Convert motifs and a sequence into neural network representations.
This is designed to convert a sequence into a representation that
can be fed as an input into a neural network. It does this by
representing a sequence based the motifs present.
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Methods
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__init__
representation
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__init__
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__init__ ( self, motifs )
Initialize an input producer with motifs to look for.
Arguments:
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Exceptions
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ValueError("Motif %s given, expected motif size %s" %( motif, self._motif_size ) )
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representation
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representation ( self, sequence )
Represent a sequence as a set of motifs.
Arguments:
This converts a sequence into a representation based on the motifs.
The representation is returned as a list of the relative amount of
each motif (number of times a motif occured divided by the total
number of motifs in the sequence). The values in the list correspond
to the input order of the motifs specified in the initializer.
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