2 # -*- coding: utf-8 -*-
9 stepSize
= intervalLength
/ n
13 interval
[i
] = a
+i
*stepSize
24 for i
in range(n
-2,0,-1):
25 interval
[i
] = interval
[i
+1] / math
.e
29 class Plf
: #means piecewise linear function
40 h
.len = int(model
.intron_len_bins
)
41 h
.limits
= model
.intron_len_limits
42 h
.penalties
= model
.intron_len_penalties
44 h
.max_len
= int(max_intron_len
)
45 h
.min_len
= int(min_intron_len
)
46 h
.transform
= model
.intron_len_transform
49 d
.len = int(model
.donor_bins
)
50 d
.limits
= model
.donor_limits
51 d
.penalties
= model
.donor_penalties
57 a
.len = int(model
.acceptor_bins
)
58 a
.limits
= model
.acceptor_limits
59 a
.penalties
= model
.acceptor_penalties
64 mmatrix
= model
.substitution_matrix
67 def set_param_palma(param
, train_with_intronlengthinformation
,\
68 min_intron_len
=None, max_intron_len
=None, min_svm_score
=None, max_svm_score
=None):
70 print 'Setting parameters ...'
72 if min_intron_len
== None:
73 if train_with_intronlengthinformation
:
80 if min_intron_len
!= None and max_intron_len
!= None:
92 h
.limits
= logspace(math
.log(min_intron_len
,10),math
.log(max_intron_len
,10),30)
93 h
.penalties
= param
[1:30]
107 d
.limits
= linspace(min_svm_score
,max_svm_score
,30)
108 d
.penalties
= param
[31:60]
122 a
.limits
= linspace(min_svm_score
,max_svm_score
,30)
123 a
.penalties
= param
[61:90]
137 mmatrix
= numpy
.matlib
.mat(param
[90:126])