Uses of Class
edu.harvard.seas.iis.abilities.classify.DataSet

Packages that use DataSet

edu.harvard.seas.iis.abilities.classify

Uses of DataSet in edu.harvard.seas.iis.abilities.classify

Subclasses of DataSet in edu.harvard.seas.iis.abilities.classify

class

UserDataSet

Methods in edu.harvard.seas.iis.abilities.classify that return DataSet

DataSet	ClassifierEvaluator.annotateDataSet(DataSet dataSet, weka.classifiers.Classifier c, InstanceFilter filter) Fills in the "Prediction probability" and "Predicted class" values in the dataSet using classifier c.
DataSet	MovementClassifier.classifyMovements(DataSet movements, boolean useTargetInformation) Assumes the movements data set has "Predicted class" and "Prediction probability" attributes present.
static DataSet	Clean.cleanOnRawValues(DataSet fullDdataSet) Method from removing obviously problematic instances (very low IDs, missed clicks, clicks on unknown targets)
static DataSet	Clean.cleanOnTransformedData(DataSet fullDdataSet) Method from removing outliers from the data.
static DataSet	Transform.combineDataSets(java.io.File inputDirectory, java.lang.String[] userNames, java.io.File outputFile) Combine data sets for several users into a single file
static DataSet	Transform.computeAdditonalFeatures(DataSet dataSet)
static DataSet	Transform.computeParticipantCodes(DataSet dataSet)
static DataSet	DataSet.fromArffFile(java.io.File f) A convenience method that creates an instance of a UserDataSet from an ARFF file
static DataSet	DataSet.fromArffFiles(java.io.File[] files) Creates a single UserDataSet object from multiple ARFF files
DataSet	ClassifierEvaluator.getDeliberateInstances(weka.classifiers.Classifier c, DataSet d) Returns a copy of the data set that only contains instances positively classified by c
DataSet	DataSet.getExplicitInstances() Convenience method which returns a subset of the data containing only explicit examples
DataSet	DataSet.getImplicitInstances() Convenience method which returns a subset of the data containing only implicit examples
DataSet	DataSet.getInstancesForUser(java.lang.String user)
DataSet	DataSet.getInstancesWithAttributeValueEqual(weka.core.Attribute att, java.lang.String attVal)
DataSet	DataSet.getInstancesWithAttributeValueGreaterThan(weka.core.Attribute att, double attVal)
DataSet	DataSet.getInstancesWithAttributeValueNotEqual(weka.core.Attribute att, java.lang.String attVal)
DataSet	DataSet.getInstancesWithAttributeValues(weka.core.Attribute att, java.util.Collection<java.lang.String> values)
static DataSet	Transform.normalize(DataSet oldData, java.lang.String[] attributesToNormalize, java.lang.String[] usersToUseForComputingNormalizationConstants, NormalizationConstants normalizationConstants) Normalizes listed features (zero mean and unit stdev); unlike the other normalize() method, it does not attempt to separate the users -- if you want normalization per user, feed it separate data sets for each user
static DataSet	Transform.normalizeUsingNormalizationConstants(DataSet dataSet, java.lang.String[] attributesToNormalize, NormalizationConstants normalizationConstants) This method uses the constants provided in the normalizationConstants to perform the normalization

Methods in edu.harvard.seas.iis.abilities.classify with parameters of type DataSet

DataSet	ClassifierEvaluator.annotateDataSet(DataSet dataSet, weka.classifiers.Classifier c, InstanceFilter filter) Fills in the "Prediction probability" and "Predicted class" values in the dataSet using classifier c.
DataSet	MovementClassifier.classifyMovements(DataSet movements, boolean useTargetInformation) Assumes the movements data set has "Predicted class" and "Prediction probability" attributes present.
static DataSet	Clean.cleanOnRawValues(DataSet fullDdataSet) Method from removing obviously problematic instances (very low IDs, missed clicks, clicks on unknown targets)
static DataSet	Clean.cleanOnTransformedData(DataSet fullDdataSet) Method from removing outliers from the data.
static DataSet	Transform.computeAdditonalFeatures(DataSet dataSet)
static double[]	ClassifierEvaluator.computeFittsLawCoefficients(DataSet dataSet)
static DataSet	Transform.computeParticipantCodes(DataSet dataSet)
ClassifierEvalStats	FeatureSelection.crossvalidateOverUsers(weka.classifiers.Classifier c, DataSet dataSet, java.lang.String[] usersToEvaluateOn, boolean annotateDataSet) Performs a per-user crossvalidation; evaluation is performed on users listed in usersToEvaluateOn; the dataSet may have data from more users -- that's ok, the data from those users are used for training but not for evaluation
java.lang.String	ClassifierEvaluator.crossvalidateOverUsers(weka.classifiers.Classifier c, DataSet dataSet, java.lang.String[] users, boolean generateAnnotatedDataSet)
double[]	ClassifierEvaluator.evaluate(weka.classifiers.Classifier c, DataSet testData) Evaluate a trained classifier c on a particular test data set
ClassifierEvalStats	FeatureSelection.evaluateFeatureSet(java.lang.String[] features, PositiveAndUnlabeledClassifier c, DataSet dataSet, boolean annotateDataSet) Uses crossvalidation to evaluate a particular classifier on a particular set of features on a particular data set
static java.lang.Double[]	DataDiagnostics.getBasicAttributeStats(DataSet dataSet)
static java.lang.Double[]	DataDiagnostics.getBasicStats(DataSet dataSet, boolean computeFittsStats)
DataSet	ClassifierEvaluator.getDeliberateInstances(weka.classifiers.Classifier c, DataSet d) Returns a copy of the data set that only contains instances positively classified by c
static java.lang.Double[]	DataDiagnostics.getPerAttributeCorrelations(DataSet dataSet)
static java.lang.Double[]	DataDiagnostics.getTTests(DataSet dataSet)
static DataSet	Transform.normalize(DataSet oldData, java.lang.String[] attributesToNormalize, java.lang.String[] usersToUseForComputingNormalizationConstants, NormalizationConstants normalizationConstants) Normalizes listed features (zero mean and unit stdev); unlike the other normalize() method, it does not attempt to separate the users -- if you want normalization per user, feed it separate data sets for each user
static void	Transform.normalizeAttribute(DataSet data, int attIndex, double mean, double var)
static DataSet	Transform.normalizeUsingNormalizationConstants(DataSet dataSet, java.lang.String[] attributesToNormalize, NormalizationConstants normalizationConstants) This method uses the constants provided in the normalizationConstants to perform the normalization
double[]	ClassifierEvaluator.runStatisticalTests(DataSet baseLine, DataSet testSet, java.lang.String[] users, java.lang.String attributeForTesting, int minNumberRequiredForTesting) Runs pairwise statistical tests to look for statistically significant differences across users on several metrics.
double	FeatureSelection.search(java.util.List<java.lang.String> startingFeatures, java.lang.String[] allowedFeatures, PositiveAndUnlabeledClassifier c, DataSet dataSet, java.lang.String prefix, double bestOEC) Given a starting set of features, searches for the best set of features to use with a particular classifier