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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.lazy.IBk
K-nearest neighbours classifier. For more information, see
Aha, D., and D. Kibler (1991) "Instance-based learning algorithms", Machine Learning, vol.6, pp. 37-66.
Valid options are:
-K num
Set the number of nearest neighbors to use in prediction
(default 1)
-W num
Set a fixed window size for incremental train/testing. As
new training instances are added, oldest instances are removed
to maintain the number of training instances at this size.
(default no window)
-I
Neighbors will be weighted by the inverse of their distance
when voting. (default equal weighting)
-F
Neighbors will be weighted by their similarity when voting.
(default equal weighting)
-X
Select the number of neighbors to use by hold-one-out cross
validation, with an upper limit given by the -K option.
-E
When k is selected by cross-validation for numeric class attributes,
minimize mean-squared error. (default mean absolute error)
-N
Turns off normalization.
| Field Summary | |
static Tag[] |
TAGS_WEIGHTING
|
static int |
WEIGHT_INVERSE
|
static int |
WEIGHT_NONE
|
static int |
WEIGHT_SIMILARITY
|
| Constructor Summary | |
IBk()
IB1 classifer. |
|
IBk(int k)
IBk classifier. |
|
| Method Summary | |
void |
buildClassifier(Instances instances)
Generates the classifier. |
java.lang.String |
crossValidateTipText()
Returns the tip text for this property |
java.lang.String |
distanceWeightingTipText()
Returns the tip text for this property |
double[] |
distributionForInstance(Instance instance)
Calculates the class membership probabilities for the given test instance. |
double |
getAttributeMax(int index)
Get an attributes maximum observed value |
double |
getAttributeMin(int index)
Get an attributes minimum observed value |
boolean |
getCrossValidate()
Gets whether hold-one-out cross-validation will be used to select the best k value |
SelectedTag |
getDistanceWeighting()
Gets the distance weighting method used. |
int |
getKNN()
Gets the number of neighbours the learner will use. |
boolean |
getMeanSquared()
Gets whether the mean squared error is used rather than mean absolute error when doing cross-validation. |
boolean |
getNoNormalization()
Gets whether normalization is turned off. |
int |
getNumTraining()
Get the number of training instances the classifier is currently using |
java.lang.String[] |
getOptions()
Gets the current settings of IBk. |
int |
getWindowSize()
Gets the maximum number of instances allowed in the training pool. |
java.lang.String |
globalInfo()
Returns a string describing classifier |
java.lang.String |
KNNTipText()
Returns the tip text for this property |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options. |
static void |
main(java.lang.String[] argv)
Main method for testing this class. |
java.lang.String |
meanSquaredTipText()
Returns the tip text for this property |
java.lang.String |
noNormalizationTipText()
Returns the tip text for this property |
void |
setCrossValidate(boolean newCrossValidate)
Sets whether hold-one-out cross-validation will be used to select the best k value |
void |
setDistanceWeighting(SelectedTag newMethod)
Sets the distance weighting method used. |
void |
setKNN(int k)
Set the number of neighbours the learner is to use. |
void |
setMeanSquared(boolean newMeanSquared)
Sets whether the mean squared error is used rather than mean absolute error when doing cross-validation. |
void |
setNoNormalization(boolean v)
Set whether normalization is turned off. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setWindowSize(int newWindowSize)
Sets the maximum number of instances allowed in the training pool. |
java.lang.String |
toString()
Returns a description of this classifier. |
void |
updateClassifier(Instance instance)
Adds the supplied instance to the training set |
java.lang.String |
windowSizeTipText()
Returns the tip text for this property |
| Methods inherited from class weka.classifiers.Classifier |
classifyInstance, debugTipText, forName, getDebug, makeCopies, setDebug |
| Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait |
| Field Detail |
public static final int WEIGHT_NONE
public static final int WEIGHT_INVERSE
public static final int WEIGHT_SIMILARITY
public static final Tag[] TAGS_WEIGHTING
| Constructor Detail |
public IBk(int k)
k - the number of nearest neighbors to use for predictionpublic IBk()
| Method Detail |
public java.lang.String globalInfo()
public java.lang.String KNNTipText()
public void setKNN(int k)
k - the number of neighbours.public int getKNN()
public java.lang.String windowSizeTipText()
public int getWindowSize()
public void setWindowSize(int newWindowSize)
newWindowSize - Value to assign to WindowSize.public java.lang.String distanceWeightingTipText()
public SelectedTag getDistanceWeighting()
public void setDistanceWeighting(SelectedTag newMethod)
public java.lang.String meanSquaredTipText()
public boolean getMeanSquared()
public void setMeanSquared(boolean newMeanSquared)
newMeanSquared - true if so.public java.lang.String crossValidateTipText()
public boolean getCrossValidate()
public void setCrossValidate(boolean newCrossValidate)
newCrossValidate - true if cross-validation should be used.public int getNumTraining()
public double getAttributeMin(int index)
throws java.lang.Exception
java.lang.Exception
public double getAttributeMax(int index)
throws java.lang.Exception
java.lang.Exceptionpublic java.lang.String noNormalizationTipText()
public boolean getNoNormalization()
public void setNoNormalization(boolean v)
v - Value to assign to DontNormalize.
public void buildClassifier(Instances instances)
throws java.lang.Exception
buildClassifier in class Classifierinstances - set of instances serving as training data
java.lang.Exception - if the classifier has not been generated successfully
public void updateClassifier(Instance instance)
throws java.lang.Exception
updateClassifier in interface UpdateableClassifierinstance - the instance to add
java.lang.Exception - if instance could not be incorporated
successfully
public double[] distributionForInstance(Instance instance)
throws java.lang.Exception
distributionForInstance in class Classifierinstance - the instance to be classified
java.lang.Exception - if an error occurred during the predictionpublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class Classifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-K num
Set the number of nearest neighbors to use in prediction
(default 1)
-W num
Set a fixed window size for incremental train/testing. As
new training instances are added, oldest instances are removed
to maintain the number of training instances at this size.
(default no window)
-I
Neighbors will be weighted by the inverse of their distance
when voting. (default equal weighting)
-F
Neighbors will be weighted by their similarity when voting.
(default equal weighting)
-X
Select the number of neighbors to use by hold-one-out cross
validation, with an upper limit given by the -K option.
-E
When k is selected by cross-validation for numeric class attributes,
minimize mean-squared error. (default mean absolute error)
setOptions in interface OptionHandlersetOptions in class Classifieroptions - the list of options as an array of strings
java.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class Classifierpublic java.lang.String toString()
public static void main(java.lang.String[] argv)
argv - should contain command line options (see setOptions)
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