def compute_laplacian_matrix(self, copy=True, return_lapsym=False, **kwargs):
if self.affinity_matrix is None:<EOL><INDENT>self.compute_affinity_matrix()<EOL><DEDENT>kwds = self.laplacian_kwds.copy()<EOL>kwds.update(kwargs)<EOL>kwds['<STR_LIT>'] = return_lapsym<EOL>result = compute_laplacian_matrix(self.affinity_matrix,<EOL>self.laplacian_method,<EOL>**kwds)<EOL>if return_lapsym:<EOL><INDENT>(self.laplacian_matrix,<EOL>self.laplacian_symmetric,<EOL>self.laplacian_weights) = result<EOL><DEDENT>else:<EOL><INDENT>self.laplacian_matrix = result<EOL><DEDENT>if copy:<EOL><INDENT>return self.laplacian_matrix.copy()<EOL><DEDENT>else:<EOL><INDENT>return self.laplacian_matrix<EOL><DEDENT>
Note: this function will compute the laplacian matrix. In order to acquire
    the existing laplacian matrix use self.laplacian_matrix as
    comptute_laplacian_matrix() will re-compute the laplacian matrix.

Parameters
----------
copy : boolean, whether to return copied version of the self.laplacian_matrix
return_lapsym : boolean, if True returns additionally the symmetrized version of
    the requested laplacian and the re-normalization weights.
**kwargs : see laplacian.py docmuentation for arguments for each method.

Returns
-------
self.laplacian_matrix : sparse matrix (N_obs, N_obs).
    The requested laplacian.
self.laplacian_symmetric : sparse matrix (N_obs, N_obs)
    The symmetric laplacian.
self.laplacian_weights : ndarray (N_obs,)
    The renormalization weights used to make
    laplacian_matrix from laplacian_symmetric