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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;
import java.util.LinkedList;
import java.util.Map;
import java.util.Queue;
import java.util.Set;
import java.util.concurrent.ConcurrentHashMap;
/**
* Abstract base extended by any concrete classifier. It implements the basic
* functionality for storing categories or features and can be used to calculate
* basic probabilities – both category and feature probabilities. The classify
* function has to be implemented by the concrete classifier class.
*
* @author Philipp Nolte
*
* @param <T> A feature class
* @param <K> A category class
*/
public abstract class Classifier<T, K> {
/**
* Initial capacity of category dictionaries.
*/
private static final int INITIAL_CATEGORY_DICTIONARY_CAPACITY = 16;
/**
* Initial capacity of feature dictionaries. It should be quite big, because
* the features will quickly outnumber the categories.
*/
private static final int INITIAL_FEATURE_DICTIONARY_CAPACITY = 32;
/**
* The initial memory capacity or how many classifications are memorized.
*/
private int memoryCapacity = 1000;
/**
* A dictionary mapping features to their number of occurrences in each
* known category.
*/
private Map<K, Map<T, Integer>> featureCountPerCategory;
/**
* A dictionary mapping features to their number of occurrences.
*/
private Map<T, Integer> totalFeatureCount;
/**
* A dictionary mapping categories to their number of occurrences.
*/
private Map<K, Integer> totalCategoryCount;
/**
* The classifier's memory. It will forget old classifications as soon as
* they become too old.
*/
private Queue<Classification<T, K>> memoryQueue;
/**
* Constructs a new classifier without any trained knowledge.
*/
public Classifier() {
this.reset();
}
/**
* Resets the <i>learned</i> feature and category counts.
*/
public void reset() {
this.featureCountPerCategory =
new ConcurrentHashMap<K, Map<T,Integer>>(
Classifier.INITIAL_CATEGORY_DICTIONARY_CAPACITY);
this.totalFeatureCount =
new ConcurrentHashMap<T, Integer>(
Classifier.INITIAL_FEATURE_DICTIONARY_CAPACITY);
this.totalCategoryCount =
new ConcurrentHashMap<K, Integer>(
Classifier.INITIAL_CATEGORY_DICTIONARY_CAPACITY);
this.memoryQueue = new LinkedList<Classification<T, K>>();
}
/**
* Returns a <code>Set</code> of features the classifier knows about.
*
* @return The <code>Set</code> of features the classifier knows about.
*/
public Set<T> getFeatures() {
return this.totalFeatureCount.keySet();
}
/**
* Returns a <code>Set</code> of categories the classifier knows about.
*
* @return The <code>Set</code> of categories the classifier knows about.
*/
public Set<K> getCategories() {
return this.totalCategoryCount.keySet();
}
/**
* Retrieves the total number of categories the classifier knows about.
*
* @return The total category count.
*/
public int getCategoriesTotal() {
int toReturn = 0;
for (Integer c: this.totalCategoryCount.values()) {
toReturn += c;
}
return toReturn;
}
/**
* Retrieves the memory's capacity.
*
* @return The memory's capacity.
*/
public int getMemoryCapacity() {
return memoryCapacity;
}
/**
* Sets the memory's capacity. If the new value is less than the old
* value, the memory will be truncated accordingly.
*
* @param memoryCapacity The new memory capacity.
*/
public void setMemoryCapacity(int memoryCapacity) {
for (int i = this.memoryCapacity; i > memoryCapacity; i--) {
this.memoryQueue.poll();
}
this.memoryCapacity = memoryCapacity;
}
/**
* Increments the count of a given feature in the given category. This is
* equal to telling the classifier, that this feature has occurred in this
* category.
*
* @param feature The feature, which count to increase.
* @param category The category the feature occurred in.
*/
public void incrementFeature(T feature, K category) {
Map<T, Integer> features =
this.featureCountPerCategory.get(category);
if (features == null) {
this.featureCountPerCategory.put(category,
new ConcurrentHashMap<T, Integer>(Classifier.INITIAL_FEATURE_DICTIONARY_CAPACITY));
features = this.featureCountPerCategory.get(category);
}
Integer count = features.get(feature);
if (count == null) {
features.put(feature, 0);
count = features.get(feature);
}
features.put(feature, ++count);
Integer totalCount = this.totalFeatureCount.get(feature);
if (totalCount == null) {
this.totalFeatureCount.put(feature, 0);
totalCount = this.totalFeatureCount.get(feature);
}
this.totalFeatureCount.put(feature, ++totalCount);
}
/**
* Increments the count of a given category. This is equal to telling the
* classifier, that this category has occurred once more.
*
* @param category The category, which count to increase.
*/
public void incrementCategory(K category) {
Integer count = this.totalCategoryCount.get(category);
if (count == null) {
this.totalCategoryCount.put(category, 0);
count = this.totalCategoryCount.get(category);
}
this.totalCategoryCount.put(category, ++count);
}
/**
* Decrements the count of a given feature in the given category. This is
* equal to telling the classifier that this feature was classified once in
* the category.
*
* @param feature The feature to decrement the count for.
* @param category The category.
*/
public void decrementFeature(T feature, K category) {
Map<T, Integer> features =
this.featureCountPerCategory.get(category);
if (features == null) {
return;
}
Integer count = features.get(feature);
if (count == null) {
return;
}
if (count.intValue() == 1) {
features.remove(feature);
if (features.size() == 0) {
this.featureCountPerCategory.remove(category);
}
} else {
features.put(feature, --count);
}
Integer totalCount = this.totalFeatureCount.get(feature);
if (totalCount == null) {
return;
}
if (totalCount.intValue() == 1) {
this.totalFeatureCount.remove(feature);
} else {
this.totalFeatureCount.put(feature, --totalCount);
}
}
/**
* Decrements the count of a given category. This is equal to telling the
* classifier, that this category has occurred once less.
