def test_max_fraction_clusters(inp, max_frac):
""" Check that ``FirstSimpleGap`` and ``FirstHistogramGap`` respect the
``max_num_clusters`` constraint, if it is set."""
n_points_per_cluster, n_clusters, _, pts = inp
max_num_clusters = max_frac * n_points_per_cluster * n_clusters
fs = FirstSimpleGap(max_fraction=max_frac)
_ = fs.fit_predict(pts)
assert fs.n_clusters_ <= np.floor(max_num_clusters)
fh = FirstHistogramGap(max_fraction=max_frac)
_ = fh.fit_predict(pts)
assert fh.n_clusters_ <= np.floor(max_num_clusters)
def test_firstsimplegap(inp):
"""For a multimodal distribution, check that ``FirstSimpleGap`` with
appropriate parameters finds the right number of clusters, and that each
has the correct number of points ``n_points_per_cluster``."""
n_points_per_cluster, n_clusters, _, pts = inp
fs = FirstSimpleGap(relative_gap_size=0.5,
max_fraction=1.,
affinity='euclidean',
memory=None,
linkage='single')
preds = fs.fit_predict(pts).astype(int)
unique, counts = np.unique(preds, return_counts=True)
# check that the nb of clusters corresponds to the nb of synth. clusters
assert unique.shape[0] == n_clusters
# check that the nb of pts in a cluster corresponds to what we expect
assert_almost_equal(counts, n_points_per_cluster)
