def fit(self):
if self.model:
self.noise = self.model.Gaussian_noise.variance[0]
return None
x_dim = self.x.shape[1] # number of input dimensions, 1 if only time
y_dim = self.y.shape[
1] # number of ouptut dimensions, typically only 1 for log OD
kern = buildKernel(x_dim, ARD=self.ARD)
m = GPRegression(self.x, self.y, kern)
if self.heteroscedastic:
kern = addFixedKernel(kern, y_dim, self.error)
m = GPRegression(self.x, self.y, kern)
m.optimize()
self.noise = m.Gaussian_noise.variance[
0] # should be negligible (<1e-10) for full model
if self.heteroscedastic:
m.kern = m.kern.parts[
0] # cannot predict with fixed kernel, so remove it
self.model = m
def wrap_kernel_with_rounding(
model: GPRegression,
hyperparameters: List[Hyperparameter]) -> GPRegression:
model.kern = RoundingKernelWrapper(model.kern, hyperparameters)
return model
