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java.lang.Object | +----weka.clusterers.ClusterEvaluation
Valid options are:
-t
-T
-d
-l
-p
-x
-c
Specify the training file.
Specify the test file to apply clusterer to.
Specify output file.
Specifiy input file.
Output predictions. Predictions are for the training file if only the
training file is specified, otherwise they are for the test file. The range
specifies attribute values to be output with the predictions.
Use '-p 0' for none.
Set the number of folds for a cross validation of the training data.
Cross validation can only be done for distribution clusterers and will
be performed if the test file is missing.
Set the class attribute. If set, then class based evaluation of clustering
is performed.
ClusterEvaluation()
clusterResultsToString()
crossValidateModel(String, Instances, int, String[])
evaluateClusterer(Clusterer, String[])
evaluateClusterer(Instances)
getClassesToClusters()
getClusterAssignments()
getNumClusters()
main(String[])
setClusterer(Clusterer)
setDoXval(boolean)
setFolds(int)
setSeed(int)
ClusterEvaluation
public ClusterEvaluation()
Constructor. Sets defaults for each member variable. Default Clusterer
is EM.
setClusterer
public void setClusterer(Clusterer clusterer)
set the clusterer
clusterer
- the clusterer to use
setDoXval
public void setDoXval(boolean x)
set whether or not to do cross validation
x
- true if cross validation is to be done
setFolds
public void setFolds(int folds)
set the number of folds to use for cross validation
folds
- the number of folds
setSeed
public void setSeed(int s)
set the seed to use for cross validation
s
- the seed.
clusterResultsToString
public java.lang.String clusterResultsToString()
return the results of clustering.
getNumClusters
public int getNumClusters()
Return the number of clusters found for the most recent call to
evaluateClusterer
getClusterAssignments
public double[] getClusterAssignments()
Return an array of cluster assignments corresponding to the most
recent set of instances clustered.
getClassesToClusters
public int[] getClassesToClusters()
Return the array (ordered by cluster number) of minimum error class to
cluster mappings
evaluateClusterer
public void evaluateClusterer(Instances test) throws java.lang.Exception
Evaluate the clusterer on a set of instances. Calculates clustering
statistics and stores cluster assigments for the instances in
m_clusterAssignments
test
- the set of instances to cluster
evaluateClusterer
public static java.lang.String evaluateClusterer(Clusterer clusterer,
java.lang.String options[]) throws java.lang.Exception
Evaluates a clusterer with the options given in an array of
strings. It takes the string indicated by "-t" as training file, the
string indicated by "-T" as test file.
If the test file is missing, a stratified ten-fold
cross-validation is performed (distribution clusterers only).
Using "-x" you can change the number of
folds to be used, and using "-s" the random seed.
If the "-p" option is present it outputs the classification for
each test instance. If you provide the name of an object file using
"-l", a clusterer will be loaded from the given file. If you provide the
name of an object file using "-d", the clusterer built from the
training data will be saved to the given file.
clusterer
- machine learning clusterer
options
- the array of string containing the options
crossValidateModel
public static java.lang.String crossValidateModel(java.lang.String clustererString,
Instances data,
int numFolds,
java.lang.String options[]) throws java.lang.Exception
Performs a cross-validation
for a distribution clusterer on a set of instances.
clustererString
- a string naming the class of the clusterer
data
- the data on which the cross-validation is to be
performed
numFolds
- the number of folds for the cross-validation
options
- the options to the clusterer
main
public static void main(java.lang.String args[])
Main method for testing this class.
args
- the options
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