A B C D E F G H I K M N P R S T U

A

argMax(double[]) - Method in class ir.classifiers.Classifier
Returns the array index with the maximum value

B

BayesResult - class ir.classifiers.BayesResult.
An object to hold the result of training a NaiveBayes classifier.
BayesResult() - Constructor for class ir.classifiers.BayesResult
 
binExamples() - Method in class ir.classifiers.CVLearningCurve
Set the fold Bins from the total Examples -- this effectively stores the training-test split

C

calculatePriors(Vector) - Method in class ir.classifiers.NaiveBayes
Calculates the class priors
calculateProbs(Example) - Method in class ir.classifiers.NaiveBayes
Calculates the prob of the testExample being generated by each category
Categories - Variable in class ir.classifiers.NaiveBayes
Vector of categories (classes) in the data
category - Variable in class ir.classifiers.Example
Category index of the example
CLASSES - Static variable in class ir.classifiers.CVLearningCurve
Stores the possible class labels
classifier - Variable in class ir.classifiers.CVLearningCurve
The classifier for which K-fold CV learning curve has to be generated
Classifier - class ir.classifiers.Classifier.
Abstract class specifying the functionality of a classifier.
Classifier() - Constructor for class ir.classifiers.Classifier
 
classPriors - Variable in class ir.classifiers.BayesResult
Stores the prior probabilities of each class
conditionalProbs(Vector) - Method in class ir.classifiers.NaiveBayes
Calculates the conditional probs of each feature in the different categories
CVLearningCurve - class ir.classifiers.CVLearningCurve.
Gives learning curves with K-fold cross validation for a classifier.

D

debug - Variable in class ir.classifiers.NaiveBayes
Flag for debug prints
debug - Variable in class ir.classifiers.CVLearningCurve
Flag for debug display
debug - Variable in class ir.classifiers.KNN
 
displayProbs(double[], Hashtable) - Method in class ir.classifiers.NaiveBayes
Displays the probs for each feature in the different categories
document - Variable in class ir.classifiers.Example
fileDocument object for the example

E

EPSILON - Variable in class ir.classifiers.NaiveBayes
Small value to be used instead of 0 in probabilities, if Laplace smoothing is not used
Example - class ir.classifiers.Example.
An object to hold training or test examples for categorization.
Example(HashMapVector, int, String, FileDocument) - Constructor for class ir.classifiers.Example
 

F

featureTable - Variable in class ir.classifiers.BayesResult
Stores the counts for each feature: an entry in the hashTable stores the array of class counts for a feature
findClassID(String) - Static method in class ir.classifiers.CVLearningCurve
Finds the class ID from the name of the document
foldBins - Variable in class ir.classifiers.CVLearningCurve
foldBins[i][j] stores the examples for class i in fold j.

G

getCategory() - Method in class ir.classifiers.Example
Returns the category of the example
getClassifier() - Method in class ir.classifiers.CVLearningCurve
Return classifier
getClassPriors() - Method in class ir.classifiers.BayesResult
Returns the class priors
getCVPredictions() - Method in class ir.classifiers.CVLearningCurve
Generate a vector of predictions ready for processing, by performing a cross-validation on the supplied dataset.
getDocument() - Method in class ir.classifiers.Example
Returns the document of the example
getEpsilon() - Method in class ir.classifiers.NaiveBayes
Returns value of EPSILON
getFeatureTable() - Method in class ir.classifiers.BayesResult
Returns the feature hash
getFoldBins() - Method in class ir.classifiers.CVLearningCurve
Return the fold Bins
getHashMapVector() - Method in class ir.classifiers.Example
Returns the hashVector of the example
getIsLaplace() - Method in class ir.classifiers.NaiveBayes
Returns value of isLaplace
getName() - Method in class ir.classifiers.Classifier
To be overloaded by the extending class
getName() - Method in class ir.classifiers.NaiveBayes
Returns the name
getName() - Method in class ir.classifiers.Rocchio
Returns the name
getName() - Method in class ir.classifiers.KNN
Returns the name
getName() - Method in class ir.classifiers.Example
Returns the name of the example
getTestCV(int) - Method in class ir.classifiers.CVLearningCurve
Creates the testing set for one fold of a cross-validation on the dataset.
getTestPrediction(Vector, Vector) - Method in class ir.classifiers.CVLearningCurve
Generate a prediction vector by performing an evaluation on the test set after training on the given training set.
getTotalExamples() - Method in class ir.classifiers.CVLearningCurve
Return all the examples
getTrainCV(int, double) - Method in class ir.classifiers.CVLearningCurve
Creates the training set for one fold of a cross-validation on the dataset.
getTrainResult() - Method in class ir.classifiers.NaiveBayes
Returns training result

H

hashVector - Variable in class ir.classifiers.Example
Representation of the example as a vector of (feature -> weight) mappings

I

ir.classifiers - package ir.classifiers
 
isLaplace - Variable in class ir.classifiers.NaiveBayes
Flag to set Laplace smoothing when estimating probabilities

K

K - Variable in class ir.classifiers.Rocchio
 
K - Variable in class ir.classifiers.KNN
 
KNN - class ir.classifiers.KNN.
 

