SYMBOL INDEX (1520 symbols across 168 files) FILE: src/main/java/mltk/cmdline/CmdLineParser.java class CmdLineParser (line 15) | public class CmdLineParser { method CmdLineParser (line 29) | public CmdLineParser(Class clazz, Object obj) { method parse (line 47) | public void parse(String[] args) throws IllegalArgumentException, Ille... method printUsage (line 86) | public void printUsage() { method processFields (line 105) | private void processFields(Field[] fields) { FILE: src/main/java/mltk/cmdline/options/HoldoutValidatedLearnerOptions.java class HoldoutValidatedLearnerOptions (line 5) | public class HoldoutValidatedLearnerOptions extends LearnerOptions { FILE: src/main/java/mltk/cmdline/options/HoldoutValidatedLearnerWithTaskOptions.java class HoldoutValidatedLearnerWithTaskOptions (line 5) | public class HoldoutValidatedLearnerWithTaskOptions extends HoldoutValid... FILE: src/main/java/mltk/cmdline/options/LearnerOptions.java class LearnerOptions (line 5) | public class LearnerOptions { FILE: src/main/java/mltk/cmdline/options/LearnerWithTaskOptions.java class LearnerWithTaskOptions (line 5) | public class LearnerWithTaskOptions extends LearnerOptions { FILE: src/main/java/mltk/core/Attribute.java class Attribute (line 9) | public abstract class Attribute implements Comparable, Copyab... type Type (line 11) | public enum Type { method getType (line 37) | public final Type getType() { method getIndex (line 46) | public final int getIndex() { method setIndex (line 55) | public final void setIndex(int index) { method getName (line 64) | public final String getName() { method compareTo (line 68) | @Override method hashCode (line 73) | @Override method equals (line 83) | @Override method toString (line 104) | @Override FILE: src/main/java/mltk/core/BinnedAttribute.java class BinnedAttribute (line 13) | public class BinnedAttribute extends Attribute { method BinnedAttribute (line 24) | public BinnedAttribute(String name, int numBins) { method BinnedAttribute (line 35) | public BinnedAttribute(String name, int numBins, int index) { method BinnedAttribute (line 49) | public BinnedAttribute(String name, Bins bins) { method BinnedAttribute (line 60) | public BinnedAttribute(String name, Bins bins, int index) { method copy (line 66) | @Override method getNumBins (line 78) | public int getNumBins() { method getBins (line 87) | public Bins getBins() { method toString (line 91) | public String toString() { method parse (line 106) | public static BinnedAttribute parse(String str) { FILE: src/main/java/mltk/core/Bins.java class Bins (line 11) | public class Bins { method Bins (line 23) | protected Bins() { method Bins (line 33) | public Bins(double[] boundaries, double[] medians) { method size (line 46) | public int size() { method getIndex (line 56) | public int getIndex(double value) { method getValue (line 72) | public double getValue(int index) { method getBoundaries (line 81) | public double[] getBoundaries() { method getMedians (line 90) | public double[] getMedians() { FILE: src/main/java/mltk/core/Copyable.java type Copyable (line 10) | public interface Copyable { method copy (line 17) | public T copy(); FILE: src/main/java/mltk/core/DenseVector.java class DenseVector (line 11) | public class DenseVector implements Vector { method DenseVector (line 20) | public DenseVector(double[] values) { method getValue (line 24) | @Override method getValues (line 29) | @Override method getValues (line 34) | @Override method setValue (line 43) | @Override method setValue (line 48) | @Override method copy (line 55) | @Override method isSparse (line 61) | @Override FILE: src/main/java/mltk/core/Instance.java class Instance (line 11) | public class Instance implements Copyable { method Instance (line 24) | public Instance(double[] values, double target, double weight) { method Instance (line 38) | public Instance(int[] indices, double[] values, double target, double ... method Instance (line 51) | public Instance(Vector vector, double target, double weight) { method Instance (line 63) | public Instance(double[] values, double target) { method Instance (line 74) | public Instance(int[] indices, double[] values, double target) { method Instance (line 84) | public Instance(Vector vector, double target) { method Instance (line 93) | public Instance(double[] values) { method Instance (line 103) | public Instance(int[] indices, double[] values) { method Instance (line 113) | public Instance(Vector vector, double[] values) { method Instance (line 122) | public Instance(Instance instance) { method isSparse (line 133) | public boolean isSparse() { method getValue (line 143) | public final double getValue(int attIndex) { method getValues (line 152) | public final double[] getValues() { method getValues (line 162) | public final double[] getValues(int... attributes) { method setValue (line 172) | public final void setValue(int attIndex, double value) { method setValue (line 182) | public final void setValue(Attribute attribute, double value) { method setValue (line 192) | public final void setValue(int[] attributes, double[] v) { method copy (line 198) | @Override method clone (line 209) | public Instance clone() { method isMissing (line 219) | public boolean isMissing(int attIndex) { method getValue (line 229) | public double getValue(Attribute att) { method getVector (line 238) | public Vector getVector() { method getWeight (line 247) | public double getWeight() { method setWeight (line 256) | public void setWeight(double weight) { method getTarget (line 265) | public double getTarget() { method setTarget (line 274) | public void setTarget(double target) { method toString (line 281) | public String toString() { method print (line 307) | protected void print(StringBuilder sb, double v) { FILE: src/main/java/mltk/core/Instances.java class Instances (line 16) | public class Instances implements Iterable, Copyable { method Instances (line 27) | public Instances(List attributes) { method Instances (line 37) | public Instances(List attributes, int capacity) { method Instances (line 47) | public Instances(List attributes, Attribute targetAtt) { method Instances (line 58) | public Instances(List attributes, Attribute targetAtt, int ... method Instances (line 69) | public Instances(Instances instances) { method add (line 80) | public void add(Instance instance) { method get (line 90) | public Instance get(int index) { method getTargetAttribute (line 99) | public final Attribute getTargetAttribute() { method setTargetAttribute (line 108) | public final void setTargetAttribute(Attribute targetAtt) { method iterator (line 112) | @Override method size (line 122) | public final int size() { method dimension (line 131) | public final int dimension() { method getAttributes (line 140) | public List getAttributes() { method getAttributes (line 150) | public List getAttributes(int... indices) { method setAttributes (line 163) | public void setAttributes(List attributes) { method clear (line 170) | public void clear() { method shuffle (line 177) | public void shuffle() { method shuffle (line 186) | public void shuffle(java.util.Random rand) { method copy (line 190) | @Override FILE: src/main/java/mltk/core/NominalAttribute.java class NominalAttribute (line 9) | public class NominalAttribute extends Attribute { method NominalAttribute (line 19) | public NominalAttribute(String name, String[] states) { method NominalAttribute (line 30) | public NominalAttribute(String name, String[] states, int index) { method copy (line 37) | public NominalAttribute copy() { method getCardinality (line 48) | public int getCardinality() { method getState (line 58) | public String getState(int index) { method getStates (line 67) | public String[] getStates() { method toString (line 71) | public String toString() { method parse (line 87) | public static NominalAttribute parse(String str) { FILE: src/main/java/mltk/core/NumericalAttribute.java class NumericalAttribute (line 9) | public class NumericalAttribute extends Attribute { method NumericalAttribute (line 16) | public NumericalAttribute(String name) { method NumericalAttribute (line 26) | public NumericalAttribute(String name, int index) { method copy (line 32) | public NumericalAttribute copy() { method toString (line 38) | public String toString() { method parse (line 48) | public static NumericalAttribute parse(String str) { FILE: src/main/java/mltk/core/Sampling.java class Sampling (line 17) | public class Sampling { method createBootstrapSample (line 25) | public static Instances createBootstrapSample(Instances instances) { method createBootstrapSample (line 49) | public static void createBootstrapSample(Instances instances, Map { method getValue (line 17) | public double getValue(int index); method getValues (line 24) | public double[] getValues(); method getValues (line 32) | public double[] getValues(int... indices); method setValue (line 40) | public void setValue(int index, double value); method setValue (line 48) | public void setValue(int[] indices, double[] v); method isSparse (line 55) | public boolean isSparse(); method copy (line 62) | public Vector copy(); FILE: src/main/java/mltk/core/Writable.java type Writable (line 12) | public interface Writable { method read (line 20) | void read(BufferedReader in) throws Exception; method write (line 28) | void write(PrintWriter out) throws Exception; FILE: src/main/java/mltk/core/io/AttributesReader.java class AttributesReader (line 23) | public class AttributesReader { method read (line 32) | public static Pair, Attribute> read(String attFile) th... method read (line 47) | public static Pair, Attribute> read(BufferedReader br)... FILE: src/main/java/mltk/core/io/InstancesReader.java class InstancesReader (line 24) | public class InstancesReader { method read (line 35) | public static Instances read(String attFile, String dataFile) throws I... method read (line 48) | public static Instances read(String attFile, String dataFile, String d... method read (line 152) | public static Instances read(String file, int targetIndex) throws IOEx... method read (line 165) | public static Instances read(String file, int targetIndex, String deli... method parseDenseInstance (line 201) | static Instance parseDenseInstance(String[] data, int classIndex) { method parseSparseInstance (line 232) | private static Instance parseSparseInstance(String[] data, TreeSet> list, int maxNumB... method discretize (line 230) | public static void discretize(Instances instances, int attIndex, Bins ... method discretize (line 250) | public static void discretize(Instances instances, int attIndex, int m... method getMedian (line 255) | static double getMedian(List stats, int start, double midP... method getStats (line 266) | static void getStats(List> list, List stat... method Discretizer (line 290) | public Discretizer() { FILE: src/main/java/mltk/core/processor/InstancesSplitter.java class InstancesSplitter (line 27) | public class InstancesSplitter { class Options (line 29) | static class Options { method main (line 66) | public static void main(String[] args) throws Exception { method createCrossValidationFolds (line 180) | public static Instances[][] createCrossValidationFolds(Instances insta... method createCrossValidationFolds (line 207) | public static Instances[][] createCrossValidationFolds(Instances insta... method createCrossValidationFolds (line 237) | public static Instances[][] createCrossValidationFolds(Instances insta... method createCrossValidationFolds (line 265) | public static Instances[][] createCrossValidationFolds(Instances insta... method split (line 294) | public static Instances[] split(Instances instances, double... ratios) { method split (line 329) | public static Instances[] split(Instances instances, double ratio) { method split (line 340) | public static Instances[] split(Instances instances, int k) { method split (line 363) | public static Instances[] split(Instances instances, String attToStrat... method split (line 376) | public static Instances[] split(Instances instances, String attToStrat... method split (line 414) | public static Instances[] split(Instances instances, String attToStrat... method getStrata (line 434) | private static List> getStrata(Instances instances, Str... FILE: src/main/java/mltk/core/processor/OneHotEncoder.java class OneHotEncoder (line 20) | public class OneHotEncoder { method process (line 29) | public Instances process(Instances instances) { FILE: src/main/java/mltk/feature/selection/BackwardElimination.java class BackwardElimination (line 22) | public class BackwardElimination { method select (line 33) | public static Pair, DoublePair> select(Instances train... method select (line 84) | public static Pair, DoublePair> select(Instances train... method evaluateModel (line 124) | private static DoublePair evaluateModel(Instances trainSet, Instances ... method evaluateModel (line 137) | private static DoublePair evaluateModel(Instances trainSet, Instances ... FILE: src/main/java/mltk/predictor/BaggedEnsemble.java class BaggedEnsemble (line 14) | public class BaggedEnsemble extends Ensemble { method BaggedEnsemble (line 19) | public BaggedEnsemble() { method BaggedEnsemble (line 28) | public BaggedEnsemble(int capacity) { method regress (line 32) | @Override method classify (line 46) | @Override method copy (line 74) | @Override FILE: src/main/java/mltk/predictor/BaggedEnsembleLearner.java class BaggedEnsembleLearner (line 12) | public class BaggedEnsembleLearner extends Learner { method BaggedEnsembleLearner (line 24) | public BaggedEnsembleLearner(int baggingIters, Learner learner) { method getBaggingIterations (line 34) | public int getBaggingIterations() { method setBaggingIterations (line 43) | public void setBaggingIterations(int baggingIters) { method getLearner (line 52) | public Learner getLearner() { method setLearner (line 61) | public void setLearner(Learner learner) { method getBags (line 70) | public Instances[] getBags() { method setBags (line 79) | public void setBags(Instances[] bags) { method build (line 83) | @Override method build (line 101) | public BaggedEnsemble build(Instances[] bags) { FILE: src/main/java/mltk/predictor/BoostedEnsemble.java