def eval(repeats,size):
result=[]
for i in range(repeats):
graphs=get_sequences_with_names(size=size, rand=(i+3)*10)
zz=fit_sample(graphs)
z=[b for a ,b in zz]
cmpath='../%s.cm' % RFAM
result+=rna.infernal_checker(z,cmfile=cmpath, cmsearchbinarypath='../toolsdata/cmsearch')
a = numpy.array(result)
mean = numpy.mean(a, axis=0)
std = numpy.std(a, axis=0)
print 'size:%d mean:%f std:%f' % (size,mean,std)
return mean,std
def eval(repeats, size):
result = []
for i in range(repeats):
graphs = get_sequences_with_names(size=size, rand=(i + 3) * 10)
zz = fit_sample(graphs)
z = [b for a, b in zz]
cmpath = '../%s.cm' % RFAM
result += rna.infernal_checker(
z, cmfile=cmpath, cmsearchbinarypath='../toolsdata/cmsearch')
a = numpy.array(result)
mean = numpy.mean(a, axis=0)
std = numpy.std(a, axis=0)
print 'size:%d mean:%f std:%f' % (size, mean, std)
return mean, std
def evaluate(repeats, size, fitsample, RFAM,inputdict,debug):
means = []
stds = []
for i in range(repeats):
if debug: print 'start rep'
sequences,void = utils.get_seq_tups(RFAM+'.fa',size,1)
zz = fitsample(sequences,inputdict)
# print zz[:3]
zz=[b for a ,b in zz]
result = rna.infernal_checker(zz, cmfile='../toolsdata/%s.cm' % RFAM,
cmsearchbinarypath='../toolsdata/cmsearch')
a = np.array(result)
means.append(np.mean(a, axis=0))
stds.append(np.std(a, axis=0))
means.sort()
stds.sort()
#print (size, means, stds)
return means[repeats / 2] * 100, stds[repeats / 2] * 100
def evaluate(repeats, size, fitsample):
print 'eval:',
means = []
stds = []
for i in range(repeats):
sequences = get_sequences_with_names(size=size, rand=10)
zz = fitsample(sequences)
# print zz[:3]
# z=[b for a ,b in zz]
result = rna.infernal_checker(
zz,
cmfile='../toolsdata/%s.cm' % RFAM,
cmsearchbinarypath='../toolsdata/cmsearch')
a = np.array(result)
means.append(np.mean(a, axis=0))
stds.append(np.std(a, axis=0))
means.sort()
stds.sort()
print(size, means, stds)
return [means[repeats / 2] * 100, stds[repeats / 2] * 100]