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SequenceScores, which can be used to score a Sequence, typically using some model assumptions.
See:
Description
| Interface Summary | |
|---|---|
| SequenceScore | This interface defines a scoring function that assigns a score to each input sequence. |
Provides all SequenceScores, which can be used to score a Sequence, typically using some model assumptions.
Important special cases of SequenceScores are DifferentiableSequenceScores, which can compute
the gradient with respect to their parameter for a given input sequence, including logistic regression, and StatisticalModels,
which can compute a proper (i.e., normalized) likelihood over the input space of sequences. These are located in the sub-package de.jstacs.sequenceScores.statisticalModels.
StatisticalModels can be further differentiated into TrainableStatisticalModels,
which can be learned from a single input DataSet, and DifferentiableStatisticalModels,
which define a proper likelihood but can also compute gradients like DifferentiableSequenceScores.
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