本文整理汇总了Java中org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance类的典型用法代码示例。如果您正苦于以下问题：Java VectorialCovariance类的具体用法？Java VectorialCovariance怎么用？Java VectorialCovariance使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
VectorialCovariance类属于org.apache.commons.math3.stat.descriptive.moment包，在下文中一共展示了VectorialCovariance类的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: MultivariateSummaryStatistics
import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
/**
* Construct a MultivariateSummaryStatistics instance
* @param k dimension of the data
* @param isCovarianceBiasCorrected if true, the unbiased sample
* covariance is computed, otherwise the biased population covariance
* is computed
*/
public MultivariateSummaryStatistics(int k, boolean isCovarianceBiasCorrected) {
this.k = k;
sumImpl = new StorelessUnivariateStatistic[k];
sumSqImpl = new StorelessUnivariateStatistic[k];
minImpl = new StorelessUnivariateStatistic[k];
maxImpl = new StorelessUnivariateStatistic[k];
sumLogImpl = new StorelessUnivariateStatistic[k];
geoMeanImpl = new StorelessUnivariateStatistic[k];
meanImpl = new StorelessUnivariateStatistic[k];
for (int i = 0; i < k; ++i) {
sumImpl[i] = new Sum();
sumSqImpl[i] = new SumOfSquares();
minImpl[i] = new Min();
maxImpl[i] = new Max();
sumLogImpl[i] = new SumOfLogs();
geoMeanImpl[i] = new GeometricMean();
meanImpl[i] = new Mean();
}
covarianceImpl =
new VectorialCovariance(k, isCovarianceBiasCorrected);
}
示例2: testMeanAndCovariance
import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
@Test
public void testMeanAndCovariance() {
VectorialMean meanStat = new VectorialMean(mean.length);
VectorialCovariance covStat = new VectorialCovariance(mean.length, true);
for (int i = 0; i < 5000; ++i) {
double[] v = generator.nextVector();
meanStat.increment(v);
covStat.increment(v);
}
double[] estimatedMean = meanStat.getResult();
RealMatrix estimatedCovariance = covStat.getResult();
for (int i = 0; i < estimatedMean.length; ++i) {
Assert.assertEquals(mean[i], estimatedMean[i], 0.07);
for (int j = 0; j <= i; ++j) {
Assert.assertEquals(covariance.getEntry(i, j),
estimatedCovariance.getEntry(i, j),
0.1 * (1.0 + FastMath.abs(mean[i])) * (1.0 + FastMath.abs(mean[j])));
}
}
}
示例3: testMeanAndCorrelation
import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
@Test
public void testMeanAndCorrelation() {
VectorialMean meanStat = new VectorialMean(mean.length);
VectorialCovariance covStat = new VectorialCovariance(mean.length, true);
for (int i = 0; i < 10000; ++i) {
double[] v = generator.nextVector();
meanStat.increment(v);
covStat.increment(v);
}
double[] estimatedMean = meanStat.getResult();
double scale;
RealMatrix estimatedCorrelation = covStat.getResult();
for (int i = 0; i < estimatedMean.length; ++i) {
Assert.assertEquals(mean[i], estimatedMean[i], 0.07);
for (int j = 0; j < i; ++j) {
scale = standardDeviation[i] * standardDeviation[j];
Assert.assertEquals(0, estimatedCorrelation.getEntry(i, j) / scale, 0.03);
}
scale = standardDeviation[i] * standardDeviation[i];
Assert.assertEquals(1, estimatedCorrelation.getEntry(i, i) / scale, 0.025);
}
}
示例4: fit
import org.apache.commons.math3.stat.descriptive.moment.VectorialCovariance; //导入依赖的package包/类
public Gaussian fit(List<double[]> data) {
k = data.get(0).length;
int n = data.size();
VectorialCovariance covCounter = new VectorialCovariance(k, true);
double[] sumCounter = new double[k];
for(double[] curDatum : data) {
for (int i = 0; i < k; i++) {
sumCounter[i] += curDatum[i];
}
covCounter.increment(curDatum);
}
for (int i = 0; i < k; i++) {
sumCounter[i] /= n;
}
mean = sumCounter;
cov = covCounter.getResult();
initialize();
return this;
}