| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
| StandardDeviation.java | 26 * is the positive square root of the variance. This implementation wraps a
27 * {@link Variance} instance. The <code>isBiasCorrected</code> property of the
28 * wrapped Variance instance is exposed, so that this class can be used to
30 * bias-corrected "sample variance") or the "population standard deviation"
31 * (the square root of the non-bias-corrected "population variance"). See
32 * {@link Variance} for more information.
47 /** Wrapped Variance instance */
48 private Variance variance = null; field in class:StandardDeviation
51 * Constructs a StandardDeviation. Sets the underlying {@link Variance}
[all...] |
| Kurtosis.java | 111 double variance = moment.m2 / (moment.n - 1); local
112 if (moment.n <= 3 || variance < 10E-20) {
119 ((n - 1) * (n -2) * (n -3) * variance * variance);
171 Variance variance = new Variance(); local
172 variance.incrementAll(values, begin, length);
173 double mean = variance.moment.m1;
174 double stdDev = FastMath.sqrt(variance.getResult())
[all...] |
| Skewness.java | 107 double variance = moment.m2 / (moment.n - 1); local
108 if (variance < 10E-20) {
113 ((n0 - 1) * (n0 -2) * FastMath.sqrt(variance) * variance);
172 final double variance = (accum - (accum2 * accum2 / length)) / (length - 1); local
179 accum3 /= variance * FastMath.sqrt(variance);
|
| /frameworks/av/media/libcpustats/ |
| CentralTendencyStatistics.cpp | 52 double CentralTendencyStatistics::variance() const function in class:CentralTendencyStatistics
54 double variance; local
58 variance = mM2 / (mN - 1);
60 variance = NAN;
62 mVariance = variance;
65 variance = mVariance;
67 return variance;
74 stddev = sqrt(variance());
|
| /tools/test/connectivity/acts/tests/google/wifi/ |
| WifiNewSetupAutoJoinTest.py | 167 variance = 5
168 attn_value = [att0 + variance * 2, att1, att2, att3]
175 variance = 5
176 attn_value = [att0 + variance, att1, att2, att3]
183 variance = 5
191 variance = 5
192 attn_value = [att0 - variance, att1, att2, att3]
206 variance = 5
207 attn_value = [att0 + variance * 2, att1, att2, attn3]
214 variance =
[all...] |
| WifiAutoJoinTest.py | 183 variance = 5
184 attenuations = ([att0 + variance * 2, att1, att2],
185 [att0 + variance, att1, att2], [att0, att1, att2],
186 [att0 - variance, att1, att2])
207 variance = 5
208 attenuations = ([att0 + variance * 2, att1, att2],
209 [att0 + variance, att1, att2], [att0, att1, att2])
230 variance = 5
231 attenuations = ([att0 - variance, att1 + variance, att2]
[all...] |
| /external/tensorflow/tensorflow/core/kernels/ |
| fused_batch_norm_op.cu.cc | 38 const T* variance, double epsilon,
43 variance, epsilon, inv_variance);
48 int sample_size, T* variance) {
50 T inv_var = variance[index];
54 variance[index] = (var > 0) ? var : 0;
61 int channels, T* variance) {
65 epsilon, sample_size, variance);
|
| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| initializers_test.py | 43 def _test_xavier(self, initializer, shape, variance, uniform):
53 self.assertAllClose(np.var(values), variance, 1e-3, 1e-3)
86 def _test_variance(self, initializer, shape, variance, factor, mode, uniform):
97 self.assertAllClose(np.var(values), variance, 1e-3, 1e-3)
104 variance=2. / 100.,
114 variance=2. / 40.,
124 variance=4. / (100. + 40.),
134 variance=2. / (100. * 40. * 5.),
144 variance=2. / (100. * 40. * 7.),
154 variance=2. / (100. * 40. * (5. + 7.))
[all...] |
| /external/guava/guava-tests/benchmark/com/google/common/math/ |
| StatsBenchmark.java | 28 * Benchmarks for various algorithms for computing the mean and/or variance.
75 private final double variance; field in class:StatsBenchmark.MeanAndVariance
77 MeanAndVariance(double mean, double variance) {
79 this.variance = variance;
84 return Doubles.hashCode(mean) * 31 + Doubles.hashCode(variance);
91 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { method
97 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { method
109 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { method
126 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) method
141 abstract MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm); method in class:StatsBenchmark.VarianceAlgorithm
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
| StatisticalSummaryValues.java | 38 /** The sample variance */
39 private final double variance; field in class:StatisticalSummaryValues
57 * @param variance the sample variance
63 public StatisticalSummaryValues(double mean, double variance, long n,
67 this.variance = variance;
113 return FastMath.sqrt(variance);
117 * @return Returns the variance.
120 return variance;
[all...] |
| AggregateSummaryStatistics.java | 198 * {@inheritDoc}. This version returns the variance of all the aggregated
332 final double variance; local
334 variance = Double.NaN;
336 variance = 0d;
338 variance = m2 / (n - 1);
340 return new StatisticalSummaryValues(mean, variance, n, max, min, sum);
|
| /external/libvpx/libvpx/vp9/encoder/ |
| vp9_blockiness.c | 23 static int variance(int sum, int sum_squared, int size) { function
45 // by dividing the blockiness by the variance of the pixels on either side
70 var_0 = variance(sum_0, sum_sq_0, size);
71 var_1 = variance(sum_1, sum_sq_1, size);
102 var_0 = variance(sum_0, sum_sq_0, size);
103 var_1 = variance(sum_1, sum_sq_1, size);
|
| /external/webrtc/webrtc/modules/audio_processing/intelligibility/ |
| intelligibility_utils_unittest.cc | 71 // Tests VarianceArray, for all variance step types.
