class TestDenseMatrix(unittest.TestCase):
def setUp(self):
self.a = np.array([[1,2,3],[4,0,5]])
self.b = np.array([[0,0,0],[0,0,0]])
self.c = np.array([[0,0],[0,0],[0,0]])
self.d = np.array([[1,0],[0,1]])
self.e = np.array([1,10])
self.f = np.array([1,10,100])
self.matrix_a = DenseMatrix(self.a)
self.matrix_b = DenseMatrix(self.b)
self.matrix_c = DenseMatrix(self.c)
self.matrix_d = DenseMatrix(self.d)
def tearDown(self):
pass
def test_init(self):
nparr = self.a
test_cases = [nparr,
np.mat(nparr),
csr_matrix(nparr),
csc_matrix(nparr),
SparseMatrix(nparr)]
for inmat in test_cases:
outmat = DenseMatrix(inmat)
self.assertIsInstance(outmat.mat, np.matrix)
numpy.testing.assert_array_equal(nparr, np.array(outmat.mat))
def test_add(self):
test_cases = [(self.matrix_a, self.matrix_a, np.mat([[2,4,6],[8,0,10]])),
(self.matrix_a, self.matrix_b, self.matrix_a.mat)
]
for (term1, term2, expected) in test_cases:
sum_ = term1 + term2
numpy.testing.assert_array_equal(sum_.mat, expected)
self.assertIsInstance(sum_, type(term1))
def test_add_raises(self):
test_cases = [(self.matrix_a, self.a),
(self.matrix_a, SparseMatrix(self.a))]
for (term1, term2) in test_cases:
self.assertRaises(TypeError, term1.__add__, term2)
def test_div(self):
test_cases = [(self.matrix_a, 2, np.mat([[0.5,1.0,1.5],[2.0,0.0,2.5]])),
(self.matrix_c, 2, np.mat(self.c))
]
for (term1, term2, expected) in test_cases:
sum_ = term1 / term2
numpy.testing.assert_array_equal(sum_.mat, expected)
self.assertIsInstance(sum_, DenseMatrix)
def test_div_raises(self):
test_cases = [(self.matrix_a, self.a, TypeError),
(self.matrix_a, SparseMatrix(self.a), TypeError),
(self.matrix_a, "3", TypeError),
(self.matrix_a, 0, ZeroDivisionError)
]
for (term1, term2, error_type) in test_cases:
self.assertRaises(error_type, term1.__div__, term2)
def test_mul(self):
test_cases = [(self.matrix_a, self.matrix_c, np.mat([[0,0],[0,0]])),
(self.matrix_d, self.matrix_a, self.matrix_a.mat),
(self.matrix_a, 2, np.mat([[2,4,6],[8,0,10]])),
(2, self.matrix_a, np.mat([[2,4,6],[8,0,10]])),
(self.matrix_a, np.int64(2), np.mat([[2,4,6],[8,0,10]])),
(np.int64(2), self.matrix_a, np.mat([[2,4,6],[8,0,10]]))
]
for (term1, term2, expected) in test_cases:
sum_ = term1 * term2
numpy.testing.assert_array_equal(sum_.mat, expected)
self.assertIsInstance(sum_, DenseMatrix)
def test_mul_raises(self):
test_cases = [(self.matrix_a, self.a),
(self.matrix_a, SparseMatrix(self.a)),
(self.matrix_a, "3"),
("3", self.matrix_a)]
for (term1, term2) in test_cases:
self.assertRaises(TypeError, term1.__mul__, term2)
def test_multiply(self):
test_cases = [(self.matrix_a, self.matrix_a, np.mat([[1,4,9],[16,0,25]])),
(self.matrix_a, self.matrix_b, np.mat(self.b))
]
for (term1, term2, expected) in test_cases:
mult1 = term1.multiply(term2)
mult2 = term2.multiply(term1)
numpy.testing.assert_array_equal(mult1.mat, expected)
numpy.testing.assert_array_equal(mult2.mat, expected)
self.assertIsInstance(mult1, DenseMatrix)
self.assertIsInstance(mult2, DenseMatrix)
def test_multiply_raises(self):
test_cases = [(self.matrix_a, self.matrix_d, ValueError),
(self.matrix_a, self.a, TypeError),
