Package smile.classification

Class SparseLogisticRegression

java.lang.Object

smile.classification.AbstractClassifier<smile.util.SparseArray>

smile.classification.SparseLogisticRegression

All Implemented Interfaces:

Serializable, ToDoubleFunction<smile.util.SparseArray>, ToIntFunction<smile.util.SparseArray>, Classifier<smile.util.SparseArray>

Direct Known Subclasses:

SparseLogisticRegression.Binomial, SparseLogisticRegression.Multinomial

public abstract class SparseLogisticRegression
extends AbstractClassifier<smile.util.SparseArray>

Logistic regression on sparse data.

See Also:

• LogisticRegression
• Maxent
• Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	SparseLogisticRegression.Binomial	Binomial logistic regression.
static class	SparseLogisticRegression.Multinomial	Multinomial logistic regression.

Nested classes/interfaces inherited from interface smile.classification.Classifier

Classifier.Trainer<T,M extends Classifier<T>>

Field Summary

Fields inherited from class smile.classification.AbstractClassifier

classes

Constructor Summary

Constructors

Constructor	Description
SparseLogisticRegression(int p, double L, double lambda, smile.util.IntSet labels)	Constructor.

Method Summary

Modifier and Type	Method	Description
double	AIC()	Returns the AIC score.
static SparseLogisticRegression.Binomial	binomial(smile.data.SparseDataset x, int[] y)	Fits binomial logistic regression.
static SparseLogisticRegression.Binomial	binomial(smile.data.SparseDataset x, int[] y, double lambda, double tol, int maxIter)	Fits binomial logistic regression.
static SparseLogisticRegression.Binomial	binomial(smile.data.SparseDataset x, int[] y, Properties params)	Fits binomial logistic regression.
static SparseLogisticRegression	fit(smile.data.SparseDataset x, int[] y)	Fits logistic regression.
static SparseLogisticRegression	fit(smile.data.SparseDataset x, int[] y, double lambda, double tol, int maxIter)	Fits logistic regression.
static SparseLogisticRegression	fit(smile.data.SparseDataset x, int[] y, Properties params)	Fits logistic regression.
double	getLearningRate()	Returns the learning rate of stochastic gradient descent.
double	loglikelihood()	Returns the log-likelihood of model.
static SparseLogisticRegression.Multinomial	multinomial(smile.data.SparseDataset x, int[] y)	Fits multinomial logistic regression.
static SparseLogisticRegression.Multinomial	multinomial(smile.data.SparseDataset x, int[] y, double lambda, double tol, int maxIter)	Fits multinomial logistic regression.
static SparseLogisticRegression.Multinomial	multinomial(smile.data.SparseDataset x, int[] y, Properties params)	Fits multinomial logistic regression.
boolean	online()	Returns true if this is an online learner.
void	setLearningRate(double rate)	Sets the learning rate of stochastic gradient descent.
boolean	soft()	Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

Methods inherited from class smile.classification.AbstractClassifier

classes, numClasses

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, predict, predict, predict, predict, predict, predict, predict, predict, score, update, update, update

Constructor Details

SparseLogisticRegression

public SparseLogisticRegression(int p,
 double L,
 double lambda,
 smile.util.IntSet labels)

Constructor.

Parameters:

p - the dimension of input data.

L - the log-likelihood of learned model.

lambda - lambda > 0 gives a "regularized" estimate of linear weights which often has superior generalization performance, especially when the dimensionality is high.

labels - the class label encoder.

Method Details

binomial

public static SparseLogisticRegression.Binomial binomial(smile.data.SparseDataset x,
 int[] y)

Fits binomial logistic regression.

Parameters:

x - training samples.

y - training labels.

Returns:

the model.

binomial

public static SparseLogisticRegression.Binomial binomial(smile.data.SparseDataset x,
 int[] y,
 Properties params)

Fits binomial logistic regression.

Parameters:

x - training samples.

y - training labels.

params - the hyper-parameters.

Returns:

the model.

binomial

public static SparseLogisticRegression.Binomial binomial(smile.data.SparseDataset x,
 int[] y,
 double lambda,
 double tol,
 int maxIter)

Fits binomial logistic regression.

Parameters:

x - training samples.

y - training labels.

lambda - lambda > 0 gives a "regularized" estimate of linear weights which often has superior generalization performance, especially when the dimensionality is high.

tol - the tolerance for stopping iterations.

maxIter - the maximum number of iterations.

Returns:

the model.

multinomial

public static SparseLogisticRegression.Multinomial multinomial(smile.data.SparseDataset x,
 int[] y)

Fits multinomial logistic regression.

Parameters:

x - training samples.

y - training labels.

Returns:

the model.

multinomial

public static SparseLogisticRegression.Multinomial multinomial(smile.data.SparseDataset x,
 int[] y,
 Properties params)

Fits multinomial logistic regression.

Parameters:

x - training samples.

y - training labels.

params - the hyper-parameters.

Returns:

the model.

multinomial

public static SparseLogisticRegression.Multinomial multinomial(smile.data.SparseDataset x,
 int[] y,
 double lambda,
 double tol,
 int maxIter)

Fits multinomial logistic regression.

Parameters:

x - training samples.

y - training labels.

lambda - lambda > 0 gives a "regularized" estimate of linear weights which often has superior generalization performance, especially when the dimensionality is high.

tol - the tolerance for stopping iterations.

maxIter - the maximum number of iterations.

Returns:

the model.

fit

public static SparseLogisticRegression fit(smile.data.SparseDataset x,
 int[] y)

Fits logistic regression.

Parameters:

x - training samples.

y - training labels.

Returns:

the model.

fit

public static SparseLogisticRegression fit(smile.data.SparseDataset x,
 int[] y,
 Properties params)

Fits logistic regression.

Parameters:

x - training samples.

y - training labels.

params - the hyper-parameters.

Returns:

the model.

fit

public static SparseLogisticRegression fit(smile.data.SparseDataset x,
 int[] y,
 double lambda,
 double tol,
 int maxIter)

Fits logistic regression.

Parameters:

x - training samples.

y - training labels.

lambda - lambda > 0 gives a "regularized" estimate of linear weights which often has superior generalization performance, especially when the dimensionality is high.

tol - the tolerance for stopping iterations.

maxIter - the maximum number of iterations.

Returns:

the model.

soft

public boolean soft()

Description copied from interface: Classifier

Returns true if this is a soft classifier that can estimate the posteriori probabilities of classification.

Returns:

true if soft classifier.

online

public boolean online()

Description copied from interface: Classifier

Returns true if this is an online learner.

Returns:

true if online learner.

setLearningRate

public void setLearningRate(double rate)

Sets the learning rate of stochastic gradient descent. It is a good practice to adapt the learning rate for different data sizes. For example, it is typical to set the learning rate to eta/n, where eta is in [0.1, 0.3] and n is the size of the training data.

Parameters:

rate - the learning rate.

getLearningRate

public double getLearningRate()

Returns the learning rate of stochastic gradient descent.

Returns:

the learning rate of stochastic gradient descent.

loglikelihood

public double loglikelihood()

Returns the log-likelihood of model.

Returns:

the log-likelihood of model.

AIC

public double AIC()

Returns the AIC score.

Returns:

the AIC score.