def test_kmeans_simple(self):
n = 10
X = np.vstack((np.random.rand(n, 2), -np.random.rand(n, 2)))
centers, labels, inertia = kmeans_mpi.kmeans(X, 3)
self.assertEqual(centers.shape, (3, 2))
np.testing.assert_array_less(labels, 3)
self.assertGreater(inertia, 0.)
centers, labels, inertia = kmeans_mpi.kmeans(X, 3, n_init=5)
self.assertEqual(centers.shape, (3, 2))
np.testing.assert_array_less(labels, 3)
self.assertGreater(inertia, 0.)
def test_kmeans_simple(self):
n = 10
X = np.vstack((np.random.rand(n, 2), -np.random.rand(n, 2)))
centers, labels, inertia = kmeans_mpi.kmeans(X, 3)
self.assertEqual(centers.shape, (3, 2))
np.testing.assert_array_less(labels, 3)
self.assertGreater(inertia, 0.0)
centers, labels, inertia = kmeans_mpi.kmeans(X, 3, n_init=5)
self.assertEqual(centers.shape, (3, 2))
np.testing.assert_array_less(labels, 3)
self.assertGreater(inertia, 0.0)
def train(self, incoming_patches):
centroids, label, inertia = \
kmeans_mpi.kmeans(incoming_patches,
self.specs['k'],
n_init = self.specs.get('n_init', 1),
max_iter = self.specs.get('max_iter', 100),
tol = self.specs.get('tol', 0.0001))
return centroids, (label, inertia)
