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

B

bias - Variable in class de.bwaldvogel.liblinear.Problem

If bias >= 0, we assume that one additional feature is added to the end of each data instance

C

copyOf(double[], int) - Static method in class de.bwaldvogel.liblinear.Linear

Java5 'backport' of Arrays.copyOf

copyOf(int[], int) - Static method in class de.bwaldvogel.liblinear.Linear

Java5 'backport' of Arrays.copyOf

crossValidation(Problem, Parameter, int, int[]) - Static method in class de.bwaldvogel.liblinear.Linear

D

de.bwaldvogel.liblinear - package de.bwaldvogel.liblinear

disableDebugOutput() - Static method in class de.bwaldvogel.liblinear.Linear

E

enableDebugOutput() - Static method in class de.bwaldvogel.liblinear.Linear

equals(Object) - Method in class de.bwaldvogel.liblinear.FeatureNode

equals(Object) - Method in class de.bwaldvogel.liblinear.Model

equals(double[], double[]) - Static method in class de.bwaldvogel.liblinear.Model

don't use Arrays.equals(double[], double[]) here, cause 0.0 and -0.0 should be handled the same

F

FeatureNode - Class in de.bwaldvogel.liblinear

FeatureNode(int, double) - Constructor for class de.bwaldvogel.liblinear.FeatureNode

G

getBias() - Method in class de.bwaldvogel.liblinear.Model

getC() - Method in class de.bwaldvogel.liblinear.Parameter

getEps() - Method in class de.bwaldvogel.liblinear.Parameter

getFeatureWeights() - Method in class de.bwaldvogel.liblinear.Model

The nr_feature*nr_class array w gives feature weights.

getFile() - Method in exception de.bwaldvogel.liblinear.InvalidInputDataException

getFilename() - Method in exception de.bwaldvogel.liblinear.InvalidInputDataException

Deprecated. use InvalidInputDataException.getFile() instead

getLabels() - Method in class de.bwaldvogel.liblinear.Model

getLine() - Method in exception de.bwaldvogel.liblinear.InvalidInputDataException

getNrClass() - Method in class de.bwaldvogel.liblinear.Model

getNrFeature() - Method in class de.bwaldvogel.liblinear.Model

getNumWeights() - Method in class de.bwaldvogel.liblinear.Parameter

the number of weights

getSolverType() - Method in class de.bwaldvogel.liblinear.Parameter

getWeightLabels() - Method in class de.bwaldvogel.liblinear.Parameter

getWeights() - Method in class de.bwaldvogel.liblinear.Parameter

H

hashCode() - Method in class de.bwaldvogel.liblinear.FeatureNode

hashCode() - Method in class de.bwaldvogel.liblinear.Model

I

index - Variable in class de.bwaldvogel.liblinear.FeatureNode

InvalidInputDataException - Exception in de.bwaldvogel.liblinear

InvalidInputDataException(String, File, int) - Constructor for exception de.bwaldvogel.liblinear.InvalidInputDataException

InvalidInputDataException(String, String, int) - Constructor for exception de.bwaldvogel.liblinear.InvalidInputDataException

InvalidInputDataException(String, File, int, Exception) - Constructor for exception de.bwaldvogel.liblinear.InvalidInputDataException

InvalidInputDataException(String, String, int, Exception) - Constructor for exception de.bwaldvogel.liblinear.InvalidInputDataException

isProbabilityModel() - Method in class de.bwaldvogel.liblinear.Model

L

l - Variable in class de.bwaldvogel.liblinear.Problem

the number of training data

Linear - Class in de.bwaldvogel.liblinear

Java port of liblinear The usage should be pretty similar to the C version of liblinear.

Linear() - Constructor for class de.bwaldvogel.liblinear.Linear

load(File) - Static method in class de.bwaldvogel.liblinear.Model

see Linear.loadModel(File)

load(Reader) - Static method in class de.bwaldvogel.liblinear.Model

see Linear.loadModel(Reader)

loadModel(Reader) - Static method in class de.bwaldvogel.liblinear.Linear

Loads the model from inputReader.

loadModel(File) - Static method in class de.bwaldvogel.liblinear.Linear

Loads the model from the file with ISO-8859-1 charset.

