def fit(self, X_trainIn, y_trainIn):
y_train_l = y_trainIn.reshape((y_trainIn.shape[0], 1))
self.m = models.GPRegression(X_trainIn, y_train_l, self.kernel_ard)
self.m.constrain_positive('')
self.m.optimize_restarts(num_restarts=10)
self.m.randomize()
self.m.optimize()
def _fit_gaussian_process(x,
y,
kernel=kern.RBF(1, 1., 10.),
num_restarts: int = 7):
"""
Fits a Gaussian process
"""
x, y = Metrics._prepare_inputs_for_fitting_gp(x, y)
gp = models.GPRegression(x, y, kernel)
gp.optimize_restarts(num_restarts=num_restarts, messages=False)
return gp, kernel