weka.attributeSelection.AttributeTransformer Maven / Gradle / Ivy

Show more of this group Show more artifacts with this name
Show all versions of weka-dev Show documentation

The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This version represents the developer version, the "bleeding edge" of development, you could say. New functionality gets added to this version.

There is a newer version: 3.9.6

Show newest version

/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see .
 */

/*
 *    AttributeTransformer.java
 *    Copyright (C) 2000-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.attributeSelection;

import weka.core.Instance;
import weka.core.Instances;

/** 
 * Abstract attribute transformer. Transforms the dataset.
 *
 * @author Mark Hall ([email protected])
 * @version $Revision: 8034 $
 */
public interface AttributeTransformer {
    // ===============
    // Public methods.
    // ===============

  /**
   * Returns just the header for the transformed data (ie. an empty
   * set of instances. This is so that AttributeSelection can
   * determine the structure of the transformed data without actually
   * having to get all the transformed data through getTransformedData().
   * @return the header of the transformed data.
   * @exception Exception if the header of the transformed data can't
   * be determined.
   */
  Instances transformedHeader() throws Exception;

  /**
   * Transform the supplied data set (assumed to be the same format
   * as the training data)
   * @return A set of instances representing the transformed data
   * @exception Exception if the attribute could not be evaluated
   */
  Instances transformedData(Instances data) throws Exception;

  /**
   * Transforms an instance in the format of the original data to the
   * transformed space
   * @return a transformed instance
   * @exception Exception if the instance could not be transformed
   */
  Instance convertInstance(Instance instance) throws Exception;
}