57 lines
1.3 KiB
Python
57 lines
1.3 KiB
Python
#!/users/dsc/bin/Python3
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#-*-coding: UTF-8 -*-
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# from Serialization import Serialization,Deserialization
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# a={"a":1,"b":2}
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# file_name="test"
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# c=Serialization(file_name,a)
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# print(c)
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# b={"c":3,"d":4}
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# e=Deserialization(file_name)
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# if(e):
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# print("e---")
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# print(e)
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# print(a)
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# print(b)
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# from mntest import mnTest,mnTrain
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# print(mnTrain())
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from ml_utiliy import ihyperDB,time_delta_split
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import datetime
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# tag_name="TCM1750_ENT-40-183-FH-2"
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# stime=1704441868000
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# etime=1704445468000
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# etime2=datetime.datetime.now()
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# stime2=etime2-datetime.timedelta(minutes=105)
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# ihd=ihyperDB()
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# print(type(ihd.value_))
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# ihd.select_raw_data(tag_name,stime,etime)
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# if(ihd.flag_):
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# print(len(ihd.value_))
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# else:
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# print("ERROR")
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# ihd.select_raw_data(tag_name,stime2,etime2)
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# if(ihd.flag_):
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# print(len(ihd.values_))
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# print(type(ihd.values_))
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# else:
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# print("ERROR")
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# [a,b]=time_delta_split(stime2,etime2)
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# print(a)
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# print(b)
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import json
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import numpy as np
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import ml_train
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sampleJson = """{"key1": "value1", "key2": "value2"}"""
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json_path="/users/dsc/code/eqpalg/alg_json/ml_param.json"
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with open(json_path,'r',encoding='utf8')as fp:
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json_data = json.load(fp)
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print(json_data)
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tm1=ml_train.TrainModle()
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# tm1.Train(json_data)
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tm1.Predict(json_data)
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