def __init__(self, Ylist, input_dim, X=None, X_variance=None,
                 initx = 'PCA', initz = 'permute',
                 num_inducing=10, Z=None, kernel=None,
                 inference_method=None, likelihoods=None, name='mrd',
                 Ynames=None, normalizer=False, stochastic=False, batchsize=10):

        self.logger = logging.getLogger(self.__class__.__name__)
        self.input_dim = input_dim
        self.num_inducing = num_inducing

        if isinstance(Ylist, dict):
            Ynames, Ylist = zip(*Ylist.items())

        self.logger.debug("creating observable arrays")
        self.Ylist = [ObsAr(Y) for Y in Ylist]
        #The next line is a fix for Python 3. It replicates the python 2 behaviour from the above comprehension
        Y = Ylist[-1]

        if Ynames is None:
            self.logger.debug("creating Ynames")
            Ynames = ['Y{}'.format(i) for i in range(len(Ylist))]
        self.names = Ynames
        assert len(self.names) == len(self.Ylist), "one name per dataset, or None if Ylist is a dict"

        if inference_method is None:
            self.inference_method = InferenceMethodList([VarDTC() for _ in range(len(self.Ylist))])
        else:
            assert isinstance(inference_method, InferenceMethodList), "please provide one inference method per Y in the list and provide it as InferenceMethodList, inference_method given: {}".format(inference_method)
            self.inference_method = inference_method

        if X is None:
            X, fracs = self._init_X(initx, Ylist)
        else:
            fracs = [X.var(0)]*len(Ylist)

        Z = self._init_Z(initz, X)
        self.Z = Param('inducing inputs', Z)
        self.num_inducing = self.Z.shape[0] # ensure M==N if M>N

        # sort out the kernels
        self.logger.info("building kernels")
        if kernel is None:
            from ..kern import RBF
            kernels = [RBF(input_dim, ARD=1, lengthscale=fracs[i]/1.) for i in range(len(Ylist))]
        elif isinstance(kernel, Kern):
            kernels = []
            for i in range(len(Ylist)):
                k = kernel.copy()
                kernels.append(k)
        else:
            assert len(kernel) == len(Ylist), "need one kernel per output"
            assert all([isinstance(k, Kern) for k in kernel]), "invalid kernel object detected!"
            kernels = kernel

        self.variational_prior = NormalPrior()
        #self.X = NormalPosterior(X, X_variance)

        if likelihoods is None:
            likelihoods = [Gaussian(name='Gaussian_noise'.format(i)) for i in range(len(Ylist))]
        else: likelihoods = likelihoods

        self.logger.info("adding X and Z")
        super(MRD, self).__init__(Y, input_dim, X=X, X_variance=X_variance, num_inducing=num_inducing,
                 Z=self.Z, kernel=None, inference_method=self.inference_method, likelihood=Gaussian(),
                 name='manifold relevance determination', normalizer=None,
                 missing_data=False, stochastic=False, batchsize=1)

        self._log_marginal_likelihood = 0

        self.unlink_parameter(self.likelihood)
        self.unlink_parameter(self.kern)

        self.num_data = Ylist[0].shape[0]
        if isinstance(batchsize, int):
            batchsize = itertools.repeat(batchsize)

        self.bgplvms = []

        for i, n, k, l, Y, im, bs in zip(itertools.count(), Ynames, kernels, likelihoods, Ylist, self.inference_method, batchsize):
            assert Y.shape[0] == self.num_data, "All datasets need to share the number of datapoints, and those have to correspond to one another"
            md = np.isnan(Y).any()
            spgp = BayesianGPLVMMiniBatch(Y, input_dim, X, X_variance,
                                          Z=Z, kernel=k, likelihood=l,
                                          inference_method=im, name=n,
                                          normalizer=normalizer,
                                          missing_data=md,
                                          stochastic=stochastic,
                                          batchsize=bs)
            spgp.kl_factr = 1./len(Ynames)
            spgp.unlink_parameter(spgp.Z)
            spgp.unlink_parameter(spgp.X)
            del spgp.Z
            del spgp.X
            spgp.Z = self.Z
            spgp.X = self.X
            self.link_parameter(spgp, i+2)
            self.bgplvms.append(spgp)

        b = self.bgplvms[0]
        self.posterior = b.posterior
        self.kern = b.kern
        self.likelihood = b.likelihood

        self.logger.info("init done")
dataset/ETHPy150Open SheffieldML/GPy/GPy/models/mrd.py/MRD.__init__/swapped operands of `/`