def __init__(self, model_type, codes, feature_names=None, alphas=None, **kwargs):
self.model_type = model_type
self.models = {}
for i, code in enumerate(codes):
model = SparseModel(model_type, feature_names, **kwargs)
if alphas is not None:
model.set_alpha(alphas[i])
self.models[code] = model
def __init__(self, model_type, codes, feature_names=None, alphas=None, **kwargs):
# generate a random order over models
self.order = list(codes)[:]
random.shuffle(self.order)
self.model_type = model_type
self.models = {}
# create models in the pre-determined order, adding one feature each time for the output of the previous model
for i, code in enumerate(self.order):
model = SparseModel(model_type, feature_names[:], **kwargs)
if alphas is not None:
model.set_alpha(alphas[i])
self.models[code] = model
feature_names.append(code)
