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)