*
* @param category The category, which count to increase.
*/
public void decrementCategory(K category) {
Integer count = this.totalCategoryCount.get(category);
if (count == null) {
return;
}
if (count.intValue() == 1) {
this.totalCategoryCount.remove(category);
} else {
this.totalCategoryCount.put(category, --count);
}
}
/**
* Retrieves the number of occurrences of the given feature in the given
* category.
*
* @param feature The feature, which count to retrieve.
* @param category The category, which the feature occurred in.
* @return The number of occurrences of the feature in the category.
*/
public int featureCount(T feature, K category) {
Map<T, Integer> features =
this.featureCountPerCategory.get(category);
if (features == null)
return 0;
Integer count = features.get(feature);
return (count == null) ? 0 : count.intValue();
}
/**
* Retrieves the number of occurrences of the given category.
*
* @param category The category, which count should be retrieved.
* @return The number of occurrences.
*/
public int categoryCount(K category) {
Integer count = this.totalCategoryCount.get(category);
return (count == null) ? 0 : count.intValue();
}
public float featureProbability(T feature, K category) {
if (this.categoryCount(category) == 0)
return 0;
return (float) this.featureCount(feature, category)
/ (float) this.categoryCount(category);
}
/**
* Retrieves the weighed average <code>P(feature|category)</code> with
* overall weight of <code>1.0</code> and an assumed probability of
* <code>0.5</code>. The probability defaults to the overall feature
* probability.
*
* @see de.daslaboratorium.machinelearning.classifier.Classifier#featureProbability(Object, Object)
* @see de.daslaboratorium.machinelearning.classifier.Classifier#featureWeighedAverage(Object, Object, IFeatureProbability, float, float)
*
* @param feature The feature, which probability to calculate.
* @param category The category.
* @return The weighed average probability.
*/
public float featureWeighedAverage(T feature, K category) {
return this.featureWeighedAverage(feature, category, null, 1.0f, 0.5f);
}
/**
* Retrieves the weighed average <code>P(feature|category)</code> with
* overall weight of <code>1.0</code>, an assumed probability of
* <code>0.5</code> and the given object to use for probability calculation.
*
* @see de.daslaboratorium.machinelearning.classifier.Classifier#featureWeighedAverage(Object, Object, IFeatureProbability, float, float)
*
* @param feature The feature, which probability to calculate.
* @param category The category.
* @param calculator The calculating object.
* @return The weighed average probability.
*/
public float featureWeighedAverage(T feature, K category, Classifier<T, K> calculator) {
return this.featureWeighedAverage(feature, category,
calculator, 1.0f, 0.5f);
}
/**
* Retrieves the weighed average <code>P(feature|category)</code> with
* the given weight and an assumed probability of <code>0.5</code> and the
* given object to use for probability calculation.
*
* @see de.daslaboratorium.machinelearning.classifier.Classifier#featureWeighedAverage(Object, Object, IFeatureProbability, float, float)
*
* @param feature The feature, which probability to calculate.
* @param category The category.
* @param calculator The calculating object.
* @param weight The feature weight.
* @return The weighed average probability.
*/
public float featureWeighedAverage(T feature, K category, Classifier<T, K> calculator, float weight) {
return this.featureWeighedAverage(feature, category,
calculator, weight, 0.5f);
}
/**
* Retrieves the weighed average <code>P(feature|category)</code> with
* the given weight, the given assumed probability and the given object to
* use for probability calculation.
*
* @param feature The feature, which probability to calculate.
* @param category The category.
* @param calculator The calculating object.
* @param weight The feature weight.
* @param assumedProbability The assumed probability.
* @return The weighed average probability.
*/
public float featureWeighedAverage(T feature, K category, Classifier<T, K> calculator, float weight, float assumedProbability) {
/*
* use the given calculating object or the default method to calculate
* the probability that the given feature occurred in the given
* category.
*/
final float basicProbability =
(calculator == null)
? this.featureProbability(feature, category)
: calculator.featureProbability(feature, category);
Integer totals = this.totalFeatureCount.get(feature);
if (totals == null)
totals = 0;
return (weight * assumedProbability + totals * basicProbability)
/ (weight + totals);
}
/**
* Train the classifier by telling it that the given features resulted in
* the given category.
*
* @param category The category the features belong to.
* @param features The features that resulted in the given category.
*/
public void learn(K category, Collection<T> features) {
this.learn(new Classification<T, K>(features, category));
}
/**
* Train the classifier by telling it that the given features resulted in
* the given category.
*
* @param classification The classification to learn.
*/
public void learn(Classification<T, K> classification) {
for (T feature : classification.getFeatureset())
this.incrementFeature(feature, classification.getCategory());
this.incrementCategory(classification.getCategory());
this.memoryQueue.offer(classification);
if (this.memoryQueue.size() > this.memoryCapacity) {
Classification<T, K> toForget = this.memoryQueue.remove();
for (T feature : toForget.getFeatureset())
this.decrementFeature(feature, toForget.getCategory());
this.decrementCategory(toForget.getCategory());
}
}
/**
* The classify method. It will retrieve the most likely category for the
* features given and depends on the concrete classifier implementation.
*
* @param features The features to classify.
* @return The category most likely.
*/
public abstract Classification<T, K> classify(Collection<T> features);
}
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