Serialized Form

Package <Unnamed>

Class Counter extends java.lang.Object implements Serializable

serialVersionUID: 1L

Serialized Fields

m_val

int m_val

Integer value stored

Class GermanStemmer extends Stemmer implements Serializable

serialVersionUID: 1L

Serialized Fields

sb

java.lang.StringBuffer sb

Buffer for the terms while stemming them.

uppercase

boolean uppercase

Indicates if a term is handled as a noun.

substCount

int substCount

Amount of characters that are removed with substitute() while stemming.

Class IteratedLovinsStemmer extends LovinsStemmer implements Serializable

serialVersionUID: 1L

Class KEAFilter extends Filter implements Serializable

serialVersionUID: 1L

Serialized Fields

m_DocumentAtt

int m_DocumentAtt

Index of attribute containing the documents

m_KeyphrasesAtt

int m_KeyphrasesAtt

Index of attribute containing the keyphrases

m_MaxPhraseLength

int m_MaxPhraseLength

The maximum length of phrases

m_MinPhraseLength

int m_MinPhraseLength

The minimum length of phrases

m_numPhrases

int m_numPhrases

The number of phrases to extract.

m_KFused

boolean m_KFused

Is keyphrase frequency attribute being used?

m_DESCRreplace

boolean m_DESCRreplace

Should non-descriptors be replaced by corresponding descriptors?

m_APfeature

boolean m_APfeature

If m_RELused = true, should the feature of their appearance/non-appearance be used?

m_NODEfeature

boolean m_NODEfeature

Is the node degree (number of related terms in candidate set) being used?

m_LENGTHfeature

boolean m_LENGTHfeature

Is the length of a phrase in words being used?

m_Debug

boolean m_Debug

Flag for debugging mode

m_DisallowInternalPeriods

boolean m_DisallowInternalPeriods

Determines whether internal periods are allowed

m_MinNumOccur

int m_MinNumOccur

The minimum number of occurences of a phrase

m_NumFeatures

int m_NumFeatures

The number of features describing a phrase

m_TfidfIndex

int m_TfidfIndex

m_FirstOccurIndex

int m_FirstOccurIndex

m_KeyFreqIndex

int m_KeyFreqIndex

m_APIndex

int m_APIndex

m_NodeIndex

int m_NodeIndex

m_LengthIndex

int m_LengthIndex

m_PunctFilter

KEAPhraseFilter m_PunctFilter

The punctuation filter used by this filter

m_NumbersFilter

NumbersFilter m_NumbersFilter

The numbers filter used by this filter

m_Classifier

DistributionClassifier m_Classifier

The actual classifier used to compute probabilities

m_Dictionary

java.util.HashMap m_Dictionary

The dictionary containing the document frequencies

m_KeyphraseDictionary

java.util.HashMap m_KeyphraseDictionary

The dictionary containing the keyphrases

m_NumDocs

int m_NumDocs

The number of documents in the global frequencies corpus

m_ClassifierData

Instances m_ClassifierData

Template for the classifier data

m_Stemmer

Stemmer m_Stemmer

The default stemmer to be used

m_Stopwords

Stopwords m_Stopwords

The list of stop words to be used

m_vocabulary

java.lang.String m_vocabulary

m_vocabularyFormat

java.lang.String m_vocabularyFormat

m_CheckForProperNouns

boolean m_CheckForProperNouns

Determines whether check for proper nouns is performed

Class KEAPhraseFilter extends Filter implements Serializable

serialVersionUID: 1L

Serialized Fields

m_SelectCols

Range m_SelectCols

Stores which columns to select as a funky range

m_DisallowInternalPeriods

boolean m_DisallowInternalPeriods

Determines whether internal periods are allowed

Class LovinsStemmer extends Stemmer implements Serializable

serialVersionUID: 1L

Class NoStemmer extends Stemmer implements Serializable

serialVersionUID: 1L

Class NumbersFilter extends Filter implements Serializable

serialVersionUID: 1L

Class PorterStemmer extends Stemmer implements Serializable

serialVersionUID: 1L

Class SremovalStemmer extends Stemmer implements Serializable

serialVersionUID: 1L

Class Stemmer extends java.lang.Object implements Serializable

Class Stopwords extends java.lang.Object implements Serializable

Class StopwordsEnglish extends Stopwords implements Serializable

serialVersionUID: 1L

Class StopwordsGerman extends Stopwords implements Serializable

serialVersionUID: 1L

Class Vocabulary extends java.lang.Object implements Serializable

serialVersionUID: 1L

Serialized Fields

useSkos

boolean useSkos

Boolean describing which vocabulary format has been chosen: true if SKOS, false if text.

VocabularyEN

java.util.HashMap VocabularyEN

Vocabulary index

VocabularyENrev

java.util.HashMap VocabularyENrev

Vocabulary reverse index

VocabularyUSE

java.util.HashMap VocabularyUSE

Vocabulary non-descriptors - descriptors list

VocabularyREL

java.util.HashMap VocabularyREL

Vocabulary related terms

m_Stemmer

Stemmer m_Stemmer

The default stemmer to be used

m_Stopwords

Stopwords m_Stopwords

The list of stop words to be used

Package weka.classifiers

Class weka.classifiers.Classifier extends java.lang.Object implements Serializable

Class weka.classifiers.DistributionClassifier extends Classifier implements Serializable

Class weka.classifiers.FilteredClassifier extends DistributionClassifier implements Serializable

Serialized Fields

m_Classifier

Classifier m_Classifier

The classifier

m_Filter

Filter m_Filter

The filter

m_FilteredInstances

Instances m_FilteredInstances

The instance structure of the filtered instances

Class weka.classifiers.NaiveBayesSimple extends DistributionClassifier implements Serializable

Serialized Fields

m_Counts

double[][][] m_Counts

All the counts for nominal attributes.

m_Means

double[][] m_Means

The means for numeric attributes.

m_Devs

double[][] m_Devs

The standard deviations for numeric attributes.

m_Priors

double[] m_Priors

The prior probabilities of the classes.

m_Instances

Instances m_Instances

The instances used for training.

