public class MotifDiscoveryAssessment extends Object
Constructor and Description |
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MotifDiscoveryAssessment() |
Modifier and Type | Method and Description |
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static ListResult |
assess(DataSet truth,
DataSet prediction,
int maxDiff)
This method computes the nucleotide and site measures.
|
static double[][] |
getSortedScoresForMotifAndFlanking(DataSet data,
DataSet pred,
String identifier)
Returns the scores read from the prediction
pred for the motif with identifier identifier and flanking sequences as annotated in
the DataSet data. |
static double[][] |
getSortedValuesForMotifAndFlanking(DataSet data,
double[][] values,
double offset,
double factor,
String identifier)
This method provides some score arrays that can be used in
AbstractPerformanceMeasure to determine some
curves or area under curves based on the values of the predictions. |
public static ListResult assess(DataSet truth, DataSet prediction, int maxDiff) throws Exception
truth
- the DataSet
annotated with the true annotationprediction
- annotated with the predicted annotationmaxDiff
- the maximal difference between predicted and true start position;
this value is used to determine the site measuresListResult
containing all NumericalResultSet
sException
- if something went wrongpublic static double[][] getSortedValuesForMotifAndFlanking(DataSet data, double[][] values, double offset, double factor, String identifier)
AbstractPerformanceMeasure
to determine some
curves or area under curves based on the values of the predictions. The scores are generated by offset+factor*values[i][j]
.data
- the datavalues
- the array of smoothed valuesoffset
- the offset that is added to the current valuesfactor
- the factor that is multiplied to the current valuesidentifier
- the identifier of the annotation of the positive classSequenceAnnotation.getIdentifier()
public static double[][] getSortedScoresForMotifAndFlanking(DataSet data, DataSet pred, String identifier)
pred
for the motif with identifier identifier
and flanking sequences as annotated in
the DataSet
data. The identifier
may be null
to obtain the scores for all motifs, irrespective of present identifiers.
The first dimension of the returned array contains the scores for the motif annotations, while the second dimension contains the scores of the flanking sequences.
Both dimensions are sorted and can be directly used in the methods of AbstractPerformanceMeasure
.
The scores for the predictions must be added to the LocatedSequenceAnnotationWithLength
representing the motifs as additional annotation using LocatedSequenceAnnotationWithLength.LocatedSequenceAnnotationWithLength(String, String, LocatedSequenceAnnotation[], Result...)
with the name of the annotation, i.e. the name of the corresponding Result
equal to "score".