samoa-api/src/main/java/org/apache/samoa/moa/learners/Learner.java

package org.apache.samoa.moa.learners;

/*
 * #%L
 * SAMOA
 * %%
 * Copyright (C) 2014 - 2015 Apache Software Foundation
 * %%
 * Licensed 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.
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 */

import org.apache.samoa.instances.InstancesHeader;
import org.apache.samoa.moa.MOAObject;
import org.apache.samoa.moa.core.Example;
import org.apache.samoa.moa.core.Measurement;
import org.apache.samoa.moa.options.OptionHandler;

/**
 * Learner interface for incremental learning models.
 * 
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @version $Revision: 7 $
 */
public interface Learner<E extends Example> extends MOAObject, OptionHandler {

  /**
   * Gets whether this learner needs a random seed. Examples of methods that needs a random seed are bagging and
   * boosting.
   * 
   * @return true if the learner needs a random seed.
   */
  public boolean isRandomizable();

  /**
   * Sets the seed for random number generation.
   * 
   * @param s
   *          the seed
   */
  public void setRandomSeed(int s);

  /**
   * Gets whether training has started.
   * 
   * @return true if training has started
   */
  public boolean trainingHasStarted();

  /**
   * Gets the sum of the weights of the instances that have been used by this learner during the training in
   * <code>trainOnInstance</code>
   * 
   * @return the weight of the instances that have been used training
   */
  public double trainingWeightSeenByModel();

  /**
   * Resets this learner. It must be similar to starting a new learner from scratch.
   * 
   */
  public void resetLearning();

  /**
   * Trains this learner incrementally using the given example.
   * 
   * @param inst
   *          the instance to be used for training
   */
  public void trainOnInstance(E example);

  /**
   * Predicts the class memberships for a given instance. If an instance is unclassified, the returned array elements
   * must be all zero.
   * 
   * @param inst
   *          the instance to be classified
   * @return an array containing the estimated membership probabilities of the test instance in each class
   */
  public double[] getVotesForInstance(E example);

  /**
   * Gets the current measurements of this learner.
   * 
   * @return an array of measurements to be used in evaluation tasks
   */
  public Measurement[] getModelMeasurements();

  /**
   * Gets the learners of this ensemble. Returns null if this learner is a single learner.
   * 
   * @return an array of the learners of the ensemble
   */
  public Learner[] getSublearners();

  /**
   * Gets the model if this learner.
   * 
   * @return the copy of this learner
   */
  public MOAObject getModel();

  /**
   * Sets the reference to the header of the data stream. The header of the data stream is extended from WEKA
   * <code>Instances</code>. This header is needed to know the number of classes and attributes
   * 
   * @param ih
   *          the reference to the data stream header
   */
  public void setModelContext(InstancesHeader ih);

  /**
   * Gets the reference to the header of the data stream. The header of the data stream is extended from WEKA
   * <code>Instances</code>. This header is needed to know the number of classes and attributes
   * 
   * @return the reference to the data stream header
   */
  public InstancesHeader getModelContext();

}