#
# 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
#
# Authors: Transport Map Team
# Website: transportmaps.mit.edu
# Support: transportmaps.mit.edu/qa/
#
import numpy as np
import scipy.stats as stats
from .DistributionBase import Distribution
__all__ = [
'DistributionFromSamples'
]
[docs]class DistributionFromSamples(Distribution):
r""" Arbitrary density built from samples
Args:
samples (type, dimension?): independent samples
"""
def __init__(self, samples, weights=None, qtype=0):
super(DistributionFromSamples,self).__init__(samples.shape[1])
self._quadratures = {qtype: {'x':samples, 'w':weights}}
[docs] def rvs(self, m, *args, **kwargs):
if m > self._quadratures[0]['x'].shape[0]:
raise ValueError("Number of rvs must be less than or equal to length of samples provided")
msamples = self._quadratures[0]['x'][0:m,:]
return msamples
[docs] def quadrature(self, qtype, qparams, *args, **kwargs):
if qtype == 0:
x = self.rvs(qparams)
w = np.ones(qparams)/qparams
else:
raise NotImplementedError("qtype not available, still to do")
return (x,w)
#if no analytical form of pdf
[docs] def kde(self, x, params=None):
r""" Evaluate :math:`\pi(x)`
"""
# kde takes data in form (#dim, #data)
density = stats.gaussian_kde(self._quadratures[0]['x'].T)
pdf_kde = density.evaluate(x.T)
return pdf_kde
[docs] def log_kde(self, x, params=None):
return np.log(self.kde(x,params))