plot_decision_boundary.py

# ref: https://blog.csdn.net/MachineLearner/article/details/104587288
def plot_decision_boundary(pred_func, X, y, figure=None):
    """Plot a decision boundary"""

    if figure is None:  # If no figure is given, create a new one
        plt.figure()
    # Set min and max values and give it some padding
    x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
    y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
    h = 0.01
    # Generate a grid of points with distance h between them
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))
    # Predict the function value for the whole grid
    Z = pred_func(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)
    # Plot the contour and training examples
    plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)
    cm_bright = ListedColormap(['#FF0000', '#0000FF'])
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cm_bright)