def predict(self, x, return_var=False):
y = SumMultiply('i,i', self.weights, x)
y_hat, var = y.get_moments()
if return_var:
return y_hat, var
else:
return y_hat
from bayespy.nodes import GaussianARD
B = GaussianARD(0, 1e-6, shape=(X.shape[1], ))
from bayespy.nodes import SumMultiply
F = SumMultiply('i,i', B, X)
from bayespy.nodes import Gamma
tau = Gamma(1e-3, 1e-3)
Y = GaussianARD(F, tau)
Y.observe(y)
from bayespy.inference import VB
Q = VB(Y, B, tau)
#Q.update(repeat=100990)
distribution = []
result = []
distribution = F.get_moments()
for min_val, max_val in zip(distribution[0], distribution[1]):
#mean = []
mean = (min_val + max_val) / 2
result.append(mean)
#result = mean
#x3 = []
#x3 = pd.DataFrame({result:buffer_data})
#x1 = x1.append(x3)
x1[buffer_data] = result
print(x1)
dataframe = pd.DataFrame(x1)
dataframe.to_csv('distribution.csv', mode='a', header=True, index=False)
'''