def nu(self):
# make new variable
v = Term.var(self.n)
# extend state
self.state = Gauss.tensor(self.state, Gauss.N(1))
self.n += 1
return v
def gp(xs, kernel):
K = [[ kernel(x1,x2) for x1 in xs] for x2 in xs ]
return Gaussian.N(np.array(K))
def __init__(self):
self.n = 0 # number of variables
self.equations = []
self.state = Gauss.N(0)