/**
* 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 java.util.ArrayList;
import java.util.List;
import com.carrotsearch.hppc.DoubleArrayList;
import com.clearnlp.classification.train.AbstractTrainSpace;
/**
* Vector containing string features.
* @since 0.1.0
* @author Jinho D. Choi ({@code jdchoi77@gmail.com})
*/
public class StringFeatureVector extends AbstractFeatureVector
{
private List<String> s_types;
private List<String> s_values;
/** Constructs a vector containing string features without weights. */
public StringFeatureVector()
{
super();
}
/**
* Constructs a vector containing string features.
* @param hasWeight {@code true} if features are assigned with different weights.
*/
public StringFeatureVector(boolean hasWeight)
{
super(hasWeight);
}
/* (non-Javadoc)
* @see edu.colorado.clear.classification.vector.AbstractFeatureVector#init()
*/
protected void init()
{
s_types = new ArrayList<String>();
s_values = new ArrayList<String>();
}
public StringFeatureVector clone()
{
StringFeatureVector copy = new StringFeatureVector(b_weight);
copy.s_types = new ArrayList<String>(s_types);
copy.s_values = new ArrayList<String>(s_values);
if (b_weight) copy.d_weights = d_weights.clone();
return copy;
}
/**
* Adds a feature.
* @param type the feature type.
* @param value the feature value.
*/
public void addFeature(String type, String value)
{
s_types .add(type);
s_values.add(value);
}
/**
* Adds a feature.
* @param type the feature type.
* @param value the feature value.
* @param weight the feature weight.
*/
public void addFeature(String type, String value, double weight)
{
s_types .add(type);
s_values.add(value);
d_weights.add(weight);
}
/**
* Adds a feature.
* @param feature {@code <type>}{@link StringFeatureVector#DELIM}{@code <value>[}{@link StringFeatureVector#DELIM}{@code <weight>]}.
*/
public void addFeature(String feature)
{
int idx0 = feature.indexOf(DELIM);
s_types.add(feature.substring(0, idx0));
if (b_weight)
{
int idx1 = feature.lastIndexOf(DELIM);
s_values .add(feature.substring(idx0+1, idx1));
d_weights.add(Double.parseDouble(feature.substring(idx1+1)));
}
else
s_values.add(feature.substring(idx0+1));
}
public void addFeatures(StringFeatureVector vector)
{
List<String> types = vector.s_types;
List<String> values = vector.s_values;
DoubleArrayList weights = vector.d_weights;
int i, size = vector.size();
for (i=0; i<size; i++)
{
s_types .add(types .get(i));
s_values.add(values.get(i));
if (weights != null) d_weights.add(weights.get(i));
}
}
/**
* Returns the index'th feature type.
* @param index the index of the feature type to return.
* @return the index'th feature type.
*/
public String getType(int index)
{
return s_types.get(index);
}
/**
* Returns the index'th feature value.
* @param index the index of the feature value to return.
* @return the index'th feature value.
*/
public String getValue(int index)
{
return s_values.get(index);
}
/**
* Returns the total number of features in this vector.
* @return the total number of features in this vector.
*/
public int size()
{
return s_types.size();
}
/* (non-Javadoc)
* @see java.lang.Object#toString()
*/
public String toString()
{
StringBuilder build = new StringBuilder();
int i, size = s_types.size();
for (i=0; i<size; i++)
{
build.append(AbstractTrainSpace.DELIM_COL);
build.append(s_types.get(i));
build.append(DELIM);
build.append(s_values.get(i));
if (b_weight)
{
build.append(DELIM);
build.append(d_weights.get(i));
}
}
return build.toString().substring(AbstractTrainSpace.DELIM_COL.length());
}
}