class BoostedEnsemble (line 11) | public class BoostedEnsemble extends Ensemble { method BoostedEnsemble (line 16) | public BoostedEnsemble() { method BoostedEnsemble (line 25) | public BoostedEnsemble(int capacity) { method regress (line 29) | @Override method classify (line 39) | @Override method remove (line 54) | public void remove(int index) { method removeLast (line 61) | public void removeLast() { method copy (line 67) | @Override FILE: src/main/java/mltk/predictor/Classifier.java type Classifier (line 11) | public interface Classifier extends Predictor { method classify (line 19) | public int classify(Instance instance); FILE: src/main/java/mltk/predictor/Ensemble.java class Ensemble (line 14) | public abstract class Ensemble implements Classifier, Regressor { method Ensemble (line 21) | public Ensemble() { method Ensemble (line 30) | public Ensemble(int capacity) { method get (line 40) | public Predictor get(int index) { method getPredictors (line 49) | public List getPredictors() { method add (line 58) | public void add(Predictor predictor) { method size (line 67) | public int size() { method clear (line 74) | public void clear() { method read (line 78) | @Override method write (line 94) | @Override FILE: src/main/java/mltk/predictor/Family.java type Family (line 9) | public enum Family { method get (line 20) | public static Family get(String name) { method Family (line 32) | Family(String name, LinkFunction link) { method getDefaultLinkFunction (line 42) | public LinkFunction getDefaultLinkFunction() { method toString (line 49) | public String toString() { FILE: src/main/java/mltk/predictor/HoldoutValidatedLearner.java class HoldoutValidatedLearner (line 13) | public abstract class HoldoutValidatedLearner extends Learner { method HoldoutValidatedLearner (line 22) | public HoldoutValidatedLearner() { method getValidSet (line 31) | public Instances getValidSet() { method setValidSet (line 40) | public void setValidSet(Instances validSet) { method getMetric (line 49) | public Metric getMetric() { method setMetric (line 58) | public void setMetric(Metric metric) { method getConvergenceTester (line 67) | public ConvergenceTester getConvergenceTester() { method setConvergenceTester (line 76) | public void setConvergenceTester(ConvergenceTester ct) { FILE: src/main/java/mltk/predictor/Learner.java class Learner (line 26) | public abstract class Learner { method isVerbose (line 35) | public boolean isVerbose() { method setVerbose (line 44) | public void setVerbose(boolean verbose) { type Task (line 52) | public enum Task { method Task (line 65) | Task(String task) { method toString (line 72) | public String toString() { method get (line 82) | public static Task get(String name) { method getDefaultMetric (line 96) | public Metric getDefaultMetric() { method build (line 119) | public abstract Predictor build(Instances instances); method isSparse (line 127) | protected boolean isSparse(Instances instances) { method getSparseDataset (line 145) | protected SparseDataset getSparseDataset(Instances instances, boolean ... method getDenseDataset (line 241) | protected DenseDataset getDenseDataset(Instances instances, boolean no... class SparseDataset (line 306) | protected class SparseDataset { method SparseDataset (line 315) | SparseDataset(int[] attrs, int[][] indices, double[][] values, doubl... class DenseDataset (line 331) | protected class DenseDataset { method DenseDataset (line 339) | DenseDataset(int[] attrs, double[][] x, double[] y, double[] stdList... FILE: src/main/java/mltk/predictor/LinkFunction.java type LinkFunction (line 11) | public enum LinkFunction { method get (line 22) | public static LinkFunction get(String name) { method LinkFunction (line 33) | LinkFunction(String name) { method applyInverse (line 43) | public double applyInverse(double x) { method toString (line 61) | public String toString() { FILE: src/main/java/mltk/predictor/Predictor.java type Predictor (line 15) | public interface Predictor extends Writable, Copyable { method read (line 23) | public void read(BufferedReader in) throws Exception; method write (line 31) | public void write(PrintWriter out) throws Exception; FILE: src/main/java/mltk/predictor/ProbabilisticClassifier.java type ProbabilisticClassifier (line 11) | public interface ProbabilisticClassifier extends Classifier { method predictProbabilities (line 19) | public double[] predictProbabilities(Instance instance); FILE: src/main/java/mltk/predictor/Regressor.java type Regressor (line 11) | public interface Regressor extends Predictor { method regress (line 19) | public double regress(Instance instance); FILE: src/main/java/mltk/predictor/evaluation/AUC.java class AUC (line 15) | public class AUC extends SimpleMetric { class DoublePairComparator (line 17) | private class DoublePairComparator implements Comparator { method compare (line 19) | @Override method AUC (line 33) | public AUC() { method eval (line 37) | @Override method eval (line 46) | @Override method eval (line 55) | protected double eval(DoublePair[] a) { FILE: src/main/java/mltk/predictor/evaluation/ConvergenceTester.java class ConvergenceTester (line 12) | public class ConvergenceTester { method parse (line 28) | public static ConvergenceTester parse(String cc) { method ConvergenceTester (line 53) | public ConvergenceTester(int minNumPoints, double c) { method ConvergenceTester (line 63) | public ConvergenceTester(int minNumPoints, int n) { method ConvergenceTester (line 74) | public ConvergenceTester(int minNumPoints, int n, double c) { method ConvergenceTester (line 87) | public ConvergenceTester(int minNumPoints, int n, double c, int capaci... method getMetric (line 105) | public Metric getMetric() { method setMetric (line 114) | public void setMetric(Metric metric) { method add (line 126) | public void add(double measure) { method getBestIndex (line 140) | public int getBestIndex() { method getBestMetricValue (line 149) | public double getBestMetricValue() { method size (line 158) | public int size() { method getMeasureList (line 167) | public List getMeasureList() { method isConverged (line 176) | public boolean isConverged() { FILE: src/main/java/mltk/predictor/evaluation/Error.java class Error (line 11) | public class Error extends SimpleMetric { method Error (line 16) | public Error() { method eval (line 20) | @Override method eval (line 33) | @Override FILE: src/main/java/mltk/predictor/evaluation/Evaluator.java class Evaluator (line 22) | public class Evaluator { method evalAreaUnderROC (line 31) | public static double evalAreaUnderROC(ProbabilisticClassifier classifi... method evalRMSE (line 49) | public static double evalRMSE(List preds, List targets) { method evalRMSE (line 66) | public static double evalRMSE(Regressor regressor, Instances instances) { method evalError (line 86) | public static double evalError(Classifier classifier, Instances instan... method evalLogisticLoss (line 107) | public static double evalLogisticLoss(Regressor regressor, Instances i... method evalMAE (line 125) | public static double evalMAE(Regressor regressor, Instances instances) { class Options (line 138) | static class Options { method main (line 168) | public static void main(String[] args) throws Exception { FILE: src/main/java/mltk/predictor/evaluation/LogLoss.java class LogLoss (line 12) | public class LogLoss extends SimpleMetric { method LogLoss (line 19) | public LogLoss() { method LogLoss (line 28) | public LogLoss(boolean isRawScore) { method eval (line 33) | @Override method eval (line 38) | @Override method isRawScore (line 53) | public boolean isRawScore() { FILE: src/main/java/mltk/predictor/evaluation/LogisticLoss.java class LogisticLoss (line 12) | public class LogisticLoss extends SimpleMetric { method LogisticLoss (line 17) | public LogisticLoss() { method eval (line 21) | @Override method eval (line 26) | @Override FILE: src/main/java/mltk/predictor/evaluation/MAE.java class MAE (line 11) | public class MAE extends SimpleMetric { method MAE (line 16) | public MAE() { method eval (line 20) | @Override method eval (line 30) | @Override FILE: src/main/java/mltk/predictor/evaluation/Metric.java class Metric (line 14) | public abstract class Metric { method Metric (line 23) | public Metric(boolean isLargerBetter) { method isLargerBetter (line 32) | public boolean isLargerBetter() { method isFirstBetter (line 43) | public boolean isFirstBetter(double a, double b) { method worstValue (line 52) | public double worstValue() { method eval (line 67) | public abstract double eval(double[] preds, Instances instances); method searchBestMetricValueIndex (line 75) | public int searchBestMetricValueIndex(List list) { FILE: src/main/java/mltk/predictor/evaluation/MetricFactory.java class MetricFactory (line 12) | public class MetricFactory { method getMetric (line 33) | public static Metric getMetric(String str) { FILE: src/main/java/mltk/predictor/evaluation/Predictor.java class Predictor (line 25) | public class Predictor { class Options (line 27) | static class Options { method main (line 69) | public static void main(String[] args) throws Exception { method predict (line 168) | public static void predict(Regressor regressor, Instances instances, S... method predict (line 194) | public static void predict(Classifier classifier, Instances instances,... FILE: src/main/java/mltk/predictor/evaluation/RMSE.java class RMSE (line 11) | public class RMSE extends SimpleMetric { method RMSE (line 16) | public RMSE() { method eval (line 20) | @Override method eval (line 31) | @Override FILE: src/main/java/mltk/predictor/evaluation/SimpleMetric.java class SimpleMetric (line 9) | public abstract class SimpleMetric extends Metric { method SimpleMetric (line 16) | public SimpleMetric(boolean isLargerBetter) { method eval (line 27) | public abstract double eval(double[] preds, double[] targets); FILE: src/main/java/mltk/predictor/function/Array1D.java class Array1D (line 17) | public class Array1D implements Regressor, UnivariateFunction { method Array1D (line 37) | public Array1D() { method Array1D (line 47) | public Array1D(int attIndex, double[] predictions) { method Array1D (line 58) | public Array1D(int attIndex, double[] predictions, double predictionOn... method getAttributeIndex (line 69) | public int getAttributeIndex() { method setAttributeIndex (line 78) | public void setAttributeIndex(int attIndex) { method getPredictions (line 87) | public double[] getPredictions() { method setPredictions (line 96) | public void setPredictions(double[] predictions) { method getPredictionOnMV (line 105) | public double getPredictionOnMV() { method setPredictionOnMV (line 114) | public void setPredictionOnMV(double predictionOnMV) { method read (line 118) | @Override method write (line 132) | @Override method regress (line 141) | @Override method add (line 157) | public Array1D add(Array1D ary) { method evaluate (line 168) | @Override method copy (line 177) | @Override FILE: src/main/java/mltk/predictor/function/Array2D.java class Array2D (line 18) | public class Array2D implements Regressor, BivariateFunction { method Array2D (line 53) | public Array2D() { method Array2D (line 64) | public Array2D(int attIndex1, int attIndex2, double[][] predictions) { method Array2D (line 80) | public Array2D(int attIndex1, int attIndex2, double[][] predictions, method getAttributeIndex1 (line 95) | public int getAttributeIndex1() { method getAttributeIndex2 (line 104) | public int getAttributeIndex2() { method getAttributeIndices (line 113) | public IntPair getAttributeIndices() { method setAttributeIndices (line 123) | public void setAttributeIndices(int attIndex1, int attIndex2) { method getPredictions (line 133) | public double[][] getPredictions() { method setPredictions (line 142) | public void setPredictions(double[][] predictions) { method getPredictionsOnMV1 (line 151) | public double[] getPredictionsOnMV1() { method setPredictionsOnMV1 (line 160) | public void setPredictionsOnMV1(double[] predictionsOnMV1) { method getPredictionsOnMV2 (line 169) | public double[] getPredictionsOnMV2() { method setPredictionsOnMV2 (line 178) | public void setPredictionsOnMV2(double[] predictionsOnMV2) { method getPredictionOnMV12 (line 187) | public double getPredictionOnMV12() { method setPredictionOnMV12 (line 196) | public void setPredictionOnMV12(double predictionOnMV12) { method read (line 200) | @Override method write (line 225) | @Override method regress (line 241) | @Override method add (line 252) | public Array2D add(Array2D ary) { method evaluate (line 271) | @Override method copy (line 285) | @Override FILE: src/main/java/mltk/predictor/function/BaggedLineCutter.java class BaggedLineCutter (line 24) | public class BaggedLineCutter extends EnsembledLineCutter { method BaggedLineCutter (line 31) | public BaggedLineCutter() { method BaggedLineCutter (line 40) | public BaggedLineCutter(boolean isClassification) { method createBags (line 51) | public void createBags(int n, int baggingIters) { method build (line 67) | @Override method build (line 72) | @Override FILE: src/main/java/mltk/predictor/function/BivariateFunction.java type BivariateFunction (line 9) | public interface BivariateFunction { method evaluate (line 18) | public double evaluate(double x, double y); FILE: src/main/java/mltk/predictor/function/CHistogram.java class CHistogram (line 9) | public class CHistogram { method CHistogram (line 21) | public CHistogram(int n) { method size (line 33) | public int size() { method hasMissingValue (line 42) | public boolean hasMissingValue() { FILE: src/main/java/mltk/predictor/function/CompressionUtils.java class CompressionUtils (line 15) | public class CompressionUtils { method compress (line 24) | public static Function1D compress(int attIndex, BaggedEnsemble baggedE... method compress (line 47) | public static Function1D compress(int attIndex, BoostedEnsemble booste... method convert (line 71) | public static Array1D convert(int n, Function1D function) { method compress (line 87) | public static Function2D compress(int attIndex1, int attIndex2, Bagged... method compress (line 112) | public static Function2D compress(int attIndex1, int attIndex2, Booste... method