diff --git a/BayesValidRox/surrogate_models/RegressionFastARD.py b/BayesValidRox/surrogate_models/RegressionFastARD.py
index 2f40199e11f92f499d87453796a7b430f295f17b..6aa4de770abe258a2b98318903755bff7caa1f19 100755
--- a/BayesValidRox/surrogate_models/RegressionFastARD.py
+++ b/BayesValidRox/surrogate_models/RegressionFastARD.py
@@ -281,7 +281,7 @@ class RegressionFastARD(LinearModel,RegressorMixin):
break
beta = n_samples - np.sum(active) + np.sum(Aa * Sdiag )
beta /= ( rss + np.finfo(np.float32).eps )
-
+
# update precision parameters of coefficients
A,converged = update_precisions(Q,S,q,s,A,active,self.tol,
n_samples,False)
diff --git a/BayesValidRox/surrogate_models/aPoly_Construction.py b/BayesValidRox/surrogate_models/aPoly_Construction.py
index 1ad0179bdfa4b11eca45a8c1b680c19bde0fcf82..3fb123acf7055a12b3cc5e82a26e5fdf2446de56 100644
--- a/BayesValidRox/surrogate_models/aPoly_Construction.py
+++ b/BayesValidRox/surrogate_models/aPoly_Construction.py
@@ -1,29 +1,41 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
-"""
-% ========================================================================
-% Construction of Data-driven Orthonormal Polynomial Basis
-% Author: Dr.-Ing. habil. Sergey Oladyshkin
-% Department of Stochastic Simulation and Safety Research for Hydrosystems
-% Institute for Modelling Hydraulic and Environmental Systems
-% Universitaet Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart
-% E-mail: Sergey.Oladyshkin@iws.uni-stuttgart.de
-% http://www.iws-ls3.uni-stuttgart.de
-% The current program is using definition of arbitrary polynomial chaos expansion (aPC) which is presented in the following manuscript:
-% Oladyshkin, S. and W. Nowak. Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion. Reliability Engineering & System Safety, Elsevier, V. 106, P. 179�190, 2012. DOI: 10.1016/j.ress.2012.05.002.
-% ========================================================================
-"""
import numpy as np
import os
-def aPoly_Construction(Data, Degree,parIdx):
- """
- Input parameters:
- % Data - raw data array
- % Degree - higher degree of the orthonormal polynomial basis
- % Poly_FileName - file name in which the orthonormal polynomial basis should be storred
+def aPoly_Construction(Data, Degree, parIdx, save=False):
"""
+ Construction of Data-driven Orthonormal Polynomial Basis
+ Author: Dr.-Ing. habil. Sergey Oladyshkin
+ Department of Stochastic Simulation and Safety Research for Hydrosystems
+ Institute for Modelling Hydraulic and Environmental Systems
+ Universitaet Stuttgart, Pfaffenwaldring 5a, 70569 Stuttgart
+ E-mail: Sergey.Oladyshkin@iws.uni-stuttgart.de
+ http://www.iws-ls3.uni-stuttgart.de
+ The current script is using definition of arbitrary polynomial chaos expansion (aPC)
+ which is presented in the following manuscript:
+ Oladyshkin, S. and W. Nowak. Data-driven uncertainty quantification using the arbitrary
+ polynomial chaos expansion. Reliability Engineering & System Safety, Elsevier, V. 106, P.
+ 179�190, 2012. DOI: 10.1016/j.ress.2012.05.002.
+
+ Parameters
+ ----------
+ Data : array
+ Raw data.
+ Degree : int
+ Maximum polynomial degree.
+ parIdx : TYPE
+ DESCRIPTION.
+ save : TYPE, optional
+ DESCRIPTION. The default is False.
+
+ Returns
+ -------
+ Polynomial : array
+ The coefficients of the orthonormal polynomials.
