def estimate_bivariate_mle_jr():
ndim = 2
size = (10000, ndim)
data = np.random.normal(size=size)
eta, lam = 4, -.9
skst = SkewStudent(eta=eta, lam=lam)
data = skst.rvs(size=size)
model = SkStJR(ndim=ndim, data=data)
out = model.fit_mle()
print(out)
model.from_theta(out.x)
fig, axes = plt.subplots(nrows=size[1], ncols=1)
for innov, ax in zip(data.T, axes):
sns.kdeplot(innov, ax=ax, label='data')
lines = [ax.get_lines()[0].get_xdata() for ax in axes]
lines = np.vstack(lines).T
marginals = model.marginals(lines)
for line, margin, ax in zip(lines.T, marginals.T, axes):
ax.plot(line, margin, label='fitted')
ax.legend()
plt.show()
def estimate_bivariate_mle_jr():
ndim = 2
size = (10000, ndim)
data = np.random.normal(size=size)
eta, lam = 4, -.9
skst = SkewStudent(eta=eta, lam=lam)
data = skst.rvs(size=size)
model = SkStJR(ndim=ndim, data=data)
out = model.fit_mle()
print(out)
model.from_theta(out.x)
fig, axes = plt.subplots(nrows=size[1], ncols=1)
for innov, ax in zip(data.T, axes):
sns.kdeplot(innov, ax=ax, label='data')
lines = [ax.get_lines()[0].get_xdata() for ax in axes]
lines = np.vstack(lines).T
marginals = model.marginals(lines)
for line, margin, ax in zip(lines.T, marginals.T, axes):
ax.plot(line, margin, label='fitted')
ax.legend()
plt.show()
def estimate_bivariate_mle_bl():
ndim = 2
size = (10000, ndim)
data = np.random.normal(size=size)
eta, lam = 4, -.9
skst = SkewStudent(eta=eta, lam=lam)
data = skst.rvs(size=size)
model = SkStBL(ndim=ndim, data=data)
out = model.fit_mle(method='L-BFGS-B')
print(out)
def estimate_bivariate_mle_bl():
ndim = 2
size = (10000, ndim)
data = np.random.normal(size=size)
eta, lam = 4, -.9
skst = SkewStudent(eta=eta, lam=lam)
data = skst.rvs(size=size)
model = SkStBL(ndim=ndim, data=data)
out = model.fit_mle(method='L-BFGS-B')
print(out)
def test_rvs(self):
"""Test ppf method."""
skewt = SkewStudent()
rvs = skewt.rvs()
self.assertIsInstance(rvs, float)
size = 2
rvs = skewt.rvs(size=size)
self.assertIsInstance(rvs, np.ndarray)
self.assertEqual(rvs.shape, (size, ))
size = (2, 3)
rvs = skewt.rvs(size=size)
self.assertIsInstance(rvs, np.ndarray)
self.assertEqual(rvs.shape, size)
def test_rvs(self):
"""Test ppf method."""
skewt = SkewStudent()
rvs = skewt.rvs()
self.assertIsInstance(rvs, float)
size = 2
rvs = skewt.rvs(size=size)
self.assertIsInstance(rvs, np.ndarray)
self.assertEqual(rvs.shape, (size, ))
size = (2, 3)
rvs = skewt.rvs(size=size)
self.assertIsInstance(rvs, np.ndarray)
self.assertEqual(rvs.shape, size)
# -*- coding: utf-8 -*-
"""Examples.
"""
from __future__ import print_function, division
import numpy as np
import matplotlib.pylab as plt
import seaborn as sns
from scipy.optimize import minimize
from skewstudent import SkewStudent
if __name__ == '__main__':
eta, lam = 5, -.5
param = [eta, lam]
sns.set_context('paper')
skewt = SkewStudent(eta=eta, lam=lam)
data = skewt.rvs(2000)
sns.kdeplot(data)
plt.show()
bounds = [(2.01, 300), (-1, 1)]
res = minimize(skewt.loglikelihood, [10, 0], args=(data,), method='SLSQP',
bounds=bounds)
print(res)
