for i in range(subnet.N):
# TODO: Track down why -np.inf causes problems with CMLE...
subnet.offset[i,i] = -20.0
for sub_size in params['sub_sizes']:
size = (sub_size, sub_size)
print 'subnetwork size = %s' % str(size)
gen = RandomSubnetworks(net, size, method = params['sampling'])
for rep in range(params['num_reps']):
subnet = gen.sample(as_network = True)
initialize(subnet, fit_model, offset_extremes = False)
fit_base_model.fit = fit_base_model.fit_convex_opt
fit_model.ignore_inner_offset = False
fit_model.fit(subnet, params['cycles'], params['sweeps'])
s_results.record(size, rep, subnet, fit_model = fit_model)
print 'S: ', fit_model.Theta
print
if params['fit_conditional']:
initialize(subnet, fit_model, offset_extremes = False)
fit_base_model.fit = fit_base_model.fit_conditional
fit_model.ignore_inner_offset = True
fit_model.fit(subnet, params['cycles'], params['sweeps'])
c_results.record(size, rep, subnet, fit_model = fit_model)
print 'C: ', fit_model.Theta
print
if params['fit_conditional_is']:
