8c02e4af36c56f0cac7edc22d3caa8e96e559655,GPy/kern/parts/rbf_inv.py,RBFInv,__init__,#RBFInv#Any#Any#Any#Any#,35

Before Change

            else:
                inv_lengthscale = np.ones(self.input_dim)

        self._set_params(np.hstack((variance, inv_lengthscale.flatten())))

        // initialize cache
        self._Z, self._mu, self._S = np.empty(shape=(3, 1))
        self._X, self._X2, self._params = np.empty(shape=(3, 1))

After Change

    .. Note: this object implements both the ARD and "spherical" version of the function
    

    def __init__(self, input_dim, variance=1., inv_lengthscale=None, ARD=False):
        //self.input_dim = input_dim
        //self.name = "rbf_inv"
        super(RBFInv, self).__init__(input_dim, variance=variance, lengthscale=1./np.array(inv_lengthscale), ARD=ARD, name="inverse rbf")
        self.ARD = ARD
        if not ARD:
            self.num_params = 2
            if inv_lengthscale is not None:
                inv_lengthscale = np.asarray(inv_lengthscale)
                assert inv_lengthscale.size == 1, "Only one lengthscale needed for non-ARD kernel"
            else:
                inv_lengthscale = np.ones(1)
        else:
            self.num_params = self.input_dim + 1
            if inv_lengthscale is not None:
                inv_lengthscale = np.asarray(inv_lengthscale)
                assert inv_lengthscale.size == self.input_dim, "bad number of lengthscales"
            else:
                inv_lengthscale = np.ones(self.input_dim)
        
        self.variance = Param("variance", variance)
        self.inv_lengthscale = Param("sensitivity", inv_lengthscale)
        self.inv_lengthscale.add_observer(self, self.update_inv_lengthscale)
        self.remove_parameter(self.lengthscale)
        self.add_parameters(self.variance, self.inv_lengthscale)
        //self._set_params(np.hstack((variance, inv_lengthscale.flatten())))

        // initialize cache

Instances