def distribution_ppwm_modular (fm_dna=traindna, order=3):
from shogun.Features import StringByteFeatures, StringCharFeatures, DNA
from shogun.Distribution import PositionalPWM
from numpy import array,e,log,exp
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_dna)
feats=StringByteFeatures(charfeat.get_alphabet())
feats.obtain_from_char(charfeat, order-1, order, 0, False)
ppwm=PositionalPWM()
ppwm.set_sigma(5.0)
ppwm.set_mean(10.0)
pwm=array([[0.0, 0.5, 0.1, 1.0],
[0.0, 0.5, 0.5, 0.0],
[1.0, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.0, 0.0]]);
ppwm.set_pwm(log(pwm))
#print ppwm.get_pwm()
ppwm.compute_w(20)
w= ppwm.get_w()
def distribution_ppwm_modular (fm_dna=traindna, order=3):
from shogun.Features import StringByteFeatures, StringCharFeatures, DNA
from shogun.Distribution import PositionalPWM
from numpy import array,e,log,exp
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_dna)
feats=StringByteFeatures(charfeat.get_alphabet())
feats.obtain_from_char(charfeat, order-1, order, 0, False)
ppwm=PositionalPWM()
ppwm.set_sigma(5.0)
ppwm.set_mean(10.0)
pwm=array([[0.0, 0.5, 0.1, 1.0],
[0.0, 0.5, 0.5, 0.0],
[1.0, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.0, 0.0]]);
ppwm.set_pwm(log(pwm))
print ppwm.get_pwm()
ppwm.compute_w(20)
w= ppwm.get_w()
def distribution_ppwm_modular (fm_dna=traindna, order=3):
from shogun.Features import StringByteFeatures, StringCharFeatures, DNA
from shogun.Distribution import PositionalPWM
from numpy import array,e,log,exp
charfeat=StringCharFeatures(DNA)
charfeat.set_features(fm_dna)
feats=StringByteFeatures(charfeat.get_alphabet())
feats.obtain_from_char(charfeat, order-1, order, 0, False)
L=20
k=3
sigma = 1;
mu = 4
ppwm=PositionalPWM()
ppwm.set_sigma(sigma)
ppwm.set_mean(mu)
pwm=array([[0.0, 0.5, 0.1, 1.0],
[0.0, 0.5, 0.5, 0.0],
[1.0, 0.0, 0.4, 0.0],
[0.0, 0.0, 0.0, 0.0]]);
pwm=array([[0.01,0.09,0.1],[0.09,0.01,0.1],[0.85,0.4,0.1],[0.05,0.5,0.7]])
ppwm.set_pwm(log(pwm))
#print(ppwm.get_pwm())
ppwm.compute_w(L)
w=ppwm.get_w()
#print(w)
#from pylab import *
#figure(1)
#pcolor(exp(w))
#pcolor(w)
#colorbar()
#figure(2)
ppwm.compute_scoring(1)
u=ppwm.get_scoring(0)
#pcolor(exp(u))
#show()
#ppwm=PositionalPWM(feats)
#ppwm.train()
#out_likelihood = histo.get_log_likelihood()
#out_sample = histo.get_log_likelihood_sample()
return w,u
