/**
* {@inheritDoc}
*/
public override UnivariateStatistic copy() //TODO: Supposed to be public override Mean copy()
{
Mean result = new Mean();
// No try-catch or advertised exception because args are guaranteed non-null
copy(this, result);
return(result);
}
/**
* Copies source to dest.
* <p>Neither source nor dest can be null.</p>
*
* @param source Mean to copy
* @param dest Mean to copy to
* @throws NullArgumentException if either source or dest is null
*/
public static void copy(Mean source, Mean dest)
{
MathUtils.checkNotNull(source);
MathUtils.checkNotNull(dest);
dest.setData(source.getDataRef());
dest.incMoment = source.incMoment;
dest.moment = (FirstMoment)source.moment.copy();
}
/**
* <p>Returns the weighted variance of the entries in the specified portion of
* the input array, or <code>Double.NaN</code> if the designated subarray
* is empty.</p>
* <p>
* Uses the formula <pre>
* Σ(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(Σ(weights[i]) - 1)
* </pre>
* where weightedMean is the weighted mean</p>
* <p>
* This formula will not return the same result as the unweighted variance when all
* weights are equal, unless all weights are equal to 1. The formula assumes that
* weights are to be treated as "expansion values," as will be the case if for example
* the weights represent frequency counts. To normalize weights so that the denominator
* in the variance computation equals the length of the input vector minus one, use <pre>
* <code>evaluate(values, MathArrays.normalizeArray(weights, values.length)); </code>
* </pre>
* <p>
* Returns 0 for a single-value (i.e. length = 1) sample.</p>
* <p>
* Throws <code>IllegalArgumentException</code> if any of the following are true:
* <ul><li>the values array is null</li>
* <li>the weights array is null</li>
* <li>the weights array does not have the same length as the values array</li>
* <li>the weights array contains one or more infinite values</li>
* <li>the weights array contains one or more NaN values</li>
* <li>the weights array contains negative values</li>
* <li>the start and length arguments do not determine a valid array</li>
* </ul></p>
* <p>
* Does not change the internal state of the statistic.</p>
* <p>
* Throws <code>MathIllegalArgumentException</code> if either array is null.</p>
*
* @param values the input array
* @param weights the weights array
* @param begin index of the first array element to include
* @param length the number of elements to include
* @return the weighted variance of the values or Double.NaN if length = 0
* @throws MathIllegalArgumentException if the parameters are not valid
* @since 2.1
*/
public double evaluate(double[] values, double[] weights, int begin, int length)
{
double var = Double.NaN;
if (test(values, weights, begin, length))
{
clear();
if (length == 1)
{
var = 0.0;
}
else if (length > 1)
{
Mean mean = new Mean();
double m = mean.evaluate(values, weights, begin, length);
var = evaluate(values, weights, m, begin, length);
}
}
return(var);
}
/**
* Copy constructor, creates a new {@code Mean} identical
* to the {@code original}
*
* @param original the {@code Mean} instance to copy
* @throws NullArgumentException if original is null
*/
public Mean(Mean original)
{
copy(original, this);
}
