weka.experiment.ResultMatrix Maven / Gradle / Ivy

/*
 *   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 .
 */

/*
 * ResultMatrix.java
 * Copyright (C) 2005-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.experiment;

import java.io.Serializable;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionHandler;
import weka.core.Utils;

/**
 * This matrix is a container for the datasets and classifier setups and their
 * statistics. Derived classes output the data in different formats. Derived
 * classes need to implement the following methods:
 * 
 * toStringMatrix()
 * toStringKey()
 * toStringHeader()
 * toStringSummary()
 * toStringRanking()
 * 
 * 
 * 
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 11542 $
 * @see #toStringMatrix()
 * @see #toStringKey()
 * @see #toStringHeader()
 * @see #toStringSummary()
 * @see #toStringRanking()
 */
public abstract class ResultMatrix implements Serializable, RevisionHandler,
  OptionHandler {

  /** for serialization. */
  private static final long serialVersionUID = 4487179306428209739L;

  /** tie. */
  public final static int SIGNIFICANCE_TIE = 0;

  /** win. */
  public final static int SIGNIFICANCE_WIN = 1;

  /** loss. */
  public final static int SIGNIFICANCE_LOSS = 2;

  /** tie string. */
  public String TIE_STRING = " ";

  /** win string. */
  public String WIN_STRING = "v";

  /** loss string. */
  public String LOSS_STRING = "*";

  /** the left parentheses for enumerating cols/rows. */
  public String LEFT_PARENTHESES = "(";

  /** the right parentheses for enumerating cols/rows. */
  public String RIGHT_PARENTHESES = ")";

  /** the column names. */
  protected String[] m_ColNames = null;

  /** the row names. */
  protected String[] m_RowNames = null;

  /** whether a column is hidden. */
  protected boolean[] m_ColHidden = null;

  /** whether a row is hidden. */
  protected boolean[] m_RowHidden = null;

  /** the significance. */
  protected int[][] m_Significance = null;

  /** the values. */
  protected double[][] m_Mean = null;

  /** the standard deviation. */
  protected double[][] m_StdDev = null;

  /** the counts for the different datasets. */
  protected double[] m_Counts = null;

  /** the standard mean precision. */
  protected int m_MeanPrec;

  /** the standard std. deviation preicision. */
  protected int m_StdDevPrec;

  /** whether std. deviations are printed as well. */
  protected boolean m_ShowStdDev;

  /** whether the average for each column should be printed. */
  protected boolean m_ShowAverage;

  /** whether the names or numbers are output as column declarations. */
  protected boolean m_PrintColNames;

  /** whether the names or numbers are output as row declarations. */
  protected boolean m_PrintRowNames;

  /**
   * whether a "(x)" is printed before each column name with "x" as the index.
   */
  protected boolean m_EnumerateColNames;

  /** whether a "(x)" is printed before each row name with "x" as the index. */
  protected boolean m_EnumerateRowNames;

  /** the size of the names of the columns. */
  protected int m_ColNameWidth;

  /** the size of the names of the rows. */
  protected int m_RowNameWidth;

  /** the size of the mean columns. */
  protected int m_MeanWidth;

  /** the size of the std dev columns. */
  protected int m_StdDevWidth;

  /** the size of the significance columns. */
  protected int m_SignificanceWidth;

  /** the size of the counts. */
  protected int m_CountWidth;

  /** contains the keys for the header. */
  protected Vector m_HeaderKeys = null;

  /** contains the values for the header. */
  protected Vector m_HeaderValues = null;

  /** the non-significant wins. */
  protected int[][] m_NonSigWins = null;

  /** the significant wins. */
  protected int[][] m_Wins = null;

  /** the wins in ranking. */
  protected int[] m_RankingWins = null;

  /** the losses in ranking. */
  protected int[] m_RankingLosses = null;

  /** the difference between wins and losses. */
  protected int[] m_RankingDiff = null;

  /** the ordering of the rows. */
  protected int[] m_RowOrder = null;

  /** the ordering of the columns. */
  protected int[] m_ColOrder = null;

  /** whether to remove the filter name from the dataaset name. */
  protected boolean m_RemoveFilterName = false;

  /**
   * initializes the matrix as 1x1 matrix.
   */
  public ResultMatrix() {
    this(1, 1);
  }

  /**
   * initializes the matrix with the given dimensions.
   * 
   * @param cols the number of columns
   * @param rows the number of rows
   */
  public ResultMatrix(int cols, int rows) {
    setSize(cols, rows);
    clear();
  }

  /**
   * initializes the matrix with the values from the given matrix.
   * 
   * @param matrix the matrix to get the values from
   */
  public ResultMatrix(ResultMatrix matrix) {
    assign(matrix);
  }

  /**
   * Returns a string describing the matrix.
   * 
   * @return a description suitable for displaying in the experimenter gui
   */
  public abstract String globalInfo();

  /**
   * Returns an enumeration of all the available options..
   * 
   * @return an enumeration of all available options.
   */
  @Override
  public Enumeration