NaiveBayes (Java Statistical Analysis Tool 0.0.8 API)

java.lang.Object
- jsat.classifiers.bayesian.NaiveBayes

All Implemented Interfaces:

Serializable, Cloneable, Classifier, Parameterized
```
public class NaiveBayes
extends Object
implements Classifier, Parameterized
```
Provides an implementation of the Naive Bayes classifier that assumes numeric features come from some continuous probability distribution. By default this implementation restricts itself to only the Gaussian distribution, and becomes Gaussian Naive Bayes. Other distributions are supported, and a KernelDensityEstimator can be used as well.

By default, this implementation assumes that the input vectors are sparse and the distribution will only be estimated by the non-zero values, and features that are zero will be ignored during prediction time. This should be turned off when using dense data by calling setSparceInput(boolean)

Naive Bayes assumes that all attributes are perfectly independent.

Author:

Edward Raff

See Also:

Serialized Form

Nested Class Summary

Nested Classes
Modifier and Type Class and Description

static class NaiveBayes.NumericalHandeling
There are multiple ways of handling numerical attributes.

Nested Classes
Modifier and Type	Class and Description
`static class`	`NaiveBayes.NumericalHandeling` There are multiple ways of handling numerical attributes.

Field Summary

Fields
Modifier and Type Field and Description

static NaiveBayes.NumericalHandeling defaultHandling
The default method of handling numeric attributes is NaiveBayes.NumericalHandeling.NORMAL.

Fields
Modifier and Type	Field and Description
`static NaiveBayes.NumericalHandeling`	`defaultHandling` The default method of handling numeric attributes is `NaiveBayes.NumericalHandeling.NORMAL`.

Constructor Summary

Constructors
Constructor and Description
`NaiveBayes()` Creates a new Gaussian Naive Bayes classifier
`NaiveBayes(NaiveBayes.NumericalHandeling numericalHandling)` Creates a new Naive Bayes classifier that uses the specific method for handling numeric features.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`CategoricalResults`	`classify(DataPoint data)` Performs classification on the given data point.
`Classifier`	`clone()`
`NaiveBayes.NumericalHandeling`	`getNumericalHandling()` Returns the method used to handle numerical attributes
`Parameter`	`getParameter(String paramName)` Returns the parameter with the given name.
`List<Parameter>`	`getParameters()` Returns the list of parameters that can be altered for this learner.
`boolean`	`isSparceInput()` Returns `true` if the Classifier assumes that data points are sparce.
`void`	`setNumericalHandling(NaiveBayes.NumericalHandeling numericalHandling)` Sets the method used by this instance for handling numerical attributes.
`void`	`setSparceInput(boolean sparceInput)` Tells the Naive Bayes classifier to assume the importance of sparseness in the numerical values.
`boolean`	`supportsWeightedData()` Indicates whether the model knows how to train using weighted data points.
`void`	`trainC(ClassificationDataSet dataSet)` Trains the classifier and constructs a model for classification using the given data set.
`void`	`trainC(ClassificationDataSet dataSet, ExecutorService threadPool)` Trains the classifier and constructs a model for classification using the given data set.

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - defaultHandling
```
public static final NaiveBayes.NumericalHandeling defaultHandling
```
    The default method of handling numeric attributes is NaiveBayes.NumericalHandeling.NORMAL.
- Constructor Detail
  - NaiveBayes
```
public NaiveBayes(NaiveBayes.NumericalHandeling numericalHandling)
```
    Creates a new Naive Bayes classifier that uses the specific method for handling numeric features.
    
    Parameters:
    
    numericalHandling - the method to use for numeric features
  - NaiveBayes
```
public NaiveBayes()
```
    Creates a new Gaussian Naive Bayes classifier
- Method Detail
  - setNumericalHandling
```
public void setNumericalHandling(NaiveBayes.NumericalHandeling numericalHandling)
```
    Sets the method used by this instance for handling numerical attributes. This has no effect on an already trained classifier, but will change the result if trained again.
    
    Parameters:
    
    numericalHandling - the method to use for numerical attributes
  - getNumericalHandling
```
public NaiveBayes.NumericalHandeling getNumericalHandling()
```
    Returns the method used to handle numerical attributes
    
    Returns:
    
    the method used to handle numerical attributes
  - isSparceInput
```
public boolean isSparceInput()
```
    Returns true if the Classifier assumes that data points are sparce.
    
    Returns:
    
    true if the Classifier assumes that data points are sparce.
    
    See Also:
    
    setSparceInput(boolean)
  - setSparceInput
```
public void setSparceInput(boolean sparceInput)
```
    Tells the Naive Bayes classifier to assume the importance of sparseness in the numerical values. This means that values of zero will be ignored in computation and classification.
    This allows faster, more efficient computation of results if the data points are indeed sparce. This will also produce different results. This value should not be changed after training and before classification.
    
    Parameters:
    
    sparceInput - true to assume sparseness in the data, false to ignore it and assume zeros are meaningful values.
    
    See Also:
    
    isSparceInput()
  - classify
```
public CategoricalResults classify(DataPoint data)
```
    Description copied from interface: Classifier
    
    Performs classification on the given data point.
    
    Specified by:
    
    classify in interface Classifier
    
    Parameters:
    
    data - the data point to classify
    
    Returns:
    
    the results of the classification.
  - trainC
```
public void trainC(ClassificationDataSet dataSet)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
  - getParameters
```
public List<Parameter> getParameters()
```
    Description copied from interface: Parameterized
    
    Returns the list of parameters that can be altered for this learner.
    
    Specified by:
    
    getParameters in interface Parameterized
    
    Returns:
    
    the list of parameters that can be altered for this learner.
  - getParameter
```
public Parameter getParameter(String paramName)
```
    Description copied from interface: Parameterized
    
    Returns the parameter with the given name. Two different strings may map to a single Parameter object. An ASCII only string, and a Unicode style string.
    
    Specified by:
    
    getParameter in interface Parameterized
    
    Parameters:
    
    paramName - the name of the parameter to obtain
    
    Returns:
    
    the Parameter in question, or null if no such named Parameter exists.
  - clone
```
public Classifier clone()
```
    Specified by:
    
    clone in interface Classifier
    
    Overrides:
    
    clone in class Object
  - supportsWeightedData
```
public boolean supportsWeightedData()
```
    Description copied from interface: Classifier
    
    Indicates whether the model knows how to train using weighted data points. If it does, the model will train assuming the weights. The values returned by this method may change depending on the parameters set for the model.
    
    Specified by:
    
    supportsWeightedData in interface Classifier
    
    Returns:
    
    true if the model supports weighted data, false otherwise
  - trainC
```
public void trainC(ClassificationDataSet dataSet,
                   ExecutorService threadPool)
```
    Description copied from interface: Classifier
    
    Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
    
    Specified by:
    
    trainC in interface Classifier
    
    Parameters:
    
    dataSet - the data set to train on
    
    threadPool - the source of threads to use.

Class NaiveBayes

Nested Class Summary

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

defaultHandling

Constructor Detail

NaiveBayes

NaiveBayes

Method Detail

setNumericalHandling

getNumericalHandling

isSparceInput

setSparceInput

classify

trainC

getParameters

getParameter

clone

supportsWeightedData

trainC