print("-----------------以下为OSELM-KNN(本文)算法（10）---------------")
acc_rem = [] #初始化精度列表
for ii in range(10):
#data.target = data.target + 1
#stdsc = StandardScaler()
#label_size = 0.5
(train_data, iter_data,
test_data) = elmUtils.splitDataWithIter(data.data, data.target,
label_size, 0.3)
iter_y = BvsbUtils.KNNClassifierResult(train_data[0], train_data[1],
iter_data[0])
bvsbc = BvsbClassifier(train_data[0],
train_data[1],
iter_data[0],
iter_y,
test_data[0],
test_data[1],
iterNum=0.1)
bvsbc.createOSELM(n_hidden=1000, active_function="sigmoid")
bvsbc.trainOSELMWithKNNButBvsb()
print(f'OSELM-BVSB-KNN 正确率为{bvsbc.score(test_data[0], test_data[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
for i in acc_rem:
print(f'{i*100:0.2f}', ) #打印每次精度
acc_mean = np.mean(acc_rem) #求出平均精度
print("{:.2f}".format(acc_mean * 100)) #打印平均精度
print('---------------------以上为OSELM-BVSB-KNN(10次)算法----------------------'
Y_train = train_data[1].copy()
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）********************")
from elm import BvsbClassifier, BvsbUtils
from sklearn import datasets
from elm import elmUtils
from sklearn.preprocessing import StandardScaler
import time
print("---------OSELM-BVSB-----------")
data = elmUtils.readDataFileToData("data/zoo.data", targetIndex=0)
print(f'数据集大小问{data.target.size}')
data.data = StandardScaler().fit_transform(data.data)
(train, iter, test) = elmUtils.splitDataWithIter(data.data, data.target, 0.05,
0.3)
print(f'训练集大小为{train[1].size}')
print(f'迭代训练集大小为{iter[1].size}')
print(f'测试集大小为{test[1].size}')
tic = time.perf_counter_ns()
bvsbc = BvsbClassifier(train[0],
train[1],
iter[0],
iter[1],
test[0],
test[1],
iterNum=0.1)
bvsbc.createOSELM(n_hidden=1000)
bvsbc.trainOSELMWithoutKNN()
toc = time.perf_counter_ns()
print(f'OSELM-BVSB-KNN 正确率为{bvsbc.score(test[0], test[1])}')
print(f'OSELM-BVSB-KNN 项目用时:{(toc - tic) / 1000 / 1000} ms')
#---------------------------数据集---------------------------------
print("-----------------以下为ELM--KNN(本文)算法（10次）---------------")
stdc = StandardScaler()
label_size = 0.05 #已标记样本比例，分别取0.05-0.1-0.2-0.3-0.4
acc_rem = [] #初始化精度列表
for ii in range(10):
data.data = stdc.fit_transform(data.data / 16.0)
(train_data, iter_data,
test_data) = elmUtils.splitDataWithIter(data.data, data.target,
label_size, 0.3)
iter_y = BvsbUtils.KNNClassifierResult(train_data[0], train_data[1],
iter_data[0]) # KNN
bvsbc = BvsbClassifier(train_data[0],
train_data[1],
iter_data[0],
iter_y,
test_data[0],
test_data[1],
iterNum=0.1)
bvsbc.createELM(n_hidden=1000,
activation_func="tanh",
alpha=1.0,
random_state=0)
bvsbc.X_test = test_data[0]
bvsbc.Y_test = test_data[1]
bvsbc.trainELMWithKNNButBvsb()
print("+++++++++++++++++++")
print(bvsbc.score(test_data[0], test_data[1]))
acc_temp = bvsbc.score(test_data[0], test_data[1]) #记录每次的精度
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))
label_size = 0.3 #已标记样本比例，分别取0.05-0.1-0.2-0.3-0.4
acc_rem = [] #初始化精度列表
for ii in range(10): #循环次数
print(f'数据集大小问{data.target.size}')
data.data = StandardScaler().fit_transform(data.data)
