43 lines
1.2 KiB
Python
43 lines
1.2 KiB
Python
from sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import Pipeline
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text_clf = Pipeline([('vect', CountVectorizer()),('tfidf', TfidfTransformer()),('clf', MultinomialNB())])
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import sys
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import yaml
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with open("example_1.yaml", 'r') as stream:
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try:
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train=yaml.safe_load(stream)
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except yaml.YAMLError as exc:
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print(exc)
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tc=text_clf.fit(train["data"],train["target"])
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print(sys.argv[1])
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answ=(tc.predict([sys.argv[1]]))[0]
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print train["target_names"][answ]
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for i in range(0, (len(train["target_names"]))):
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print (str(i)+" "+ train["target_names"][i])
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ca=int(raw_input("Correct answer.."))
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if ca == answ:
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print ("Yes I got it right")
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else:
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print("should I remember this?")
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a=raw_input("shoudIrememberthis?")
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if a == "y":
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train["data"].append(sys.argv[1])
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train["target"].append(ca)
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print yaml.dump(train,default_flow_style=False)
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file=open("example_1.yaml","w")
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file.write(yaml.dump(train,default_flow_style=False))
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file.close()
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else:
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print ("Ok, I already forgot")
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