def initial_pool(self):
initial_wrapper = _Initialpoolsampling(self.eps0 , self.data, self.model, \
self.distance , self.prior_dict)
#print wrapper
#print wrapper(1)
if self.N_threads == 1:
results = []
for i in range(self.N_particles):
results.append(initial_wrapper(i))
else:
self.pool = Pool(self.N_threads)
self.mapfn = self.pool.map
results = self.mapfn(initial_wrapper , range(self.N_particles))
self.pool.close()
#self.pool.terminate()
#self.pool.join()
results = np.array(results).T
self.theta_t = results[1:self.n_params+1,:]
self.w_t = results[self.n_params+1,:]
self.rhos = results[self.n_params+2,:]
self.sig_t = 2. * utils.covariance(self.theta_t , self.w_t)
plot_thetas(self.theta_t, self.w_t, self.prior_dict, 0,
basename=self.basename)
def initial_pool(self):
initial_wrapper = _Initialpoolsampling(self.eps0 , self.data, self.model, \
self.distance , self.prior_dict)
#print wrapper
#print wrapper(1)
if self.N_threads == 1:
results = []
for i in range(self.N_particles):
results.append(initial_wrapper(i))
else:
self.pool = Pool(self.N_threads)
self.mapfn = self.pool.map
results = self.mapfn(initial_wrapper , range(self.N_particles))
self.pool.close()
#self.pool.terminate()
#self.pool.join()
results = np.array(results).T
self.theta_t = results[1:self.n_params+1,:]
self.w_t = results[self.n_params+1,:] / self.N_particles
self.rhos = results[self.n_params+2:,:]
self.sig_t = 2. * utils.covariance(self.theta_t , self.w_t)
plot_thetas(self.theta_t, self.w_t, self.prior_dict, 0,
basename=self.basename)
def run_abc(self):
"""
Run PMC_ABC
"""
self.initial_pool()
t = 1
while t < self.T:
self.eps_t = np.percentile(self.rhos, 75)
print "iteration= " , t , "threshold= " , self.eps_t
importance_wrapper = _Importancepoolsampling(self.eps_t, self.data, self.model, \
self.distance, self.prior_dict, \
self.theta_t, self.w_t, self.sig_t)
if self.N_threads == 1 :
results = []
for i in range(self.N_particles):
results.append(importance_wrapper(i))
else:
self.pool = Pool(self.N_threads)
self.mapfn = self.pool.map
results = self.mapfn(importance_wrapper, range(self.N_particles))
self.pool.close()
#self.pool.terminate()
#self.pool.join()
results = np.array(results).T
self.theta_t = results[1:self.n_params+1,:]
self.w_t = results[self.n_params+1,:]
self.rhos = results[self.n_params+2,:]
self.sig_t = 2. * utils.covariance(self.theta_t, self.w_t)
plot_thetas(self.theta_t, self.w_t, self.prior_dict, t,
basename=self.basename,
fig_name="{0}_{1}.png".format(self.basename, str(t)))
t += 1
def run_abc(self):
"""
Run PMC_ABC
"""
self.initial_pool()
t = 1
while t < self.T:
self.eps_t = np.percentile(np.atleast_2d(self.rhos), 75, axis=1)
print "iteration= " , t , "threshold= " , self.eps_t
importance_wrapper = _Importancepoolsampling(self.eps_t, self.data, self.model, \
self.distance, self.prior_dict, \
self.theta_t, self.w_t, self.sig_t)
if self.N_threads == 1 :
results = []
for i in range(self.N_particles):
results.append(importance_wrapper(i))
else:
self.pool = Pool(self.N_threads)
self.mapfn = self.pool.map
results = self.mapfn(importance_wrapper, range(self.N_particles))
self.pool.close()
#self.pool.terminate()
#self.pool.join()
results = np.array(results).T
self.theta_t = results[1:self.n_params+1,:]
self.w_t = results[self.n_params+1,:]
self.rhos = results[self.n_params+2:,:]
self.sig_t = 2. * utils.covariance(self.theta_t, self.w_t)
plot_thetas(self.theta_t, self.w_t, self.prior_dict, t,
basename=self.basename,
fig_name="{0}_{1}.png".format(self.basename, str(t)))
t += 1