edu.illinois.cs.cogcomp.lbjava.learn

Class BiasedWeightVector

java.lang.Object

edu.illinois.cs.cogcomp.lbjava.learn.SparseWeightVector

edu.illinois.cs.cogcomp.lbjava.learn.BiasedWeightVector

All Implemented Interfaces:

Serializable, Cloneable

public class BiasedWeightVector
extends SparseWeightVector

Same as the SparseWeightVector class that it extends, except that this vector also contains a bias term which is added to every dot product and affected by every vector addition operation.

Author:

Nick Rizzolo

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
protected double	bias The current bias weight.
protected static double	defaultInitialBias Default value for initialBias.
protected double	initialBias The first value for bias.

Fields inherited from class edu.illinois.cs.cogcomp.lbjava.learn.SparseWeightVector

defaultCapacity, defaultWeight, weights

Constructor Summary

Constructors

Constructor and Description
BiasedWeightVector() Instantiates this biased vector with default parameter values.
BiasedWeightVector(double b) Instantiates this biased vector with the specified initial bias.

Method Summary

Modifier and Type	Method and Description
void	clear() Empties the weight map.
double	dot(int[] exampleFeatures, double[] exampleValues, double defaultW) Takes the dot product of this BiasedWeightVector with the argument vector, using the specified default weight when one is not yet present in this vector.
SparseWeightVector	emptyClone() Returns a new, empty weight vector with the same parameter settings as this one.
void	read(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in) Reads the representation of a weight vector with this object's run-time type from the given stream, overwriting the data in this object.
void	scaledAdd(int[] exampleFeatures, double[] exampleValues, double factor, double defaultW) Self-modifying vector addition where the argument vector is first scaled by the given factor.
void	write(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessOutputStream out) Writes the weight vector's internal representation in binary form.
void	write(PrintStream out) Outputs the contents of this BiasedWeightVector into the specified PrintStream.
void	write(PrintStream out, Lexicon lex) Outputs the contents of this BiasedWeightVector into the specified PrintStream.

Methods inherited from class edu.illinois.cs.cogcomp.lbjava.learn.SparseWeightVector

clone, dot, getWeight, getWeight, pairwiseMultiply, readWeightVector, scaledAdd, scaledAdd, scaledMultiply, scaledMultiply, setWeight, setWeight, size, toString, toString, toStringJustWeights, toStringJustWeights

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Field Detail

defaultInitialBias

protected static final double defaultInitialBias

Default value for initialBias.

See Also:

Constant Field Values

initialBias

protected double initialBias

The first value for bias.

bias

protected double bias

The current bias weight.

Constructor Detail

BiasedWeightVector

public BiasedWeightVector()

Instantiates this biased vector with default parameter values.

BiasedWeightVector

public BiasedWeightVector(double b)

Instantiates this biased vector with the specified initial bias.

Parameters:

b - The inital bias.

Method Detail

dot

public double dot(int[] exampleFeatures,
                  double[] exampleValues,
                  double defaultW)

Takes the dot product of this BiasedWeightVector with the argument vector, using the specified default weight when one is not yet present in this vector.

Overrides:

dot in class SparseWeightVector

Parameters:

exampleFeatures - The example's array of feature indices

exampleValues - The example's array of feature values

defaultW - The default weight.

Returns:

The computed dot product.

scaledAdd

public void scaledAdd(int[] exampleFeatures,
                      double[] exampleValues,
                      double factor,
                      double defaultW)

Self-modifying vector addition where the argument vector is first scaled by the given factor.

Overrides:

scaledAdd in class SparseWeightVector

Parameters:

exampleFeatures - The example's array of feature indices

exampleValues - The example's array of feature values

factor - The scaling factor.

defaultW - An initial weight for previously unseen features.

clear

public void clear()

Empties the weight map.

Overrides:

clear in class SparseWeightVector

write

public void write(PrintStream out)

Outputs the contents of this BiasedWeightVector into the specified PrintStream. The string representation is the same as in the super class, with two added lines just after the "Begin" annotation that give the values of bias and initialBias.

Overrides:

write in class SparseWeightVector

Parameters:

out - The stream to write to.

write

public void write(PrintStream out,
                  Lexicon lex)

Outputs the contents of this BiasedWeightVector into the specified PrintStream. The string representation is the same as in the super class, with two added lines just after the "Begin" annotation that give the values of bias and initialBias.

Overrides:

write in class SparseWeightVector

Parameters:

out - The stream to write to.

lex - The feature lexicon.

write

public void write(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessOutputStream out)

Writes the weight vector's internal representation in binary form.

Overrides:

write in class SparseWeightVector

Parameters:

out - The output stream.

read

public void read(edu.illinois.cs.cogcomp.core.datastructures.vectors.ExceptionlessInputStream in)

Reads the representation of a weight vector with this object's run-time type from the given stream, overwriting the data in this object.

This method is appropriate for reading weight vectors as written by write(ExceptionlessOutputStream).

Overrides:

read in class SparseWeightVector

Parameters:

in - The input stream.

emptyClone

public SparseWeightVector emptyClone()

Returns a new, empty weight vector with the same parameter settings as this one.

Overrides:

emptyClone in class SparseWeightVector

Returns:

An empty weight vector.