| /external/apache-commons-math/src/main/java/org/apache/commons/math/random/ |
| UncorrelatedRandomVectorGenerator.java | 39 /** Mean vector. */
40 private final double[] mean; field in class:UncorrelatedRandomVectorGenerator
47 * its mean and standard deviation vectors.</p>
48 * @param mean expected mean values for each component
53 public UncorrelatedRandomVectorGenerator(double[] mean,
56 if (mean.length != standardDeviation.length) {
57 throw new DimensionMismatchException(mean.length, standardDeviation.length);
59 this.mean = mean.clone()
[all...] |
| /external/apache-commons-math/src/main/java/org/apache/commons/math/distribution/ |
| 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) {
54 this(mean, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
58 * Create a exponential distribution with the given mean.
59 * @param mean mean of this distribution
[all...] |
| 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){
64 this(mean, sd, DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
68 * Create a normal distribution using the given mean, standard deviation and
71 * @param mean mean for this distributio
[all...] |
| ExponentialDistribution.java | 34 * Modify the mean.
35 * @param mean the new mean.
39 void setMean(double mean);
42 * Access the mean.
43 * @return the mean.
|
| NormalDistribution.java | 35 * Access the mean.
36 * @return mean for this distribution
40 * Modify the mean.
41 * @param mean for this distribution
45 void setMean(double mean);
|
| /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/ |
| ctor_double.pass.cpp | 25 assert(d.mean() == 1);
30 assert(d.mean() == 3.5);
|
| param_ctor.pass.cpp | 27 assert(p.mean() == 1);
33 assert(p.mean() == 10);
|
| ctor_param.pass.cpp | 27 assert(d.mean() == 0.25);
|
| param_assign.pass.cpp | 29 assert(p.mean() == .7);
|
| param_copy.pass.cpp | 28 assert(p.mean() == .125);
|
| /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.pois/rand.dist.pois.poisson/ |
| ctor_double.pass.cpp | 25 assert(d.mean() == 1);
30 assert(d.mean() == 3.5);
|
| param_ctor.pass.cpp | 27 assert(p.mean() == 1);
33 assert(p.mean() == 10);
|
| /external/guava/guava-tests/benchmark/com/google/common/math/ |
| StatsBenchmark.java | 28 * Benchmarks for various algorithms for computing the mean and/or variance.
37 double mean(double[] values) { method
47 double mean(double[] values) { method
61 double mean(double[] values) { method
62 double mean = values[0]; local
64 mean = mean + (values[i] - mean) / (i + 1);
66 return mean;
70 abstract double mean(double[] values) method in class:StatsBenchmark.MeanAlgorithm
74 private final double mean; field in class:StatsBenchmark.MeanAndVariance
98 double mean = meanAlgorithm.mean(values); local
110 double mean = meanAlgorithm.mean(values); local
130 double mean = values[0]; local
[all...] |
| /external/libcxx/test/std/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.normal/ |
| ctor_double_double.pass.cpp | 15 // explicit normal_distribution(result_type mean = 0, result_type stddev = 1);
25 assert(d.mean() == 0);
31 assert(d.mean() == 14.5);
37 assert(d.mean() == 14.5);
|
| param_ctor.pass.cpp | 27 assert(p.mean() == 0);
34 assert(p.mean() == 10);
41 assert(p.mean() == 10);
|
| ctor_param.pass.cpp | 27 assert(d.mean() == 0.25);
|
| /ndk/sources/cxx-stl/llvm-libc++/libcxx/test/numerics/rand/rand.dis/rand.dist.norm/rand.dist.norm.normal/ |
| ctor_double_double.pass.cpp | 15 // explicit normal_distribution(result_type mean = 0, result_type stddev = 1);
25 assert(d.mean() == 0);
31 assert(d.mean() == 14.5);
37 assert(d.mean() == 14.5);
|
| param_ctor.pass.cpp | 27 assert(p.mean() == 0);
34 assert(p.mean() == 10);
41 assert(p.mean() == 10);
|
| ctor_param.pass.cpp | 27 assert(d.mean() == 0.25);
|
| /external/webrtc/webrtc/common_audio/vad/ |
| vad_gmm.h | 19 // normal distribution with mean and standard deviation (|mean|, |std|).
23 // - mean : mean input in the statistical model, Q7.
29 // |delta| = (|input| - |mean|) / |std|^2.
33 // 1 / |std| * exp(-(|input| - |mean|)^2 / (2 * |std|^2));
35 int16_t mean,
|
| /external/webrtc/webrtc/modules/audio_processing/vad/ |
| gmm.h | 27 // where kth row is the mean of the kth mixture.
28 const double* mean; member in struct:webrtc::GmmParameters
|
| /external/opencv3/modules/ml/test/ |
| test_mltests.cpp | 75 float mean = 0, sigma = 0; local
78 mean += res[k];
80 mean = mean /icount;
83 sigma += (res[k] - mean)*(res[k] - mean);
86 printf("%f, %f\n", mean, sigma);
95 float mean, sigma; local
102 resultNode["mean"] >> mean; local
[all...] |
| /external/opencv3/modules/core/src/ |
| pca.cpp | 98 mean.create( mean_sz, ctype );
105 _mean.convertTo(mean, ctype);
109 calcCovarMatrix( data, covar, mean, covar_flags, ctype );
116 Mat tmp_data, tmp_mean = repeat(mean, data.rows/mean.rows, data.cols/mean.cols);
117 if( data.type() != ctype || tmp_mean.data == mean.data )
158 fs << "mean" << mean; local
169 cv::read(fs["mean"], mean)
[all...] |
| /external/valgrind/VEX/test/ |
| rounderr.c | 43 /* Compute the arithmetic mean of a dataset using the recurrence relation
44 mean_(n) = mean(n-1) + (data[n] - mean(n-1))/(n+1) */
45 long double mean = 0; local
50 mean += (data[i * stride] - mean) / (i + 1);
52 return mean;
58 /* Compute the arithmetic mean of a dataset using the
|
| /libcore/support/src/test/java/tests/util/ |
| SummaryStatistics.java | 47 /** Mean of the values seen. */
48 public double mean() { method in class:SummaryStatistics
54 return (squaresSum / numValues) - square(mean());
64 return stddev() / mean();
71 sb.append(",mean=");
72 sb.append(mean()); method
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