def _pcca_connected(P, n, return_rot=False):
    """
    PCCA+ spectral clustering method with optimized memberships [1]_

    Clusters the first n_cluster eigenvectors of a transition matrix in order to cluster the states.
    This function assumes that the transition matrix is fully connected.

    Parameters
    ----------
    P : ndarray (n,n)
        Transition matrix.

    n : int
        Number of clusters to group to.

    Returns
    -------
    chi by default, or (chi,rot) if return_rot = True

    chi : ndarray (n x m)
        A matrix containing the probability or membership of each state to be assigned to each cluster.
        The rows sum to 1.

    rot_mat : ndarray (m x m)
        A rotation matrix that rotates the dominant eigenvectors to yield the PCCA memberships, i.e.:
        chi = np.dot(evec, rot_matrix

    References
    ----------
    [1] S. Roeblitz and M. Weber, Fuzzy spectral clustering by PCCA+:
        application to Markov state models and data classification.
        Adv Data Anal Classif 7, 147-179 (2013).

    """

    # test connectivity
    from msmtools.estimation import connected_sets

    labels = connected_sets(P)
    n_components = len(labels)  # (n_components, labels) = connected_components(P, connection='strong')
    if (n_components > 1):
        raise ValueError("Transition matrix is disconnected. Cannot use pcca_connected.")

    from msmtools.analysis import stationary_distribution

    pi = stationary_distribution(P)
    # print "statdist = ",pi

    from msmtools.analysis import is_reversible

    if not is_reversible(P, mu=pi):
        raise ValueError("Transition matrix does not fulfill detailed balance. "
                         "Make sure to call pcca with a reversible transition matrix estimate")
    # TODO: Susanna mentioned that she has a potential fix for nonreversible matrices by replacing each complex conjugate
    #      pair by the real and imaginary components of one of the two vectors. We could use this but would then need to
    #      orthonormalize all eigenvectors e.g. using Gram-Schmidt orthonormalization. Currently there is no theoretical
    #      foundation for this, so I'll skip it for now.

    # right eigenvectors, ordered
    from msmtools.analysis import eigenvectors

    evecs = eigenvectors(P, n)

    # orthonormalize
    for i in range(n):
        evecs[:, i] /= math.sqrt(np.dot(evecs[:, i] * pi, evecs[:, i]))
    # make first eigenvector positive
    evecs[:, 0] = np.abs(evecs[:, 0])

    # Is there a significant complex component?
    if not np.alltrue(np.isreal(evecs)):
        warnings.warn(
            "The given transition matrix has complex eigenvectors, so it doesn't exactly fulfill detailed balance "
            + "forcing eigenvectors to be real and continuing. Be aware that this is not theoretically solid.")
    evecs = np.real(evecs)

    # create initial solution using PCCA+. This could have negative memberships
    (chi, rot_matrix) = _pcca_connected_isa(evecs, n)

    #print "initial chi = \n",chi

    # optimize the rotation matrix with PCCA++.
    rot_matrix = _opt_soft(evecs, rot_matrix, n)

    # These memberships should be nonnegative
    memberships = np.dot(evecs[:, :], rot_matrix)

    # We might still have numerical errors. Force memberships to be in [0,1]
    # print "memberships unnormalized: ",memberships
    memberships = np.maximum(0.0, memberships)
    memberships = np.minimum(1.0, memberships)
    # print "memberships unnormalized: ",memberships
    for i in range(0, np.shape(memberships)[0]):
        memberships[i] /= np.sum(memberships[i])

    # print "final chi = \n",chi

    return memberships
def _pcca_connected ( P , n , return_rot = False ) : from msmtools . estimation import connected_sets labels = connected_sets ( P ) n_components = len ( labels ) if ( n_components > 1 ) : raise ValueError ( "Transition matrix is disconnected. Cannot use pcca_connected." ) from msmtools . analysis import stationary_distribution pi = stationary_distribution ( P ) from msmtools . analysis import is_reversible if not is_reversible ( P , mu = pi ) : raise ValueError ( "Transition matrix does not fulfill detailed balance. " "Make sure to call pcca with a reversible transition matrix estimate" ) from msmtools . analysis import eigenvectors evecs = eigenvectors ( P , n ) for i in range ( n ) : evecs [ : , i ] /= math . sqrt ( np . dot ( evecs [ : , i ] * pi , evecs [ : , i ] ) ) evecs [ : , 0 ] = np . abs ( evecs [ : , 0 ] ) if not np . alltrue ( np . isreal ( evecs ) ) : warnings . warn ( "The given transition matrix has complex eigenvectors, so it doesn't exactly fulfill detailed balance " + "forcing eigenvectors to be real and continuing. Be aware that this is not theoretically solid." ) evecs = np . real ( evecs ) ( chi , rot_matrix ) = _pcca_connected_isa ( evecs , n ) rot_matrix = _opt_soft ( evecs , rot_matrix , n ) memberships = np . dot ( evecs [ : , : ] , rot_matrix ) memberships = np . maximum ( 0.0 , memberships ) memberships = np . minimum ( 1.0 , memberships ) for i in range ( 0 , np . shape ( memberships ) [ 0 ] ) : memberships [ i ] /= np . sum ( memberships [ i ] ) return memberships
PCCA+ spectral clustering method with optimized memberships [1]_

    Clusters the first n_cluster eigenvectors of a transition matrix in order to cluster the states.
    This function assumes that the transition matrix is fully connected.

    Parameters
    ----------
    P : ndarray (n,n)
        Transition matrix.

    n : int
        Number of clusters to group to.

    Returns
    -------
    chi by default, or (chi,rot) if return_rot = True

    chi : ndarray (n x m)
        A matrix containing the probability or membership of each state to be assigned to each cluster.
        The rows sum to 1.

    rot_mat : ndarray (m x m)
        A rotation matrix that rotates the dominant eigenvectors to yield the PCCA memberships, i.e.:
        chi = np.dot(evec, rot_matrix

    References
    ----------
    [1] S. Roeblitz and M. Weber, Fuzzy spectral clustering by PCCA+:
        application to Markov state models and data classification.
        Adv Data Anal Classif 7, 147-179 (2013).