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- from stock import cnn_predict
- from stock import dnn_predict
- from stock import kmeans
- def dnn_and_kmeans():
- # cnn_result = cnn_predict.predict()
- dnn_result = dnn_predict.predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
- cnn_result = kmeans.class_fic(file_path="D:\\data\\quantization\\stock2_20.log")
- print('计算完成')
- with open('dnn_and_kmeans.txt', 'a') as f:
- up_num = 0
- up_right = 0
- i = 0
- for m in cnn_result: #3位 时间是字符串
- # print('find', m)
- for n in dnn_result: #5位 时间是int
- if m[0][0] == n[0][0] and int(m[0][1]) == n[0][1]:
- print('AND', m)
- f.write(str(m) + '\n')
- if n[1][0] == 1:
- up_right = up_right + 1.13
- elif n[1][1] == 1:
- up_right = up_right + 1.05
- elif n[1][2] == 1:
- up_right = up_right + 1
- else:
- up_right = up_right - 0.15
- up_num = up_num + 1
- i = i + 1
- break
- print(up_right, up_num, up_right / up_num)
- def and_predict():
- # cnn_result = cnn_predict.predict()
- dnn_result = dnn_predict.predict(file_path='D:\\data\\quantization\\stock6_5_test.log', model_path='5d_dnn_seq.h5')
- cnn_result = dnn_predict.predict(file_path='D:\\data\\quantization\\stock6_test.log', model_path='15m_dnn_seq.h5')
- print('计算完成')
- with open('and_predict.txt', 'a') as f:
- up_num = 0
- up_right = 0
- i = 0
- for m in cnn_result: #3位 时间是字符串
- # print('find', m)
- for n in dnn_result: #5位 时间是int
- if m[0][0] == n[0][0] and int(m[0][1][:10].replace('-', '')) == n[0][1]:
- print('AND', m, n)
- f.write(str(m) + '\n')
- if n[1][0] == 1:
- up_right = up_right + 1
- elif n[1][1] == 1:
- up_right = up_right + 0.2
- up_num = up_num + 1
- i = i + 1
- break
- print(up_right, up_num, up_right / up_num)
- if __name__ == '__main__':
- # and_predict()
- dnn_and_kmeans()
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