public final class DiscreteNaiveBayesModel extends Object implements IgniteModel<Vector,Double>, Exportable<DiscreteNaiveBayesModel>, Serializable
y
belongs to a class C_k, k in [0..K]
as
p(C_k,y) =x_1*p_k1^x *...*x_i*p_ki^x_i
. Where x_i
is a discrete feature, p_ki
is a prior
probability probability of class p(x|C_k)
. Returns the number of the most possible class.Constructor and Description |
---|
DiscreteNaiveBayesModel(double[][][] probabilities,
double[] clsProbabilities,
double[] labels,
double[][] bucketThresholds,
DiscreteNaiveBayesSumsHolder sumsHolder) |
Modifier and Type | Method and Description |
---|---|
double[][] |
getBucketThresholds() |
double[] |
getClsProbabilities() |
double[][][] |
getProbabilities() |
DiscreteNaiveBayesSumsHolder |
getSumsHolder() |
Double |
predict(Vector vector)
Make a prediction for the specified input arguments.
|
<P> void |
saveModel(Exporter<DiscreteNaiveBayesModel,P> exporter,
P path)
Save model by the given path.
|
public DiscreteNaiveBayesModel(double[][][] probabilities, double[] clsProbabilities, double[] labels, double[][] bucketThresholds, DiscreteNaiveBayesSumsHolder sumsHolder)
probabilities
- Probabilities of features for classes.clsProbabilities
- Prior probabilities for classes.bucketThresholds
- The threshold to convert a feature to a binary value.sumsHolder
- Amount values which are abouve the threshold per label.labels
- Labels.public <P> void saveModel(Exporter<DiscreteNaiveBayesModel,P> exporter, P path)
saveModel
in interface Exportable<DiscreteNaiveBayesModel>
exporter
- Exporter.path
- Path to saved model.public Double predict(Vector vector)
Model
public double[][][] getProbabilities()
public double[] getClsProbabilities()
public double[][] getBucketThresholds()
public DiscreteNaiveBayesSumsHolder getSumsHolder()
GridGain In-Memory Computing Platform : ver. 8.9.14 Release Date : November 5 2024