def ar_model():

    # Wczytanie danych X
    dataframe = read_csv('ALIOR.mst', usecols=[2], engine='python', skipfooter=3)
    dataset = dataframe.values
    dataset = dataset.astype('float32')
    dataset_X = dataset[1:51, :]
    # print('dataset_X.shape =', dataset_X.shape)
    # print(dataset_X)

    # Wczytanie danych Y
    dataframe = read_csv('ALIOR.mst', usecols=[1], engine='python', skipfooter=3)
    dataset = dataframe.values
    dataset = dataset.astype('int')
    dataset_Y = dataset[1:51, :]
    # print('dataset_Y.shape =', dataset_Y.shape)
    # print(dataset_Y)

    y = dataset_X

    # normalize the dataset
    x = dataset_Y
    import datetime as dt

    # x = [dt.datetime.strptime([:,0], '%m/%d/%Y').date() for d in x]
    plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y%m%d'))
    plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=5))
    plt.plot(x,y)
    plt.gcf().autofmt_xdate()


    #
    # c = np.hstack([x * x * x, x * x, x, np.ones(x.shape)])
    # v = np.linalg.pinv(c) @ y
    #
    # c1 = np.hstack([1 / x, np.ones(x.shape)])
    # v1 = np.linalg.pinv(c1) @ y
    #
    #
    # plt.plot(y, 'ro')
    # plt.plot(x, v[0] * x * x * x + v[1] * x * x + v[2] * x + v[3], )
    # plt.plot(x, v1[0] / x + v1[1])
    plt.show()