Searched defs:mean (Results 51 - 75 of 182) sorted by relevance

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/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/
H A Deval_param.pass.cpp49 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
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/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.weibull/
H A Deval_param.pass.cpp50 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);
/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.int/
H A Deval.pass.cpp48 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
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H A Deval_param.pass.cpp48 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);
/external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.uni/rand.dist.uni.real/
H A Deval.pass.cpp48 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
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H A Deval_param.pass.cpp48 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);
/external/opencv/cv/src/
H A Dcvadapthresh.cpp48 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 );
/external/opencv/ml/src/
H A Dmltestset.cpp65 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 );
/external/tensorflow/tensorflow/compiler/tf2xla/kernels/
H A Drandom_ops.cc105 // 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);
/external/tensorflow/tensorflow/core/grappler/costs/
H A Drobust_stats.cc88 // 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)
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/external/tensorflow/tensorflow/examples/android/jni/object_tracking/
H A Dutils_neon.cc64 "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
/external/webp/src/utils/
H A Dfilters_utils.c38 int mean = p[0]; local
40 const int diff0 = SDIFF(p[i], mean);
50 mean = (3 * mean + p[i] + 2) >> 2;
/external/webrtc/webrtc/modules/audio_processing/test/
H A Dtest_utils.h109 float mean = 0; local
115 mean += ref[i];
119 mean /= length;
120 *variance -= mean * mean;
/external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/
H A DExponentialDistributionImpl.java43 /** 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
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H A DNormalDistributionImpl.java49 /** 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
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H A DPoissonDistributionImpl.java55 * 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
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/
H A DStatUtils.java60 /** 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
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/
H A DVariance.java30 * 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
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/external/eigen/bench/btl/data/
H A Dmean.cxx2 // 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;
/external/fio/
H A Dsteadystate.c133 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,
/external/google-benchmark/src/
H A Dstatistics.cc64 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)));
/external/guava/guava/src/com/google/common/math/
H A DDoubleMath.java390 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
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H A DIntMath.java571 * 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.
/external/guava/guava-gwt/src-super/com/google/common/math/super/com/google/common/math/
H A DIntMath.java418 * 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.
/external/libcxx/utils/google-benchmark/src/
H A Dstatistics.cc64 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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