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/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements. See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied. See the License for the
* specific language governing permissions and limitations
* under the License.
*/
package opennlp.maxent;
import java.io.IOException;
import opennlp.model.DataIndexer;
import opennlp.model.EventStream;
import opennlp.model.Prior;
/**
* A Factory class which uses instances of GISTrainer to create and train
* GISModels.
*/
public class GIS {
/**
* Set this to false if you don't want messages about the progress of model
* training displayed. Alternately, you can use the overloaded version of
* trainModel() to conditionally enable progress messages.
*/
public static boolean PRINT_MESSAGES = true;
/**
* If we are using smoothing, this is used as the "number" of times we want
* the trainer to imagine that it saw a feature that it actually didn't see.
* Defaulted to 0.1.
*/
public static double SMOOTHING_OBSERVATION = 0.1;
/**
* Train a model using the GIS algorithm, assuming 100 iterations and no
* cutoff.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(EventStream eventStream) throws IOException {
return trainModel(eventStream, 100, 0, false, PRINT_MESSAGES);
}
/**
* Train a model using the GIS algorithm, assuming 100 iterations and no
* cutoff.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(EventStream eventStream, boolean smoothing)
throws IOException {
return trainModel(eventStream, 100, 0, smoothing, PRINT_MESSAGES);
}
/**
* Train a model using the GIS algorithm.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param iterations
* The number of GIS iterations to perform.
* @param cutoff
* The number of times a feature must be seen in order to be relevant
* for training.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(EventStream eventStream, int iterations,
int cutoff) throws IOException {
return trainModel(eventStream, iterations, cutoff, false, PRINT_MESSAGES);
}
/**
* Train a model using the GIS algorithm.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param iterations
* The number of GIS iterations to perform.
* @param cutoff
* The number of times a feature must be seen in order to be relevant
* for training.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @param printMessagesWhileTraining
* Determines whether training status messages are written to STDOUT.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(EventStream eventStream, int iterations,
int cutoff, boolean smoothing, boolean printMessagesWhileTraining)
throws IOException {
GISTrainer trainer = new GISTrainer(printMessagesWhileTraining);
trainer.setSmoothing(smoothing);
trainer.setSmoothingObservation(SMOOTHING_OBSERVATION);
return trainer.trainModel(eventStream, iterations, cutoff);
}
/**
* Train a model using the GIS algorithm.
*
* @param eventStream
* The EventStream holding the data on which this model will be
* trained.
* @param iterations
* The number of GIS iterations to perform.
* @param cutoff
* The number of times a feature must be seen in order to be relevant
* for training.
* @param sigma
* The standard deviation for the gaussian smoother.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(EventStream eventStream, int iterations,
int cutoff, double sigma) throws IOException {
GISTrainer trainer = new GISTrainer(PRINT_MESSAGES);
if (sigma > 0)
trainer.setGaussianSigma(sigma);
return trainer.trainModel(eventStream, iterations, cutoff);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer,
boolean smoothing) {
return trainModel(iterations, indexer, true, smoothing, null, 0);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer) {
return trainModel(iterations, indexer, true, false, null, 0);
}
/**
* Train a model using the GIS algorithm with the specified number of
* iterations, data indexer, and prior.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param modelPrior
* The prior distribution for the model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer,
Prior modelPrior, int cutoff) {
return trainModel(iterations, indexer, true, false, modelPrior, cutoff);
}
/**
* Train a model using the GIS algorithm.
*
* @param iterations
* The number of GIS iterations to perform.
* @param indexer
* The object which will be used for event compilation.
* @param printMessagesWhileTraining
* Determines whether training status messages are written to STDOUT.
* @param smoothing
* Defines whether the created trainer will use smoothing while
* training the model.
* @param modelPrior
* The prior distribution for the model.
* @param cutoff
* The number of times a predicate must occur to be used in a model.
* @return The newly trained model, which can be used immediately or saved to
* disk using an opennlp.maxent.io.GISModelWriter object.
*/
public static GISModel trainModel(int iterations, DataIndexer indexer,
boolean printMessagesWhileTraining, boolean smoothing, Prior modelPrior,
int cutoff) {
GISTrainer trainer = new GISTrainer(printMessagesWhileTraining);
trainer.setSmoothing(smoothing);
trainer.setSmoothingObservation(SMOOTHING_OBSERVATION);
if (modelPrior != null) {
return trainer.trainModel(iterations, indexer, modelPrior, cutoff);
} else {
return trainer.trainModel(iterations, indexer, cutoff);
}
}
}
