机器学习实战：基于Scikit-Learn和TensorFlow 第5章 支持向量机 学习笔记(硬间隔)

数据挖掘作业，需要实现支持向量机进行分类，记录学习记录

环境:win10,Python 3.7.0

SVM的基本思想：在类别之间拟合可能的最宽的间距，也叫作最大间隔分类

书上提供的源代码绘制了两个图，一个是没用SVM的一个是用了SVM的，我做出了修改只画出使用了硬间隔SVM的图像，图像保存在当前目录的images文件夹下，如果没有此文件夹则需要进行创建

代码如下：

import numpy as np
import os
import matplotlib
import matplotlib.pyplot as plt
import warnings
from sklearn.svm import SVC
from sklearn import datasets
np.random.seed(42)

plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12

# Where to save the figures
# 设定图片保存路径，这里写了一个函数，后面直接调用即可
PROJECT_ROOT_DIR = "."
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images")
#保存图片
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
#画出分类界限
def plot_svc_decision_boundary(svm_clf, xmin, xmax):
w = svm_clf.coef_[0]
b = svm_clf.intercept_[0]
x0 = np.linspace(xmin, xmax, 200)
decision_boundary = -w[0]/w[1] * x0 - b/w[1]

margin = 1/w[1]
gutter_up = decision_boundary + margin
gutter_down = decision_boundary - margin

svs = svm_clf.support_vectors_
plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')
plt.plot(x0, decision_boundary, "k-", linewidth=2)
plt.plot(x0, gutter_up, "k--", linewidth=2)
plt.plot(x0, gutter_down, "k--", linewidth=2)
# 忽略无用警告
warnings.filterwarnings(action="ignore", message="^internal gelsd")

iris = datasets.load_iris()
X = iris["data"][:, (2, 3)] # petal length, petal width
y = iris["target"]
setosa_or_versicolor = (y == 0) | (y == 1)
X = X[setosa_or_versicolor]
y = y[setosa_or_versicolor]
# SVM Classifier model
svm_clf = SVC(kernel="linear", C=float("inf"))
svm_clf.fit(X, y)

plot_svc_decision_boundary(svm_clf, 0, 5.5)
plt.plot(X[:, 0][y==1], X[:, 1][y==1], "bs")
plt.plot(X[:, 0][y==0], X[:, 1][y==0], "yo")
plt.xlabel("Petal length", fontsize=14)
plt.ylabel("Petal width", fontsize=14)
plt.axis([0, 5.5, 0, 2])

save_fig("硬间隔SVM分类")
plt.show()