/external/python/cpython3/Lib/ |
H A D | statistics.py | 13 mean Arithmetic mean (average) of data. 14 harmonic_mean Harmonic mean of data. 22 Calculate the arithmetic mean ("the average") of data: 24 >>> mean([-1.0, 2.5, 3.25, 5.75]) 63 If you have previously calculated the mean, you can pass it as the optional 67 >>> mu = mean(data) 82 'mean', 'mode', 'harmonic_mean', 291 def mean(data): function 292 """Return the sample arithmetic mean o [all...] |
/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
H A D | batch_norm_op.cc | 69 // calculated mean and variance. 76 // space 1 & 2". They are used to pass the per-batch mean and 78 // them to the mean and variance calculated by BatchNormTraining. 86 // Directly send input to output as mean and variance in inference mode. 126 auto mean = ctx->Input(3); variable 151 b->BatchNormGrad(activations, scale, mean, var, grad_backprop, 177 // scratch2 = sum(y_backprop * (x - mean)) 179 b->Mul(grad_backprop, b->Sub(activations, mean, {feature_index})),
|
/external/tensorflow/tensorflow/compiler/xla/service/gpu/ |
H A D | gpu_layout_assignment_test.cc | 109 // The shape of the scale, offset, mean, and variance inputs to 128 auto* mean = builder.AddInstruction( local 129 HloInstruction::CreateParameter(3, aux_shape, "mean")); 141 {operand, scale, offset, mean, variance, epsilon, feature_index}, 247 // The shape of the scale, mean, and variance inputs to BatchNormGrad. These 270 auto* mean = builder.AddInstruction( local 271 HloInstruction::CreateParameter(2, scale_shape, "mean")); 286 {operand, scale, mean, var, grad_offset, epsilon,
|
H A D | cudnn_batchnorm_thunk.cc | 82 const BufferAllocation::Slice& mean, 89 mean_(mean), 166 // batch mean, and batch variance. We want to make our descriptors based on 212 const BufferAllocation::Slice& scale, const BufferAllocation::Slice& mean, 222 mean_(mean), 79 CudnnBatchNormForwardInferenceThunk( const BufferAllocation::Slice& operand, const BufferAllocation::Slice& scale, const BufferAllocation::Slice& offset, const BufferAllocation::Slice& mean, const BufferAllocation::Slice& variance, float epsilon, int64 feature_index, const BufferAllocation::Slice& output, const HloInstruction* hlo) argument 210 CudnnBatchNormBackwardThunk( const BufferAllocation::Slice& operand, const BufferAllocation::Slice& scale, const BufferAllocation::Slice& mean, const BufferAllocation::Slice& inv_stddev, const BufferAllocation::Slice& grad_output, float epsilon, int64 feature_index, const BufferAllocation::Slice& output_grad_data, const BufferAllocation::Slice& output_grad_scale, const BufferAllocation::Slice& output_grad_offset, const BufferAllocation::Slice& output_tuple, const HloInstruction* hlo) argument
|
/external/tensorflow/tensorflow/compiler/xla/tests/ |
H A D | literal_test_util.h | 219 // values using a normal distribution with given mean and stddev standard 226 const Shape& shape, E* engine, T mean, T stddev); 229 // values using a normal distribution with given mean and stddev standard 236 const Shape& shape, T mean, T stddev); 363 LiteralTestUtil::CreateRandomLiteral(const Shape& shape, E* engine, T mean, argument 366 std::normal_distribution<NativeT> generator(mean, stddev); 375 LiteralTestUtil::CreateRandomLiteral(const Shape& shape, T mean, T stddev) { argument 377 return CreateRandomLiteral<type>(shape, &engine, mean, stddev);
|
/external/tensorflow/tensorflow/core/kernels/ |
H A D | batch_norm_op.cc | 49 const Tensor& mean = context->input(1); variable 57 OP_REQUIRES(context, mean.dims() == 1, 58 errors::InvalidArgument("mean must be 1-dimensional", 59 mean.shape().DebugString())); 75 context->eigen_device<Device>(), input.tensor<T, 4>(), mean.vec<T>(), 99 const Tensor& mean = context->input(1); variable 107 OP_REQUIRES(context, mean.dims() == 1, 108 errors::InvalidArgument("mean must be 1-dimensional", 109 mean.shape().DebugString())); 125 {1}, 1, mean [all...] |
