Ejemplo n.º 1
0
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
Ejemplo n.º 2
0
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')