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))