def estimate(self, X, y, theta=None):
if theta is None:
theta = self.theta0
self.gpr = GPR(theta, self.covfunc, X, y)
self.theta = self.gpr.estimate(theta, self.covfunc, X, y)
return self.theta
def estimate(self, X, y, **kwargs):
theta = kwargs.pop('theta', None)
if theta is None:
theta = self.theta0
self.gpr = GPR(theta, self.covfunc, X, y)
self.theta = self.gpr.estimate(theta, self.covfunc, X, y)
return self
def __init__(self, X=None, y=None, theta=None):
self.covfunc = CovSum(X, ('CovLin', 'CovSqExpARD'))
self.theta0 = np.zeros(self.covfunc.get_n_params() + 1)
self.theta = self.theta0
print("Initialising GPR")
if (theta is not None) and (X is not None) and (y is not None):
self.gpr = GPR(theta, self.covfunc, X, y)
self._n_params = self.covfunc.get_n_params() + 1
else:
self.gpr = GPR()
def __init__(self, **kwargs): #X=None, y=None, theta=None,
X = kwargs.pop('X', None)
y = kwargs.pop('y', None)
theta = kwargs.pop('theta', None)
self.covfunc = CovSum(X, ('CovLin', 'CovSqExpARD'))
self.theta0 = np.zeros(self.covfunc.get_n_params() + 1)
self.theta = self.theta0
if (theta is not None) and (X is not None) and (y is not None):
self.gpr = GPR(theta, self.covfunc, X, y)
self._n_params = self.covfunc.get_n_params() + 1
else:
self.gpr = GPR()
class NormGPR(NormBase):
""" Classical GPR-based normative modelling approach
"""
def __init__(self, **kwargs): #X=None, y=None, theta=None,
X = kwargs.pop('X', None)
y = kwargs.pop('y', None)
theta = kwargs.pop('theta', None)
self.covfunc = CovSum(X, ('CovLin', 'CovSqExpARD'))
self.theta0 = np.zeros(self.covfunc.get_n_params() + 1)
self.theta = self.theta0
if (theta is not None) and (X is not None) and (y is not None):
self.gpr = GPR(theta, self.covfunc, X, y)
self._n_params = self.covfunc.get_n_params() + 1
else:
self.gpr = GPR()
@property
def n_params(self):
if not hasattr(self,'_n_params'):
self._n_params = self.covfunc.get_n_params() + 1
return self._n_params
@property
def neg_log_lik(self):
return self.gpr.nlZ
def estimate(self, X, y, **kwargs):
theta = kwargs.pop('theta', None)
if theta is None:
theta = self.theta0
self.gpr = GPR(theta, self.covfunc, X, y)
self.theta = self.gpr.estimate(theta, self.covfunc, X, y)
return self
def predict(self, Xs, X, y, **kwargs):
theta = kwargs.pop('theta', None)
if theta is None:
theta = self.theta
yhat, s2 = self.gpr.predict(theta, X, y, Xs)
# only return the marginal variances
if len(s2.shape) == 2:
s2 = np.diag(s2)
return yhat, s2