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----------------------'
) #运行程序
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
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算法----------------------') #运行程序
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))
import numpy as np
from sklearn.preprocessing import StandardScaler
from elm import elmUtils
import time
print("------------")
# digits = load_digits()
data = datasets.load_breast_cancer()
stdc = StandardScaler() # 均值归一化
data.data = stdc.fit_transform(data.data / 16.0)
label_size=0.3
(train_data, iter_data, test_data) = elmUtils.splitDataWithIter(data.data,data.target, label_size, 0.2)
tic = time.perf_counter_ns()
bvsbc = BvsbClassifier(train_data[0], train_data[1], iter_data[0], iter_data[1], 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.trainELMWithoutKNN()
toc = time.perf_counter_ns()
print("+++++++++++++++++++")
print(bvsbc.score(test_data[0], test_data[1]))
print("ELM-BVSB 项目用时:%d" % ((toc - tic) / 1000 / 1000))