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 get_full_name(name):
return enron_utils.FULLNAMES.get(name, name)
def get_title(name):
return enron_utils.TITLES.get(name, "?")
for cluster, payload in suspicious.values():
cnames = cluster_names(cluster)
ctitles = map(get_title, cnames)
print payload["desc"]
for n, t in zip(cnames, ctitles):
print "\t", get_full_name(n), '\t\t"{}"'.format(t)
print
# #Given the uncertainty behind these latent clusters, we can visualize the variablity within these assignments with a z-matrix
#
# ###Ordering the z-matrix allows us to group members of each possible cluster together
# In[ ]:
zmat = query.zmatrix(domain=0, latents=infers)
zmat = zmatrix_reorder(zmat, zmatrix_heuristic_block_ordering(zmat))
# In[ ]:
plt.imshow(zmat, cmap=plt.cm.binary, interpolation='nearest')
plt.xlabel('people (sorted)')
plt.ylabel('people (sorted)')
def get_full_name(name):
return enron_utils.FULLNAMES.get(name, name)
def get_title(name):
return enron_utils.TITLES.get(name, "?")
for cluster, payload in suspicious.values():
cnames = cluster_names(cluster)
ctitles = map(get_title, cnames)
print payload["desc"]
for n, t in zip(cnames, ctitles):
print "\t", get_full_name(n), '\t\t"{}"'.format(t)
print
# #Given the uncertainty behind these latent clusters, we can visualize the variablity within these assignments with a z-matrix
#
# ###Ordering the z-matrix allows us to group members of each possible cluster together
# In[ ]:
zmat = query.zmatrix(domain=0, latents=infers)
zmat = zmatrix_reorder(zmat, zmatrix_heuristic_block_ordering(zmat))
# In[ ]:
plt.imshow(zmat, cmap=plt.cm.binary, interpolation='nearest')
plt.xlabel('people (sorted)')
plt.ylabel('people (sorted)')