The following document contains the results of PMD's CPD 6.55.0.
Duplications
File |
Line |
org/hipparchus/stat/regression/MillerUpdatingRegression.java |
978 |
org/hipparchus/stat/regression/MillerUpdatingRegression.java |
1089 |
int idx1 = 0;
int idx2;
int _i;
int _j;
for (int i = 0; i < beta.length; i++) {
_i = newIndices[i];
for (int j = 0; j <= i; j++, idx1++) {
_j = newIndices[j];
if (_i > _j) {
idx2 = _i * (_i + 1) / 2 + _j;
} else {
idx2 = _j * (_j + 1) / 2 + _i;
}
covNew[idx1] = cov[idx2];
}
}
return new RegressionResults(
betaNew, new double[][]{covNew}, true, this.nobs, rnk,
this.sumy, this.sumsqy, this.sserr, this.hasIntercept, false);
}
}
|
File |
Line |
org/hipparchus/stat/inference/ChiSquareTest.java |
87 |
org/hipparchus/stat/inference/GTest.java |
90 |
public double chiSquare(final double[] expected, final long[] observed)
throws MathIllegalArgumentException {
if (expected.length < 2) {
throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
expected.length, 2);
}
MathUtils.checkDimension(expected.length, observed.length);
MathArrays.checkPositive(expected);
MathArrays.checkNonNegative(observed);
double sumExpected = 0d;
double sumObserved = 0d;
for (int i = 0; i < observed.length; i++) {
sumExpected += expected[i];
sumObserved += observed[i];
}
double ratio = 1.0d;
|
File |
Line |
org/hipparchus/stat/correlation/KendallsCorrelation.java |
123 |
org/hipparchus/stat/correlation/PearsonsCorrelation.java |
231 |
int nVars = matrix.getColumnDimension();
RealMatrix outMatrix = new BlockRealMatrix(nVars, nVars);
for (int i = 0; i < nVars; i++) {
for (int j = 0; j < i; j++) {
double corr = correlation(matrix.getColumn(i), matrix.getColumn(j));
outMatrix.setEntry(i, j, corr);
outMatrix.setEntry(j, i, corr);
}
outMatrix.setEntry(i, i, 1d);
}
return outMatrix;
}
/**
* Computes the Kendall's Tau rank correlation matrix for the columns of
* the input rectangular array. The columns of the array represent values
* of variables to be correlated.
*
* @param matrix matrix with columns representing variables to correlate
* @return correlation matrix
*/
public RealMatrix computeCorrelationMatrix(final double[][] matrix) {
|