/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/moment/ |
H A D | StandardDeviation.java | 26 * is the positive square root of the variance. This implementation wraps a 30 * bias-corrected "sample variance") or the "population standard deviation" 31 * (the square root of the non-bias-corrected "population variance"). See 48 private Variance variance = null; field in class:StandardDeviation 55 variance = new Variance(); 64 variance = new Variance(m2); 84 * @param isBiasCorrected whether or not the variance computation will use 88 variance = new Variance(isBiasCorrected); 98 * @param isBiasCorrected whether or not the variance computation will use 103 variance [all...] |
H A D | Kurtosis.java | 111 double variance = moment.m2 / (moment.n - 1); 112 if (moment.n <= 3 || variance < 10E-20) { 119 ((n - 1) * (n -2) * (n -3) * variance * variance); 171 Variance variance = new Variance(); 172 variance.incrementAll(values, begin, length); 173 double mean = variance.moment.m1; 174 double stdDev = FastMath.sqrt(variance.getResult());
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H A D | Skewness.java | 107 double variance = moment.m2 / (moment.n - 1); 108 if (variance < 10E-20) { 113 ((n0 - 1) * (n0 -2) * FastMath.sqrt(variance) * variance); 172 final double variance = (accum - (accum2 * accum2 / length)) / (length - 1); 179 accum3 /= variance * FastMath.sqrt(variance);
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/external/linux-tools-perf/src/tools/perf/util/ |
H A D | stat.c | 44 double variance, variance_mean; local 49 variance = stats->M2 / (stats->n - 1); 50 variance_mean = variance / stats->n;
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/external/guava/guava-tests/benchmark/com/google/common/math/ |
H A D | 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) { argument 79 this.variance = variance; 84 return Doubles.hashCode(mean) * 31 + Doubles.hashCode(variance); 91 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { 97 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { 109 MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm) { 126 MeanAndVariance variance(doubl 141 abstract MeanAndVariance variance(double[] values, MeanAlgorithm meanAlgorithm); method in class:StatsBenchmark.VarianceAlgorithm [all...] |
/external/lldb/tools/lldb-perf/lib/ |
H A D | Metric.cpp | 76 T variance; local 78 variance = M2 / n; 80 variance = M2 / (n - 1); 81 return sqrt(variance);
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/descriptive/ |
H A D | 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, argument 67 this.variance = variance; 113 return FastMath.sqrt(variance); 117 * @return Returns the variance. 120 return variance; [all...] |
H A D | AggregateSummaryStatistics.java | 198 * {@inheritDoc}. This version returns the variance of all the aggregated 332 final double variance; 334 variance = Double.NaN; 336 variance = 0d; 338 variance = m2 / (n - 1); 340 return new StatisticalSummaryValues(mean, variance, n, max, min, sum);
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H A D | SummaryStatistics.java | 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 116 private StorelessUnivariateStatistic varianceImpl = variance; 154 // If mean, variance or geomean have been overridden, 230 * Returns the variance of the values that have been added. 234 * @return the variance 237 if (varianceImpl == variance) { 321 outBuffer.append("variance [all...] |
/external/libvpx/libvpx/vp9/encoder/ |
H A D | vp9_variance.c | 21 void variance(const uint8_t *a, int a_stride, function 117 variance(a, a_stride, b, b_stride, W, H, sse, &sum); \ 162 variance(src_ptr, source_stride, ref_ptr, ref_stride, 16, 16, sse, sum); 168 variance(src_ptr, source_stride, ref_ptr, ref_stride, 8, 8, sse, sum); 175 variance(src, src_stride, ref, ref_stride, 16, 16, sse, &sum); 183 variance(src, src_stride, ref, ref_stride, 16, 8, sse, &sum); 191 variance(src, src_stride, ref, ref_stride, 8, 16, sse, &sum); 199 variance(src, src_stride, ref, ref_stride, 8, 8, sse, &sum);
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H A D | vp9_variance.h | 20 void variance(const uint8_t *a, int a_stride,
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H A D | vp9_aq_variance.c | 128 variance(x->plane[0].src.buf, x->plane[0].src.stride,
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/inference/ |
H A D | TTestImpl.java | 191 return t(StatUtils.mean(observed), mu, StatUtils.variance(observed), 235 * and <strong><code>var</code></strong> is the pooled variance estimate: 239 * with <strong><code>var1<code></strong> the variance of the first sample and 240 * <strong><code>var2</code></strong> the variance of the second sample. 256 StatUtils.variance(sample1), StatUtils.variance(sample2), 276 * <strong><code> var1</code></strong> is the variance of the first sample; 277 * <strong><code> var2</code></strong> is the variance of the second sample; 293 StatUtils.variance(sample1), StatUtils.variance(sample [all...] |
/external/libvpx/libvpx/vp8/common/ |
