nn

nn.py

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]]))