classifier.java

一个数据挖掘软件ALPHAMINERR的整个过程的JAVA版源代码

/*
 *    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 2 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, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

 /**
  * Title: XELOPES Data Mining Library
  * Description: The XELOPES library is an open platform-independent and data-source-independent library for Embedded Data Mining.
  * Copyright: Copyright (c) 2002 Prudential Systems Software GmbH
  * Company: ZSoft (www.zsoft.ru), Prudsys (www.prudsys.com)
  * @author Valentine Stepanenko ([e-mail])
  * @version 1.0
  */

package com.prudsys.pdm.Cwm.Core;

import java.util.Collection;
import java.util.Iterator;
import java.util.List;


/**
 * A classifier is an element that describes structural and behavioral features;
 * it comes in several specific forms, including class, data type, interface,
 * component, and others that are defined in other metamodel packages.
 *
 *
 *
 * Classifier is often used as a type.
 *
 *
 *
 * In the metamodel, a Classifier may declare a collection of Features, such as
 * Attributes, Operations and Methods. It has a name, which is unique in the
 * Namespace enclosing the Classifier. Classifier is an abstract metaclass.
 *
 *
 *
 * Classifier is a child of Namespace. As a Namespace, a Classifier may declare
 * other Classifiers nested in its scope. Nested Classifiers may be accessed by
 * other Classifiers only if the nested Classifiers have adequate visibility.
 * There are no data value or state consequences of nested Classifiers, i.e., it
 * is not an aggregation or composition.
 */
public abstract class Classifier extends Namespace implements org.omg.cwm.objectmodel.core.Classifier
{
   /**
    * An abstract Classifier is not instantiable.
    */
   public Boolean isAbstract;
   public Feature feature[];
   public StructuralFeature structuralFeature[];

   /**
    * @roseuid 3C5E9E9102BA
    */
   public Classifier()
   {

   }

   public java.lang.Boolean getIsAbstract() {

     return isAbstract.getBoolean();
   }

   public void setIsAbstract(java.lang.Boolean isAbstract) {

     Boolean b = new Boolean();
     b.setBoolean(isAbstract);
     this.isAbstract = b;
   }

   public boolean isAbstractValue() {

     return isAbstract.isBooleanValue();
   }

   public void setIsAbstract(boolean isAbstract) {

     setIsAbstract( new java.lang.Boolean(isAbstract) );
   }

   public int getFeatureSize() {

     return (feature == null) ? 0 : feature.length;
   }

   public Feature getFeature(int index) {

     return (Feature) CWMTools.getElement(feature, index);
   }

   public List getFeature() {

     return CWMTools.ArrayToList(feature);
   }

   public void setFeature(Collection feature) {

     this.feature = new Feature[ feature.size() ];
     Iterator it = feature.iterator();
     for (int i = 0; i < feature.size(); i++)
       this.feature[i] = (Feature) it.next();
   }

   public void addFeature( org.omg.cwm.objectmodel.core.Feature input) {

     int size = (feature == null) ? 0 : feature.length;
     Feature[] oldData = feature;
     feature = new Feature[size+1];
     if (size > 0) System.arraycopy(oldData, 0, feature, 0, size);
     feature[size] = (Feature) input;
   }

   public void removeFeature( org.omg.cwm.objectmodel.core.Feature input) {

     int size = (feature == null) ? 0 : feature.length;
     if (size == 0)
       return;

     int ipos = -1;
     for (int i = 0; i < size; i++)
       if (feature[i].equals(input)) {
         ipos = i;
         break;
       }
     if (ipos == -1)
       return;

     Feature[] oldData = feature;
     feature = new Feature[size-1];
     for (int i = 0; i < ipos; i++)
       feature[i] = oldData[i];
     for (int i = ipos+1; i < size; i++)
       feature[i-1] = oldData[i];
   }

   public void moveFeatureBefore( org.omg.cwm.objectmodel.core.Feature before, org.omg.cwm.objectmodel.core.Feature input){};

   public void moveFeatureAfter( org.omg.cwm.objectmodel.core.Feature before, org.omg.cwm.objectmodel.core.Feature input){};

   public StructuralFeature[] getStructuralFeature() {

     return structuralFeature;
   }

   public void setStructuralFeature(StructuralFeature[] structuralFeature) {

     this.structuralFeature = structuralFeature;
   }
}