SVMTest.py 文件源码

def visualizeCrossValidation(results):
    # Visualize the cross-validation results
    x_scatter = [math.log10(x[0]) for x in results]
    y_scatter = [math.log10(x[1]) for x in results]

    # plot training accuracy
    marker_size = 100
    colors = [results[x][0] for x in results]
    plt.subplot(2, 1, 1)
    plt.scatter(x_scatter, y_scatter, marker_size, c=colors)
    plt.colorbar()
    plt.xlabel('log learning rate')
    plt.ylabel('log regularization strength')
    plt.title('CIFAR-10 training accuracy')

    # plot validation accuracy
    colors = [results[x][1] for x in results] # default size of markers is 20
    plt.subplot(2, 1, 2)
    plt.scatter(x_scatter, y_scatter, marker_size, c=colors)
    plt.colorbar()
    plt.xlabel('log learning rate')
    plt.ylabel('log regularization strength')
    plt.title('CIFAR-10 validation accuracy')
    plt.show()