| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
| D | StandardDeviation.java | 48 private Variance variance = null; field in StandardDeviation
55 variance = new Variance(); in StandardDeviation()
64 variance = new Variance(m2); in StandardDeviation()
88 variance = new Variance(isBiasCorrected); in StandardDeviation()
103 variance = new Variance(isBiasCorrected, m2); in StandardDeviation()
111 variance.increment(d); in increment()
118 return variance.getN(); in getN()
126 return FastMath.sqrt(variance.getResult()); in getResult()
134 variance.clear(); in clear()
153 return FastMath.sqrt(variance.evaluate(values)); in evaluate()
[all …]
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| D | Kurtosis.java | 111 double variance = moment.m2 / (moment.n - 1); in getResult() local
112 if (moment.n <= 3 || variance < 10E-20) { in getResult()
119 ((n - 1) * (n -2) * (n -3) * variance * variance); in getResult()
171 Variance variance = new Variance(); in evaluate() local
172 variance.incrementAll(values, begin, length); in evaluate()
173 double mean = variance.moment.m1; in evaluate()
174 double stdDev = FastMath.sqrt(variance.getResult()); in evaluate()
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| D | Skewness.java | 107 double variance = moment.m2 / (moment.n - 1); in getResult() local
108 if (variance < 10E-20) { in getResult()
113 ((n0 - 1) * (n0 -2) * FastMath.sqrt(variance) * variance); in getResult()
172 final double variance = (accum - (accum2 * accum2 / length)) / (length - 1); in evaluate() local
179 accum3 /= variance * FastMath.sqrt(variance); in evaluate()
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| /external/tensorflow/tensorflow/contrib/layers/python/layers/ |
| D | initializers_test.py | 43 def _test_xavier(self, initializer, shape, variance, uniform): argument
53 self.assertAllClose(np.var(values), variance, 1e-3, 1e-3)
86 def _test_variance(self, initializer, shape, variance, factor, mode, uniform): argument
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 …]
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| /external/guava/guava-tests/benchmark/com/google/common/math/ |
| D | StatsBenchmark.java | 75 private final double variance; field in StatsBenchmark.MeanAndVariance
77 MeanAndVariance(double mean, double variance) { in MeanAndVariance() argument
79 this.variance = variance; in MeanAndVariance()
84 return Doubles.hashCode(mean) * 31 + Doubles.hashCode(variance); in hashCode()
91 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
97 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
109 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
126 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
141 abstract MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm); in variance() method in StatsBenchmark.VarianceAlgorithm
168 tmp += varianceAlgorithm.variance(values[i & 0xFF], meanAlgorithm).hashCode(); in meanAndVariance()
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| /external/webrtc/webrtc/modules/audio_processing/intelligibility/ |
| D | intelligibility_utils_unittest.cc | 87 EXPECT_EQ(0, variance_array.variance()[0]); in TEST()
90 EXPECT_EQ(0, variance_array.variance()[0]); in TEST()
100 EXPECT_GE(variance_array.variance()[j], 0.0f); in TEST()
101 EXPECT_LE(variance_array.variance()[j], 1.0f); in TEST()
105 EXPECT_EQ(0, variance_array.variance()[0]); in TEST()
141 EXPECT_EQ(0, variance_array.variance()[j]); in TEST()
143 EXPECT_NEAR(kTestVarianceBufferNotFull, variance_array.variance()[j], in TEST()
146 EXPECT_NEAR(kTestVarianceBufferFull1, variance_array.variance()[j], in TEST()
149 EXPECT_NEAR(kTestVarianceBufferFull2, variance_array.variance()[j], in TEST()
152 EXPECT_EQ(0, variance_array.variance()[j]); in TEST()
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | fused_batch_norm_op.cu.cc | 38 const T* variance, double epsilon, in operator ()() argument
43 variance, epsilon, inv_variance); in operator ()()
48 int sample_size, T* variance) { in InvVarianceToVarianceKernel() argument
50 T inv_var = variance[index]; in InvVarianceToVarianceKernel()
54 variance[index] = (var > 0) ? var : 0; in InvVarianceToVarianceKernel()
61 int channels, T* variance) { in operator ()() argument
65 epsilon, sample_size, variance); in operator ()()
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| D | quantized_instance_norm.cc | 42 const uint32_t cols, float* mean, float* variance) { in ColMeanAndVariance() argument
128 vst1q_f32(variance + col_offset, vmulq_n_f32(M2A[3], inv_rows)); in ColMeanAndVariance()
129 vst1q_f32(variance + col_offset + 4, vmulq_n_f32(M2A[2], inv_rows)); in ColMeanAndVariance()
130 vst1q_f32(variance + col_offset + 8, vmulq_n_f32(M2A[1], inv_rows)); in ColMeanAndVariance()
131 vst1q_f32(variance + col_offset + 12, vmulq_n_f32(M2A[0], inv_rows)); in ColMeanAndVariance()
150 const float32x4_t variance[4] = {vld1q_f32(variance_ptr + col_offset), in MinAndMax() local
155 vrsqrteq_f32(vaddq_f32(variance[0], eps)), in MinAndMax()
156 vrsqrteq_f32(vaddq_f32(variance[1], eps)), in MinAndMax()
157 vrsqrteq_f32(vaddq_f32(variance[2], eps)), in MinAndMax()
158 vrsqrteq_f32(vaddq_f32(variance[3], eps))}; in MinAndMax()
[all …]
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| /external/python/cpython3/Doc/library/ |
| D | 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
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 usually
358 When called with the entire population, this gives the population variance
359 σ². When called on a sample instead, this is the biased sample variance
[all …]
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | 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.
|
| D | 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.
|
| D | 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.
|
| D | 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.
