Support Vector Machine

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57

from __future__ import annotations

import japanize_matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_moons
from sklearn.metrics import accuracy_score
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

def run_svm_demo(
    n_samples: int = 400,
    noise: float = 0.25,
    random_state: int = 42,
    title: str = "พื้นที่การจำแนกของ SVM (RBF kernel)",
    xlabel: str = "คุณลักษณะที่ 1",
    ylabel: str = "คุณลักษณะที่ 2",
) -> dict[str, float]:
    """Train linear and RBF SVMs and plot the RBF decision boundary."""
    japanize_matplotlib.japanize()
    X, y = make_moons(n_samples=n_samples, noise=noise, random_state=random_state)

    linear_clf = make_pipeline(StandardScaler(), SVC(kernel="linear", C=1.0))
    linear_clf.fit(X, y)

    rbf_clf = make_pipeline(StandardScaler(), SVC(kernel="rbf", C=5.0, gamma=0.5))
    rbf_clf.fit(X, y)

    linear_acc = float(accuracy_score(y, linear_clf.predict(X)))
    rbf_acc = float(accuracy_score(y, rbf_clf.predict(X)))

    grid_x, grid_y = np.meshgrid(
        np.linspace(X[:, 0].min() - 0.5, X[:, 0].max() + 0.5, 400),
        np.linspace(X[:, 1].min() - 0.5, X[:, 1].max() + 0.5, 400),
    )
    grid = np.c_[grid_x.ravel(), grid_y.ravel()]
    rbf_scores = rbf_clf.predict(grid).reshape(grid_x.shape)

    fig, ax = plt.subplots(figsize=(6, 5))
    ax.contourf(grid_x, grid_y, rbf_scores, alpha=0.2, cmap="coolwarm")
    ax.scatter(X[:, 0], X[:, 1], c=y, cmap="coolwarm", edgecolor="k", s=30)
    ax.set_title(title)
    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    fig.tight_layout()
    plt.show()

    return {"linear_accuracy": linear_acc, "rbf_accuracy": rbf_acc}

metrics = run_svm_demo(
    title="พื้นที่การจำแนกของ SVM (RBF kernel)",
    xlabel="คุณลักษณะที่ 1",
    ylabel="คุณลักษณะที่ 2",
)
print(f"ความแม่นยำ SVM เชิงเส้น: {metrics['linear_accuracy']:.3f}")
print(f"ความแม่นยำ SVM แบบ RBF: {metrics['rbf_accuracy']:.3f}")