def test_zmatrix():
assignments = [
[2, 345, 2, 2],
[3, 345, 2, 2],
[3, 9, 1, 1],
]
zmat = query.zmatrix(assignments)
assert_true(query._is_square_ndarray(zmat))
def test_zmatrix_heuristic_block_reordering():
assignments = [
[2, 345, 2, 2],
[3, 345, 2, 2],
[3, 9, 1, 1],
]
zmat = query.zmatrix(assignments)
order = query.zmatrix_heuristic_block_ordering(zmat)
assert_true(query._is_permutation(order, zmat.shape[0]))
def test_zmatrix_reorder():
assignments = [
[2, 345, 2, 2],
[3, 345, 2, 2],
[3, 9, 1, 1],
]
zmat = query.zmatrix(assignments)
reordered = query.zmatrix_reorder(zmat, np.arange(zmat.shape[0]))
assert_almost_equals(np.abs(zmat - reordered).max(), 0.)
def zmatrix(latents):
"""Compute a z-matrix (cluster co-assignment matrix). The ij-th entry of a
z-matrix is a real value scalar between [0, 1] indicating the frequency of
how often entities i and j appear in the same cluster.
Parameters
----------
latents : list of mixturemodel latent objects
The latents should all be points in the state space of the same
structural model. The implementation currently does not check for this.
Returns
-------
zmat : (N, N) ndarray
Notes
-----
Currently does not support a sparse zmatrix representation, so only use
this for small N.
"""
return query.zmatrix([latent.assignments() for latent in latents])