public class RepeatedSubSamplingExperiment extends ClassifierAssessment<RepeatedSubSamplingAssessParameterSet>
myAbstractClassifier, myModel, myTempMeanResultSets, skipLastClassifiersDuringClassifierTraining
Modifier  Constructor and Description 


RepeatedSubSamplingExperiment(AbstractClassifier... aCs)
Creates a new
RepeatedSubSamplingExperiment from a set of
AbstractClassifier s. 

RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
This constructor allows to assess a collection of given
AbstractClassifier s and those constructed using the given
TrainableStatisticalModel s by a
RepeatedSubSamplingExperiment . 
protected 
RepeatedSubSamplingExperiment(AbstractClassifier[] aCs,
TrainableStatisticalModel[][] aMs,
boolean buildClassifiersByCrossProduct,
boolean checkAlphabetConsistencyAndLength)
Creates a new
RepeatedSubSamplingExperiment from an array of
AbstractClassifier s and a twodimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. 

RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct,
TrainableStatisticalModel[]... aMs)
Creates a new
RepeatedSubSamplingExperiment from a set of
TrainableStatisticalModel s. 
Modifier and Type  Method and Description 

protected void 
evaluateClassifier(NumericalPerformanceMeasureParameterSet mp,
RepeatedSubSamplingAssessParameterSet assessPS,
DataSet[] s,
double[][] weights,
ProgressUpdater pU)
Evaluates the classifier.

