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- # -*- encoding:utf-8 -*-
- import numpy as np
- from keras.models import load_model
- import joblib
- model_path = '161_18d_lstm_5D_ma5_s_seq.h5'
- data_dir = 'D:\\data\\quantization\\dmi\\'
- args = 'stock160_18d_train1_A'
- def read_data(path):
- lines = []
- with open(path) as f:
- for line in f.readlines()[:]:
- line = eval(line.strip())
- if line[-2][0].startswith('0') or line[-2][0].startswith('3'):
- lines.append(line)
- size = len(lines[0])
- train_x=[s[:size - 2] for s in lines]
- train_y=[s[size-1] for s in lines]
- return np.array(train_x),np.array(train_y),lines
- def _score(fact, line):
- with open('mix_predict_dmi_18d.txt', 'a') as f:
- f.write(str([line[-2], line[-1]]) + "\n")
- up_right = 0
- up_error = 0
- if fact[0] == 1:
- up_right = up_right + 1.12
- elif fact[1] == 1:
- up_right = up_right + 1.06
- elif fact[2] == 1:
- up_right = up_right + 1
- up_error = up_error + 0.5
- elif fact[3] == 1:
- up_right = up_right + 0.94
- up_error = up_error + 1
- else:
- up_error = up_error + 1
- up_right = up_right + 0.88
- return up_right,up_error
- def mul_predict(name="10_18d"):
- r = 0
- p = 0
- for x in range(0, 12):
- win_dnn, up_ratio,down_ratio = predict(data_dir + name + '_' + str(x) + ".log", x) # stock160_18d_trai_0
- r = r + up_ratio
- p = p + down_ratio
- print(r, p)
- def predict(file_path='', idx=-1):
- test_x,test_y,lines=read_data(file_path)
- print(idx, 'Load data success')
- test_x_a = test_x[:,:18*24]
- test_x_a = test_x_a.reshape(test_x.shape[0], 18, 24)
- # test_x_b = test_x[:, 18*16:18*16+10*18]
- # test_x_b = test_x_b.reshape(test_x.shape[0], 18, 10, 1)
- test_x_c = test_x[:,18*24:]
- model=load_model(model_path.split('.')[0] + '_' + str(idx) + '.h5')
- score = model.evaluate([test_x_c, test_x_a, ], test_y)
- print('LSTM', score)
- up_num = 0
- up_error = 0
- up_right = 0
- down_num = 0
- down_error = 0
- down_right = 0
- i = 0
- result=model.predict([test_x_c, test_x_a, ])
- win_dnn = []
- for r in result:
- fact = test_y[i]
- if idx in [-2]:
- if r[0] > 0.5 or r[1] > 0.5:
- pass
- else:
- if r[0] > 0.6 or r[1] > 0.6:
- tmp_right,tmp_error = _score(fact, lines[i])
- up_right = tmp_right + up_right
- up_error = tmp_error + up_error
- up_num = up_num + 1
- elif r[3] > 0.7 or r[4] > 0.7:
- if fact[0] == 1:
- down_error = down_error + 1
- down_right = down_right + 1.12
- elif fact[1] == 1:
- down_error = down_error + 1
- down_right = down_right + 1.06
- elif fact[2] == 1:
- down_error = down_error + 0.5
- down_right = down_right + 1
- elif fact[3] == 1:
- down_right = down_right + 0.94
- else:
- down_right = down_right + 0.88
- down_num = down_num + 1
- i = i + 1
- if up_num == 0:
- up_num = 1
- if down_num == 0:
- down_num = 1
- print('LSTM', up_right, up_num, up_right/up_num, up_error/up_num, down_right/down_num, down_error/down_num)
- return win_dnn,up_right/up_num,down_right/down_num
- if __name__ == '__main__':
- mul_predict(args)
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