def initialize_model(asrf):
# store the rate model code in the asrf for future reference
asrf.fit['rate_model'] = inspect.getsource(rate_model)
asrf.fit['out_age_mesh'] = range(probabilistic_utils.MAX_AGE)
# do normal approximation first, to generate a good starting point
M,C = probabilistic_utils.normal_approx(asrf)
# define the model variables
rate_model.setup_rate_model(asrf)
def initialized_rate_vars(rf, rate_stoch=None):
# store the rate model code in the asrf for future reference
try:
rf.fit["rate_model"] = inspect.getsource(rate_model)
except:
rf.fit["rate_model"] = "WARNING: could not find source code"
rf.fit["out_age_mesh"] = range(probabilistic_utils.MAX_AGE)
# do normal approximation first, to generate a good starting point
M, C = probabilistic_utils.normal_approx(rf)
rate_model.setup_rate_model(rf, rate_stoch)
return probabilistic_utils.flatten(rf.vars.values()), rf.rate_stoch
def map_fit(asrfs):
all_vars = []
rate_vars = {}
rate_stochs = []
for asrf in asrfs:
# store the rate model code in the asrf for future reference
asrf.fit['rate_model'] = inspect.getsource(rate_model)
asrf.fit['out_age_mesh'] = range(probabilistic_utils.MAX_AGE)
# do normal approximation first, to generate a good starting point
M,C = probabilistic_utils.normal_approx(asrf)
# define the model variables
vars = rate_model.model_vars(asrf)
rate_vars[asrf.id] = vars
rate_stochs += [ vars['asrf_%d'%asrf.id] ]
all_vars += vars.values()
@mc.potential
def smooth_across_regions(rate_list=rate_stochs):
logp = 0.
for ii in range(len(rate_list)):
for jj in range(ii+1, len(rate_list)):
logp += mc.normal_like(np.diff(np.log(rate_list[ii]))-np.diff(np.log(rate_list[jj])), 0., 1./(.1)**2)
return logp
all_vars += [ smooth_across_regions ]
map = mc.MAP(all_vars)
print "searching for maximum likelihood point estimate"
iterlim = 500
method = 'fmin_powell'
map.fit(verbose=10, iterlim=iterlim, method=method)
for asrf in asrfs:
rate_model.save_map(rate_vars[asrf.id], asrf)
return all_vars, rate_vars
