def convSparseToShog(data,delFeature=False):
resFeat = SparseRealFeatures()
resFeat.create_sparse_feature_matrix(len(data))
for iRec in xrange(len(data)):
feat = data[iRec]["feature"]
resFeat.set_sparse_feature_vector(iRec,feat["ind"].astype('i4')-1,feat["val"].astype('f8'))
if delFeature:
data[iRec]["feature"] = None
return resFeat
def getSparseRealFeatures(self,sequences,method="frequences"):
maxSeqLen = max( ( len(seq) for seq in sequences ) )
kmer_ind = numpy.zeros(maxSeqLen,dtype='i8')
if method == 'frequences':
kmer_val = numpy.zeros(maxSeqLen,dtype='f4')
else:
kmer_val = numpy.zeros(maxSeqLen,dtype='i4')
kmerMethod = getattr(self,method)
resFeat = SparseRealFeatures()
resFeat.create_sparse_feature_matrix(len(sequences))
for iSeq in xrange(len(sequences)):
seq = sequences[iSeq]
if isinstance(seq,str):
seq = numpy.fromstring(seq,'S1')
self.process(seq)
(size,total) = kmerMethod(kmer_val,kmer_ind)
#print size, total, kmer_val[:10],kmer_ind[:10]
resFeat.set_sparse_feature_vector(iSeq,kmer_ind[:size].astype('i4')-1,kmer_val[:size].astype('f8'))
#pdb.set_trace()
return resFeat
