/** * Gets the currently set performance evaluation measure used for selecting * attributes for the decision table * * @return the performance evaluation measure */ public SelectedTag getEvaluationMeasure() { return new SelectedTag(m_evaluationMeasure, TAGS_EVALUATION); }
/** * Gets the method used. Will be one of METHOD_1_AGAINST_ALL, * METHOD_ERROR_RANDOM, METHOD_ERROR_EXHAUSTIVE, or METHOD_1_AGAINST_1. * * @return the current method. */ public SelectedTag getMethod() { return new SelectedTag(m_Method, TAGS_METHOD); }
/** * Get the metric type * * @return the type of metric to use for ranking rules */ public SelectedTag getMetricType() { return new SelectedTag(m_metricType, TAGS_SELECTION); }
/** * Gets the method used for pruning. * * @return the pruning method to use. */ public SelectedTag getPruningMethod() { return new SelectedTag(m_PruningMethod, TAGS_PRUNING); }
/** * Gets the source location method of the cost matrix. Will be one of * MATRIX_ON_DEMAND or MATRIX_SUPPLIED. * * @return the cost matrix source. */ public SelectedTag getCostMatrixSource() { return new SelectedTag(m_MatrixSource, TAGS_MATRIX_SOURCE); }
/** * Gets the estimator * * @return the estimator */ public SelectedTag getEstimator() { return new SelectedTag(paceEstimator, TAGS_ESTIMATOR); }
/** * Gets how the training data will be transformed. Will be one of * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE. * * @return the filtering mode */ public SelectedTag getFilterType() { return new SelectedTag(m_filterType, TAGS_FILTER); }
/** * Gets the type of algorithm. * * @return the algorithm type */ public SelectedTag getAlgorithmType() { return new SelectedTag(m_AlgorithmType, TAGS_ALGORITHMTYPE); }
/** * Getter method. */ public SelectedTag getSplitMethod() { return new SelectedTag(m_SplitMethod, TAGS_SPLITMETHOD); }
/** * Gets the currently set type of GUI to display. * * @return the current GUI Type. */ public SelectedTag getGUIType() { return new SelectedTag(m_GUIType, TAGS_GUI); }
/** * returns the default function * * @return the default function */ protected SelectedTag defaultFunction() { return new SelectedTag(FUNCTION_1, FUNCTION_TAGS); }
/** * Get the current loss function. * * @return the current loss function. */ public SelectedTag getLossFunction() { return new SelectedTag(m_loss, TAGS_SELECTION); }
/** * returns the default pattern * * @return the default pattern */ protected SelectedTag defaultPattern() { return new SelectedTag(RANDOM, TAGS_PATTERN); }
/** * Gets the combination rule used * * @return the combination rule used */ public SelectedTag getCombinationRule() { return new SelectedTag(m_CombinationRule, TAGS_RULES); }
/** * Gets the type of algorithm to use * * @return the current algorithm type. */ public SelectedTag getAlgorithm() { return new SelectedTag(m_Algorithm, TAGS_ALGORITHM); }
/** * Gets the type of preprocessing to use * * @return the current preprocessing type. */ public SelectedTag getPreprocessing() { return new SelectedTag(m_Preprocessing, TAGS_PREPROCESSING); }
/** * Gets the criterion used for evaluating the classifier performance. * * @return the current evaluation criterion. */ public SelectedTag getEvaluation() { return new SelectedTag(m_Evaluation, m_Metrics.getTags()); }
/** * Gets the currently set performance evaluation measure used for selecting * attributes for the decision table * * @return the performance evaluation measure */ public SelectedTag getEvaluationMeasure() { return new SelectedTag(m_evaluationMeasure.getIDStr(), TAGS_EVALUATION); }
/** * Gets the criterion used for evaluating the classifier performance. * * @return the current evaluation criterion. */ public SelectedTag getEvaluation() { return new SelectedTag(m_Evaluation, m_Metrics.getTags()); }
public void testAddDate() { m_Filter = getFilter(); ((Add) m_Filter).setAttributeType(new SelectedTag(Attribute.DATE, Add.TAGS_TYPE)); testBuffered(); testType(Attribute.DATE); }