代碼來源?https://github.com/mbeyeler/opencv-machine-learning/blob/master/notebooks/06.01-Implementing-Your-First-Support-Vector-Machine.ipynb

from sklearn import datasets
import cv2
import matplotlib.pyplot as plt
import numpy as np
from sklearn import model_selection as ms
from sklearn import metricsX, y = datasets.make_classification(n_samples=100, n_features=2,n_redundant=0, n_classes=2,random_state=7816)plt.style.use('ggplot')
plt.set_cmap('jet')plt.figure(figsize=(10, 6))
plt.scatter(X[:, 0], X[:, 1], c=y, s=100)
plt.xlabel('x values')
plt.ylabel('y values')X = X.astype(np.float32)
y = y * 2 - 1X_train, X_test, y_train, y_test = ms.train_test_split(X, y, test_size=0.2, random_state=42
)def plot_decision_boundary(svm, X_test, y_test):# create a mesh to plot inh = 0.02  # step size in meshx_min, x_max = X_test[:, 0].min() - 1, X_test[:, 0].max() + 1y_min, y_max = X_test[:, 1].min() - 1, X_test[:, 1].max() + 1xx, yy = np.meshgrid(np.arange(x_min, x_max, h),np.arange(y_min, y_max, h))X_hypo = np.c_[xx.ravel().astype(np.float32),yy.ravel().astype(np.float32)]_, zz = svm.predict(X_hypo)zz = zz.reshape(xx.shape)plt.contourf(xx, yy, zz, cmap=plt.cm.coolwarm, alpha=0.8)plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=200)kernels = [cv2.ml.SVM_LINEAR, cv2.ml.SVM_INTER, cv2.ml.SVM_SIGMOID, cv2.ml.SVM_RBF]plt.figure(figsize=(14, 8))
for idx, kernel in enumerate(kernels):svm = cv2.ml.SVM_create()svm.setKernel(kernel)svm.train(X_train, cv2.ml.ROW_SAMPLE, y_train)_, y_pred = svm.predict(X_test)plt.subplot(2, 2, idx + 1)plot_decision_boundary(svm, X_test, y_test)plt.title('accuracy = %.2f' % metrics.accuracy_score(y_test, y_pred))
plt.show()

結果如下：

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總結

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