from sklearn.datasets import load_iris
from sklearn import svm
iris = load_iris()
print iris.data
[[ 5.1 3.5 1.4 0.2] [ 4.9 3. 1.4 0.2] [ 4.7 3.2 1.3 0.2] [ 4.6 3.1 1.5 0.2] [ 5. 3.6 1.4 0.2] [ 5.4 3.9 1.7 0.4] [ 4.6 3.4 1.4 0.3] [ 5. 3.4 1.5 0.2] [ 4.4 2.9 1.4 0.2] [ 4.9 3.1 1.5 0.1] [ 5.4 3.7 1.5 0.2] [ 4.8 3.4 1.6 0.2] [ 4.8 3. 1.4 0.1] [ 4.3 3. 1.1 0.1] [ 5.8 4. 1.2 0.2] [ 5.7 4.4 1.5 0.4] [ 5.4 3.9 1.3 0.4] [ 5.1 3.5 1.4 0.3] [ 5.7 3.8 1.7 0.3] [ 5.1 3.8 1.5 0.3] [ 5.4 3.4 1.7 0.2] [ 5.1 3.7 1.5 0.4] [ 4.6 3.6 1. 0.2] [ 5.1 3.3 1.7 0.5] [ 4.8 3.4 1.9 0.2] [ 5. 3. 1.6 0.2] [ 5. 3.4 1.6 0.4] [ 5.2 3.5 1.5 0.2] [ 5.2 3.4 1.4 0.2] [ 4.7 3.2 1.6 0.2] [ 4.8 3.1 1.6 0.2] [ 5.4 3.4 1.5 0.4] [ 5.2 4.1 1.5 0.1] [ 5.5 4.2 1.4 0.2] [ 4.9 3.1 1.5 0.1] [ 5. 3.2 1.2 0.2] [ 5.5 3.5 1.3 0.2] [ 4.9 3.1 1.5 0.1] [ 4.4 3. 1.3 0.2] [ 5.1 3.4 1.5 0.2] [ 5. 3.5 1.3 0.3] [ 4.5 2.3 1.3 0.3] [ 4.4 3.2 1.3 0.2] [ 5. 3.5 1.6 0.6] [ 5.1 3.8 1.9 0.4] [ 4.8 3. 1.4 0.3] [ 5.1 3.8 1.6 0.2] [ 4.6 3.2 1.4 0.2] [ 5.3 3.7 1.5 0.2] [ 5. 3.3 1.4 0.2] [ 7. 3.2 4.7 1.4] [ 6.4 3.2 4.5 1.5] [ 6.9 3.1 4.9 1.5] [ 5.5 2.3 4. 1.3] [ 6.5 2.8 4.6 1.5] [ 5.7 2.8 4.5 1.3] [ 6.3 3.3 4.7 1.6] [ 4.9 2.4 3.3 1. ] [ 6.6 2.9 4.6 1.3] [ 5.2 2.7 3.9 1.4] [ 5. 2. 3.5 1. ] [ 5.9 3. 4.2 1.5] [ 6. 2.2 4. 1. ] [ 6.1 2.9 4.7 1.4] [ 5.6 2.9 3.6 1.3] [ 6.7 3.1 4.4 1.4] [ 5.6 3. 4.5 1.5] [ 5.8 2.7 4.1 1. ] [ 6.2 2.2 4.5 1.5] [ 5.6 2.5 3.9 1.1] [ 5.9 3.2 4.8 1.8] [ 6.1 2.8 4. 1.3] [ 6.3 2.5 4.9 1.5] [ 6.1 2.8 4.7 1.2] [ 6.4 2.9 4.3 1.3] [ 6.6 3. 4.4 1.4] [ 6.8 2.8 4.8 1.4] [ 6.7 3. 5. 1.7] [ 6. 2.9 4.5 1.5] [ 5.7 2.6 3.5 1. ] [ 5.5 2.4 3.8 1.1] [ 5.5 2.4 3.7 1. ] [ 5.8 2.7 3.9 1.2] [ 6. 2.7 5.1 1.6] [ 5.4 3. 4.5 1.5] [ 6. 3.4 4.5 1.6] [ 6.7 3.1 4.7 1.5] [ 6.3 2.3 4.4 1.3] [ 5.6 3. 4.1 1.3] [ 5.5 2.5 4. 1.3] [ 5.5 2.6 4.4 1.2] [ 6.1 3. 4.6 1.4] [ 5.8 2.6 4. 1.2] [ 5. 2.3 3.3 1. ] [ 5.6 2.7 4.2 1.3] [ 5.7 3. 