input_secondc2 = input_secondc2.values
#放入11分类器中进一步分类
r_finally3 = np.array(second_check2(input_secondc2))[:, np.newaxis]
r_finally = np.vstack((r_finally1, r_finally2, r_finally3))
r_real = np.vstack(
(input_firstc[:, -1][:, np.newaxis], input_secondc1[:, -1][:,
np.newaxis],
input_secondc2[:, -1][:, np.newaxis]))
return r_finally, r_real
if __name__ == '__main__':
p = r'/home/xiaosong/桌面/pny相关数据/data_pny/PNY_all.pickle'
input = LoadFile(p=p)
np.random.shuffle(input)
dataset_4feature, dataset_dense, label = input[:, :
4], input[:, 4:
-1], input[:,
-1][:,
np.
newaxis]
dataset_fft = fft_transformer(dataset_dense, 100)
dataset = np.hstack((dataset_4feature, dataset_fft, label))
dataset_guiyi_2 = guiyi(dataset)
print(dataset_guiyi_2.shape)
r_finally, r_real = check(input=dataset_guiyi_2[:5100, :])
r_1 = np.where(np.abs(r_finally - r_real) < 1e-2, 1, 0)
r_sum = np.sum(r_1)
acc = r_sum / r_1.shape[0]
print('5100个测试样本的预测精确度为: %s' % acc)
return fft_abs
if __name__ == '__main__':
p = r'/home/xiaosong/桌面/OLDENBURG_all.pickle'
dataset = LoadFile(p)
nums_cl = [[6557, 0], [611, 2], [101, 2], [13, 2], [554, 2], [155, 2],
[100, 2], [1165, 1], [1993, 1], [947, 2], [1133, 2], [1152, 1],
[542, 2], [754, 2], [2163, 1]]
dataset_output = making(nums_cl=nums_cl, dataset=dataset)
print(dataset_output.shape)
checkclassifier(dataset_output[:, -1])
# SaveFile(dataset_output, savepickle_p=r'/home/xiaosong/桌面/OLDENBURG_3cl.pickle')
dataset_4feature, dataset_dense, label = dataset_output[:, :
4], dataset_output[:,
4:
-1], dataset_output[:,
-1][:,
np
.
newaxis]
dataset_fft = fft_transformer(dataset_dense, 100)
dataset = np.hstack((dataset_4feature, dataset_fft, label))
dataset_guiyi = guiyi(dataset)
print(dataset_guiyi.shape)
# print(np.min(dataset_guiyi, axis=0))
SaveFile(data=dataset_guiyi,
savepickle_p=r'/home/xiaosong/桌面/OLDENBURG_3cl.pickle')
dataset_onehot = onehot(dataset_guiyi)
print(np.sum(dataset_onehot[:, -3:], axis=0))