smile.classification

Class Maxent.Binomial

java.lang.Object

smile.classification.Maxent

smile.classification.Maxent.Binomial

All Implemented Interfaces:

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

Enclosing class:

Maxent

public static class Maxent.Binomial
extends Maxent

Binomial maximum entropy classifier. The dependent variable is nominal of two levels.

See Also:

Serialized Form

Nested Class Summary

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

Maxent.Binomial, Maxent.Multinomial

Constructor Summary

Constructors

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

Method Summary

Modifier and Type	Method and 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(int[] x) Predicts the class label of an instance.
int	predict(int[] x, double[] posteriori) Predicts the class label of an instance and also calculate a posteriori probabilities.
void	update(int[] x, int y) Online update the classifier with a new training instance.

Methods inherited from class smile.classification.Maxent

AIC, binomial, binomial, binomial, dimension, fit, fit, fit, getLearningRate, loglikelihood, 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

Binomial

public Binomial(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 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.

predict

public int predict(int[] 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(int[] 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(int[] 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.