def cov(self, theta, x, z=None):
self.ell = np.exp(theta[0])
self.sf2 = np.exp(2 * theta[1])
if z is None:
z = x
R = squared_dist(x / self.ell, z / self.ell)
K = self.sf2 * np.exp(-R / 2)
return K
def dcov(self, theta, x, i):
K = self.cov(theta, x)
if i < self.D: # return derivative of lengthscale parameter
dK = K * squared_dist(x[:, i] / self.ell[i], x[:, i] / self.ell[i])
return dK
elif i == self.D: # return derivative of signal variance parameter
dK = 2 * K
return dK
else:
raise ValueError("Invalid covariance function parameter")
def cov(self, theta, x, z=None):
self.ell = np.exp(theta[0:self.D])
self.sf2 = np.exp(2 * theta[self.D])
if z is None:
z = x
R = squared_dist(x.dot(np.diag(1. / self.ell)),
z.dot(np.diag(1. / self.ell)))
K = self.sf2 * np.exp(-R / 2)
return K
def dcov(self, theta, x, i):
self.ell = np.exp(theta[0])
self.sf2 = np.exp(2 * theta[1])
R = squared_dist(x / self.ell, x / self.ell)
if i == 0: # return derivative of lengthscale parameter
dK = self.sf2 * np.exp(-R / 2) * R
return dK
elif i == 1: # return derivative of signal variance parameter
dK = 2 * self.sf2 * np.exp(-R / 2)
return dK
else:
raise ValueError("Invalid covariance function parameter")
