weka.estimators

Class Estimator

java.lang.Object

weka.estimators.Estimator

All Implemented Interfaces:

Serializable, Cloneable, CapabilitiesHandler, OptionHandler, RevisionHandler

Direct Known Subclasses:

DiscreteEstimator, DiscreteEstimatorBayes, KernelEstimator, MahalanobisEstimator, NormalEstimator, PoissonEstimator

public abstract class Estimator
extends Object
implements Cloneable, Serializable, OptionHandler, CapabilitiesHandler, RevisionHandler

Abstract class for all estimators. Example code for a nonincremental estimator

   // create a histogram for estimation
   EqualWidthEstimator est = new EqualWidthEstimator();
   est.addValues(instances, attrIndex);
 

Example code for an incremental estimator (incremental estimators must implement interface IncrementalEstimator)

   // Create a discrete estimator that takes values 0 to 9
   DiscreteEstimator newEst = new DiscreteEstimator(10, true);

   // Create 50 random integers first predicting the probability of the
   // value, then adding the value to the estimator
   Random r = new Random(seed);
   for(int i = 0; i < 50; i++) {
     current = Math.abs(r.nextInt() % 10);
     System.out.println(newEst);
     System.out.println("Prediction for " + current 
                        + " = " + newEst.getProbability(current));
     newEst.addValue(current, 1);
   }
 

Example code for a main method for an estimator.

 public static void main(String [] argv) {

   try {
     LoglikeliEstimator est = new LoglikeliEstimator();      
     Estimator.buildEstimator((Estimator) est, argv, false);      
     System.out.println(est.toString());
   } catch (Exception ex) {
     ex.printStackTrace();
     System.out.println(ex.getMessage());
   }
 }
 

Version:

$Revision: 5539 $

Author:

Gabi Schmidberger ([email protected]), Len Trigg ([email protected])

See Also:

Serialized Form

Constructor Summary

Constructors

Constructor and Description
Estimator()

Method Summary

Methods

Modifier and Type	Method and Description
void	addValue(double data, double weight) Add a new data value to the current estimator.
void	addValues(Instances data, int attrIndex) Initialize the estimator with a new dataset.
void	addValues(Instances data, int attrIndex, double min, double max, double factor) Initialize the estimator with all values of one attribute of a dataset.
void	addValues(Instances data, int attrIndex, int classIndex, int classValue) Initialize the estimator using only the instance of one class.
void	addValues(Instances data, int attrIndex, int classIndex, int classValue, double min, double max) Initialize the estimator using only the instance of one class.
static void	buildEstimator(Estimator est, Instances instances, int attrIndex, int classIndex, int classValueIndex, boolean isIncremental)
static void	buildEstimator(Estimator est, String[] options, boolean isIncremental) Build an estimator using the options.
static Estimator	clone(Estimator model) Creates a deep copy of the given estimator using serialization.
String	debugTipText() Returns the tip text for this property
boolean	equals(Object obj) Tests whether the current estimation object is equal to another estimation object
static Estimator	forName(String name, String[] options) Creates a new instance of a estimatorr given it's class name and (optional) arguments to pass to it's setOptions method.
Capabilities	getCapabilities() Returns the Capabilities of this Estimator.
boolean	getDebug() Get whether debugging is turned on.
String[]	getOptions() Gets the current settings of the Estimator.
abstract double	getProbability(double data) Get a probability estimate for a value.
Enumeration	listOptions() Returns an enumeration describing the available options.
static Estimator[]	makeCopies(Estimator model, int num) Creates a given number of deep copies of the given estimator using serialization.
static Estimator	makeCopy(Estimator model) Creates a deep copy of the given estimator using serialization.
void	setDebug(boolean debug) Set debugging mode.
void	setOptions(String[] options) Parses a given list of options.
void	testCapabilities(Instances data, int attrIndex) Test if the estimator can handle the data.

Methods inherited from class java.lang.Object

getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Methods inherited from interface weka.core.RevisionHandler

getRevision

Constructor Detail

Estimator

public Estimator()

Method Detail

addValue

public void addValue(double data,
            double weight)

Add a new data value to the current estimator.

Parameters:

data - the new data value

weight - the weight assigned to the data value

addValues

public void addValues(Instances data,
             int attrIndex)
               throws Exception

Initialize the estimator with a new dataset. Finds min and max first.

Parameters:

data - the dataset used to build this estimator

attrIndex - attribute the estimator is for

Throws:

Exception - if building of estimator goes wrong

addValues

public void addValues(Instances data,
             int attrIndex,
             double min,
             double max,
             double factor)
               throws Exception

Initialize the estimator with all values of one attribute of a dataset. Some estimator might ignore the min and max values.