compress (line 138) | public static Array2D compress(int attIndex1, int attIndex2, int n1, i... method convert (line 173) | public static Array2D convert(int n1, int n2, Function2D function) { FILE: src/main/java/mltk/predictor/function/CubicSpline.java class CubicSpline (line 17) | public class CubicSpline implements Regressor, UnivariateFunction { method CubicSpline (line 32) | public CubicSpline(int attIndex, double intercept, double[] knots, dou... method CubicSpline (line 46) | public CubicSpline(double intercept, double[] knots, double[] w) { method CubicSpline (line 56) | public CubicSpline(double[] knots, double[] w) { method CubicSpline (line 65) | public CubicSpline(double[] knots) { method CubicSpline (line 72) | public CubicSpline() { method read (line 76) | @Override method write (line 86) | @Override method evaluate (line 97) | @Override method h (line 113) | public static double h(double x, double k) { method regress (line 121) | @Override method getAttributeIndex (line 131) | public int getAttributeIndex() { method setAttributeIndex (line 140) | public void setAttributeIndex(int attIndex) { method getCoefficients (line 149) | public double[] getCoefficients() { method getKnots (line 158) | public double[] getKnots() { method getIntercept (line 167) | public double getIntercept() { method copy (line 171) | @Override FILE: src/main/java/mltk/predictor/function/EnsembledLineCutter.java class EnsembledLineCutter (line 8) | public abstract class EnsembledLineCutter extends Learner { method build (line 18) | @Override method build (line 31) | public BaggedEnsemble build(Instances instances, int attIndex, int num... method build (line 36) | public abstract BaggedEnsemble build(Instances instances, Attribute at... method getAttributeIndex (line 43) | public int getAttributeIndex() { method setAttributeIndex (line 52) | public void setAttributeIndex(int attIndex) { method getBaggingIters (line 61) | public int getBaggingIters() { method setBaggingIters (line 70) | public void setBaggingIters(int baggingIters) { method isClassification (line 79) | public boolean isClassification() { method setClassification (line 88) | public void setClassification(boolean isClassification) { method getNumIntervals (line 97) | public int getNumIntervals() { method setNumIntervals (line 106) | public void setNumIntervals(int numIntervals) { FILE: src/main/java/mltk/predictor/function/Function1D.java class Function1D (line 25) | public class Function1D implements Regressor, UnivariateFunction { method getConstantFunction (line 55) | public static Function1D getConstantFunction(int attIndex, double pred... method setZero (line 64) | public void setZero() { method isZero (line 75) | public boolean isZero() { method isConstant (line 85) | public boolean isConstant() { method multiply (line 96) | public Function1D multiply(double c) { method divide (line 108) | public Function1D divide(double c) { method add (line 120) | public Function1D add(double c) { method subtract (line 132) | public Function1D subtract(double c) { method Function1D (line 141) | public Function1D() { method Function1D (line 152) | public Function1D(int attIndex, double[] splits, double[] predictions) { method Function1D (line 164) | public Function1D(int attIndex, double[] splits, double[] predictions,... method add (line 177) | public Function1D add(Function1D func) { method getAttributeIndex (line 220) | public int getAttributeIndex() { method setAttributeIndex (line 229) | public void setAttributeIndex(int attIndex) { method getSplits (line 238) | public double[] getSplits() { method setSplits (line 247) | public void setSplits(double[] splits) { method getPredictions (line 256) | public double[] getPredictions() { method setPredictions (line 265) | public void setPredictions(double[] predictions) { method getPredictionOnMV (line 274) | public double getPredictionOnMV() { method setPredictionOnMV (line 283) | public void setPredictionOnMV(double predictionOnMV) { method read (line 287) | @Override method write (line 306) | @Override method regress (line 317) | @Override method evaluate (line 322) | @Override method copy (line 331) | @Override method getSegmentIndex (line 344) | protected int getSegmentIndex(double x) { FILE: src/main/java/mltk/predictor/function/Function2D.java class Function2D (line 25) | public class Function2D implements Regressor, BivariateFunction { method Function2D (line 68) | public Function2D() { method Function2D (line 81) | public Function2D(int attIndex1, int attIndex2, double[] splits1, doub... method Function2D (line 97) | public Function2D(int attIndex1, int attIndex2, method getConstantFunction (line 118) | public static Function2D getConstantFunction(int attIndex1, int attInd... method getAttributeIndex1 (line 129) | public int getAttributeIndex1() { method getAttributeIndex2 (line 138) | public int getAttributeIndex2() { method getAttributeIndices (line 147) | public IntPair getAttributeIndices() { method setAttributeIndices (line 157) | public void setAttributeIndices(int attIndex1, int attIndex2) { method getPredictions (line 167) | public double[][] getPredictions() { method setPredictions (line 176) | public void setPredictions(double[][] predictions) { method getPredictionsOnMV1 (line 185) | public double[] getPredictionsOnMV1() { method setPredictionsOnMV1 (line 194) | public void setPredictionsOnMV1(double[] predictionsOnMV1) { method getPredictionsOnMV2 (line 203) | public double[] getPredictionsOnMV2() { method setPredictionsOnMV2 (line 212) | public void setPredictionsOnMV2(double[] predictionsOnMV2) { method getPredictionOnMV12 (line 221) | public double getPredictionOnMV12() { method setPredictionOnMV12 (line 230) | public void setPredictionOnMV12(double predictionOnMV12) { method multiply (line 240) | public Function2D multiply(double c) { method divide (line 256) | public Function2D divide(double c) { method add (line 272) | public Function2D add(double c) { method subtract (line 288) | public Function2D subtract(double c) { method add (line 304) | public Function2D add(Function2D func) { method read (line 386) | @Override method write (line 419) | @Override method regress (line 439) | @Override method evaluate (line 444) | @Override method copy (line 460) | @Override method getSegmentIndex (line 481) | protected IntPair getSegmentIndex(double x1, double x2) { FILE: src/main/java/mltk/predictor/function/Histogram2D.java class Histogram2D (line 13) | public class Histogram2D { class Table (line 24) | public static class Table { method Table (line 36) | public Table(int n, int m) { method computeHistogram2D (line 56) | public static void computeHistogram2D(Instances instances, int f1, int... method computeTable (line 88) | public static Table computeTable(Histogram2D hist2d, CHistogram cHist1... method fillTable (line 138) | protected static void fillTable(Table table, int i, int j, CHistogram ... method Histogram2D (line 156) | public Histogram2D(int n, int m) { method computeCHistogram (line 172) | public Pair computeCHistogram() { FILE: src/main/java/mltk/predictor/function/LineCutter.java class LineCutter (line 25) | public class LineCutter extends Learner { class Interval (line 27) | static class Interval implements Comparable { method Interval (line 43) | Interval() { method Interval (line 47) | Interval(int start, int end, double sum, double weight) { method compareTo (line 55) | @Override method getPrediction (line 66) | double getPrediction() { method isFinalized (line 70) | boolean isFinalized() { method isInteriorNode (line 74) | boolean isInteriorNode() { method isLeaf (line 78) | boolean isLeaf() { method LineCutter (line 93) | public LineCutter() { method LineCutter (line 102) | public LineCutter(boolean isClassification) { method build (line 107) | @Override method build (line 120) | public Function1D build(Instances instances, Attribute attribute, int ... method build (line 208) | public Function1D build(Instances instances, int attIndex, int numInte... method getAttributeIndex (line 218) | public int getAttributeIndex() { method setAttributeIndex (line 227) | public void setAttributeIndex(int attIndex) { method isClassification (line 236) | public boolean isClassification() { method setClassification (line 245) | public void setClassification(boolean isClassification) { method getNumIntervals (line 254) | public int getNumIntervals() { method setNumIntervals (line 263) | public void setNumIntervals(int numIntervals) { method getHistograms (line 267) | protected static void getHistograms(List> pairs, Lis... method build (line 295) | protected static Function1D build(int attIndex, List histogr... method sumUp (line 365) | protected static double[] sumUp(List histograms, int start, ... method split (line 376) | protected static void split(List histograms, Interval parent) { method split (line 380) | protected static void split(List histograms, Interval parent... method inorder (line 436) | protected static void inorder(Interval parent, List splits, Li... FILE: src/main/java/mltk/predictor/function/LinearFunction.java class LinearFunction (line 15) | public class LinearFunction implements Regressor, UnivariateFunction { method LinearFunction (line 30) | public LinearFunction() { method LinearFunction (line 39) | public LinearFunction(double beta) { method LinearFunction (line 49) | public LinearFunction(int attIndex, double beta) { method read (line 54) | @Override method write (line 60) | @Override method evaluate (line 67) | @Override method regress (line 72) | @Override method getSlope (line 77) | public double getSlope() { method setSlope (line 81) | public void setSlope(double beta) { method getAttributeIndex (line 85) | public int getAttributeIndex() { method copy (line 89) | @Override FILE: src/main/java/mltk/predictor/function/SquareCutter.java class SquareCutter (line 21) | public class SquareCutter extends Learner { method SquareCutter (line 30) | public SquareCutter() { method SquareCutter (line 39) | public SquareCutter(boolean lineSearch) { method setAttIndices (line 49) | public void setAttIndices(int attIndex1, int attIndex2) { method build (line 54) | public Function2D build(Instances instances) { method findCuts (line 141) | protected static void findCuts(Histogram2D.Table table, int v1, int[] ... method findCuts (line 199) | protected static void findCuts(Histogram2D.Table table, int[] v1, int ... method getPredictor (line 257) | protected static void getPredictor(Histogram2D.Table table, int v1, in... method getPredictor (line 285) | protected static void getPredictor(Histogram2D.Table table, int[] v1, ... method getRSS (line 313) | protected static double getRSS(Histogram2D.Table table, int v1, int v2... method getRSS (line 355) | protected static double getRSS(Histogram2D.Table table, int[] v1, int ... method getFunction2D (line 398) | protected static Function2D getFunction2D(int attIndex1, int attIndex2... method getFunction2D (line 515) | protected static Function2D getFunction2D(int attIndex1, int attIndex2... method lineSearch (line 648) | protected static void lineSearch(Instances instances, int attIndex1, i... FILE: src/main/java/mltk/predictor/function/SubagSequence.java class SubagSequence (line 17) | class SubagSequence { class SampleDelta (line 19) | static class SampleDelta { method SampleDelta (line 24) | SampleDelta(Set toAdd, Set toDel) { method getDistance (line 37) | int getDistance() { class Sample (line 43) | static class Sample { method Sample (line 48) | Sample(Set set) { method computeDistance (line 57) | static int computeDistance(Sample s1, Sample s2) { method computeDelta (line 62) | static SampleDelta computeDelta(Sample s1, Sample s2) { method getWeight (line 71) | int getWeight() { method SubagSequence (line 83) | SubagSequence(int n, int m, int baggingIters) { method createSubsample (line 92) | Sample createSubsample(int n, int m) { method computeSequence (line 104) | private void computeSequence(Sample[] samples) { FILE: src/main/java/mltk/predictor/function/SubaggedLineCutter.java class SubaggedLineCutter (line 22) | public class SubaggedLineCutter extends EnsembledLineCutter { method SubaggedLineCutter (line 31) | public SubaggedLineCutter() { method SubaggedLineCutter (line 40) | public SubaggedLineCutter(boolean isClassification) { method createSubags (line 52) | public void createSubags(int n, double subsampleRatio, int baggingIter... method build (line 57) | @Override class Histogram (line 62) | class Histogram { method Histogram (line 65) | Histogram(double[][] histogram) { method copy (line 69) | Histogram copy() { method build (line 90) | public BaggedEnsemble build(Instances instances, int attIndex, int num... method build (line 104) | public BaggedEnsemble build(Instances instances, Attribute attribute, ... method buildFromHistogram (line 220) | protected Function1D buildFromHistogram(int attIndex, Histogram histog... FILE: src/main/java/mltk/predictor/function/UnivariateFunction.java type UnivariateFunction (line 9) | public interface UnivariateFunction { method evaluate (line 17) | public double evaluate(double x); FILE: src/main/java/mltk/predictor/gam/DenseDesignMatrix.java class DenseDesignMatrix (line 10) | class DenseDesignMatrix { method DenseDesignMatrix (line 16) | DenseDesignMatrix(double[][][] x, double[][] knots, double[][] std) { method createCubicSplineDesignMatrix (line 22) | static DenseDesignMatrix createCubicSplineDesignMatrix(double[][] data... FILE: src/main/java/mltk/predictor/gam/GA2MLearner.java class GA2MLearner (line 51) | public class GA2MLearner extends HoldoutValidatedLearner { class Options (line 53) | static class Options extends HoldoutValidatedLearnerWithTaskOptions { method main (line 99) | public static void main(String[] args) throws Exception { method GA2MLearner (line 174) | public GA2MLearner() { method getBaggingIters (line 188) | public int getBaggingIters() { method setBaggingIters (line 197) | public void setBaggingIters(int baggingIters) { method getMaxNumIters (line 206) | public int getMaxNumIters() { method setMaxNumIters (line 215) | public void setMaxNumIters(int maxNumIters) { method getLearningRate (line 224) | public