(self.matrix_a, SparseMatrix(self.a), TypeError),
]
for (term1, term2, error_type) in test_cases:
self.assertRaises(error_type, term1.multiply, term2)
def test_scale_rows(self):
outcome = np.mat([[1,2,3],[40,0,50]])
test_cases = [(self.matrix_a, self.e, outcome),
(self.matrix_a, np.mat(self.e).T, outcome),
]
for (term1, term2, expected) in test_cases:
term1 = term1.scale_rows(term2)
numpy.testing.assert_array_equal(term1.mat, expected)
def test_scale_columns(self):
test_cases = [(self.matrix_a, self.f, np.mat([[1,20,300],[4,0,500]]))]
for (term1, term2, expected) in test_cases:
term1 = term1.scale_columns(term2)
numpy.testing.assert_array_equal(term1.mat, expected)
def test_scale_raises(self):
test_cases = [(self.matrix_a, self.f, ValueError, self.matrix_a.scale_rows),
(self.matrix_a, self.e, ValueError, self.matrix_a.scale_columns),
(self.matrix_a, self.b, ValueError, self.matrix_a.scale_rows),
(self.matrix_a, self.b, ValueError, self.matrix_a.scale_columns),
(self.matrix_a, "3", TypeError, self.matrix_a.scale_rows),
]
for (term1, term2, error_type, function) in test_cases:
self.assertRaises(error_type, function, term2)
def test_plog(self):
m = DenseMatrix(np.mat([[0.5,1.0,1.5],[2.0,0.0,2.5]]))
m_expected = np.mat([[0.,0.,0.4054],[ 0.6931,0.,0.9162]])
a_expected = np.mat([[0.,0.6931,1.0986],[1.3862,0.,1.6094]])
test_cases = [(self.matrix_a.copy(), a_expected),
(m, m_expected)
]
for (term, expected) in test_cases:
term.plog()
numpy.testing.assert_array_almost_equal(term.mat, expected, 3)
class TestDenseMatrix(unittest.TestCase):
def setUp(self):
self.a = np.array([[1, 2, 3], [4, 0, 5]])
self.b = np.array([[0, 0, 0], [0, 0, 0]])
self.c = np.array([[0, 0], [0, 0], [0, 0]])
self.d = np.array([[1, 0], [0, 1]])
self.e = np.array([1, 10])
self.f = np.array([1, 10, 100])
self.matrix_a = DenseMatrix(self.a)
self.matrix_b = DenseMatrix(self.b)
self.matrix_c = DenseMatrix(self.c)
self.matrix_d = DenseMatrix(self.d)
def tearDown(self):
pass
def test_init(self):
nparr = self.a
test_cases = [nparr, np.mat(nparr), csr_matrix(nparr), csc_matrix(nparr), SparseMatrix(nparr)]
for inmat in test_cases:
outmat = DenseMatrix(inmat)
self.assertIsInstance(outmat.mat, np.matrix)
numpy.testing.assert_array_equal(nparr, np.array(outmat.mat))
def test_add(self):
test_cases = [
(self.matrix_a, self.matrix_a, np.mat([[2, 4, 6], [8, 0, 10]])),
(self.matrix_a, self.matrix_b, self.matrix_a.mat),
]
for (term1, term2, expected) in test_cases:
sum_ = term1 + term2
numpy.testing.assert_array_equal(sum_.mat, expected)
self.assertIsInstance(sum_, type(term1))
def test_add_raises(self):
test_cases = [(self.matrix_a, self.a), (self.matrix_a, SparseMatrix(self.a))]
for (term1, term2) in test_cases:
self.assertRaises(TypeError, term1.__add__, term2)
def test_div(self):
test_cases = [
(self.matrix_a, 2, np.mat([[0.5, 1.0, 1.5], [2.0, 0.0, 2.5]])),
(self.matrix_c, 2, np.mat(self.c)),
]
for (term1, term2, expected) in test_cases:
sum_ = term1 / term2
numpy.testing.assert_array_equal(sum_.mat, expected)
self.assertIsInstance(sum_, DenseMatrix)
def test_div_raises(self):
test_cases = [
(self.matrix_a, self.a, TypeError),
