執行環境 anaconda 

python 版本 2.7.13

包含詳細資料集和資料的使用，視覺化結果，很快入門，程式碼如下

# -*- coding: utf-8 -*-
__author__ = 'LinearSVC線性分類支援向量機：包含懲罰項的'
# 導包
import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model, cross_validation, svm


# 資料集：鳶尾花資料集
'''
資料數 150
資料類別 3 (setosa, versicolor, virginica)
 
每個資料包含4個屬性：sepal萼片長度、萼片寬度、petal花瓣長度、花瓣寬度'''
def load_data_classfication():
    iris = datasets.load_iris()
    X_train = iris.data
    y_train = iris.target
    return cross_validation.train_test_split(X_train, y_train, test_size=0.25, random_state=0, stratify=y_train)


# 求得分類函式引數w, b
# 得出預測準確度
'''
呼叫預設線性分類函式，預設引數定義如下：
 
penalty = 'l2' 懲罰項loss = 'squared_hinge' 合頁損失函式的平方dual = True 解決對偶問題tol = 0.0001 終止迭代的閾值C = 1.0 懲罰引數multi_class = 'ovr' 多分類問題的策略：採用 one-vs-rest策略fit_intercept = True 計算截距，即決策函式中的常數項intercept-scaling = 1 例項X變成向量[X, intercept-scaling]，此時相當於添加了一個人工特徵，該特徵對所有例項都是常數值。class-weight = None 認為類權重是1
verbose = 0 表示不開啟
 
verbose輸出random_state = None 使用預設的隨機數生成器max_iter = 1000 指定最大的迭代次數'''
def test_LinearSVC(*data):
    X_train, X_test, y_train, y_test = data
    cls = svm.LinearSVC()
    cls.fit(X_train, y_train)
    print('Coefficients: %s, intercept %s' % (cls.coef_, cls.intercept_))
    print('Score: %.2f' % cls.score(X_test, y_test))

# 考察損失函式的影響
def test_LinearSVC_loss(*data):
    X_train, X_test, y_train, y_test = data
    losses = ['hinge', 'squared_hinge']
    for loss in losses:
        cls = svm.LinearSVC(loss=loss)
        cls.fit(X_train, y_train)
        print('Loss: %s' % loss)
        print('Coefficients: %s, intercept %s' % (cls.coef_, cls.intercept_))
        print('Score: %.2f' % cls.score(X_test, y_test))

# 考察懲罰項引數影響 dual=True, penalty='l2'不支援
def test_LinearSVC_L12(*data):
    X_train, X_test, y_train, y_test = data
    L12 = ['l1', 'l2']
    for p in L12:
        cls = svm.LinearSVC(penalty=p, dual=False)
        cls.fit(X_train, y_train)
        print('Loss: %s' % p)
        print('Coefficients: %s, intercept %s' % (cls.coef_, cls.intercept_))
        print('Score: %.2f' % cls.score(X_test, y_test))

# 考察懲罰項
def test_LinearSVC_C(*data):
    X_train, X_test, y_train, y_test = data
    Cs = np.logspace(-2, 1)
    train_scores = []
    test_scores = []
    for C in Cs:
        cls = svm.LinearSVC(C=C)
        cls.fit(X_train, y_train)
        train_scores.append(cls.score(X_train, y_train))
        test_scores.append(cls.score(X_test, y_test))

    # 繪圖
fig = plt.figure()
    ax = fig.add_subplot(1, 1, 1)
    ax.plot(Cs, train_scores, label='Training_score')
    ax.plot(Cs, test_scores, label='Testing_score')
    ax.set_xlabel(r'C')
    ax.set_ylabel(r'score')
    ax.set_xscale('log')
    ax.set_title('LinearSVC')
    ax.legend(loc='best')
    #plt.show()
plt.savefig('C_LinearSVC.png')
# 呼叫test_LinearSVC函式
if __name__ == '__main__':

    X_train, X_test, y_train, y_test = load_data_classfication() #結果1
    #test_LinearSVC(X_train, X_test, y_train, y_test) #結果2    #test_LinearSVC_loss(X_train, X_test, y_train, y_test)  #結果3    #test_LinearSVC_L12(X_train, X_test, y_train, y_test) #結果4
test_LinearSVC_C(X_train, X_test, y_train, y_test) #結果5
'''結果1
Coefficients: [[ 0.20959406  0.39924006 -0.817388   -0.44232006]
 [-0.12446638 -0.78463813  0.51620539 -1.02217595]
 [-0.80311193 -0.87621986  1.21370374  1.810064  ]], intercept [ 0.11973666  2.02931847 -1.4439232 ]
Score: 0.97
'''

# 結果3

# 結果4

# 結果5