Field f = fieldValuePair.getFirst(); if( !Modifier.isStatic( f.getModifiers() ) )
Field f = fieldValuePair.getFirst(); if( !Modifier.isStatic( f.getModifiers() ) )
Field f = fieldValuePair.getFirst(); if( !Modifier.isStatic( f.getModifiers() ) )
@Override public int hashCode() { int hash = 3; hash = 23 * hash + ObjectUtil.hashCodeSafe(this.getFirst()); hash = 23 * hash + ObjectUtil.hashCodeSafe(this.getSecond()); return hash; }
@Override public int hashCode() { int hash = 3; hash = 23 * hash + ObjectUtil.hashCodeSafe(this.getFirst()); hash = 23 * hash + ObjectUtil.hashCodeSafe(this.getSecond()); return hash; }
@Override public int hashCode() { int hash = 3; hash = 23 * hash + ObjectUtil.hashCodeSafe(this.getFirst()); hash = 23 * hash + ObjectUtil.hashCodeSafe(this.getSecond()); return hash; }
/** * 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 ); }
public boolean equals( final Pair<FirstType, SecondType> other) { return other != null && ObjectUtil.equalsSafe(this.getFirst(), other.getFirst()) && ObjectUtil.equalsSafe(this.getSecond(), other.getSecond()); }
public boolean equals( final Pair<FirstType, SecondType> other) { return other != null && ObjectUtil.equalsSafe(this.getFirst(), other.getFirst()) && ObjectUtil.equalsSafe(this.getSecond(), other.getSecond()); }
public boolean equals( final Pair<FirstType, SecondType> other) { return other != null && ObjectUtil.equalsSafe(this.getFirst(), other.getFirst()) && ObjectUtil.equalsSafe(this.getSecond(), other.getSecond()); }
public Double evaluate( Double input ) { double yn = 0.0; this.getState().getFirst().addFirst( input ); int n = 0; for( Double xn : this.getState().getFirst() ) { final double bn = this.getMovingAverageCoefficients().getElement( n ); yn += bn*xn; n++; } n = 0; for( Double ynm1 : this.getState().getSecond() ) { final double an = this.getAutoRegressiveCoefficients().getElement( n ); yn -= an*ynm1; n++; } this.getState().getSecond().addFirst( yn ); return yn; }
public Double evaluate( Double input ) { double yn = 0.0; this.getState().getFirst().addFirst( input ); int n = 0; for( Double xn : this.getState().getFirst() ) { final double bn = this.getMovingAverageCoefficients().getElement( n ); yn += bn*xn; n++; } n = 0; for( Double ynm1 : this.getState().getSecond() ) { final double an = this.getAutoRegressiveCoefficients().getElement( n ); yn -= an*ynm1; n++; } this.getState().getSecond().addFirst( yn ); return yn; }
public Double evaluate( Double input ) { double yn = 0.0; this.getState().getFirst().addFirst( input ); int n = 0; for( Double xn : this.getState().getFirst() ) { final double bn = this.getMovingAverageCoefficients().getElement( n ); yn += bn*xn; n++; } n = 0; for( Double ynm1 : this.getState().getSecond() ) { final double an = this.getAutoRegressiveCoefficients().getElement( n ); yn -= an*ynm1; n++; } this.getState().getSecond().addFirst( yn ); return yn; }
/** * {@inheritDoc} * * @param fold {@inheritDoc} */ protected void runTrial( final PartitionedDataset<FoldDataType> fold) { // Perform the learning algorithm on this fold for the first learner. final LearnedType learned1 = this.getLearners().getFirst().learn(fold.getTrainingSet()); // Compute the statistic and add it to the collection for the first // learner. final StatisticType statistic1 = this.getPerformanceEvaluator().evaluatePerformance( learned1, fold.getTestingSet()); this.getStatistics().getFirst().add(statistic1); // Perform the learning algorithm on this fold for the second learner. final LearnedType learned2 = this.getLearners().getSecond().learn(fold.getTrainingSet()); // Compute the statistic and add it to the collection for the second // learner. final StatisticType statistic2 = this.getPerformanceEvaluator().evaluatePerformance( learned2, fold.getTestingSet()); this.getStatistics().getSecond().add(statistic2); }
/** * {@inheritDoc} * * @param fold {@inheritDoc} */ protected void runTrial( final PartitionedDataset<FoldDataType> fold) { // Perform the learning algorithm on this fold for the first learner. final LearnedType learned1 = this.getLearners().getFirst().learn(fold.getTrainingSet()); // Compute the statistic and add it to the collection for the first // learner. final StatisticType statistic1 = this.getPerformanceEvaluator().evaluatePerformance( learned1, fold.getTestingSet()); this.getStatistics().getFirst().add(statistic1); // Perform the learning algorithm on this fold for the second learner. final LearnedType learned2 = this.getLearners().getSecond().learn(fold.getTrainingSet()); // Compute the statistic and add it to the collection for the second // learner. final StatisticType statistic2 = this.getPerformanceEvaluator().evaluatePerformance( learned2, fold.getTestingSet()); this.getStatistics().getSecond().add(statistic2); }
/** * {@inheritDoc} * * @param fold {@inheritDoc} */ protected void runTrial( final PartitionedDataset<FoldDataType> fold) { // Perform the learning algorithm on this fold for the first learner. final LearnedType learned1 = this.getLearners().getFirst().learn(fold.getTrainingSet()); // Compute the statistic and add it to the collection for the first // learner. final StatisticType statistic1 = this.getPerformanceEvaluator().evaluatePerformance( learned1, fold.getTestingSet()); this.getStatistics().getFirst().add(statistic1); // Perform the learning algorithm on this fold for the second learner. final LearnedType learned2 = this.getLearners().getSecond().learn(fold.getTrainingSet()); // Compute the statistic and add it to the collection for the second // learner. final StatisticType statistic2 = this.getPerformanceEvaluator().evaluatePerformance( learned2, fold.getTestingSet()); this.getStatistics().getSecond().add(statistic2); }