H A D | batch_norm_op.h | 29 typename TTypes<T>::ConstVec mean, 35 const int depth = mean.dimension(0); 54 mean.reshape(one_by_depth).broadcast(rest_by_one)) * 63 mean.reshape(one_by_depth).broadcast(rest_by_one)) * 76 typename TTypes<T>::ConstVec mean, 85 const int depth = mean.dimension(0); 122 mean.reshape(one_by_depth).broadcast(rest_by_one))) 28 operator ()(const Device& d, typename TTypes<T, 4>::ConstTensor input, typename TTypes<T>::ConstVec mean, typename TTypes<T>::ConstVec var, typename TTypes<T>::ConstVec beta, typename TTypes<T>::ConstVec gamma, T variance_epsilon, bool scale_after_normalization, typename TTypes<T, 4>::Tensor output) argument 75 operator ()(const Device& d, typename TTypes<T, 4>::ConstTensor input, typename TTypes<T>::ConstVec mean, typename TTypes<T>::ConstVec var, typename TTypes<T>::ConstVec gamma, typename TTypes<T, 4>::ConstTensor out_backprop, T variance_epsilon, bool scale_after_normalization, typename TTypes<T, 4>::Tensor dx, typename TTypes<T>::Vec dm, typename TTypes<T>::Vec dv, typename TTypes<T>::Vec db, typename TTypes<T>::Vec dg, typename TTypes<T>::Vec scratch1, typename TTypes<T>::Vec scratch2) argument
|
H A D | adjust_contrast_op.cc | 220 // Calculate the mean of the inputs. 222 // Broadcast the mean into the outputs. 230 // Reduce the mean of the inputs along the image dimension, i.e. dim_1, in a 231 // 3D tensor. Effectively means(i, k) = inputs(i, :, k).mean(). 233 typename TTypes<float, 2>::Tensor mean, 239 TTypes<float, 1>::Tensor mean_flat(&mean(0, 0), mean.size()); 255 // Sum the input(i, :, k) into mean(i, k). Repeatedly splits the input 232 ReduceMeanAcrossImage(typename TTypes<float, 3>::ConstTensor input, typename TTypes<float, 2>::Tensor mean, typename TTypes<float, 3>::Tensor scratch) argument
|
H A D | debug_ops.h | 290 double mean = std::numeric_limits<double>::quiet_NaN(); variable 341 mean = sum / non_inf_nan_count; 348 variance += (x - mean) * (x - mean); 367 output_tensor->vec<double>()(10) = mean;
|
H A D | eigen_attention.h | 165 // Initialize the glimpse with white noise: compute the mean and sigma 173 TensorFixedSize<Scalar, Sizes<> > mean; local 174 mean.device(device) = input.template chip<3>(i) 177 .mean(); 183 mean.reshape(Sizes<1, 1>()).broadcast(input_size)) 185 .mean() 196 (mean.reshape(Sizes<1, 1>()).broadcast(glimpse_size) +
|
H A D | parameterized_truncated_normal_op_gpu.cu.cc | 80 T mean = means[single_mean ? 0 : batch_id]; local 87 if (Eigen::numext::isinf(minval) || maxval < mean) { 99 const T normMin = (minval - mean) / stddev; 100 const T normMax = (maxval - mean) / stddev; 150 data[offset] = z[i] * stddev + mean; 175 data[offset] = z * stddev + mean;
|
H A D | quantized_batch_norm_op.cc | 32 const float input_max, const Tensor& mean, 40 auto mean_flat = mean.flat<T1>(); 46 const int depth = mean.dim_size(0); 95 const float input_max, const Tensor& mean, 103 auto mean_flat = mean.flat<T1>(); 109 const int depth = mean.dim_size(0); 178 const Tensor& mean = context->input(3); variable 194 OP_REQUIRES(context, mean.dims() == 1, 195 errors::InvalidArgument("mean must be 1-dimensional", 196 mean 31 ReferenceBatchNorm(const Tensor& input, const float input_min, const float input_max, const Tensor& mean, float mean_min, float mean_max, const Tensor& var, float var_min, float var_max, const Tensor& beta, float beta_min, float beta_max, const Tensor& gamma, float gamma_min, float gamma_max, float variance_epsilon, bool scale_after_normalization, Tensor* output, float* output_min, float* output_max) argument 94 FixedPointBatchNorm(const Tensor& input, const float input_min, const float input_max, const Tensor& mean, float mean_min, float mean_max, const Tensor& var, float var_min, float var_max, const Tensor& beta, float beta_min, float beta_max, const Tensor& gamma, float gamma_min, float gamma_max, float variance_epsilon, bool scale_after_normalization, Tensor* output, float* output_min, float* output_max) argument [all...] |
/external/tensorflow/tensorflow/stream_executor/cuda/ |
H A D | cuda_rng.cc | 209 bool CUDARng::DoPopulateRandGaussianInternal(Stream *stream, ElemT mean, argument 221 func(parent_, rng_, CUDAMemoryMutable(v), element_count, mean, stddev); 232 bool CUDARng::DoPopulateRandGaussian(Stream *stream, float mean, float stddev, argument 234 return DoPopulateRandGaussianInternal(stream, mean, stddev, v, 238 bool CUDARng::DoPopulateRandGaussian(Stream *stream, double mean, double stddev, argument 240 return DoPopulateRandGaussianInternal(stream, mean, stddev, v,
