Adapt to API change in "Commons Numbers".
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@ -114,7 +114,7 @@ public class UnivariatePeriodicInterpolator
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y[index] = yval[i];
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}
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SortInPlace.ASCENDING.accept(x, y);
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SortInPlace.ASCENDING.apply(x, y);
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final UnivariateFunction f = interpolator.interpolate(x, y);
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return new UnivariateFunction() {
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@ -78,7 +78,7 @@ public class PolynomialFunctionLagrangeForm implements UnivariateFunction {
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coefficientsComputed = false;
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if (!verifyInterpolationArray(x, y, false)) {
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SortInPlace.ASCENDING.accept(this.x, this.y);
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SortInPlace.ASCENDING.apply(this.x, this.y);
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// Second check in case some abscissa is duplicated.
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verifyInterpolationArray(this.x, this.y, true);
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}
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@ -183,7 +183,7 @@ public class PolynomialFunctionLagrangeForm implements UnivariateFunction {
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System.arraycopy(x, 0, xNew, 0, x.length);
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System.arraycopy(y, 0, yNew, 0, y.length);
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SortInPlace.ASCENDING.accept(xNew, yNew);
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SortInPlace.ASCENDING.apply(xNew, yNew);
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// Second check in case some abscissa is duplicated.
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verifyInterpolationArray(xNew, yNew, true);
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return evaluateInternal(xNew, yNew, z);
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@ -767,7 +767,7 @@ public class PSquarePercentile extends AbstractStorelessUnivariateStatistic
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new double[] { xval[1], xval[1 + delta] };
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final double[] yBad =
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new double[] { yval[1], yval[1 + delta] };
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SortInPlace.ASCENDING.accept(xBad, yBad);// since d can be +/- 1
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SortInPlace.ASCENDING.apply(xBad, yBad);// since d can be +/- 1
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univariateFunction = linear.interpolate(xBad, yBad);
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markerHeight = univariateFunction.value(xD);
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}
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@ -1150,7 +1150,7 @@ public class Percentile extends AbstractUnivariateStatistic implements Serializa
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@Override
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public double evaluate(final double[] work, final double[] sampleWeights,
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final double p) {
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SortInPlace.ASCENDING.accept(work, sampleWeights);
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SortInPlace.ASCENDING.apply(work, sampleWeights);
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double[] sk = new double[work.length];
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for(int k = 0; k < work.length; k++) {
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sk[k] = 0;
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