data_processing.py 文件源码

def test_image():
    import matplotlib.pyplot as plt

    con = config.Config()
    data = DataGenerator(con)
    xedges = [_ / 7 for _ in range(-14, 15)]
    yedges = [_ / 7 for _ in range(-14, 15)]
    image_data = {}
    for x, y in data.get_train_data(1):
        e, v = scipy.linalg.eigh(
            x.values.reshape((10, 10)))  # np.linalg.eig will return the complex data sometimes...

        for i in range(1, len(v)):
            new_v = preprocessing.scale(v[i])

            for k in range(0, len(new_v), 2):
                if k not in image_data:
                    image_data[k] = {}
                    image_data[k][0] = [new_v[k]]
                    image_data[k][1] = [new_v[k + 1]]
                else:
                    image_data[k][0].append(new_v[k])
                    image_data[k][1].append(new_v[k + 1])

        for k in image_data.keys():
            H, new_xedges, new_yedges = np.histogram2d(image_data[k][0], image_data[k][1], bins=(xedges, yedges))
            print(H)
            plt.imshow(H, cmap=plt.cm.gray, interpolation='nearest', origin='low',
                       extent=[xedges[0], xedges[-1], yedges[0], yedges[-1]])
            plt.show()