Package ai.sklearn4j.naive_bayes

Class GaussianNaiveBayes

  • public class GaussianNaiveBayes
extends BaseNaiveBayes
    Naive Bayes classifier for normal distributed models.

    • Constructor Detail

      • GaussianNaiveBayes

        public GaussianNaiveBayes()

    • Method Detail

      • jointLogLikelihood

        protected NumpyArray<Double> jointLogLikelihood​(NumpyArray<Double> x)
        Compute the unnormalized posterior log probability of X.

        I.e. ``log P(c) + log P(x|c)`` for all rows x of X, as an array-like of shape (n_samples, n_classes).

        predict, predictProbabilities, and predictLogProbabilities pass the input over to jointLogLikelihood.

        Specified by:
        jointLogLikelihood in class BaseNaiveBayes
        Parameters:
        x - An array-like of shape (n_samples, n_classes).
        Returns:
        The unnormalized posterior log probability of X.

      • getClassCounts

        public NumpyArray<Double> getClassCounts()
        Gets the class counts.
        Returns:
        The value of class counts.

      • setClassCounts

        public void setClassCounts​(NumpyArray<Double> classCounts)
        Sets the class counts.
        Parameters:
        classCounts - New value to be stored.

      • getClassPriors

        public NumpyArray<Double> getClassPriors()
        Gets the class priors.
        Returns:
        The value of class priors.

      • setClassPriors

        public void setClassPriors​(NumpyArray<Double> classPriors)
        Sets the class priors.
        Parameters:
        classPriors - New value to be stored.

      • getClasses

        public NumpyArray<Long> getClasses()
        Gets the classes.
        Returns:
        The value of classes.

      • setClasses

        public void setClasses​(NumpyArray<Long> classes)
        Sets the classes.
        Parameters:
        classes - New value to be stored.

      • getPriors

        public NumpyArray<Double> getPriors()
        Gets the priors.
        Returns:
        The value of priors.

      • setPriors

        public void setPriors​(NumpyArray<Double> priors)
        Sets the priors.
        Parameters:
        priors - New value to be stored.

      • getFeatureNamesIn

        public String[] getFeatureNamesIn()
        Gets the feature names.
        Returns:
        The value of feature names.

      • setFeatureNamesIn

        public void setFeatureNamesIn​(String[] featureNamesIn)
        Sets the feature names.
        Parameters:
        featureNamesIn - New value to be stored.

      • getNumberOfFeatures

        public int getNumberOfFeatures()
        Gets the number of features.
        Returns:
        The value of number of features.

      • setNumberOfFeatures

        public void setNumberOfFeatures​(int numberOfFeatures)
        Sets the number of features.
        Parameters:
        numberOfFeatures - New value to be stored.

      • getSigma

        public NumpyArray<Double> getSigma()
        Gets the variance of the features.
        Returns:
        The value of variance of the features.

      • setSigma

        public void setSigma​(NumpyArray<Double> sigma)
        Sets the variance of the features.
        Parameters:
        sigma - New value to be stored.

      • getTheta

        public NumpyArray<Double> getTheta()
        Gets the mean of the features.
        Returns:
        The value of mean of the features.

      • setTheta

        public void setTheta​(NumpyArray<Double> theta)
        Sets the mean of the features.
        Parameters:
        theta - New value to be stored.