37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
import numpy
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from keras.models import load_model
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from data import load_pay_data, load_gift_data
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import matplotlib.pyplot as plt
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# x_train, y_train, tx_train, ty_train, _ = load_pay_data(160)
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# model = load_model("./predict_pay")
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# p_data = model.predict(tx_train)
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# for i in range(len(p_data)):
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# comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
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# print(comp, p_data[i][0], ty_train[i])
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# if abs(comp) >= 1:
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# print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])
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x_train, y_train, tx_train, ty_train, _ = load_gift_data(160)
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model = load_model("./predict_gift")
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p_data = model.predict(tx_train)
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for i in range(len(p_data)):
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comp = (p_data[i][0] - ty_train[i]) / ty_train[i]
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print(comp, p_data[i][0], ty_train[i])
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if abs(comp) >= 0.1:
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print("测结果:", p_data[i][0], "测:", tx_train[i], "真实:", ty_train[i])
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plt.plot(ty_train)
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plt.plot(p_data)
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plt.show()
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# data = numpy.reshape([[15, 2359688 / 10000000, 255968 / 1000000, 10 / 10000]],(1, 4, 1))
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# print( model.predict(data))
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