbtxt | 8 argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
|
| tensorflow.initializers.truncated_normal.pbtxt | 8 argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
|
| tensorflow.keras.initializers.-random-normal.pbtxt | 8 argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
|
| tensorflow.keras.initializers.-truncated-normal.pbtxt | 8 argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
|
| tensorflow.random_normal_initializer.pbtxt | 8 argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
|
| tensorflow.truncated_normal_initializer.pbtxt | 8 argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"<dtype: \'float32\'>\"], "
|
| /libcore/support/src/test/java/tests/util/ |
| SummaryStatistics.java | 63 public double stddev() { method in class:SummaryStatistics
69 return stddev() / mean();
80 sb.append(",stddev=");
81 sb.append(stddev()); method
|
| /external/tensorflow/tensorflow/python/kernel_tests/ |
| parameterized_truncated_normal_op_test.py | 41 stddev = None variable in class:TruncatedNormalMoments
45 def __init__(self, mean, stddev, minval, maxval):
48 self.stddev = np.double(stddev)
69 dist = scipy.stats.norm(loc=self.mean, scale=self.stddev)
76 m = ((k - 1) * self.stddev**2 * m_k_minus_2 + self.mean * m_k_minus_1 -
77 self.stddev * numerator / denominator)
112 def validateMoments(self, shape, mean, stddev, minval, maxval, seed=1618):
119 samples = random_ops.parameterized_truncated_normal(shape, mean, stddev,
124 expected_moments = TruncatedNormalMoments(mean, stddev, minval, maxval
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| noise.py | 41 stddev: float, standard deviation of the noise distribution.
52 def __init__(self, stddev, **kwargs):
55 self.stddev = stddev
61 shape=K.shape(inputs), mean=0., stddev=self.stddev)
66 config = {'stddev': self.stddev}
105 stddev = np.sqrt(self.rate / (1.0 - self.rate))
107 shape=K.shape(inputs), mean=1.0, stddev=stddev
[all...] |
| /external/tensorflow/tensorflow/contrib/tensor_forest/hybrid/python/layers/ |
| decisions_to_data.py | 44 mean=params.weight_init_mean, stddev=params.weight_init_std))
50 mean=params.weight_init_mean, stddev=params.weight_init_std))
85 mean=params.weight_init_mean, stddev=params.weight_init_std))
91 mean=params.weight_init_mean, stddev=params.weight_init_std))
130 mean=params.weight_init_mean, stddev=params.weight_init_std))
136 mean=params.weight_init_mean, stddev=params.weight_init_std))
168 mean=params.weight_init_mean, stddev=params.weight_init_std))
174 mean=params.weight_init_mean, stddev=params.weight_init_std))
220 mean=params.weight_init_mean, stddev=params.weight_init_std))
226 mean=params.weight_init_mean, stddev=params.weight_init_std)
[all...] |
| /test/suite_harness/common/util/src/com/android/compatibility/common/util/ |
| Stat.java | 31 * Collection of statistical propertirs like average, max, min, and stddev
39 public StatResult(double average, double min, double max, double stddev, int dataCount) {
43 mStddev = stddev;
49 * Calculate statistics properties likes average, min, max, and stddev for the given array
71 double stddev = Math.sqrt(variance); local
72 return new StatResult(average, min, max, stddev, data.length);
76 * Calculate statistics properties likes average, min, max, and stddev for the given array
|
