def constructLikelihoodDiff(self, high_pickle_fn, low_pickle_fn):
"""
calculate difference of the conditional prob of whole data set given low and high class
"""
condi_dist_h = nb.loadCondiDistribution(high_pickle_fn)
condi_dist_l = nb.loadCondiDistribution(low_pickle_fn)
high_pdf = nb.buildPdf(condi_dist_h)
low_pdf = nb.buildPdf(condi_dist_l)
ener = self.all_set[:, 1:]
log_condi_predicted_high = nb.calculateConditionalProb(ener, high_pdf)
log_condi_predicted_low = nb.calculateConditionalProb(ener, low_pdf)
self.likelihood_diff = log_condi_predicted_low - log_condi_predicted_high
def calculateLikelihoodDiff():
h5_path = 'all_decoy.h5'
sub_path = 'noncentralized_path/low_decoy'
ener_matx = loadEnerMatx(h5_path, sub_path)
condi_dist_fn = 'low.dist'
low_pdf = nb.loadCondiDistribution(condi_dist_fn)
low_pdf = nb.buildPdf(low_pdf)
condi_dist_fn = 'high.dist'
high_pdf = nb.loadCondiDistribution(condi_dist_fn)
high_pdf = nb.buildPdf(high_pdf)
likelihood_low_diff = nb.getLikelihoodDiff(ener_matx, high_pdf, low_pdf)
sub_path = 'noncentralized_path/high_decoy'
ener_matx = loadEnerMatx(h5_path, sub_path)
likelihood_high_diff = nb.getLikelihoodDiff(ener_matx, high_pdf, low_pdf)
return likelihood_high_diff, likelihood_low_diff
# h = np.loadtxt('06ff_high_decoy.mat', delimiter=' ')
h5_path = 'all_decoy.h5'
sub_path = 'noncentralized_path/low_decoy'
condi_dist_fn = 'low.dist'
f = h5py.File(h5_path)
dset = f[sub_path][()]
ener_matx = dset[:, 1:]
condi_dist_h = nb.loadCondiDistribution(condi_dist_fn)
h = ener_matx
total_samples = 1000
high_pdf = nb.buildPdf(condi_dist_h)
for i in range(h.shape[1]):
high0_pdf, fitting_para = high_pdf[i]
print high0_pdf, fitting_para
h0 = h[:,i]
max_val = h0.max()
min_val = h0.min()
step = (max_val - min_val) / float(total_samples)
samples = np.arange(min_val, max_val, step)
if len(fitting_para) == 3:
arr = high0_pdf(samples, fitting_para[0], loc=fitting_para[1], scale=fitting_para[2])
