| /frameworks/av/media/libcpustats/ |
| CentralTendencyStatistics.cpp | 52 double CentralTendencyStatistics::variance() const function in class:CentralTendencyStatistics
54 double variance; local
58 variance = mM2 / (mN - 1);
60 variance = NAN;
62 mVariance = variance;
65 variance = mVariance;
67 return variance;
74 stddev = sqrt(variance());
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
| StandardDeviation.java | 26 * is the positive square root of the variance. This implementation wraps a
27 * {@link Variance} instance. The <code>isBiasCorrected</code> property of the
28 * wrapped Variance instance is exposed, so that this class can be used to
30 * bias-corrected "sample variance") or the "population standard deviation"
31 * (the square root of the non-bias-corrected "population variance"). See
32 * {@link Variance} for more information.
47 /** Wrapped Variance instance */
48 private Variance variance = null; field in class:StandardDeviation
51 * Constructs a StandardDeviation. Sets the underlying {@link Variance}
[all...] |
| Variance.java | 27 * Computes the variance of the available values. By default, the unbiased
28 * "sample variance" definitional formula is used:
30 * variance = sum((x_i - mean)^2) / (n - 1) </p>
38 * <li> The <code>getResult</code> method computes the variance using
46 * Chan, Golub, Levesque, <i>Algorithms for Computing the Sample Variance</i>,
54 * The "population variance" ( sum((x_i - mean)^2) / n ) can also
68 public class Variance extends AbstractStorelessUnivariateStatistic implements Serializable, WeightedEvaluation {
73 /** SecondMoment is used in incremental calculation of Variance*/
77 * Boolean test to determine if this Variance should also increment
78 * the second moment, this evaluates to false when this Variance i
[all...] |
| Kurtosis.java | 111 double variance = moment.m2 / (moment.n - 1); local
112 if (moment.n <= 3 || variance < 10E-20) {
119 ((n - 1) * (n -2) * (n -3) * variance * variance);
171 Variance variance = new Variance(); local
172 variance.incrementAll(values, begin, length);
173 double mean = variance.moment.m1;
174 double stdDev = FastMath.sqrt(variance.getResult())
[all...] |
| Skewness.java | 107 double variance = moment.m2 / (moment.n - 1); local
108 if (variance < 10E-20) {
113 ((n0 - 1) * (n0 -2) * FastMath.sqrt(variance) * variance);
172 final double variance = (accum - (accum2 * accum2 / length)) / (length - 1); local
179 accum3 /= variance * FastMath.sqrt(variance);
|
| /external/guava/guava-tests/benchmark/com/google/common/math/ |
| StatsBenchmark.java | 28 * Benchmarks for various algorithms for computing the mean and/or variance.
75 private final double variance; field in class:StatsBenchmark.MeanAndVariance
77 MeanAndVariance(double mean, double variance) {
79 this.variance = variance;
84 return Doubles.hashCode(mean) * 31 + Doubles.hashCode(variance);
91 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { method
97 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { method
109 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { method
126 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) method
141 abstract MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm); method in class:StatsBenchmark.VarianceAlgorithm
[all...] |
| /tools/test/connectivity/acts/tests/google/wifi/ |
| WifiNewSetupAutoJoinTest.py | 167 variance = 5
168 attn_value = [att0 + variance * 2, att1, att2, att3]
175 variance = 5
176 attn_value = [att0 + variance, att1, att2, att3]
183 variance = 5
191 variance = 5
192 attn_value = [att0 - variance, att1, att2, att3]
206 variance = 5
207 attn_value = [att0 + variance * 2, att1, att2, attn3]
214 variance =
[all...] |
| WifiAutoJoinTest.py | 183 variance = 5
184 attenuations = ([att0 + variance * 2, att1, att2],
185 [att0 + variance, att1, att2], [att0, att1, att2],
186 [att0 - variance, att1, att2])
207 variance = 5
208 attenuations = ([att0 + variance * 2, att1, att2],
209 [att0 + variance, att1, att2], [att0, att1, att2])
230 variance = 5
231 attenuations = ([att0 - variance, att1 + variance, att2]
[all...] |
| /external/libmpeg2/common/ |
| icv_variance.c | 26 * This file contains the functions to compute variance
60 * Computes variance of a given 8x4 block
63 * Compute variance of a given 8x4 block
78 * Variance
104 /* variance */
110 /* calculates the variance only for field area not frame one. */
|
| /external/autotest/server/tests/netpipe/ |
| control.srv | 19 variance - NetPIPE chooses the message sizes at regular intervals,
32 variance = 17
36 upper_bound=upper_bound, variance=variance)
|
| control.stress.srv | 21 variance - NetPIPE chooses the message sizes at regular intervals,
43 variance = 17
50 upper_bound=upper_bound, variance=variance)
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
| StatisticalSummaryValues.java | 38 /** The sample variance */
39 private final double variance; field in class:StatisticalSummaryValues
57 * @param variance the sample variance
63 public StatisticalSummaryValues(double mean, double variance, long n,
67 this.variance = variance;
113 return FastMath.sqrt(variance);
117 * @return Returns the variance.
