| /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/guava/android/guava-tests/benchmark/com/google/common/math/ |
| D | StatsBenchmark.java | 74 private final double variance; field in StatsBenchmark.MeanAndVariance
76 MeanAndVariance(double mean, double variance) { in MeanAndVariance() argument
78 this.variance = variance; in MeanAndVariance()
83 return Doubles.hashCode(mean) * 31 + Doubles.hashCode(variance); in hashCode()
90 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
96 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
108 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
125 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
140 abstract MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm); in variance() method in StatsBenchmark.VarianceAlgorithm
166 tmp += varianceAlgorithm.variance(values[i & 0xFF], meanAlgorithm).hashCode(); in meanAndVariance()
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| /external/guava/guava-tests/benchmark/com/google/common/math/ |
| D | StatsBenchmark.java | 74 private final double variance; field in StatsBenchmark.MeanAndVariance
76 MeanAndVariance(double mean, double variance) { in MeanAndVariance() argument
78 this.variance = variance; in MeanAndVariance()
83 return Doubles.hashCode(mean) * 31 + Doubles.hashCode(variance); in hashCode()
90 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
96 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
108 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
125 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { in variance() method
140 abstract MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm); in variance() method in StatsBenchmark.VarianceAlgorithm
166 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/python/keras/layers/preprocessing/ |
| D | normalization.py | 95 self.variance = self._add_state_variable(
112 variance = array_ops.reshape(self.variance, self._broadcast_shape)
113 return (inputs - mean) / math_ops.sqrt(variance)
160 variance = np.var(values, axis=reduction_axes, dtype=np.float64)
164 sanitized_accumulator = self._create_accumulator(count, mean, variance)
187 accumulator.variance + np.square(accumulator.mean - combined_mean))
201 _VARIANCE_NAME: accumulator.variance
224 _VARIANCE_NAME: accumulator.variance.tolist()
235 def _create_accumulator(self, count, mean, variance): argument
240 np.nan_to_num(variance))
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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.
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| D | api_def_FusedBatchNormV3.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
85 The data type for the scale, offset, mean, and variance.
91 A small float number added to the variance of x.
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| 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.
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| 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.
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| D | api_def_FusedBatchNormGradV3.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
75 Unused placeholder to match the variance input
88 The data type for the scale, offset, mean, and variance.
94 A small float number added to the variance of x.
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| /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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| /external/tensorflow/tensorflow/compiler/mlir/xla/tests/ |
| D | unfuse_batch_norm.mlir | 11 %mean: tensor<256xf32>, %variance: tensor<256xf32>)
25 %0 = "xla_hlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
42 %mean: tensor<256xf32>, %variance: tensor<256xf32>)
44 %0 = "xla_hlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
57 %mean: tensor<256xf64>, %variance: tensor<256xf64>)
59 %0 = "xla_hlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
72 %mean: tensor<256xf16>, %variance: tensor<256xf16>)
74 %0 = "xla_hlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
85 %mean: tensor<256xf16>, %variance: tensor<256xf16>)
89 %0 = "xla_hlo.batch_norm_inference"(%x, %scale, %offset, %mean, %variance)
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| /external/libaom/libaom/av1/encoder/ |
| D | var_based_part.c | 100 v->variance = in get_variance()
161 vt.part_variances->none.variance < threshold) { in set_vt_partitioning()
172 vt.part_variances->none.variance > (threshold << 4))) { in set_vt_partitioning()
178 vt.part_variances->none.variance < threshold) { in set_vt_partitioning()
188 if (vt.part_variances->vert[0].variance < threshold && in set_vt_partitioning()
189 vt.part_variances->vert[1].variance < threshold && in set_vt_partitioning()
203 if (vt.part_variances->horz[0].variance < threshold && in set_vt_partitioning()
204 vt.part_variances->horz[1].variance < threshold && in set_vt_partitioning()
415 if ((vt->part_variances).none.variance < (thresholds[0] >> 1)) in set_low_temp_var_flag_64x64()
419 if (vt->part_variances.horz[i].variance < (thresholds[0] >> 2)) in set_low_temp_var_flag_64x64()
[all …]
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| D | blockiness.c | 31 static int variance(int sum, int sum_squared, int size) { in variance() function
78 var_0 = variance(sum_0, sum_sq_0, size); in blockiness_vertical()
79 var_1 = variance(sum_1, sum_sq_1, size); in blockiness_vertical()
110 var_0 = variance(sum_0, sum_sq_0, size); in blockiness_horizontal()
111 var_1 = variance(sum_1, sum_sq_1, size); in blockiness_horizontal()
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| /external/tensorflow/tensorflow/core/kernels/ |
| 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/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/ops/ |
| D | batch_norm_benchmark.py | 40 def batch_norm_op(tensor, mean, variance, beta, gamma, scale): argument
46 tensor, mean, variance, beta, gamma, 0.001, scale)
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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| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | normalization_v2.py | 184 variance = y_squared_mean - math_ops.square(mean)
192 variance = math_ops.reduce_mean(
199 variance = array_ops.squeeze(variance, axes)
202 math_ops.cast(variance, dtypes.float16))
204 return (mean, variance)
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| D | normalization.py | 547 variance=self.moving_variance,
552 output, mean, variance = tf_utils.smart_cond(
559 array_ops.size(inputs) / array_ops.size(variance), variance.dtype)
560 factor = (sample_size - math_ops.cast(1.0, variance.dtype)) / sample_size
561 variance *= factor
581 self.moving_stddev, math_ops.sqrt(variance + self.epsilon),
589 return self._assign_moving_average(self.moving_variance, variance,
597 def _renorm_correction_and_moments(self, mean, variance, training, argument
600 stddev = math_ops.sqrt(variance + self.epsilon)
651 out_variance = array_ops.identity(variance)
[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/libhevc/encoder/ |
| D | ihevce_stasino_helpers.c | 145 ULWORD64 variance; in ihevce_calc_variance() local
152 variance = 0; in ihevce_calc_variance()
175 variance = in ihevce_calc_variance()
181 variance = ((total_elements * sq_sum) - (sum * sum)); in ihevce_calc_variance()
187 *pu4_variance = variance; in ihevce_calc_variance()
238 LWORD64 variance; in ihevce_calc_variance_signed() local
245 variance = 0; in ihevce_calc_variance_signed()
261 variance = ((total_elements * sq_sum) - (sum * sum)); // / (total_elements * (total_elements) ) in ihevce_calc_variance_signed()
265 *pu4_variance = variance; in ihevce_calc_variance_signed()
323 ULWORD64 variance; in ihevce_calc_chroma_variance() local
[all …]
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| /external/libvpx/libvpx/vp9/encoder/ |
| D | vp9_blockiness.c | 24 static int variance(int sum, int sum_squared, int size) { in variance() function
71 var_0 = variance(sum_0, sum_sq_0, size); in blockiness_vertical()
72 var_1 = variance(sum_1, sum_sq_1, size); in blockiness_vertical()
103 var_0 = variance(sum_0, sum_sq_0, size); in blockiness_horizontal()
104 var_1 = variance(sum_1, sum_sq_1, size); in blockiness_horizontal()
|