类 PaceRegression
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.functions.PaceRegression
- 所有已实现的接口:
Serializable
,Cloneable
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
,WeightedInstancesHandler
public class PaceRegression
extends Classifier
implements OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler
Class for building pace regression linear models and using them for prediction.
Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions.
The current work of the pace regression theory, and therefore also this implementation, do not handle:
- missing values
- non-binary nominal attributes
- the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20)
For more information see:
Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand.
Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002. BibTeX:
Under regularity conditions, pace regression is provably optimal when the number of coefficients tends to infinity. It consists of a group of estimators that are either overall optimal or optimal under certain conditions.
The current work of the pace regression theory, and therefore also this implementation, do not handle:
- missing values
- non-binary nominal attributes
- the case that n - k is small where n is the number of instances and k is the number of coefficients (the threshold used in this implmentation is 20)
For more information see:
Wang, Y (2000). A new approach to fitting linear models in high dimensional spaces. Hamilton, New Zealand.
Wang, Y., Witten, I. H.: Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference in Machine Learning, Sydney, Australia, 650-657, 2002. BibTeX:
@phdthesis{Wang2000, address = {Hamilton, New Zealand}, author = {Wang, Y}, school = {Department of Computer Science, University of Waikato}, title = {A new approach to fitting linear models in high dimensional spaces}, year = {2000} } @inproceedings{Wang2002, address = {Sydney, Australia}, author = {Wang, Y. and Witten, I. H.}, booktitle = {Proceedings of the Nineteenth International Conference in Machine Learning}, pages = {650-657}, title = {Modeling for optimal probability prediction}, year = {2002} }Valid options are:
-D Produce debugging output. (default no debugging output)
-E <estimator> The estimator can be one of the following: eb -- Empirical Bayes estimator for noraml mixture (default) nested -- Optimal nested model selector for normal mixture subset -- Optimal subset selector for normal mixture pace2 -- PACE2 for Chi-square mixture pace4 -- PACE4 for Chi-square mixture pace6 -- PACE6 for Chi-square mixture ols -- Ordinary least squares estimator aic -- AIC estimator bic -- BIC estimator ric -- RIC estimator olsc -- Ordinary least squares subset selector with a threshold
-S <threshold value> Threshold value for the OLSC estimator
- 版本:
- $Revision: 5523 $
- 作者:
- Yong Wang (yongwang@cs.waikato.ac.nz), Gabi Schmidberger (gabi@cs.waikato.ac.nz)
- 另请参阅:
-
字段概要
字段 -
构造器概要
构造器 -
方法概要
修饰符和类型方法说明void
buildClassifier
(Instances data) Builds a pace regression model for the given data.boolean
checkForMissing
(Instance instance, Instances model) Checks if an instance has a missing value.double
classifyInstance
(Instance instance) Classifies the given instance using the linear regression function.double[]
Returns the coefficients for this linear model.Returns the tip text for this propertyReturns the tip text for this propertyReturns default capabilities of the classifier.boolean
getDebug()
Controls whether debugging output will be printedGets the estimatorString[]
Gets the current settings of the classifier.Returns the revision string.Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.double
Gets the threshold for olsc estimatorReturns a string describing this classifierReturns an enumeration describing the available options.static void
Generates a linear regression function predictor.int
Get the number of coefficients used in the modelvoid
setDebug
(boolean debug) Controls whether debugging output will be printedvoid
setEstimator
(SelectedTag estimator) Sets the estimator.void
setOptions
(String[] options) Parses a given list of options.void
setThreshold
(double newThreshold) Set threshold for the olsc estimatorReturns the tip text for this propertytoString()
Outputs the linear regression model as a string.从类继承的方法 weka.classifiers.Classifier
distributionForInstance, forName, makeCopies, makeCopy
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字段详细资料
-
TAGS_ESTIMATOR