M

main(String[]) - Static method in class ir.classifiers.TestClassifier
 

N

NaiveBayes - class ir.classifiers.NaiveBayes.
Implements the NaiveBayes Classifier with Laplace smoothing.
NaiveBayes(String[], boolean) - Constructor for class ir.classifiers.NaiveBayes
Create an naive bayes classifier with these attributes
name - Static variable in class ir.classifiers.NaiveBayes
Name of classifier
name - Variable in class ir.classifiers.Rocchio
 
name - Variable in class ir.classifiers.KNN
 
name - Variable in class ir.classifiers.Example
Name of the example
numCategories - Variable in class ir.classifiers.NaiveBayes
Number of categories
numClasses - Variable in class ir.classifiers.CVLearningCurve
Number of classes in the data
numExamples - Variable in class ir.classifiers.NaiveBayes
Number of training examples, set by train function
numFeatures - Variable in class ir.classifiers.NaiveBayes
Number of features
numFolds - Variable in class ir.classifiers.CVLearningCurve
Number of folds of cross validation to run

P

POINTS - Static variable in class ir.classifiers.CVLearningCurve
Points at which the learning curve is plotted

R

Rocchio - class ir.classifiers.Rocchio.
 
rocchioMeanVectors - Variable in class ir.classifiers.Rocchio
 

S

setCategories(String[]) - Method in class ir.classifiers.NaiveBayes
Set vector of categories (classes) in the data with the input string of categories
setCategory(int) - Method in class ir.classifiers.Example
Sets the category of the example
setClassifier(Classifier) - Method in class ir.classifiers.CVLearningCurve
Set the classifier
setClassPriors(double[]) - Method in class ir.classifiers.BayesResult
Sets the class priors
setDebug(boolean) - Method in class ir.classifiers.NaiveBayes
Sets the debug flag
setDocument(FileDocument) - Method in class ir.classifiers.Example
Sets the document of the example
setEpsilon(double) - Method in class ir.classifiers.NaiveBayes
Sets the value of EPSILON (default 1e-6)
setFeatureTable(Hashtable) - Method in class ir.classifiers.BayesResult
Sets the feature hash
setFoldBins(Vector[][]) - Method in class ir.classifiers.CVLearningCurve
Set the fold Bins
setHashMapVector(HashMapVector) - Method in class ir.classifiers.Example
Sets the hashVector of the example
setInvertedIndex(InvertedIndex) - Method in class ir.classifiers.Classifier
For classifiers that use invertedIndices, this function sets them -- needed for efficient memory management
setInvertedIndex(InvertedIndex) - Method in class ir.classifiers.NaiveBayes
Since NaiveBayes does not use an inverted Index, this function does nothing in the case of NaiveBayes
setInvertedIndex(InvertedIndex) - Method in class ir.classifiers.Rocchio
 
setInvertedIndex(InvertedIndex) - Method in class ir.classifiers.KNN
 
setLaplace(boolean) - Method in class ir.classifiers.NaiveBayes
Sets the Laplace smoothing flag
setName(String) - Method in class ir.classifiers.Example
Sets the name of the example
setTotalExamples(String) - Method in class ir.classifiers.CVLearningCurve
Sets the totalExamples by reading in file from directory dirName
setTotalExamples(Vector[]) - Method in class ir.classifiers.CVLearningCurve
Set all the examples

T

test(Example) - Method in class ir.classifiers.Classifier
Returns true if the predicted category of the test example matches the correct category, false otherwise
test(Example) - Method in class ir.classifiers.NaiveBayes
Categorizes the test example using the trained Naive Bayes classifier, returning true if the predicted category is same as the actual category
test(Example) - Method in class ir.classifiers.Rocchio
 
test(Example) - Method in class ir.classifiers.KNN
 
TestClassifier - class ir.classifiers.TestClassifier.
Wrapper class to test classifiers using k-fold CV.
TestClassifier() - Constructor for class ir.classifiers.TestClassifier
 
testTime - Variable in class ir.classifiers.CVLearningCurve
Total Testing time
toString() - Method in class ir.classifiers.Example
Returns the String representation of the example object
totalExamples - Variable in class ir.classifiers.CVLearningCurve
Stores all the examples for each class
train(Vector) - Method in class ir.classifiers.Classifier
Trains the classifier on the training examples
train(Vector) - Method in class ir.classifiers.NaiveBayes
Trains the Naive Bayes classifier - estimates the prior probs and calculates the counts for each feature in different categories
train(Vector) - Method in class ir.classifiers.Rocchio
 
train(Vector) - Method in class ir.classifiers.KNN
 
trainDocs - Variable in class ir.classifiers.Rocchio
 
trainDocs - Variable in class ir.classifiers.KNN
 
trainResult - Variable in class ir.classifiers.NaiveBayes
Stores the training result, set by the train function
trainTime - Variable in class ir.classifiers.CVLearningCurve
Total Training time

U

usesInvertedIndex() - Method in class ir.classifiers.Classifier
To indicated whether this classifier uses inverted index
usesInvertedIndex() - Method in class ir.classifiers.NaiveBayes
Function to indicate that this class does not use an inverted index
usesInvertedIndex() - Method in class ir.classifiers.Rocchio
Function to indicate that this class uses an inverted index
usesInvertedIndex() - Method in class ir.classifiers.KNN
Function to indicate that this class uses an inverted index

A B C D E F G H I K M N P R S T U