机器学习sklearn19.0——SVM算法

一、线性分类器

二、支持向量机

关于感知机模型是学习SVM的基础，详细链接参照博客：http://www.cnblogs.com/pinard/p/6042320.html

三、线性支持向量机的软间隔最大化模型

                                                

四、线性不可分支持向量机与核函数

五、sklearn中SVM算法库的使用

六、支持向量机高斯核调参总结

七、SVM算法案例代码

（1）鸢尾花SVM数据分类

#!/usr/bin/env python# -*- coding:utf-8 -*-# Author:ZhengzhengLiu#鸢尾花数据SVM分类import numpy as npimport pandas as pdimport matplotlib as mplimport matplotlib.pyplot as pltfrom sklearn import svm     #svm导入from sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_score#解决中文显示问题mpl.rcParams['font.sans-serif']=[u'simHei']mpl.rcParams['axes.unicode_minus']=False#数据读取path = "datas/iris.data"data = pd.read_csv(path,header=None)iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'x,y = data[list(range(4))],data[4]y = pd.Categorical(y).codes     #将文本数据进行编码，如：a b c编码为 0 1 2x = x[[0,1]]#数据分割x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.6,random_state=28)# svm.SVC API说明：# 功能：使用SVM分类器进行模型构建# 参数说明：# C: 误差项的惩罚系数，默认为1.0；一般为大于0的一个数字，C越大表示在训练过程中对于总误差的关注度越高，也就是说当C越大的时候，对于训练集的表现会越好，# 但是有可能引发过度拟合的问题(overfiting)# kernel：指定SVM内部函数的类型，可选值：linear、poly、rbf、sigmoid、precomputed(基本不用，有前提要求，要求特征属性数目和样本数目一样)；默认是rbf；# degree：当使用多项式函数作为svm内部的函数的时候，给定多项式的项数，默认为3# gamma：当SVM内部使用poly、rbf、sigmoid的时候，核函数的系数值，当默认值为auto的时候，实际系数为1/n_features# coef0: 当核函数为poly或者sigmoid的时候，给定的独立系数，默认为0# probability：是否启用概率估计，默认不启动，不太建议启动# shrinking：是否开启收缩启发式计算，默认为True# tol: 模型构建收敛参数，当模型的的误差变化率小于该值的时候，结束模型构建过程，默认值:1e-3# cache_size：在模型构建过程中，缓存数据的最大内存大小，默认为空，单位MB# class_weight：给定各个类别的权重，默认为空# max_iter：最大迭代次数，默认-1表示不限制# decision_function_shape: 决策函数，可选值：ovo和ovr，默认为None；推荐使用ovr；（1.7以上版本才有）#数据svm分类器构建clf = svm.SVC(C=1,kernel="linear")#模型训练clf.fit(x_train,y_train)#计算模型的准确率print("score:",clf.score(x_train,y_train))print("训练集准确率:",accuracy_score(y_train,clf.predict(x_train)))print("score:",clf.score(x_test,y_test))print("测试集准确率:",accuracy_score(y_test,clf.predict(x_test)))#计算决策函数的结构值与预测值#decision_function 计算的是样本x到各个分割平面的距离<也就是决策函数的值>print("decision_function:\n",clf.decision_function(x_train))print("npredict\n",clf.predict(x_train))#-画图N = 500x1_min,x2_min = x.min()x1_max,x2_max = x.max()t1 = np.linspace(x1_min,x1_max,N)t2 = np.linspace(x2_min,x2_max,N)x1,x2 = np.meshgrid(t1,t2)      #生成网格采样点grid_show = np.dstack((x1.flat,x2.flat))[0]     #测试点grid_hat = clf.predict(grid_show)       #预测分类值grid_hat = grid_hat.reshape(x1.shape)   #使之与输入的形状相同cm_light = mpl.colors.ListedColormap(["#A0FFA0","#FFA0A0","#A0A0FF"])cm_dark = mpl.colors.ListedColormap(["g","r","b"])plt.figure(facecolor="w")#区域图plt.pcolormesh(x1,x2,grid_hat,cmap=cm_light)#所有样本点plt.scatter(x[0],x[1],c=y,edgecolors="k",s=50,cmap=cm_dark)#测试数据集plt.scatter(x_test[0],x_test[1],s=120,facecolor="none",zorder=10)   #圈中测试集样本plt.xlabel(iris_feature[0],fontsize=13)plt.ylabel(iris_feature[1],fontsize=13)plt.xlim(x1_min,x1_max)plt.ylim(x2_min,x2_max)plt.title("鸢尾花SVM特征分类",fontsize=16)plt.grid(b=True,ls=":")plt.tight_layout(pad=1.5)plt.savefig("鸢尾花SVM特征分类.png")plt.show()#运行结果：score: 0.85训练集准确率: 0.85score: 0.711111111111测试集准确率: 0.711111111111decision_function: [[-0.1735954   1.07024224  2.10335316] [-0.24032342  2.13157018  1.10875323] [ 0.99570201  2.0769661  -0.0726681 ] [-0.16985611  1.00818979  2.16166632] [-0.19326418  1.07479258  2.1184716 ] [ 2.1722349  -0.11684293  0.94460803] [-0.07947693  0.95668703  2.1227899 ] [ 2.07787335 -0.0797157   1.00184235] [-0.31924164  1.07334357  2.24589807] [-0.12255379  1.02784016  2.09471363] [-0.43725436  1.10064561  2.33660874] [-0.20895059  1.09371845  2.11523215] [-0.37426563  1.10137012  2.27289551] [-0.22488009  1.03621634  2.18866375] [-0.10288501  1.02328982  2.07959518] [ 2.20385081  0.92173331 -0.12558412] [ 2.09380285  0.97778641 -0.07158926] [ 2.18418203  0.92628365 -0.11046568] [ 2.12493258 -0.1364933   1.01156072] [-0.22861938  1.09826879  2.13035059] [-0.26023529  1.05969255  2.20054274] [ 2.15256612 -0.11229259  0.95972647] [-0.19724656  1.06041705  2.13682951] [-0.08321622  1.01873948  2.06447674] [-0.22488009  1.03621634  2.18866375] [-0.23658413  1.06951773  2.16706639] [-0.16587374  1.02256532  2.14330842] [-0.09865955  2.11409333  0.98456622] [-0.20122893  1.04604152  2.15518741] [ 1.01935317  2.08679128 -0.10614445] [-0.12255379  1.02784016  2.09471363] [-0.11060667  2.07096674  1.03963992] [-0.24032342  2.13157018  1.10875323] [-0.31176307  0.94923867  2.3625244 ] [-0.15790899  1.05131637  2.10659262] [-0.29160811  1.09754428  2.19406383] [ 2.19962535 -0.16907019  0.96944484] [-0.11060667  2.07096674  1.03963992] [-0.26421766  1.04531702  2.21890064] [ 2.18792131 -0.1357688   0.94784748] [ 2.15654849 -0.09791706  0.94136857] [-0.24479196  0.9643387   2.28045326] [ 2.11721092 -0.08881638  0.97160546] [-0.20122893  1.04604152  2.15518741] [-0.26771387  2.18379745  1.08391642] [-0.06306126  2.1670451   0.89601617] [ 2.13314042  0.96868573 -0.10182615] [-0.5         1.17634909  2.32365091] [-0.33891043  1.07789391  2.26101652] [ 2.15654849 -0.09791706  0.94136857] [-0.0712691   2.06186606  1.00940303] [ 2.19612915  0.96941024 -0.16553939] [ 2.13314042  0.96868573 -0.10182615] [-0.22488009  1.03621634  2.18866375] [-0.27169624  2.16942192  1.10227432] [ 2.1881644   0.94065918 -0.12882358] [ 2.2270158  -0.22129746  0.99428166] [ 2.26659645 -0.15397016  0.8873737 ] [ 2.15630541 -0.17434504  1.01803963] [ 2.11347163  0.97323607 -0.08670771]]npredict [2 1 1 2 2 0 2 0 2 2 2 2 2 2 2 0 0 0 0 2 2 0 2 2 2 2 2 1 2 1 2 1 1 2 2 2 0 1 2 0 0 2 0 2 1 1 0 2 2 0 1 0 0 2 1 0 0 0 0 0]