RD.Initialize_Data(file)
print('Number of Positive: ', RD.Num_positive)
print('Number of Negative: ', RD.Num_negative)
nominal_feature = []
data = RD.get_feature()
num_samples = data.shape[0]
num_features = data.shape[1]
num_bins = 100
bounds = np.zeros((num_bins + 1, num_features))
for i in range(num_features):
if i not in nominal_feature:
bounds[:, i] = np.histogram(data[:, i], bins=num_bins)[1]
nf = RD.get_negative_feature()
transfer(nf, bounds, num_features, nominal_feature)
pf = RD.get_positive_feature()
transfer(pf, bounds, num_features, nominal_feature)
bayes = BayesianNetwork.from_samples(nf, algorithm='chow-liu')
pt = bayes.log_probability(nf).sum()
print('Chow-Liu', pt)
'''
bayes = BayesianNetwork.from_samples(nf, algorithm='exact-dp')
pt = bayes.log_probability(nf).sum()
print('Exact Shortest:', pt)
bayes = BayesianNetwork.from_samples(nf, algorithm='exact') # Error
pt = bayes.log_probability(nf).sum()
print('Exact A*', pt)
