#
# This file is part of TransportMaps.
#
# TransportMaps is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# TransportMaps is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with TransportMaps. If not, see <http://www.gnu.org/licenses/>.
#
# Transport Maps Library
# Copyright (C) 2015-2018 Massachusetts Institute of Technology
# Uncertainty Quantification group
# Department of Aeronautics and Astronautics
#
# Author: Transport Map Team
# Website: transportmaps.mit.edu
# Support: transportmaps.mit.edu/qa/
#
import numpy as np
from TransportMaps.Likelihoods.LikelihoodBase import LogLikelihood
from TransportMaps.Distributions.Inference.InferenceBase import BayesPosteriorDistribution
from TransportMaps.Algorithms.SequentialInference.LinearSequentialInference import \
LinearFilter
from .Coradia175VehicleStateSpace import *
__all__ = ['ParametersLogLikelihood',
'ParametersPosterior']
[docs]class ParametersLogLikelihood(LogLikelihood):
def __init__(self, y, vehicle, par_name_list):
self._vehicle = vehicle
self._par_name_list = par_name_list
dim = len(par_name_list)
super(ParametersLogLikelihood, self).__init__(y, dim)
@property
[docs] def vehicle(self):
return self._vehicle
@property
[docs] def par_name_list(self):
return self._par_name_list
[docs] def evaluate(self, x, *args, **kwargs):
if x.shape[1] != self.dim:
raise ValueError("Input dimension mismatch.")
m = x.shape[0]
out = np.zeros(m)
for i in range(m):
pi_hyper = ParametersPrior(self._par_name_list)
pi_prior = StateSpacePrior(self.vehicle, self._par_name_list, x[i,:])
pi_trans = StateSpaceTransition(self.vehicle, self._par_name_list, x[i,:])
FLT = LinearFilter(pi_hyper=pi_hyper)
for n, y in enumerate(self.y):
# Define log-likelihood
if y is None: # Missing data
ll = None
else:
ll = StateSpaceLogLikelihood(
y, self.vehicle, self._par_name_list, init_coeffs=x[i,:])
# Define transition / prior
if n > 0:
pin = pi_trans
else:
pin = pi_prior
# Assimilation
FLT.assimilate(pin, ll)
out[i] = FLT.marginal_log_likelihood
return out
[docs] def grad_x(self, x, *args, **kwargs):
if x.shape[1] != self.dim:
raise ValueError("Input dimension mismatch.")
m = x.shape[0]
out = np.zeros((m,len(self._par_name_list)))
for i in range(m):
pi_hyper = ParametersPrior(self._par_name_list)
pi_prior = StateSpacePrior(self.vehicle, self._par_name_list, x[i,:])
pi_trans = StateSpaceTransition(self.vehicle, self._par_name_list, x[i,:])
FLT = LinearFilter(ders=1, pi_hyper=pi_hyper)
for n, y in enumerate(self.y):
# Define log-likelihood
if y is None: # Missing data
ll = None
else:
ll = StateSpaceLogLikelihood(
y, self.vehicle, self._par_name_list, init_coeffs=x[i,:])
# Define transition / prior
if n > 0:
pin = pi_trans
else:
pin = pi_prior
# Assimilation
FLT.assimilate(pin, ll)
out[i] = FLT.grad_marginal_log_likelihood
return out
[docs] def tuple_grad_x(self, x, *args, **kwargs):
if x.shape[1] != self.dim:
raise ValueError("Input dimension mismatch.")
m = x.shape[0]
ev = np.zeros(m)
gx = np.zeros((m,len(self._par_name_list)))
for i in range(m):
pi_hyper = ParametersPrior(self._par_name_list)
pi_prior = StateSpacePrior(self.vehicle, self._par_name_list, x[i,:])
pi_trans = StateSpaceTransition(self.vehicle, self._par_name_list, x[i,:])
FLT = LinearFilter(ders=1, pi_hyper=pi_hyper)
for n, y in enumerate(self.y):
# Define log-likelihood
if y is None: # Missing data
ll = None
else:
ll = StateSpaceLogLikelihood(
y, self.vehicle, self._par_name_list, init_coeffs=x[i,:])
# Define transition / prior
if n > 0:
pin = pi_trans
else:
pin = pi_prior
# Assimilation
FLT.assimilate(pin, ll)
ev[i] = FLT.marginal_log_likelihood
gx[i] = FLT.grad_marginal_log_likelihood
return (ev, gx)
[docs]class ParametersPosterior(BayesPosteriorDistribution):
def __init__(self, y, vehicle, par_name_list, T=None, Z=None):
self.T = T
self.Z = Z
prior = ParametersPrior(par_name_list)
logL = ParametersLogLikelihood(y, vehicle, par_name_list)
super(ParametersPosterior,self).__init__(logL, prior)