def __init__(self, fit_params=None, verbose=False):
if fit_params is None or fit_params == 'mle':
fit_params = lambda structure, data: to_mvn(*gn_params(structure, data, sparse=True))
elif fit_params == 'ridge':
fit_params = lambda structure, data: to_mvn(*gn_params(structure, data, sparse=True, l2_reg=0.1))
elif callable(fit_params):
fit_params = fit_params
else:
raise NotImplementedError('Only mle estimation is currently available')
self.fit_params = fit_params
self.verbose = verbose
variables = list(range(5))
gen_mean = np.zeros(len(variables))
gen_var = np.zeros(len(variables)) + 0.2
gen_weight = 2
diff_norm_mean = 0
diff_norm_cov = 0
for i, s in enumerate(seeds):
print('Test {0}/{1}'.format(i + 1, len(seeds)))
graph = random_dag(variables, rng=rng)
beta = graph.T * gen_weight
mvn = to_mvn(gen_mean, gen_var, beta, return_mvn=True)
n_samples = 200
sample_seed = rng.randint(0, 2**32 - 1)
data_gn = sample_from_gn(graph, gen_mean, gen_var, beta, n_samples,
sample_seed)
data_mvn = mvn.rvs(n_samples, sample_seed)
mean_gn = np.mean(data_gn, axis=0)
mean_mvn = np.mean(data_mvn, axis=0)
cov_gn = np.cov(data_gn, rowvar=False)
cov_mvn = np.cov(data_mvn, rowvar=False)
mean_norm = norm(mean_mvn - mean_gn)
return mean / graph.n_nodes
for i, s in enumerate(seeds):
print('Test {0}/{1}'.format(i + 1, len(seeds)))
graph = random_dag(variables, rng=rng)
beta = graph.T * gen_weight
sample_seed = rng.randint(0, 2**32-1)
data_gn = sample_from_gn(graph, gen_mean, gen_var, beta, n_samples, sample_seed)
mu, var, b = gn_params(graph, data_gn, sparse=True)
mu, sigma = to_mvn(mu, var, b)
b = b.A
# print(beta)
# print(b)
mean_norm = norm(gen_mean - mu)
var_norm = norm(gen_var - var)
# noinspection PyTypeChecker
beta_norm = norm(beta - b)
sklearn_check = sklearn_fit(graph, b)
total_mean += mean_norm
total_cov += var_norm
total_beta += beta_norm
def parameter_estimator(structure, data):
return to_mvn(*gn_params_ridge(structure, data, sparse=True, l2_reg=0.1))