def getCovMatrix(self,x=None,z=None,mode=None):
        self.checkInputGetCovMatrix(x,z,mode)
        c   = np.exp(self.hyp[0])             # inhomogeneous offset
        sf2 = np.exp(2.*self.hyp[1])          # signal variance
        ord = self.para[0]                    # order of polynomial
        if np.abs(ord-np.round(ord)) < 1e-8:  # remove numerical error from format of parameter
            ord = int(round(ord))
        assert(1. >= ord)                     # only nonzero integers for ord
        ord = int(ord)
        if mode == 'self_test':               # self covariances for the test cases
            nn,D = z.shape
            A = np.reshape(np.sum(z*z,1), (nn,1))
        elif mode == 'train':                 # compute covariance matix for dataset x
            n,D = x.shape
            A = np.dot(x,x.T)  + np.eye(n)*1e-10
        elif mode == 'cross':                 # compute covariance between data sets x and z
            A = np.dot(x,z.T)
        A = sf2 * (c + A)**ord
        return A
dataset/ETHPy150Open marionmari/pyGPs/pyGPs/Core/cov.py/Poly.getCovMatrix/swapped operands of `>=`