two version of R2R are hereHEADmaster

1 files changed, 71 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/networkx/linalg/tests/test_spectrum.py b/.venv/lib/python3.12/site-packages/networkx/linalg/tests/test_spectrum.py
new file mode 100644
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+++ b/.venv/lib/python3.12/site-packages/networkx/linalg/tests/test_spectrum.py
@@ -0,0 +1,71 @@
+import pytest
+
+np = pytest.importorskip("numpy")
+pytest.importorskip("scipy")
+
+import networkx as nx
+from networkx.generators.degree_seq import havel_hakimi_graph
+
+
+class TestSpectrum:
+    @classmethod
+    def setup_class(cls):
+        deg = [3, 2, 2, 1, 0]
+        cls.G = havel_hakimi_graph(deg)
+        cls.P = nx.path_graph(3)
+        cls.WG = nx.Graph(
+            (u, v, {"weight": 0.5, "other": 0.3}) for (u, v) in cls.G.edges()
+        )
+        cls.WG.add_node(4)
+        cls.DG = nx.DiGraph()
+        nx.add_path(cls.DG, [0, 1, 2])
+
+    def test_laplacian_spectrum(self):
+        "Laplacian eigenvalues"
+        evals = np.array([0, 0, 1, 3, 4])
+        e = sorted(nx.laplacian_spectrum(self.G))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.laplacian_spectrum(self.WG, weight=None))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.laplacian_spectrum(self.WG))
+        np.testing.assert_almost_equal(e, 0.5 * evals)
+        e = sorted(nx.laplacian_spectrum(self.WG, weight="other"))
+        np.testing.assert_almost_equal(e, 0.3 * evals)
+
+    def test_normalized_laplacian_spectrum(self):
+        "Normalized Laplacian eigenvalues"
+        evals = np.array([0, 0, 0.7712864461218, 1.5, 1.7287135538781])
+        e = sorted(nx.normalized_laplacian_spectrum(self.G))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight=None))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG))
+        np.testing.assert_almost_equal(e, evals)
+        e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight="other"))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_adjacency_spectrum(self):
+        "Adjacency eigenvalues"
+        evals = np.array([-np.sqrt(2), 0, np.sqrt(2)])
+        e = sorted(nx.adjacency_spectrum(self.P))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_modularity_spectrum(self):
+        "Modularity eigenvalues"
+        evals = np.array([-1.5, 0.0, 0.0])
+        e = sorted(nx.modularity_spectrum(self.P))
+        np.testing.assert_almost_equal(e, evals)
+        # Directed modularity eigenvalues
+        evals = np.array([-0.5, 0.0, 0.0])
+        e = sorted(nx.modularity_spectrum(self.DG))
+        np.testing.assert_almost_equal(e, evals)
+
+    def test_bethe_hessian_spectrum(self):
+        "Bethe Hessian eigenvalues"
+        evals = np.array([0.5 * (9 - np.sqrt(33)), 4, 0.5 * (9 + np.sqrt(33))])
+        e = sorted(nx.bethe_hessian_spectrum(self.P, r=2))
+        np.testing.assert_almost_equal(e, evals)
+        # Collapses back to Laplacian:
+        e1 = sorted(nx.bethe_hessian_spectrum(self.P, r=1))
+        e2 = sorted(nx.laplacian_spectrum(self.P))
+        np.testing.assert_almost_equal(e1, e2)