public class LinearRegression extends AbstractClassifier implements OptionHandler, WeightedInstancesHandler
-S <number of selection method> Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
-C Do not try to eliminate colinear attributes.
-R <double> Set ridge parameter (default 1.0e-8).
-minimal Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data)
-additional-stats Output additional statistics.
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
Modifier and Type | Field and Description |
---|---|
static int |
SELECTION_GREEDY
Attribute selection method: Greedy method
|
static int |
SELECTION_M5
Attribute selection method: M5 method
|
static int |
SELECTION_NONE
Attribute selection method: No attribute selection
|
static Tag[] |
TAGS_SELECTION
Attribute selection methods
|
BATCH_SIZE_DEFAULT, NUM_DECIMAL_PLACES_DEFAULT
Constructor and Description |
---|
LinearRegression() |
Modifier and Type | Method and Description |
---|---|
java.lang.String |
attributeSelectionMethodTipText()
Returns the tip text for this property
|
void |
buildClassifier(Instances data)
Builds a regression model for the given data.
|
double |
classifyInstance(Instance instance)
Classifies the given instance using the linear regression function.
|
double[] |
coefficients()
Returns the coefficients for this linear model.
|
java.lang.String |
eliminateColinearAttributesTipText()
Returns the tip text for this property
|
SelectedTag |
getAttributeSelectionMethod()
Gets the method used to select attributes for use in the linear regression.
|
Capabilities |
getCapabilities()
Returns default capabilities of the classifier.
|
boolean |
getEliminateColinearAttributes()
Get the value of EliminateColinearAttributes.
|
boolean |
getMinimal()
Returns whether to be more memory conservative or being able to output the
model as string.
|
java.lang.String[] |
getOptions()
Gets the current settings of the classifier.
|
boolean |
getOutputAdditionalStats()
Get whether to output additional statistics (such as std.
|
java.lang.String |
getRevision()
Returns the revision string.
|
double |
getRidge()
Get the value of Ridge.
|
boolean |
getUseQRDecomposition()
Get whether to use QR decomposition.
|
java.lang.String |
globalInfo()
Returns a string describing this classifier
|
java.util.Enumeration<Option> |
listOptions()
Returns an enumeration describing the available options.
|
static void |
main(java.lang.String[] argv)
Generates a linear regression function predictor.
|
java.lang.String |
minimalTipText()
Returns the tip text for this property.
|
int |
numParameters()
Get the number of coefficients used in the model
|
java.lang.String |
outputAdditionalStatsTipText()
Returns the tip text for this property.
|
java.lang.String |
ridgeTipText()
Returns the tip text for this property
|
void |
setAttributeSelectionMethod(SelectedTag method)
Sets the method used to select attributes for use in the linear regression.
|
void |
setEliminateColinearAttributes(boolean newEliminateColinearAttributes)
Set the value of EliminateColinearAttributes.
|
void |
setMinimal(boolean value)
Sets whether to be more memory conservative or being able to output the
model as string.
|
void |
setOptions(java.lang.String[] options)
Parses a given list of options.
|
void |
setOutputAdditionalStats(boolean additional)
Set whether to output additional statistics (such as std.
|
void |
setRidge(double newRidge)
Set the value of Ridge.
|
void |
setUseQRDecomposition(boolean useQR)
Set whether to use QR decomposition.
|
java.lang.String |
toString()
Outputs the linear regression model as a string.
|
void |
turnChecksOff()
Turns off checks for missing values, etc.
|
void |
turnChecksOn()
Turns on checks for missing values, etc.
|
java.lang.String |
useQRDecompositionTipText()
Returns the tip text for this property.
|
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
makeCopy
public static final int SELECTION_M5
public static final int SELECTION_NONE
public static final int SELECTION_GREEDY
public static final Tag[] TAGS_SELECTION
public static void main(java.lang.String[] argv)
argv
- the optionspublic java.lang.String globalInfo()
public Capabilities getCapabilities()
getCapabilities
in interface Classifier
getCapabilities
in interface CapabilitiesHandler
getCapabilities
in class AbstractClassifier
Capabilities
public void buildClassifier(Instances data) throws java.lang.Exception
buildClassifier
in interface Classifier
data
- the training data to be used for generating the linear
regression functionjava.lang.Exception
- if the classifier could not be built successfullypublic double classifyInstance(Instance instance) throws java.lang.Exception
classifyInstance
in interface Classifier
classifyInstance
in class AbstractClassifier
instance
- the test instancejava.lang.Exception
- if classification can't be done successfullypublic java.lang.String toString()
toString
in class java.lang.Object
public java.util.Enumeration<Option> listOptions()
listOptions
in interface OptionHandler
listOptions
in class AbstractClassifier
public double[] coefficients()
public java.lang.String[] getOptions()
getOptions
in interface OptionHandler
getOptions
in class AbstractClassifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
-S <number of selection method> Set the attribute selection method to use. 1 = None, 2 = Greedy. (default 0 = M5' method)
-C Do not try to eliminate colinear attributes.
-R <double> Set ridge parameter (default 1.0e-8).
-minimal Conserve memory, don't keep dataset header and means/stdevs. Model cannot be printed out if this option is enabled. (default: keep data)
-additional-stats Output additional statistics.
-output-debug-info If set, classifier is run in debug mode and may output additional info to the console
-do-not-check-capabilities If set, classifier capabilities are not checked before classifier is built (use with caution).
-use-qr If set, QR decomposition will be used to find coefficients.
setOptions
in interface OptionHandler
setOptions
in class AbstractClassifier
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String ridgeTipText()
public double getRidge()
public void setRidge(double newRidge)
newRidge
- Value to assign to Ridge.public java.lang.String eliminateColinearAttributesTipText()
public boolean getEliminateColinearAttributes()
public void setEliminateColinearAttributes(boolean newEliminateColinearAttributes)
newEliminateColinearAttributes
- Value to assign to
EliminateColinearAttributes.public int numParameters()
public java.lang.String attributeSelectionMethodTipText()
public SelectedTag getAttributeSelectionMethod()
public void setAttributeSelectionMethod(SelectedTag method)
method
- the attribute selection method to use.public java.lang.String minimalTipText()
public boolean getMinimal()
public void setMinimal(boolean value)
value
- if true memory will be conservedpublic java.lang.String outputAdditionalStatsTipText()
public boolean getOutputAdditionalStats()
public void setOutputAdditionalStats(boolean additional)
additional
- true if additional stats are to be outputpublic java.lang.String useQRDecompositionTipText()
public boolean getUseQRDecomposition()
public void setUseQRDecomposition(boolean useQR)
useQR
- true if QR decomposition is to be usedpublic void turnChecksOff()
public void turnChecksOn()
public java.lang.String getRevision()
getRevision
in interface RevisionHandler
getRevision
in class AbstractClassifier