Class SparseLogisticRegression.Binomial

java.lang.Object

smile.classification.AbstractClassifier<SparseArray>

smile.classification.SparseLogisticRegression

smile.classification.SparseLogisticRegression.Binomial

All Implemented Interfaces:

Serializable, ToDoubleFunction<SparseArray>, ToIntFunction<SparseArray>, Classifier<SparseArray>

Enclosing class:

SparseLogisticRegression

public static class SparseLogisticRegression.Binomial
extends SparseLogisticRegression

Binomial logistic regression. The dependent variable is nominal of two levels.

See Also:

• Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class SparseLogisticRegression

SparseLogisticRegression.Binomial, SparseLogisticRegression.Multinomial

Nested classes/interfaces inherited from interface Classifier

Classifier.Trainer<T,M>

Field Summary

Fields inherited from class AbstractClassifier

classes

Constructor Summary

Constructors

Constructor	Description
Binomial(double[] w, double L, double lambda, IntSet labels)	Constructor.

Method Summary

Modifier and Type	Method	Description
double[]	coefficients()	Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors.
int	predict(SparseArray x)	Predicts the class label of an instance.
int	predict(SparseArray x, double[] posteriori)	Predicts the class label of an instance and also calculate a posteriori probabilities.
double	score(SparseArray x)	The raw prediction score.
void	update(SparseArray x, int y)	Online update the classifier with a new training instance.

Methods inherited from class SparseLogisticRegression

AIC, binomial, binomial, fit, fit, fit, getLearningRate, loglikelihood, multinomial, multinomial, online, setLearningRate, soft

Methods inherited from class AbstractClassifier

classes, numClasses

Methods inherited from class Object

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

Methods inherited from interface Classifier

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

Constructor Details

Binomial

public Binomial(double[] w,
 double L,
 double lambda,
 IntSet labels)

Constructor.

Parameters:

w - the weights.

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

coefficients

public double[] coefficients()

Returns an array of size (p+1) containing the linear weights of binary logistic regression, where p is the dimension of feature vectors. The last element is the weight of bias.

Returns:

the linear weights.

score

public double score(SparseArray x)

Description copied from interface: Classifier

The raw prediction score.

Parameters:

x - the instance to be classified.

Returns:

the raw prediction score.

predict

public int predict(SparseArray x)

Description copied from interface: Classifier

Predicts the class label of an instance.

Parameters:

x - the instance to be classified.

Returns:

the predicted class label.

predict

public int predict(SparseArray x,
 double[] posteriori)

Description copied from interface: Classifier

Predicts the class label of an instance and also calculate a posteriori probabilities. Classifiers may NOT support this method since not all classification algorithms are able to calculate such a posteriori probabilities.

Parameters:

x - an instance to be classified.

posteriori - a posteriori probabilities on output.

Returns:

the predicted class label

update

public void update(SparseArray x,
 int y)

Description copied from interface: Classifier

Online update the classifier with a new training instance. In general, this method may be NOT multi-thread safe.

Parameters:

x - the training instance.

y - the training label.