def test_min_dim(self):
a = np.array([1])
b = np.array([1, 2])
exp = [1]
act = supp.min_dim(a, b)
self.assertListEqual(exp, act)
a = np.array([[1], [1]])
b = np.array([1, 2])
exp = [2]
act = supp.min_dim(a, b)
self.assertListEqual(exp, act)
def sqrtxy(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.sqrt(
np.square(np.resize(x, new_dim)) +
np.square(np.resize(y, new_dim))) / np.sqrt(2.0)
return np.sqrt(np.square(x) + np.square(y)) / np.sqrt(2.0)
def test_ypow_array(self):
x, y = np.random.rand(5, 10), np.random.rand(3, 2)
dim = supp.min_dim(x, y)
x, p = np.resize(x, dim), np.resize(y, dim)
exp = np.abs(x)**np.abs(y)
act = mat.ypow(x, y, p)
equal = np.equal(exp, act).all()
self.assertEqual(equal, True)
def test_cpow_array(self):
x, p, y = np.random.rand(5, 10), np.random.rand(3, 2), 0
dim = supp.min_dim(x, p)
x, p = np.resize(x, dim), np.resize(p, dim)
exp = np.abs(x)**(p + 1)
act = mat.cpow(x, y, p)
equal = np.equal(exp, act).all()
self.assertEqual(equal, True)
def test_sqrt_array(self):
x, y = np.random.rand(5, 10), np.random.rand(3, 2)
dim = supp.min_dim(x, y)
x, p = np.resize(x, dim), np.resize(y, dim)
exp = np.sqrt(x)
act = mat.sqrt(x, y, 0)
equal = np.equal(exp, act).all()
self.assertEqual(equal, True)
def test_inv_np_array_0(self):
x, y = np.random.rand(5, 10), np.random.rand(3, 2)
dim = supp.min_dim(x, y)
x, p = np.resize(x, dim), np.resize(y, dim)
exp = 1 / x
act = mat.inv(x, y, 0)
equal = np.equal(exp, act).all()
self.assertEqual(equal, True)
def test_add_np_array_1(self):
x, y = np.random.rand(2, 3), np.random.rand(3, 2)
dim = supp.min_dim(x, y)
x, y = np.resize(x, dim), np.resize(y, dim)
exp = (x + y) / 2.0
act = mat.add(x, y, 0)
equal = np.equal(exp, act).all()
self.assertEqual(equal, True)
def test_cmult_np_array_1(self):
x, y = np.random.rand(5, 10), np.random.rand(3, 2)
p = np.random.rand(100, 10)
dim = supp.min_dim(x, p)
x, p = np.resize(x, dim), np.resize(p, dim)
exp = x * p
act = mat.cmult(x, y, p)
equal = np.equal(exp, act).all()
self.assertEqual(equal, True)
def ypow(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return (np.abs(np.resize(x, new_dim))**np.abs(np.resize(y, new_dim)))
return np.abs(x)**np.abs(y)
def mult(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.resize(x, new_dim) * np.resize(y, new_dim)
return x * y
def aminus(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.abs(np.resize(x, new_dim) - np.resize(y, new_dim)) / 2.0
return np.abs(x - y) / 2.0
def add(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return (np.resize(x, new_dim) + np.resize(y, new_dim)) / 2.0
return (x + y) / 2.0
def gt(x, y, p):
if is_np(x) and is_np(y):
new_dim = min_dim(x, y)
return np.resize(x, new_dim) > np.resize(y, new_dim)
return x > y
