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- from sklearn.neural_network import MLPClassifier
- X = []
- y = []
- with open('letter-recognition.data') as f:
- lines = f.readlines()
- for line in lines:
- line = line.replace("\n", "")
- parseLine = line.split(",")
- y.append(parseLine[0])
- parseLine.pop(0)
- parseLine = list(map(int, parseLine))
- X.append(parseLine)
- clf = MLPClassifier(solver='lbfgs', hidden_layer_sizes=(500, ), random_state=1, activation='identity', max_iter=5000, learning_rate='adaptive')
- print(clf.fit(X[:15999], y[:15999]))
- test_data_x = clf.predict(X[16000:19999])
- test_data_y = y[16000:19999]
- accuracy = 0
- for index,test_x in enumerate(test_data_x):
- if test_x == test_data_y[index]:
- accuracy = accuracy + 1
- print(accuracy/len(test_data_y))
- print(clf.predict([[4,4,4,6,2,7,7,14,2,5,6,8,6,8,0,8]]))
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