H A D | variance_c.c | 12 #include "variance.h" 34 static void variance( function 76 variance(src_ptr, source_stride, ref_ptr, recon_stride, 16, 16, &var, &avg); 92 variance(src_ptr, source_stride, ref_ptr, recon_stride, 8, 16, &var, &avg); 108 variance(src_ptr, source_stride, ref_ptr, recon_stride, 16, 8, &var, &avg); 125 variance(src_ptr, source_stride, ref_ptr, recon_stride, 8, 8, &var, &avg); 141 variance(src_ptr, source_stride, ref_ptr, recon_stride, 4, 4, &var, &avg); 157 variance(src_ptr, source_stride, ref_ptr, recon_stride, 16, 16, &var, &avg);
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/external/opencv/cvaux/src/ |
H A D | cvbgfg_gaussmix.cpp | 79 icvMatchTest(...) assumes what all color channels component exhibit the same variance 120 //Rw is the learning rate for weight and Rg is leaning rate for mean and variance 126 //The list is maintained in sorted order using w/sqrt(variance) as a key 132 //v[n+1] = v[n] + Rg*((x[n+1] - u[n])*(x[n+1] - u[n])) - v[n]) variance 206 bg_model->g_point[n].g_values[0].variance[m] = var_init; 214 bg_model->g_point[n].g_values[k].variance[m] = var_init; 390 var_threshold += g_point->g_values[k].variance[m]; 420 sum_d2 += (d*d) / (g_point->g_values[k].variance[m] * g_point->g_values[k].variance[m]); 452 g_point->g_values[k].variance[ [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/ |
H A D | StatUtils.java | 63 /** variance */ 301 * Returns the variance of the entries in the input array, or 312 * @return the variance of the values or Double.NaN if the array is empty 315 public static double variance(final double[] values) { method in class:StatUtils 320 * Returns the variance of the entries in the specified portion of 335 * @return the variance of the values or Double.NaN if length = 0 339 public static double variance(final double[] values, final int begin, method in class:StatUtils 345 * Returns the variance of the entries in the specified portion of 366 * @return the variance of the values or Double.NaN if length = 0 370 public static double variance(fina method in class:StatUtils 397 public static double variance(final double[] values, final double mean) { method in class:StatUtils [all...] |
/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/correlation/ |
H A D | Covariance.java | 162 Variance variance = new Variance(biasCorrected); 170 outMatrix.setEntry(i, i, variance.evaluate(matrix.getColumn(i)));
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/external/apache-commons-math/src/main/java/org/apache/commons/math/stat/clustering/ |
H A D | KMeansPlusPlusClusterer.java | 41 /** Split the cluster with largest distance variance. */ 64 * algorithm iterations is to split the cluster with largest distance variance. 201 * Get a random point from the {@link Cluster} with the largest distance variance. 213 // compute the distance variance of the current cluster 219 final double variance = stat.getResult(); 221 // select the cluster with the largest variance 222 if (variance > maxVariance) { 223 maxVariance = variance;
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/external/fio/tools/plot/ |
H A D | fio2gnuplot | 253 variance = map(lambda x: (x - avg)**2, disk_perf[disk]) 254 standard_deviation = math.sqrt(average(variance)) 267 variance = map(lambda x: (x - avg)**2, global_disk_perf) 268 standard_deviation = math.sqrt(average(variance))
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/external/deqp/modules/glshared/ |
H A D | glsStateChangePerfTestCases.cpp | 59 double variance; member in struct:deqp::gls::__anon3779::ResultStats 79 result.variance += (val - result.mean) * (val - result.mean); 82 result.variance /= values.size(); 548 log << TestLog::Message << "Interleaved variance: " << interleaved.variance << TestLog::EndMessage; 554 log << TestLog::Message << "Batched variance: " << batched.variance << TestLog::EndMessage; 702 log << TestLog::Message << "Iteration variance time: " << varIteration << TestLog::EndMessage;
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/external/antlr/antlr-3.4/runtime/Ruby/lib/antlr3/ |
H A D | profile.rb | 84 def variance method in class:ANTLR3.Profile.DataSet 90 sqrt( variance )
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/external/libvpx/libvpx/vp8/common/x86/ |
H A D | mfqe_sse2.asm | 165 ; unsigned int *variance, 4 255 ; (variance + 128) >> 8
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H A D | loopfilter_sse2.asm | 248 pandn xmm4, xmm5 ; high edge variance additive 310 ; calculate breakout conditions and high edge variance 362 ; calculate breakout conditions and high edge variance 584 ; calculate breakout conditions and high edge variance 634 ; calculate breakout conditions and high edge variance 955 ; calculate filter mask and high edge variance 1020 ; calculate filter mask and high edge variance 1169 ; calculate filter mask and high edge variance 1236 ; calculate filter mask and high edge variance
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/external/harfbuzz_ng/test/shaping/ |
H A D | hb_test_tools.py | 185 def variance (self): member in class:Stats 190 return self.variance () ** .5
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/external/libvpx/libvpx/vp9/common/x86/ |
H A D | vp9_loopfilter_mmx.asm | 133 ; calculate high edge variance 191 pandn mm4, mm5 ; high edge variance additive 426 ; calculate high edge variance 509 pandn mm4, mm5 ; high edge variance additive
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