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Showing content from http://accord-framework.net/docs/html/T_Accord_MachineLearning_Bayes_NaiveBayesLearning.htm below:

Naïve Bayes learning algorithm for discrete distribution models.

Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

[SerializableAttribute]
public class NaiveBayesLearning : NaiveBayesLearningBase<NaiveBayes, GeneralDiscreteDistribution, int, IndependentOptions<GeneralDiscreteOptions>, GeneralDiscreteOptions>, 
	ISupervisedLearning<NaiveBayes, int[], double[]>, ISupervisedLearning<NaiveBayes, int[], int>

<SerializableAttribute>
Public Class NaiveBayesLearning
	Inherits NaiveBayesLearningBase(Of NaiveBayes, GeneralDiscreteDistribution, Integer, IndependentOptions(Of GeneralDiscreteOptions), GeneralDiscreteOptions)
	Implements ISupervisedLearning(Of NaiveBayes, Integer(), Double()), 
	ISupervisedLearning(Of NaiveBayes, Integer(), Integer)

The NaiveBayesLearning type exposes the following members.

Gets or sets the distribution creation function. This function can be used to specify how the initial distributions of the model should be created. By default, this function attempts to call the empty constructor of the distribution using Activator.CreateInstance().

Gets or sets whether the class priors should be estimated from the data.

Gets or sets the model being learned.

Gets or sets the fitting options to use when estimating the class-specific distributions.

Gets or sets the parallelization options for this algorithm.

Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.

Creates an instance of the model to be learned.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Fits one of the distributions in the naive bayes model.

Serves as the default hash function.

Gets the Type of the current instance.

Learns a model that can map the given inputs to the given outputs.

Learns a model that can map the given inputs to the given outputs.

Learns a model that can map the given inputs to the given outputs.

Creates a shallow copy of the current Object.

Returns a string that represents the current object.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

For basic examples on how to learn a Naive Bayes algorithm, please see NaiveBayes page. The following examples show how to set more specialized learning settings for discrete models.

int[][] inputs =
{
    
    new [] { 0, 1 }, 
    new [] { 0, 2 }, 
    new [] { 0, 1 }, 
    new [] { 1, 2 }, 
    new [] { 0, 2 }, 
    new [] { 0, 2 }, 
    new [] { 1, 1 }, 
    new [] { 0, 1 }, 
    new [] { 1, 1 }, 
};

int[] outputs = 
{
    0, 0, 0, 1, 1, 1, 2, 2, 2,
};





var bayes = new NaiveBayes(classes: 3, symbols: new[] { 2, 3 });


var learning = new NaiveBayesLearning()
{
    Model = bayes
};


learning.Options.InnerOption.UseLaplaceRule = true;


learning.Learn(inputs, outputs);


int answer = bayes.Decide(new int[] { 0, 1 });

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