87 EXPECT_EQ(0, variance_array.variance()[0]);
90 EXPECT_EQ(0, variance_array.variance()[0]);
100 EXPECT_GE(variance_array.variance()[j], 0.0f);
101 EXPECT_LE(variance_array.variance()[j], 1.0f);
105 EXPECT_EQ(0, variance_array.variance()[0]);
141 EXPECT_EQ(0, variance_array.variance()[j]);
143 EXPECT_NEAR(kTestVarianceBufferNotFull, variance_array.variance()[j],
146 EXPECT_NEAR(kTestVarianceBufferFull1, variance_array.variance()[j],
149 EXPECT_NEAR(kTestVarianceBufferFull2, variance_array.variance()[j]
[all...] |
| /external/webrtc/webrtc/modules/audio_processing/test/ |
| test_utils.h | 107 float ComputeSNR(const T* ref, const T* test, size_t length, float* variance) {
110 *variance = 0;
114 *variance += ref[i] * ref[i];
118 *variance /= length;
120 *variance -= mean * mean;
124 snr = 10 * log10(*variance / mse);
|
| /external/autotest/server/tests/netpipe/ |
| netpipe.py | 7 def run_once(self, pair, buffer, upper_bound, variance):
43 buffer, upper_bound, variance,
46 buffer, upper_bound, variance,
|
| /external/libmpeg2/common/arm/ |
| icv_variance_a9.s | 26 @* This file contains definitions of routines for variance caclulation
42 @* @brief computes variance of a 8x4 block
46 @* This functions computes variance of a 8x4 block
61 @* variance value in r0
|
| /frameworks/av/media/libcpustats/include/cpustats/ |
| CentralTendencyStatistics.h | 45 // return the variance of all samples so far
46 double variance() const;
65 // cached variance, and n at time of caching
|
| /hardware/intel/common/libva/va/vendor/intel/ |
| va_intel_statistics.h | 41 * number of non-zero coefficients, MB variance and MB pixel average.
156 unsigned int variance; member in struct:_VAStatsStatistics16x16Intel
|
| /external/autotest/client/common_lib/cros/ |
| perf_stat_lib.py | 73 variance = sum([(elem - mean) ** 2 for elem in num_list]) / (n -1)
74 return round(sqrt(variance), 2
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/inference/ |
| TTestImpl.java | 191 return t(StatUtils.mean(observed), mu, StatUtils.variance(observed),
235 * and <strong><code>var</code></strong> is the pooled variance estimate:
239 * with <strong><code>var1<code></strong> the variance of the first sample and
240 * <strong><code>var2</code></strong> the variance of the second sample.
256 StatUtils.variance(sample1), StatUtils.variance(sample2),
276 * <strong><code> var1</code></strong> is the variance of the first sample;
277 * <strong><code> var2</code></strong> is the variance of the second sample;
293 StatUtils.variance(sample1), StatUtils.variance(sample2)
[all...] |
| /external/autotest/client/site_tests/video_WebRtcCamera/ |
| ssim.js | 29 // square root. The latter is actually an unbiased estimate of the variance,
30 // not the exact variance.
43 return {mean : meanA, variance : accu / a.length};
70 var sigmaX2 = statsX.variance;
74 var sigmaY2 = statsY.variance;
|
| /external/autotest/client/site_tests/video_WebRtcPeerConnectionWithCamera/ |
| ssim.js | 29 // square root. The latter is actually an unbiased estimate of the variance,
30 // not the exact variance.
43 return {mean : meanA, variance : accu / a.length};
70 var sigmaX2 = statsX.variance;
74 var sigmaY2 = statsY.variance;
|
| /external/webrtc/webrtc/voice_engine/test/auto_test/standard/ |
| video_sync_test.cc | 58 // Computes the standard deviation by first estimating the sample variance
66 float variance = 0; local
68 variance += (*start - mean) * (*start - mean) / (num_elements - 1);
70 return sqrt(variance);
|
| /external/tensorflow/tensorflow/python/ops/ |
| batch_norm_benchmark.py | 39 def batch_norm_op(tensor, mean, variance, beta, gamma, scale):
45 variance, beta, gamma,
51 # batch_norm = (tensor - mean) * tf.rsqrt(variance + 0.001)
55 def batch_norm_py(tensor, mean, variance, beta, gamma, scale):
57 return nn_impl.batch_normalization(tensor, mean, variance, beta, gamma if
61 def batch_norm_slow(tensor, mean, variance, beta, gamma, scale):
62 batch_norm = (tensor - mean) * math_ops.rsqrt(variance + 0.001)
99 mean, variance = nn_impl.moments(tensor, axes, keep_dims=keep_dims)
102 variance = array_ops.ones(moment_shape)
106 tensor = batch_norm_py(tensor, mean, variance, beta, gamma, scale
[all...] |
| /frameworks/av/drm/libmediadrm/protos/ |
| metrics.proto | 59 optional double variance = 4;
|
