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()
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