class SquaredExponentialCovariance(Covariance):
def __init__(self, sigma, scale):
"""
sigma - kernel width
scale - kernel scalling
"""
Covariance.__init__(self)
self.kernel=GaussianKernel(sigma);
self.scale=scale
def compute(self, X, Y=None):
return (self.scale**2)*self.kernel.kernel(X, Y)
@abstractmethod
def gen_num_hyperparameters(self):
return 2
@abstractmethod
def get_hyperparameters(self):
return array([self.sigma, self.scale])
@abstractmethod
def set_hyperparameters(self, theta):
assert(len(shape(theta))==1)
assert(len(theta)==2)
self.kernel.sigma=theta[0]
self.kernel.scale=theta[1]
class SquaredExponentialCovariance(Covariance):
def __init__(self, sigma, scale):
"""
sigma - kernel width
scale - kernel scalling
"""
Covariance.__init__(self)
self.kernel = GaussianKernel(sigma)
self.scale = scale
def compute(self, X, Y=None):
return (self.scale**2) * self.kernel.kernel(X, Y)
@abstractmethod
def gen_num_hyperparameters(self):
return 2
@abstractmethod
def get_hyperparameters(self):
return array([self.sigma, self.scale])
@abstractmethod
def set_hyperparameters(self, theta):
assert (len(shape(theta)) == 1)
assert (len(theta) == 2)
self.kernel.sigma = theta[0]
self.kernel.scale = theta[1]
