【364】SVM 通過 sklearn 可視化實現

先看下效果圖：

# 先調入需要的模塊

import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm
import seaborn as sb

# 生成幾個數據點

data = np.array([
    [0.1, 0.7],
    [0.3, 0.6],
    [0.4, 0.1],
    [0.5, 0.4],
    [0.8, 0.04],
    [0.42, 0.6],
    [0.9, 0.4],
    [0.6, 0.5],
    [0.7, 0.2],
    [0.7, 0.67],
    [0.27,0.8],
    [0.5, 0.72]
    ])
    

target = [1] * 6 + [0] * 6

x_line = np.linspace(0, 1, 100)
y_line = 1 - x_line
plt.scatter(data[:6, 0], data[:6, 1], marker='o', s=100, lw=3)
plt.scatter(data[6:, 0], data[6:, 1], marker='x', s=100, lw=3)
plt.plot(x_line, y_line)

# 定義計算域、文字說明等

C = 0.0001  # SVM regularization parameter, since Scikit-learn doesn't allow C=0
# linear_svc = svm.SVC(kernel='linear', C=C).fit(data, target)

# create a mesh to plot in
h = 0.002
x_min, x_max = data[:, 0].min() - 0.2, data[:, 0].max() + 0.2
y_min, y_max = data[:, 1].min() - 0.2, data[:, 1].max() + 0.2
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                     np.arange(y_min, y_max, h))

# title for the plots
titles = ['SVC with linear kernel',
          'SVC with RBF kernel',
          'SVC with polynomial (degree 3) kernel']

# RBF Kernel

plt.figure(figsize=(16, 15))

for i, gamma in enumerate([1, 5, 15, 35, 45, 55]):
    rbf_svc = svm.SVC(kernel='rbf', gamma=gamma, C=C).fit(data, target)
    
    # ravel - flatten
    # c_ - vstack
    # #把后面兩個壓扁之后變成了x1和x2，然后進行判斷，得到結果在壓縮成一個矩形
    Z = rbf_svc.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    
    plt.subplot(3, 2, i + 1)
    plt.subplots_adjust(wspace=0.4, hspace=0.4)
    plt.contourf(xx, yy, Z, cmap=plt.cm.ocean, alpha=0.6)

    # Plot the training points
    plt.scatter(data[:6, 0], data[:6, 1], marker='o', color='r', s=100, lw=3)
    plt.scatter(data[6:, 0], data[6:, 1], marker='x', color='k', s=100, lw=3)
    
    plt.title('RBF SVM with $\gamma=$' + str(gamma))
    
plt.show()