def plot_bidensity():
lam = [1.5, -2]
mvsn = MvSN(ndim=len(lam), lam=lam)
mvsn.plot_bidensity()
eta = 20
skst = MvSt(ndim=2, eta=eta)
skst.plot_bidensity()
eta, lam = [20, 5], [1.5, .5]
skst = SkStJR(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
eta, lam = 20, [1.5, .5]
skst = SkStBL(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
eta, lam = 20, [1.5, -2]
skst = SkStDM(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
eta, lam = 20, [1.5, -2]
skst = SkStAC(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
def plot_bidensity():
lam = [1.5, -2]
mvsn = MvSN(ndim=len(lam), lam=lam)
mvsn.plot_bidensity()
eta = 20
skst = MvSt(ndim=2, eta=eta)
skst.plot_bidensity()
eta, lam = [20, 5], [1.5, .5]
skst = SkStJR(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
eta, lam = 20, [1.5, .5]
skst = SkStBL(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
eta, lam = 20, [1.5, -2]
skst = SkStDM(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
eta, lam = 20, [1.5, -2]
skst = SkStAC(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
def test_rvs(self):
"""Test simulation."""
lam = [.5, 1.5, 2]
skst = MvSN(ndim=len(lam), lam=lam)
size = 10
rvs = skst.rvs(size=size)
self.assertEqual(rvs.shape, (size, len(lam)))
def test_rvs(self):
"""Test simulation."""
lam = [.5, 1.5, 2]
skst = MvSN(ndim=len(lam), lam=lam)
size = 10
rvs = skst.rvs(size=size)
self.assertEqual(rvs.shape, (size, len(lam)))
def test_param_array(self):
"""Test pdf."""
ndim, nobs = 1, 10
size = (nobs, ndim)
lam = np.ones((nobs, ndim)) * .5
skst = MvSN(ndim=ndim, lam=lam)
data = np.random.normal(size=size)
pdf = skst.pdf(data)
self.assertEqual(pdf.ndim, ndim)
self.assertEqual(pdf.shape, (size[0], ))
def test_likelihood(self):
"""Test log-likelihood."""
lam = [.5, 1.5, 2]
theta = np.array(lam)
size = (10, len(lam))
data = np.random.normal(size=size)
skst = MvSN(ndim=len(lam), lam=lam, data=data)
logl1 = skst.likelihood(theta)
logl2 = skst.likelihood(theta * 2)
self.assertIsInstance(logl1, float)
self.assertNotEqual(logl1, logl2)
npt.assert_array_equal(skst.data, data)
def test_cdf(self):
"""Test cdf."""
lam = 1.5
skst = MvSN(ndim=1, lam=lam)
cdf = skst.cdf(np.zeros(1))
self.assertIsInstance(cdf, float)
lam = [1.5, .5]
skst = MvSN(ndim=len(lam), lam=lam)
cdf = skst.cdf(np.zeros(2) - 10)
self.assertIsInstance(cdf, float)
def test_init(self):
"""Test __init__."""
skst = MvSN(ndim=3)
self.assertIsInstance(skst.lam, np.ndarray)
lam = [.5, 1.5]
skst = MvSN(ndim=len(lam), lam=lam)
npt.assert_array_equal(skst.lam, np.array(lam))
mu, sigma = [.5, .4], np.ones((2, 2))
skst = MvSN(ndim=len(lam), lam=lam, mu=mu, sigma=sigma)
npt.assert_array_equal(skst.mu, np.array(mu))
npt.assert_array_equal(skst.sigma, np.array(sigma))
npt.assert_array_equal(skst.const_mu(), np.array(mu))
npt.assert_array_equal(skst.const_sigma(), np.array(sigma))
lam = [1.5, .5]
skst.from_theta(np.array(lam))
npt.assert_array_equal(skst.lam, np.array(lam))
size = len(lam)
data = np.random.normal(size=size)
skst = MvSN(ndim=len(lam), data=data)
npt.assert_array_equal(skst.data, np.atleast_2d(data))
def test_likelihood(self):
"""Test log-likelihood."""
lam = [.5, 1.5, 2]
theta = np.array(lam)
size = (10, len(lam))
data = np.random.normal(size=size)
skst = MvSN(ndim=len(lam), lam=lam, data=data)
logl1 = skst.likelihood(theta)
logl2 = skst.likelihood(theta * 2)
self.assertIsInstance(logl1, float)
self.assertNotEqual(logl1, logl2)
npt.assert_array_equal(skst.data, data)
def test_quantile(self):
"""Test quantile."""
lam = 1.5
skst = MvSN(ndim=1, lam=lam)
arg = -2.
