def setUp(self):
rng = np.random.RandomState(42)
Cluster.global_rng = rng
Genotype.global_rng = rng
setattr(Cluster, "num_dims", 2)
clust1 = Cluster(50)
clust1.mean = np.array([0, 0])
clust1.cov = np.array([[1, 0], [0, 1]])
clust2 = Cluster(30)
clust2.mean = np.array([5, 5])
clust2.cov = np.array([[5, 0], [0, 10]])
self.indiv1 = Genotype([clust1, clust2])
self.indiv1.create_views()
self.indiv1.resample_values()
clust3 = Cluster(50)
clust3.mean = np.array([2, 2])
clust3.cov = np.array([[2, 0], [0, 2]])
clust4 = Cluster(30)
clust4.mean = np.array([10, 10])
clust4.cov = np.array([[4, 0], [0, 2]])
self.indiv2 = Genotype([clust3, clust4])
self.indiv2.create_views()
self.indiv2.resample_values()
def test_eigenratio_eccentric(self):
clust1 = Cluster(50)
clust1.mean = np.array([0, 0])
clust1.cov = np.array([[10, 0], [0, 1]])
clust2 = Cluster(80)
clust2.mean = np.array([0, 0])
clust2.cov = np.array([[9.9, 0], [0, 1]])
indiv = Genotype([clust1, clust2])
eigen_ratio = hawks.constraints.eigenval_ratio(indiv)
self.assertEqual(eigen_ratio, 10)
def test_overlap_same(self):
clust1 = Cluster(4000)
clust1.mean = np.array([0, 0])
clust1.cov = np.array([[1, 0], [0, 1]])
clust2 = Cluster(4000)
clust2.mean = np.array([0, 0])
clust2.cov = np.array([[1, 0], [0, 1]])
indiv = Genotype([clust1, clust2])
indiv.create_views()
indiv.resample_values()
overlap = hawks.constraints.overlap(indiv)
self.assertAlmostEqual(overlap, 0.5, places=1)
def test_overlap_separated(self):
clust1 = Cluster(50)
clust1.mean = np.array([0, 0])
clust1.cov = np.array([[1, 0], [0, 1]])
clust2 = Cluster(80)
clust2.mean = np.array([10, 10])
clust2.cov = np.array([[1, 0], [0, 1]])
indiv = Genotype([clust1, clust2])
indiv.create_views()
indiv.resample_values()
overlap = hawks.constraints.overlap(indiv)
self.assertEqual(overlap, 0)