《机器学习(周志华)》笔记--支持向量机（5）--SVM算法实验代码记录：线性SVM、非线性SVM

五、SVM算法实验代码

1、线性SVM

import numpy as np
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
data = load_iris()
x = data.data
y = data.target
x = x[y<2,:2]
y = y[y<2]
plt.scatter(x[y==0,0],x[y==0,1],color='red')
plt.scatter(x[y==1,0],x[y==1,1],color='blue')
plt.legend(labels=['c1','c2'],loc='upper left')
plt.xlabel('x')
plt.ylabel('y')
plt.show()

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(x)
x = scaler.transform(x)


from sklearn.svm import LinearSVC
svc = LinearSVC(C=0.01)
svc.fit(x,y)

print(svc.coef_)
print(svc.intercept_)

def plot_decision_boundary1(model,axis):
    x0,x1=np.meshgrid(
        np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)).reshape(-1,1),
        np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*100)).reshape(-1,1)
    )
    x_new=np.c_[x0.ravel(),x1.ravel()]
    y_predict=model.predict(x_new)
    zz=y_predict.reshape(x0.shape)
    from matplotlib.colors import ListedColormap
    custom_cmap=ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
    plt.contourf(x0,x1,zz,linewidth=5,cmap=custom_cmap)
    
    w = model.coef_[0]
    b = model.intercept_[0]
    plot_x = np.linspace(axis[0],axis[1],200)
    up_y = -w[0]/w[1]*plot_x - b/w[1] + 1/w[1]
    down_y = -w[0]/w[1]*plot_x - b/w[1] - 1/w[1]
    up_index = (up_y>=axis[2]) & (up_y<=axis[3])
    down_index = (down_y>=axis[2]) & (down_y<=axis[3])
    plt.plot(plot_x[up_index],up_y[up_index],c='black')
    plt.plot(plot_x[down_index],down_y[down_index],c='black')

plot_decision_boundary1(svc,axis=[-3,3,-3,3])
plt.scatter(x[y==0,0],x[y==0,1],c='r')
plt.scatter(x[y==1,0],x[y==1,1],c='b')
plt.show()

2、非线性SVM

#-------------异或问题-----------------
import numpy as np
from sklearn.svm import LinearSVC
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
X = np.array([[0,0,0],[1,1,1],[0,1,0],[1,0,0]])
y = np.array([0,0,1,1])
svc=LinearSVC(C=1e9)
svc.fit(X,y)
x_plot = np.linspace(0,1,100)
y_plot = np.linspace(0,1,100)

x_plot,y_plot = np.meshgrid(x_plot,y_plot)
A=svc.coef_[0][0]
B=svc.coef_[0][1]
C=svc.coef_[0][2]
D=svc.intercept_[0]
z_plot=(-D-A*x_plot-B*y_plot)/C

fig = plt.figure(figsize=(12, 8))
ax = Axes3D(fig)
ax.plot_surface(x_plot,y_plot,z_plot)
ax.scatter([0,1],[0,1],[0,1],c='red',s=100)
ax.scatter([0,1],[1,0],[0,0],c='blue',s=100)
plt.show()


print(svc.coef_)
print(svc.intercept_)

#=======非线性通过特征映射转换为线性问题========
import numpy as np
from sklearn import datasets
import matplotlib.pyplot as plt
X,y = datasets.make_moons(noise=0.15,random_state=666)
plt.scatter(X[y==0,0],X[y==0,1],c='red')
plt.scatter(X[y==1,0],X[y==1,1],c='blue')
plt.show()


from sklearn.preprocessing import PolynomialFeatures,StandardScaler
from sklearn.svm import LinearSVC
from sklearn.pipeline import Pipeline
def PolynomialSVC(degree,C=1.0):
    return Pipeline([
        ('poly',PolynomialFeatures(degree=degree)),
        ('std_scaler',StandardScaler()),
        ('linearSVC',LinearSVC(C=1e9))
    ])
poly_svc = PolynomialSVC(degree=3)
poly_svc.fit(X,y)
plot_decision_boundary(poly_svc,axis=[-1.5,2.5,-1.0,1.5])
plt.scatter(X[y==0,0],X[y==0,1],c='red')
plt.scatter(X[y==1,0],X[y==1,1],c='blue')
plt.show()

#---------------------非线性SVM-----------------
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.pipeline import Pipeline
def plot_decision_boundary(model,axis):
    x0,x1=np.meshgrid(
        np.linspace(axis[0],axis[1],int((axis[1]-axis[0])*100)).reshape(-1,1),
        np.linspace(axis[2],axis[3],int((axis[3]-axis[2])*100)).reshape(-1,1)
    )
    x_new=np.c_[x0.ravel(),x1.ravel()]
    y_predict=model.predict(x_new)
    zz=y_predict.reshape(x0.shape)
    from matplotlib.colors import ListedColormap
    custom_cmap=ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])
    plt.contourf(x0,x1,zz,linewidth=5,cmap=custom_cmap)
def RBFKernelSVC(gamma=1.0):
    return Pipeline([
        ('std_scaler',StandardScaler()),
        ('svc',SVC(kernel='rbf',gamma=gamma))
    ])
svc = RBFKernelSVC(gamma=100)
svc.fit(X,y)
plot_decision_boundary(svc,axis=[-1.5,2.5,-1.0,1.5])
plt.scatter(X[y==0,0],X[y==0,1],c='red')
plt.scatter(X[y==1,0],X[y==1,1],c='blue')
plt.show()

import numpy as np
from sklearn.svm import LinearSVC
X = np.array([[1,1],[1,2],[2,1],[0,0],[1,0],[0,1]])
y = np.array([0,0,0,1,1,1])
svc=LinearSVC(C=1e9)
svc.fit(X,y)

import numpy as np
X0=np.array([[1,1],[1,2],[2,1]])
X1=np.array([[0,0],[1,0],[0,1]])
import matplotlib.pyplot as plt
plt.grid()
plt.scatter(X0[:,0],X0[:,1],c='red',marker='o',s=100)
plt.scatter(X1[:,0],X1[:,1],c='blue',marker='s',s=100)
plt.show()

poly = PolynomialFeatures(degree=3)
X= poly.fit_transform(np.array([[3,4]]))
print(X)