/external/webrtc/webrtc/modules/audio_coding/test/ |
H A D | utility.cc | 235 int16_t CircularBuffer::ArithMean(double& mean) { argument 240 mean = _sum / (double) _buffLen; 244 mean = _sum / (double) _idx;
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/external/webrtc/webrtc/modules/audio_processing/intelligibility/ |
H A D | intelligibility_utils.cc | 43 complex<float> NewMean(complex<float> mean, complex<float> data, size_t count) { argument 44 return mean + (data - mean) / static_cast<float>(count); 47 void AddToMean(complex<float> data, size_t count, complex<float>* mean) { argument 48 (*mean) = NewMean(*mean, data, count); 163 complex<float> mean; local 168 mean = history_[i][history_cursor_]; 175 complex<float> old_mean = mean; 177 mean [all...] |
/external/ImageMagick/Magick++/lib/ |
H A D | Statistic.cpp | 357 double Magick::ChannelStatistics::mean() const function in class:Magick::ChannelStatistics 410 _mean(channelStatistics_->mean),
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/external/ImageMagick/MagickCore/ |
H A D | statistic.h | 41 mean, member in struct:_ChannelStatistics
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H A D | threshold.c | 177 % o bias: the mean bias. 308 mean; 333 mean=(double) (channel_sum[channel]/number_pixels+bias); 335 p[center+i] <= mean ? 0 : QuantumRange),q); 302 mean; local
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/external/apache-commons-math/src/main/java/org/apache/commons/math/random/ |
H A D | RandomDataImpl.java | 347 * The Poisson process (and hence value returned) is bounded by 1000 * mean.</li> 353 * @param mean mean of the Poisson distribution. 355 * @throws NotStrictlyPositiveException if {@code mean <= 0}. 357 public long nextPoisson(double mean) { argument 358 if (mean <= 0) { 359 throw new NotStrictlyPositiveException(LocalizedFormats.MEAN, mean); 365 if (mean < pivot) { 366 double p = FastMath.exp(-mean); 371 while (n < 1000 * mean) { 482 nextExponential(double mean) argument [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
H A D | SummaryStatistics.java | 67 /** SecondMoment is used to compute the mean and variance */ 88 /** mean of values that have been added */ 89 protected Mean mean = new Mean(); field in class:SummaryStatistics 109 /** Geometric mean statistic implementation - can be reset by setter. */ 113 private StorelessUnivariateStatistic meanImpl = mean; 154 // If mean, variance or geomean have been overridden, 196 * Returns the mean of the values that have been added. 200 * @return the mean 203 if (mean == meanImpl) { 267 * Returns the geometric mean o [all...] |
/external/eigen/Eigen/src/Core/ |
H A D | Redux.h | 445 * \sa trace(), prod(), mean() 456 /** \returns the mean of all coefficients of *this 462 DenseBase<Derived>::mean() const function in class:Eigen::DenseBase 479 * \sa sum(), mean(), trace()
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H A D | VectorwiseOp.h | 105 EIGEN_MEMBER_FUNCTOR(mean, (Size-1)*NumTraits<Scalar>::AddCost + NumTraits<Scalar>::MulCost); 401 /** \returns a row (or column) vector expression of the mean 404 * \sa DenseBase::mean() */ 406 const MeanReturnType mean() const function in class:Eigen::VectorwiseOp
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/external/harfbuzz_ng/test/shaping/ |
H A D | hb_test_tools.py | 258 def mean (self): member in class:Stats 276 return (self.mean () - population.mean ()) / population.stddev ()
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/external/libopus/src/ |
H A D | mlp_train.c | 95 double mean = 0; local 98 mean += outputs[i*outDim+j]; 99 mean /= nbSamples; 101 net->weights[nbLayers-2][j*(topo[nbLayers-2]+1)] = mean;
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/external/libvpx/libvpx/vpx_dsp/ |
H A D | avg.c | 169 int sse = 0, mean = 0, var; local 173 mean += diff; // mean: dynamic range 16 bits. 177 // (mean * mean): dynamic range 31 bits. 178 var = sse - ((mean * mean) >> (bwl + 2));
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/external/libvpx/libvpx/vpx_dsp/x86/ |
H A D | avg_intrin_sse2.c | 386 int16_t mean; local 421 mean = _mm_extract_epi16(sum, 0); 423 return _mm_cvtsi128_si32(sse) - ((mean * mean) >> (bwl + 2));
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/external/ltp/testcases/misc/math/fptests/ |
H A D | fptest01.c | 134 dtw = 1. / nproc; /* mean process work time */ 337 static double mean; variable 344 mean = m; 367 return (mean + stdev * x1); 371 return (mean + stdev * x2);
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H A D | fptest02.c | 133 dtw = 1. / nproc; /* mean process work time */ 318 static double mean; variable 327 mean = m; 342 return (mean + stdev * x1); 346 return (mean + stdev * x2);
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/external/opencv/cv/src/ |
