# 计算插值样本
# 3.随机选取K中的一个样本
np.random.seed(self.random_state)
choice_all = k_sample_index.flatten()
# print('choice_all',choice_all)
# print('choice_all[choice_all != 0]',choice_all != 0)
choosed = np.random.choice(choice_all[choice_all != 0])
# print('choosed',choosed)
# 4. 在正样本和随机样本之间选出一个点
diff = sample.iloc[choosed, ] - sample.iloc[i, ]
# print('diff',type(diff), diff)
gap = np.random.rand(1, n_atters)
# print('gap', gap)
# print('sample.iloc[i,]', sample.iloc[i,])
new.loc[i] = [x for x in sample.iloc[i, ] + gap.flatten() * diff]
# print('new',new)
label_out = np.r_[label_out, tp_less] ##给新增加的一行数据添加label标签
print('new', new)
new_sample = pd.concat([x_data, new])
new_sample.reset_index(inplace=True, drop=True)
return new_sample, label_out
if __name__ == '__main__':
x = pd.DataFrame(data=x, columns=columns)
y = y
smt = SMOTE()
x_new, y_new = smt.over_sample(x, y)
print(Counter(y_new))
