/** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test was * acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numClasses, ArrayList<String> accepts) { return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, TestInstances.CLASS_IS_LAST, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); }
/** * Runs a text on the datasets with the given characteristics. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @param missingLevel the percentage of missing values * @param predictorMissing true if the missing values may be in the predictors * @param classMissing true if the missing values may be in the class * @param numTrain the number of instances in the training set * @param numClasses the number of classes * @param accepts the acceptable string in an exception * @return index 0 is true if the test was passed, index 1 is true if test was * acceptable */ protected boolean[] runBasicTest(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType, int missingLevel, boolean predictorMissing, boolean classMissing, int numTrain, int numClasses, ArrayList<String> accepts) { return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, TestInstances.CLASS_IS_LAST, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); }
boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
/** * Checks whether the scheme can handle zero training instances. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is true if test was * acceptable */ protected boolean[] canHandleZeroTraining(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("handle zero training instances"); printAttributeSummary(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); ArrayList<String> accepts = new ArrayList<String>(); accepts.add("train"); accepts.add("value"); int numTrain = 0, numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); }
/** * Checks whether the scheme can handle zero training instances. * * @param nominalPredictor if true use nominal predictor attributes * @param numericPredictor if true use numeric predictor attributes * @param stringPredictor if true use string predictor attributes * @param datePredictor if true use date predictor attributes * @param relationalPredictor if true use relational predictor attributes * @param multiInstance whether multi-instance is needed * @param classType the class type (NUMERIC, NOMINAL, etc.) * @return index 0 is true if the test was passed, index 1 is true if test was * acceptable */ protected boolean[] canHandleZeroTraining(boolean nominalPredictor, boolean numericPredictor, boolean stringPredictor, boolean datePredictor, boolean relationalPredictor, boolean multiInstance, int classType) { print("handle zero training instances"); printAttributeSummary(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType); print("..."); ArrayList<String> accepts = new ArrayList<String>(); accepts.add("train"); accepts.add("value"); int numTrain = 0, numClasses = 2, missingLevel = 0; boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses, accepts); }
boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, Attribute.NOMINAL, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
int numTrain = getNumInstances(), numClasses = 2; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, classIndex, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
boolean predictorMissing = false, classMissing = false; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, classIndex, missingLevel, predictorMissing, classMissing, numTrain, numClasses,
int numTrain = getNumInstances(), numClasses = 2; return runBasicTest(nominalPredictor, numericPredictor, stringPredictor, datePredictor, relationalPredictor, multiInstance, classType, missingLevel, predictorMissing, classMissing, numTrain, numClasses,