def propose(self):
"""
Propose a move.
"""
tau = (self.adaptive_scale_factor * self.proposal_sd) ** 2
self.stochastic.value = \
pm.rlognormal(np.log(self.stochastic.value), tau)
def propose(self):
"""
Propose a move.
"""
tau = (self.adaptive_scale_factor * self.proposal_sd)**2
self.stochastic.value = \
pm.rlognormal(np.log(self.stochastic.value), tau)
def settlement_size_samples(mu, tau, cutoff, sum_mu, sum_tau, pop_accounted, N):
"""
Returns N samples from the distribution of unsampled settlement sizes.
Settlement sizes are drawn from a truncated lognormal distribution
conditional on their sum being equal to sum_val.
At the SIR stage, 100 samples are proposed and 10 are retained.
:Parameters:
- mu : float
The mean parameter.
- tau : float
The precision parameter.
- cutoff : float
The truncation value.
- sum_mu : float
The mean of the lognormal distribution of total population.
- sum_tau : float
The precision parameter of the lognormal distribution of total population.
- pop_accounted : integer
The total population accounted for by the GRUMP urban extents.
- N : integer
The number of samples to return.
"""
N_sum_vals = N/10
# Compute moments and characteristic function for single population size,
# to be used in all posterior evaluations.
lnorms = np.exp(geto_truncnorm(mu, tau, log(cutoff), 10000))
sum_moments = np.mean(lnorms), np.var(lnorms)
oFT = robust_CF(mu, tau, cutoff)
# Generate values for total population in region not accounted for by
# GRUMP urban extents.
sum_vals = pm.rlognormal(sum_mu, sum_tau, size=N_sum_vals)-pop_accounted
where_not_OK = np.where(sum_vals < 0)
while len(where_not_OK[0]) > 0:
sum_vals[where_not_OK] = pm.rlognormal(sum_mu, sum_tau, size=len(where_not_OK[0]))-pop_accounted
where_not_OK = np.where(sum_vals < 0)
# Create 10 samples using SIR for each sum.
samps = []
for sum_val in sum_vals:
tries = 0
while tries < 10:
try:
# Find posterior of N given this sum, and draw a single sample from it.
Nmesh, p = robust_posterior(mu, tau, cutoff, sum_val, prior_fun=None, sum_moments=sum_moments, oFT=oFT)
N = Nmesh[int(pm.rcategorical(p))]
# Draw 10 samples for the sizes of the constituent populations given their number and
# the total population size.
w,i,s = SIR_simplex_sample(mu, tau, cutoff, sum_val, N, N_proposals=1000, N_samps=10)
break
except RuntimeError:
print 'Failed at N=%f, Nmesh=%s, p=%s. Trying again'%(N,Nmesh,p)
tries += 1
if tries==10:
import sys
a,b,c = sys.exc_info()
raise a,b,c
samps.extend(list(s.T))
# Return, you're done!
return samps
