def main_rep_1():
orig_theta = [5., 5.]
orig_ds = abc.generate_dataset_rep(orig_theta)
#orig_ds = abc.add_gaussian_noise_full(orig_ds)
param_range = np.arange(3., 7., 0.1)
param_range_1 = np.arange(3, 7, 0.1)
likelihood_vals = np.zeros((len(param_range), len(param_range_1)))
for ind1, th in enumerate(param_range):
for ind2, th1 in enumerate(param_range_1):
sim_ds = abc.generate_dataset_rep([th, th1])
l = log_likelihood(sim_ds, orig_ds)
likelihood_vals[ind1, ind2] = l
print th, th1, l
print "========================"
i,j = np.unravel_index(likelihood_vals.argmax(), likelihood_vals.shape)
print "max likelihood ", likelihood_vals[i, j], " at: ()", i, " ,", j, ")"
fig = plt.figure()
ax = fig.gca(projection='3d')
m1, m2 = np.meshgrid(param_range, param_range_1)
print np.shape(m1), np.shape(m2), np.shape(likelihood_vals)
surf = ax.plot_surface(m1, m2, likelihood_vals.T, rstride=1, cstride=1, cmap=cm.jet, linewidth=0.1, antialiased=True)
fig.colorbar(surf, shrink=0.5, aspect=5)
ax.set_xlabel('alpha')
ax.set_ylabel('beta')
ax.set_zlabel('likelihood')
plt.figure()
plt.imshow(likelihood_vals)
plt.show()
def main_rep():
orig_theta = [216., 205.]
orig_ds = abc.generate_dataset_rep(orig_theta)
param_range = np.arange(0, 250, 5.)
likelihood_vals = []
for th in param_range:
sim_ds = abc.generate_dataset_rep([th])
likelihood_vals.append(log_likelihood_fourier(sim_ds, orig_ds))
plt.plot(param_range, likelihood_vals)
plt.show()