/** * Set the loss function to use. * * @param function the loss function to use. */ public void setLossFunction(SelectedTag function) { if (function.getTags() == TAGS_SELECTION) { m_loss = function.getSelectedTag().getID(); } }
/** * Sets the attribute type to be deleted by the filter. * * @param type a TAGS_ATTRIBUTETYPE of the new type the filter should delete */ public void setAttributeType(SelectedTag type) { if (type.getTags() == TAGS_ATTRIBUTETYPE) { m_attTypeToDelete = type.getSelectedTag().getID(); } }
/** * Sets how the training data will be transformed. Should be one of * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE. * * @param newType the new filtering mode */ public void setFilterType(SelectedTag newType) { if (newType.getTags() == TAGS_FILTER) { m_filterType = newType.getSelectedTag().getID(); } }
/** * Sets the pattern type. * * @param value new pattern type */ public void setPattern(SelectedTag value) { if (value.getTags() == TAGS_PATTERN) { m_Pattern = value.getSelectedTag().getID(); } }
/** * Returns whether predictions can be discarded (depends on selected measure). */ protected boolean canDiscardPredictions() { switch (m_Owner.getEvaluation().getSelectedTag().getID()) { case DefaultEvaluationMetrics.EVALUATION_AUC: case DefaultEvaluationMetrics.EVALUATION_PRC: return false; default: return true; } }
/** * The new method for weighting the instances. * * @param method the new method */ public void setWeightMethod(SelectedTag method) { if (method.getTags() == TAGS_WEIGHTMETHOD) { m_WeightMethod = method.getSelectedTag().getID(); } }
/** * Sets the algorithm type. * * @param newType the new algorithm type */ public void setAlgorithmType(SelectedTag newType) { if (newType.getTags() == TAGS_ALGORITHMTYPE) { m_AlgorithmType = newType.getSelectedTag().getID(); } }
/** * Sets how the training data will be transformed. Should be one of * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE. * * @param newType the new filtering mode */ public void setFilterType(SelectedTag newType) { if (newType.getTags() == TAGS_FILTER) { m_filterType = newType.getSelectedTag().getID(); } }
/** * The new method for weighting the instances. * * @param method the new method */ public void setWeightMethod(SelectedTag method) { if (method.getTags() == MultiInstanceToPropositional.TAGS_WEIGHTMETHOD) { m_WeightMethod = method.getSelectedTag().getID(); } }
/** * Sets how the training data will be transformed. Should be one of * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE. * * @param newType the new filtering mode */ public void setFilterType(SelectedTag newType) { if (newType.getTags() == TAGS_FILTER) { m_filterType = newType.getSelectedTag().getID(); } }
/** * Sets the performance evaluation measure to use for selecting attributes for * the decision table * * @param newMethod the new performance evaluation metric to use */ public void setEvaluationMeasure(SelectedTag newMethod) { if (newMethod.getTags() == TAGS_EVALUATION) { m_evaluationMeasure = newMethod.getSelectedTag().getID(); } }
/** * Sets the method used to select attributes for use in the linear regression. * * @param method the attribute selection method to use. */ public void setAttributeSelectionMethod(SelectedTag method) { if (method.getTags() == TAGS_SELECTION) { m_AttributeSelection = method.getSelectedTag().getID(); } }
/** * set cross validation strategy to be used in searching for networks. * * @param newCVType : cross validation strategy */ public void setCVType(SelectedTag newCVType) { if (newCVType.getTags() == TAGS_CV_TYPE) { m_nCVType = newCVType.getSelectedTag().getID(); } } // setCVType
/** * Sets the method used to for pruning. * * @param value the pruning method to use. */ public void setPruningMethod(SelectedTag value) { if (value.getTags() == TAGS_PRUNING) { m_PruningMethod = value.getSelectedTag().getID(); } }
public void setLinkType(SelectedTag newLinkType) { if (newLinkType.getTags() == TAGS_LINK_TYPE) { m_nLinkType = newLinkType.getSelectedTag().getID(); } }
/** * Set the split criterion to use (either Gini or info gain). * * @param crit the criterion to use */ public void setSplitCriterion(SelectedTag crit) { if (crit.getTags() == TAGS_SELECTION) { m_selectedSplitMetric = crit.getSelectedTag().getID(); } }
/** * Sets the type of attribute to generate. * * @param value the attribute type */ public void setAttributeType(SelectedTag value) { if (value.getTags() == TAGS_TYPE) { m_AttributeType = value.getSelectedTag().getID(); } }
/** * Sets the cluster type. * * @param value the new cluster type. * @see SubspaceCluster#TAGS_CLUSTERTYPE */ public void setClusterType(SelectedTag value) { if (value.getTags() == SubspaceCluster.TAGS_CLUSTERTYPE) { m_clustertype = value.getSelectedTag().getID(); } }
/** * Set the loss function to use. * * @param function the loss function to use. */ public void setLossFunction(SelectedTag function) { if (function.getTags() == TAGS_SELECTION) { m_loss = function.getSelectedTag().getID(); } }
/** * Sets the criterion to use for evaluating the classifier performance. * * @param value the evaluation criterion */ public void setEvaluation(SelectedTag value) { if (value.getTags() == m_Metrics.getTags()) { m_Evaluation = value.getSelectedTag().getID(); } }