boosting.py 文件源码

def __init__(self, X, y, estimator = DecisionTreeClassifier, itern = 20, mode = "sign"):
        self.X = X
        self.y = y
        self.estimator = estimator
        self.itern = itern
        self.mode = mode
        self.m = self.X.shape[0] # number of samples
        self.cls_list = [] # list used to store classes' name and numbers
#        if type(y[0]) != np.ndarray:
#           self.y = y.reshape(len(y),-1)
        for i in range(self.m):
            for cls in self.y[i]:
                if cls not in self.cls_list:
                    self.cls_list.append(cls)
        self.k = len(self.cls_list) # number of classes
        self.boost = self.train()