SVM的章节已经讲完，具体内容请参考：《》

常规操作：

1、头文件引入SVM相关的包

2、防止中文乱码3、去警告4、读取数据5、数据分割训练集和测试集 8:2

import numpy as npimport pandas as pdimport matplotlib as mplimport matplotlib.pyplot as pltimport warningsfrom sklearn import svm#svm导入from sklearn.svm import SVCfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.exceptions import ChangedBehaviorWarning## 设置属性防止中文乱码mpl.rcParams['font.sans-serif'] = [u'SimHei']mpl.rcParams['axes.unicode_minus'] = Falsewarnings.filterwarnings('ignore', category=ChangedBehaviorWarning)## 读取数据# 'sepal length', 'sepal width', 'petal length', 'petal width'iris_feature = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'path = './datas/iris.data'  # 数据文件路径data = pd.read_csv(path, header=None)x, y = data[list(range(4))], data[4]y = pd.Categorical(y).codes #把文本数据进行编码，比如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, random_state=0, train_size=0.8)

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__API 说明：__$\color{red}{sklearn.svm.SVC}$

引用： from sklearn.svm import SVC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

gamma值越大，训练集的拟合就越好，但是会造成过拟合，导致测试集拟合变差。

gamma值越小，模型的泛化能力越好，训练集和测试集的拟合相近，但是会导致训练集出现欠拟合问题，从而准确率变低，导致测试集准确率也变低。

clf = SVC(C=1,kernel='rbf',gamma=0.1)## 模型训练clf.fit(x_train, y_train)

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计算模型的准确率/精度

print (clf.score(x_train, y_train)) print ('训练集准确率：', accuracy_score(y_train, clf.predict(x_train)))print (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以及预测值(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))

