X_iter = iter_data[0].copy()
len_iter = len(X_iter)
i = 1
while len(X_iter) > (len_iter / 2):
nbr = BvsbUtils.KNNClassifier(X_train, Y_train) # KNN
# iter_y=nbr.predict(X_iter)
pred = nbr.predict_proba(X_iter)
iter_y = np.argmax(pred, axis=1)
classMax = np.max(pred, axis=1) # 获取未标记样本的最大隶属度
sortIndex = np.argsort(classMax) # 排序后的原下标
iter_index = np.sort(sortIndex[-select_h:])
sort_h_y = iter_y[iter_index] # 返回原来数据置信度最大的h个数
sort_h_data = X_iter[iter_index]
len_curr_iter = len(sort_h_y)
bvsbc = BvsbClassifier(X_train, Y_train, sort_h_data, sort_h_y, test_data[0], test_data[1], iterNum=0.1)
bvsbc.createELM(n_hidden=hidden_nums, activation_func="tanh", alpha=1.0, random_state=0)
_data_index = bvsbc.fitAndGetUpdateDataIndex(limit=int(0.2 * len_curr_iter))
if len(_data_index) != 0:
X_train = np.r_[bvsbc.X_train, sort_h_data[_data_index]]
Y_train = np.r_[bvsbc.Y_train, sort_h_y[_data_index]]
X_iter = np.delete(X_iter, iter_index[_data_index], axis=0)
else:
print("没有数据被加入训练集，训练结束")
break
print(f"第{ii} 次训练,第{i}次迭代: 正确率为:{bvsbc.score(test_data[0], test_data[1])}")
i += 1
acc_temp = bvsbc.score(test_data[0], test_data[1]) # 记录每次的精度
acc_rem.append(acc_temp) # 将每次的精度存入列表
print("***************ELM-BVSB-KNN加权算法（10次精度）********************")
for i in acc_rem:
data = datasets.load_digits()
stdc = StandardScaler() # 均值归一化
label_size = 0.3
data.data = stdc.fit_transform(data.data / 16.0)
train, iter, test = elmUtils.splitDataWithIter(data.data, data.target,
label_size, 0.2)
Y_iter = BvsbUtils.KNNClassifierResult(train[0], train[1], iter[0])
print(Y_iter.size)
tic = time.perf_counter_ns()
bvsbc = BvsbClassifier(train[0],
train[1],
iter[0],
Y_iter,
test[0],
test[1],
iterNum=0.1)
bvsbc.createELM(n_hidden=1000,
activation_func="sigmoid",
alpha=1.0,
random_state=0)
bvsbc.X_test = test[0]
bvsbc.Y_test = test[1]
bvsbc.trainELMWithKNNButBvsb()
toc = time.perf_counter_ns()
print(bvsbc.score(test[0], test[1]))
print("ELM-BVSB 项目用时:%d" % ((toc - tic) / 1000 / 1000))