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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.
##############################################################################################################################################################################
import numpy as np
import Tasmanian
def example_09():
print("\n---------------------------------------------------------------------------------------------------\n")
print("Example 9: comparison between local polynomial refinement strategies\n")
iNumInputs = 2 # using two inputs for testing
# test the error on a uniform dense grid with 10K points
iTestGridSize = 100
dx = np.linspace(-1.0, 1.0, iTestGridSize) # sample on a uniform grid
aMeshX, aMeshY = np.meshgrid(dx, dx)
aTestPoints = np.column_stack([aMeshX.reshape((iTestGridSize**2, 1)),
aMeshY.reshape((iTestGridSize**2, 1))])
def get_error(grid, model, aTestPoints):
aGridResult = grid.evaluateBatch(aTestPoints)
aModelResult = np.empty((aTestPoints.shape[0], 1), np.float64)
for i in range(aTestPoints.shape[0]):
aModelResult[i,:] = model(aTestPoints[i,:])
return np.max(np.abs(aModelResult[:,0] - aGridResult[:,0]))
def sharp_model(aX):
return np.ones((1,)) * np.exp(-aX[0]) / (1.0 + 100.0 * np.exp(-10.0 * aX[1]))
grid_classic = Tasmanian.makeLocalPolynomialGrid(iNumInputs, 1, 2,
iOrder = -1, sRule = "localp")
grid_fds = Tasmanian.copyGrid(grid_classic)
print("Using batch refinement:")
print(" classic fds")
print(" points error points error")
fTolerance = 1.E-5;
while((grid_classic.getNumNeeded() > 0) or (grid_fds.getNumNeeded() > 0)):
Tasmanian.loadNeededValues(lambda x, tid: sharp_model(x), grid_classic, 4);
Tasmanian.loadNeededValues(lambda x, tid: sharp_model(x), grid_fds, 4);
# print the results at this stage
print("{0:>8d}{1:>14.4e}{2:>8d}{3:>14.4e}".format(
grid_classic.getNumLoaded(), get_error(grid_classic, sharp_model, aTestPoints),
grid_fds.getNumLoaded(), get_error(grid_fds, sharp_model, aTestPoints)))
# setting refinement for each grid
grid_classic.setSurplusRefinement(fTolerance, 0, "classic");
grid_fds.setSurplusRefinement(fTolerance, 0, "fds");
# reset the grid and repeat using construction
grid_classic = Tasmanian.makeLocalPolynomialGrid(iNumInputs, 1, 2,
iOrder = -1, sRule = "localp")
grid_fds.copyGrid(grid_classic)
print("\nUsing construction:")
print(" classic fds")
print(" points error points error")
iNumThreads = 1
for budget in range(50, 800, 100):
Tasmanian.constructSurplusSurrogate(
lambda x, tid : sharp_model(x.reshape((2,))).reshape((1,1)),
budget, iNumThreads, 1, grid_classic, fTolerance, "classic")
Tasmanian.constructSurplusSurrogate(
lambda x, tid : sharp_model(x.reshape((2,))).reshape((1,1)),
budget, iNumThreads, 1, grid_fds, fTolerance, "fds")
print("{0:>8d}{1:>14.4e}{2:>8d}{3:>14.4e}".format(
grid_classic.getNumLoaded(), get_error(grid_classic, sharp_model, aTestPoints),
grid_fds.getNumLoaded(), get_error(grid_fds, sharp_model, aTestPoints)))
if (__name__ == "__main__"):
example_09()
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