/**
* Copyright 2014, Emory University
*
* Licensed 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 edu.emory.clir.clearnlp.classification.instance;
import edu.emory.clir.clearnlp.classification.vector.AbstractFeatureVector;
import edu.emory.clir.clearnlp.classification.vector.SparseFeatureVector;
/**
* @since 3.0.0
* @author Jinho D. Choi ({@code jinho.choi@emory.edu})
*/
public class IntInstance
{
private int i_label;
private SparseFeatureVector f_vector;
public IntInstance(int label, SparseFeatureVector vector)
{
set(label, vector);
}
public int getLabel()
{
return i_label;
}
public SparseFeatureVector getFeatureVector()
{
return f_vector;
}
public void set(int label, SparseFeatureVector vector)
{
setLabel(label);
setFeatureVector(vector);
}
public void setLabel(int label)
{
i_label = label;
}
public void setFeatureVector(SparseFeatureVector vector)
{
f_vector = vector;
}
public boolean isLabel(int label)
{
return i_label == label;
}
public String toString()
{
return i_label + AbstractFeatureVector.DELIM_FEATURE + f_vector.toString();
}
}