本文整理汇总了Java中org.apache.commons.math.stat.descriptive.moment.Variance.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Java Variance.evaluate方法的具体用法？Java Variance.evaluate怎么用？Java Variance.evaluate使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类org.apache.commons.math.stat.descriptive.moment.Variance的用法示例。
在下文中一共展示了Variance.evaluate方法的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: prepareVariance
import org.apache.commons.math.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
private void prepareVariance() {
this.var = new double[this.feats[0].length];
Matrix m = new DenseMatrix(feats);
double[] colArr = new double[this.feats.length];
Variance v = new Variance();
for (int i = 0; i < this.var.length; i++) {
m.column(i).storeOn(colArr, 0);
this.var[i] = v.evaluate(colArr);
}
}
示例2: testConsistency
import org.apache.commons.math.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/**
* Verify that diagonal entries are consistent with Variance computation and matrix matches
* column-by-column covariances
*/
public void testConsistency() {
final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
// Variances on the diagonal
Variance variance = new Variance();
for (int i = 0; i < 5; i++) {
assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
}
// Symmetry, column-consistency
assertEquals(covarianceMatrix.getEntry(2, 3),
new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);
// All columns same -> all entries = column variance
RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
for (int i = 0; i < 3; i++) {
repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
}
RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
double columnVariance = variance.evaluate(matrix.getColumn(0));
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
}
}
// Check bias-correction defaults
double[][] data = matrix.getData();
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data),Double.MIN_VALUE);
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data, true),Double.MIN_VALUE);
double[] x = data[0];
double[] y = data[1];
assertEquals(new Covariance().covariance(x, y),
new Covariance().covariance(x, y, true), Double.MIN_VALUE);
}
示例3: testConsistency
import org.apache.commons.math.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/**
* Verify that diagonal entries are consistent with Variance computation and matrix matches
* column-by-column covariances
*/
@Test
public void testConsistency() {
final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
// Variances on the diagonal
Variance variance = new Variance();
for (int i = 0; i < 5; i++) {
Assert.assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
}
// Symmetry, column-consistency
Assert.assertEquals(covarianceMatrix.getEntry(2, 3),
new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
Assert.assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);
// All columns same -> all entries = column variance
RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
for (int i = 0; i < 3; i++) {
repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
}
RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
double columnVariance = variance.evaluate(matrix.getColumn(0));
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
Assert.assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
}
}
// Check bias-correction defaults
double[][] data = matrix.getData();
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data),Double.MIN_VALUE);
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data, true),Double.MIN_VALUE);
double[] x = data[0];
double[] y = data[1];
Assert.assertEquals(new Covariance().covariance(x, y),
new Covariance().covariance(x, y, true), Double.MIN_VALUE);
}
示例4: testConsistency
import org.apache.commons.math.stat.descriptive.moment.Variance; //导入方法依赖的package包/类
/**
* Verify that diagonal entries are consistent with Variance computation and matrix matches
* column-by-column covariances
*/
public void testConsistency() {
final RealMatrix matrix = createRealMatrix(swissData, 47, 5);
final RealMatrix covarianceMatrix = new Covariance(matrix).getCovarianceMatrix();
// Variances on the diagonal
Variance variance = new Variance();
for (int i = 0; i < 5; i++) {
assertEquals(variance.evaluate(matrix.getColumn(i)), covarianceMatrix.getEntry(i,i), 10E-14);
}
// Symmetry, column-consistency
assertEquals(covarianceMatrix.getEntry(2, 3),
new Covariance().covariance(matrix.getColumn(2), matrix.getColumn(3), true), 10E-14);
assertEquals(covarianceMatrix.getEntry(2, 3), covarianceMatrix.getEntry(3, 2), Double.MIN_VALUE);
// All columns same -> all entries = column variance
RealMatrix repeatedColumns = new Array2DRowRealMatrix(47, 3);
for (int i = 0; i < 3; i++) {
repeatedColumns.setColumnMatrix(i, matrix.getColumnMatrix(0));
}
RealMatrix repeatedCovarianceMatrix = new Covariance(repeatedColumns).getCovarianceMatrix();
double columnVariance = variance.evaluate(matrix.getColumn(0));
for (int i = 0; i < 3; i++) {
for (int j = 0; j < 3; j++) {
assertEquals(columnVariance, repeatedCovarianceMatrix.getEntry(i, j), 10E-14);
}
}
// Check bias-correction defaults
double[][] data = matrix.getData();
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data),Double.MIN_VALUE);
TestUtils.assertEquals("Covariances",
covarianceMatrix, new Covariance().computeCovarianceMatrix(data, true),Double.MIN_VALUE);
double[] x = data[0];
double[] y = data[1];
assertEquals(new Covariance().covariance(x, y),
new Covariance().covariance(x, y, true), Double.MIN_VALUE);
}