Python MSMLib.ergodic_trim方法代码示例

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
def construct_counts_matrix(assignments):
    """Build and return a counts matrix from assignments.
    
    Symmetrize either with transpose or MLE based on the value of the
    self.symmetrize variable
        
    Also modifies the assignments file that you pass it to reflect ergodic
    trimming
    
    Parameters
    ----------
    assignments : np.ndarray
        2D array of MSMBuilder assignments
    
    Returns
    -------
    counts : scipy.sparse.csr_matrix
        transition counts
    
    """
        
    n_states  = np.max(assignments.flatten()) + 1
    raw_counts = MSMLib.get_count_matrix_from_assignments(assignments, n_states,
                                               lag_time=Project().lagtime,
                                               sliding_window=True)
        
    ergodic_counts = None
    if Project().trim:
        raise NotImplementedError(('Trimming is not yet supported because '
                                   'we need to keep track of the mapping from trimmed to '
                                   ' untrimmed states for joint clustering to be right'))
        try:
            ergodic_counts, mapping = MSMLib.ergodic_trim(raw_counts)
            MSMLib.apply_mapping_to_assignments(assignments, mapping)
            counts = ergodic_counts
        except Exception as e:
            logger.warning("MSMLib.ergodic_trim failed with message '{0}'".format(e))

    else:
        logger.info("Ignoring ergodic trimming")
        counts = raw_counts
        
    if Project().symmetrize == 'transpose':
        logger.debug('Transpose symmetrizing')
        counts = counts + counts.T
    elif Project().symmetrize == 'mle':
        logger.debug('MLE symmetrizing')
        counts = MSMLib.mle_reversible_count_matrix(counts)
    elif Project().symmetrize == 'none' or (not Project().symmetrize):
        logger.debug('Skipping symmetrization')
    else:
        raise ValueError("Could not understand symmetrization method: %s" % Project().symmetrize)
        
    return counts

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
def test_trim_states():
    
    # run the (just tested) ergodic trim
    counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
    trimmed, mapping = MSMLib.ergodic_trim(counts)
    
    # now try the segmented method
    states_to_trim = MSMLib.ergodic_trim_indices(counts)
    trimmed_counts = MSMLib.trim_states(states_to_trim, counts, assignments=None)
    
    eq(trimmed.todense(), trimmed_counts.todense())

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
    def compare_kyle_to_lutz(self, raw_counts):
        """Kyle wrote the most recent MLE code.  We compare to the previous
        code that was written by Lutz.
        """

        counts = MSMLib.ergodic_trim(raw_counts)[0]

        x_kyle = MSMLib.mle_reversible_count_matrix(counts)
        x_kyle /= x_kyle.sum()

        x_lutz = MSMLib.__mle_reversible_count_matrix_lutz__(counts)
        x_lutz /= x_lutz.sum()

        eq(x_kyle.toarray(), x_lutz.toarray())

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
def test_trim_states():

    # run the (just tested) ergodic trim
    counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
    trimmed, mapping = MSMLib.ergodic_trim(counts)

    # now try the segmented method
    states_to_trim = MSMLib.ergodic_trim_indices(counts)
    trimmed_counts = MSMLib.trim_states(
        states_to_trim, counts, assignments=None)

    eq(trimmed.todense(), trimmed_counts.todense())

    assignments = np.array([np.arange(counts.shape[0])])
    states_to_trim = MSMLib.ergodic_trim_indices(counts)
    trimmed_counts, trimmed_assignments = MSMLib.trim_states(states_to_trim, counts, assignments=assignments)  # Test that code works with assignments given
    
    trimmed_assignments_ref = np.array([[0, 1, -1]])  # State 2 is strong-disconnected so set to -1
    
    eq(trimmed_assignments, trimmed_assignments_ref)

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
def test_ergodic_trim():
    counts = scipy.sparse.csr_matrix(np.matrix("2 1 0; 1 2 0; 0 0 1"))
    trimmed, mapping = MSMLib.ergodic_trim(counts)

    eq(trimmed.todense(), np.matrix("2 1; 1 2"))
    eq(mapping, np.array([0, 1, -1]))

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
    def compare_kyle_to_reference(self, raw_counts, reference):
        """Compare MLE estimated reversible counts to a reference matrix"""
        counts = MSMLib.ergodic_trim(raw_counts)[0]
        x_kyle = MSMLib.mle_reversible_count_matrix(counts)

        eq(x_kyle.toarray(), reference.toarray())

# 需要导入模块: from msmbuilder import MSMLib [as 别名]
# 或者: from msmbuilder.MSMLib import ergodic_trim [as 别名]
def test_ergodic_trim():
    counts = scipy.sparse.csr_matrix(np.matrix('2 1 0; 1 2 0; 0 0 1'))
    trimmed, mapping = MSMLib.ergodic_trim(counts)
    
    npt.assert_equal(trimmed.todense(), np.matrix('2 1; 1 2'))
    npt.assert_equal(mapping, np.array([0, 1, -1]))