def buildMultiresolutionMatrix(xTrain,maxOrder,maxDetailLevel):
# Construct samples
samples = uq.uqSamples(xTrain.shape[1])
currSample = uq.stdVec()
for loopA in range(xTrain.shape[0]):
currSample.clear()
for loopB in range(xTrain.shape[1]):
currSample.push_back(xTrain[loopA,loopB])
samples.addOneSample(currSample)
# Set Parameters for multiresolution basis
maxOrder = maxOrder
addLegendrePoly = True
addMW = True
useBinaryPartitions = False
mwMatType = uq.kMWFixedMaxDetailLevel
mwMatIncludeNullColumns = True
useExactMW = False
mwQuadOrder = 10
measure = npToStdMat(np.ones((2*mwQuadOrder,1)))
# print np.array(measure)
maxColumns = 0
maxDetailLevel = maxDetailLevel
# Construct Multiresolution Matrix on samples
mwMat = uq.uqMWMatrix(maxOrder,samples, \
addLegendrePoly,addMW,useBinaryPartitions, \
mwMatType,mwMatIncludeNullColumns, \
useExactMW,mwQuadOrder,measure, \
maxColumns,maxDetailLevel)
# Return Matrix
return mwMat
def buildRegressionMatrix(pTrainVals,ord):
# Construct samples
samples = uq.uqSamples(pTrainVals.shape[1])
currSample = uq.stdVec()
for loopA in range(pTrainVals.shape[0]):
currSample.clear()
for loopB in range(pTrainVals.shape[1]):
currSample.push_back(pTrainVals[loopA,loopB])
samples.addOneSample(currSample)
# Construct Polynomial Matrix on samples
polyMat = uq.uqPolyMatrix(samples,ord,uq.kPolyLegendre,uq.kMIPartialOrder)
# Return Matrix
return polyMat
# Imports
import sys
sys.path.insert(0, '../../py')
import tulipUQ as uq
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
# Construct three sample objects
randomSamples = uq.uqSamples()
sparseGridSamples = uq.uqSamples()
cartesianGridSamples = uq.uqSamples()
# Add variables to random sampling
randomSamples.addVariable('Var1', uq.kSAMPLEUniform, 0.0, 1.0)
randomSamples.addVariable('Var2', uq.kSAMPLEUniform, 0.0, 1.0)
randomSamples.generateRandomSamples(100)
s1 = uq.stdMat()
randomSamples.getValues(s1)
npS1 = np.array(s1)
# Add variables to sparse grid samples
sparseGridSamples.addVariable('Var1', uq.kSAMPLEUniform, 0.0, 1.0)
sparseGridSamples.addVariable('Var2', uq.kSAMPLEUniform, 0.0, 1.0)
sparseGridSamples.generateSparseGrid(5)
s2 = uq.stdMat()
sparseGridSamples.getValues(s2)
npS2 = np.array(s2)
# Imports
import sys
sys.path.insert(0, '../../py')
import tulipUQ as uq
import numpy as np
import matplotlib.pyplot as plt
# =============
# MAIN FUNCTION
# =============
if __name__ == "__main__":
# Construct samples
samples = uq.uqSamples()
samples.addVariable('Var1', uq.kSAMPLEUniform, -1.0, 1.0)
samples.addVariable('Var2', uq.kSAMPLEUniform, -1.0, 1.0)
samples.generateCartesianGrid(10, uq.kCC, uq.kHaarRange)
# Construct Polynomial Matrix on samples
polyMat = uq.uqPolyMatrix(samples, 5, uq.kPolyLegendre, uq.kMIPartialOrder)
# Eval functional value with simple function
rhs = uq.stdVec()
rhs.resize(polyMat.getRowCount())
for loopA in xrange(polyMat.getRowCount()):
rhs[loopA] = samples.getValuesAt(loopA, 0) + samples.getValuesAt(
loopA, 1)
# Construct the regression algorithm
bcs = uq.uqAlgorithmBCS()
#marginalFile = ""
#error = approx.importFromTextFile(marginalFile)
#if(error != 0):
# print 'ERROR: Cannot Read Approximant From File.'
# sys.exit(-1)
# EVALUATE RANGE
#tmpLimits = uq.stdVec()
#approx.getExtremes(tmpLimits)
#measureSize = fabs(tmpLimits[1] - tmpLimits[0])
#print 'CURRENT LIMITS: %f %f' % (tmpLimits[0],tmpLimits[1])
# FORM SAMPLES USING INTEGRATION GRID IN 1D
mwOrder = 2
mwQuadOrder = 30
measure1DGridPoints = uq.uqSamples()
measure1DGridPoints.addVariable('grid1D', uq.kSAMPLEUniform, 0.0, 1.0)
measure1DGridPoints.generateCartesianGrid(mwQuadOrder, uq.kDoubleCC,
uq.kHaarRange)
intLocations = uq.stdVec()
#scale1DGridOnPartition(measure1DGridPoints,tmpLimits[0],tmpLimits[1],intLocations);
scale1DGridOnPartition(measure1DGridPoints, 0.0, 1.0, intLocations)
# EVALUATE MARGINAL AT QUADRATURE POINTS
measure = uq.stdMat()
tmp = uq.stdVec()
currInt = 0.0
currLoc = 0.0
currVal = 0.0
for loopA in xrange(intLocations.size()):
import os
import numpy as np
import time
import glob
#===============================================================================
# USER INPUTS HERE
INPUTS_FILE = 'sampledInputs.txt'
OUTPUTS_FILE = 'cylinderResults.txt'
#===============================================================================
if __name__ == '__main__':
inputs = uq.uqSamples()
# Create the Uncertainty Propagation Action Object
mwBcs = ac.acActionUP_MWBCS()
# Set Options
mwBcs.opts.inputMode = ac.imTable
mwBcs.opts.tableInputFileName = INPUTS_FILE
mwBcs.opts.tableOutputFileName = OUTPUTS_FILE
mwBcs.opts.addBoundaryPoints = False
mwBcs.opts.boundaryPointOther = 30
mwBcs.opts.numIniSamples = 0
mwBcs.opts.doRefineSpace = True
mwBcs.opts.doRefineSamples = False