/usr/share/pyshared/ase/utils/bee.py is in python-ase 3.6.0.2515-1.1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | import numpy as np
# NB! This module was ported from a 4 year old CamposASE2 module.
"""Bayesian Error Estimation
For details, see: "Bayesian Error Estimation in Density Functional
Theory", J. J. Mortensen, K. Kaasbjerg, S. L. Frederiksen,
J. K. Norskov, J. P. Sethna, K. W. Jacobsen, Phys. Rev. Lett. 95,
216401 (2005)."""
# T
# cost(c) = cost0 + 0.5 * (c - c0) H (c - c0)
#
# Cost function minimum value:
cost0 = 3.4660625596
# Best fit parameters:
c0 = np.array([1.000787451, 0.1926284063, 1.896191546])
# Hessian:
# H = np.array([[ 1.770035168e+03, -3.732470432e+02, -2.105836167e+02],
# [-3.732470432e+02, 1.188857209e+02, 6.054102443e+01],
# [-2.105836167e+02, 6.054102443e+01, 3.211200293e+01]])
#
# 0.5 * np * T = cost0 (np=3: number of parameters)
T = cost0 * 2 / 3
def make_ensemble(N=1000, seed=None):
np.random.seed(seed) # None means /dev/urandom seed
M = np.array([(0.066, -0.812, 1.996),
(0.055, 0.206, 0.082),
(-0.034, 0.007, 0.004)])
alpha = np.random.normal(0.0, 1.0, (N, 3))
return c0 + np.dot(alpha, M)
c = make_ensemble()
def get_ensemble_energies(atoms, c=c):
if hasattr(atoms, 'get_calculator'):
coefs = atoms.get_calculator().get_ensemble_coefficients()
else:
coefs = atoms
return coefs[0] + np.dot(c, coefs[1:])
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