def METHOD_NAME(G, R):
    """Filters the activation matrix G, and returns a flattened copy."""
    #import pylab as plt
    #plt.imshow(G, interpolation="nearest", aspect="auto")
    #plt.show()
    idx = np.argmax(G, axis=1)
    max_idx = np.arange(G.shape[0])
    max_idx = (max_idx, idx.flatten())
    G[:, :] = 0
    G[max_idx] = idx + 1
    # TODO: Order matters?
    G = np.sum(G, axis=1)
    G = median_filter(G[:, np.newaxis], R)
    return G.flatten()