A B C D E F G H I L M N O P R S T W X Y

A

accuracy() - Method in class types.Evaluation

AdaBoost - Class in classification

AdaBoost(int, Alphabet, Alphabet, FeatureFunction) - Constructor for class classification.AdaBoost

add(Evaluation) - Method in class types.Evaluation

add(int, double) - Method in class types.SparseVector

add(double[], double[], double[], double) - Static method in class types.StaticUtils

u = v + d*w

algo - package algo

Alphabet - Class in types

This class is used to map from some descriptive features (e.g.

Alphabet() - Constructor for class types.Alphabet

apply(SparseVector, int) - Method in class classification.CompleteFeatureFunction

apply(SparseVector[], int[]) - Method in class sequence.OneYwithXFeatureFunction

apply(SparseVector[], int, int, int) - Method in class sequence.OneYwithXFeatureFunction

apply(SparseVector[], int[]) - Method in interface sequence.SequenceFeatureFunction

apply the feature function to an entire sequence of y's

apply(SparseVector[], int, int, int) - Method in interface sequence.SequenceFeatureFunction

apply the feature function to a pair of tags (label positions)

apply(SparseVector[], int[]) - Method in class sequence.TwoYwithXFeatureFunction

apply(SparseVector[], int, int, int) - Method in class sequence.TwoYwithXFeatureFunction

apply(SparseVector, int) - Method in interface types.FeatureFunction

argmax(double[]) - Static method in class types.StaticUtils

computes arg max_i v[i]

B

batchTrain(ArrayList<ClassificationInstance>) - Method in class classification.AdaBoost

batchTrain(ArrayList<ClassificationInstance>) - Method in class classification.MaxEntropy

batchTrain(ArrayList<ClassificationInstance>) - Method in class classification.Mira

batchTrain(ArrayList<ClassificationInstance>) - Method in class classification.NaiveBayes

batchTrain(ArrayList<ClassificationInstance>) - Method in class classification.Perceptron

batchTrain(ArrayList<SequenceInstance>) - Method in class sequence.CRF

batchTrain(ArrayList<SequenceInstance>) - Method in class sequence.Mira

batchTrain(ArrayList<SequenceInstance>) - Method in class sequence.Perceptron

C

calculate(int, int) - Method in interface classification.Loss

calculate(int, int) - Method in class classification.PrecRecLoss

calculate(int[], int[]) - Method in class sequence.HammingLoss

calculate(int[], int[]) - Method in interface sequence.Loss

calculate(int[], int[]) - Method in class sequence.PrecRecLoss

classification - package classification

ClassificationInstance - Class in types

ClassificationInstance(Alphabet, Alphabet, SparseVector, Object) - Constructor for class types.ClassificationInstance

claz - Variable in class sequence.EvalResult

CompleteFeatureFunction - Class in classification

CompleteFeatureFunction(Alphabet, Alphabet) - Constructor for class classification.CompleteFeatureFunction

computeAccuracy(LinearClassifier, ArrayList<ClassificationInstance>) - Static method in class types.StaticUtils

The accuracy of classifier h on the data set data.

computeAccuracyS(LinearTagger, ArrayList<SequenceInstance>) - Static method in class types.StaticUtils

The accuracy of classifier h on the data set data.

ConjugateGradient - Class in algo

ConjugateGradient(int) - Constructor for class algo.ConjugateGradient

correct - Variable in class types.Evaluation

CRF - Class in sequence

CRF(double, Alphabet, Alphabet, SequenceFeatureFunction) - Constructor for class sequence.CRF

D

defalutFeatureIndex - Variable in class classification.CompleteFeatureFunction

this is the last feature for each y

DifferentiableObjective - Interface in types

dotProduct(SparseVector, double[]) - Static method in class types.StaticUtils

the dot product between a sparse vector v and a dense vector w.

dotProduct(double[], double[]) - Static method in class types.StaticUtils

E

end - Variable in class io.Span

equals(Object) - Method in class sequence.EvalResult

eval(LinearClassifier, ArrayList<ClassificationInstance>, int) - Static method in class classification.Evaluate