H A D | _cvkdtree.hpp | 91 accum_type mean = 0; local 93 mean += deref(ctor(*k), j); 94 mean /= last - first; 97 accum_type diff = accum_type(deref(ctor(*k), j)) - mean;
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/external/opencv/cxcore/src/ |
H A D | cxmeansdv.cpp | 314 mean[idx] = tmp = scale*(double)total##idx; \ 378 CvSize size, double* mean, double* sdv ), \ 379 (src, step, size, mean, sdv) ) \ 413 CvSize size, double* mean, double* sdv ), \ 414 (src, step, size, mean, sdv) ) \ 438 double* mean, double* sdv ) \ 473 int cn, int coi, double* mean, double* sdv )\ 498 CvSize size, double* mean, double* sdv ), \ 499 (src, step, mask, maskstep, size, mean, sdv))\ 533 CvSize size, double* mean, doubl 678 CvScalar mean = {{0,0,0,0}}; local [all...] |
/external/tensorflow/tensorflow/examples/android/jni/object_tracking/ |
H A D | utils.h | 236 const float mean); 247 // Get mean. 267 const float mean) { 271 squared_sum += Square(values[i] - mean); 279 const float mean) { 282 (num_vals >= 8) ? ComputeStdDevNeon(values, num_vals, mean) : 284 ComputeStdDevCpu(values, num_vals, mean); 329 // Find the mean and then subtract so that the new mean is 0.0. 330 const float mean local 265 ComputeStdDevCpu(const float* const values, const int num_vals, const float mean) argument 277 ComputeStdDev(const float* const values, const int num_vals, const float mean) argument [all...] |
/external/tensorflow/tensorflow/python/ops/ |
H A D | metrics_impl.py | 268 @tf_export('metrics.mean') 269 def mean(values, function 274 """Computes the (weighted) mean of the given values. 276 The `mean` function creates two local variables, `total` and `count` 278 returned as `mean` which is an idempotent operation that simply divides 282 `update_op` operation that updates these variables and returns the `mean`. 293 metrics_collections: An optional list of collections that `mean` 300 mean: A `Tensor` representing the current mean, the value of `total` divided 312 raise RuntimeError('tf.metrics.mean i [all...] |
/external/deqp/modules/gles2/performance/ |
H A D | es2pDrawCallBatchingTests.cpp | 788 double mean; member in struct:deqp::gles2::Performance::__anon5023::Statistics 795 double mean = 0.0; local 798 mean += (double)samples[i]; 800 mean /= (double)samples.size(); 807 standardDeviation += (x - mean) * (x - mean); 817 stats.mean = mean; 880 log << TestLog::Message << "Batched samples; Count: " << m_batchedSamplesUs.size() << ", Mean: " << batchedStats.mean << "us, Standard deviation: " << batchedStats.standardDeviation << "us, Standard error of mean [all...] |
/external/deqp/modules/glshared/ |
H A D | glsStateChangePerfTestCases.cpp | 58 double mean; member in struct:deqp::gls::__anon5527::ResultStats 74 result.mean = ((double)sum) / (double)values.size(); 79 result.variance += (val - result.mean) * (val - result.mean); 546 log << TestLog::Message << "Interleaved mean: " << interleaved.mean << TestLog::EndMessage; 552 log << TestLog::Message << "Batched mean: " << batched.mean << TestLog::EndMessage; 558 log << TestLog::Message << "Batched/Interleaved mean ratio: " << (interleaved.mean/batche [all...] |
/external/fio/ |
H A D | gettime.c | 263 double delta, mean, S; local 268 S = delta = mean = 0.0; 271 delta = cycles[i] - mean; 273 mean += delta / (i + 1.0); 274 S += delta * (cycles[i] - mean); 295 if ((fmax(this, mean) - fmin(this, mean)) > S) 308 dprint(FD_TIME, "min=%llu, max=%llu, mean=%f, S=%f\n", 310 (unsigned long long) maxc, mean, S);
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H A D | iolog.h | 17 fio_fp64_t mean; member in struct:io_stat
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/external/tensorflow/tensorflow/compiler/xla/ |
H A D | reference_util.cc | 409 const Array4D<float>& input, const Array4D<float>& mean, 413 *MapArray4D(input, mean, [](float a, float b) { return a - b; }); 408 BatchNorm4D( const Array4D<float>& input, const Array4D<float>& mean, const Array4D<float>& var, const Array4D<float>& scale, const Array4D<float>& offset, float epsilon) argument
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/external/tensorflow/tensorflow/compiler/xla/service/ |
H A D | batchnorm_expander.cc | 232 auto mean = add(HloInstruction::CreateBinary( local 236 HloInstruction::CreateBroadcast(operand_shape, mean, {feature_index})); 244 feature_shape, HloOpcode::kMultiply, mean, mean)); 283 auto tuple = HloInstruction::CreateTuple({shifted_normalized, mean, var}); 317 HloInstruction* mean = batch_norm->mutable_operand(3); local 349 HloInstruction::CreateBroadcast(operand_shape, mean, {feature_index})); 406 // sum(output_grad * (activation - mean(activation))) * rsqrt(var + epsilon) 413 // (N * output_grad - sum(output_grad) - (activation - mean(activation)) * 414 // sum(output_grad * (activation - mean(activatio 432 HloInstruction* mean = batch_norm->mutable_operand(2); local [all...] |