def __init__(self, penalty=None, alpha=1.0, search_eta=None, n_iter=1000, eps=1e-5): if penalty not in [None, "l1", "l2"]: raise ValueError() self.penalty_ = penalty self.alpha_ = alpha baseRegression.__init__(self, search_eta=search_eta, n_iter=n_iter, eps=eps) if (search_eta is None) and (penalty == "l1"): self.search_eta = self.search_eta_decrescendo return
def __init__(self, prob_func=None, penalty="l2", class_weight=None, C=1.0, search_eta=None, n_iter=1000, eps=1e-5): if penalty not in ["l1", "l2"]: raise ValueError() if prob_func not in [None, "sigmoid", "softmax"]: raise ValueError() if (not isinstance(class_weight, dict)) and (class_weight not in [None, "balanced"]): raise ValueError() self.penalty_ = penalty self.class_weight = class_weight self.alpha_ = 1.0 / C self.prob_func_ = prob_func baseRegression.__init__(self, search_eta=search_eta, n_iter=n_iter, eps=eps) if (search_eta is None) and (penalty == "l1"): self.search_eta = self.search_eta_decrescendo return