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/*
* The MIT License (MIT)
* ------------------
*
* Copyright (c) 2012-2014 Philipp Nolte
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/*
* This software was taken from https://github.com/ptnplanet/Java-Naive-Bayes-Classifier
* and inserted into the loklak class hierarchy to be enhanced and extended
* by @0rb1t3r. After optimization in loklak it was inserted into the net.yacy.cora.bayes
* package. It shall be used to create custom search navigation filters.
* The original copyright notice was copied from the README.mnd
* from https://github.com/ptnplanet/Java-Naive-Bayes-Classifier/blob/master/README.md
* The original package domain was de.daslaboratorium.machinelearning.classifier
*/
package net.yacy.cora.bayes;
import java.util.Collection;
/**
* A basic wrapper reflecting a classification. It will store both featureset
* and resulting classification.
*
* @author Philipp Nolte
*
* @param <T> The feature class.
* @param <K> The category class.
*/
public class Classification<T, K> {
/**
* The classified featureset.
*/
private Collection<T> featureset;
/**
* The category as which the featureset was classified.
*/
private K category;
/**
* The probability that the featureset belongs to the given category.
*/
private float probability;
/**
* Constructs a new Classification with the parameters given and a default
* probability of 1.
*
* @param featureset The featureset.
* @param category The category.
*/
public Classification(Collection<T> featureset, K category) {
this(featureset, category, 1.0f);
}
/**
* Constructs a new Classification with the parameters given.
*
* @param featureset The featureset.
* @param category The category.
* @param probability The probability.
*/
public Classification(Collection<T> featureset, K category, float probability) {
this.featureset = featureset;
this.category = category;
this.probability = probability;
}
/**
* Retrieves the featureset classified.
*
* @return The featureset.
*/
public Collection<T> getFeatureset() {
return featureset;
}
/**
* Retrieves the classification's probability.
* @return
*/
public float getProbability() {
return this.probability;
}
/**
* Retrieves the category the featureset was classified as.
*
* @return The category.
*/
public K getCategory() {
return category;
}
/**
* {@inheritDoc}
*/
@Override
public String toString() {
return "Classification [category=" + this.category
+ ", probability=" + this.probability
+ ", featureset=" + this.featureset
+ "]";
}
}
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