8fd8288fb88b4e14a4150bc23d3a4372d0f60de9,GPy/models/gplvm.py,GPLVM,__init__,#GPLVM#Any#Any#Any#Any#Any#Any#,29

Before Change

            kernel = kern.rbf(input_dim, ARD=input_dim>1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
        likelihood = Gaussian(Y, normalize=normalize_Y)
        GP.__init__(self, X, likelihood, kernel, normalize_X=False)
        self._set_params(self._get_params())

    def initialise_latent(self, init, input_dim, Y):
        if init == "PCA":
            return PCA(Y, input_dim)[0]

After Change

    :type init: "PCA"|"random"

    
    def __init__(self, Y, input_dim, init="PCA", X = None, kernel=None, normalize_Y=False):
        if X is None:
            X = self.initialise_latent(init, input_dim, Y)
        if kernel is None:
            kernel = kern.rbf(input_dim, ARD=input_dim>1) + kern.bias(input_dim, np.exp(-2)) + kern.white(input_dim, np.exp(-2))
        likelihood = Gaussian(Y, normalize=normalize_Y)
        GP.__init__(self, X, likelihood, kernel, normalize_X=False)
        self.ensure_default_constraints()

    def initialise_latent(self, init, input_dim, Y):
        if init == "PCA":
            return PCA(Y, input_dim)[0]

Instances