|
/external/walt/android/WALT/app/src/main/java/org/chromium/latency/walt/ |
H A D | Utils.java | 46 public static double mean(double[] x) { method in class:Utils 84 double m = mean(a);
|
/external/webrtc/webrtc/base/ |
H A D | profiler.h | 97 double mean() const { return mean_; } function in class:rtc::ProfilerEvent
|
H A D | random_unittest.cc | 60 // Expect the result to be within 3 standard deviations of the mean. 141 // Expect the result to be within 3 standard deviations of the mean, 142 // or more generally, within sigma_level standard deviations of the mean. 143 double mean = static_cast<double>(samples) / bucket_count; local 144 EXPECT_NEAR(buckets[i], mean, sigma_level * sqrt(mean)); 181 // Expect the result to be within 3 standard deviations of the mean, 182 // or more generally, within sigma_level standard deviations of the mean. 183 double mean = static_cast<double>(samples) / bucket_count; local 184 EXPECT_NEAR(buckets[i], mean, sigma_leve 235 double mean = static_cast<double>(samples) / bucket_count; local [all...] |
H A D | rollingaccumulator.h | 125 // between (0.0, 1.0], otherwise the non-weighted mean is returned. 153 double mean = sum_ * count_inv; local 154 return mean_2 - (mean * mean);
|
/external/guava/guava/src/com/google/common/math/ |
H A D | LongMath.java | 748 * Returns the arithmetic mean of {@code x} and {@code y}, rounded toward 753 public static long mean(long x, long y) { method in class:LongMath 754 // Efficient method for computing the arithmetic mean.
|
/external/libchrome/base/trace_event/ |
H A D | memory_dump_scheduler.cc | 237 uint64_t mean = 0; local 246 mean += polling_state_->last_memory_totals_kb[i]; 248 mean = mean / PollingTriggerState::kMaxNumMemorySamples; 251 variance += (polling_state_->last_memory_totals_kb[i] - mean) * 252 (polling_state_->last_memory_totals_kb[i] - mean); 261 bool is_stddev_low = variance < mean / 500 * mean / 500; 265 // (mean + 3.69 * stddev) corresponds to a value that is higher than current 267 return (current_memory_total_kb - mean) * (current_memory_total_k [all...] |
/external/mesa3d/src/gallium/auxiliary/util/ |
H A D | u_cache.c | 295 double mean = (double)cache->count/(double)cache->size; local 296 double stddev = sqrt(mean); 299 double z = fabs(cache->entries[i].count - mean)/stddev;
|
/external/opencv/cxcore/src/ |
H A D | cxmean.cpp | 165 mean[0] = scale*(double)tmp##0 171 mean[0] = t0; \ 172 mean[1] = t1 179 mean[0] = t0; \ 180 mean[1] = t1; \ 181 mean[2] = t2 187 mean[0] = t0; \ 188 mean[1] = t1; \ 191 mean[2] = t0; \ 192 mean[ 384 CvScalar mean = {{0,0,0,0}}; local [all...] |
/external/tensorflow/tensorflow/contrib/lite/kernels/ |
H A D | mean.cc | 27 namespace mean { namespace in namespace:tflite::ops::builtin 220 } // namespace mean 223 static TfLiteRegistration r = {mean::Init, mean::Free, mean::Prepare, 224 mean::Eval<mean::kReference>};
|
/external/tensorflow/tensorflow/python/kernel_tests/ |
H A D | parameterized_truncated_normal_op_test.py | 40 mean = None variable in class:TruncatedNormalMoments 45 def __init__(self, mean, stddev, minval, maxval): 47 self.mean = np.double(mean) 69 dist = scipy.stats.norm(loc=self.mean, scale=self.stddev) 76 m = ((k - 1) * self.stddev**2 * m_k_minus_2 + self.mean * m_k_minus_1 - 112 def validateMoments(self, shape, mean, stddev, minval, maxval, seed=1618): 119 samples = random_ops.parameterized_truncated_normal(shape, mean, stddev, 124 expected_moments = TruncatedNormalMoments(mean, stddev, minval, maxval) 134 mean, [all...] |
/external/tensorflow/tensorflow/tools/graph_transforms/ |
H A D | fold_old_batch_norms.cc | 72 Tensor mean = GetNodeTensorAttr(mean_node, "value"); local 79 const int64 num_cols = mean.shape().dim_size(0); 102 (-mean.flat<float>()(i) * (*scale_values)[i]) + beta.flat<float>()(i);
|
/external/webrtc/webrtc/common_audio/ |
H A D | audio_converter_unittest.cc | 59 float mean = 0; local 65 mean += ref.channels()[i][j]; 72 mean /= length; 73 variance -= mean * mean;
|