Package | Description |
---|---|
de.unibi.techfak.scie.classifiers | |
de.unibi.techfak.scie.classifiers.annotators | |
de.unibi.techfak.scie.classifiers.data |
Modifier and Type | Interface and Description |
---|---|
static interface |
TrainingUtils.ParameterSweep<C extends Classifier>
This is an interface to define a parameter sweep on some classifier.
|
Modifier and Type | Class and Description |
---|---|
class |
LibLinearClassifier
This is a wrapper for Benedikt Waldvogels liblinear implementation for Java.
|
Modifier and Type | Method and Description |
---|---|
static <C extends Classifier> |
TrainingUtils.crossValidationSweep(ArrayList<LabeledDataPoint> trainingData,
C classifier,
int folds,
int times,
TrainingUtils.ParameterSweep<C> sweep,
Comparator<ClassifierEvaluation> comparator,
boolean verbose)
Does the cross validation but does the given parameter sweep in an outer
loop.
|
static <C extends Classifier> |
TrainingUtils.simpleParameterSweep(ArrayList<LabeledDataPoint> trainingData,
C classifier,
double testRatio,
TrainingUtils.ParameterSweep<C> sweep,
int times,
Comparator<ClassifierEvaluation> comparator,
boolean verbose)
Does the simple training but does the given parameter sweep in an outer
loop.
|
Modifier and Type | Method and Description |
---|---|
static ClassifierEvaluation |
TrainingUtils.crossValidation(ArrayList<LabeledDataPoint> trainingData,
Classifier classifier,
int folds,
int times,
Comparator<ClassifierEvaluation> comparator,
boolean verbose)
Trains the given classifier using crossvalidation and taking the result
with the best evaluation result according to the given comparator.
|
static ClassifierEvaluation |
TrainingUtils.simpleTraining(ArrayList<LabeledDataPoint> trainingData,
Classifier classifier,
double testRatio,
int times,
Comparator<ClassifierEvaluation> comparator,
boolean verbose)
Chooses randomly testRatio datapoints for testing, trains the classifier
with the rest and reports precision, recall and accuracy for the test
set.
|
Constructor and Description |
---|
ClassifierEvaluation.SetEvaluation(Classifier classifier,
ArrayList<LabeledDataPoint> data,
int roc_steps) |
ClassifierEvaluation.SetEvaluation(Classifier classifier,
ArrayList<LabeledDataPoint> data,
int roc_steps,
boolean verbose) |
ClassifierEvaluation(Classifier classifier,
ArrayList<LabeledDataPoint> trainingData,
ArrayList<LabeledDataPoint> testData) |
ClassifierEvaluation(Classifier classifier,
ArrayList<LabeledDataPoint> trainingData,
ArrayList<LabeledDataPoint> testData,
boolean verbose) |
ClassifierEvaluation(Classifier classifier,
ArrayList<LabeledDataPoint> trainingData,
ArrayList<LabeledDataPoint> testData,
int roc_steps) |
ClassifierEvaluation(Classifier classifier,
ArrayList<LabeledDataPoint> trainingData,
ArrayList<LabeledDataPoint> testData,
int roc_steps,
boolean verbose) |
Modifier and Type | Method and Description |
---|---|
Classifier |
RelationAnnotator.getCoreClassifier() |
Classifier |
RelationAnnotator.getRelationClassifier() |
Classifier |
SlotSpecification.getSlotClassifier() |
Constructor and Description |
---|
RelationAnnotator(Class<C> coreClass,
Classifier coreClassifier,
Classifier relationClassifier) |
SlotSpecification(Class<S> slotClass,
Classifier slotClassifier) |
Modifier and Type | Method and Description |
---|---|
Classifier |
RelationDataPoint.RelationTrainingDataReader.getCoreClassifier() |
Constructor and Description |
---|
RelationDataPoint.RelationTrainingDataReader(Class<C> coreClass,
Classifier coreClassifier,
String rawRelationClass) |
Copyright © 2014. All rights reserved.