protected void runTrial( final PartitionedDataset<FoldDataType> fold) { // Perform the learning algorithm on this fold. final LearnedType learned = getLearner().learn(fold.getTrainingSet()); // Compute the statistic of the learned object on the testing set. final Collection<FoldDataType> testingSet = fold.getTestingSet(); final StatisticType statistic = this.getPerformanceEvaluator().evaluatePerformance( learned, testingSet); statistics.add(statistic); }
protected void runTrial( final PartitionedDataset<FoldDataType> fold) { // Perform the learning algorithm on this fold. final LearnedType learned = getLearner().learn(fold.getTrainingSet()); // Compute the statistic of the learned object on the testing set. final Collection<FoldDataType> testingSet = fold.getTestingSet(); final StatisticType statistic = this.getPerformanceEvaluator().evaluatePerformance( learned, testingSet); statistics.add(statistic); }
protected void runTrial( final PartitionedDataset<FoldDataType> fold) { // Perform the learning algorithm on this fold. final LearnedType learned = getLearner().learn(fold.getTrainingSet()); // Compute the statistic of the learned object on the testing set. final Collection<FoldDataType> testingSet = fold.getTestingSet(); final StatisticType statistic = this.getPerformanceEvaluator().evaluatePerformance( learned, testingSet); statistics.add(statistic); }
/** * Runs one trial in the experiment. * * @param data The data to use. */ protected void runTrial( final PartitionedDataset<? extends InputDataType> data) { // Perform the learning algorithm on this fold. final LearnedType learned = getLearner().learn(data.getTrainingSet()); // Compute the statistic of the learned object on the testing set. final Collection<? extends InputDataType> testingSet = data.getTestingSet(); final StatisticType statistic = this.getPerformanceEvaluator().evaluatePerformance( learned, testingSet); statistics.add(statistic); }
/** * Runs one trial in the experiment. * * @param data The data to use. */ protected void runTrial( final PartitionedDataset<? extends InputDataType> data) { // Perform the learning algorithm on this fold. final LearnedType learned = getLearner().learn(data.getTrainingSet()); // Compute the statistic of the learned object on the testing set. final Collection<? extends InputDataType> testingSet = data.getTestingSet(); final StatisticType statistic = this.getPerformanceEvaluator().evaluatePerformance( learned, testingSet); statistics.add(statistic); }
/** * Runs one trial in the experiment. * * @param data The data to use. */ protected void runTrial( final PartitionedDataset<? extends InputDataType> data) { // Perform the learning algorithm on this fold. final LearnedType learned = getLearner().learn(data.getTrainingSet()); // Compute the statistic of the learned object on the testing set. final Collection<? extends InputDataType> testingSet = data.getTestingSet(); final StatisticType statistic = this.getPerformanceEvaluator().evaluatePerformance( learned, testingSet); statistics.add(statistic); }
/** * {@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); }