/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/ |
H A D | eval_param.pass.cpp | 49 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local 55 double dbl = (u[i] - mean); 66 double x_mean = p.mean(); 67 double x_var = p.mean(); 70 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 90 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local 96 double dbl = (u[i] - mean); 107 double x_mean = p.mean(); 108 double x_var = p.mean(); 111 assert(std::abs((mean 131 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local [all...] |
/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/ |
H A D | eval_param.pass.cpp | 50 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local 56 double dbl = (u[i] - mean); 75 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 95 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local 101 double dbl = (u[i] - mean); 120 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 140 double mean = std::accumulate(u.begin(), u.end(), 0.0) / u.size(); local 146 double dbl = (u[i] - mean); 165 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
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/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/ |
H A D | eval.pass.cpp | 48 double mean = std::accumulate(u.begin(), u.end(), local 55 double dbl = (u[i] - mean); 71 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 89 double mean = std::accumulate(u.begin(), u.end(), 96 double dbl = (u[i] - mean); 112 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 130 double mean = std::accumulate(u.begin(), u.end(), 137 double dbl = (u[i] - mean); 153 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 171 double mean [all...] |
H A D | eval_param.pass.cpp | 48 double mean = std::accumulate(u.begin(), u.end(), local 55 double dbl = (u[i] - mean); 71 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
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/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/ |
H A D | eval.pass.cpp | 48 D::result_type mean = std::accumulate(u.begin(), u.end(), local 55 D::result_type dbl = (u[i] - mean); 70 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 88 D::result_type mean = std::accumulate(u.begin(), u.end(), 95 D::result_type dbl = (u[i] - mean); 110 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 128 D::result_type mean = std::accumulate(u.begin(), u.end(), 135 D::result_type dbl = (u[i] - mean); 150 assert(std::abs((mean - x_mean) / x_mean) < 0.01); 168 D::result_type mean [all...] |
H A D | eval_param.pass.cpp | 48 D::result_type mean = std::accumulate(u.begin(), u.end(), local 55 D::result_type dbl = (u[i] - mean); 70 assert(std::abs((mean - x_mean) / x_mean) < 0.01);
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/external/opencv/cv/src/ |
H A D | cvadapthresh.cpp | 48 CvMat* mean = 0; local 70 mean = dst; 72 CV_CALL( mean = cvCreateMat( rows, cols, CV_8UC1 )); 74 CV_CALL( cvSmooth( src, mean, method == CV_ADAPTIVE_THRESH_MEAN_C ? 89 const uchar* m = mean->data.ptr + i*mean->step; 98 if( mean != dst ) 99 cvReleaseMat( &mean );
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/external/opencv/ml/src/ |
H A D | mltestset.cpp | 65 CvMat* mean = NULL; local 107 CV_CALL( mean = cvCreateMat( 1, num_features, CV_32FC1 ) ); 108 CV_CALL( cvSetZero( mean ) ); 114 CV_CALL( cvRandMVNormal( mean, cov, *samples ) ); 165 cvReleaseMat( &mean );
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/external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
H A D | random_ops.cc | 105 // Normal distribution with a mean of 0 and a standard deviation of 1: 134 xla::ComputationDataHandle mean = XlaHelpers::Zero(b, dtype); variable 137 b->RngNormal(mean, stddev, xla_shape); 151 // while (any(candidate < mean-2*sd || candidate > mean+2*sd)) { 152 // out_of_range_mask := candidate < mean-2*sd || candidate > mean+2*sd 172 xla::ComputationDataHandle mean = XlaHelpers::Zero(b, dtype); variable 174 b->Select(to_resample, b->RngNormal(mean, stddev, xla_shape), candidate);
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/external/tensorflow/tensorflow/core/grappler/costs/ |
H A D | robust_stats.cc | 88 // Computes an updated mean using Huber's weighting function (values beyond 89 // the margin are weighted by margin / abs(value - mean). 90 double UpdateHuberMean(const std::vector<double> &sorted_values, double mean, argument 96 if (d < mean - margin) { 98 } else if (d > mean + margin) { 108 // the Huber mean drifts slightly off the median and there are no values 109 // within the margin. In that case, just return the old mean, and the caller 114 return mean; 119 // uses it to compute a Huber robust mean (sandwich mean) [all...] |
/external/tensorflow/tensorflow/examples/android/jni/object_tracking/ |
H A D | utils_neon.cc | 64 "Neon mismatch with CPU mean! %.10f vs %.10f", 73 const int num_vals, const float mean) { 77 const float32x4_t mean_vec = vdupq_n_f32(-mean); 94 squared_sum += Square(values[offset] - mean); 100 const float std_dev_cpu = ComputeStdDevCpu(values, num_vals, mean); 72 ComputeStdDevNeon(const float* const values, const int num_vals, const float mean) argument
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/external/webp/src/utils/ |
H A D | filters_utils.c | 38 int mean = p[0]; local 40 const int diff0 = SDIFF(p[i], mean); 50 mean = (3 * mean + p[i] + 2) >> 2;
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/external/webrtc/webrtc/modules/audio_processing/test/ |
H A D | test_utils.h | 109 float mean = 0; local 115 mean += ref[i]; 119 mean /= length; 120 *variance -= mean * mean;
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/external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
H A D | ExponentialDistributionImpl.java | 43 /** The mean of this distribution. */ 44 private double mean; field in class:ExponentialDistributionImpl 50 * Create a exponential distribution with the given mean. 51 * @param mean mean of this distribution. 53 public ExponentialDistributionImpl(double mean) { argument 54 this(mean, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); 58 * Create a exponential distribution with the given mean. 59 * @param mean mean o 64 ExponentialDistributionImpl(double mean, double inverseCumAccuracy) argument 77 setMean(double mean) argument [all...] |