double getLearningRate() { method setLearningRate (line 233) | public void setLearningRate(double learningRate) { method getTask (line 242) | public Task getTask() { method setTask (line 251) | public void setTask(Task task) { method getGAM (line 260) | public GAM getGAM() { method setGAM (line 269) | public void setGAM(GAM gam) { method getPairs (line 278) | public List getPairs() { method setPairs (line 287) | public void setPairs(List pairs) { method buildClassifier (line 300) | public void buildClassifier(GAM gam, List terms, Instances tr... method buildClassifier (line 447) | public void buildClassifier(GAM gam, List terms, Instances tr... method buildRegressor (line 563) | public void buildRegressor(GAM gam, List terms, Instances tra... method buildRegressor (line 708) | public void buildRegressor(GAM gam, List terms, Instances tra... method build (line 813) | @Override method indexOf (line 848) | private int indexOf(List terms, IntPair pair) { FILE: src/main/java/mltk/predictor/gam/GAM.java class GAM (line 22) | public class GAM implements ProbabilisticClassifier, Regressor { class RegressorList (line 24) | class RegressorList implements Iterable { method RegressorList (line 28) | RegressorList() { method iterator (line 32) | @Override class TermList (line 38) | class TermList implements Iterable { method TermList (line 42) | TermList() { method iterator (line 46) | @Override method GAM (line 59) | public GAM() { method getIntercept (line 70) | public double getIntercept() { method setIntercept (line 79) | public void setIntercept(double intercept) { method read (line 83) | @Override method write (line 105) | @Override method add (line 125) | public void add(int[] term, Regressor regressor) { method regress (line 130) | @Override method classify (line 139) | @Override method predictProbabilities (line 145) | @Override method getTerms (line 157) | public List getTerms() { method getRegressors (line 166) | public List getRegressors() { method copy (line 170) | @Override FILE: src/main/java/mltk/predictor/gam/GAMLearner.java class GAMLearner (line 49) | public class GAMLearner extends HoldoutValidatedLearner { class Options (line 51) | static class Options extends HoldoutValidatedLearnerWithTaskOptions { method main (line 89) | public static void main(String[] args) throws Exception { method GAMLearner (line 147) | public GAMLearner() { method getBaggingIters (line 163) | public int getBaggingIters() { method setBaggingIters (line 172) | public void setBaggingIters(int baggingIters) { method getMaxNumIters (line 181) | public int getMaxNumIters() { method setMaxNumIters (line 190) | public void setMaxNumIters(int maxNumIters) { method getMaxNumLeaves (line 199) | public int getMaxNumLeaves() { method setMaxNumLeaves (line 208) | public void setMaxNumLeaves(int maxNumLeaves) { method getLearningRate (line 217) | public double getLearningRate() { method setLearningRate (line 226) | public void setLearningRate(double learningRate) { method getSubsamplingRatio (line 235) | public double getSubsamplingRatio() { method setSubsamplingRatio (line 244) | public void setSubsamplingRatio(double alpha) { method getTask (line 253) | public Task getTask() { method setTask (line 262) | public void setTask(Task task) { method setBaseLearner (line 271) | public void setBaseLearner(String option) { method buildClassifier (line 302) | public GAM buildClassifier(Instances trainSet, Instances validSet, int... method buildClassifier (line 444) | public GAM buildClassifier(Instances trainSet, int maxNumIters, int ma... method buildRegressor (line 559) | public GAM buildRegressor(Instances trainSet, Instances validSet, int ... method buildRegressor (line 695) | public GAM buildRegressor(Instances trainSet, int maxNumIters, int max... method build (line 792) | @Override FILE: src/main/java/mltk/predictor/gam/GAMUtils.java class GAMUtils (line 13) | class GAMUtils { method getGAM (line 15) | static GAM getGAM(GLM glm, List attList) { FILE: src/main/java/mltk/predictor/gam/SPLAMLearner.java class SPLAMLearner (line 33) | public class SPLAMLearner extends Learner { class Options (line 35) | static class Options extends LearnerWithTaskOptions { method main (line 73) | public static void main(String[] args) throws Exception { class ModelStructure (line 113) | static class ModelStructure { method ModelStructure (line 121) | ModelStructure(byte[] structure) { method equals (line 125) | @Override method hashCode (line 139) | @Override method SPLAMLearner (line 164) | public SPLAMLearner() { method build (line 176) | @Override method buildBinaryClassifier (line 210) | public GAM buildBinaryClassifier(int[] attrs, double[][][] x, double[]... method buildBinaryClassifier (line 310) | public GAM buildBinaryClassifier(int[] attrs, int[][] indices, double[... method buildClassifier (line 408) | public GAM buildClassifier(Instances trainSet, boolean isSparse, int n... method buildClassifier (line 508) | public GAM buildClassifier(Instances trainSet, int maxNumIters, int nu... method buildRegressor (line 523) | public GAM buildRegressor(Instances trainSet, boolean isSparse, int ma... method buildRegressor (line 623) | public GAM buildRegressor(Instances trainSet, int maxNumIters, int num... method buildRegressor (line 640) | public GAM buildRegressor(int[] attrs, double[][][] x, double[] y, dou... method buildRegressor (line 741) | public GAM buildRegressor(int[] attrs, int[][] indices, double[][][] v... method fitIntercept (line 839) | public boolean fitIntercept() { method fitIntercept (line 848) | public void fitIntercept(boolean fitIntercept) { method getAlpha (line 857) | public double getAlpha() { method getEpsilon (line 866) | public double getEpsilon() { method getLambda (line 875) | public double getLambda() { method getMaxNumIters (line 884) | public int getMaxNumIters() { method getNumKnots (line 893) | public int getNumKnots() { method getTask (line 902) | public Task getTask() { method isVerbose (line 911) | public boolean isVerbose() { method refit (line 920) | public boolean refit() { method refit (line 929) | public void refit(boolean refit) { method setAlpha (line 938) | public void setAlpha(double alpha) { method setEpsilon (line 947) | public void setEpsilon(double epsilon) { method setLambda (line 956) | public void setLambda(double lambda) { method setMaxNumIters (line 965) | public void setMaxNumIters(int maxNumIters) { method setNumKnots (line 974) | public void setNumKnots(int numKnots) { method setTask (line 983) | public void setTask(Task task) { method setVerbose (line 992) | public void setVerbose(boolean verbose) { method findMaxLambda (line 996) | public double findMaxLambda(Instances trainSet, Task task, int numKnot... method findMaxLambda (line 1029) | protected double findMaxLambda(double[][][] x, double[] rTrain, double... method findMaxLambda (line 1067) | protected double findMaxLambda(double[][][] x, double[] y, double[] pT... method testZeroPoint (line 1104) | protected boolean testZeroPoint(double[][][] x, double[] y, double[] t... method testZeroPoint (line 1152) | protected boolean testZeroPoint(double[][][] x, double[] y, double[] p... method computeGradient (line 1200) | protected void computeGradient(double[][] block, double[] rTrain, doub... method computeGradient (line 1206) | protected void computeGradient(int[] index, double[][] block, double[]... method doOnePass (line 1216) | protected boolean doOnePass(double[][][] x, double[] tl1, double[] tl2... method doOnePass (line 1287) | protected boolean doOnePass(double[][][] x, double[] y, double[] tl1, ... method doOnePass (line 1359) | protected boolean doOnePass(int[][] indices, double[][][] values, doub... method doOnePass (line 1431) | protected boolean doOnePass(int[][] indices, double[][][] values, doub... method extractStructure (line 1507) | protected byte[] extractStructure(double[][] w) { method getGAM (line 1525) | protected GAM getGAM(int[] attrs, double[][] knots, double[][] w, doub... method getPenalty (line 1546) | protected double getPenalty(double[] w, double lambda1, double lambda2) { method getPenalty (line 1557) | protected double getPenalty(double[][] coef, double[] lambda1, double[... method getRegularizationParameters (line 1565) | protected void getRegularizationParameters(double lambda, double alpha... method refitClassifier (line 1572) | protected GAM refitClassifier(int[] attrs, byte[] struct, double[][][]... method refitClassifier (line 1624) | protected GAM refitClassifier(int[] attrs, byte[] struct, int[][] indi... method refitRegressor (line 1684) | protected GAM refitRegressor(int[] attrs, byte[] struct, double[][][] ... method refitRegressor (line 1747) | protected GAM refitRegressor(int[] attrs, byte[] struct, int[][] indic... FILE: src/main/java/mltk/predictor/gam/ScorecardModelLearner.java class ScorecardModelLearner (line 22) | public class ScorecardModelLearner extends Learner { class Options (line 24) | static class Options extends LearnerWithTaskOptions { method main (line 51) | public static void main(String[] args) throws Exception { method ScorecardModelLearner (line 89) | public ScorecardModelLearner() { method getLambda (line 102) | public double getLambda() { method setLambda (line 111) | public void setLambda(double lambda) { method getMaxNumIters (line 120) | public int getMaxNumIters() { method setMaxNumIters (line 129) | public void setMaxNumIters(int maxNumIters) { method getTask (line 138) | public Task getTask() { method setTask (line 147) | public void setTask(Task task) { method buildClassifier (line 159) | public GAM buildClassifier(Instances trainSet, int maxNumIters, double... method buildRegressor (line 181) | public GAM buildRegressor(Instances trainSet, int maxNumIters, double ... method build (line 195) | @Override FILE: src/main/java/mltk/predictor/gam/SparseDesignMatrix.java class SparseDesignMatrix (line 10) | class SparseDesignMatrix { method SparseDesignMatrix (line 17) | SparseDesignMatrix(int[][] indices, double[][][] values, double[][] kn... method createCubicSplineDesignMatrix (line 24) | static SparseDesignMatrix createCubicSplineDesignMatrix(int n, int[][]... FILE: src/main/java/mltk/predictor/gam/interaction/FAST.java class FAST (line 39) | public class FAST { class FASTThread (line 41) | static class FASTThread extends Thread { method FASTThread (line 46) | FASTThread(Instances instances) { method add (line 51) | public void add(Element pair) { method run (line 55) | public void run() { class Options (line 60) | static class Options { method main (line 98) | public static void main(String[] args) throws Exception { method computeWeights (line 175) | public static void computeWeights(Instances instances, List pair, CHistogram[... method getPredictor (line 265) | protected static void getPredictor(Histogram2D.Table table, int v1, in... method getRSS (line 280) | protected static double getRSS(Histogram2D.Table table, int v1, int v2... FILE: src/main/java/mltk/predictor/gam/tool/Diagnostics.java class Diagnostics (line 28) | public class Diagnostics { type Mode (line 36) | public enum Mode { method Mode (line 49) | Mode(String mode) { method toString (line 53) | public String toString() { method getEnum (line 63) | public static Mode getEnum(String mode) { method diagnose (line 81) | public static List> diagnose(GAM gam, Instances instanc... method diagnose (line 92) | public static List> diagnose(GAM gam, Instances instanc... class Options (line 130) | static class Options { method main (line 164) | public static void main(String[] args) throws Exception { FILE: src/main/java/mltk/predictor/gam/tool/Visualizer.java class Visualizer (line 38) | public class Visualizer { type Terminal (line 46) | public enum Terminal { method Terminal (line 59) | Terminal(String term) { method toString (line 63) | public String toString() { method getEnum (line 73) | public static Terminal getEnum(String term) { method generateGnuplotScripts (line 93) | public static void generateGnuplotScripts(GAM gam, Instances instances... class Options (line 474) | static class Options { method main (line 508) | public static void main(String[] args) throws Exception { FILE: src/main/java/mltk/predictor/glm/ElasticNetLearner.java class ElasticNetLearner (line 28) | public class ElasticNetLearner extends GLMLearner { class Options (line 30) | static class Options extends LearnerWithTaskOptions { method main (line 61) | public static void main(String[] args) throws Exception { method ElasticNetLearner (line 98) | public ElasticNetLearner() { method build (line 104) | @Override method build (line 123) | @Override method buildBinaryClassifier (line 155) | public GLM buildBinaryClassifier(int[] attrs, double[][] x, double[] y... method buildBinaryClassifier (line 212) | public GLM buildBinaryClassifier(int[] attrs, int[][] indices, double[... method buildBinaryClassifiers (line 270) | public GLM[] buildBinaryClassifiers(int[] attrs, double[][] x, double[... method buildBinaryClassifiers (line 341) | public GLM[] buildBinaryClassifiers(int[] attrs, int[][] indices, doub... method buildClassifier (line 407) | public GLM buildClassifier(Instances trainSet, boolean isSparse, int m... method buildClassifier (line 519) | public GLM buildClassifier(Instances trainSet, int maxNumIters, double... method buildClassifiers (line 534) | public GLM[] buildClassifiers(Instances trainSet, boolean isSparse, in... method buildClassifiers (line 669) | public GLM[] buildClassifiers(Instances trainSet, int maxNumIters, int... method buildGaussianRegressor (line 684) | public GLM buildGaussianRegressor(Instances trainSet, boolean isSparse... method buildGaussianRegressor (line 722) | public GLM buildGaussianRegressor(Instances trainSet, int maxNumIters,... method buildGaussianRegressor (line 739) | public GLM buildGaussianRegressor(int[] attrs, double[][] x, double[] ... method buildGaussianRegressor (line 798) | public GLM buildGaussianRegressor(int[] attrs, int[][] indices, double... method buildGaussianRegressors (line 855) | public GLM[] buildGaussianRegressors(Instances trainSet, boolean isSpa... method buildGaussianRegressors (line 901) | public