smile.classification

Class LogisticRegression.Multinomial

java.lang.Object

smile.classification.LogisticRegression

smile.classification.LogisticRegression.Multinomial

All Implemented Interfaces:

java.io.Serializable, java.util.function.ToDoubleFunction<double[]>, java.util.function.ToIntFunction<double[]>, Classifier<double[]>, OnlineClassifier<double[]>, SoftClassifier<double[]>

Enclosing class:

LogisticRegression

public static class LogisticRegression.Multinomial
extends LogisticRegression

Multinomial logistic regression. The dependent variable is nominal with more than two levels.

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class smile.classification.LogisticRegression

LogisticRegression.Binomial, LogisticRegression.Multinomial

Constructor Summary

Constructors

Constructor and Description
Multinomial(double[][] w, double L, double lambda, smile.util.IntSet labels) Constructor.

Method Summary

Modifier and Type	Method and Description
double[][]	coefficients() Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors.
int	predict(double[] x) Predicts the class label of an instance.
int	predict(double[] x, double[] posteriori) Predicts the class label of an instance and also calculate a posteriori probabilities.
void	update(double[] x, int y) Online update the classifier with a new training instance.

Methods inherited from class smile.classification.LogisticRegression

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

Methods inherited from class java.lang.Object

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

Methods inherited from interface smile.classification.OnlineClassifier

update

Methods inherited from interface smile.classification.Classifier

applyAsDouble, applyAsInt, f, predict

Constructor Detail

Multinomial

public Multinomial(double[][] w,
                   double L,
                   double lambda,
                   smile.util.IntSet labels)

Constructor.

Parameters:

w - the weights.

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

coefficients

public double[][] coefficients()

Returns a 2d-array of size (k-1) x (p+1), containing the linear weights of multi-class logistic regression, where k is the number of classes and p is the dimension of feature vectors. The last element of each row is the weight of bias.

predict

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

Description copied from interface: SoftClassifier

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 - the array to store a posteriori probabilities on output.

Returns:

the predicted class label

update

public void update(double[] x,
                   int y)

Description copied from interface: OnlineClassifier

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

Parameters:

x - training instance.

y - training label.