Compute evaluation of entire instance list, with respect to a single tag.

eval(LinearClassifier, ArrayList<ClassificationInstance>) - Static method in class classification.Evaluate

eval(LinearClassifier, ClassificationInstance, int) - Static method in class classification.Evaluate

Compute evaluation of entire instance list, with respect to a single tag.

eval(int, int, int) - Static method in class classification.Evaluate

eval(LinearTagger, ArrayList<SequenceInstance>, int) - Static method in class sequence.Evaluate

Compute evaluation of entire instance list, with respect to a single tag.

eval(LinearTagger, SequenceInstance, int) - Static method in class sequence.Evaluate

Compute evaluation of entire instance list, with respect to a single tag.

eval(int[], int[], int) - Static method in class sequence.Evaluate

EvalResult - Class in sequence

EvalResult(String, String) - Constructor for class sequence.EvalResult

Evaluate - Class in classification

Evaluate() - Constructor for class classification.Evaluate

Evaluate - Class in sequence

Evaluate() - Constructor for class sequence.Evaluate

Evaluation - Class in types

Evaluation(int, int, int, int, int) - Constructor for class types.Evaluation

exp(double[]) - Static method in class types.StaticUtils

res = exp(vec)

exp(double[][]) - Static method in class types.StaticUtils

res = exp(vec)

exp(double[][][]) - Static method in class types.StaticUtils

res = exp(vec)

experiments - package experiments

F

FeatureFunction - Interface in types

fn - Variable in class types.Evaluation

fp - Variable in class types.Evaluation

fscore() - Method in class types.Evaluation

G

getGradient(double[]) - Method in class experiments.TestMaximization

getGradient(double[]) - Method in interface types.DifferentiableObjective

getIndexAt(int) - Method in class types.SparseVector

getNumParameters() - Method in class experiments.TestMaximization

getNumParameters() - Method in interface types.DifferentiableObjective

getParameters(double[]) - Method in class experiments.TestMaximization

getParameters(double[]) - Method in interface types.DifferentiableObjective

getValue() - Method in class experiments.TestMaximization

getValue() - Method in interface types.DifferentiableObjective

getValueAt(int) - Method in class types.SparseVector

GradientAscent - Class in algo

GradientAscent() - Constructor for class algo.GradientAscent

H

HammingLoss - Class in sequence

This class implements Hamming Loss function

HammingLoss() - Constructor for class sequence.HammingLoss

hashCode() - Method in class sequence.EvalResult

I

InternetAdReader - Class in io

InternetAdReader(Alphabet, Alphabet) - Constructor for class io.InternetAdReader

InternetAds - Class in experiments

InternetAds() - Constructor for class experiments.InternetAds

io - package io

L

label(SparseVector[]) - Method in class sequence.LinearTagger

use the Viterbi algorithm to find arg_max_y f(x,y) .

label(SparseVector) - Method in class types.LinearClassifier

computes the classification according to this linear classifier.

LinearClassifier - Class in types

A linear model for classification.

LinearClassifier(Alphabet, Alphabet, FeatureFunction) - Constructor for class types.LinearClassifier

LinearTagger - Class in sequence

A linear model for sequence classification.

LinearTagger(Alphabet, Alphabet, SequenceFeatureFunction) - Constructor for class sequence.LinearTagger

lineSearch(DifferentiableObjective, double[], double[]) - Method in class algo.ConjugateGradient

finds the maximizer of o(parameters + lambda*direction)

loadTagger(String) - Static method in class types.StaticUtils

lookupCollection(Collection<String>, Alphabet) - Static method in class types.StaticUtils

lookupIndex(int) - Method in class types.Alphabet

lookupInt(int) - Method in class types.Alphabet

returns the string representation of feature associated with a index.

lookupObject(Object) - Method in class types.Alphabet

returns the index associated with a feature.

