prediction_dict
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@@ -53,16 +53,17 @@ def load_estimation_model(inputfilename, outputfilename, begin, end, csv_export=
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model.load_state_dict(torch.load("em.pth"))
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model.load_state_dict(torch.load("em.pth"))
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my_prediction = model.forward(data)
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my_prediction = model.forward(data)
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my_prediction[0][0] = 1000 * float(my_prediction[0][0])
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prediction_dict = {}
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my_prediction[0][1] = 1000 * float(my_prediction[0][1])
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prediction_dict["F1"] = 1000 * float(my_prediction[0][0])
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my_prediction[0][2] = 1000 * float(my_prediction[0][2])
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prediction_dict["F2"] = 1000 * float(my_prediction[0][1])
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my_prediction[0][3] = 1000 * float(my_prediction[0][3])
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prediction_dict["F3"] = 1000 * float(my_prediction[0][2])
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prediction_dict["F4"] = 1000 * float(my_prediction[0][3])
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if csv_export:
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if csv_export:
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with open(outputfilename, "w") as wf:
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with open(outputfilename, "w") as wf:
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wf.write("NAME,begin,end,F1,F2,F3,F4\n")
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wf.write("NAME,begin,end,F1,F2,F3,F4\n")
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wf.write(name + "," + str(begin) + "," + str(end) + "," + \
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wf.write(name + "," + str(begin) + "," + str(end) + "," + \
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str(my_prediction[0][0]) + "," + str(my_prediction[0][1]) + "," + \
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str(prediction_dict["F1"]) + "," + str(prediction_dict["F2"]) + "," + \
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str(my_prediction[0][2]) + "," + str(my_prediction[0][3]) + "\n")
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str(prediction_dict["F3"]) + "," + str(prediction_dict["F4"]) + "\n")
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return my_prediction
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return prediction_dict
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