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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.
/*
* 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 .
*/
/**
* KStarConstants.java
* Copyright (C) 1995-2012 Univeristy of Waikato
* Java port to Weka by Abdelaziz Mahoui ([email protected]).
*
*/
package weka.classifiers.lazy.kstar;
/**
* @author Len Trigg ([email protected])
* @author Abdelaziz Mahoui ([email protected])
* @version $Revision 1.0 $
*/
public interface KStarConstants {
/** Some usefull constants */
int ON = 1;
int OFF = 0;
int NUM_RAND_COLS = 5;
double FLOOR = 0.0;
double FLOOR1 = 0.1;
double INITIAL_STEP = 0.05;
double LOG2 = 0.693147181;
double EPSILON = 1.0e-5;
/** How close the root finder for numeric and nominal have to get */
int ROOT_FINDER_MAX_ITER = 40;
double ROOT_FINDER_ACCURACY = 0.01;
/** Blend setting modes */
int B_SPHERE = 1; /* Use sphere of influence */
int B_ENTROPY = 2; /* Use entropic blend setting */
/** Missing value handling mode */
/* Ignore the instance with the missing value */
int M_DELETE = 1;
/* Treat missing values as maximally different */
int M_MAXDIFF = 2;
/* Normilize over the attributes */
int M_NORMAL = 3;
/* Average column entropy curves */
int M_AVERAGE = 4;
}
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