Loss - Interface in classification

This class defines the contract we hava for loss function

Loss - Interface in sequence

This class defines the contract we hava for loss function

M

main(String[]) - Static method in class classification.AdaBoost

main(String[]) - Static method in class experiments.InternetAds

main(String[]) - Static method in class experiments.Newsgroups

main(String[]) - Static method in class experiments.PartOfSpeech

main(String[]) - Static method in class experiments.TestMaximization

MaxEntropy - Class in classification

MaxEntropy(double, Alphabet, Alphabet, FeatureFunction) - Constructor for class classification.MaxEntropy

maximize(DifferentiableObjective) - Method in class algo.ConjugateGradient

maximize(DifferentiableObjective) - Method in class algo.GradientAscent

Mira - Class in classification

Mira(boolean, int, Alphabet, Alphabet, CompleteFeatureFunction, Loss) - Constructor for class classification.Mira

Mira - Class in sequence

Mira(boolean, int, Alphabet, Alphabet, SequenceFeatureFunction, Loss) - Constructor for class sequence.Mira

N

NaiveBayes - Class in classification

NaiveBayes(double, double, Alphabet, Alphabet) - Constructor for class classification.NaiveBayes

Newsgroups - Class in experiments

Newsgroups() - Constructor for class experiments.Newsgroups

NewsgroupsReader - Class in io

NewsgroupsReader(Alphabet, Alphabet) - Constructor for class io.NewsgroupsReader

numEntries() - Method in class types.SparseVector

numGradientCalls - Variable in class experiments.TestMaximization

numInputs - Variable in class sequence.TwoYwithXFeatureFunction

numValueCalls - Variable in class experiments.TestMaximization

O

OneYwithXFeatureFunction - Class in sequence

OneYwithXFeatureFunction(Alphabet, Alphabet) - Constructor for class sequence.OneYwithXFeatureFunction

P

params - Variable in class experiments.TestMaximization

PartOfSpeech - Class in experiments

PartOfSpeech() - Constructor for class experiments.PartOfSpeech

PartOfSpeechReader - Class in io

PartOfSpeechReader(Alphabet, Alphabet) - Constructor for class io.PartOfSpeechReader

Perceptron - Class in classification

Perceptron(boolean, int, Alphabet, Alphabet, FeatureFunction) - Constructor for class classification.Perceptron

Perceptron - Class in sequence

Perceptron(boolean, int, Alphabet, Alphabet, SequenceFeatureFunction) - Constructor for class sequence.Perceptron

plusEquals(double[], SparseVector) - Static method in class types.StaticUtils

update: w = w + v

plusEquals(SparseVector, SparseVector) - Static method in class types.StaticUtils

w <- w + v

plusEquals(double[], SparseVector, double) - Static method in class types.StaticUtils

w <- w + d*v

plusEquals(double[], double[], double) - Static method in class types.StaticUtils

w <- w + d*v

precision() - Method in class types.Evaluation

PrecRecLoss - Class in classification

This class implements a loss function for high precision or recall.

PrecRecLoss(int, double) - Constructor for class classification.PrecRecLoss

PrecRecLoss - Class in sequence

This class implements a loss function for high precision or recall.

PrecRecLoss(int, double) - Constructor for class sequence.PrecRecLoss

printArray(double[]) - Method in class classification.AdaBoost

R

readFile(String) - Method in class io.InternetAdReader

readFile(String) - Method in class io.NewsgroupsReader

readFile(String) - Method in class io.PartOfSpeechReader

readObject(ObjectInputStream) - Method in class sequence.LinearTagger

readObject(ObjectInputStream) - Method in class types.LinearClassifier

recall() - Method in class types.Evaluation

reset() - Method in class experiments.TestMaximization

S

saveTagger(LinearTagger, String) - Static method in class types.StaticUtils

scores(SparseVector[]) - Method in class sequence.LinearTagger

at each position 0<=tscores(SparseVector) - Method in class types.LinearClassifier

computes the score of each label 'y' defined as f(x,y) .

sequence - package sequence

SequenceFeatureFunction - Interface in sequence

SequenceInstance - Class in sequence

SequenceInstance(Alphabet, Alphabet, SparseVector[], Object[]) - Constructor for class sequence.SequenceInstance

setParameters(double[]) - Method in class experiments.TestMaximization

setParameters(double[]) - Method in interface types.DifferentiableObjective

shuffle(ArrayList, long) - Static method in class types.StaticUtils

shuffle a list (intended as a list of instances) in place using seed as the random seed.

size() - Method in class types.Alphabet

Span - Class in io

Span(int, int, String) - Constructor for class io.Span

span - Variable in class sequence.EvalResult

SparseVector - Class in types

a sparse vector implementation.

