| /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 …]
|
| 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()
|
| 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()
|
| /external/tensorflow/tensorflow/python/keras/layers/preprocessing/ |
| D | normalization.py | 91 def __init__(self, axis=-1, mean=None, variance=None, **kwargs): argument
110 if isinstance(variance, variables.Variable):
113 if mean is not None and variance is not None:
115 variance = convert_to_ndarray(variance)
116 elif mean is not None or variance is not None:
119 'must be set. Got mean: {} and variance: {}'.format(mean, variance))
121 self.variance_val = variance
156 self.variance = self.add_weight(
175 self.variance.assign(variance_val)
199 total_variance = ((self.variance +
[all …]
|
| D | normalization_v1.py | 85 variance = kwargs.pop('variance', None)
99 if isinstance(variance, variables.Variable):
102 if mean is not None and variance is not None:
104 variance = convert_to_ndarray(variance)
105 elif mean is not None or variance is not None:
108 'must be set. Got mean: {} and variance: {}'.format(mean, variance))
111 self.variance_val = variance
148 self.variance = self._add_state_variable(
177 variance = array_ops.reshape(self.variance, self._broadcast_shape)
179 math_ops.maximum(math_ops.sqrt(variance), K.epsilon()))
[all …]
|
| /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()
|
| /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()
|
| /external/tensorflow/tensorflow/lite/delegates/gpu/metal/kernels/ |
| D | mean_stddev_normalization_test.mm | 31 // zero mean, zero variance
35 // zero mean, small variance
39 // zero mean, large variance
43 // small mean, zero variance
47 // small mean, small variance
51 // small mean, large variance
55 // large mean, zero variance
59 // large mean, small variance
63 // large mean, large variance
|
| /external/tensorflow/tensorflow/core/kernels/mkl/ |
| D | mkl_fused_batch_norm_op_test.cc | 46 const Tensor& mean, const Tensor& variance,
52 const Tensor& scale, const Tensor& mean, const Tensor& variance,
100 Tensor variance(dtype, {depth}); in VerifyTensorsClose() local
101 variance.flat<T>() = in VerifyTensorsClose()
102 variance.flat<T>().template setRandom<random_gen_>().abs(); in VerifyTensorsClose()
111 run(input, scale, offset, mean, variance, exponential_avg_factor, in VerifyTensorsClose()
113 run_mkl(input, scale, offset, mean, variance, exponential_avg_factor, in VerifyTensorsClose()
154 Tensor variance(dtype, {out_channels}); in VerifyTensorsCloseForGrad() local
155 variance.flat<T>() = in VerifyTensorsCloseForGrad()
156 variance.flat<T>().template setRandom<random_gen_>().abs(); in VerifyTensorsCloseForGrad()
[all …]
|
| /external/webrtc/third_party/abseil-cpp/absl/random/internal/ |
| D | distribution_test_util_test.cc | 162 m.variance = 1; in TEST()
167 m.variance = 1; in TEST()
172 m.variance = 100; in TEST()
180 m.variance = 1; in TEST()
185 m.variance = 1; in TEST()
190 m.variance = 100; in TEST()
|
| /external/libtextclassifier/abseil-cpp/absl/random/internal/ |
| D | distribution_test_util_test.cc | 162 m.variance = 1; in TEST()
167 m.variance = 1; in TEST()
172 m.variance = 100; in TEST()
180 m.variance = 1; in TEST()
185 m.variance = 1; in TEST()
190 m.variance = 100; in TEST()
|
| D | chi_square.cc | 125 const double variance = 2.0 / (9 * dof); in ChiSquareValue() local
127 if (variance != 0) { in ChiSquareValue()
128 return std::pow(z * std::sqrt(variance) + mean, 3.0) * dof; in ChiSquareValue()
172 const double variance = 2.0 / (9 * dof); in ChiSquarePValue() local
174 if (variance != 0) { in ChiSquarePValue()
175 const double z = (chi_square_scaled - mean) / std::sqrt(variance); in ChiSquarePValue()
|
| /external/openscreen/third_party/abseil/src/absl/random/internal/ |
| D | distribution_test_util_test.cc | 162 m.variance = 1; in TEST()
167 m.variance = 1; in TEST()
172 m.variance = 100; in TEST()
180 m.variance = 1; in TEST()
185 m.variance = 1; in TEST()
190 m.variance = 100; in TEST()
|
| D | chi_square.cc | 125 const double variance = 2.0 / (9 * dof); in ChiSquareValue() local
127 if (variance != 0) { in ChiSquareValue()
128 return std::pow(z * std::sqrt(variance) + mean, 3.0) * dof; in ChiSquareValue()
172 const double variance = 2.0 / (9 * dof); in ChiSquarePValue() local
174 if (variance != 0) { in ChiSquarePValue()
175 const double z = (chi_square_scaled - mean) / std::sqrt(variance); in ChiSquarePValue()
|
| /external/abseil-cpp/absl/random/internal/ |
| D | distribution_test_util_test.cc | 162 m.variance = 1; in TEST()
167 m.variance = 1; in TEST()
172 m.variance = 100; in TEST()
180 m.variance = 1; in TEST()
185 m.variance = 1; in TEST()
190 m.variance = 100; in TEST()
|
| D | chi_square.cc | 125 const double variance = 2.0 / (9 * dof); in ChiSquareValue() local
127 if (variance != 0) { in ChiSquareValue()
128 return std::pow(z * std::sqrt(variance) + mean, 3.0) * dof; in ChiSquareValue()
172 const double variance = 2.0 / (9 * dof); in ChiSquarePValue() local
174 if (variance != 0) { in ChiSquarePValue()
175 const double z = (chi_square_scaled - mean) / std::sqrt(variance); in ChiSquarePValue()
|
| /external/rust/crates/grpcio-sys/grpc/third_party/abseil-cpp/absl/random/internal/ |
| D | distribution_test_util_test.cc | 162 m.variance = 1; in TEST()
167 m.variance = 1; in TEST()
172 m.variance = 100; in TEST()
180 m.variance = 1; in TEST()
185 m.variance = 1; in TEST()
190 m.variance = 100; in TEST()
|
| /external/angle/third_party/abseil-cpp/absl/random/internal/ |
| D | distribution_test_util_test.cc | 162 m.variance = 1; in TEST()
167 m.variance = 1; in TEST()
172 m.variance = 100; in TEST()
180 m.variance = 1; in TEST()
185 m.variance = 1; in TEST()
190 m.variance = 100; in TEST()
|
| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| 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.
|
| 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.
|
| /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()
|
| /external/tensorflow/tensorflow/python/keras/layers/ |
| D | normalization.py | 564 def _maybe_add_or_remove_bessels_correction(variance, remove=True): argument
571 return variance
573 array_ops.size(inputs) / array_ops.size(variance), variance.dtype)
576 math_ops.cast(1.0, variance.dtype)) / sample_size
579 sample_size - math_ops.cast(1.0, variance.dtype))
580 return variance * factor
588 variance=_maybe_add_or_remove_bessels_correction(
604 variance=self.moving_variance,
617 output, mean, variance = control_flow_util.smart_cond(
619 variance = _maybe_add_or_remove_bessels_correction(variance, remove=True)
[all …]
|
| /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 …]
|
| /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 …]
|