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#!@Tasmanian_string_python_hashbang@
##############################################################################################################################################################################
# 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 Tasmanian import DREAM
import numpy
def example_01():
print("\n---------------------------------------------------------------------------------------------------\n")
print("EXAMPLE 1: make your own probability distribution")
print(" sample from the Gaussian distribution: f(x) = exp(-x^2)")
print(" ignoring scaling constants, using 3000 samples")
print(" See the comments in example_dream_01.cpp\n")
iNumDimensions = 1
iNumChains = 30
iNumBurnupIterations = 200
iNumCollectIterations = 1000 # total samples are iNumChains x iNumCollectIterations
state = DREAM.State(iNumChains, GridOrIntDimensions = iNumDimensions)
# initialize with uniform samples on [-2, 2]
state.setState(DREAM.genUniformSamples((-2.0,), (2.0,), state.getNumChains()))
DREAM.Sample(iNumBurnupIterations, iNumCollectIterations,
lambda x : numpy.exp( - x[:,0]**2 ), # Gaussian mean 0.0 variance 0.5
DREAM.Domain("unbounded"),
state,
DREAM.IndependentUpdate("uniform", 0.5),
DREAM.DifferentialUpdate(90))
aMean, aVariance = state.getHistoryMeanVariance()
print("Using regular form:")
print("mean {0:13.6f} error {1:14.6e}".format(aMean[0], numpy.abs(aMean[0])))
print("variance{0:13.6f} error {1:14.6e}".format(aVariance[0], numpy.abs(aVariance[0] -0.5)))
# reset the state
state = DREAM.State(iNumChains, GridOrIntDimensions = iNumDimensions)
# initialize with uniform samples on [-2, 2]
state.setState(DREAM.genUniformSamples((-2.0,), (2.0,), state.getNumChains()))
DREAM.Sample(iNumBurnupIterations, iNumCollectIterations,
lambda x : - x[:,0]**2, # Gaussian mean 0.0 variance 0.5
DREAM.Domain("unbounded"),
state,
DREAM.IndependentUpdate("uniform", 0.5),
DREAM.DifferentialUpdate(90),
typeForm = DREAM.typeLogform)
aMean, aVariance = state.getHistoryMeanVariance()
print("Using logarithm form:")
print("mean {0:13.6f} error {1:14.6e}".format(aMean[0], numpy.abs(aMean[0])))
print("variance{0:13.6f} error {1:14.6e}".format(aVariance[0], numpy.abs(aVariance[0] -0.5)))
if __name__ == "__main__":
example_01()
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