brains.py 文件源码

def plot_decision_boundary(X, Y, model):
    # X - some data in 2dimensional np.array
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01),
                         np.arange(y_min, y_max, 0.01))

    # here "model" is your model's prediction (classification) function
    Z = model(np.c_[xx.ravel(), yy.ravel()])

    # Put the result into a color plot
    Z = Z.reshape(xx.shape)
    plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)
    plt.axis('off')

    for i in x:
        print i

    # Plot also the training points
    plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)


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