smile.classification

Class SparseLogisticRegression

java.lang.Object

smile.classification.SparseLogisticRegression

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<smile.util.SparseArray>, java.util.function.ToIntFunction<smile.util.SparseArray>, Classifier<smile.util.SparseArray>, OnlineClassifier<smile.util.SparseArray>, SoftClassifier<smile.util.SparseArray>

Direct Known Subclasses:

SparseLogisticRegression.Binomial, SparseLogisticRegression.Multinomial

public abstract class SparseLogisticRegression
extends java.lang.Object
implements SoftClassifier<smile.util.SparseArray>, OnlineClassifier<smile.util.SparseArray>

Logistic regression on sparse data.

See Also:

LogisticRegression, Maxent, Serialized Form

Nested Class Summary

Nested Classes

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

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and 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, java.util.Properties prop) 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, java.util.Properties prop) 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, java.util.Properties prop) Fits multinomial logistic regression.
void	setLearningRate(double rate) Sets the learning rate of stochastic gradient descent.

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.classification.SoftClassifier

predict, predict

Methods inherited from interface smile.classification.OnlineClassifier

update, update

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, predict, predict, score

Constructor Detail

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

labels - class labels

Method Detail

binomial

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

Fits binomial logistic regression.

Parameters:

x - training samples.

y - training labels.

binomial

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

Fits binomial logistic regression.

Parameters:

x - training samples.

y - training labels.

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 - λ > 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.

multinomial

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

Fits multinomial logistic regression.

Parameters:

x - training samples.

y - training labels.

multinomial

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

Fits multinomial logistic regression.

Parameters:

x - training samples.

y - training labels.

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 - λ > 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.

fit

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

Fits logistic regression.

Parameters:

x - training samples.

y - training labels.

fit

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

Fits logistic regression.

Parameters:

x - training samples.

y - training labels.

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 - λ > 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.

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.

loglikelihood

public double loglikelihood()

Returns the log-likelihood of model.

AIC

public double AIC()

Returns the AIC score.