H A D | NormalDistributionImpl.java | 49 /** The mean of this distribution. */ 50 private double mean = 0; field in class:NormalDistributionImpl 59 * Create a normal distribution using the given mean and standard deviation. 60 * @param mean mean for this distribution 63 public NormalDistributionImpl(double mean, double sd){ argument 64 this(mean, sd, DEFAULT_INVERSE_ABSOLUTE_ACCURACY); 68 * Create a normal distribution using the given mean, standard deviation and 71 * @param mean mean fo 76 NormalDistributionImpl(double mean, double sd, double inverseCumAccuracy) argument 105 setMean(double mean) argument [all...] |
H A D | PoissonDistributionImpl.java | 55 * Holds the Poisson mean for the distribution. 57 private double mean; field in class:PoissonDistributionImpl 73 * Create a new Poisson distribution with the given the mean. The mean value 76 * @param p the Poisson mean 84 * Create a new Poisson distribution with the given mean, convergence criterion 87 * @param p the Poisson mean 99 * Create a new Poisson distribution with the given mean and convergence criterion. 101 * @param p the Poisson mean 111 * Create a new Poisson distribution with the given mean an [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/ |
H A D | StatUtils.java | 60 /** mean */ 69 /** geometric mean */ 222 * Returns the arithmetic mean of the entries in the input array, or 231 * @return the mean of the values or Double.NaN if the array is empty 234 public static double mean(final double[] values) { method in class:StatUtils 239 * Returns the arithmetic mean of the entries in the specified portion of 251 * @return the mean of the values or Double.NaN if length = 0 255 public static double mean(final double[] values, final int begin, method in class:StatUtils 261 * Returns the geometric mean of the entries in the input array, or 270 * @return the geometric mean o 370 variance(final double[] values, final double mean, final int begin, final int length) argument 397 variance(final double[] values, final double mean) argument [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
H A D | Variance.java | 30 * variance = sum((x_i - mean)^2) / (n - 1) </p> 32 * where mean is the {@link Mean} and <code>n</code> is the number 54 * The "population variance" ( sum((x_i - mean)^2) / n ) can also 252 Mean mean = new Mean(); 253 double m = mean.evaluate(values, begin, length); 268 * where weightedMean is the weighted mean</p> 312 Mean mean = new Mean(); 313 double m = mean.evaluate(values, weights, begin, length); 327 * where weightedMean is the weighted mean</p> 364 * the input array, using the precomputed mean valu 388 evaluate(final double[] values, final double mean, final int begin, final int length) argument 441 evaluate(final double[] values, final double mean) argument 490 evaluate(final double[] values, final double[] weights, final double mean, final int begin, final int length) argument 564 evaluate(final double[] values, final double[] weights, final double mean) argument [all...] |
/external/eigen/bench/btl/data/ |
H A D | mean.cxx | 2 // File : mean.cxx 112 cout << " <TH ALIGN=CENTER> <a href=""#mean_marker""> in cache <BR> mean perf <BR> Mflops </a></TH>" << endl ; 114 cout << " <TH ALIGN=CENTER> <a href=""#mean_marker""> out of cache <BR> mean perf <BR> Mflops </a></TH>" << endl ; 157 double mean=0.0; local 165 mean+=tab_mflops[i]; 173 INFOS("no data for mean calculation"); 177 return mean/nb_sample;
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/external/fio/ |
H A D | steadystate.c | 133 double mean; local 162 mean = (double) ss->sum_y / ss->dur; 167 diff = ss->iops_data[i] - mean; 169 diff = ss->bw_data[i] - mean; 174 ss->criterion = 100.0 * ss->deviation / mean; 178 dprint(FD_STEADYSTATE, "sum_y: %llu, mean: %f, max diff: %f, " 180 (unsigned long long) ss->sum_y, mean,
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/external/google-benchmark/src/ |
H A D | statistics.cc | 64 const auto mean = StatisticsMean(v); local 65 if (v.size() == 0) return mean; 72 return Sqrt(v.size() / (v.size() - 1.0) * (avg_squares - Sqr(mean)));
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/external/guava/guava/src/com/google/common/math/ |
H A D | DoubleMath.java | 390 private double mean = 0.0; field in class:DoubleMath.MeanAccumulator 396 mean += (value - mean) / count; 399 double mean() { method in class:DoubleMath.MeanAccumulator 400 checkArgument(count > 0, "Cannot take mean of 0 values"); 401 return mean; 406 * Returns the arithmetic mean of the values. There must be at least one value, and they must all 410 public static double mean(double... values) { method in class:DoubleMath 415 return accumulator.mean(); 419 * Returns the arithmetic mean o 423 public static double mean(int... values) { method in class:DoubleMath 437 public static double mean(long... values) { method in class:DoubleMath 451 public static double mean(Iterable<? extends Number> values) { method in class:DoubleMath 465 public static double mean(Iterator<? extends Number> values) { method in class:DoubleMath [all...] |
H A D | IntMath.java | 571 * Returns the arithmetic mean of {@code x} and {@code y}, rounded towards 576 public static int mean(int x, int y) { method in class:IntMath 577 // Efficient method for computing the arithmetic mean.
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/external/guava/guava-gwt/src-super/com/google/common/math/super/com/google/common/math/ |
H A D | IntMath.java | 418 * Returns the arithmetic mean of {@code x} and {@code y}, rounded towards 423 public static int mean(int x, int y) { method in class:IntMath 424 // Efficient method for computing the arithmetic mean.
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/external/libcxx/utils/google-benchmark/src/ |
H A D | statistics.cc | 64 const auto mean = StatisticsMean(v); local 65 if (v.size() == 0) return mean; 72 return Sqrt(v.size() / (v.size() - 1.0) * (avg_squares - Sqr(mean)));
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