SVM非线性简单实现

绝地灬酷狼 2023-07-16 03:53 11阅读 0赞

小彩笔的学习笔记

from sklearn.datasets import make_circles
    X,y = make_circles(100, factor=0.1, noise=.1)
     
    X.shape
     
    y.shape
     
    plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
    plt.show()

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clf = SVC(kernel = "linear").fit(X,y)
    plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
    plot_svc_decision_function(clf)
    clf.score(X,y)

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#定义一个由x计算出来的新维度r
    r = np.exp(-(X**2).sum(1))
     
    rlim = np.linspace(min(r),max(r),100)
     
    from mpl_toolkits import mplot3d
     
    #定义一个绘制三维图像的函数
    #elev表示上下旋转的角度
    #azim表示平行旋转的角度
    def plot_3D(elev=30,azim=30,X=X,y=y):
        ax = plt.subplot(projection="3d")
        ax.scatter3D(X[:,0],X[:,1],r,c=y,s=50,cmap='rainbow')
        ax.view_init(elev=elev,azim=azim)
        ax.set_xlabel("x")
        ax.set_ylabel("y")
        ax.set_zlabel("r")
        plt.show()
        
    plot_3D()

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#如果放到jupyter notebook中运行
    from sklearn.svm import SVC
    import matplotlib.pyplot as plt
    import numpy as np
     
    from sklearn.datasets import make_circles
    X,y = make_circles(100, factor=0.1, noise=.1)
    plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
     
    def plot_svc_decision_function(model,ax=None):
        if ax is None:
            ax = plt.gca()
        xlim = ax.get_xlim()
        ylim = ax.get_ylim()
        
        x = np.linspace(xlim[0],xlim[1],30)
        y = np.linspace(ylim[0],ylim[1],30)
        Y,X = np.meshgrid(y,x) 
        xy = np.vstack([X.ravel(), Y.ravel()]).T
        P = model.decision_function(xy).reshape(X.shape)
        
        ax.contour(X, Y, P,colors="k",levels=[-1,0,1],alpha=0.5,linestyles=["--","-","--"])
        ax.set_xlim(xlim)
        ax.set_ylim(ylim)
     
    clf = SVC(kernel = "linear").fit(X,y)
    plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
    plot_svc_decision_function(clf)
     
    r = np.exp(-(X**2).sum(1))
     
    rlim = np.linspace(min(r),max(r),0.2)
     
    from mpl_toolkits import mplot3d
     
    def plot_3D(elev=30,azim=30,X=X,y=y):
        ax = plt.subplot(projection="3d")
        ax.scatter3D(X[:,0],X[:,1],r,c=y,s=50,cmap='rainbow')
        ax.view_init(elev=elev,azim=azim)
        ax.set_xlabel("x")
        ax.set_ylabel("y")
        ax.set_zlabel("r")
        plt.show()
     
    from ipywidgets import interact,fixed
    interact(plot_3D,elev=[0,30,60,90],azip=(-180,180),X=fixed(X),y=fixed(y))
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 3]

clf = SVC(kernel = "rbf").fit(X,y)
    plt.scatter(X[:,0],X[:,1],c=y,s=50,cmap="rainbow")
    plot_svc_decision_function(clf)

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dlaXhpbl80MjI2MDEwMg_size_16_color_FFFFFF_t_70 4]

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