def __init__(self,
n_components,
method="sklearn",
init="rand_orth",
orthogonalize=True,
tol=1e-5,
maxiter=100,
seed=None):
# Model options.
self.n_components = n_components
self.orthogonalize = orthogonalize
# Optimization parameters.
self.tol = tol
self.maxiter = maxiter
self.seed = seed
# Model parameters.
self._factors = None
# Check that optimization method is recognized.
METHODS = ("als", "sklearn")
if method not in METHODS:
raise_no_method(self, method, METHODS)
else:
self.method = method
# Check that initialization method is recognized.
INITS = ("rand_orth", )
if init not in INITS:
raise_no_init(self, init, INITS)
else:
self.init = init
def __init__(
self, n_components, method="em", init="rand",
n_restarts=1, seed=None, tol=1e-5, maxiter=100):
# Model options.
self.n_components = n_components
# Optimization parameters.
self.maxiter = maxiter
self.seed = seed
self.tol = tol
self.n_restarts = n_restarts
# Model parameters.
self._factors = None
# Check that optimization method is recognized.
METHODS = ("em",)
if method not in METHODS:
raise_no_method(self, method, METHODS)
else:
self.method = method
# Check that initialization method is recognized.
INITS = ("rand",)
if init not in INITS:
raise_no_init(self, init, INITS)
else:
self.init = init
def __init__(self,
n_components,
method="lloyds",
init="rand",
n_restarts=1,
seed=None,
maxiter=100,
verbose=False):
# Model options.
self.n_components = n_components
self.verbose = verbose
# Optimization parameters.
self.maxiter = maxiter
self.n_restarts = n_restarts
self.seed = seed
# Model parameters.
self._factors = None
# Check that optimization method is recognized.
METHODS = ("lloyds", )
if method not in METHODS:
raise_no_method(self, method, METHODS)
else:
self.method = method
# Check that initialization method is recognized.
INITS = ("rand", "soft")
if init not in INITS:
raise_no_init(self, init, INITS)
else:
self.init = init
def __init__(self,
n_components,
loss,
method="cd",
init="randn",
tol=1e-5,
maxiter=100,
seed=None):
# Model options.
self.n_components = n_components
self.loss = loss
LOSSES = ("poisson", )
if loss not in LOSSES:
raise ValueError("Did not recognize 'loss' option. "
"Saw '{}'. Expected one of {}."
"".format(loss, LOSSES))
# Determine link and inverse link functions.
INVLINKS = {
"poisson": np.exp,
}
LINKS = {"poisson": np.log}
self._invlink = INVLINKS[loss]
self._link = LINKS[loss]
# Optimization parameters.
self.tol = tol
self.maxiter = maxiter
self.seed = seed
# Model parameters.
self._factors = None
# Check that optimization method is recognized.
METHODS = ("cd", )
if method not in METHODS:
raise_no_method(self, method, METHODS)
else:
self.method = method
# Check that initialization method is recognized.
INITS = ("randn", )
if init not in INITS:
raise_no_init(self, init, INITS)
else:
self.init = init
def __init__(self,
n_components,
method="hals",
init="rand",
n_restarts=1,
tol=1e-5,
maxiter=100,
seed=None):
# Model hyperparameters.
self.n_components = n_components
if n_components <= 0:
raise ValueError("Expected n_components to be an integer >= 1.")
# Optimization parameters.
self.tol = tol
self.maxiter = maxiter
self.seed = seed
self.n_restarts = n_restarts
# Model parameters.
self._factors = None
# Check that optimization method is recognized.
METHODS = ("hals", )
if method not in METHODS:
raise_no_method(self, method, METHODS)
else:
self.method = method
# Check that initialization method is recognized.
INITS = ("rand", )
if init not in INITS:
raise_no_init(self, init, INITS)
else:
self.init = init
def __init__(self,
n_components,
method="cd",
tol=1e-5,
maxiter=100,
seed=None):
# Model options.
self.n_components = n_components
# Optimization parameters.
self.tol = tol
self.maxiter = maxiter
self.seed = seed
# Model parameters.
self._factors = None
# Check that optimization method is recognized.
METHODS = ("cd", )
if method not in METHODS:
raise_no_method(self, method, METHODS)
else:
self.method = method