def __init__(self, indim: int, outdim: int, order: int, default=0.,\
tol: float=1e-3, random_state: Union[int, RandomState] = None, **kwargs):
self.indim = indim
self.outdim = outdim
self.default = default
self.order = order
self.transformer = PolynomialFeatures(degree=order, include_bias=False)
# self.powers = np.arange(1, self.order + 1)[:, None]
kwargs['random_state'] = random_state # to be passed to SGDRegressor
kwargs['fit_intercept'] = True # learn bias term for polynomial
n_features = len(self._project(np.zeros((1, indim)))[0])
self.models = []
for _ in range(outdim):
model = SGDRegressor(tol=tol, **kwargs)
# SGDRegressor does not assign weight arrays until the first call
# to 'fit()'. Allocating them early so the weights property is
# accessible from the get-go. One initialized, they won't be reset
# by the model.
model._allocate_parameter_mem(n_classes=outdim,
n_features=n_features)
self.models.append(model)
