如何根据模型来画决策面？

_author__ = 'm.bashari' import numpy as np from sklearn import datasets, linear_model import matplotlib.pyplot as plt def generate_data(): np.random.seed(0) X, y = datasets.make_moons(200, noise=0.20) return X, y def visualize(X, y, clf): # plt.scatter(X[:, 0], X[:, 1], s=40, c=y, cmap=plt.cm.Spectral) # plt.show() plot_decision_boundary(lambda x: clf.predict(x), X, y) plt.title("Logistic Regression") def plot_decision_boundary(pred_func, X, y): # 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 gid 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) plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Spectral) plt.show() def classify(X, y): clf = linear_model.LogisticRegressionCV() clf.fit(X, y) return clf def main(): X, y = generate_data() # visualize(X, y) clf = classify(X, y) visualize(X, y, clf) if __name__ == "__main__": main()

https://github.com/dennybritz/nn-from-scratch/blob/master/simple_classification.py

       如何根据模型来画决策面？我们可以看 函数plot_decision_boundary。 从平面上按照0.5的间距设置网格点，每个点都用模型预测一下结果，然后用contourf函数来画曲线。