(train, iter, test) = elmUtils.splitDataWithIter(data.data, data.target,
0.2, 0.3)
print(f'训练集大小为{train[1].size}')
print(f'迭代训练集大小为{iter[1].size}')
print(f'测试集大小为{test[1].size}')
#tic = time.perf_counter_ns()
bvsbc = BvsbClassifier(train[0],
train[1],
iter[0],
iter[1],
test[0],
test[1],
iterNum=0.1)
bvsbc.createOSELM(n_hidden=1000)
bvsbc.trainOSELMWithoutKNN()
#toc = time.perf_counter_ns()
print(f'OSELM-BVSB 正确率为{bvsbc.score(test[0], test[1])}')
# print(f'OSELM-BVSB项目用时:{(toc - tic) / 1000 / 1000} ms')
acc_temp = bvsbc.score(test[0], test[1]) #记录每次的精度
acc_rem.append(acc_temp) #将每次的精度存入列表
print("*****************************************************")
for i in acc_rem:
print(f'{i*100:0.2f}', ) #打印每次精度
from sklearn.svm import SVC
from elm import BvsbClassifier, BvsbUtils
from elm import elmUtils
print("---------OSELM-BVSB-KNN-----------")
data = datasets.load_digits()
#data=elmUtils.readDataFileToData("data/zoo.data", targetIndex=0)
#data=elmUtils.readDataFileToData("./data/abalone.data", targetIndex=-1, transformIndex=[0])
stdc = StandardScaler()
#data.data, data.target = stdc.fit_transform(data.data) / 16.0, data.target
#data.data = BvsbUtils.dimensionReductionWithPCA(data.data, 0.95)
label_size = 0.05
(train_data, iter_data, test_data) = elmUtils.splitDataWithIter(data.data, data.target, label_size, 0.2)
iter_y = BvsbUtils.SVMClassifierResult(train_data[0], train_data[1], iter_data[0])
tic = time.perf_counter_ns()
bvsbc = BvsbClassifier(train_data[0], train_data[1], iter_data[0], iter_y, test_data[0], test_data[1],
iterNum=0.2)
bvsbc.createOSELM(n_hidden=1000,active_function="sigmoid")
bvsbc.trainOSELMWithBvsb()
toc = time.perf_counter_ns()
tdata=np.vstack((iter_data[0],test_data[0]))
ttarget=np.hstack((iter_data[1],test_data[1]))
print(ttarget.shape)
print(f'OSELM-BVSB-KNN 正确率为{bvsbc.score(tdata,ttarget)}')
print(f'OSELM-BVSB-KNN 正确率为{bvsbc.score(test_data[0],test_data[1])}')
print(f'OSELM-BVSB-KNN 项目用时:{(toc - tic) / 1000 / 1000} ms')
#data = sklearn.datasets.fetch_covtype()
#data = datasets.load_iris()
#data = datasets.load_digits()
#ata = datasets.load_wine()#############################################有问题，跑不了
#data = datasets.load_breast_cancer()
stdc=StandardScaler()
#data.data,data.target=stdc.fit_transform(data.data)/16.0, data.target
#data.data=BvsbUtils.dimensionReductionWithPCA(data.data,0.95)
label_size=0.3
(train_data, iter_data, test_data) = elmUtils.splitDataWithIter(data.data,data.target, label_size, 0.2)
iter_y=BvsbUtils.KNNClassifierResult(train_data[0],train_data[1],iter_data[0])
tic = time.perf_counter_ns()
bvsbc = BvsbClassifier(train_data[0], train_data[1], iter_data[0], iter_y, test_data[0], test_data[1],
iterNum=0.1)
bvsbc.createOSELM(n_hidden=1000)
bvsbc.trainOSELMWithBvsb()
toc = time.perf_counter_ns()
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
time_temp = (toc - tic) / 1000 / 1000 #记录每次的时间
time_rem.append(time_temp) #将每次的时间存入列表
print(f'OSELM-BVSB-KNN 正确率为{bvsbc.score(test_data[0], test_data[1])}')
print(f'OSELM-BVSB-KNN 项目用时:{(toc - tic) / 1000 / 1000} ms')
for i in acc_rem:
print(f'每一次的精度：{i*100:0.2f}',) #打印每次精度