def __init__(self, dim=None, nIterMC=30): Cached.__init__(self) self._nIterMC = nIterMC self._reuse = True self._KiZo = None self._tol = 1e-6 if dim is not None: self.initialize(dim)
def __init__(self, y, W=None, Wstar=None): """ Args: y: phenotype vector [N, 1] W: fixed effect design [N, K] Wstar: out-of-sample fixed effect design for predictions [No, K] """ Cached.__init__(self) self.y = y self.W = W self.Wstar = Wstar self.setFIinv(None) self._set_relay(None)
def __init__(self, Y, W, Wstar=None): """ Args: Y: phenotype matrix [N, 1] W: fixed effect design [N, K] Wstar: out-of-sample fixed effect design for predictions [No, K] """ Cached.__init__(self) self.Y = Y self.W = W self.B = sp.zeros((self._K,1)) self.Wstar = Wstar self.setFIinv(None)
def __init__(self, Y, W, Wstar=None): """ Args: Y: phenotype matrix [N, 1] W: fixed effect design [N, K] Wstar: out-of-sample fixed effect design for predictions [No, K] """ Cached.__init__(self) self.Y = Y self.W = W self.B = sp.zeros((self._K, 1)) self.Wstar = Wstar self.setFIinv(None)
def __init__(self, mean, covar): """ covar: Limix covariance function mean: Limix linear Mean function """ Cached.__init__(self) if not issubclass(type(mean), MeanBase): raise TypeError('Parameter mean must have MeanBase inheritance.') if not issubclass(type(covar), Covariance): raise TypeError('Parameter covar must have Covariance ' 'inheritance.') self.covar = covar self.mean = mean self.Areml = cov_reml(self) self._observe()
def __init__(self,dim=None): Cached.__init__(self) if dim is not None: self.initialize(dim)