/** * 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 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 function for generating the data. * * @param value the function. * @see #FUNCTION_TAGS */ public void setFunction(SelectedTag value) { if (value.getTags() == FUNCTION_TAGS) { m_Function = value.getSelectedTag().getID(); } }
/** * Sets the combination rule to use. Values other than * * @param newRule the combination rule method to use */ public void setCombinationRule(SelectedTag newRule) { if (newRule.getTags() == TAGS_RULES) { m_CombinationRule = newRule.getSelectedTag().getID(); } }
/** * set quality measure to be used in searching for networks. * * @param newScoreType the new score type */ public void setScoreType(SelectedTag newScoreType) { if (newScoreType.getTags() == TAGS_SCORE_TYPE) { m_nScoreType = newScoreType.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(); } }
/** * Gets the current value as text. * * @return a value of type 'String' */ public String getAsText() { SelectedTag s = (SelectedTag)getValue(); return s.getSelectedTag().getReadable(); }
/** * 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 method to use for handling missing values. Values other than * M_NORMAL, M_AVERAGE, M_MAXDIFF and M_DELETE will be ignored. * * @param newMode the method to use for handling missing values. */ public void setMissingMode(SelectedTag newMode) { if (newMode.getTags() == TAGS_MISSING) { m_MissingMode = newMode.getSelectedTag().getID(); } }
/** * Sets the distance weighting method used. Values other than * WEIGHT_NONE, WEIGHT_INVERSE, or WEIGHT_SIMILARITY will be ignored. * * @param newMethod the distance weighting method to use */ public void setDistanceWeighting(SelectedTag newMethod) { if (newMethod.getTags() == TAGS_WEIGHTING) { m_DistanceWeighting = newMethod.getSelectedTag().getID(); } }
/** * Set the initialization method to use * * @param method the initialization method to use */ public void setInitializationMethod(SelectedTag method) { if (method.getTags() == TAGS_SELECTION) { m_initializationMethod = method.getSelectedTag().getID(); } }
/** * Sets the source location of the cost matrix. Values other than * MATRIX_ON_DEMAND or MATRIX_SUPPLIED will be ignored. * * @param newMethod the cost matrix location method. */ public void setCostMatrixSource(SelectedTag newMethod) { if (newMethod.getTags() == TAGS_MATRIX_SOURCE) { m_MatrixSource = newMethod.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(); } }