RepeatedSubSamplingAssessParameterSet 
getAssessParameterSet()
This method returns an instance of
ClassifierAssessmentAssessParameterSet that can be used in the assess methods. 
assess, assess, assess, assess, getClassifier, getNameOfAssessment, prepareAssessment, test, train
protected RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, TrainableStatisticalModel[][] aMs, boolean buildClassifiersByCrossProduct, boolean checkAlphabetConsistencyAndLength) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
RepeatedSubSamplingExperiment
from an array of
AbstractClassifier
s and a twodimensional array of TrainableStatisticalModel
s, which are combined to additional classifiers. If
buildClassifiersByCrossProduct
is true
, the
crossproduct of all TrainableStatisticalModel
s in aMs
is built to
obtain these classifiers.aCs
 the predefined classifiersaMs
 the TrainableStatisticalModel
s that are used to build additional
classifiersbuildClassifiersByCrossProduct
 Determines how classifiers are constructed using the given
models. Suppose a kclass problem. In this case, each
classifier is supposed to consist of k models, one responsible
for each class. S_i
be the set of all models in
aMs[i]
. Let S
be the set
S_1 x S_2 x ... x S_k
(crossproduct).true
: all possible classifiers consisting of a
subset (set of k models) of S are constructed false
: one classifier consisting of the models
aMs[0][i]
,aMs[1][i]
,...,
aMs[k][i]
for a fixed i
is
constructed. In this case, all second dimensions of
aMs
have to be equal, say m
. In
total m
classifiers are constructed.checkAlphabetConsistencyAndLength
 indicates if alphabets and lengths shall be checked for
consistencyIllegalArgumentException
 if the classifiers have different lengthsWrongAlphabetException
 if the classifiers use different alphabetsCloneNotSupportedException
 if something went wrong while cloningClassDimensionException
 if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier[],
TrainableStatisticalModel[][], boolean, boolean)
public RepeatedSubSamplingExperiment(AbstractClassifier... aCs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
RepeatedSubSamplingExperiment
from a set of
AbstractClassifier
s.aCs
 contains the classifiers to be assessed.assess( ... )
.s
in order (s[0]
contains foreground data, s[1]
contains
background data)
IllegalArgumentException
 if the classifiers have different lengthsWrongAlphabetException
 if not all given classifiers are defined on the same
AlphabetContainer
CloneNotSupportedException
 if something went wrong while cloningClassDimensionException
 if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier...)
public RepeatedSubSamplingExperiment(boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
RepeatedSubSamplingExperiment
from a set of
TrainableStatisticalModel
s. The argument buildClassifiersByCrossProduct
determines how these TrainableStatisticalModel
s are combined to classifiers.buildClassifiersByCrossProduct
 S_i
be the set of all models in
aMs[i]
. Let S
be the set
S_1 x S_2 x ... x S_k
(crossproduct).true
: all possible classifiers consisting of a
subset (set of k models) of S
are constructed false
: one classifier consisting of the models
aMs[0][i]
,aMs[1][i]
,...,
aMs[k][i]
for a fixed i
is
constructed. In this case, all second dimensions of
aMs
have to be equal, say m
. In
total m
classifiers are constructed.aMs
 aMs[i]
) contains the
models according to class i
.s
... . s
in order (s[0]
contains foreground data, s[1]
contains
background data)
IllegalArgumentException
 if the classifiers have different lengthsWrongAlphabetException
 if not all given classifiers are defined on the same
AlphabetContainer
CloneNotSupportedException
 if something went wrong while cloningClassDimensionException
 if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(boolean, TrainableStatisticalModel[][])
public RepeatedSubSamplingExperiment(AbstractClassifier[] aCs, boolean buildClassifiersByCrossProduct, TrainableStatisticalModel[]... aMs) throws IllegalArgumentException, WrongAlphabetException, CloneNotSupportedException, ClassDimensionException
AbstractClassifier
s and those constructed using the given
TrainableStatisticalModel
s by a
RepeatedSubSamplingExperiment
. aCs
 contains some AbstractClassifier
that should be
assessed in addition to the AbstractClassifier
constructed using the given
TrainableStatisticalModel
sbuildClassifiersByCrossProduct
 S_i
be the set of all models in
aMs[i]
. Let S
be the set
S_1 x S_2 x ... x S_k
(crossproduct).true
: all possible classifiers consisting of a
subset (set of k models) of S
are constructed false
: one classifier consisting of the models
aMs[0][i]
,aMs[1][i]
,...,
aMs[k][i]
for a fixed i
is
constructed. In this case, all second dimensions of
aMs
have to be equal, say m
. In
total m
classifiers are constructed.aMs
 aMs[i]
) contains the
models according to class i
.s
... . s
in order (s[0]
contains foreground data, s[1]
contains
background data)
IllegalArgumentException
 if the classifiers have different lengthsWrongAlphabetException
 if not all given classifiers are defined on the same
AlphabetContainer
CloneNotSupportedException
 if something went wrong while cloningClassDimensionException
 if there is something wrong with the class dimension of the
classifierClassifierAssessment.ClassifierAssessment(AbstractClassifier[],
boolean, TrainableStatisticalModel[][])
protected void evaluateClassifier(NumericalPerformanceMeasureParameterSet mp, RepeatedSubSamplingAssessParameterSet assessPS, DataSet[] s, double[][] weights, ProgressUpdater pU) throws IllegalArgumentException, Exception
evaluateClassifier
in class ClassifierAssessment<RepeatedSubSamplingAssessParameterSet>
mp
 defines which performance measures are used to assess
classifierspU
 allows to abort this assessment by setting
pU.isCancelled()=true
. The last step of this
assessment is continued afterwards this assessment stops.s
 contains the data to be used for assessment. The order of the
data sets is important. s
. If
the models are trained directly, the order of given models
during initiation of this assessment object determines, which
data set will be used for training which model. In general the
first model will be trained using the first data set in
s
... . s
in order (s[0]
contains foreground data, s[1]
contains
background data)
assessPS
 contains parameters for a run of this
RepeatedSubSamplingExperiment
. Must be of type
RepeatedSubSamplingExperiment
.weights
 the (nonnegative) weights for the data;
weight for each data set (first dimension) and each sequence (second dimension),
can be null
which is the same as weight 1 for all sequences in all data setsIllegalArgumentException
 if the given assessPS
is not of type
RepeatedSubSamplingExperiment
Exception
 if something went wrongpublic RepeatedSubSamplingAssessParameterSet getAssessParameterSet() throws Exception
ClassifierAssessment
ClassifierAssessmentAssessParameterSet
that can be used in the assess
methods.getAssessParameterSet
in class ClassifierAssessment<RepeatedSubSamplingAssessParameterSet>
ClassifierAssessmentAssessParameterSet
that can be used in the assess
methods.Exception
 if the parameter set could not be created properlyClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, DataSet...)
,
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, ProgressUpdater, DataSet[])
,
ClassifierAssessment.assess(NumericalPerformanceMeasureParameterSet, ClassifierAssessmentAssessParameterSet, ProgressUpdater, DataSet[], double[][])