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
| D | StatisticalSummaryValues.java | 39 private final double variance; field in StatisticalSummaryValues
63 public StatisticalSummaryValues(double mean, double variance, long n, in StatisticalSummaryValues() argument
67 this.variance = variance; in StatisticalSummaryValues()
113 return FastMath.sqrt(variance); in getStandardDeviation()
120 return variance; in getVariance()
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| D | AggregateSummaryStatistics.java | 332 final double variance; in aggregate() local
334 variance = Double.NaN; in aggregate()
336 variance = 0d; in aggregate()
338 variance = m2 / (n - 1); in aggregate()
340 return new StatisticalSummaryValues(mean, variance, n, max, min, sum); in aggregate()
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| D | SummaryStatistics.java | 92 protected Variance variance = new Variance(); field in SummaryStatistics
116 private StorelessUnivariateStatistic varianceImpl = variance;
237 if (varianceImpl == variance) { in getVariance()
344 if (varianceImpl != variance) { in clear()
701 if (source.variance == source.varianceImpl) { in copy()
702 dest.variance = (Variance) dest.varianceImpl; in copy()
704 Variance.copy(source.variance, dest.variance); in copy()
|
| /external/tensorflow/tensorflow/python/ops/ |
| D | batch_norm_benchmark.py | 39 def batch_norm_op(tensor, mean, variance, beta, gamma, scale): argument
45 variance, beta, gamma,
55 def batch_norm_py(tensor, mean, variance, beta, gamma, scale): argument
57 return nn_impl.batch_normalization(tensor, mean, variance, beta, gamma if
61 def batch_norm_slow(tensor, mean, variance, beta, gamma, scale): argument
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)
108 tensor = batch_norm_op(tensor, mean, variance, beta, gamma, scale)
[all …]
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| D | nn_impl.py | 644 variance = math_ops.subtract(
648 return (mean, variance)
692 variance = math_ops.reduce_mean(
699 variance = array_ops.squeeze(variance, axes)
702 math_ops.cast(variance, dtypes.float16))
704 return (mean, variance)
782 variance, argument
829 with ops.name_scope(name, "batchnorm", [x, mean, variance, scale, offset]):
830 inv = math_ops.rsqrt(variance + variance_epsilon)
843 variance=None, argument
[all …]
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| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/inference/ |
| D | TTestImpl.java | 191 return t(StatUtils.mean(observed), mu, StatUtils.variance(observed), in t()
256 StatUtils.variance(sample1), StatUtils.variance(sample2), in homoscedasticT()
293 StatUtils.variance(sample1), StatUtils.variance(sample2), in t()
412 return tTest( StatUtils.mean(sample), mu, StatUtils.variance(sample), in tTest()
576 StatUtils.variance(sample1), StatUtils.variance(sample2), in tTest()
618 StatUtils.mean(sample2), StatUtils.variance(sample1), in homoscedasticTTest()
619 StatUtils.variance(sample2), sample1.length, in homoscedasticTTest()
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| /external/webrtc/webrtc/modules/audio_processing/test/ |
| D | test_utils.h | 107 float ComputeSNR(const T* ref, const T* test, size_t length, float* variance) { in ComputeSNR() argument
110 *variance = 0; in ComputeSNR()
114 *variance += ref[i] * ref[i]; in ComputeSNR()
118 *variance /= length; in ComputeSNR()
120 *variance -= mean * mean; in ComputeSNR()
124 snr = 10 * log10(*variance / mse); in ComputeSNR()
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| /external/tensorflow/tensorflow/python/layers/ |
| D | normalization.py | 395 variance=self.moving_variance,
400 output, mean, variance = utils.smart_cond(
407 array_ops.size(inputs) / array_ops.size(variance), variance.dtype)
408 factor = (sample_size - math_ops.cast(1.0, variance.dtype)) / sample_size
409 variance *= factor
422 variance, one_minus_decay)
432 def _renorm_correction_and_moments(self, mean, variance, training): argument
434 stddev = math_ops.sqrt(variance + self.epsilon)
563 mean, variance = nn.moments(inputs, reduction_axes, keep_dims=keep_dims)
571 variance = utils.smart_cond(training,
[all …]
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| /external/libvpx/libvpx/vp9/encoder/ |
| D | vp9_blockiness.c | 23 static int variance(int sum, int sum_squared, int size) { in variance() function
70 var_0 = variance(sum_0, sum_sq_0, size); in blockiness_vertical()
71 var_1 = variance(sum_1, sum_sq_1, size); in blockiness_vertical()
102 var_0 = variance(sum_0, sum_sq_0, size); in blockiness_horizontal()
103 var_1 = variance(sum_1, sum_sq_1, size); in blockiness_horizontal()
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| /external/webrtc/webrtc/common_audio/ |
| D | audio_converter_unittest.cc | 58 float variance = 0; in ComputeSNR() local
64 variance += ref.channels()[i][j] * ref.channels()[i][j]; in ComputeSNR()
71 variance /= length; in ComputeSNR()
73 variance -= mean * mean; in ComputeSNR()
76 snr = 10 * std::log10(variance / mse); in ComputeSNR()
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| /external/autotest/server/tests/netpipe/ |
| D | control.srv | 19 variance - NetPIPE chooses the message sizes at regular intervals,
32 variance = 17
36 upper_bound=upper_bound, variance=variance)
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| /external/libmpeg2/common/arm/ |
| D | 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
|