python/set_param_palma.py

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