def __init__(self, Y, input_dim, init='PCA', X=None, kernel=None, name="gplvm"):

        """
        :param Y: observed data
        :type Y: np.ndarray
        :param input_dim: latent dimensionality
        :type input_dim: int
        :param init: initialisation method for the latent space
        :type init: 'PCA'|'random'
        """
        if X is None:
            from ..util.initialization import initialize_latent
            X, fracs = initialize_latent(init, input_dim, Y)
        else:
            fracs = np.ones(input_dim)
        if kernel is None:
            kernel = kern.RBF(input_dim, lengthscale=fracs, ARD=1 > input_dim) + kern.Bias(input_dim, np.exp(-2))

        likelihood = Gaussian()

        super(GPLVM, self).__init__(X, Y, kernel, likelihood, name='GPLVM')

        self.X = Param('latent_mean', X)
        self.link_parameter(self.X, index=0)
dataset/ETHPy150Open SheffieldML/GPy/GPy/models/gplvm.py/GPLVM.__init__/swapped operands of `>`