GLM[] buildGaussianRegressors(Instances trainSet, int maxNumIte... method buildGaussianRegressors (line 920) | public GLM[] buildGaussianRegressors(int[] attrs, double[][] x, double... method buildGaussianRegressors (line 997) | public GLM[] buildGaussianRegressors(int[] attrs, int[][] indices, dou... method doOnePassGaussian (line 1059) | protected void doOnePassGaussian(double[][] x, double[] sq, final doub... method doOnePassGaussian (line 1084) | protected void doOnePassGaussian(int[][] indices, double[][] values, d... method doOnePassBinomial (line 1114) | protected void doOnePassBinomial(double[][] x, double[] theta, double[... method doOnePassBinomial (line 1145) | protected void doOnePassBinomial(int[][] indices, double[][] values, d... method findMaxLambdaGaussian (line 1182) | protected double findMaxLambdaGaussian(double[][] x, double[] y, doubl... method findMaxLambdaGaussian (line 1203) | protected double findMaxLambdaGaussian(int[][] indices, double[][] val... method findMaxLambdaBinomial (line 1228) | protected double findMaxLambdaBinomial(double[][] x, double[] y, doubl... method findMaxLambdaBinomial (line 1253) | protected double findMaxLambdaBinomial(int[][] indices, double[][] val... method getL1Ratio (line 1287) | public double getL1Ratio() { method getLambda (line 1296) | public double getLambda() { method getTask (line 1305) | public Task getTask() { method setL1Ratio (line 1314) | public void setL1Ratio(double l1Ratio) { method setLambda (line 1323) | public void setLambda(double lambda) { method setTask (line 1332) | public void setTask(Task task) { FILE: src/main/java/mltk/predictor/glm/GLM.java class GLM (line 22) | public class GLM implements ProbabilisticClassifier, Regressor { method GLM (line 42) | public GLM() { method GLM (line 51) | public GLM(int dimension) { method GLM (line 61) | public GLM(int numClasses, int dimension) { method GLM (line 72) | public GLM(double[] intercept, double[][] w) { method GLM (line 83) | public GLM(double[] intercept, double[][] w, LinkFunction link) { method coefficients (line 97) | public double[][] coefficients() { method coefficients (line 107) | public double[] coefficients(int k) { method intercept (line 116) | public double[] intercept() { method intercept (line 126) | public double intercept(int k) { method read (line 130) | @Override method write (line 145) | @Override method regress (line 162) | @Override method classify (line 167) | @Override method predict (line 179) | public double predict(Instance instance) { method predictProbabilities (line 183) | @Override method copy (line 207) | @Override method regress (line 216) | protected double regress(double intercept, double[] coef, Instance ins... FILE: src/main/java/mltk/predictor/glm/GLMLearner.java class GLMLearner (line 14) | public abstract class GLMLearner extends Learner { method GLMLearner (line 24) | public GLMLearner() { method fitIntercept (line 37) | public boolean fitIntercept() { method fitIntercept (line 46) | public void fitIntercept(boolean fitIntercept) { method getEpsilon (line 55) | public double getEpsilon() { method setEpsilon (line 64) | public void setEpsilon(double epsilon) { method getMaxNumIters (line 73) | public int getMaxNumIters() { method setMaxNumIters (line 82) | public void setMaxNumIters(int maxNumIters) { method getFamily (line 91) | public Family getFamily() { method setFamily (line 100) | public void setFamily(Family family) { method build (line 112) | public abstract GLM build(Instances trainSet, Family family); FILE: src/main/java/mltk/predictor/glm/GLMOptimUtils.java class GLMOptimUtils (line 8) | class GLMOptimUtils { method getGLM (line 10) | static GLM getGLM(int[] attrs, double[] w, double intercept, LinkFunct... method computeRidgeLoss (line 21) | static double computeRidgeLoss(double[] residual, double[] w, double l... method computeRidgeLoss (line 27) | static double computeRidgeLoss(double[] pred, double[] y, double[] w, ... method computeLassoLoss (line 33) | static double computeLassoLoss(double[] residual, double[] w, double l... method computeLassoLoss (line 39) | static double computeLassoLoss(double[] pred, double[] y, double[] w, ... method computeElasticNetLoss (line 45) | static double computeElasticNetLoss(double[] residual, double[] w, dou... method computeElasticNetLoss (line 51) | static double computeElasticNetLoss(double[] pred, double[] y, double[... method computeGroupLassoLoss (line 57) | static double computeGroupLassoLoss(double[] residual, double[][] w, d... method computeGroupLassoLoss (line 65) | static double computeGroupLassoLoss(double[] pred, double[] y, double[... FILE: src/main/java/mltk/predictor/glm/GroupLassoLearner.java class GroupLassoLearner (line 35) | public class GroupLassoLearner extends GLMLearner { class DenseDesignMatrix (line 37) | class DenseDesignMatrix { method DenseDesignMatrix (line 42) | DenseDesignMatrix(int[][] groups, double[][][] x) { class ModelStructure (line 49) | static class ModelStructure { method ModelStructure (line 53) | ModelStructure(boolean[] structure) { method equals (line 57) | @Override method hashCode (line 71) | @Override class SparseDesignMatrix (line 81) | class SparseDesignMatrix { method SparseDesignMatrix (line 87) | SparseDesignMatrix(int[][] groups, int[][][] indices, double[][][] v... method GroupLassoLearner (line 102) | public GroupLassoLearner() { method build (line 109) | @Override method build (line 131) | @Override method buildBinaryClassifier (line 164) | public GLM buildBinaryClassifier(int[][] attrs, double[][][] x, double... method buildBinaryClassifier (line 269) | public GLM buildBinaryClassifier(int[][] attrs, int[][][] indices, dou... method buildBinaryClassifiers (line 392) | public List buildBinaryClassifiers(int[][] attrs, double[][][] x,... method buildBinaryClassifiers (line 523) | public List buildBinaryClassifiers(int[][] attrs, int[][][] indic... method buildClassifier (line 667) | public GLM buildClassifier(Instances trainSet, boolean isSparse, List<... method buildClassifier (line 782) | public GLM buildClassifier(Instances trainSet, List groups, int... method buildClassifiers (line 797) | public List buildClassifiers(Instances trainSet, boolean isSparse... method buildClassifiers (line 950) | public List buildClassifiers(Instances trainSet, List grou... method buildGaussianRegressor (line 965) | public GLM buildGaussianRegressor(Instances trainSet, boolean isSparse... method buildGaussianRegressor (line 1006) | public GLM buildGaussianRegressor(Instances trainSet, List grou... method buildGaussianRegressor (line 1021) | public GLM buildGaussianRegressor(int[][] attrs, double[][][] x, doubl... method buildGaussianRegressor (line 1127) | public GLM buildGaussianRegressor(int[][] groups, int[][][] indices, d... method buildGaussianRegressors (line 1230) | public List buildGaussianRegressors(Instances trainSet, boolean i... method buildGaussianRegressors (line 1277) | public List buildGaussianRegressors(Instances trainSet, List buildGaussianRegressors(int[][] groups, double[][][] ... method buildGaussianRegressors (line 1434) | public List buildGaussianRegressors(int[][] groups, int[][][] ind... method getLambda (line 1566) | public double getLambda() { method getNumLambdas (line 1575) | public int getNumLambdas() { method getTask (line 1584) | public Task getTask() { method refit (line 1593) | public boolean refit() { method refit (line 1602) | public void refit(boolean refit) { method setLambda (line 1611) | public void setLambda(double lambda) { method setNumLambdas (line 1620) | public void setNumLambdas(int numLambdas) { method setTask (line 1629) | public void setTask(Task task) { method getGroups (line 1638) | public List getGroups() { method setGroups (line 1647) | public void setGroups(List groups) { method computeGradient (line 1651) | protected void computeGradient(double[][] block, double[] rTrain, doub... method computeGradient (line 1657) | protected void computeGradient(int[][] index, double[][] block, double... method computePenalty (line 1669) | protected double computePenalty(double[] w, double lambda) { method computePenalty (line 1673) | protected double computePenalty(double[][] w, double[] lambdas) { method createDesignMatrix (line 1681) | protected DenseDesignMatrix createDesignMatrix(DenseDataset dd, List buildBinaryClassifiers(int[] attrs, double[][] x, dou... method buildBinaryClassifiers (line 390) | public List buildBinaryClassifiers(int[] attrs, int[][] indices, ... method buildClassifier (line 467) | public GLM buildClassifier(Instances trainSet, boolean isSparse, int m... method buildClassifier (line 577) | public GLM buildClassifier(Instances trainSet, int maxNumIters, double... method buildClassifiers (line 591) | public List buildClassifiers(Instances trainSet, boolean isSparse... method buildClassifiers (line 741) | public List buildClassifiers(Instances trainSet, int maxNumIters,... method buildGaussianRegressor (line 754) | public GLM buildGaussianRegressor(Instances trainSet, boolean isSparse... method buildGaussianRegressor (line 791) | public GLM buildGaussianRegressor(Instances trainSet, int maxNumIters,... method buildGaussianRegressor (line 807) | public GLM buildGaussianRegressor(int[] attrs, double[][] x, double[] ... method buildGaussianRegressor (line 868) | public GLM buildGaussianRegressor(int[] attrs, int[][] indices, double... method buildGaussianRegressors (line 928) | public List buildGaussianRegressors(Instances trainSet, boolean i... method buildGaussianRegressors (line 973) | public List buildGaussianRegressors(Instances trainSet, int maxNu... method buildGaussianRegressors (line 990) | public List buildGaussianRegressors(int[] attrs, double[][] x, do... method buildGaussianRegressors (line 1075) | public List buildGaussianRegressors(int[] attrs, int[][] indices,... method doOnePassGaussian (line 1146) | protected void doOnePassGaussian(double[][] x, double[] sq, final doub... method doOnePassGaussian (line 1171) | protected void doOnePassGaussian(int[][] indices, double[][] values, d... method doOnePassBinomial (line 1201) | protected void doOnePassBinomial(double[][] x, double[] theta, double[... method doOnePassBinomial (line 1232) | protected void doOnePassBinomial(int[][] indices, double[][] values, d... method findMaxLambdaGaussian (line 1269) | protected double findMaxLambdaGaussian(double[][] x, double[] y) { method findMaxLambdaGaussian (line 1289) | protected double findMaxLambdaGaussian(int[][] indices, double[][] val... method findMaxLambdaBinomial (line 1313) | protected double findMaxLambdaBinomial(double[][] x, double[] y, doubl... method findMaxLambdaBinomial (line 1339) | protected double findMaxLambdaBinomial(int[][] indices, double[][] val... method getLambda (line 1372) | public double getLambda() { method getNumLambdas (line 1381) | public int getNumLambdas() { method getTask (line 1390) | public Task getTask() { method refit (line 1399) | public boolean refit() { method refit (line 1408) | public void refit(boolean refit) { method refitGaussianRegressor (line 1412) | protected GLM refitGaussianRegressor(int[] attrs, boolean[] selected, ... method refitGaussianRegressor (line 1455) | protected GLM refitGaussianRegressor(int[] attrs, boolean[] selected, ... method refitClassifier (line 1503) | protected GLM refitClassifier(int[] attrs, boolean[] selected, double[... method refitClassifier (line 1539) | protected GLM refitClassifier(int[] attrs, boolean[] selected, int[][]... method setLambda (line 1586) | public void setLambda(double lambda) { method setNumLambdas (line 1595) | public void setNumLambdas(int numLambdas) { method setTask (line 1604) | public void setTask(Task task) { FILE: src/main/java/mltk/predictor/glm/RidgeLearner.java class RidgeLearner (line 26) | public class RidgeLearner extends GLMLearner { class Options (line 28) | static class Options extends LearnerWithTaskOptions { method main (line 55) | public static void main(String[] args) throws Exception { method RidgeLearner (line 90) | public RidgeLearner() { method build (line 95) | @Override method build (line 114) | @Override method buildBinaryClassifier (line 145) | public GLM buildBinaryClassifier(int[] attrs, double[][] x, double[] y... method buildBinaryClassifier (line 197) | public GLM buildBinaryClassifier(int[] attrs, int[][] indices, double[... method buildBinaryClassifiers (line 249) | public GLM[] buildBinaryClassifiers(int[] attrs, double[][] x, double[... method buildBinaryClassifiers (line 309) | public GLM[] buildBinaryClassifiers(int[] attrs, int[][] indices, doub... method buildClassifier (line 367) | public GLM buildClassifier(Instances trainSet, boolean isSparse, int m... method buildClassifier (line 477) | public GLM buildClassifier(Instances trainSet, int maxNumIters, double... method buildClassifiers (line 490) | public GLM[] buildClassifiers(Instances trainSet, boolean isSparse, in... method buildClassifiers (line 619) | public GLM[] buildClassifiers(Instances trainSet, int maxNumIters, dou... method buildGaussianRegressor (line 632) | public GLM buildGaussianRegressor(Instances trainSet, boolean isSparse... method buildGaussianRegressor (line 669) | public GLM buildGaussianRegressor(Instances trainSet, int maxNumIters,... method buildGaussianRegressor (line 685) | public GLM buildGaussianRegressor(int[] attrs, double[][] x, double[] ... method buildGaussianRegressor (line 738) | public GLM buildGaussianRegressor(int[] attrs, int[][] indices, double... method buildGaussianRegressors (line 789) | public GLM[] buildGaussianRegressors(Instances trainSet, boolean isSpa... method buildGaussianRegressors (line 831) | public GLM[] buildGaussianRegressors(Instances trainSet, int maxNumIte... method buildGaussianRegressors (line 847) | public GLM[] buildGaussianRegressors(int[] attrs, double[][] x, double... method buildGaussianRegressors (line 911) | public GLM[] buildGaussianRegressors(int[] attrs, int[][] indices, dou... method doOnePassGaussian (line 963) | protected void doOnePassGaussian(double[][] x, double[] sq, final doub... method doOnePassGaussian (line 980) | protected void