def test_wide_sense_distr(self):
std_wide_sense_1d = distrs.WideSenseDistr(dim=1)
npt.assert_almost_equal(std_wide_sense_1d.mean, 0.)
npt.assert_almost_equal(std_wide_sense_1d.cov, 1.)
npt.assert_almost_equal(std_wide_sense_1d.vol, 1.)
with self.assertRaises(NotImplementedError):
std_wide_sense_1d.sample()
std_wide_sense_2d = distrs.WideSenseDistr(dim=2)
npt.assert_almost_equal(std_wide_sense_2d.mean, npu.col_of(2, 0.))
npt.assert_almost_equal(std_wide_sense_2d.cov, np.eye(2))
npt.assert_almost_equal(std_wide_sense_2d.vol, np.eye(2))
with self.assertRaises(NotImplementedError):
std_wide_sense_2d.sample()
sd1=3.; sd2=4.; cor=-.5
wide_sense_2d = distrs.WideSenseDistr(mean=[1., 2.], cov=stats.make_cov_2d(sd1=sd1, sd2=sd2, cor=cor))
npt.assert_almost_equal(wide_sense_2d.mean, npu.col(1., 2.))
npt.assert_almost_equal(wide_sense_2d.cov, [[sd1*sd1, cor*sd1*sd2], [cor*sd1*sd2, sd2*sd2]])
npt.assert_almost_equal(wide_sense_2d.vol, [[sd1, 0.], [cor*sd2, np.sqrt(1.-cor*cor)*sd2]])
with self.assertRaises(NotImplementedError):
wide_sense_2d.sample()
wide_sense_2d = distrs.WideSenseDistr(mean=[1., 2.], vol=stats.make_vol_2d(sd1=sd1, sd2=sd2, cor=cor))
npt.assert_almost_equal(wide_sense_2d.mean, npu.col(1., 2.))
npt.assert_almost_equal(wide_sense_2d.cov, [[sd1*sd1, cor*sd1*sd2], [cor*sd1*sd2, sd2*sd2]])
npt.assert_almost_equal(wide_sense_2d.vol, [[sd1, 0.], [cor*sd2, np.sqrt(1.-cor*cor)*sd2]])
with self.assertRaises(NotImplementedError):
wide_sense_2d.sample()
def create_2d(pct_drift1, pct_drift2, pct_sd1, pct_sd2, pct_cor):
return GeometricBrownianMotion(
npu.col(pct_drift1, pct_drift2),
stats.make_vol_2d(pct_sd1, pct_sd2, pct_cor))
def create_2d(mean1, mean2, sd1, sd2, cor):
return WienerProcess(npu.col(mean1, mean2),
stats.make_vol_2d(sd1, sd2, cor))
def test_normal_distr(self):
rnd.random_state(np.random.RandomState(seed=42), force=True)
std_normal_1d = distrs.NormalDistr(dim=1)
npt.assert_almost_equal(std_normal_1d.mean, 0.)
npt.assert_almost_equal(std_normal_1d.cov, 1.)
npt.assert_almost_equal(std_normal_1d.vol, 1.)
sample = std_normal_1d.sample()
self.assertEqual(np.shape(sample), (1, 1))
npt.assert_almost_equal(sample, [[ 0.49671415]])
sample = std_normal_1d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 1))
npt.assert_almost_equal(sample, [
[-0.1382643 ],
[ 0.64768854],
[ 1.52302986],
[-0.23415337],
[-0.23413696],
[ 1.57921282],
[ 0.76743473],
[-0.46947439],
[ 0.54256004],
[-0.46341769]])
std_normal_2d = distrs.NormalDistr(dim=2)
npt.assert_almost_equal(std_normal_2d.mean, npu.col_of(2, 0.))
