/*
 * 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 org.apache.samoa.learners.clusterers;

import java.io.Serializable;

import org.apache.samoa.instances.Instance;
import org.apache.samoa.instances.Instances;
import org.apache.samoa.moa.cluster.Clustering;

/**
 * Learner interface for non-distributed learners.
 * 
 * @author abifet
 */
public interface LocalClustererAdapter extends Serializable {

  /**
   * Creates a new learner object.
   * 
   * @return the learner
   */
  LocalClustererAdapter create();

  /**
   * 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
   */
  double[] getVotesForInstance(Instance inst);

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

  /**
   * Trains this classifier incrementally using the given instance.
   * 
   * @param inst
   *          the instance to be used for training
   */
  void trainOnInstance(Instance inst);

  /**
   * Sets where to obtain the information of attributes of Instances
   * 
   * @param dataset
   *          the dataset that contains the information
   */
  public void setDataset(Instances dataset);

  public Instances getDataset();

  public boolean implementsMicroClusterer();

  public Clustering getMicroClusteringResult();

}