boosting.py 文件源码

def __init__(self, X, y, code_dic = None, estimator = DecisionTreeClassifier, itern = 20):
        self.X = X
        self.y = y
        self.estimator = estimator
        self.itern = itern
        self.m = self.X.shape[0] # number of samples
        self.cls_list = [] # list used to store classes' name and numbers
        for i in range(self.m):
            if y[i] not in self.cls_list:
                self.cls_list.append(y[i])
        if code_dic != None:
            self.k = len(code_dic[cls_list[0]]) # dimension of encoding space
        else:
            self.k = len(self.cls_list)
            if code_dic == None: # generate default encode dictionary
                code_dic = {} 
                for i in range(self.k):
                    code = np.array([-1] * self.k)
                    code[i] = 1
                    code_dic[self.cls_list[i]] = code
        self.code_dic = code_dic #store {label: array-like code}
        self.boost = self.train()