输出：

0.85训练集准确率： 0.850.733333333333测试集准确率： 0.733333333333

decision_function:[[-0.25039727  1.0886331   2.16176417][ 1.03478736  2.11650098 -0.15128834][ 2.23214438  1.00598335 -0.23812773][-0.19163546  2.1175139   1.07412155][-0.32152579  1.14496276  2.17656303][ 1.02173467  2.16988825 -0.19162293][ 2.14580325  0.95677746 -0.10258071][-0.23566638  2.17796366  1.05770273][-0.13008471  2.12075927  1.00932543][-0.19844194  2.1995431   0.99889884][-0.36343522  1.08701831  2.27641692][ 2.30535715  1.04393285 -0.34929   ][-0.35915878  1.06384614  2.29531264][ 2.29333629  0.99860275 -0.29193904][ 2.21795456  0.97111601 -0.18907056][ 0.92054508  2.2724345  -0.19297958][-0.2997012   1.10328323  2.19641797][-0.2730624   1.03890272  2.23415968][-0.33839217  2.26132199  1.07707018][-0.44273262  1.17653689  2.26619573][-0.15877661  2.21746358  0.94131303][-0.44724083  1.02472152  2.42251931][-0.17202518  1.05287918  2.119146  ][-0.14988387  2.23343312  0.91645074][-0.31861821  1.16774019  2.15087802][-0.29622421  1.14950193  2.14672228][ 1.0664275   2.1904298  -0.2568573 ][-0.35991183  1.20227659  2.15763525][-0.35330602  1.04124945  2.31205657][-0.2997012   1.10328323  2.19641797][-0.05522314  2.03779287  1.01743027][ 2.25203496  1.06973396 -0.32176891][-0.17449621  2.18085941  0.9936368 ][-0.11021164  2.18046075  0.92975089][-0.05865155  2.14084287  0.91780868][-0.12662311  2.21612151  0.9105016 ][-0.19163546  2.1175139   1.07412155][-0.38070881  1.0296007   2.35110811][ 2.24957743  0.96861839 -0.21819582][ 2.35477694  1.05478502 -0.40956196][-0.34332437  1.16288782  2.18043655][-0.06527735  2.12119172  0.94408563][ 2.14185505  1.03254567 -0.17440072][ 2.27389225  0.85571723 -0.12960948][-0.35915878  1.06384614  2.29531264][ 2.30724951  1.05732668 -0.3645762 ][-0.13008471  2.12075927  1.00932543][ 1.00329378  2.20214884 -0.20544262][ 2.37889994  0.99914274 -0.37804268][-0.38865303  2.25320429  1.13544874][-0.29145938  0.96854255  2.32291684][-0.09164014  2.14161983  0.95002031][ 2.22623117  1.08968182 -0.31591299][-0.4096892   1.06746523  2.34222397][-0.33660296  1.0467762   2.28982676][-0.2997012   1.10328323  2.19641797][-0.32152579  1.14496276  2.17656303][ 2.33278328  0.94341849 -0.27620177][ 2.32663406  1.00960575 -0.33623981][-0.25094655  1.06568299  2.18526357][-0.2730624   1.03890272  2.23415968][ 2.13304331  1.19108118 -0.32412449][-0.11663626  1.03526731  2.08136896][ 2.19635991  1.09554303 -0.29190293][-0.19042462  2.21791314  0.97251148][-0.35915878  1.06384614  2.29531264][ 2.37987847  1.02502782 -0.40490629][ 2.31697854  0.97865204 -0.29563057][-0.42101983  1.06048387  2.36053596][ 2.26321395  1.00248244 -0.26569639][ 2.3322641   1.06231608 -0.39458018][ 2.2645061   0.93262533 -0.19713143][-0.17206568  2.24979256  0.92227312][-0.31794906  1.05203355  2.2659155 ][-0.44593685  1.03180134  2.41413551][ 2.26321395  1.00248244 -0.26569639][ 2.22247594  1.07534695 -0.29782289][ 2.20680036  1.02662003 -0.23342039][-0.11748127  2.16161947  0.9558618 ][-0.32277435  1.09831759  2.22445676][ 2.21795026  1.05994599 -0.27789625][ 2.21270515  1.04364305 -0.2563482 ][-0.2986835   1.12654041  2.17214309][ 2.14185505  1.03254567 -0.17440072][-0.5         1.07338601  2.42661399][ 1.0415998   2.20742886 -0.24902865][-0.30569708  0.92274296  2.38295412][-0.32111039  1.07499685  2.24611354][ 2.36439692  0.89257767 -0.25697458][-0.1613555   2.11948124  1.04187426][ 2.161655    0.92086513 -0.08252013][-0.47608835  1.04954709  2.42654126][ 2.33278328  0.94341849 -0.27620177][ 2.30535715  1.04393285 -0.34929   ][-0.47075253  1.07424442  2.39650811][ 2.24367895  1.03936622 -0.28304517][-0.14575094  1.03325696  2.11249398][-0.11748127  2.16161947  0.9558618 ][-0.17449621  2.18085941  0.9936368 ][-0.16701198  2.19987473  0.96713725][-0.22523374  1.06936924  2.1558645 ][-0.34404723  1.09287868  2.25116855][-0.35991183  1.20227659  2.15763525][-0.34404723  1.09287868  2.25116855][ 2.16544172  1.10090524 -0.26634696][-0.14988387  2.23343312  0.91645074][-0.32111039  1.07499685  2.24611354][-0.17449621  2.18085941  0.9936368 ][ 2.23827935  1.02296045 -0.2612398 ][-0.34541291  1.11637043  2.22904248][ 0.96788879  2.12033521 -0.088224  ][-0.07704422  2.07965201  0.99739221][-0.3958175   1.23359604  2.16222145][ 2.13504156  1.01391343 -0.14895499][ 2.31059852  0.96260146 -0.27319998][ 2.22247594  1.07534695 -0.29782289][-0.27283046  1.13075432  2.14207614][-0.17449621  2.18085941  0.9936368 ][-0.29717239  0.92710063  2.37007176][ 2.33180515  1.03788212 -0.36968728]]predict:[2 1 0 1 2 1 0 1 1 1 2 0 2 0 0 1 2 2 1 2 1 2 2 1 2 2 1 2 2 2 1 0 1 1 1 1 12 0 0 2 1 0 0 2 0 1 1 0 1 2 1 0 2 2 2 2 0 0 2 2 0 2 0 1 2 0 0 2 0 0 0 1 22 0 0 0 1 2 0 0 2 0 2 1 2 2 0 1 0 2 0 0 2 0 2 1 1 1 2 2 2 2 0 1 2 1 0 2 11 2 0 0 0 2 1 2 0]

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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(['#00FFCC', '#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)## 所以样本点plt.scatter(x[0], x[1], c=y, edgecolors='k', s=50, cmap=cm_dark)      # 样本## 测试数据集plt.scatter(x_test[0], x_test[1], s=120, facecolors='none', zorder=10)     # 圈中测试集样本## lable列表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(u'鸢尾花SVM特征分类', fontsize=16)plt.grid(b=True, ls=':')plt.tight_layout(pad=1.5)plt.show()