ai.libs.mlplan.metamining.similaritymeasures

Class F1Optimizer

java.lang.Object

ai.libs.mlplan.metamining.similaritymeasures.F1Optimizer

All Implemented Interfaces:

IHeterogenousSimilarityMeasureComputer

public class F1Optimizer
extends java.lang.Object
implements IHeterogenousSimilarityMeasureComputer

Constructor Summary

Constructors

Constructor and Description
F1Optimizer()

Method Summary

Modifier and Type	Method and Description
void	build(org.nd4j.linalg.api.ndarray.INDArray X, org.nd4j.linalg.api.ndarray.INDArray W, org.nd4j.linalg.api.ndarray.INDArray R) Learns a matrix U that minimizes F1 (W is ignored here)
double	computeSimilarity(org.nd4j.linalg.api.ndarray.INDArray x, org.nd4j.linalg.api.ndarray.INDArray w) Compute the 'quality of the match' of given feature values for a new problem instance based on the training.
double	getCost(org.nd4j.linalg.api.ndarray.INDArray U) This evaluates F1
float	getFirstDerivative(org.nd4j.linalg.api.ndarray.INDArray U, int k, int l) This compute the derivative of F1 for the (k,l)-th element of the U matrix
org.nd4j.linalg.api.ndarray.INDArray	getGradientAsMatrix(org.nd4j.linalg.api.ndarray.INDArray U) This computes the gradient of F1 in matrix form
org.nd4j.linalg.api.ndarray.INDArray	getU()
org.nd4j.linalg.api.ndarray.INDArray	getX()
de.jungblut.math.DoubleVector	matrix2vector(org.nd4j.linalg.api.ndarray.INDArray matrix) collapses a matrix of the Nd4j framework into a double vector of Thomas Jungblut's framework
org.nd4j.linalg.api.ndarray.INDArray	vector2matrix(de.jungblut.math.DoubleVector vector, int m, int n) creates a matrix of the Nd4j framework from a vector of Thomas Jungblut's math framework

Methods inherited from class java.lang.Object

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

Constructor Detail

F1Optimizer

public F1Optimizer()

Method Detail

build

public void build(org.nd4j.linalg.api.ndarray.INDArray X,
                  org.nd4j.linalg.api.ndarray.INDArray W,
                  org.nd4j.linalg.api.ndarray.INDArray R)

Learns a matrix U that minimizes F1 (W is ignored here)

Specified by:

build in interface IHeterogenousSimilarityMeasureComputer

Parameters:

X - Feature values for instances of the first measure (One row = features of one instance, e.g. meta features of a data set)

W - Feature values for instances of the second measure (One row = features of one instance, e.g. a characterization of a machine learning pipeline)

R - A matrix giving an indication of how good of a match a specific instance of the first measure is to a specific instance of the second measure, i.e. how well a pipeline performs on a data set

vector2matrix

public org.nd4j.linalg.api.ndarray.INDArray vector2matrix(de.jungblut.math.DoubleVector vector,
                                                          int m,
                                                          int n)

creates a matrix of the Nd4j framework from a vector of Thomas Jungblut's math framework

Parameters:

vector -

m -

n -

Returns:

matrix2vector

public de.jungblut.math.DoubleVector matrix2vector(org.nd4j.linalg.api.ndarray.INDArray matrix)

collapses a matrix of the Nd4j framework into a double vector of Thomas Jungblut's framework

Parameters:

matrix -

Returns:

getCost

public double getCost(org.nd4j.linalg.api.ndarray.INDArray U)

This evaluates F1

Parameters:

RRT -

U -

X -

Returns:

getGradientAsMatrix

public org.nd4j.linalg.api.ndarray.INDArray getGradientAsMatrix(org.nd4j.linalg.api.ndarray.INDArray U)

This computes the gradient of F1 in matrix form

Parameters:

R -

U -

X -

Returns:

getFirstDerivative

public float getFirstDerivative(org.nd4j.linalg.api.ndarray.INDArray U,
                                int k,
                                int l)

This compute the derivative of F1 for the (k,l)-th element of the U matrix

Parameters:

RRT -

U -

X -

k -

l -

Returns:

computeSimilarity

public double computeSimilarity(org.nd4j.linalg.api.ndarray.INDArray x,
                                org.nd4j.linalg.api.ndarray.INDArray w)

Description copied from interface: IHeterogenousSimilarityMeasureComputer

Compute the 'quality of the match' of given feature values for a new problem instance based on the training.

Specified by:

computeSimilarity in interface IHeterogenousSimilarityMeasureComputer

Parameters:

x - Feature values for the instance for the first measure (e.g. meta data of a new data set)

w - Feature values for the instance for the second measure (e.g. a characterization of machine learning pipeline)

Returns:

The quality of the match, or similarity for the given vectors

getX

public org.nd4j.linalg.api.ndarray.INDArray getX()

getU

public org.nd4j.linalg.api.ndarray.INDArray getU()