GaussianProcess.py 文件源码

def fit(self, X, y):
        """
        Fits a Gaussian Process regressor

        Parameters
        ----------
        X: np.ndarray, shape=(nsamples, nfeatures)
            Training instances to fit the GP.
        y: np.ndarray, shape=(nsamples,)
            Corresponding continuous target values to X.

        """
        self.X = X
        self.y = y
        self.nsamples = self.X.shape[0]
        if self.optimize:
            grads = None
            if self.usegrads:
                grads = self._grad
            self.optHyp(param_key=self.covfunc.parameters, param_bounds=self.covfunc.bounds, grads=grads)

        self.K = self.covfunc.K(self.X, self.X)
        self.L = cholesky(self.K).T
        self.alpha = solve(self.L.T, solve(self.L, y - self.mprior))
        self.logp = -.5 * np.dot(self.y, self.alpha) - np.sum(np.log(np.diag(self.L))) - self.nsamples / 2 * np.log(
            2 * np.pi)