SVM支持向量机

#############################  支持向量机的核函数 #######################################
#导入numpy
import numpy as np
#导入画图工具
import matplotlib.pyplot as plt
#导入支持向量机svm
from sklearn import svm
#导入数据集生成工具
from sklearn.datasets import make_blobs

#先创建50个数据点,让他们分为两类
X,y = make_blobs(n_samples=50,centers=2,random_state=6)
#创建一个线性内核的支持向量机模型
clf = svm.SVC(kernel = ‘linear‘,C=1000)
clf.fit(X,y)
#把数据点画出来
plt.scatter(X[:, 0],X[:, 1],c=y,s=30,cmap=plt.cm.Paired)

#建立图像坐标
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
#生成两个等差数列
xx = np.linspace(xlim[0],xlim[1],30)
yy = np.linspace(ylim[0],ylim[1],30)
YY,XX = np.meshgrid(yy,xx)
xy = np.vstack([XX.ravel(),YY.ravel()]).T
Z = clf.decision_function(xy).reshape(XX.shape)

#把分类的决定边界画出来
ax.contour(XX,YY,Z,colors=‘k‘,levels=[-1,0,1],alpha=0.5,linestyles=[‘--‘,‘-‘,‘--‘])
ax.scatter(clf.support_vectors_[:, 0],clf.support_vectors_[:, 1],s=100,linewidth=1,facecolors=‘none‘)
plt.show()

#创建一个RBF内核的支持向量机模型
clf_rbf = svm.SVC(kernel=‘rbf‘,C=1000)
clf_rbf.fit(X,y)

#把数据点画出来
plt.scatter(X[:, 0],X[:, 1],c=y,s=30,cmap=plt.cm.Paired)

#建立图像坐标
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()
#生成两个等差数列
xx = np.linspace(xlim[0],xlim[1],30)
yy = np.linspace(ylim[0],ylim[1],30)
YY,XX = np.meshgrid(yy,xx)
xy = np.vstack([XX.ravel(),YY.ravel()]).T
Z = clf_rbf.decision_function(xy).reshape(XX.shape)

#把分类的决定边界画出来
ax.contour(XX,YY,Z,colors=‘k‘,levels=[-1,0,1],alpha=0.5,linestyles=[‘--‘,‘-‘,‘--‘])
ax.scatter(clf.support_vectors_[:, 0],clf.support_vectors_[:, 1],s=100,linewidth=1,facecolors=‘none‘)
plt.show()

#导入红酒数据集
from sklearn.datasets import load_wine
#导入支持向量机svm
from sklearn import svm
#定义一个函数用来画图
def make_meshgrid(x,y,h=.02):
    x_min,x_max = x.min() - 1,x.max() + 1
    y_min,y_max = y.min() - 1,y.max() + 1
    xx,yy = np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))
    return xx,yy
#定义一个绘制等高线的函数
def plot_contours(ax,clf,xx,yy,**params):
    Z = clf.predict(np.c_[xx.ravel(),yy.ravel()])
    Z = Z.reshape(xx.shape)
    out = ax.contourf(xx,yy,Z,**params)
    return out

#使用酒的数据集
wine = load_wine()
#选取数据集的前两个特征
X = wine.data[:, :2]
y = wine.target

C = 1.0 #svm的正则化参数
models = (svm.SVC(kernel=‘linear‘,C=C),svm.LinearSVC(C=C),svm.SVC(kernel=‘rbf‘,gamma=0.7,C=C),svm.SVC(kernel=‘poly‘,degree=3,C=C))
models = (clf.fit(X,y) for clf in models)

#设定图题
titles = (‘SVC with linear kernel‘,‘LineatSVC (linear kernel)‘,‘SVC with RBF kernel‘,‘SVC with polynomial (degree 3) kernel‘)

#设定一个子图形的个数和排列方式
flg, sub = plt.subplots(2, 2)
plt.subplots_adjust(wspace=0.4,hspace=0.4)
#使用前面定义的函数进行画图
X0,X1, = X[:, 0],X[:, 1]
xx,yy = make_meshgrid(X0,X1)

for clf,title,ax in zip(models,titles,sub.flatten()):
    plot_contours(ax,clf,xx,yy,cmap=plt.cm.plasma,alpha=0.8)
    ax.scatter(X0,X1,c=y,cmap=plt.cm.plasma,s=20,edgecolors=‘k‘)
    ax.set_xlim(xx.min(),xx.max())
    ax.set_ylim(yy.min(),yy.max())
    ax.set_xlabel(‘Feature 0‘)
    ax.set_ylabel(‘Featuer 1‘)
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)
#将图型显示出来
plt.show()

C = 1.0 #svm的正则化参数
models = (svm.SVC(kernel=‘rbf‘,gamma=0.1,C=C),svm.SVC(kernel=‘rbf‘,gamma=1,C=C),svm.SVC(kernel=‘rbf‘,gamma=10,C=C))
models = (clf.fit(X,y) for clf in models)

#设定图题
titles = (‘gamma = 0.1‘,‘gamma = 1‘,‘gamma = 10‘,)

#设定一个子图形的个数和排列方式
flg, sub = plt.subplots(1,3,figsize = (10,3))
#使用定义好的函数进行画图
X0,X1, = X[:, 0],X[:, 1]
xx,yy = make_meshgrid(X0,X1)

for clf,title,ax in zip(models,titles,sub.flatten()):
    plot_contours(ax,clf,xx,yy,cmap=plt.cm.plasma,alpha=0.8)
    ax.scatter(X0,X1,c=y,cmap=plt.cm.plasma,s=20,edgecolors=‘k‘)
    ax.set_xlim(xx.min(),xx.max())
    ax.set_ylim(yy.min(),yy.max())
    ax.set_xlabel(‘Feature 0‘)
    ax.set_ylabel(‘Featuer 1‘)
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)
#将图型显示出来
plt.show()