def main_hopf_1():
dx_dt = abc.dx_dt # simplest system with hopf bifurcation
orig_theta = [2.2]
orig_ds = abc.generate_dataset(dx_dt, orig_theta)
#orig_ds = abc.add_gaussian_noise_full(orig_ds)
param_range = np.arange(1, 4, 0.05)
likelihood_vals = []
for th in param_range:
sim_ds = abc.generate_dataset(dx_dt, [th])
likelihood_vals.append(log_likelihood(sim_ds, orig_ds))
print "max: ", max(likelihood_vals)
print "max val: ", param_range[likelihood_vals.index(max(likelihood_vals))]
plt.plot(param_range, likelihood_vals)
plt.show()
def main_lv():
lv = abc.lv
orig_theta = [1.]
t = np.linspace(0, 90, 1000)
orig_ds = abc.generate_dataset(lv, orig_theta)
param_range = np.linspace(0, 4, 100)
likelihood_vals = []
for th in param_range:
sim_ds = abc.generate_dataset(lv, [th])
likelihood_vals.append(log_likelihood(sim_ds, orig_ds))
plt.plot(param_range, likelihood_vals)
#plt.figure()
#plt.plot(t, orig_ds)
plt.show()
def lv_test():
theta = [1, 1]
ds = abc.generate_dataset(lv, theta)
ds = abc.add_gaussian_noise(ds)
population = abc.smc(lv, ds, [30.0, 16.0, 6.0, 5.0, 4.3])
sys.exit(0)
def lv_test():
theta = [1,1]
ds = abc.generate_dataset(lv, theta)
ds = abc.add_gaussian_noise(ds)
population = abc.smc(lv, ds, [30.0, 16.0, 6.0, 5.0, 4.3])
sys.exit(0)