Parameters:

data - the dataset used to build this estimator

attrIndex - attribute the estimator is for

min - minimal border of range

max - maximal border of range

factor - number of instances has been reduced to that factor

Throws:

Exception - if building of estimator goes wrong

addValues

public void addValues(Instances data,
             int attrIndex,
             int classIndex,
             int classValue)
               throws Exception

Initialize the estimator using only the instance of one class. It is using the values of one attribute only.

Parameters:

data - the dataset used to build this estimator

attrIndex - attribute the estimator is for

classIndex - index of the class attribute

classValue - the class value

Throws:

Exception - if building of estimator goes wrong

addValues

public void addValues(Instances data,
             int attrIndex,
             int classIndex,
             int classValue,
             double min,
             double max)
               throws Exception

Initialize the estimator using only the instance of one class. It is using the values of one attribute only.

Parameters:

data - the dataset used to build this estimator

attrIndex - attribute the estimator is for

classIndex - index of the class attribute

classValue - the class value

min - minimal value of this attribute

max - maximal value of this attribute

Throws:

Exception - if building of estimator goes wrong

getProbability

public abstract double getProbability(double data)

Get a probability estimate for a value.

Parameters:

data - the value to estimate the probability of

Returns:

the estimated probability of the supplied value

buildEstimator

public static void buildEstimator(Estimator est,
                  String[] options,
                  boolean isIncremental)
                           throws Exception

Build an estimator using the options. The data is given in the options.

Parameters:

est - the estimator used

options - the list of options

isIncremental - true if estimator is incremental

Throws:

Exception - if something goes wrong or the user requests help on command options

buildEstimator

public static void buildEstimator(Estimator est,
                  Instances instances,
                  int attrIndex,
                  int classIndex,
                  int classValueIndex,
                  boolean isIncremental)
                           throws Exception

Throws:

Exception

clone

public static Estimator clone(Estimator model)
                       throws Exception

Creates a deep copy of the given estimator using serialization.

Parameters:

model - the estimator to copy

Returns:

a deep copy of the estimator

Throws:

Exception - if an error occurs

makeCopy

public static Estimator makeCopy(Estimator model)
                          throws Exception

Creates a deep copy of the given estimator using serialization.

Parameters:

model - the estimator to copy

Returns:

a deep copy of the estimator

Throws:

Exception - if an error occurs

makeCopies

public static Estimator[] makeCopies(Estimator model,
                     int num)
                              throws Exception

Creates a given number of deep copies of the given estimator using serialization.

Parameters:

model - the estimator to copy

num - the number of estimator copies to create.

Returns:

an array of estimators.

Throws:

Exception - if an error occurs

equals

public boolean equals(Object obj)

Tests whether the current estimation object is equal to another estimation object

Overrides:

equals in class Object

Parameters:

obj - the object to compare against

Returns:

true if the two objects are equal

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options. Valid options are:

-D
If set, estimator is run in debug mode and may output additional info to the console.

Specified by:

setOptions in interface OptionHandler

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the Estimator.

Specified by:

getOptions in interface OptionHandler

Returns:

an array of strings suitable for passing to setOptions

forName

public static Estimator forName(String name,
                String[] options)
                         throws Exception

Creates a new instance of a estimatorr given it's class name and (optional) arguments to pass to it's setOptions method. If the classifier implements OptionHandler and the options parameter is non-null, the classifier will have it's options set.

Parameters:

name - the fully qualified class name of the estimatorr

options - an array of options suitable for passing to setOptions. May be null.

Returns:

the newly created classifier, ready for use.

Throws:

Exception - if the classifier name is invalid, or the options supplied are not acceptable to the classifier

setDebug

public void setDebug(boolean debug)

Set debugging mode.

Parameters:

debug - true if debug output should be printed

getDebug

public boolean getDebug()

Get whether debugging is turned on.

Returns:

true if debugging output is on

debugTipText

public String debugTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getCapabilities

public Capabilities getCapabilities()

Returns the Capabilities of this Estimator. Derived estimators have to override this method to enable capabilities.

Specified by:

getCapabilities in interface CapabilitiesHandler

Returns:

the capabilities of this object

See Also:

Capabilities

testCapabilities

public void testCapabilities(Instances data,
                    int attrIndex)
                      throws Exception

Test if the estimator can handle the data.

Parameters:

data - the dataset the estimator takes an attribute from

attrIndex - the index of the attribute

Throws:

Exception

See Also:

Capabilities