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##############################################################################################################################################################################
# Copyright (c) 2017, Miroslav Stoyanov
#
# This file is part of
# Toolkit for Adaptive Stochastic Modeling And Non-Intrusive ApproximatioN: TASMANIAN
#
# Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions
# and the following disclaimer in the documentation and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse
# or promote products derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES,
# INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED.
# IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY,
# OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
# OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# UT-BATTELLE, LLC AND THE UNITED STATES GOVERNMENT MAKE NO REPRESENTATIONS AND DISCLAIM ALL WARRANTIES, BOTH EXPRESSED AND IMPLIED.
# THERE ARE NO EXPRESS OR IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE, OR THAT THE USE OF THE SOFTWARE WILL NOT INFRINGE ANY PATENT,
# COPYRIGHT, TRADEMARK, OR OTHER PROPRIETARY RIGHTS, OR THAT THE SOFTWARE WILL ACCOMPLISH THE INTENDED RESULTS OR THAT THE SOFTWARE OR ITS USE WILL NOT RESULT IN INJURY OR DAMAGE.
# THE USER ASSUMES RESPONSIBILITY FOR ALL LIABILITIES, PENALTIES, FINES, CLAIMS, CAUSES OF ACTION, AND COSTS AND EXPENSES, CAUSED BY, RESULTING FROM OR ARISING OUT OF,
# IN WHOLE OR IN PART THE USE, STORAGE OR DISPOSAL OF THE SOFTWARE.
##############################################################################################################################################################################
from ctypes import c_char_p, c_int, c_double, c_void_p, POINTER, CDLL, create_string_buffer, RTLD_GLOBAL
import numpy as np
import sys
from TasmanianConfig import __path_libdream__
from TasmanianConfig import TasmanianInputError as InputError
from TasmanianSG import TasmanianSparseGrid as SparseGrid
pLibDTSG = CDLL(__path_libdream__, mode = RTLD_GLOBAL)
pLibDTSG.tsgMakeDreamState.restype = c_void_p
pLibDTSG.tsgMakeDreamState.argtypes = [c_int, c_int]
pLibDTSG.tsgDeleteDreamState.argtypes = [c_void_p]
pLibDTSG.tsgDreamStateGetDims.restype = c_int
pLibDTSG.tsgDreamStateGetDims.argtypes = [c_void_p]
pLibDTSG.tsgDreamStateGetChains.restype = c_int
pLibDTSG.tsgDreamStateGetChains.argtypes = [c_void_p]
pLibDTSG.tsgDreamStateGetNumHistory.restype = c_int
pLibDTSG.tsgDreamStateGetNumHistory.argtypes = [c_void_p]
pLibDTSG.tsgDreamStateSet.argtypes = [c_void_p, POINTER(c_double)]
pLibDTSG.tsgDreamStateGetHistory.argtypes = [c_void_p, POINTER(c_double)]
pLibDTSG.tsgDreamStateGetHistoryPDF.argtypes = [c_void_p, POINTER(c_double)]
pLibDTSG.tsgDreamStateGetMeanVar.argtypes = [c_void_p, POINTER(c_double), POINTER(c_double)]
pLibDTSG.tsgDreamStateGetMode.argtypes = [c_void_p, POINTER(c_double)]
pLibDTSG.tsgDreamStateGetRate.restype = c_double
pLibDTSG.tsgDreamStateGetRate.argtypes = [c_void_p]
class DreamState:
'''
Wrapper to class TasDREAM::TasmanianDREAM
'''
def __init__(self, iNumChains, GridOrIntDimensions):
'''
Constructs a new state with given number of chains and dimensions.
The dimensions can be given as an integer or inferred from
a Tasmanian.SparseGrid object.
iNumChains: positive integer indicating the number of chains.
GridOrIntDimensions: either a positive integer or a non-empty
Tasmanian.SparseGrid object that gives the dimensions
'''
self.TasmanainDreamState = True
if (isinstance(GridOrIntDimensions, SparseGrid)):
self.pStatePntr = pLibDTSG.tsgMakeDreamState(iNumChains, GridOrIntDimensions.getNumDimensions())
else:
self.pStatePntr = pLibDTSG.tsgMakeDreamState(iNumChains, GridOrIntDimensions)
def __del__(self):
'''
Deletes an instance of the DREAM state.
'''
pLibDTSG.tsgDeleteDreamState(self.pStatePntr)
def getNumDimensions(self):
'''
Return the number of dimensions.
'''
return pLibDTSG.tsgDreamStateGetDims(self.pStatePntr)
def getNumChains(self):
'''
Return the number of chains.
'''
return pLibDTSG.tsgDreamStateGetChains(self.pStatePntr)
def getNumHistory(self):
'''
Return the number of samples saved in the history.
'''
return pLibDTSG.tsgDreamStateGetNumHistory(self.pStatePntr)
def setState(self, llfNewState):
'''
Set a new state for the DREAM chains.
llfNewState: is a two dimensional numpy.ndarray with
.shape[0] = .getNumChains()
.shape[1] = .getNumDimensions()
'''
iNumChains = self.getNumChains()
iNumDims = self.getNumDimensions()
if (llfNewState.shape[0] != iNumChains):
raise InputError("llfNewState", "llfNewState.shape[0] should match the number of chains")
if (llfNewState.shape[1] != iNumDims):
raise InputError("llfNewState", "llfNewState.shape[1] should match the number of dimensions")
pLibDTSG.tsgDreamStateSet(self.pStatePntr,
np.ctypeslib.as_ctypes(llfNewState.reshape((iNumChains * iNumDims,))))
def getHistory(self):
'''
Returns the saved history in a two dimensional numpy.ndarray
'''
iNumDims = self.getNumDimensions()
iNumHistory = self.getNumHistory()
aResult = np.empty((iNumDims * iNumHistory,), np.float64)
pLibDTSG.tsgDreamStateGetHistory(self.pStatePntr, np.ctypeslib.as_ctypes(aResult))
return aResult.reshape((iNumHistory, iNumDims))
def getHistoryPDF(self):
'''
Returns the saved pdf in a one dimensional numpy.ndarray
'''
iNumHistory = self.getNumHistory()
aResult = np.empty((NumHistory,), np.float64)
pLibDTSG.tsgDreamStateGetHistoryPDF(self.pStatePntr, np.ctypeslib.as_ctypes(aResult))
return aResult
def getHistoryMeanVariance(self):
'''
Returns two one dimensional numpy.ndarrays corresponding
to the mean and variance of the history.
'''
iNumDims = self.getNumDimensions()
aMean = np.empty((iNumDims,))
aVar = np.empty((iNumDims,))
pLibDTSG.tsgDreamStateGetMeanVar(self.pStatePntr,
np.ctypeslib.as_ctypes(aMean),
np.ctypeslib.as_ctypes(aVar))
return aMean, aVar
def getApproximateMode(self):
'''
Returns the approximate mode, i.e., the sample with highest probability density.
'''
iNumDims = self.getNumDimensions()
aMode = np.empty((iNumDims,))
pLibDTSG.tsgDreamStateGetMode(self.pStatePntr, np.ctypeslib.as_ctypes(aMode))
return aMode
def getAcceptanceRate(self):
'''
Return the acceptance rate accumulated with the history.
'''
return pLibDTSG.tsgDreamStateGetRate(self.pStatePntr)
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