4.2 1.2] [ 5.7 2.9 4.2 1.3] [ 6.2 2.9 4.3 1.3] [ 5.1 2.5 3. 1.1] [ 5.7 2.8 4.1 1.3] [ 6.3 3.3 6. 2.5] [ 5.8 2.7 5.1 1.9] [ 7.1 3. 5.9 2.1] [ 6.3 2.9 5.6 1.8] [ 6.5 3. 5.8 2.2] [ 7.6 3. 6.6 2.1] [ 4.9 2.5 4.5 1.7] [ 7.3 2.9 6.3 1.8] [ 6.7 2.5 5.8 1.8] [ 7.2 3.6 6.1 2.5] [ 6.5 3.2 5.1 2. ] [ 6.4 2.7 5.3 1.9] [ 6.8 3. 5.5 2.1] [ 5.7 2.5 5. 2. ] [ 5.8 2.8 5.1 2.4] [ 6.4 3.2 5.3 2.3] [ 6.5 3. 5.5 1.8] [ 7.7 3.8 6.7 2.2] [ 7.7 2.6 6.9 2.3] [ 6. 2.2 5. 1.5] [ 6.9 3.2 5.7 2.3] [ 5.6 2.8 4.9 2. ] [ 7.7 2.8 6.7 2. ] [ 6.3 2.7 4.9 1.8] [ 6.7 3.3 5.7 2.1] [ 7.2 3.2 6. 1.8] [ 6.2 2.8 4.8 1.8] [ 6.1 3. 4.9 1.8] [ 6.4 2.8 5.6 2.1] [ 7.2 3. 5.8 1.6] [ 7.4 2.8 6.1 1.9] [ 7.9 3.8 6.4 2. ] [ 6.4 2.8 5.6 2.2] [ 6.3 2.8 5.1 1.5] [ 6.1 2.6 5.6 1.4] [ 7.7 3. 6.1 2.3] [ 6.3 3.4 5.6 2.4] [ 6.4 3.1 5.5 1.8] [ 6. 3. 4.8 1.8] [ 6.9 3.1 5.4 2.1] [ 6.7 3.1 5.6 2.4] [ 6.9 3.1 5.1 2.3] [ 5.8 2.7 5.1 1.9] [ 6.8 3.2 5.9 2.3] [ 6.7 3.3 5.7 2.5] [ 6.7 3. 5.2 2.3] [ 6.3 2.5 5. 1.9] [ 6.5 3. 5.2 2. ] [ 6.2 3.4 5.4 2.3] [ 5.9 3. 5.1 1.8]]
from sklearn.cross_validation import train_test_split
X = iris.data
y = iris.target
X_train, X_test,y_train,y_test = train_test_split(X,y,test_size = 0.2, random_state = 0)#to reproduce the result, we need to set the random_state
/Users/zjm/anaconda/lib/python2.7/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20. "This module will be removed in 0.20.", DeprecationWarning)
model = svm.LinearSVC()
#train the model
model.fit(X_train,y_train)
LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True, intercept_scaling=1, loss='squared_hinge', max_iter=1000, multi_class='ovr', penalty='l2', random_state=None, tol=0.0001, verbose=0)
model.predict(X_test)
array([2, 1, 0, 2, 0, 2, 0, 1, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 0, 0, 2, 1, 0, 0, 2, 0, 0, 1, 1, 0])
print(model.score(X_test, y_test))
1.0