#!/usr/bin/env python3
"``naginterface.library.mv.gaussian_mixture`` Python Example."
# NAG Copyright 2017-2019.
# pylint: disable=invalid-name
from naginterfaces.library import mv
[docs]def main():
"""
Example for :func:`naginterfaces.library.mv.gaussian_mixture`.
Fits a mixture of Gaussians for a given (co)variance structure.
>>> main()
naginterfaces.library.mv.gaussian_mixture Python Example Results.
Fits a Gaussian mixture model with pooled covariance structure to
New Haven schools test data.
The final membership probabilities are:
[
9.50176891e-01, 4.98231095e-02
3.32590884e-06, 9.99996674e-01
9.99613355e-01, 3.86644659e-04
9.99920087e-01, 7.99127116e-05
3.89990173e-02, 9.61000983e-01
9.32704894e-01, 6.72951064e-02
9.88809712e-01, 1.11902877e-02
4.12521422e-03, 9.95874786e-01
9.72521486e-01, 2.74785140e-02
9.99691952e-01, 3.08048285e-04
2.17221867e-01, 7.82778133e-01
7.69380852e-01, 2.30619148e-01
9.99973063e-01, 2.69370297e-05
6.11334389e-03, 9.93886656e-01
4.41893305e-02, 9.55810670e-01
3.50057883e-04, 9.99649942e-01
9.99902971e-01, 9.70286734e-05
4.02698414e-05, 9.99959730e-01
9.73798317e-01, 2.62016830e-02
3.02036785e-04, 9.99697963e-01
6.94705604e-02, 9.30529440e-01
4.16030240e-03, 9.95839698e-01
3.08391490e-02, 9.69160851e-01
9.91157909e-01, 8.84209120e-03
4.15339034e-04, 9.99584661e-01
]
The log-likehood is -29.683060270297.
There were 14 function evaluations required.
"""
print('naginterfaces.library.mv.gaussian_mixture Python Example Results.')
print('Fits a Gaussian mixture model with pooled covariance structure to')
print('New Haven schools test data.')
x = [
[2.7, 3.2, 4.5, 4.8],
[3.9, 3.8, 5.9, 6.2],
[4.8, 4.1, 6.8, 5.5],
[3.1, 3.5, 4.3, 4.6],
[3.4, 3.7, 5.1, 5.6],
[3.1, 3.4, 4.1, 4.7],
[4.6, 4.4, 6.6, 6.1],
[3.1, 3.3, 4.0, 4.9],
[3.8, 3.7, 4.7, 4.9],
[5.2, 4.9, 8.2, 6.9],
[3.9, 3.8, 5.2, 5.4],
[4.1, 4.0, 5.6, 5.6],
[5.7, 5.1, 7.0, 6.3],
[3.0, 3.2, 4.5, 5.0],
[2.9, 3.3, 4.5, 5.1],
[3.4, 3.3, 4.4, 5.0],
[4.0, 4.2, 5.2, 5.4],
[3.0, 3.0, 4.6, 5.0],
[4.0, 4.1, 5.9, 5.8],
[3.0, 3.2, 4.4, 5.1],
[3.6, 3.6, 5.3, 5.4],
[3.1, 3.2, 4.6, 5.0],
[3.2, 3.3, 5.4, 5.3],
[3.0, 3.4, 4.2, 4.7],
[3.8, 4.0, 6.9, 6.7],
]
nvar = 4
ng = 2
sopt = 2
gm = mv.gaussian_mixture(x, nvar, ng, sopt, riter=5)
print('The final membership probabilities are:')
print('[')
for i in range(gm.prob.shape[0]):
print(
' ' +
', '.join(['{:10.8e}']*gm.prob.shape[1]).format(*gm.prob[i, :])
)
print(']')
print('The log-likehood is {:.12f}.'.format(gm.loglik))
print('There were {:d} function evaluations required.'.format(gm.niter))
if __name__ == '__main__':
import doctest
import sys
sys.exit(
doctest.testmod(
None, verbose=True, report=False,
optionflags=doctest.REPORT_NDIFF,
).failed
)
