def test_lognormal_mul_div_pow(self):
a = random.expovariate(1 / 10)
# Multiplication by constant
Ymul = self.Y * a
self.assertEqual(Ymul.mu, self.Y.mu + np.log(a))
self.assertEqual(np.log(Ymul.sigma), np.log(self.Y.sigma))
# Division by Constant
Ydiv = self.Y / a
self.assertEqual(Ydiv.mu, self.Y.mu - np.log(a))
self.assertEqual(np.log(Ydiv.sigma), np.log(self.Y.sigma))
# Multiplication by another LogNormal
Y2 = rvpy.LogNormal(-10 + 20 * random.random(), 10 * random.random())
Z = self.Y * Y2
self.assertIsInstance(Z, rvpy.LogNormal)
self.assertEqual(Z.mu, self.Y.mu + Y2.mu)
self.assertEqual(Z.sigma, self.Y.sigma + Y2.sigma)
# Inversion
Yinv = 1 / self.Y
self.assertIsInstance(Yinv, rvpy.LogNormal)
self.assertEqual(Yinv.mu, -self.Y.mu)
self.assertEqual(Yinv.sigma, self.Y.sigma)
def test_pow(self):
X = rvpy.StandardNormal()
Y = rvpy.pow(X, 2)
self.assertIsInstance(X, rvpy.StandardNormal)
self.assertIsInstance(Y, rvpy.ChiSq)
Z = rvpy.LogNormal()
W = rvpy.pow(Z, .321)
self.assertIsInstance(Z, rvpy.LogNormal)
self.assertIsInstance(W, rvpy.LogNormal)
def test_lognormal_conversion(self):
with self.assertRaises(AssertionError):
rvpy.LogNormal(0, 0)
with self.assertRaises(AssertionError):
rvpy.LogNormal(0, -1)
with self.assertRaises(TypeError):
rvpy.LogNormal() + rvpy.LogNormal()
with self.assertRaises(TypeError):
rvpy.LogNormal()**0
with self.assertRaises(TypeError):
rvpy.LogNormal() * 0
def test_log(self):
X = rvpy.LogNormal()
Y = rvpy.log(X)
self.assertIsInstance(X, rvpy.LogNormal)
self.assertIsInstance(Y, rvpy.Normal)
def test_sqrt(self):
X = rvpy.LogNormal()
Y = rvpy.sqrt(X)
self.assertIsInstance(X, rvpy.LogNormal)
self.assertIsInstance(Y, rvpy.LogNormal)
def setUp(self):
self.X = rvpy.LogNormal()
self.Y = rvpy.LogNormal(-10 + 20 * random.random(),
10 * random.random())