doOnePassGaussian(int[][] indices, double[][] values, d... method doOnePassBinomial (line 1002) | protected void doOnePassBinomial(double[][] x, double[] theta, double[... method doOnePassBinomial (line 1024) | protected void doOnePassBinomial(int[][] indices, double[][] values, d... method getLambda (line 1057) | public double getLambda() { method getTask (line 1066) | public Task getTask() { method setLambda (line 1075) | public void setLambda(double lambda) { method setTask (line 1084) | public void setTask(Task task) { FILE: src/main/java/mltk/predictor/io/PredictorReader.java class PredictorReader (line 14) | public class PredictorReader { method read (line 23) | public static Predictor read(String path) throws Exception { method read (line 43) | public static T read(String path, Class clazz... method read (line 55) | public static Predictor read(BufferedReader in) throws Exception { method read (line 73) | public static T read(BufferedReader in, Class... FILE: src/main/java/mltk/predictor/io/PredictorWriter.java class PredictorWriter (line 13) | public class PredictorWriter { method write (line 22) | public static void write(Predictor predictor, String path) throws Exce... FILE: src/main/java/mltk/predictor/tree/DecisionTable.java class DecisionTable (line 17) | public class DecisionTable implements RTree { method DecisionTable (line 27) | public DecisionTable() { method DecisionTable (line 39) | public DecisionTable(int[] attIndices, double[] splits, method getAttributeIndices (line 52) | public int[] getAttributeIndices() { method getSplits (line 61) | public double[] getSplits() { method multiply (line 65) | @Override method read (line 70) | @Override method write (line 82) | @Override method copy (line 95) | @Override method regress (line 104) | @Override method regress (line 125) | public double regress(long predIdx) { FILE: src/main/java/mltk/predictor/tree/DecisionTableLearner.java class DecisionTableLearner (line 40) | public class DecisionTableLearner extends RTreeLearner { type Mode (line 48) | public enum Mode { method DecisionTableLearner (line 60) | public DecisionTableLearner() { method setParameters (line 66) | @Override method isRobust (line 90) | @Override method getConstructionMode (line 100) | public Mode getConstructionMode() { method setConstructionMode (line 109) | public void setConstructionMode(Mode mode) { method getMaxDepth (line 118) | public int getMaxDepth() { method setMaxDepth (line 127) | public void setMaxDepth(int maxDepth) { method getNumPasses (line 136) | public int getNumPasses() { method setNumPasses (line 145) | public void setNumPasses(int numPasses) { method build (line 149) | @Override method buildOnePassGreedy (line 174) | public DecisionTable buildOnePassGreedy(Instances instances, int maxDe... method buildMultiPassCyclic (line 324) | public DecisionTable buildMultiPassCyclic(Instances instances, int max... method buildMultiPassRandom (line 506) | public DecisionTable buildMultiPassRandom(Instances instances, int max... method processGains (line 685) | protected void processGains(List uniqueValues, double[] localG... method evalSplits (line 721) | protected double[] evalSplits(List uniqueValues, List p... method getStats (line 69) | protected boolean getStats(Instances instances, double[] stats) { FILE: src/main/java/mltk/predictor/tree/RegressionTree.java class RegressionTree (line 14) | public class RegressionTree implements RTree { method RegressionTree (line 24) | public RegressionTree() { method RegressionTree (line 33) | public RegressionTree(TreeNode root) { method getRoot (line 42) | public TreeNode getRoot() { method setRoot (line 51) | public void setRoot(TreeNode root) { method getLeafNode (line 61) | public RegressionTreeLeaf getLeafNode(Instance instance) { method multiply (line 79) | public void multiply(double c) { method multiply (line 89) | protected void multiply(TreeNode node, double c) { method regress (line 100) | @Override method read (line 105) | @Override method write (line 113) | @Override method copy (line 120) | @Override FILE: src/main/java/mltk/predictor/tree/RegressionTreeLeaf.java class RegressionTreeLeaf (line 12) | public class RegressionTreeLeaf extends TreeNode { method RegressionTreeLeaf (line 19) | public RegressionTreeLeaf() { method RegressionTreeLeaf (line 28) | public RegressionTreeLeaf(double prediction) { method isLeaf (line 32) | @Override method setPrediction (line 42) | public void setPrediction(double prediction) { method getPrediction (line 51) | public double getPrediction() { method read (line 55) | @Override method write (line 60) | @Override method copy (line 66) | @Override FILE: src/main/java/mltk/predictor/tree/RegressionTreeLearner.java class RegressionTreeLearner (line 30) | public class RegressionTreeLearner extends RTreeLearner { class Options (line 32) | static class Options extends LearnerOptions { method main (line 58) | public static void main(String[] args) throws Exception { type Mode (line 90) | public enum Mode { method RegressionTreeLearner (line 104) | public RegressionTreeLearner() { method build (line 109) | @Override method setParameters (line 130) | @Override method isRobust (line 158) | @Override method getAlpha (line 168) | public double getAlpha() { method getConstructionMode (line 177) | public Mode getConstructionMode() { method getMaxDepth (line 186) | public int getMaxDepth() { method getMaxNumLeaves (line 195) | public int getMaxNumLeaves() { method getMinLeafSize (line 204) | public int getMinLeafSize() { method setAlpha (line 213) | public void setAlpha(double alpha) { method setConstructionMode (line 222) | public void setConstructionMode(Mode mode) { method setMaxDepth (line 231) | public void setMaxDepth(int maxDepth) { method setMaxNumLeaves (line 240) | public void setMaxNumLeaves(int maxNumLeaves) { method setMinLeafSize (line 249) | public void setMinLeafSize(int minLeafSize) { method buildAlphaLimitedTree (line 253) | protected RegressionTree buildAlphaLimitedTree(Instances instances, do... method buildDepthLimitedTree (line 258) | protected RegressionTree buildDepthLimitedTree(Instances instances, in... method buildMinLeafSizeLimitedTree (line 321) | protected RegressionTree buildMinLeafSizeLimitedTree(Instances instanc... method buildNumLeafLimitedTree (line 358) | protected RegressionTree buildNumLeafLimitedTree(Instances instances, ... method createNode (line 433) | protected TreeNode createNode(Dataset dataset, int limit, double[] sta... method split (line 482) | protected void split(Dataset data, TreeInteriorNode node, Dataset left... method split (line 486) | protected DoublePair split(List uniqueValues, List... method traverse (line 519) | protected void traverse(TreeNode node, Map parent) { FILE: src/main/java/mltk/predictor/tree/TreeInteriorNode.java class TreeInteriorNode (line 14) | public class TreeInteriorNode extends TreeNode { method TreeInteriorNode (line 24) | public TreeInteriorNode() { method TreeInteriorNode (line 34) | public TreeInteriorNode(int attIndex, double splitPoint) { method getLeftChild (line 44) | public TreeNode getLeftChild() { method getRightChild (line 53) | public TreeNode getRightChild() { method getSplitAttributeIndex (line 62) | public int getSplitAttributeIndex() { method getSplitPoint (line 71) | public double getSplitPoint() { method isLeaf (line 75) | @Override method goLeft (line 86) | public boolean goLeft(Instance instance) { method read (line 91) | @Override method write (line 108) | @Override method copy (line 119) | @Override FILE: src/main/java/mltk/predictor/tree/TreeLearner.java class TreeLearner (line 24) | public abstract class TreeLearner extends Learner { method isRobust (line 36) | public abstract boolean isRobust(); method cache (line 44) | public void cache(Instances instances) { method evictCache (line 51) | public void evictCache() { method setParameters (line 60) | public abstract void setParameters(String mode); class Dataset (line 62) | protected static class Dataset { method create (line 64) | static Dataset create(Instances instances) { method create (line 105) | static Dataset create(Dataset dataset, Instances instances) { method Dataset (line 138) | Dataset() { method Dataset (line 142) | Dataset(Instances instances) { method merge (line 147) | static Dataset merge(Dataset left, Dataset right) { method split (line 197) | void split(int attIndex, double split, Dataset left, Dataset right) { FILE: src/main/java/mltk/predictor/tree/TreeNode.java class TreeNode (line 12) | public abstract class TreeNode implements Writable, Copyable { method isLeaf (line 19) | public abstract boolean isLeaf(); FILE: src/main/java/mltk/predictor/tree/ensemble/BaggedRTrees.java class BaggedRTrees (line 14) | public class BaggedRTrees extends RTreeList { method BaggedRTrees (line 19) | public BaggedRTrees() { method BaggedRTrees (line 28) | public BaggedRTrees(int n) { method regress (line 41) | public double regress(Instance instance) { FILE: src/main/java/mltk/predictor/tree/ensemble/BoostedDTables.java class BoostedDTables (line 23) | public class BoostedDTables implements Copyable { class IndexElement (line 25) | static class IndexElement implements Comparable { method IndexElement (line 31) | public IndexElement(double cut, int tid, int pos) { method compareTo (line 37) | @Override method copy (line 42) | public IndexElement copy() { class Index (line 48) | static class Index { method Index (line 52) | public Index(IndexElement[] elements) { method setPredIdx (line 56) | void setPredIdx(long[] predIndices, double v) { method copy (line 68) | public Index copy() { method BoostedDTables (line 86) | public BoostedDTables() { method BoostedDTables (line 95) | public BoostedDTables(BoostedRTrees trees) { method buildIndex (line 106) | public void buildIndex() { method add (line 143) | public void add(DecisionTable dt) { method get (line 153) | public DecisionTable get(int index) { method removeLast (line 160) | public void removeLast() { method size (line 171) | public int size() { method set (line 181) | public void set(int index, DecisionTable dt) { method regress (line 191) | public double regress(Instance instance) { method copy (line 216) | @Override method copy (line 227) | public BoostedDTables copy(boolean copyIndexes) { method read (line 248) | public void read(BufferedReader in) throws Exception { method write (line 269) | public void write(PrintWriter out) throws Exception { FILE: src/main/java/mltk/predictor/tree/ensemble/BoostedRTrees.java class BoostedRTrees (line 16) | public class BoostedRTrees extends RTreeList { method BoostedRTrees (line 21) | public BoostedRTrees() { method BoostedRTrees (line 30) | public BoostedRTrees(int n) { method regress (line 43) | public double regress(Instance instance) { method copy (line 51) | @Override method read (line 60) | public void read(BufferedReader in) throws Exception { method write (line 74) | public void write(PrintWriter out) throws Exception { FILE: src/main/java/mltk/predictor/tree/ensemble/RTreeList.java class RTreeList (line 16) | public class RTreeList implements Iterable, Copyable { method RTreeList (line 23) | public RTreeList() { method RTreeList (line 32) | public RTreeList(int capacity) { method add (line 41) | public void add(RTree tree) { method copy (line 45) | @Override method get (line 60) | public RTree get(int index) { method iterator (line 64) | @Override method removeLast (line 72) | public void removeLast() { method set (line 84) | public void set(int index, RTree rt) { method size (line 93) | public int size() { FILE: src/main/java/mltk/predictor/tree/ensemble/TreeEnsembleLearner.java class TreeEnsembleLearner (line 12) | public abstract class TreeEnsembleLearner extends HoldoutValidatedLearner { method getTreeLearner (line 16) | public TreeLearner getTreeLearner() { method setTreeLearner (line 20) | public void setTreeLearner(TreeLearner treeLearner) { FILE: src/main/java/mltk/predictor/tree/ensemble/ag/AdditiveGroves.java class AdditiveGroves (line 18) | public class AdditiveGroves implements Regressor { method AdditiveGroves (line 25) | public AdditiveGroves() { method read (line 29) | @Override method write (line 49) | @Override method regress (line 63) | @Override method copy (line 77) | @Override FILE: src/main/java/mltk/predictor/tree/ensemble/ag/AdditiveGrovesLearner.java class AdditiveGrovesLearner (line 39) | public class AdditiveGrovesLearner extends Learner { class Options (line 41) | static class Options extends LearnerOptions { method main (line 87) | public static void main(String[] args) throws Exception { class PerformanceMatrix (line 125) | class PerformanceMatrix { method PerformanceMatrix (line 130) | PerformanceMatrix(int maxNumTrees, int numAlphas, int baggingIters, ... method expand (line 135) | void expand(int maxNumTrees, int numAlphas, int baggingIters) { method eval (line 152) | void eval(int t, int a, int b, double[] preds, double[] targets) { method getBestParameters (line 156) | IntPair getBestParameters() { method analyzeBagging (line 188) | boolean analyzeBagging(int t, int a) { class ModelMatrix (line 194) | class ModelMatrix { method ModelMatrix (line 198) | ModelMatrix(int maxNumTrees, int numAlphas) { method expand (line 202) | void expand(int maxNumTrees, int numAlphas, int baggingIters) { method add (line 212) | void add(int t, int a, RegressionTree[] grove) { class PredictionMatrix (line 221) | class PredictionMatrix { method PredictionMatrix (line 226) | PredictionMatrix(int tn, int an, int n) { method expand (line 231) | void expand(int tn, int an) { method AdditiveGrovesLearner (line 257) | public AdditiveGrovesLearner() { method getMetric (line 270) | public SimpleMetric getMetric() { method setMetric (line 280) | public void setMetric(SimpleMetric metric) { method getBestNumTrees (line 291) | public int getBestNumTrees() { method getBestBaggingIters (line 300) | public int getBestBaggingIters() { method getBestAlpha (line 309) | public double getBestAlpha() { method getNumTrees (line 318) | public int getNumTrees() { method setNumTrees (line 327) | public void setNumTrees(int numTrees) { method getMinAlpha (line 336) | public double getMinAlpha() { method