支持向量机SVM代码实践

传统机器学习的最后一类算法了，接下来要进入神经网络了。

一，代码

import numpy as np
import matplotlib.pyplot as plt
from sklearn import  svm
from sklearn.datasets import make_blobs
from sklearn.datasets import load_wine

'''
X, y = make_blobs(n_samples=50, centers=2, random_state=6)
clf = svm.SVC(kernel='rbf', C=1000)
clf.fit(X, y)
plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
ax = plt.gca()
xlim = ax.get_xlim()
ylim = ax.get_ylim()

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)

ax.contour(XX, YY, Z, color='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()
'''

# 定义一个函数来画图
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，选取数据集的前两个特征
wine = load_wine()
X = wine.data[:, :2]
y = wine.target

# 设定正则化参数
C = 1.0
'''

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)

titles = ('SVC with linear kernel',
          'LinearSVC (linear kernel)',
          'SVC with RBF kernel',
          'SVC with polynomial (degree 3) kernel')
'''
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)

titles = ('gamma = 0.1',
          'gamma = 1',
          'gamma = 10')
# 设定一个子图形的个数和排列方式
# fig, sub = plt.subplots(2, 2)
# plt.subplots_adjust(wspace=0.4, hspace=0.4)
fig, sub = plt.subplots(1, 3, figsize=(10, 3))
# 使用前面定义的函数画图
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('Feature 1')
    ax.set_xticks(())
    ax.set_yticks(())
    ax.set_title(title)

plt.show()

二，效果

2022-04-26 11_42_17-MessageCenterUI.png 2022-04-26 11_25_05-MessageCenterUI.png 2022-04-26 10_55_25-MessageCenterUI.png