/**
* Copyright (c) 2009/09-2012/08, Regents of the University of Colorado
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
/**
* Copyright 2012/09-2013/04, 2013/11-Present, University of Massachusetts Amherst
* Copyright 2013/05-2013/10, IPSoft Inc.
*
* 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 com.clearnlp.classification.train;
import java.util.ArrayList;
import java.util.List;
import com.clearnlp.classification.model.SparseModel;
import com.clearnlp.classification.vector.SparseFeatureVector;
import com.clearnlp.util.pair.Pair;
/**
* Train space containing sparse vectors.
* @since 1.0.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class SparseTrainSpace extends AbstractTrainSpace
{
/** Casted from {@likn AbstractTrainSpace#m_model}. */
private SparseModel s_model;
/** The list of all labels. */
private List<String> s_ys;
/**
* Constructs a train space containing sparse vectors.
* @param hasWeight {@code true} if features are assigned with different weights.
*/
public SparseTrainSpace(boolean hasWeight)
{
super(new SparseModel(), hasWeight);
s_model = (SparseModel)m_model;
s_ys = new ArrayList<String>();
}
/**
* Adds a training instance to this space.
* @param label the label to be added.
* @param vector the feature vector to be added.
*/
public void addInstance(String label, SparseFeatureVector vector)
{
int[] x = vector.getIndices();
s_model.addLabel(label);
s_model.addFeatures(x);
s_ys.add(label);
a_xs.add(x);
if (b_weight) a_vs.add(vector.getWeights());
}
/**
* Adds a training instance to this space.
* @param line {@code <label>}{@link AbstractTrainSpace#DELIM_COL}{@link SparseFeatureVector#toString()}.
*/
public void addInstance(String line)
{
Pair<String,SparseFeatureVector> instance = toInstance(line, b_weight);
addInstance(instance.o1, instance.o2);
}
@Override
public void build(boolean clearInstances)
{
LOG.info("Building:\n");
s_model.initLabelArray();
for (String label : s_ys)
a_ys.add(s_model.getLabelIndex(label));
LOG.info("- # of labels : "+s_model.getLabelSize()+"\n");
LOG.info("- # of features : "+s_model.getFeatureSize()+"\n");
LOG.info("- # of instances: "+a_ys.size()+"\n");
if (clearInstances) s_ys.clear();
}
@Override
public void build()
{
build(true);
}
/**
* Returns the pair of label and feature vector parsed from the specific string.
* @param line the string to get the pair of label and feature vector from.
* @param hasWeight {@code true} if this vector has a different weight for each feature.
* @return the pair of label and feature vector parsed from the specific string.
*/
static public Pair<String,SparseFeatureVector> toInstance(String line, boolean hasWeight)
{
String[] tmp = line.split(DELIM_COL);
String label = tmp[0];
SparseFeatureVector vector = new SparseFeatureVector(hasWeight);
int i, size = tmp.length;
for (i=1; i<size; i++)
vector.addFeature(tmp[i]);
return new Pair<String,SparseFeatureVector>(label, vector);
}
}