Package weka.core

Class weka.core.Attribute extends java.lang.Object implements Serializable

Serialized Fields

m_Name

java.lang.String m_Name

The attribute's name.

m_Type

int m_Type

The attribute's type.

m_Values

FastVector m_Values

The attribute's values (if nominal or string).

m_Hashtable

java.util.Hashtable m_Hashtable

Mapping of values to indices (if nominal or string).

m_Index

int m_Index

The attribute's index.

Class weka.core.FastVector extends java.lang.Object implements Serializable

Serialized Fields

m_Objects

java.lang.Object[] m_Objects

The array of objects.

m_Size

int m_Size

The current size;

m_CapacityIncrement

int m_CapacityIncrement

The capacity increment

m_CapacityMultiplier

double m_CapacityMultiplier

The capacity multiplier.

Class weka.core.Instance extends java.lang.Object implements Serializable

Serialized Fields

m_Dataset

Instances m_Dataset

The dataset the instance has access to. Null if the instance doesn't have access to any dataset. Only if an instance has access to a dataset, it knows about the actual attribute types.

m_AttValues

double[] m_AttValues

The instance's attribute values.

m_Weight

double m_Weight

The instance's weight.

Class weka.core.Instances extends java.lang.Object implements Serializable

Serialized Fields

m_RelationName

java.lang.String m_RelationName

The dataset's name.

m_Attributes

FastVector m_Attributes

The attribute information.

m_Instances

FastVector m_Instances

The instances.

m_ClassIndex

int m_ClassIndex

The class attribute's index

m_ValueBuffer

double[] m_ValueBuffer

Buffer of values for sparse instance

m_IndicesBuffer

int[] m_IndicesBuffer

Buffer of indices for sparse instance

Class weka.core.Queue extends java.lang.Object implements Serializable

Serialized Fields

m_Head

weka.core.Queue.QueueNode m_Head

Store a reference to the head of the queue

m_Tail

weka.core.Queue.QueueNode m_Tail

Store a reference to the tail of the queue

m_Size

int m_Size

Store the current number of elements in the queue

Class weka.core.Queue.QueueNode extends java.lang.Object implements Serializable

Serialized Fields

m_Next

weka.core.Queue.QueueNode m_Next

The next node in the queue

m_Contents

java.lang.Object m_Contents

The nodes contents

Class weka.core.Range extends java.lang.Object implements Serializable

serialVersionUID: 1L

Serialized Fields

m_RangeStrings

java.util.Vector m_RangeStrings

Record the string representations of the columns to delete

m_Invert

boolean m_Invert

Whether matching should be inverted

m_SelectFlags

boolean[] m_SelectFlags

The array of flags for whether an column is selected

m_Upper

int m_Upper

Store the maximum value permitted in the range. -1 indicates that no upper value has been set

Class weka.core.SerializedObject extends java.lang.Object implements Serializable

Serialized Fields

m_Serialized

byte[] m_Serialized

Stores the serialized object

m_Compressed

boolean m_Compressed

True if the object has been compressed during storage

Class weka.core.SparseInstance extends Instance implements Serializable

Serialized Fields

m_Indices

int[] m_Indices

The index of the attribute associated with each stored value.

m_NumAttributes

int m_NumAttributes

The maximum number of values that can be stored.

Class weka.core.UnassignedClassException extends java.lang.RuntimeException implements Serializable

Class weka.core.UnassignedDatasetException extends java.lang.RuntimeException implements Serializable

Class weka.core.UnsupportedClassTypeException extends WekaException implements Serializable

Class weka.core.WekaException extends java.lang.Exception implements Serializable

Package weka.filters

Class weka.filters.DiscretizeFilter extends Filter implements Serializable

Serialized Fields

m_DiscretizeCols

Range m_DiscretizeCols

Stores which columns to Discretize

m_NumBins

int m_NumBins

The number of bins to divide the attribute into

m_CutPoints

double[][] m_CutPoints

Store the current cutpoints

m_UseMDL

boolean m_UseMDL

True if discretisation will be done by MDL rather than binning

m_MakeBinary

boolean m_MakeBinary

Output binary attributes for discretized attributes.

m_UseBetterEncoding

boolean m_UseBetterEncoding

Use better encoding of split point for MDL.

m_UseKononenko

boolean m_UseKononenko

Use Kononenko's MDL criterion instead of Fayyad et al.'s

m_FindNumBins

boolean m_FindNumBins

Find the number of bins using cross-validated entropy.

m_UseEqualFrequency

boolean m_UseEqualFrequency

Use equal-frequency binning if unsupervised discretization turned on

Class weka.filters.Filter extends java.lang.Object implements Serializable

Serialized Fields

m_Debug

boolean m_Debug

Debugging mode

m_OutputFormat

Instances m_OutputFormat

The output format for instances

m_OutputQueue

Queue m_OutputQueue

The output instance queue

m_OutputStringAtts

int[] m_OutputStringAtts

Indices of string attributes in the output format

m_InputStringAtts

int[] m_InputStringAtts

Indices of string attributes in the input format

m_InputFormat

Instances m_InputFormat

The input format for instances

m_NewBatch

boolean m_NewBatch

Record whether the filter is at the start of a batch