完善了运算
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parent
2a2e5f9d27
commit
aa956261b8
3
.gitignore
vendored
3
.gitignore
vendored
@ -1,3 +1,4 @@
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__pycache__/*
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__pycache__/*
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Temp/
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Temp/
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Scales/
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Scales/
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Work/
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21
file_load.py
21
file_load.py
@ -40,7 +40,7 @@ def calc_similarity(scale):
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for i in scale:
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for i in scale:
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item.append(i)
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item.append(i)
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vec.append(client.embeddings.create(
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vec.append(client.embeddings.create(
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input=scale[i], model="text-embedding-3-small" # nomic-embed-text text-embedding-3-small
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input=scale[i], model="text-embedding-3-large" # nomic-embed-text text-embedding-3-small
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).data[0].embedding)
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).data[0].embedding)
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simi=cosine_similarity(vec)
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simi=cosine_similarity(vec)
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que=[]
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que=[]
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@ -73,24 +73,17 @@ def make_data():
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for j in range(1,20):
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for j in range(1,20):
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s += ',' + str(random.randint(0,4))
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s += ',' + str(random.randint(0,4))
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s+='\n'
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s+='\n'
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with open("Temp/data.csv","w") as data:
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with open("data.csv","w") as data:
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data.write(s)
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data.write(s)
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def corelation(sort:bool=True):
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def corelation():
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data = pandas.read_csv("data.csv")
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data = pandas.read_csv("Work/data.csv")
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que=[]
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que={}
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for i in data:
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for i in data:
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for j in data:
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for j in data:
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try:
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try:
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if(i != j):
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que[i,j]=data[i].corr(data[j])
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# que[i,j]["psr"]=data[i].corr(data[j])
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que.append({"from":j,"to":i,"psr":data[i].corr(data[j])})
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else:
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pass
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except:
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except:
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pass
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pass
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if sort:
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return que
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return sorted(que,key = lambda t : abs(t["psr"]), reverse=True)
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else:
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return que
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20
main.py
20
main.py
@ -10,23 +10,21 @@ import numpy
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similarity = file_load.similarity()
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similarity = file_load.similarity()
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corelation = file_load.corelation()
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corelation = file_load.corelation()
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table = {}
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for i in corelation:
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table[i["from"],i["to"]]=i["psr"]
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x=[]
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x=[]
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y=[]
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y=[]
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s="similarity, corelation\n"
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for i in similarity:
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for i in similarity:
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x.append(abs(table[i["from"],i["to"]]))
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try:
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y.append(i["similarity"])
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s+=str(i["similarity"])+','+str(corelation[i["from"],i["to"]])+'\n'
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x.append(i["similarity"])
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y.append(corelation[i["from"],i["to"]])
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except:
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pass
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print(numpy.corrcoef(x,y)[0,1])
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print(numpy.corrcoef(x,y)[0,1])
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s="similarity, corelation\n"
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with open("Work/point.csv","w") as point:
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for i in similarity:
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s+=str(i["similarity"])+','+str(table[i["from"],i["to"]])+'\n'
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with open("Temp/point.csv","w") as point:
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point.write(s)
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point.write(s)
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