Class MultiClassNaiveBayes
- java.lang.Object
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- eu.h2020.helios_social.core.info_control.classifier.MultiClassNaiveBayes
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- All Implemented Interfaces:
java.io.Serializable
public class MultiClassNaiveBayes extends java.lang.Object implements java.io.Serializable
Multi-class Naive Bayes text classifier- See Also:
- Serialized Form
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Constructor Summary
Constructors Constructor Description MultiClassNaiveBayes()
Creates MultiClassNaiveBayes classifierMultiClassNaiveBayes(int maxSamples)
Create MultiClassNaiveBayes classifierMultiClassNaiveBayes(int maxSamples, int maxTextLength)
Create MultiClassNaiveBayes classifierMultiClassNaiveBayes(int maxSamples, int maxTextLength, double alpha)
Creates MultiClassNaiveBayes classifier
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Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
addSample(java.lang.String category, java.lang.String text)
Adds training sample for the classifierjava.util.List<Classification>
classify(java.lang.String text)
Classifies the input textvoid
removeCategory(java.lang.String category)
Removes a category from the training datavoid
reset()
Resets the classifier into initial state
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Constructor Detail
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MultiClassNaiveBayes
public MultiClassNaiveBayes()
Creates MultiClassNaiveBayes classifier
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MultiClassNaiveBayes
public MultiClassNaiveBayes(int maxSamples)
Create MultiClassNaiveBayes classifier- Parameters:
maxSamples
- the maximum number of training samples
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MultiClassNaiveBayes
public MultiClassNaiveBayes(int maxSamples, int maxTextLength)
Create MultiClassNaiveBayes classifier- Parameters:
maxSamples
- the maximum number of training samplesmaxTextLength
- the maximum length of input text for the classifier
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MultiClassNaiveBayes
public MultiClassNaiveBayes(int maxSamples, int maxTextLength, double alpha)
Creates MultiClassNaiveBayes classifier- Parameters:
maxSamples
- the maximum number of training samplesmaxTextLength
- the maximum length of input text for the classifieralpha
- the additional smoothing term. For the so-called Lidstone smoothing alpha < 1.0 and for Laplace smoothing alpha == 1.
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Method Detail
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addSample
public void addSample(@NonNull java.lang.String category, @NonNull java.lang.String text)
Adds training sample for the classifier- Parameters:
category
- the classified category of the text. It corresponds to context idtext
- the input text.
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removeCategory
public void removeCategory(@NonNull java.lang.String category)
Removes a category from the training data- Parameters:
category
- the category (i.e. context id)
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classify
public java.util.List<Classification> classify(@NonNull java.lang.String text)
Classifies the input text- Parameters:
text
- the input text to be classified- Returns:
- the list of possible classifications with probability values
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reset
public void reset()
Resets the classifier into initial state
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