def initial_pool(prior_obj, eps0, N_particles, N_threads=1):
""" Initial Pool
"""
args_list = [[i, prior_obj, eps0, N_particles]
for i in xrange(N_particles)]
if N_threads > 1:
pool = InterruptiblePool(processes=N_threads)
mapfn = pool.map
results = mapfn(initial_pool_sampling, args_list)
pool.close()
pool.terminate()
pool.join()
else:
results = []
for arg in args_list:
results.append(initial_pool_sampling(arg))
results = np.array(results).T
theta_t = results[1:prior_obj.n_params + 1, :]
w_t = results[prior_obj.n_params + 1, :]
rhos = results[prior_obj.n_params + 2, :]
sig_t = covariance(theta_t, w_t)
return theta_t, w_t, rhos, sig_t
def pmc_abc(N_threads = N_threads):
# initial pool
theta_t, w_t, rhos, sig_t = initial_pool()
t = 0 # iternation number
plot_thetas(theta_t , w_t, t)
plt.savefig("/home/mj/public_html/scatter_hod_gaussian_t"+str(t)+".png")
plt.close()
while t < N_iter:
eps_t = np.percentile(rhos, 75)
print 'New Distance Threshold Eps_t = ', eps_t
theta_t_1 = theta_t.copy()
w_t_1 = w_t.copy()
sig_t_1 = sig_t.copy()
"""these lines are borrowed from initial sampling to double-check multiprocessing"""
#pool = InterruptiblePool(processes = N_threads)
#mapfn = pool.map
#args_list = [i for i in xrange(N_particles)]
#results = mapfn(initial_pool_sampling, args_list)
#pool.close()
#pool.terminate()
#pool.join()
pool = InterruptiblePool(processes = N_threads)
mapfn = pool.map
args_list = [[i, theta_t_1, w_t_1, sig_t_1, eps_t] for i in xrange(N_particles)]
#results = []
#for args in args_list:
# pool_sample = importance_pool_sampling(args)
# results.append( pool_sample )
results = mapfn(importance_pool_sampling, args_list)
pool.close()
pool.terminate()
pool.join()
sig_t = np.cov(theta_t)
results = np.array(results).T
theta_t = results[1:n_params+1,:]
w_t = results[n_params+1,:]
rhos = results[n_params+2,:]
sig_t = np.cov(theta_t)
t += 1
plot_thetas(theta_t, w_t , t)
plt.savefig("/home/mj/public_html/scatter_hod_gaussian_t"+str(t)+".png")
plt.close()
def pmc_abc(prior_dict, N_particles=100, N_iter=30, eps0=20.0, N_threads=1):
"""
"""
prior_obj = Prior(prior_dict)
# initial pool
theta_t, w_t, rhos, sig_t = initial_pool(prior_obj,
eps0,
N_particles,
N_threads=N_threads)
t = 0 # iternation number
#plot_thetas(theta_t , w_t, prior_dict, t)
while t < N_iter:
eps_t = np.percentile(rhos, 75)
print 'New Distance Threshold Eps_t = ', eps_t
theta_t_1 = theta_t.copy()
w_t_1 = w_t.copy()
sig_t_1 = sig_t.copy()
args_list = [[i, prior_obj, theta_t_1, w_t_1, sig_t_1, eps_t]
for i in xrange(N_particles)]
if N_threads > 1:
pool = InterruptiblePool(processes=N_threads)
mapfn = pool.map
results = mapfn(importance_pool_sampling, args_list)
pool.close()
pool.terminate()
pool.join()
else:
results = []
for args in args_list:
pool_sample = importance_pool_sampling(args)
results.append(pool_sample)
results = np.array(results).T
theta_t = results[1:prior_obj.n_params + 1, :]
w_t = results[prior_obj.n_params + 1, :]
rhos = results[prior_obj.n_params + 2, :]
sig_t = covariance(theta_t, w_t)
t += 1
plot_thetas(theta_t, w_t, prior_dict, t)
def pmc_abc(N_threads=N_threads):
# initial pool
theta_t, w_t, rhos, sig_t = initial_pool()
w_t = w_t / np.sum(w_t)
t = 0 # iternation number
plot_thetas(theta_t, w_t, t)
while t < N_iter:
if t < 4:
eps_t = np.percentile(np.atleast_2d(rhos), 20, axis=1)
else:
eps_t = np.percentile(np.atleast_2d(rhos), 50, axis=1)
print 'New Distance Threshold Eps_t = ', eps_t, "t=", t
theta_t_1 = theta_t.copy()
w_t_1 = w_t.copy()
sig_t_1 = sig_t.copy()
args_list = [[i, theta_t_1, w_t_1, sig_t_1, eps_t]
for i in xrange(N_particles)]
"""serial"""
results = []
#for args in args_list:
# pool_sample = importance_pool_sampling(args)
# results.append( pool_sample )
"""parallel"""
pool = InterruptiblePool(processes=N_threads)
mapfn = pool.map
results = mapfn(importance_pool_sampling, args_list)
pool.close()
pool.terminate()
pool.join()
results = np.array(results).T
theta_t = results[1:n_params + 1, :]
w_t = results[n_params + 1, :]
w_t = w_t / np.sum(w_t)
rhos = results[n_params + 2:, :]
#sig_t = knn_sigma(theta_t , k = 10)
sig_t = 2. * covariance(theta_t, w_t)
t += 1
plot_thetas(theta_t, w_t, t)
def initial_pool():
pool = InterruptiblePool(processes = N_threads)
mapfn = pool.map
args_list = [i for i in xrange(N_particles)]
results = mapfn(initial_pool_sampling, args_list)
pool.close()
pool.terminate()
pool.join()
results = np.array(results).T
theta_t = results[1:n_params+1,:]
w_t = results[n_params+1,:]
rhos = results[n_params+2,:]
sig_t = np.cov(theta_t)
return theta_t, w_t, rhos, sig_t
def initial_pool():
args_list = np.arange(N_particles)
"""serial"""
#results = []
#for arg in args_list:
# results.append(initial_pool_sampling(arg))
"""parallel"""
pool = InterruptiblePool(processes=N_threads)
mapfn = pool.map
results = mapfn(initial_pool_sampling, args_list)
pool.close()
pool.terminate()
pool.join()
results = np.array(results).T
theta_t = results[1:n_params + 1, :]
w_t = results[n_params + 1, :]
w_t = w_t / np.sum(w_t)
rhos = results[n_params + 2:, :]
sig_t = covariance(theta_t, w_t)
return theta_t, w_t, rhos, sig_t
def pmc_abc(self):
"""
"""
self.rhos = self.initial_pool()
while self.t < self.T:
self.eps_t = np.percentile(self.rhos, 75)
print 'Epsilon t', self.eps_t
self.theta_t_1 = self.theta_t.copy()
self.w_t_1 = self.w_t.copy()
self.sig_t_1 = self.sig_t.copy()
pool = InterruptiblePool(self.Nthreads)
mapfn = pool.map
args_list = [ i for i in xrange(self.N) ]
results = mapfn(unwrap_self_importance_sampling, zip([self]*len(args_list), args_list))
pool.close()
pool.terminate()
pool.join()
pars = np.array(results).T
self.theta_t = pars[1:self.n_params+1,:].copy()
self.w_t = pars[self.n_params+1,:].copy()
self.rhos = pars[self.n_params+2,:].copy()
self.sig_t = 2.0 * np.cov(self.theta_t)
self.t += 1
self.writeout()
self.plotout()
return None