npt.assert_almost_equal(std_normal_2d.cov, np.eye(2))
npt.assert_almost_equal(std_normal_2d.vol, np.eye(2))
sample = std_normal_2d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 2))
npt.assert_almost_equal(sample, [
[-0.46572975, 0.24196227],
[-1.91328024, -1.72491783],
[-0.56228753, -1.01283112],
[ 0.31424733, -0.90802408],
[-1.4123037 , 1.46564877],
[-0.2257763 , 0.0675282 ],
[-1.42474819, -0.54438272],
[ 0.11092259, -1.15099358],
[ 0.37569802, -0.60063869],
[-0.29169375, -0.60170661]])
sd1=3.; sd2=4.; cor=-.5
normal_2d = distrs.NormalDistr(mean=[1., 2.], cov=stats.make_cov_2d(sd1=sd1, sd2=sd2, cor=cor))
npt.assert_almost_equal(normal_2d.mean, npu.col(1., 2.))
npt.assert_almost_equal(normal_2d.cov, [[sd1*sd1, cor*sd1*sd2], [cor*sd1*sd2, sd2*sd2]])
npt.assert_almost_equal(normal_2d.vol, [[sd1, 0.], [cor*sd2, np.sqrt(1.-cor*cor)*sd2]])
sample = normal_2d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 2))
npt.assert_almost_equal(sample, [
[-3.09581812, 9.06710684],
[ 5.00400357, -1.07912958],
[ 4.10821238, -2.42324481],
[ 2.58989516, -7.05256838],
[ 2.07671635, 3.61955714],
[ 0.38728403, 2.5195548 ],
[-1.36010204, -0.88681309],
[ 1.63968707, -1.29329703],
[-0.61960168, 6.44566548],
[ 5.53451941, -4.36131646]])
normal_2d = distrs.NormalDistr(mean=[1., 2.], vol=stats.make_vol_2d(sd1=sd1, sd2=sd2, cor=cor))
npt.assert_almost_equal(normal_2d.mean, npu.col(1., 2.))
npt.assert_almost_equal(normal_2d.cov, [[sd1*sd1, cor*sd1*sd2], [cor*sd1*sd2, sd2*sd2]])
npt.assert_almost_equal(normal_2d.vol, [[sd1, 0.], [cor*sd2, np.sqrt(1.-cor*cor)*sd2]])
sample = normal_2d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 2))
npt.assert_almost_equal(sample, [
[ 0.4624506 , -0.26705979],
[ 1.76344545, 5.54913479],
[-2.76038957, 4.57609973],
[ 2.35608833, 1.20642031],
[-2.1218454 , 5.16796697],
[-0.85307657, -0.00850715],
[ 5.28771297, -1.62048489],
[-2.12592264, 7.1016208 ],
[-0.46508111, 6.26189296],
[ 3.15543223, -0.04269231]])
def test_log_normal_distr(self):
rnd.random_state(np.random.RandomState(seed=42), force=True)
std_log_normal_1d = distrs.LogNormalDistr(dim=1)
npt.assert_almost_equal(std_log_normal_1d.mean, [[ 1.6487213]])
npt.assert_almost_equal(std_log_normal_1d.cov, [[ 4.6707743]])
npt.assert_almost_equal(std_log_normal_1d.vol, [[ 2.1611974]])
sample = std_log_normal_1d.sample(size=1)
self.assertEqual(np.shape(sample), (1, 1))
npt.assert_almost_equal(sample, [[ 1.6433127]])
sample = std_log_normal_1d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 1))
npt.assert_almost_equal(sample, [
[ 0.87086849],
[ 1.91111824],
[ 4.58609939],
[ 0.79124045],
[ 0.79125344],
[ 4.85113557],
[ 2.15423297],
[ 0.62533086],
[ 1.72040554],
[ 0.62912979]])
std_log_normal_2d = distrs.LogNormalDistr(dim=2)