estimator types
-
-
构造器详细资料
-
PaceRegression
public PaceRegression()
-
-
方法详细资料
-
globalInfo
Returns a string describing this classifier- 返回:
- a description of the classifier suitable for displaying in the explorer/experimenter gui
-
getTechnicalInformation
Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.- 指定者:
getTechnicalInformation
在接口中TechnicalInformationHandler
- 返回:
- the technical information about this class
-
getCapabilities
Returns default capabilities of the classifier.- 指定者:
getCapabilities
在接口中CapabilitiesHandler
- 覆盖:
getCapabilities
在类中Classifier
- 返回:
- the capabilities of this classifier
- 另请参阅:
-
buildClassifier
Builds a pace regression model for the given data.- 指定者:
buildClassifier
在类中Classifier
- 参数:
data
- the training data to be used for generating the linear regression function- 抛出:
Exception
- if the classifier could not be built successfully
-
checkForMissing
Checks if an instance has a missing value.- 参数:
instance
- the instancemodel
- the data- 返回:
- true if missing value is present
-
classifyInstance
Classifies the given instance using the linear regression function.- 覆盖:
classifyInstance
在类中Classifier
- 参数:
instance
- the test instance- 返回:
- the classification
- 抛出:
Exception
- if classification can't be done successfully
-
toString
Outputs the linear regression model as a string. -
listOptions
Returns an enumeration describing the available options.- 指定者:
listOptions
在接口中OptionHandler
- 覆盖:
listOptions
在类中Classifier
- 返回:
- an enumeration of all the available options.
-
setOptions
Parses a given list of options. Valid options are:-D Produce debugging output. (default no debugging output)
-E <estimator> The estimator can be one of the following: eb -- Empirical Bayes estimator for noraml mixture (default) nested -- Optimal nested model selector for normal mixture subset -- Optimal subset selector for normal mixture pace2 -- PACE2 for Chi-square mixture pace4 -- PACE4 for Chi-square mixture pace6 -- PACE6 for Chi-square mixture ols -- Ordinary least squares estimator aic -- AIC estimator bic -- BIC estimator ric -- RIC estimator olsc -- Ordinary least squares subset selector with a threshold
-S <threshold value> Threshold value for the OLSC estimator
- 指定者:
setOptions
在接口中OptionHandler
- 覆盖:
setOptions
在类中Classifier
- 参数:
options
- the list of options as an array of strings- 抛出:
Exception
- if an option is not supported
-
coefficients
public double[] coefficients()Returns the coefficients for this linear model.- 返回:
- the coefficients for this linear model
-
getOptions
Gets the current settings of the classifier.- 指定者:
getOptions
在接口中OptionHandler
- 覆盖:
getOptions
在类中Classifier
- 返回:
- an array of strings suitable for passing to setOptions
-
numParameters
public int numParameters()Get the number of coefficients used in the model- 返回:
- the number of coefficients
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debugTipText
Returns the tip text for this property- 覆盖:
debugTipText
在类中Classifier
- 返回:
- tip text for this property suitable for displaying in the explorer/experimenter gui
-
setDebug
public void setDebug(boolean debug) Controls whether debugging output will be printed- 覆盖:
setDebug
在类中Classifier
- 参数:
debug
- true if debugging output should be printed
-
getDebug
public boolean getDebug()Controls whether debugging output will be printed- 覆盖:
getDebug
在类中Classifier
- 返回:
- true if debugging output should be printed
-
estimatorTipText
Returns the tip text for this property- 返回:
- tip text for this property suitable for displaying in the explorer/experimenter gui
-
getEstimator
Gets the estimator- 返回:
- the estimator
-
setEstimator
Sets the estimator.- 参数:
estimator
- the new estimator
-
thresholdTipText
Returns the tip text for this property- 返回:
- tip text for this property suitable for displaying in the explorer/experimenter gui
-
setThreshold
public void setThreshold(double newThreshold) Set threshold for the olsc estimator- 参数:
newThreshold
- the threshold for the olsc estimator
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getThreshold
public double getThreshold()Gets the threshold for olsc estimator- 返回:
- the threshold
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getRevision
Returns the revision string.- 指定者:
getRevision
在接口中RevisionHandler
- 覆盖:
getRevision
在类中Classifier
- 返回:
- the revision
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main
Generates a linear regression function predictor.- 参数:
argv
- the options
-