| /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());
|
| /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}
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| Variance.java | 27 * Computes the variance of the available values. By default, the unbiased
28 * "sample variance" definitional formula is used:
30 * variance = sum((x_i - mean)^2) / (n - 1) </p>
38 * <li> The <code>getResult</code> method computes the variance using
46 * Chan, Golub, Levesque, <i>Algorithms for Computing the Sample Variance</i>,
54 * The "population variance" ( sum((x_i - mean)^2) / n ) can also
68 public class Variance extends AbstractStorelessUnivariateStatistic implements Serializable, WeightedEvaluation {
73 /** SecondMoment is used in incremental calculation of Variance*/
77 * Boolean test to determine if this Variance should also increment
78 * the second moment, this evaluates to false when this Variance i
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| 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...] |
| /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/tensorflow/tensorflow/core/api_def/base_api/ |
| api_def_FusedBatchNormV2.pbtxt | 29 name: "variance"
31 A 1D Tensor for population variance. Used for inference only;
51 A 1D Tensor for the computed batch variance, to be used by
52 TensorFlow to compute the running variance.
65 A 1D Tensor for the computed batch variance (inverted variance
78 The data type for the scale, offset, mean, and variance.
84 A small float number added to the variance of x.
|
| api_def_FusedBatchNorm.pbtxt | 29 name: "variance"
31 A 1D Tensor for population variance. Used for inference only;
51 A 1D Tensor for the computed batch variance, to be used by
52 TensorFlow to compute the running variance.
65 A 1D Tensor for the computed batch variance (inverted variance
78 A small float number added to the variance of x.
|
| api_def_FusedBatchNormGradV2.pbtxt | 34 variance (inverted variance in the cuDNN case) to be reused in
36 for the population variance to be reused in both 1st and 2nd
67 Unused placeholder to match the variance input
80 The data type for the scale, offset, mean, and variance.
86 A small float number added to the variance of x.
|
| api_def_FusedBatchNormGrad.pbtxt | 34 variance (inverted variance in the cuDNN case) to be reused in
36 for the population variance to be reused in both 1st and 2nd
67 Unused placeholder to match the variance input
80 A small float number added to the variance of x.
|
| /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/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.))
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| /external/libmpeg2/common/ |
| icv_variance.c | 26 * This file contains the functions to compute variance
60 * Computes variance of a given 8x4 block
63 * Compute variance of a given 8x4 block
78 * Variance
104 /* variance */
110 /* calculates the variance only for field area not frame one. */
|
| /external/autotest/server/tests/netpipe/ |
| control.srv | 19 variance - NetPIPE chooses the message sizes at regular intervals,
32 variance = 17
36 upper_bound=upper_bound, variance=variance)
|
| control.stress.srv | 21 variance - NetPIPE chooses the message sizes at regular intervals,
43 variance = 17
50 upper_bound=upper_bound, variance=variance)
|
| /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;
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| /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/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);
|
| quantized_instance_norm.cc | 34 // Single pass mean and variance.
35 // Shape of `input` is [rows x cols], shape of both `mean` and `variance`
37 // Note, `mean` and `variance` are of 'i' (not scaled).
42 const uint32_t cols, float* mean, float* variance) {
118 // Write the final mean and variance for the 16 columns.
128 vst1q_f32(variance + col_offset, vmulq_n_f32(M2A[3], inv_rows));
129 vst1q_f32(variance + col_offset + 4, vmulq_n_f32(M2A[2], inv_rows));
130 vst1q_f32(variance + col_offset + 8, vmulq_n_f32(M2A[1], inv_rows));
131 vst1q_f32(variance + col_offset + 12, vmulq_n_f32(M2A[0], inv_rows));
135 // Compute min and max of (input - mean) / sqrt(variance + epsilon)
150 const float32x4_t variance[4] = {vld1q_f32(variance_ptr + col_offset), local
207 const float32x4_t variance[4] = {vld1q_f32(variance_ptr + col_offset + 12), local
[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);
|
| /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
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/regression/ |
| MultipleLinearRegression.java | 43 * Estimates the variance of the regression parameters, ie Var(b).
45 * @return The [k,k] array representing the variance of b
57 * Returns the variance of the regressand, ie Var(y).
59 * @return The double representing the variance of y
|
| /external/tensorflow/tensorflow/java/src/test/java/org/tensorflow/op/ |
| ScopeTest.java | 159 Variance<Integer> var1 = Variance.create(s.withName("example"), data, Integer.class);
160 assertEquals("example/variance", var1.output().op().name());
168 Variance<Integer> var2 = Variance.create(s, data, Integer.class);
169 assertEquals("variance/variance", var2.output().op().name());
172 assertNotNull(g.operation("variance/squared_deviation"));
173 assertNotNull(g.operation("variance/Mean"));
174 // assertNotNull(g.operation("variance/zero"))
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| /external/python/cpython3/Doc/library/ |
| statistics.rst | 58 :func:`pvariance` Population variance of data.
60 :func:`variance` Sample variance of data.
297 variance). See :func:`pvariance` for arguments and other details.
307 Return the population variance of *data*, a non-empty iterable of real-valued
308 numbers. Variance, or second moment about the mean, is a measure of the
309 variability (spread or dispersion) of data. A large variance indicates that
310 the data is spread out; a small variance indicates it is clustered closely
317 Use this function to calculate the variance from the entire population. To
318 estimate the variance from a sample, the :func:`variance` function is usuall
[all...] |
| /external/webrtc/webrtc/test/ |
| statistics.cc | 31 double Statistics::Variance() const {
38 return sqrt(Variance());
|
