public class MaxEntShell extends Object
| Modifier and Type | Method and Description |
|---|---|
static Classification[] |
classify(Classifier classifier,
PipeInputIterator data)
Compute the maxent classifications for unlabeled instances given
by an iterator.
|
static Classification |
classify(Classifier classifier,
String[] features)
Compute the maxent classification of an instance.
|
static Classification[] |
classify(Classifier classifier,
String[][] features)
Compute the maxent classifications of an array of instances
|
static Classifier |
load(File modelFile)
Load a classifier from a file.
|
static void |
main(String[] args)
Command-line wrapper to train, test, or run a maxent
classifier.
|
static double |
test(Classifier classifier,
PipeInputIterator data)
Test a maxent classifier.
|
static double |
test(Classifier classifier,
String[][] features,
String[] labels)
Test a maxent classifier.
|
static Classifier |
train(PipeInputIterator data,
double var,
File save)
Train a maxent classifier.
|
static Classifier |
train(String[][] features,
String[] labels,
double var,
File save)
Train a maxent classifier.
|
public static Classifier train(String[][] features, String[] labels, double var, File save) throws IOException
features
represents the features of a training instance. The label for
that instance is in the corresponding position of
labels.features - Each row gives the on features of an instancelabels - Each position gives the label of an instancevar - Gaussian prior variance for trainingsave - if non-null, save the trained model to this fileIOException - if the trained model cannot be savedpublic static Classifier train(PipeInputIterator data, double var, File save) throws IOException
data returns
training instances with a TokenSequence as data and a
target object. The tokens in the instance data will be converted to
features.data - the iterator over training instancesvar - Gaussian prior variance for training.save - if non-null, save the trained model to this fileIOException - if the trained model cannot be savedpublic static double test(Classifier classifier, String[][] features, String[] labels)
classifier - the classifier to testfeatures - an array of instances represented as arrays of featureslabels - corresponding labelspublic static double test(Classifier classifier, PipeInputIterator data)
classifier - the classifier to testdata - an iterator over labeled instancespublic static Classification classify(Classifier classifier, String[] features)
classifier - the classifierfeatures - the features that are on for this instancepublic static Classification[] classify(Classifier classifier, String[][] features)
classifier - the classifierfeatures - each row represents the on features for an instancepublic static Classification[] classify(Classifier classifier, PipeInputIterator data)
classifier - the classifierdata - the iterator over unlabeled instancespublic static Classifier load(File modelFile) throws IOException, ClassNotFoundException
modelFile - the fileIOException - if the file cannot be opened or readClassNotFoundException - if the file does not deserializepublic static void main(String[] args) throws Exception
args - the command line arguments. Options (shell and Java quoting should be added as needed):
--help booleantrue for longer documentation. Default is false.--prefix-code Java-code--gaussian-variance positive-number--train filenane--test filename--classify filename--model filenameException - if an error occursCopyright © 2019 JULIE Lab, Germany. All rights reserved.