%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')