python 绘制新训练的感知器模型的决策区域，并可视化它分离不同样本的程度

Posted 2021-05-09

from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt

def plot_decision_regions(X, y, classifier,test_idx=None, resolution=0.02):
  # setup marker generator and color map
  markers = ('s', 'x', 'o', '^', 'v')
  colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
  cmap = ListedColormap(colors[:len(np.unique(y))])
  
  # plot the decision surface
  x1_min, x1_max = X[:, 0].min() - 1, X[:, 0].max() + 1
  x2_min, x2_max = X[:, 1].min() - 1, X[:, 1].max() + 1
  xx1, xx2 = np.meshgrid(np.arange(x1_min, x1_max, resolution),
                         np.arange(x2_min, x2_max, resolution))
  Z = classifier.predict(np.array([xx1.ravel(), xx2.ravel()]).T)
  Z = Z.reshape(xx1.shape)
  plt.contourf(xx1, xx2, Z, alpha=0.4, cmap=cmap)
  plt.xlim(xx1.min(), xx1.max())
  plt.ylim(xx2.min(), xx2.max())
  
  # plot all samples
  X_test, y_test = X[test_idx, :], y[test_idx]
  for idx, cl in enumerate(np.unique(y)):
    plt.scatter(x=X[y == cl, 0], y=X[y == cl, 1],alpha=0.8, 
              c=cmap(idx),marker=markers[idx], label=cl)
    
    # highlight test samples
    if test_idx:
      X_test, y_test = X[test_idx, :], y[test_idx]
      plt.scatter(X_test[:, 0], X_test[:, 1], c='',alpha=1.0, linewidth=1, 
                 marker='o',s=55, label='test set')
                 
                 
# Executing above function
X_combined_std = np.vstack((X_train_std, X_test_std))
y_combined = np.hstack((y_train, y_test))
plot_decision_regions(X=X_combined_std,
                      y=y_combined,
                      classifier=ppn,
                      test_idx=range(105,150))

plt.xlabel('petal length [standardized]')
plt.ylabel('petal width [standardized]')
plt.legend(loc='upper left')
plt.show()