setMinAlpha (line 345) | public void setMinAlpha(double minAlpha) { method getBaggingIters (line 354) | public int getBaggingIters() { method setBaggingIters (line 363) | public void setBaggingIters(int baggingIters) { method buildRegressor (line 374) | public AdditiveGroves buildRegressor(Instances trainSet, Instances val... method runLayeredTraining (line 484) | public AdditiveGroves runLayeredTraining(Instances trainSet, int baggi... method build (line 531) | @Override method getAlpha (line 545) | protected double getAlpha(int an) { method getAlphaIdx (line 558) | protected int getAlphaIdx(double alpha, int n) { method backfit (line 567) | protected void backfit(Instances trainSet, double alpha, RegressionTre... method regress (line 615) | protected double regress(RegressionTree[] trees, Instance instance) { method runLayeredTraining (line 623) | protected void runLayeredTraining(Instances trainSet, Instances validS... method evalRMSE (line 680) | protected double evalRMSE(List indices, double[] residual) { method update (line 690) | protected void update(Instances trainSet, RegressionTree[] grove, doub... FILE: src/main/java/mltk/predictor/tree/ensemble/brt/BDT.java class BDT (line 26) | public class BDT implements ProbabilisticClassifier, Regressor { method constructBDT (line 36) | public static BDT constructBDT(BRT brt) { method BDT (line 50) | public BDT() { method BDT (line 59) | public BDT(int k) { method getDecisionTreeList (line 72) | public BoostedDTables getDecisionTreeList(int k) { method classify (line 76) | @Override method read (line 82) | @Override method write (line 95) | @Override method regress (line 105) | @Override method predictProbabilities (line 110) | @Override method copy (line 135) | @Override FILE: src/main/java/mltk/predictor/tree/ensemble/brt/BRT.java class BRT (line 21) | public class BRT implements ProbabilisticClassifier, Regressor { method BRT (line 28) | public BRT() { method BRT (line 37) | public BRT(int k) { method getRegressionTreeList (line 50) | public BoostedRTrees getRegressionTreeList(int k) { method classify (line 54) | @Override method read (line 60) | @Override method write (line 73) | @Override method regress (line 83) | @Override method predictProbabilities (line 88) | @Override method copy (line 113) | @Override FILE: src/main/java/mltk/predictor/tree/ensemble/brt/BRTLearner.java class BRTLearner (line 12) | public abstract class BRTLearner extends TreeEnsembleLearner { method BRTLearner (line 21) | public BRTLearner() { method getAlpha (line 39) | public double getAlpha() { method setAlpha (line 49) | public void setAlpha(double alpha) { method getLearningRate (line 58) | public double getLearningRate() { method setLearningRate (line 67) | public void setLearningRate(double learningRate) { method getMaxNumIters (line 76) | public int getMaxNumIters() { method setMaxNumIters (line 85) | public void setMaxNumIters(int maxNumIters) { FILE: src/main/java/mltk/predictor/tree/ensemble/brt/BRTUtils.java class BRTUtils (line 7) | class BRTUtils { method parseTreeLearner (line 9) | public static TreeLearner parseTreeLearner(String baseLearner) { FILE: src/main/java/mltk/predictor/tree/ensemble/brt/LADBoostLearner.java class LADBoostLearner (line 35) | public class LADBoostLearner extends BRTLearner { class Options (line 37) | static class Options extends HoldoutValidatedLearnerOptions { method main (line 74) | public static void main(String[] args) throws Exception { method LADBoostLearner (line 124) | public LADBoostLearner() { method build (line 128) | @Override method buildRegressor (line 148) | public BRT buildRegressor(Instances trainSet, Instances validSet, int ... method buildRegressor (line 262) | public BRT buildRegressor(Instances trainSet, int maxNumIters) { FILE: src/main/java/mltk/predictor/tree/ensemble/brt/LSBoostLearner.java class LSBoostLearner (line 29) | public class LSBoostLearner extends BRTLearner { class Options (line 31) | static class Options extends HoldoutValidatedLearnerOptions { method main (line 68) | public static void main(String[] args) throws Exception { method LSBoostLearner (line 118) | public LSBoostLearner() { method build (line 122) | @Override method buildRegressor (line 142) | public BRT buildRegressor(Instances trainSet, Instances validSet, int ... method buildRegressor (line 235) | public BRT buildRegressor(Instances trainSet, int maxNumIters) { FILE: src/main/java/mltk/predictor/tree/ensemble/brt/LogitBoostLearner.java class LogitBoostLearner (line 40) | public class LogitBoostLearner extends BRTLearner { class Options (line 42) | static class Options extends HoldoutValidatedLearnerOptions { method main (line 79) | public static void main(String[] args) throws Exception { method LogitBoostLearner (line 131) | public LogitBoostLearner() { method build (line 135) | @Override method buildBinaryClassifier (line 155) | public BRT buildBinaryClassifier(Instances trainSet, Instances validSe... method buildBinaryClassifier (line 274) | public BRT buildBinaryClassifier(Instances trainSet, int maxNumIters) { method buildClassifier (line 386) | public BRT buildClassifier(Instances trainSet, Instances validSet, int... method buildClassifier (line 526) | public BRT buildClassifier(Instances trainSet, int maxNumIters) { method setTreeLearner (line 648) | @Override method computeProbabilities (line 656) | protected void computeProbabilities(double[] pred, double[] prob) { method computeProbabilities (line 662) | protected void computeProbabilities(double[][] pred, double[][] prob) { FILE: src/main/java/mltk/predictor/tree/ensemble/brt/RobustDecisionTableLearner.java class RobustDecisionTableLearner (line 18) | public class RobustDecisionTableLearner extends DecisionTableLearner { method getStats (line 20) | protected boolean getStats(Instances instances, double[] stats) { method getHistogram (line 44) | protected void getHistogram(Instances instances, List p... FILE: src/main/java/mltk/predictor/tree/ensemble/brt/RobustRegressionTreeLearner.java class RobustRegressionTreeLearner (line 19) | public class RobustRegressionTreeLearner extends RegressionTreeLearner { method isRobust (line 21) | public boolean isRobust() { method getStats (line 25) | protected boolean getStats(Instances instances, double[] stats) { method getHistogram (line 49) | protected void getHistogram(Instances instances, List p... FILE: src/main/java/mltk/predictor/tree/ensemble/rf/RandomForest.java class RandomForest (line 18) | public class RandomForest implements Regressor { method RandomForest (line 25) | public RandomForest() { method RandomForest (line 34) | public RandomForest(int capacity) { method read (line 38) | @Override method write (line 53) | @Override method copy (line 64) | @Override method regress (line 73) | @Override method add (line 91) | public void add(RTree rt) { method get (line 101) | public RTree get(int index) { method getTreeList (line 110) | public RTreeList getTreeList() { method size (line 119) | public int size() { FILE: src/main/java/mltk/predictor/tree/ensemble/rf/RandomForestLearner.java class RandomForestLearner (line 20) | public class RandomForestLearner extends Learner { class Options (line 22) | static class Options extends LearnerOptions { method main (line 55) | public static void main(String[] args) throws Exception { method build (line 110) | @Override method RandomForestLearner (line 125) | public RandomForestLearner() { method getBaggingIterations (line 136) | public int getBaggingIterations() { method setBaggingIterations (line 145) | public void setBaggingIterations(int baggingIters) { method getRegressionTreeLearner (line 154) | public RegressionTreeLearner getRegressionTreeLearner() { method setRegressionTreeLearner (line 163) | public void setRegressionTreeLearner(RegressionTreeLearner rtLearner) { FILE: src/main/java/mltk/predictor/tree/ensemble/rf/RandomRegressionTreeLearner.java class RandomRegressionTreeLearner (line 26) | public class RandomRegressionTreeLearner extends RegressionTreeLearner { method RandomRegressionTreeLearner (line 34) | public RandomRegressionTreeLearner() { method getNumFeatures (line 45) | public int getNumFeatures() { method setNumFeatures (line 54) | public void setNumFeatures(int numFeatures) { method build (line 58) | @Override method createNode (line 85) | protected TreeNode createNode(Dataset dataset, int limit, double[] sta... FILE: src/main/java/mltk/util/ArrayUtils.java class ArrayUtils (line 12) | public class ArrayUtils { method toIntArray (line 20) | public static int[] toIntArray(List list) { method toDoubleArray (line 34) | public static double[] toDoubleArray(List list) { method toIntArray (line 48) | public static int[] toIntArray(double[] a) { method toString (line 64) | public static String toString(double[] a, int start, int end) { method parseDoubleArray (line 80) | public static double[] parseDoubleArray(String str) { method parseDoubleArray (line 91) | public static double[] parseDoubleArray(String str, String delimiter) { method parseIntArray (line 109) | public static int[] parseIntArray(String str) { method parseIntArray (line 120) | public static int[] parseIntArray(String str, String delimiter) { method parseLongArray (line 138) | public static long[] parseLongArray(String str) { method parseLongArray (line 149) | public static long[] parseLongArray(String str, String delimiter) { method isConstant (line 170) | public static boolean isConstant(double[] a, int begin, int end, doubl... method isConstant (line 188) | public static boolean isConstant(int[] a, int begin, int end, int c) { method isConstant (line 206) | public static boolean isConstant(byte[] a, int begin, int end, byte c) { method getMedian (line 221) | public static double getMedian(double[] a) { method findInsertionPoint (line 242) | public static int findInsertionPoint(double[] a, double key) { FILE: src/main/java/mltk/util/Element.java class Element (line 10) | public class Element implements Comparable> { method Element (line 21) | public Element(T element, double weight) { method compareTo (line 26) | @Override FILE: src/main/java/mltk/util/MathUtils.java class MathUtils (line 9) | public class MathUtils { method equals (line 28) | public static boolean equals(double a, double b) { method indicator (line 38) | public static int indicator(boolean b) { method isFirstBetter (line 50) | public static boolean isFirstBetter(double a, double b, boolean isLarg... method isInteger (line 64) | public static boolean isInteger(double v) { method isZero (line 74) | public static boolean isZero(double v) { method sigmoid (line 84) | public static double sigmoid(double a) { method sign (line 94) | public static int sign(double a) { method sign (line 110) | public static int sign(int a) { method divide (line 128) | public static double divide(double a, double b, double dv) { FILE: src/main/java/mltk/util/OptimUtils.java class OptimUtils (line 11) | public class OptimUtils { method getGain (line 21) | public static double getGain(double sum, double weight) { method getProbability (line 35) | public static double getProbability(double pred) { method getResidual (line 46) | public static double getResidual(double pred, double target) { method getPseudoResidual (line 57) | public static double getPseudoResidual(double pred, double cls) { method computePseudoResidual (line 68) | public static void computePseudoResidual(double[] prediction, double[]... method computeProbabilities (line 80) | public static void computeProbabilities(double[] pred, double[] prob) { method computeLogisticLoss (line 93) | public static double computeLogisticLoss(double pred, double cls) { method computeLogisticLoss (line 108) | public static double computeLogisticLoss(double[] pred, double[] y) { method computeLogLoss (line 123) | public static double computeLogLoss(double prob, double y) { method computeLogLoss (line 135) | public static double computeLogLoss(double p, double y, boolean isRawS... method computeLogLoss (line 153) | public static double computeLogLoss(double[] prob, double[] y) { method computeLogLoss (line 165) | public static double computeLogLoss(double[] p, double[] y, boolean is... method computeQuadraticLoss (line 179) | public static double computeQuadraticLoss(double[] residual) { method fitIntercept (line 189) | public static double fitIntercept(double[] residual) { method fitIntercept (line 203) | public static double fitIntercept(double[] prediction, double[] residu... method isConverged (line 234) | public static boolean isConverged(double prevLoss, double currLoss, do... method isConverged (line 249) | public static boolean isConverged(double[] p, boolean isLargerBetter) { method isConverged (line 283) | public static boolean isConverged(List list, boolean isLargerB... FILE: src/main/java/mltk/util/Permutation.java class Permutation (line 9) | public class Permutation { method Permutation (line 18) | public Permutation(int n) { method permute (line 30) | public Permutation permute() { method size (line 45) | public int size() { method getPermutation (line 54) | public int[] getPermutation() { FILE: src/main/java/mltk/util/Queue.java class Queue (line 12) | public class Queue { method Queue (line 19) | public Queue() { method enqueue (line 28) | public void enqueue(T item) { method dequeue (line 37) | public T dequeue() { method isEmpty (line 48) | public boolean isEmpty() { FILE: src/main/java/mltk/util/Random.java class Random (line 9) | public class Random { method Random (line 14) | protected Random() { method getInstance (line 23) | public static Random getInstance() { method setSeed (line 35) | public void setSeed(long seed) { method nextInt (line 45) | public int nextInt() { method nextInt (line 56) | public int nextInt(int n) { method nextDouble (line 66) | public double nextDouble() { method nextFloat (line 76) | public float nextFloat() { method nextGaussian (line 86) | public double nextGaussian() { method nextLong (line 96) | public long nextLong() { method nextBoolean (line 106) | public boolean nextBoolean() { method nextBytes (line 115) | public void nextBytes(byte[] bytes) { method getRandom (line 124) | public java.util.Random