Add a new unit test suite for the dunshire.matrices module.

[dunshire.git] / test / matrices_test.py

diff --git a/test/matrices_test.py b/test/matrices_test.py
new file mode 100644 (file)
index 0000000..195549f
--- /dev/null
+++ b/test/matrices_test.py
@@ -0,0 +1,88 @@
+"""
+Unit tests for the functions in the ``matrices`` module.
+"""
+
+from unittest import TestCase
+
+from cvxopt import matrix
+
+from dunshire.matrices import (append_col, append_row, condition_number,
+                               eigenvalues, eigenvalues_re, identity,
+                               inner_product, norm)
+from dunshire.options import ABS_TOL
+from .randomgen import random_matrix, random_natural, random_scalar
+
+class AppendColTest(TestCase):
+
+    def test_size_increases(self):
+        """
+        If we append a column to a matrix, the result should be bigger
+        than the original matrix.
+        """
+        dims = random_natural()
+        mat1 = random_matrix(dims)
+        mat2 = random_matrix(dims)
+        bigmat = append_col(mat1, mat2)
+        self.assertTrue(bigmat.size[0] >= mat1.size[0])
+        self.assertTrue(bigmat.size[1] >= mat1.size[1])
+
+
+class AppendRowTest(TestCase):
+
+    def test_size_increases(self):
+        """
+        If we append a row to a matrix, the result should be bigger
+        than the original matrix.
+        """
+        dims = random_natural()
+        mat1 = random_matrix(dims)
+        mat2 = random_matrix(dims)
+        bigmat = append_row(mat1, mat2)
+        self.assertTrue(bigmat.size[0] >= mat1.size[0])
+        self.assertTrue(bigmat.size[1] >= mat1.size[1])
+
+
+class EigenvaluesTest(TestCase):
+
+    def test_eigenvalues_input_not_clobbered(self):
+        mat = random_matrix(random_natural())
+        symmat = mat + mat.trans()
+        symmat_copy = matrix(symmat, symmat.size)
+        eigs = eigenvalues(symmat)
+        self.assertTrue(norm(symmat - symmat_copy) < ABS_TOL)
+
+    def test_eigenvalues_re_input_not_clobbered(self):
+        mat = random_matrix(random_natural())
+        mat_copy = matrix(mat, mat.size)
+        eigs = eigenvalues_re(mat)
+        self.assertTrue(norm(mat - mat_copy) < ABS_TOL)
+
+    def test_eigenvalues_of_symmetric_are_real(self):
+        mat = random_matrix(random_natural())
+        symmat = mat + mat.trans()
+        eigs1 = sorted(eigenvalues(symmat))
+        eigs2 = sorted(eigenvalues_re(symmat))
+        diffs = [abs(e1-e2) for (e1,e2) in zip(eigs1,eigs2)]
+        self.assertTrue(all([diff < ABS_TOL for diff in diffs]))
+
+
+    def test_eigenvalues_of_identity(self):
+        mat = identity(random_natural(), typecode='d')
+        eigs1 = eigenvalues(mat)
+        eigs2 = eigenvalues_re(mat)
+        self.assertTrue(all([abs(e1 - 1) < ABS_TOL for e1 in eigs1]))
+        self.assertTrue(all([abs(e2 - 1) < ABS_TOL for e2 in eigs2]))
+
+
+class InnerProductTest(TestCase):
+
+    def test_norm_is_nonnegative(self):
+        vec = matrix([random_scalar() for _ in range(random_natural())])
+        self.assertTrue(inner_product(vec,vec) >= 0)
+
+
+def ConditionNumberTest(TestCase):
+
+    def test_condition_number_ge_one(self):
+        mat = random_matrix(random_natural())
+        self.assertTrue(condition_number(mat) >= 1)