SparseVector() - Constructor for class types.SparseVector

split(ArrayList<ClassificationInstance>, int) - Static method in class types.StaticUtils

splitS(ArrayList<SequenceInstance>, int) - Static method in class types.StaticUtils

start - Variable in class io.Span

startGrowth() - Method in class types.Alphabet

StaticUtils - Class in types

Some methods that are useful to have in linear models.

StaticUtils() - Constructor for class types.StaticUtils

stopGrowth() - Method in class types.Alphabet

at test time, we need to stop the growth of the alphabet so we do not increase the size of the feature vector in case the user tries to use features not encountered at training time.

sum(double[]) - Static method in class types.StaticUtils

T

TestMaximization - Class in experiments

TestMaximization(double[], double[]) - Constructor for class experiments.TestMaximization

tn - Variable in class types.Evaluation

toString() - Method in class types.ClassificationInstance

toString() - Method in class types.Evaluation

toString() - Method in class types.SparseVector

total - Variable in class types.Evaluation

tp - Variable in class types.Evaluation

trainAdaBoost(int, ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.InternetAds

trainCRF(ArrayList<SequenceInstance>, Alphabet, Alphabet) - Static method in class experiments.PartOfSpeech

trainMaxEnt(ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.InternetAds

trainMaxEnt(ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.Newsgroups

trainMira(boolean, int, ArrayList<SequenceInstance>, Alphabet, Alphabet) - Static method in class experiments.PartOfSpeech

trainNaivBayes(ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.InternetAds

trainNaivBayes(ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.Newsgroups

trainPerceptron(boolean, int, ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.InternetAds

trainPerceptron(boolean, int, ArrayList<ClassificationInstance>, Alphabet, Alphabet) - Static method in class experiments.Newsgroups

trainPerceptron(boolean, int, ArrayList<SequenceInstance>, Alphabet, Alphabet) - Static method in class experiments.PartOfSpeech

twoNormSquared(double[]) - Static method in class types.StaticUtils

TwoYwithXFeatureFunction - Class in sequence

TwoYwithXFeatureFunction(Alphabet, Alphabet) - Constructor for class sequence.TwoYwithXFeatureFunction

type - Variable in class io.Span

types - package types

W

w - Variable in class sequence.LinearTagger

w - Variable in class types.LinearClassifier

writeObject(ObjectOutputStream) - Method in class sequence.LinearTagger

writeObject(ObjectOutputStream) - Method in class types.LinearClassifier

wSize() - Method in class classification.CompleteFeatureFunction

wSize() - Method in class sequence.OneYwithXFeatureFunction

wSize() - Method in interface sequence.SequenceFeatureFunction

wSize() - Method in class sequence.TwoYwithXFeatureFunction

wSize() - Method in interface types.FeatureFunction

X

x - Variable in class sequence.SequenceInstance

x - Variable in class types.ClassificationInstance

xAlphabet - Variable in class sequence.SequenceInstance

xAlphabet - Variable in class types.ClassificationInstance

xAlphabet - Variable in class types.LinearClassifier

xAsize - Variable in class sequence.OneYwithXFeatureFunction

xAsize - Variable in class sequence.TwoYwithXFeatureFunction

Y

y - Variable in class sequence.SequenceInstance

y - Variable in class types.ClassificationInstance

yAlphabet - Variable in class sequence.SequenceInstance

yAlphabet - Variable in class types.ClassificationInstance

yAlphabet - Variable in class types.LinearClassifier

yAsize - Variable in class sequence.OneYwithXFeatureFunction

yAsize - Variable in class sequence.TwoYwithXFeatureFunction

A B C D E F G H I L M N O P R S T W X Y