/**
* 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.vector;
import com.carrotsearch.hppc.IntArrayList;
import com.clearnlp.classification.train.AbstractTrainSpace;
import com.clearnlp.util.UTMath;
/**
* Vector containing sparse features.
* @since 1.0.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class SparseFeatureVector extends AbstractFeatureVector
{
private IntArrayList i_indices;
/** Constructs a vector containing sparse features without weights. */
public SparseFeatureVector()
{
super();
}
/**
* Constructs a vector containing sparse features.
* @param hasWeight {@code true} if features are assigned with different weights.
*/
public SparseFeatureVector(boolean hasWeight)
{
super(hasWeight);
}
/* (non-Javadoc)
* @see edu.colorado.clear.classification.vector.AbstractFeatureVector#init()
*/
protected void init()
{
i_indices = new IntArrayList();
}
/**
* Adds a feature.
* @param index the feature type.
*/
public void addFeature(int index)
{
i_indices.add(index);
}
/**
* Adds a feature.
* @param index the feature index.
* @param weight the feature weight.
*/
public void addFeature(int index, double weight)
{
i_indices.add(index);
d_weights.add(weight);
}
public void addFeatures(int[] indices)
{
i_indices.add(indices);
}
public void addFeatures(int[] indices, double[] weights)
{
i_indices.add(indices);
d_weights.add(weights);
}
/**
* Adds a feature.
* @param feature {@code <index>}{@link SparseFeatureVector#DELIM}{@code [}{@link SparseFeatureVector#DELIM}{@code <weight>]}.
*/
public void addFeature(String feature)
{
if (b_weight)
{
String[] tmp = feature.split(DELIM);
i_indices.add(Integer.parseInt(tmp[0]));
d_weights.add(Double.parseDouble(tmp[1]));
}
else
i_indices.add(Integer.parseInt(feature));
}
/**
* Returns the index'th feature index.
* @param index the index of the feature index to return.
* @return the index'th feature index.
*/
public int getIndex(int index)
{
return i_indices.get(index);
}
/**
* Returns all feature indices.
* @return all feature indices.
*/
public int[] getIndices()
{
return i_indices.toArray();
}
/**
* Returns the total number of features in this vector.
* @return the total number of features in this vector.
*/
public int size()
{
return i_indices.size();
}
/** Trims the internal buffer to the current size. */
public void trimToSize()
{
i_indices.trimToSize();
if (b_weight) d_weights.trimToSize();
}
public boolean isEmpty()
{
return i_indices.isEmpty();
}
public double getSumOfSquaredWeights()
{
int size = i_indices.size();
if (b_weight)
{
double sum = 0; int i;
for (i=0; i<size; i++)
sum += UTMath.sq(d_weights.get(i));
return sum;
}
else
return size;
}
/* (non-Javadoc)
* @see java.lang.Object#toString()
*/
public String toString()
{
StringBuilder build = new StringBuilder();
int i, size = i_indices.size();
for (i=0; i<size; i++)
{
build.append(AbstractTrainSpace.DELIM_COL);
build.append(i_indices.get(i));
if (b_weight)
{
build.append(DELIM);
build.append(d_weights.get(i));
}
}
return build.toString().substring(AbstractTrainSpace.DELIM_COL.length());
}
}