def __init__(self, n_components=None, max_iter=None, tol=None, random_state=None): PCA.__init__(self, n_components=n_components) self.V = None self.W = None self.H = None self.recovered_V = None
def __init__(self, n_components=None, copy=True, whiten=False, svd_solver='auto', tol=0.0, iterated_power='auto', random_state=None): PCA.__init__(self, n_components=n_components, copy=copy, whiten=whiten, svd_solver=svd_solver, tol=tol, iterated_power=iterated_power, random_state=random_state)
def __init__(self, X, var_names=None, n_components=0.95, transform=True, **kwargs): """Initialize PCA and perform fit.""" self.X = X self.var_names = (list(range(self.X.shape[1])) if var_names is None else var_names) sklearn_PCA.__init__(self, n_components=n_components, **kwargs) if transform: self.fit_transform(self.X) else: self.fit(self.X)
def __init__(self, *args, **kwargs): self.num_outliers_ = kwargs.pop('num_outliers') return PCA.__init__(self, *args, **kwargs)