npt.assert_almost_equal(std_log_normal_2d.mean, [
[ 1.6487213],
[ 1.6487213]])
npt.assert_almost_equal(std_log_normal_2d.cov, [
[ 4.6707743, 0. ],
[ 0. , 4.6707743]])
npt.assert_almost_equal(std_log_normal_2d.vol, [
[ 2.1611974, 0. ],
[ 0. , 2.1611974]])
sample = std_log_normal_2d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 2))
npt.assert_almost_equal(sample, [
[ 0.62767689, 1.27374614],
[ 0.14759544, 0.17818769],
[ 0.5699039 , 0.36318929],
[ 1.36922835, 0.40332037],
[ 0.2435815 , 4.33035173],
[ 0.79789657, 1.06986043],
[ 0.24056903, 0.58019982],
[ 1.11730841, 0.31632232],
[ 1.45600738, 0.54846123],
[ 0.74699727, 0.54787583]])
sd1=.4; sd2=.4; cor=-.5
log_normal_2d = distrs.LogNormalDistr(mean_of_log=[1., 1.3], cov_of_log=stats.make_cov_2d(sd1=sd1, sd2=sd2, cor=cor))
npt.assert_almost_equal(log_normal_2d.mean_of_log, npu.col(1., 1.3))
npt.assert_almost_equal(log_normal_2d.cov_of_log, [[sd1*sd1, cor*sd1*sd2], [cor*sd1*sd2, sd2*sd2]])
npt.assert_almost_equal(log_normal_2d.vol_of_log, [[sd1, 0.], [cor*sd2, np.sqrt(1.-cor*cor)*sd2]])
npt.assert_almost_equal(log_normal_2d.mean, [[ 2.9446796], [ 3.9749016]])
npt.assert_almost_equal(log_normal_2d.cov, [[ 1.5045366, -0.8999087], [-0.8999087, 2.7414445]])
npt.assert_almost_equal(log_normal_2d.vol, [[ 1.2265956, 0. ], [-0.7336637, 1.484312 ]])
sample = log_normal_2d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 2))
npt.assert_almost_equal(sample, [
[ 1.42711164, 6.95143797],
[ 4.62238496, 2.99848502],
[ 4.32618186, 2.50643161],
[ 4.10913455, 1.42691268],
[ 2.94320341, 4.55346303],
[ 2.50304159, 3.80468825],
[ 2.24476532, 2.45957906],
[ 3.18112082, 2.60781028],
[ 2.01884543, 5.66848303],
[ 5.34174201, 2.12565878]])
log_normal_2d = distrs.LogNormalDistr(mean_of_log=[1., 1.3], vol_of_log=stats.make_vol_2d(sd1=sd1, sd2=sd2, cor=cor))
npt.assert_almost_equal(log_normal_2d.mean_of_log, npu.col(1., 1.3))
npt.assert_almost_equal(log_normal_2d.cov_of_log, [[sd1*sd1, cor*sd1*sd2], [cor*sd1*sd2, sd2*sd2]])
npt.assert_almost_equal(log_normal_2d.vol_of_log, [[sd1, 0.], [cor*sd2, np.sqrt(1.-cor*cor)*sd2]])
npt.assert_almost_equal(log_normal_2d.mean, npu.col(2.9446796, 3.9749016))
npt.assert_almost_equal(log_normal_2d.cov, [[ 1.5045366, -0.8999087], [-0.8999087, 2.7414445]])
npt.assert_almost_equal(log_normal_2d.vol, [[ 1.2265956, 0. ], [-0.7336637, 1.484312 ]])
sample = log_normal_2d.sample(size=10)
self.assertEqual(np.shape(sample), (10, 2))
npt.assert_almost_equal(sample, [
[ 2.71288329, 2.80448293],
[ 2.70285608, 5.57387658],
[ 1.66454464, 4.28346127],
[ 3.23285936, 3.52238521],
[ 1.76160691, 4.67441442],
[ 2.32343609, 2.75776026],
[ 4.8398479 , 2.85230385],
[ 1.67494888, 5.78583855],
[ 2.06409776, 5.58431178],
[ 3.6537541 , 3.15441508]])
