svm_kernel_test1.py

import numpy as np
import matplotlib.pyplot as plt
import svm_kernel

plt.axes().set_aspect("equal")
np.random.seed(0)
X0 = np.random.randn(100, 2)
X1 = np.random.randn(100, 2) + np.array([2.5, 3])
y = np.array([1] * 100 + [-1] * 100)
X = np.r_[X0, X1]

model = svm_kernel.SVC()
model.fit(X, y)

xmin, xmax = X[:, 0].min(), X[:, 0].max()
ymin, ymax = X[:, 1].min(), X[:, 1].max()

plt.scatter(X0[:, 0], X0[:, 1], color="k", marker="*")
plt.scatter(X1[:, 0], X1[:, 1], color="k", marker="+")
xmesh, ymesh = np.meshgrid(np.linspace(xmin, xmax, 200), np.linspace(ymin, ymax, 200))
Z = model.predict(np.c_[xmesh.ravel(), ymesh.ravel()]).reshape(xmesh.shape)
plt.contour(xmesh, ymesh, Z, levels=[0], colors="k")

print("正しく分類できた数:", (model.predict(X) == y).sum())
plt.show()