def get_bgibbs_stats(np_file = '../Result/Regular_Gibbs_ferrof_80000.npy', max_lag = 500, start = 20000):
energies = np.load(np_file)
E_samples = energies[0]
total_acf = np.zeros(max_lag+1)
for energy in energies:
total_acf += map(lambda i: utils.compute_acf(energy[start:], i), xrange(max_lag+1))
average_acf = total_acf/float(len(energies))
return E_samples, average_acf
def plot_acf(self, max_lag = 500, start = 0):
plot_name = "acf"
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.plot(map(lambda i: utils.compute_acf(self.E_samples[start:], i),
xrange(max_lag+1)))
ax.set_ylim(0, 1)
ax.set_xlim(0, max_lag)
self.save_current_plot(plot_name)
def acf_plot_multi_energies(self, energies, max_lag=500, res_num=0):
total_acf = zeros(max_lag + 1)
for energy in energies:
total_acf += map(lambda i: utils.compute_acf(energy[1:], i), xrange(max_lag + 1))
total_acf = total_acf / float(len(energies))
plt.figure()
plt.plot(total_acf)
plt.ylim(ymax=1.0)
plt.ylim(ymin=0.0)
plt.grid(True)
save_current_plot(self.rbm.name, "Acf_total", self.name + "_s" + str(self.n), res_num)
def get_sard_tests_stats(graph_name = 'ISG3D_UAI', policy_num = 1000, res_num = 0, max_lag = 500, hdf5_fn='adaptive_saw.h5'):
node = "/%s/%s/p%d/r%d" % (graph_name, 'SARD_EGO', policy_num, res_num)
h5file = t.openFile(hdf5_fn, mode = "a")
group = h5file.getNode(node)
E_samples = group.E_samples[:]
rewards = group.rewards[:]
nadaptations = group.nadaptations.read()
nruns_per_param_update = group.nruns_per_param_update.read()
acf = map(lambda i: utils.compute_acf(E_samples[nruns_per_param_update*nadaptations:], i), xrange(max_lag+1))
h5file.close()
return E_samples, acf, [rewards[nruns_per_param_update*n+1][0] for n in xrange(nadaptations)]
def plots(self, max_lag=500, res_num=0, energies=None, save_plots=False):
if energies is None:
energies = self.energies
plt.figure()
plt.plot(energies)
if save_plots:
save_current_plot(self.rbm.name, "E_samples", self.name + "_" + str(self.n), res_num)
plt.figure()
plt.plot(map(lambda i: utils.compute_acf(energies[1:], i), xrange(max_lag + 1)))
plt.ylim(ymax=1.0)
plt.ylim(ymin=0.0)
plt.grid(True)
if save_plots:
save_current_plot(self.rbm.name, "Acf", self.name + "_s" + str(self.n), res_num)
if not save_plots:
plt.show()
def get_gibbs_stats(np_file = '../Result/Regular_Gibbs_ferrof_80000.npy', max_lag = 500, start = 20000):
E_samples = np.load(np_file)
acf = map(lambda i: utils.compute_acf(E_samples[start:], i), xrange(max_lag+1))
return E_samples, acf
def get_sw_stats(matfile_name='../Result/ferro_frus_sw80000_nonzero_h_temp1.mat', E_sample_key = 'energy_vec', max_lag = 500, start = 20000):
import scipy.io as sio
matfile = sio.loadmat(matfile_name)
acf = map(lambda i: utils.compute_acf(matfile['energy_vec'][start:], i), xrange(max_lag+1))
return matfile[E_sample_key], acf
def compute_acf(self, max_lag = 500, subsample = 1): self.acf = map(lambda i: utils.compute_acf(self.energies[::subsample], i), xrange(max_lag+1))
sard_unif_test = tests.SARD_Unif_Test(**testspec_sard_unif)
sard_unif_test.run()
plt.figure()
plt.subplot(311)
line1 = plt.plot(sard_unif_test.MH_ratios, 'b*')
plt.subplot(312)
line2 = plt.plot(sard_unif_test.E_samples, '-b')
plt.grid(True)
max_lag = 500
plt.subplot(313)
plt.plot(map(lambda i: utils.compute_acf(sard_unif_test.E_samples[1:], i), xrange(max_lag+1)))
plt.grid(True)
plt.ylim(ymax=1.0)
plt.ylim(ymin=0.0)
plt.show()
else:
#=====================================================
# Run SARDONICS with parameter adaptation
#=====================================================
# Total number of moves (including the moves during adaptation)
nmoves = 10**5
# Set total number of adaptation
