Lines Matching defs:values

27  * Computes the variance of the available values.  By default, the unbiased
44  * full array of values in memory to execute a two-pass algorithm.
48  * Note that adding values using <code>increment</code> or
51  * <code>evaluate</code> with the full array of values. The former approach
52  * should only be used when the full array of values is not available.</p>
149      * <p>If all values are available, it is more accurate to use
150      * {@link #evaluate(double[])} rather than adding values one at a time
153      * list of values together to execute a two-pass algorithm.
210      * @param values the input array
211      * @return the variance of the values or Double.NaN if length = 0
215     public double evaluate(final double[] values) {
216         if (values == null) {
219         return evaluate(values, 0, values.length);
235      * @param values the input array
238      * @return the variance of the values or Double.NaN if length = 0
243     public double evaluate(final double[] values, final int begin, final int length) {
247         if (test(values, begin, length)) {
253                 double m = mean.evaluate(values, begin, length);
254                 var = evaluate(values, m, begin, length);
266      *   &Sigma;(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)
272      * weights are to be treated as "expansion values," as will be the case if for example
275      *   <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code>
281      * <ul><li>the values array is null</li>
283      *     <li>the weights array does not have the same length as the values array</li>
284      *     <li>the weights array contains one or more infinite values</li>
285      *     <li>the weights array contains one or more NaN values</li>
286      *     <li>the weights array contains negative values</li>
294      * @param values the input array
298      * @return the weighted variance of the values or Double.NaN if length = 0
302     public double evaluate(final double[] values, final double[] weights,
307         if (test(values, weights,begin, length)) {
313                 double m = mean.evaluate(values, weights, begin, length);
314                 var = evaluate(values, weights, m, begin, length);
325      *   &Sigma;(weights[i]*(values[i] - weightedMean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)
331      * weights are to be treated as "expansion values," as will be the case if for example
334      *   <code>evaluate(values, MathUtils.normalizeArray(weights, values.length)); </code>
340      * <ul><li>the values array is null</li>
342      *     <li>the weights array does not have the same length as the values array</li>
343      *     <li>the weights array contains one or more infinite values</li>
344      *     <li>the weights array contains one or more NaN values</li>
345      *     <li>the weights array contains negative values</li>
352      * @param values the input array
354      * @return the weighted variance of the values
358     public double evaluate(final double[] values, final double[] weights) {
359         return evaluate(values, weights, 0, values.length);
380      * @param values the input array
384      * @return the variance of the values or Double.NaN if length = 0
388     public double evaluate(final double[] values, final double mean,
393         if (test(values, begin, length)) {
401                     dev = values[i] - mean;
436      * @param values the input array
438      * @return the variance of the values or Double.NaN if the array is empty
441     public double evaluate(final double[] values, final double mean) {
442         return evaluate(values, mean, 0, values.length);
451      *   &Sigma;(weights[i]*(values[i] - mean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)
461      * weights are to be treated as "expansion values," as will be the case if for example
464      *   <code>evaluate(values, MathUtils.normalizeArray(weights, values.length), mean); </code>
470      * <ul><li>the values array is null</li>
472      *     <li>the weights array does not have the same length as the values array</li>
473      *     <li>the weights array contains one or more infinite values</li>
474      *     <li>the weights array contains one or more NaN values</li>
475      *     <li>the weights array contains negative values</li>
481      * @param values the input array
486      * @return the variance of the values or Double.NaN if length = 0
490     public double evaluate(final double[] values, final double[] weights,
495         if (test(values, weights, begin, length)) {
503                     dev = values[i] - mean;
524      * <p>Returns the weighted variance of the values in the input array, using
528      *   &Sigma;(weights[i]*(values[i] - mean)<sup>2</sup>)/(&Sigma;(weights[i]) - 1)
538      * weights are to be treated as "expansion values," as will be the case if for example
541      *   <code>evaluate(values, MathUtils.normalizeArray(weights, values.length), mean); </code>
547      * <ul><li>the values array is null</li>
549      *     <li>the weights array does not have the same length as the values array</li>
550      *     <li>the weights array contains one or more infinite values</li>
551      *     <li>the weights array contains one or more NaN values</li>
552      *     <li>the weights array contains negative values</li>
557      * @param values the input array
560      * @return the variance of the values or Double.NaN if length = 0
564     public double evaluate(final double[] values, final double[] weights, final double mean) {
565         return evaluate(values, weights, mean, 0, values.length);