cdf = skst.cdf(arg)
ppf = skst.ppf(cdf)
self.assertAlmostEqual(ppf, arg)
arg = -.1 * np.ones(3)
cdf = skst.cdf_vec(arg)
quantiles = skst.ppf_vec(cdf)
npt.assert_array_almost_equal(arg, quantiles)
def test_dimensions(self):
"""Test dimensions."""
lam = .5
mvsn = MvSN(lam=lam, ndim=1)
self.assertEqual(mvsn.ndim, 1)
def test_pdf(self):
"""Test pdf."""
lam = .5
skst = MvSN(ndim=1, lam=lam)
size = (10, 1)
data = np.random.normal(size=size)
pdf = skst.pdf(data)
self.assertEqual(pdf.ndim, 1)
self.assertEqual(pdf.shape, (size[0], ))
lam = [.5, 1.5, 2]
skst = MvSN(ndim=len(lam), lam=lam)
size = (10, len(lam))
data = np.random.normal(size=size)
pdf = skst.pdf(data)
self.assertEqual(pdf.ndim, 1)
self.assertEqual(pdf.shape, (size[0], ))
def test_cdf(self):
"""Test cdf."""
lam = 1.5
skst = MvSN(ndim=1, lam=lam)
cdf = skst.cdf(np.zeros(1))
self.assertIsInstance(cdf, float)
lam = [1.5, .5]
skst = MvSN(ndim=len(lam), lam=lam)
cdf = skst.cdf(np.zeros(2) - 10)
self.assertIsInstance(cdf, float)
def test_quantile(self):
"""Test quantile."""
lam = 1.5
skst = MvSN(ndim=1, lam=lam)
arg = -2.
cdf = skst.cdf(arg)
ppf = skst.ppf(cdf)
self.assertAlmostEqual(ppf, arg)
arg = -.1 * np.ones(3)
cdf = skst.cdf_vec(arg)
quantiles = skst.ppf_vec(cdf)
npt.assert_array_almost_equal(arg, quantiles)
def test_pdf(self):
"""Test pdf."""
lam = .5
skst = MvSN(ndim=1, lam=lam)
size = (10, 1)
data = np.random.normal(size=size)
pdf = skst.pdf(data)
self.assertEqual(pdf.ndim, 1)
self.assertEqual(pdf.shape, (size[0], ))
lam = [.5, 1.5, 2]
skst = MvSN(ndim=len(lam), lam=lam)
size = (10, len(lam))
data = np.random.normal(size=size)
pdf = skst.pdf(data)
self.assertEqual(pdf.ndim, 1)
self.assertEqual(pdf.shape, (size[0], ))
def plot_bidensity_simulated():
size = int(1e4)
lam = [1.5, -2]
mvsn = MvSN(ndim=len(lam), lam=lam)
mvsn.plot_bidensity()
rvs = mvsn.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta = 20
skst = MvSt(ndim=2, eta=eta)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = [20, 5], [1.5, .5]
skst = SkStJR(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = 20, [1.5, .5]
skst = SkStBL(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = 20, [1.5, -2]
skst = SkStDM(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = 20, [1.5, -2]
skst = SkStAC(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
def plot_bidensity_simulated():
size = int(1e4)
lam = [1.5, -2]
mvsn = MvSN(ndim=len(lam), lam=lam)
mvsn.plot_bidensity()
rvs = mvsn.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta = 20
skst = MvSt(ndim=2, eta=eta)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = [20, 5], [1.5, .5]
skst = SkStJR(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = 20, [1.5, .5]
skst = SkStBL(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = 20, [1.5, -2]
skst = SkStDM(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
eta, lam = 20, [1.5, -2]
skst = SkStAC(ndim=len(lam), eta=eta, lam=lam)
skst.plot_bidensity()
rvs = skst.rvs(size=size)
sns.kdeplot(rvs, shade=True)
plt.axis('square')
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.show()
def test_init(self):
"""Test __init__."""
skst = MvSN(ndim=3)
self.assertIsInstance(skst.lam, np.ndarray)
lam = [.5, 1.5]
skst = MvSN(ndim=len(lam), lam=lam)
npt.assert_array_equal(skst.lam, np.array(lam))
mu, sigma = [.5, .4], np.ones((2, 2))
skst = MvSN(ndim=len(lam), lam=lam, mu=mu, sigma=sigma)
npt.assert_array_equal(skst.mu, np.array(mu))
npt.assert_array_equal(skst.sigma, np.array(sigma))
npt.assert_array_equal(skst.const_mu(), np.array(mu))
npt.assert_array_equal(skst.const_sigma(), np.array(sigma))
lam = [1.5, .5]
skst.from_theta(np.array(lam))
npt.assert_array_equal(skst.lam, np.array(lam))
size = len(lam)
data = np.random.normal(size=size)
skst = MvSN(ndim=len(lam), data=data)
npt.assert_array_equal(skst.data, np.atleast_2d(data))
