def fit_model_and_calc_idr(r1,
r2,
starting_point=None,
max_iter=idr.MAX_ITER_DEFAULT,
convergence_eps=idr.CONVERGENCE_EPS_DEFAULT,
fix_mu=False,
fix_sigma=False):
# in theory we would try to find good starting point here,
# but for now just set it to somethign reasonable
if type(starting_point) == type(None):
starting_point = (DEFAULT_MU, DEFAULT_SIGMA, DEFAULT_RHO,
DEFAULT_MIX_PARAM)
idr.log("Initial parameter values: [%s]" %
" ".join("%.2f" % x for x in starting_point))
# fit the model parameters
idr.log("Fitting the model parameters", 'VERBOSE')
if idr.PROFILE:
import cProfile
cProfile.runctx(
"""theta, loss = estimate_model_params(
r1,r2,
starting_point,
max_iter=max_iter,
convergence_eps=convergence_eps,
fix_mu=fix_mu, fix_sigma=fix_sigma)
""",
{'estimate_model_params': estimate_model_params}, {
'r1': r1,
'r2': r2,
'starting_point': starting_point,
'max_iter': max_iter,
'convergence_eps': convergence_eps,
'fix_mu': fix_mu,
'fix_sigma': fix_sigma
})
assert False
theta, loss = estimate_model_params(r1,
r2,
starting_point,
max_iter=max_iter,
convergence_eps=convergence_eps,
fix_mu=fix_mu,
fix_sigma=fix_sigma)
idr.log("Finished running IDR on the datasets", 'VERBOSE')
idr.log("Final parameter values: [%s]" % " ".join("%.2f" % x
for x in theta))
# calculate the global IDR
localIDRs, IDRs = calc_IDR(numpy.array(theta), r1, r2)
return localIDRs, IDRs
def fit_model_and_calc_idr(r1, r2,
starting_point=None,
max_iter=idr.MAX_ITER_DEFAULT,
convergence_eps=idr.CONVERGENCE_EPS_DEFAULT,
fix_mu=False, fix_sigma=False ):
# in theory we would try to find good starting point here,
# but for now just set it to somethign reasonable
if type(starting_point) == type(None):
starting_point = (DEFAULT_MU, DEFAULT_SIGMA,
DEFAULT_RHO, DEFAULT_MIX_PARAM)
idr.log("Initial parameter values: [%s]" % " ".join(
"%.2f" % x for x in starting_point))
# fit the model parameters
idr.log("Fitting the model parameters", 'VERBOSE');
if idr.PROFILE:
import cProfile
cProfile.runctx("""theta, loss = estimate_model_params(
r1,r2,
starting_point,
max_iter=max_iter,
convergence_eps=convergence_eps,
fix_mu=fix_mu, fix_sigma=fix_sigma)
""",
{'estimate_model_params': estimate_model_params},
{'r1':r1, 'r2':r2,
'starting_point': starting_point,
'max_iter': max_iter,
'convergence_eps': convergence_eps,
'fix_mu': fix_mu, 'fix_sigma': fix_sigma} )
assert False
theta, loss = estimate_model_params(
r1, r2,
starting_point,
max_iter=max_iter,
convergence_eps=convergence_eps,
fix_mu=fix_mu, fix_sigma=fix_sigma)
idr.log("Finished running IDR on the datasets", 'VERBOSE')
idr.log("Final parameter values: [%s]"%" ".join("%.2f" % x for x in theta))
# calculate the global IDR
localIDRs, IDRs = calc_IDR(numpy.array(theta), r1, r2)
return localIDRs, IDRs