2-4、支持向量机断带实例
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVC
from sklearn.datasets import make_blobs
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
X, y = make_blobs(random_state=27)
fig, sub = plt.subplots(2, 1, figsize=(5, 8))
titles = ("向量机断带为假", "向量机断带为真")
for break_ties, title, ax in zip((False, True), titles, sub.flatten()):
svm = SVC(
kernel="linear", C=1, break_ties=break_ties, decision_function_shape="ovr"
).fit(X, y)
xlim = [X[:, 0].min(), X[:, 0].max()]
ylim = [X[:, 1].min(), X[:, 1].max()]
xs = np.linspace(xlim[0], xlim[1], 1000)
ys = np.linspace(ylim[0], ylim[1], 1000)
xx, yy = np.meshgrid(xs, ys)
pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
colors = [plt.cm.Accent(i) for i in [0, 4, 7]]
points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap="Accent")
classes = [(0, 1), (0, 2), (1, 2)]
line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5)
ax.imshow(
-pred.reshape(xx.shape),
cmap="Accent",
alpha=0.2,
extent=(xlim[0], xlim[1], ylim[1], ylim[0]),
)
for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes):
line2 = -(line * coef[1] + intercept) / coef[0]
ax.plot(line2, line, "-", c=colors[col[0]])
ax.plot(line2, line, "--", c=colors[col[1]])
ax.set_xlim(xlim)
ax.set_ylim(ylim)
ax.set_title(title)
ax.set_aspect("equal")
plt.show()
2-8、SVC 线性内核
import numpy as np
from sklearn import svm, datasets
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
iris = datasets.load_iris()
#输入数据集iris
X = iris.data[:, :2]
#使用前两个功能
y = iris.target
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
h = (x_max / x_min)/100
#创建网格，用原始数据绘制支持向量机边界。
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
X_plot = np.c_[xx.ravel(), yy.ravel()]
C = 1.0#给出正则化参数的值。
clf = svm.SVC(kernel='linear',C=C, decision_function_shape = 'ovr').fit(X, y)
#创建SVM分类器
Z = clf.predict(X_plot)
Z = Z.reshape(xx.shape)
plt.figure(figsize = (15, 5))
plt.subplot(121)
plt.contourf(xx, yy, Z, cmap = plt.cm.tab10, alpha = 0.3)
plt.scatter(X[:, 0], X[:, 1], c = y, cmap = plt.cm.Set1)
plt.xlabel('萼片长度')
plt.ylabel('萼片宽度')
plt.xlim(xx.min(), xx.max())
plt.title('SVC 线性内核')