(self.matrix_a, SparseMatrix(self.a), TypeError),
(self.matrix_a, "3", TypeError),
(self.matrix_a, 0, ZeroDivisionError),
]
for (term1, term2, error_type) in test_cases:
self.assertRaises(error_type, term1.__div__, term2)
def test_mul(self):
test_cases = [
(self.matrix_a, self.matrix_c, np.mat([[0, 0], [0, 0]])),
(self.matrix_d, self.matrix_a, self.matrix_a.mat),
(self.matrix_a, 2, np.mat([[2, 4, 6], [8, 0, 10]])),
(2, self.matrix_a, np.mat([[2, 4, 6], [8, 0, 10]])),
(self.matrix_a, np.int64(2), np.mat([[2, 4, 6], [8, 0, 10]])),
(np.int64(2), self.matrix_a, np.mat([[2, 4, 6], [8, 0, 10]])),
]
for (term1, term2, expected) in test_cases:
sum_ = term1 * term2
numpy.testing.assert_array_equal(sum_.mat, expected)
self.assertIsInstance(sum_, DenseMatrix)
def test_mul_raises(self):
test_cases = [
(self.matrix_a, self.a),
(self.matrix_a, SparseMatrix(self.a)),
(self.matrix_a, "3"),
("3", self.matrix_a),
]
for (term1, term2) in test_cases:
self.assertRaises(TypeError, term1.__mul__, term2)
def test_multiply(self):
test_cases = [
(self.matrix_a, self.matrix_a, np.mat([[1, 4, 9], [16, 0, 25]])),
(self.matrix_a, self.matrix_b, np.mat(self.b)),
]
for (term1, term2, expected) in test_cases:
mult1 = term1.multiply(term2)
mult2 = term2.multiply(term1)
numpy.testing.assert_array_equal(mult1.mat, expected)
numpy.testing.assert_array_equal(mult2.mat, expected)
self.assertIsInstance(mult1, DenseMatrix)
self.assertIsInstance(mult2, DenseMatrix)
def test_multiply_raises(self):
test_cases = [
(self.matrix_a, self.matrix_d, ValueError),
(self.matrix_a, self.a, TypeError),
(self.matrix_a, SparseMatrix(self.a), TypeError),
]
for (term1, term2, error_type) in test_cases:
self.assertRaises(error_type, term1.multiply, term2)
def test_scale_rows(self):
outcome = np.mat([[1, 2, 3], [40, 0, 50]])
test_cases = [(self.matrix_a, self.e, outcome), (self.matrix_a, np.mat(self.e).T, outcome)]
for (term1, term2, expected) in test_cases:
term1 = term1.scale_rows(term2)
numpy.testing.assert_array_equal(term1.mat, expected)
def test_scale_columns(self):
test_cases = [(self.matrix_a, self.f, np.mat([[1, 20, 300], [4, 0, 500]]))]
for (term1, term2, expected) in test_cases:
term1 = term1.scale_columns(term2)
numpy.testing.assert_array_equal(term1.mat, expected)
def test_scale_raises(self):
test_cases = [
(self.matrix_a, self.f, ValueError, self.matrix_a.scale_rows),
(self.matrix_a, self.e, ValueError, self.matrix_a.scale_columns),
(self.matrix_a, self.b, ValueError, self.matrix_a.scale_rows),
(self.matrix_a, self.b, ValueError, self.matrix_a.scale_columns),
(self.matrix_a, "3", TypeError, self.matrix_a.scale_rows),
]
for (term1, term2, error_type, function) in test_cases:
self.assertRaises(error_type, function, term2)
def test_plog(self):
m = DenseMatrix(np.mat([[0.5, 1.0, 1.5], [2.0, 0.0, 2.5]]))
m_expected = np.mat([[0.0, 0.0, 0.4054], [0.6931, 0.0, 0.9162]])
a_expected = np.mat([[0.0, 0.6931, 1.0986], [1.3862, 0.0, 1.6094]])
test_cases = [(self.matrix_a.copy(), a_expected), (m, m_expected)]
for (term, expected) in test_cases:
term.plog()
numpy.testing.assert_array_almost_equal(term.mat, expected, 3)