getRandom() { FILE: src/main/java/mltk/util/Stack.java class Stack (line 14) | public class Stack { method Stack (line 21) | public Stack() { method push (line 30) | public void push(T item) { method peek (line 39) | public T peek() { method pop (line 51) | public T pop() { method isEmpty (line 62) | public boolean isEmpty() { FILE: src/main/java/mltk/util/StatUtils.java class StatUtils (line 9) | public class StatUtils { method max (line 17) | public static int max(int[] a) { method max (line 33) | public static double max(double[] a) { method indexOfMax (line 49) | public static int indexOfMax(int[] a) { method indexOfMax (line 67) | public static int indexOfMax(double[] a) { method min (line 85) | public static int min(int[] a) { method min (line 101) | public static double min(double[] a) { method indexOfMin (line 117) | public static int indexOfMin(int[] a) { method indexOfMin (line 135) | public static int indexOfMin(double[] a) { method sum (line 153) | public static double sum(double[] a) { method sumSq (line 167) | public static double sumSq(double[] a) { method sumSq (line 179) | public static double sumSq(double[] a, int fromIndex, int toIndex) { method mean (line 193) | public static double mean(double[] a) { method mean (line 204) | public static double mean(double[] a, int n) { method variance (line 218) | public static double variance(double[] a) { method variance (line 229) | public static double variance(double[] a, int n) { method sd (line 245) | public static double sd(double[] a) { method sd (line 256) | public static double sd(double[] a, int n) { method rms (line 266) | public static double rms(double[] a) { method mad (line 282) | public static double mad(double[] a, double centralPoint) { FILE: src/main/java/mltk/util/UFSets.java class UFSets (line 9) | public class UFSets { method UFSets (line 18) | public UFSets(int size) { method union (line 31) | public void union(int root1, int root2) { method find (line 48) | public int find(int i) { FILE: src/main/java/mltk/util/VectorUtils.java class VectorUtils (line 13) | public class VectorUtils { method add (line 21) | public static void add(double[] a, double v) { method subtract (line 33) | public static void subtract(double[] a, double v) { method multiply (line 45) | public static void multiply(double[] a, double v) { method divide (line 57) | public static void divide(double[] a, double v) { method l2norm (line 69) | public static double l2norm(double[] a) { method l2norm (line 79) | public static double l2norm(Vector v) { method l1norm (line 89) | public static double l1norm(double[] a) { method l1norm (line 103) | public static double l1norm(Vector v) { method dotProduct (line 114) | public static double dotProduct(double[] a, double[] b) { method dotProduct (line 129) | public static double dotProduct(DenseVector a, DenseVector b) { method dotProduct (line 140) | public static double dotProduct(SparseVector a, DenseVector b) { method dotProduct (line 158) | public static double dotProduct(DenseVector a, SparseVector b) { method dotProduct (line 169) | public static double dotProduct(SparseVector a, SparseVector b) { method correlation (line 198) | public static double correlation(double[] a, double[] b) { FILE: src/main/java/mltk/util/tuple/DoublePair.java class DoublePair (line 9) | public class DoublePair { method DoublePair (line 20) | public DoublePair(double v1, double v2) { method hashCode (line 25) | @Override method equals (line 37) | @Override FILE: src/main/java/mltk/util/tuple/IntDoublePair.java class IntDoublePair (line 9) | public class IntDoublePair { method IntDoublePair (line 20) | public IntDoublePair(int v1, double v2) { method hashCode (line 25) | @Override method equals (line 36) | @Override FILE: src/main/java/mltk/util/tuple/IntDoublePairComparator.java class IntDoublePairComparator (line 11) | public class IntDoublePairComparator implements Comparator { method IntDoublePairComparator (line 16) | public IntDoublePairComparator() { method IntDoublePairComparator (line 20) | public IntDoublePairComparator(boolean firstIsKey) { method IntDoublePairComparator (line 24) | public IntDoublePairComparator(boolean firstIsKey, boolean ascending) { method compare (line 29) | @Override FILE: src/main/java/mltk/util/tuple/IntPair.java class IntPair (line 9) | public class IntPair { method IntPair (line 20) | public IntPair(int v1, int v2) { method hashCode (line 25) | @Override method equals (line 34) | @Override FILE: src/main/java/mltk/util/tuple/IntTriple.java class IntTriple (line 9) | public class IntTriple { method IntTriple (line 22) | public IntTriple(int v1, int v2, int v3) { method hashCode (line 28) | @Override method equals (line 38) | @Override FILE: src/main/java/mltk/util/tuple/LongDoublePair.java class LongDoublePair (line 9) | public class LongDoublePair { method LongDoublePair (line 20) | public LongDoublePair(long v1, double v2) { method hashCode (line 25) | @Override method equals (line 36) | @Override method toString (line 52) | @Override FILE: src/main/java/mltk/util/tuple/LongDoublePairComparator.java class LongDoublePairComparator (line 11) | public class LongDoublePairComparator implements Comparator { method Pair (line 22) | public Pair(T1 v1, T2 v2) { method hashCode (line 27) | @Override method equals (line 36) | @Override FILE: src/main/java/mltk/util/tuple/Triple.java class Triple (line 12) | public class Triple { method Triple (line 25) | public Triple(T1 v1, T2 v2, T3 v3) { method hashCode (line 31) | @Override method equals (line 41) | @Override FILE: src/test/java/mltk/core/BinsTest.java class BinsTest (line 8) | public class BinsTest { method testBins (line 10) | @Test FILE: src/test/java/mltk/core/InstancesTestHelper.java class InstancesTestHelper (line 6) | public class InstancesTestHelper { method getInstance (line 15) | public static InstancesTestHelper getInstance() { method getDenseClassificationDataset (line 22) | public Instances getDenseClassificationDataset() { method getDenseRegressionDataset (line 26) | public Instances getDenseRegressionDataset() { method getDenseClassificationDatasetWMissing (line 30) | public Instances getDenseClassificationDatasetWMissing() { method getDenseRegressionDatasetWMissing (line 34) | public Instances getDenseRegressionDatasetWMissing() { method InstancesTestHelper (line 38) | private InstancesTestHelper() { FILE: src/test/java/mltk/core/io/AttributesReaderTest.java class AttributesReaderTest (line 20) | public class AttributesReaderTest { method testIO (line 22) | @Test FILE: src/test/java/mltk/core/io/InstancesReaderTest.java class InstancesReaderTest (line 8) | public class InstancesReaderTest { method testDenseFormat (line 10) | @Test FILE: src/test/java/mltk/core/processor/DiscretizerTest.java class DiscretizerTest (line 11) | public class DiscretizerTest { method testMissingValue (line 13) | @Test FILE: src/test/java/mltk/core/processor/InstancesSplitterTest.java class InstancesSplitterTest (line 11) | public class InstancesSplitterTest { method testSamplingTrainValid (line 13) | @Test method testSamplingTrainValidTest (line 25) | @Test method testStratifiedSampling (line 39) | @Test FILE: src/test/java/mltk/predictor/evaluation/AUCTest.java class AUCTest (line 8) | public class AUCTest { method test1 (line 10) | @Test method test2 (line 18) | @Test FILE: src/test/java/mltk/predictor/evaluation/ConvergenceTesterTest.java class ConvergenceTesterTest (line 6) | public class ConvergenceTesterTest { method test1 (line 8) | @Test method test2 (line 26) | @Test method test3 (line 44) | @Test method test4 (line 62) | @Test method testParse (line 80) | @Test FILE: src/test/java/mltk/predictor/evaluation/ErrorTest.java class ErrorTest (line 8) | public class ErrorTest { method testLabel (line 10) | @Test method testProbability (line 18) | @Test FILE: src/test/java/mltk/predictor/evaluation/LogLossTest.java class LogLossTest (line 8) | public class LogLossTest { method testProb (line 10) | @Test method testRawScore (line 18) | @Test FILE: src/test/java/mltk/predictor/evaluation/LogisticLossTest.java class LogisticLossTest (line 8) | public class LogisticLossTest { method test (line 10) | @Test FILE: src/test/java/mltk/predictor/evaluation/MAETest.java class MAETest (line 8) | public class MAETest { method test (line 10) | @Test FILE: src/test/java/mltk/predictor/evaluation/MetricFactoryTest.java class MetricFactoryTest (line 6) | public class MetricFactoryTest { method test (line 8) | @Test FILE: src/test/java/mltk/predictor/evaluation/RMSETest.java class RMSETest (line 8) | public class RMSETest { method test (line 10) | @Test FILE: src/test/java/mltk/predictor/glm/GLMTest.java class GLMTest (line 15) | public class GLMTest { method testIO (line 17) | @Test FILE: src/test/java/mltk/predictor/tree/DecisionTableLearnerTest.java class DecisionTableLearnerTest (line 12) | public class DecisionTableLearnerTest { method testDecisionTableLearner1 (line 14) | @Test method testDecisionTableLearner2 (line 27) | @Test FILE: src/test/java/mltk/predictor/tree/DecisionTableTest.java class DecisionTableTest (line 17) | public class DecisionTableTest { method testIO (line 19) | @Test method testRegress (line 42) | @Test FILE: src/test/java/mltk/predictor/tree/DecisionTableTestHelper.java class DecisionTableTestHelper (line 3) | public class DecisionTableTestHelper { method getInstance (line 10) | public static DecisionTableTestHelper getInstance() { method getTable1 (line 17) | public DecisionTable getTable1() { method getTable2 (line 21) | public DecisionTable getTable2() { method DecisionTableTestHelper (line 25) | private DecisionTableTestHelper() { method buildDecisionTable1 (line 30) | private void buildDecisionTable1() { method buildDecisionTable2 (line 39) | private void buildDecisionTable2() { FILE: src/test/java/mltk/predictor/tree/RegressionTreeLearnerTest.java class RegressionTreeLearnerTest (line 12) | public class RegressionTreeLearnerTest { method testRegressionTreeLearner1 (line 14) | @Test method testRegressionTreeLearner2 (line 28) | @Test FILE: src/test/java/mltk/predictor/tree/RegressionTreeTest.java class RegressionTreeTest (line 15) | public class RegressionTreeTest { method testIO (line 17) | @Test FILE: src/test/java/mltk/predictor/tree/RegressionTreeTestHelper.java class RegressionTreeTestHelper (line 3) | public class RegressionTreeTestHelper { method getInstance (line 10) | public static RegressionTreeTestHelper getInstance() { method getTree1 (line 17) | public RegressionTree getTree1() { method getTree2 (line 21) | public RegressionTree getTree2() { method RegressionTreeTestHelper (line 25) | private RegressionTreeTestHelper() { method buildTree1 (line 30) | private void buildTree1() { method buildTree2 (line 44) | private void buildTree2() { FILE: src/test/java/mltk/predictor/tree/ensemble/BoostedDTablesTest.java class BoostedDTablesTest (line 16) | public class BoostedDTablesTest { method testIO (line 18) | @Test FILE: src/test/java/mltk/predictor/tree/ensemble/BoostedRTreesTest.java class BoostedRTreesTest (line 18) | public class BoostedRTreesTest { method testIO (line 20) | @Test FILE: src/test/java/mltk/predictor/tree/ensemble/brt/BDTTest.java class BDTTest (line 22) | public class BDTTest { method BDTTest (line 26) | public BDTTest() { method testIO (line 37) | @Test method testRegress (line 67) | @Test FILE: src/test/java/mltk/predictor/tree/ensemble/brt/BRTTest.java class BRTTest (line 20) | public class BRTTest { method testIO (line 22) | @Test FILE: src/test/java/mltk/predictor/tree/ensemble/brt/BRTUtilsTest.java class BRTUtilsTest (line 10) | public class BRTUtilsTest { method testParseRegressionTreeLearner1 (line 12) | @Test method testParseRegressionTreeLearner2 (line 23) | @Test method testParseRegressionTreeLearner3 (line 34) | @Test method testParseRegressionTreeLearner4 (line 45) | @Test method testParseRobustRegressionTreeLearner1 (line 56) | @Test method testParseRobustRegressionTreeLearner2 (line 67) | @Test method testParseRobustRegressionTreeLearner3 (line 78) | @Test method testParseRobustRegressionTreeLearner4 (line 89) | @Test FILE: src/test/java/mltk/predictor/tree/ensemble/brt/LogitBoostLearnerTest.java class LogitBoostLearnerTest (line 12) | public class LogitBoostLearnerTest { method testLogitBoostLearner (line 14) | @Test FILE: src/test/java/mltk/util/ArrayUtilsTest.java class ArrayUtilsTest (line 6) | public class ArrayUtilsTest { method testParseDoubleArray (line 8) | @Test method testParseIntArray (line 15) | @Test method testIsConstant (line 22) | @Test method testGetMedian (line 37) | @Test FILE: src/test/java/mltk/util/MathUtilsTest.java class MathUtilsTest (line 6) | public class MathUtilsTest { method testEquals (line 8) | @Test method testIndicator (line 14) | @Test method testIsFirstBetter (line 20) | @Test method testIsInteger (line 26) | @Test method testIsZero (line 32) | @Test method testSigmoid (line 38) | @Test method testSign (line 43) | @Test FILE: src/test/java/mltk/util/OptimUtilsTest.java class OptimUtilsTest (line 6) | public class OptimUtilsTest { method testGetProbability (line 8) | @Test method testGetResidual (line 13) | @Test method testGetPseudoResidual (line 18) | @Test method testComputeLogisticLoss (line 24) | @Test method testIsConverged (line 32) | @Test FILE: src/test/java/mltk/util/StatUtilsTest.java class StatUtilsTest (line 6) | public class StatUtilsTest { method testMax (line 11) | @Test method testIndexOfMax (line 17) | @Test method testMin (line 23) | @Test method testIndexOfMin (line 29) | @Test method testSum (line 35) | @Test method testSumSq (line 40) | @Test method testMean (line 46) | @Test method testVariance (line 51) | @Test method testStd (line 56) | @Test method testRms (line 61) | @Test FILE: src/test/java/mltk/util/VectorUtilsTest.java class VectorUtilsTest (line 6) | public class VectorUtilsTest { method testAdd (line 8) | @Test method testSubtract (line 16) | @Test method testMultiply (line 24) | @Test method testDivide (line 32) | @Test method testL2norm (line 40) | @Test method testL1norm (line 46) | @Test method testDotProduct (line 52) | @Test method testCorrelation (line 59) | @Test