014. basic of scikit-learn, basic of svm
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# @
import sklearn import svm
# I create svm classifier instance
clf = svm.SVC()
# And then, we will use fit() for training and predict() for predicting
# 1 argument : data
# 2 argument : label
# We will deal with xor data
clf.fit(
[[0,0],
[1,0],
[0,1],
[1,1]],
[[0, 1, 1, 0]])
results = clf.predict([0,0], [1,0])
print(results)
# output will be list of [0,1]
# @
from sklearn import metrics
datas = [[0,0], [1,0], [0,1], [1,1]]
labels = [0, 1, 1, 0]
examples = [[0,0], [1,0]]
examples_label = [0,1]
clf = svm.SVC()
clf.fit(datas, labels)
results = clf.predict(examples)
print(results)
score = metrics.accuracy_score(examples_label, results)
print("accuracy : ", score)
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