def test_max_likelihood():
import pdb
pdb.set_trace()
x = [1., 2.]
obs = gaussian_generator(mu=x[0], sigma=x[1])
pdf = norm(x[0], x[1]).pdf
func_to_minimize = lambda x: -get_log_likelihood( norm(x[0], x[1]).pdf, obs )
solver = Solver(func_to_minimize, minimize)
x0 = [0., 0.5]
res = solver.solve(x0)
print res
def test_max_likelihood(params=None, params0=None, method="L-BFGS-B"):
# simple test with a two gaussian mixture
if not params:
params = {"mu": [0.0, 5.0], "sigma": [0.01, 0.01], "p": [0.5]}
obs = gaussian_mix_generator(params["mu"], params["sigma"], params["p"])
"""
def penalize(params):
penalization = 0
if True in (np.array(params['p'])<0.01):
penalization += 10000000.
if True in (np.array(params['sigma'])<0.01):
penalization += 10000000.
return penalization
"""
func_to_minimize = lambda params: -get_log_likelihood(GaussianMix(params).pdf, obs)
solver = Solver(
func_to_minimize, minimize, preprocess=GaussianMix.preprocess_func, postprocess=GaussianMix.postprocess_func
)
bounds = ((None, None), (None, None), (0.0, None), (0.0, None), (0.0, 1.0))
# the initial point is set by kmeans
k = len(params["mu"])
kmeans_res = kmeans2(obs, k)
mu0 = kmeans_res[0]
sigma0 = np.zeros(k)
p0 = np.zeros(k)
for id in range(k):
sigma0[id] = obs[np.where(kmeans_res[1] == id)].std()
p0[id] = np.where(kmeans_res[1] == id)[0].size * 1.0 / obs.size
params0 = {"mu": mu0.tolist(), "sigma": sigma0.tolist(), "p": p0[:-1].tolist()}
print "initial point", params0
res = solver.solve(params0, method=method, bounds=bounds)
print "final point", res
print "function value for the correct point", func_to_minimize(params)
print "function value for the initial point", func_to_minimize(params0)
print "function value for the final point", func_to_minimize(res)