M

main(String[]) - Static method in class de.bwaldvogel.liblinear.Predict

main(String[]) - Static method in class de.bwaldvogel.liblinear.Train

Model - Class in de.bwaldvogel.liblinear

Model stores the model obtained from the training procedure use Linear.loadModel(File) and Linear.saveModel(File, Model) to load/save it

Model() - Constructor for class de.bwaldvogel.liblinear.Model

N

n - Variable in class de.bwaldvogel.liblinear.Problem

the number of features (including the bias feature if bias >= 0)

P

Parameter - Class in de.bwaldvogel.liblinear

Parameter(SolverType, double, double) - Constructor for class de.bwaldvogel.liblinear.Parameter

predict(Model, FeatureNode[]) - Static method in class de.bwaldvogel.liblinear.Linear

Predict - Class in de.bwaldvogel.liblinear

Predict() - Constructor for class de.bwaldvogel.liblinear.Predict

predictProbability(Model, FeatureNode[], double[]) - Static method in class de.bwaldvogel.liblinear.Linear

predictValues(Model, FeatureNode[], double[]) - Static method in class de.bwaldvogel.liblinear.Linear

Problem - Class in de.bwaldvogel.liblinear

Describes the problem For example, if we have the following training data: LABEL ATTR1 ATTR2 ATTR3 ATTR4 ATTR5 ----- ----- ----- ----- ----- ----- 1 0 0.1 0.2 0 0 2 0 0.1 0.3 -1.2 0 1 0.4 0 0 0 0 2 0 0.1 0 1.4 0.5 3 -0.1 -0.2 0.1 1.1 0.1 and bias = 1, then the components of problem are: l = 5 n = 6 y -> 1 2 1 2 3 x -> [ ] -> (2,0.1) (3,0.2) (6,1) (-1,?)

Problem() - Constructor for class de.bwaldvogel.liblinear.Problem

R

readFromFile(File, double) - Static method in class de.bwaldvogel.liblinear.Problem

see Train.readProblem(File, double)

readProblem(File, double) - Static method in class de.bwaldvogel.liblinear.Train

reads a problem from LibSVM format

resetRandom() - Static method in class de.bwaldvogel.liblinear.Linear

resets the PRNG this is i.a. needed for regression testing (eg. the Weka wrapper)

S

save(File) - Method in class de.bwaldvogel.liblinear.Model

see Linear.saveModel(java.io.File, Model)

save(Writer) - Method in class de.bwaldvogel.liblinear.Model

see Linear.saveModel(Writer, Model)

saveModel(Writer, Model) - Static method in class de.bwaldvogel.liblinear.Linear

Writes the model to the modelOutput.

saveModel(File, Model) - Static method in class de.bwaldvogel.liblinear.Linear

Writes the model to the file with ISO-8859-1 charset.

setC(double) - Method in class de.bwaldvogel.liblinear.Parameter

C is the cost of constraints violation.

setDebugOutput(PrintStream) - Static method in class de.bwaldvogel.liblinear.Linear

setEps(double) - Method in class de.bwaldvogel.liblinear.Parameter

eps is the stopping criterion.

setSolverType(SolverType) - Method in class de.bwaldvogel.liblinear.Parameter

setWeights(double[], int[]) - Method in class de.bwaldvogel.liblinear.Parameter

nr_weight, weight_label, and weight are used to change the penalty for some classes (If the weight for a class is not changed, it is set to 1).

SolverType - Enum in de.bwaldvogel.liblinear

T

toString() - Method in class de.bwaldvogel.liblinear.FeatureNode

toString() - Method in exception de.bwaldvogel.liblinear.InvalidInputDataException

toString() - Method in class de.bwaldvogel.liblinear.Model

train(Problem, Parameter) - Static method in class de.bwaldvogel.liblinear.Linear

Train - Class in de.bwaldvogel.liblinear

Train() - Constructor for class de.bwaldvogel.liblinear.Train

V

value - Variable in class de.bwaldvogel.liblinear.FeatureNode

valueOf(String) - Static method in enum de.bwaldvogel.liblinear.SolverType

Returns the enum constant of this type with the specified name.

values() - Static method in enum de.bwaldvogel.liblinear.SolverType

Returns an array containing the constants of this enum type, in the order they are declared.

X

x - Variable in class de.bwaldvogel.liblinear.Problem

array of sparse feature nodes

Y

y - Variable in class de.bwaldvogel.liblinear.Problem

an array containing the target values

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