/** * Performs a U-test on the score-class pairs. The first element in the * pair is a score, the second is a flag to determine which group the score * belongs to. For example {<true,1.0>, <false,0.9> means that data1=1.0 * and data2=0.9 and so forth. This is useful for computing that * classified data partitions data better than chance. * @param scoreClassPairs * Pairs of scores with the corresponding class "label" for the score * @return * Statistics from the Mann-Whitney U-test */ public MannWhitneyUConfidence.Statistic evaluateNullHypothesis( Collection<? extends InputOutputPair<? extends Number, Boolean>> scoreClassPairs ) { DefaultPair<LinkedList<Number>, LinkedList<Number>> pair = DatasetUtil.splitDatasets( scoreClassPairs ); LinkedList<Number> data1 = pair.getFirst(); LinkedList<Number> data2 = pair.getSecond(); return this.evaluateNullHypothesis( data1, data2 ); }
/** * Performs a U-test on the score-class pairs. The first element in the * pair is a score, the second is a flag to determine which group the score * belongs to. For example {<true,1.0>, <false,0.9> means that data1=1.0 * and data2=0.9 and so forth. This is useful for computing that * classified data partitions data better than chance. * @param scoreClassPairs * Pairs of scores with the corresponding class "label" for the score * @return * Statistics from the Mann-Whitney U-test */ public MannWhitneyUConfidence.Statistic evaluateNullHypothesis( Collection<? extends InputOutputPair<? extends Number, Boolean>> scoreClassPairs ) { DefaultPair<LinkedList<Number>, LinkedList<Number>> pair = DatasetUtil.splitDatasets( scoreClassPairs ); LinkedList<Number> data1 = pair.getFirst(); LinkedList<Number> data2 = pair.getSecond(); return this.evaluateNullHypothesis( data1, data2 ); }
/** * Performs a U-test on the score-class pairs. The first element in the * pair is a score, the second is a flag to determine which group the score * belongs to. For example {<true,1.0>, <false,0.9> means that data1=1.0 * and data2=0.9 and so forth. This is useful for computing that * classified data partitions data better than chance. * @param scoreClassPairs * Pairs of scores with the corresponding class "label" for the score * @return * Statistics from the Mann-Whitney U-test */ public MannWhitneyUConfidence.Statistic evaluateNullHypothesis( Collection<? extends InputOutputPair<? extends Number, Boolean>> scoreClassPairs ) { DefaultPair<LinkedList<Number>, LinkedList<Number>> pair = DatasetUtil.splitDatasets( scoreClassPairs ); LinkedList<Number> data1 = pair.getFirst(); LinkedList<Number> data2 = pair.getSecond(); return this.evaluateNullHypothesis( data1, data2 ); }
DatasetUtil.splitDatasets(data); LinkedList<? extends Vector> d1 = pair.getFirst(); LinkedList<? extends Vector> d0 = pair.getSecond();
DatasetUtil.splitDatasets(data); LinkedList<? extends Vector> d1 = pair.getFirst(); LinkedList<? extends Vector> d0 = pair.getSecond();
DatasetUtil.splitDatasets(data); LinkedList<? extends Vector> d1 = pair.getFirst(); LinkedList<? extends Vector> d0 = pair.getSecond();