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.len = len(self.limits)
  52
  53       ps.limits = QPalmaDP.createDoubleArrayFromList(self.limits)
  54       ps.penalties = QPalmaDP.createDoubleArrayFromList(self.penalties)
  55
  56       ps.max_len = self.max_len
  57       ps.min_len = self.min_len
  58       ps.transform = 0
  59       ps.name = self.name
  60
  61       return ps
  62
  63
  64 def set_params_pa():
  65    h = plf()
  66    h.len = int(model.intron_len_bins)
  67    h.limits = model.intron_len_limits
  68    h.penalties = model.intron_len_penalties
  69    h.name = 'h'
  70    h.max_len   = int(max_intron_len)
  71    h.min_len   = int(min_intron_len)
  72    h.transform = model.intron_len_transform
  73
  74    d = plf()
  75    d.len = int(model.donor_bins)
  76    d.limits = model.donor_limits
  77    d.penalties = model.donor_penalties
  78    d.name = 'd'
  79    d.max_len   = 100
  80    d.min_len   = -100
  81
  82    a = plf()
  83    a.len = int(model.acceptor_bins)
  84    a.limits = model.acceptor_limits
  85    a.penalties = model.acceptor_penalties
  86    a.name = 'a'
  87    a.max_len   = 100
  88    a.min_len   = -100
  89
  90    mmatrix = model.substitution_matrix
  91
  92
  93 def set_param_palma(param, train_with_intronlengthinformation,\
  94                                                 min_intron_len=None, max_intron_len=None, min_svm_score=None, max_svm_score=None):
  95
  96    print 'Setting parameters ...'
  97
  98    if min_intron_len == None:
  99       if train_with_intronlengthinformation:
 100          min_intron_len=20
 101          max_intron_len=1000
 102       else:
 103          min_intron_len = 1
 104          max_intron_len = 2
 105
 106    if min_intron_len != None and max_intron_len != None:
 107       min_svm_score=-5
 108       max_svm_score=5
 109
 110    h = Plf()
 111    d = Plf()
 112    a = Plf()
 113
 114    ####################
 115    # Gapfunktion
 116    ####################
 117    h.limits    = logspace(math.log(min_intron_len,10),math.log(max_intron_len,10),30)
 118    h.penalties = param[0:30].flatten().tolist()[0]
 119    #h.transform = '+1'
 120    h.transform = ''
 121    h.name      = 'h'
 122    h.max_len   =  100000
 123    h.min_len   =  4
 124    h.id        = 1
 125    h.use_svm   = 0
 126    h.next_id   = 0
 127
 128
 129    ####################
 130    # Donorfunktion
 131    ####################
 132    d.limits    = linspace(min_svm_score,max_svm_score,30)
 133    d.penalties = param[30:60].flatten().tolist()[0]
 134    #d.transform = '+1'
 135    d.transform = ''
 136    d.name      = 'd'
 137    d.max_len   = 100
 138    d.min_len   = -100
 139    d.id        = 1
 140    d.use_svm   = 0
 141    d.next_id   = 0
 142
 143
 144    ####################
 145    # Acceptorfunktion
 146    ####################
 147    a.limits    = linspace(min_svm_score,max_svm_score,30)
 148    a.penalties = param[60:90].flatten().tolist()[0]
 149    #a.transform = '+1'
 150    a.transform = ''
 151    a.name      = 'a'
 152    a.max_len   = 100
 153    a.min_len   = -100
 154    a.id        = 1
 155    a.use_svm   = 0
 156    a.next_id   = 0
 157
 158
 159    ####################
 160    # Matchmatrix
 161    ####################
 162    mmatrix = numpy.matlib.mat(param[90:126])
 163    mmatrix.reshape(6,6)
 164
 165    return h,d,a,mmatrix
 166
 167 if __name__ == '__main__':
 168    #min_intron_len=20
 169    #max_intron_len=1000
 170    #print logspace(math.log(min_intron_len,10),math.log(max_intron_len,10),30)
 171
 172    min_svm_score=-5
 173    max_svm_score=5
 174    print linspace(min_svm_score,max_svm_score,30)