%pylab inline

from sklearn import datasets
digits = datasets.load_digits()

digits.data

digits.target

digits.images[0]

from sklearn import svm
clf = svm.SVC(gamma=0.001, C=100.)

clf.fit(digits.data[:-1], digits.target[:-1])

clf.predict(digits.data[-1])

plt.figure(figsize=(2, 2))
plt.imshow(digits.images[-1], interpolation='nearest', cmap=plt.cm.binary)

print digits.target[-1]

from sklearn import svm
from sklearn import datasets
clf = svm.SVC()
iris = datasets.load_iris()
X, y = iris.data, iris.target
clf.fit(X, y)

import pickle
s = pickle.dumps(clf)
clf2 = pickle.loads(s)
clf2.predict(X[0])

y[0]

from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl')