def testcase(self):
    '[summary]\n        '
    import matplotlib.pyplot as plt
    X = np.random.randn(10000, 2)
    pt = power_tracker(100, 100, 100)
    print('All at once: power={}'.format(pt.transform(X)))
    pt = power_tracker(100, 1000, 1000)
    print('alpha_mu={} alpha_pow={}'.format(pt.alpha_mu, pt.alpha_power))
    step = 30
    idxs = list(range(step, X.shape[0], step))
    powers = np.zeros((len(idxs), X.shape[(- 1)]))
    mus = np.zeros((len(idxs), X.shape[(- 1)]))
    for (i, j) in enumerate(idxs):
        powers[i, :] = np.sqrt(pt.transform(X[(j - step):j, :]))
        mus[i, :] = pt.mean()
    for d in range(X.shape[(- 1)]):
        plt.subplot(X.shape[(- 1)], 1, (d + 1))
        plt.plot(X[:, d])
        plt.plot(idxs, mus[:, d])
        plt.plot(idxs, powers[:, d])
[summary]
def testcase(self):
    '\n        '
    import matplotlib.pyplot as plt
    X = np.random.randn(10000, 2)
    pt = power_tracker(100, 100, 100)
    print('All at once: power={}'.format(pt.transform(X)))
    pt = power_tracker(100, 1000, 1000)
    print('alpha_mu={} alpha_pow={}'.format(pt.alpha_mu, pt.alpha_power))
    step = 30
    idxs = list(range(step, X.shape[0], step))
    powers = np.zeros((len(idxs), X.shape[(- 1)]))
    mus = np.zeros((len(idxs), X.shape[(- 1)]))
    for (i, j) in enumerate(idxs):
        powers[i, :] = np.sqrt(pt.transform(X[(j - step):j, :]))
        mus[i, :] = pt.mean()
    for d in range(X.shape[(- 1)]):
        plt.subplot(X.shape[(- 1)], 1, (d + 1))
        plt.plot(X[:, d])
        plt.plot(idxs, mus[:, d])
        plt.plot(idxs, powers[:, d])