python/set_param_palma.py

   1 #!/usr/bin/env python
   2 # -*- coding: utf-8 -*-
   3
   4 import math
   5 import numpy.matlib
   6 import QPalmaDP
   7 import pdb
   8
   9 def linspace(a,b,n):
  10    intervalLength = b-a
  11    stepSize = 1.0*intervalLength / (n-1)
  12
  13    interval = [0]*n
  14    interval[0] = a
  15    interval[-1] = b
  16    for i in range(1,n-1):
  17       interval[i] = a+(i*stepSize)
  18
  19    return interval
  20
  21 def logspace(a,b,n):
  22    interval = [0]*n
  23    begin = 10.0**a
  24    end = 10.0**b
  25    intervalSize = 1.0*(b-a)/(n-1)
  26    interval[0] = begin
  27    interval[-1] = end
  28
  29    for i in range(1,n-1):
  30       interval[i] = 10**(a+i*intervalSize)
  31
  32    return interval
  33
  34 def log10(x):
  35    return math.log(x)/math.log(10)
  36
  37 class Plf: #means piecewise linear function
  38
  39    def __init_(self):
  40       self.len = 0
  41       self.limits = []
  42       self.penalties = []
  43       self.transform = ''
  44       self.name = ''
  45       self.max_len = 0
  46       self.min_len = 0
  47
  48    def convert2SWIG(self):
  49       ps = QPalmaDP.penalty_struct()
  50
  51       ps.limits = QPalmaDP.createDoubleArrayFromList(self.limits)
  52       ps.penalties = QPalmaDP.createDoubleArrayFromList([elem[0] for elem in self.penalties.tolist()])
  53
  54       ps.max_len = self.max_len
  55       ps.min_len = self.min_len
  56       ps.transform = 0
  57       ps.name = self.name
  58
  59       return ps
  60
  61
  62 def set_params_pa():
  63    h = plf()
  64    h.len = int(model.intron_len_bins)
  65    h.limits = model.intron_len_limits
  66    h.penalties = model.intron_len_penalties
  67    h.name = 'h'
  68    h.max_len   = int(max_intron_len)
  69    h.min_len   = int(min_intron_len)
  70    h.transform = model.intron_len_transform
  71
  72    d = plf()
  73    d.len = int(model.donor_bins)
  74    d.limits = model.donor_limits
  75    d.penalties = model.donor_penalties
  76    d.name = 'd'
  77    d.max_len   = 100
  78    d.min_len   = -100
  79
  80    a = plf()
  81    a.len = int(model.acceptor_bins)
  82    a.limits = model.acceptor_limits
  83    a.penalties = model.acceptor_penalties
  84    a.name = 'a'
  85    a.max_len   = 100
  86    a.min_len   = -100
  87
  88    mmatrix = model.substitution_matrix
  89
  90
  91 def set_param_palma(param, train_with_intronlengthinformation,\
  92                                                 min_intron_len=None, max_intron_len=None, min_svm_score=None, max_svm_score=None):
  93
  94    print 'Setting parameters ...'
  95
  96    if min_intron_len == None:
  97       if train_with_intronlengthinformation:
  98          min_intron_len=20
  99          max_intron_len=1000
 100       else:
 101          min_intron_len = 1
 102          max_intron_len = 2
 103
 104    if min_intron_len != None and max_intron_len != None:
 105       min_svm_score=-5
 106       max_svm_score=5
 107
 108    h = Plf()
 109    d = Plf()
 110    a = Plf()
 111
 112    ####################
 113    # Gapfunktion
 114    ####################
 115    h.limits    = logspace(math.log(min_intron_len,10),math.log(max_intron_len,10),30)
 116    h.penalties = param[0:30]
 117    #h.transform = '+1'
 118    h.transform = ''
 119    h.name      = 'h'
 120    h.max_len   =  100000
 121    h.min_len   =  4
 122    h.id        = 1
 123    h.use_svm   = 0
 124    h.next_id   = 0
 125
 126
 127    ####################
 128    # Donorfunktion
 129    ####################
 130    d.limits    = linspace(min_svm_score,max_svm_score,30)
 131    d.penalties = param[30:60]
 132    #d.transform = '+1'
 133    d.transform = ''
 134    d.name      = 'd'
 135    d.max_len   = 100
 136    d.min_len   = -100
 137    d.id        = 1
 138    d.use_svm   = 0
 139    d.next_id   = 0
 140
 141
 142    ####################
 143    # Acceptorfunktion
 144    ####################
 145    a.limits    = linspace(min_svm_score,max_svm_score,30)
 146    a.penalties = param[60:90]
 147    #a.transform = '+1'
 148    a.transform = ''
 149    a.name      = 'a'
 150    a.max_len   = 100
 151    a.min_len   = -100
 152    a.id        = 1
 153    a.use_svm   = 0
 154    a.next_id   = 0
 155
 156
 157    ####################
 158    # Matchmatrix
 159    ####################
 160    mmatrix = numpy.matlib.mat(param[90:126])
 161    mmatrix.reshape(6,6)
 162
 163    return h,d,a,mmatrix
 164
 165 if __name__ == '__main__':
 166    #min_intron_len=20
 167    #max_intron_len=1000
 168    #print logspace(math.log(min_intron_len,10),math.log(max_intron_len,10),30)
 169
 170    min_svm_score=-5
 171    max_svm_score=5
 172    print linspace(min_svm_score,max_svm_score,30)