#用各种方式在iris数据集上数据分类

 #载入iris数据集，其中每个特征向量有四个维度，有三种类别

 from sklearn import datasets

 iris = datasets.load_iris()

 print ("The iris' target names: ",iris.target_names)

 x = iris.data

 y = iris.target

 #待分类的两个样本

 test_vector = [[1,-1,2.6,-2],[0,0,7,0.8]]

 #线性回归

 from sklearn import linear_model

 linear = linear_model.LinearRegression()

 linear.fit(x,y)

 print ("linear's score: ",linear.score(x,y))

 print ("w:",linear.coef_)

 print ("b:",linear.intercept_)

 print ("predict: ",linear.predict(test_vector))

 #逻辑回归

 LR = linear_model.LogisticRegression()

 LR.fit(x,y)

 print ("LogisticRegression:",LR.predict(test_vector))

 #决策树

 from sklearn import tree

 TR = tree.DecisionTreeClassifier(criterion='entropy')

 TR.fit(x,y)

 print ("DecisionTree:",TR.predict(test_vector))

 #支持向量机

 from sklearn import svm

 SV = svm.SVC()

 SV.fit(x,y)

 print ("svm:",SV.predict(test_vector))

 #朴素贝叶斯

 from sklearn import naive_bayes

 NB = naive_bayes.GaussianNB()

 NB.fit(x,y)

 print ("naive_bayes:",NB.predict(test_vector))

 #K近邻

 from sklearn import neighbors

 KNN = neighbors.KNeighborsClassifier(n_neighbors = 3)

 KNN.fit(x,y)

 print ("KNeighbors:",KNN.predict(test_vector))

 '''

 he iris' target names:  ['setosa' 'versicolor' 'virginica']

 linear's score:  0.930422367533

 w: [-0.10974146 -0.04424045  0.22700138  0.60989412]

 b: 0.192083994828

 predict:  [-0.50300167  2.26900897]

 LogisticRegression: [1 2]

 DecisionTree: [1 2]

 svm: [2 2]

 naive_bayes: [2 2]

 KNeighbors: [0 1]

 '''