120 return variance;
[all...] |
| SummaryStatistics.java | 26 import org.apache.commons.math.stat.descriptive.moment.Variance;
45 * default implementation for the variance can be overridden by calling
67 /** SecondMoment is used to compute the mean and variance */
91 /** variance of values that have been added */
92 protected Variance variance = new Variance(); field in class:SummaryStatistics
115 /** Variance statistic implementation - can be reset by setter. */
116 private StorelessUnivariateStatistic varianceImpl = variance;
154 // If mean, variance or geomean have been overridden
[all...] |
| /external/webrtc/webrtc/modules/audio_processing/intelligibility/ |
| intelligibility_utils_unittest.cc | 71 // Tests VarianceArray, for all variance step types.
87 EXPECT_EQ(0, variance_array.variance()[0]);
90 EXPECT_EQ(0, variance_array.variance()[0]);
100 EXPECT_GE(variance_array.variance()[j], 0.0f);
101 EXPECT_LE(variance_array.variance()[j], 1.0f);
105 EXPECT_EQ(0, variance_array.variance()[0]);
141 EXPECT_EQ(0, variance_array.variance()[j]);
143 EXPECT_NEAR(kTestVarianceBufferNotFull, variance_array.variance()[j],
146 EXPECT_NEAR(kTestVarianceBufferFull1, variance_array.variance()[j],
149 EXPECT_NEAR(kTestVarianceBufferFull2, variance_array.variance()[j]
[all...] |
| /external/webrtc/webrtc/modules/audio_processing/test/ |
| test_utils.h | 107 float ComputeSNR(const T* ref, const T* test, size_t length, float* variance) {
110 *variance = 0;
114 *variance += ref[i] * ref[i];
118 *variance /= length;
120 *variance -= mean * mean;
124 snr = 10 * log10(*variance / mse);
|
| /frameworks/av/media/libcpustats/include/cpustats/ |
| CentralTendencyStatistics.h | 45 // return the variance of all samples so far
46 double variance() const;
65 // cached variance, and n at time of caching
|
| /external/apache-commons-math/src/main/java/org/apache/commons/math/stat/regression/ |
| MultipleLinearRegression.java | 43 * Estimates the variance of the regression parameters, ie Var(b).
45 * @return The [k,k] array representing the variance of b
57 * Returns the variance of the regressand, ie Var(y).
59 * @return The double representing the variance of y
|
| GLSMultipleLinearRegression.java | 36 * whose variance is
103 * Calculates the variance on the beta.
107 * @return The beta variance matrix
118 * Calculates the estimated variance of the error term using the formula
125 * @return error variance
|
| /external/webrtc/webrtc/test/ |
| statistics.cc | 31 double Statistics::Variance() const {
38 return sqrt(Variance());
|
| /device/google/contexthub/firmware/os/algos/calibration/gyroscope/ |
| gyro_stillness_detect.h | 23 * is computed using non-overlapping windows of signal variance
48 // Variance threshold for the stillness confidence score.
51 // Delta about the variance threshold for calculation of the
72 // variance for the current window (used for stillness detection).
81 // Latest computed variance.
|
| gyro_stillness_detect.c | 33 // Set the delta about the variance threshold for calculation
41 // Set the variance threshold parameter for the stillness
58 // online mean and variance statistics:
87 // Reset current window mean and variance.
106 // Online window mean and variance ("one-pass" accumulation).
137 // Update the final calculation of window mean and variance.
161 // Define the variance thresholds.
172 // Sensor variance exceeds the upper threshold (i.e., motion detected).
180 // Sensor variance is below the lower threshold (i.e., stillness
|
| /external/autotest/client/site_tests/video_WebRtcCamera/ |
| ssim.js | 29 // square root. The latter is actually an unbiased estimate of the variance,
30 // not the exact variance.
43 return {mean : meanA, variance : accu / a.length};
70 var sigmaX2 = statsX.variance;
74 var sigmaY2 = statsY.variance;
|
| /external/autotest/client/site_tests/video_WebRtcPeerConnectionWithCamera/ |
| ssim.js | 29 // square root. The latter is actually an unbiased estimate of the variance,
30 // not the exact variance.
43 return {mean : meanA, variance : accu / a.length};
70 var sigmaX2 = statsX.variance;
74 var sigmaY2 = statsY.variance;
|
| /external/libmpeg2/common/x86/ |
| icv_variance_ssse3.c | 26 * This file contains the functions to compute variance
60 * Computes variance of a given 8x4 block
63 * Compute variance of a given 8x4 block
78 * Variance
153 /* Compute variance */
|
| /cts/apps/CameraITS/tests/scene1/ |
| test_dng_noise_model.py | 25 DIFF_THRESH = 0.0005 # absolute variance delta threshold
26 FRAC_THRESH = 0.2 # relative variance delta threshold
35 # defined as being within an absolute variance delta or relative variance
36 # delta of the expected variance, whichever is larger. This is to allow the
92 # non-uniform lighting or vignetting doesn't affect the variance
116 pylab.ylabel('Center patch variance')
|
