def plot_gp_pred(sigma, **fillargs):
# pdb.set_trace()
nugget = (sigma ** 2 / (0.1 + d.astype('float') ** 2))
gp = GaussianProcess(corr='squared_exponential', nugget=nugget)
gp.fit(np.atleast_2d(range(n)).T, np.atleast_2d(d).T)
x = np.atleast_2d(np.linspace(0, n - 1)).T
y_pred, MSE = gp.predict(x, eval_MSE=True)
pylab.plot(x, y_pred)
pylab.fill_between(x.T[0], y_pred + MSE, y_pred - MSE, **fillargs)
def _calculate(self):
from sklearn.gaussian_process.gaussian_process import GaussianProcess
X = self.xs.reshape((self.xs.shape[0], 1))
y = self.ys
yserr = self.yserr
nugget = (yserr / y) ** 2
gp = GaussianProcess(
# nugget=nugget
)
gp.fit(X, y)
return gp