/** * {@inheritDoc} */ @Override public Skewness copy() { Skewness result = new Skewness(); // No try-catch or advertised exception because args are guaranteed non-null copy(this, result); return result; }
@Override public Double summarize(NumericColumn<?> column) { double[] data = removeMissing(column); return new Skewness().evaluate(data, 0, data.length); } };
@Override public Number getExpectedValue(int start, int length) { if (length < 3) { return null; } double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Skewness skewness = new Skewness(); return skewness.evaluate(values); }
@Override public Number getExpectedValue(int start, int length) { if (length < 3) { return null; } double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Skewness skewness = new Skewness(); return skewness.evaluate(values); }
public Skew() { super(new org.apache.commons.math3.stat.descriptive.moment.Skewness()); } }
public Skew() { super(new org.apache.commons.math3.stat.descriptive.moment.Skewness()); } }
@Override public double reduce(double[] data) { return new Skewness().evaluate(data, 0, data.length); } };
/** * {@inheritDoc} */ @Override public Skewness copy() { Skewness result = new Skewness(); // No try-catch or advertised exception because args are guaranteed non-null copy(this, result); return result; }
/** * {@inheritDoc} */ @Override public Skewness copy() { Skewness result = new Skewness(); // No try-catch or advertised exception because args are guaranteed non-null copy(this, result); return result; }
@Override public Double summarize(NumericColumn<?> column) { double[] data = removeMissing(column); return new Skewness().evaluate(data, 0, data.length); } };
/** * Calculates the skewness. * * @param fittedData * If {@code true}, calculation is performed on polynomial fitted * values, otherwise from sampled data * * @return the skewness of intersection counts */ public double getSkewness(final boolean fittedData) { if (fittedData) validateFit(); final Skewness s = new Skewness(); return s.evaluate(fittedData ? fCounts : inputCounts); }
@Override public Number getExpectedValue(int start, int length) { if (length < 3) { return null; } double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Skewness skewness = new Skewness(); return skewness.evaluate(values); }
@Override public Number getExpectedValue(int start, int length) { if (length < 3) { return null; } double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Skewness skewness = new Skewness(); return skewness.evaluate(values); }
@Override public Number getExpectedValue(int start, int length) { if (length < 3) { return null; } double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Skewness skewness = new Skewness(); return skewness.evaluate(values); }
@Override public Number getExpectedValue(int start, int length) { if (length < 3) { return null; } double[] values = new double[length]; for (int i = 0; i < length; i++) { values[i] = start + i; } Skewness skewness = new Skewness(); return skewness.evaluate(values); }
public static void main(String[] args) { final double[] values = new java.util.Random().doubles(5000).toArray(); final Skew stat1 = new Skew(); final org.apache.commons.math3.stat.descriptive.moment.Skewness stat2 = new org.apache.commons.math3.stat.descriptive.moment.Skewness(); for (double value : values) { stat1.add(value); stat2.increment(value); } final double result1 = stat1.getValue(); final double result2 = stat2.getResult(); if (result1 != result2) { throw new RuntimeException("Error: " + result1 + " != " + result2); } }
return new Product(); case Skewness: return new Skewness(); case StandardDeviation: return new StandardDeviation();