+ """
+
# Initialization
d=Degree #Degree of polinomial expansion
dd=d+1 #Degree of polinomial for roots defenition
@@ -115,7 +127,8 @@ def aPoly_Construction(Data, Degree,parIdx):
# Polynomial[:,k] = Polynomial[:,k]/(MeanOfData**(k))
# Save the Polynomial matrix
- os.makedirs('polyCoeffs',exist_ok=True)
- np.save('polyCoeffs/par_{}.npy'.format(parIdx+1), Polynomial)
+ if save:
+ os.makedirs('polyCoeffs',exist_ok=True)
+ np.save('polyCoeffs/par_{}.npy'.format(parIdx+1), Polynomial)
return Polynomial
\ No newline at end of file
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index dcf927cff59de44d4d733205bf167733310794ef..c265d99a0069690002cdc9cacffafa1c904927ed 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -794,8 +794,6 @@ class Metamodel:
self.LCerror = self.AutoVivification()
if self.ExpDesignFlag != 'sequential':
- self.allBasisIndices = self.AutoVivification()
-
# Read observations or MCReference
if Model.MeasurementFile is not None or len(Model.Observations) != 0:
self.Observations = Model.read_Observation()
@@ -809,15 +807,15 @@ class Metamodel:
degNew = range(1,maxDeg+1)[np.argmin(abs(M_uptoMax(maxDeg)-ndim*nSamples*d))]
degNew = degNew if self.ExpDesignFlag == 'sequential' else self.MaxPceDegree
self.q = np.array(self.q) if not np.isscalar(self.q) else np.array([self.q])
- if degNew > self.MinPceDegree:
+ if degNew > self.MinPceDegree and self.RegMethod.lower() != 'fastard':
self.DegreeArray = np.arange(self.MinPceDegree,degNew+1)
else:
self.DegreeArray = np.array([degNew])
# Generate all basis indices
+ self.allBasisIndices = self.AutoVivification()
for deg in self.DegreeArray:
if deg not in np.fromiter(self.allBasisIndices.keys(), dtype=float):
- # self.allBasisIndices = self.AutoVivification()
# Generate the polynomial basis indices
for qidx, q in enumerate(self.q):
self.allBasisIndices[str(deg)][str(q)] = self.PolyBasisIndices(degree=deg, q=q)
diff --git a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
index d00381c5181b4b39cc2d69ebf6c05b7d27c30c15..cb3b4221d44ede2844a47cebfabd031686f60bb7 100755
--- a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
+++ b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
@@ -88,7 +88,7 @@ if __name__ == "__main__":
MetaModelOpts = Metamodel(Inputs)
# Select if you want to preserve the spatial/temporal depencencies
- MetaModelOpts.DimRedMethod = 'PCA'
+ # MetaModelOpts.DimRedMethod = 'PCA'
# MetaModelOpts.varPCAThreshold = 100 #99.999
# Select your metamodel method
@@ -118,7 +118,7 @@ if __name__ == "__main__":
MetaModelOpts.q = np.linspace(0.3,0.8,3) if ndim<5 else 0.75
# Print summary of the regression results
- MetaModelOpts.DisplayFlag = True
+ # MetaModelOpts.DisplayFlag = True
# ------------------------------------------------
# ------ Experimental Design Configuration -------
@@ -128,8 +128,8 @@ if __name__ == "__main__":
MetaModelOpts.addExpDesign()
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.Method = 'normal'
- NrofInitSamples = 350 #5*ndim #50
+ MetaModelOpts.ExpDesign.Method = 'sequential'
+ NrofInitSamples = 5*ndim #50
MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
# Sampling methods
@@ -229,7 +229,7 @@ if __name__ == "__main__":
# Compute and print RMSE error
PostPCE.accuracyCheckMetaModel(nSamples=200)
- # sys.exit('STOP!!')
+ sys.exit('STOP!!')
#=====================================================
#======== Bayesian inference with Emulator ==========
#=====================================================
@@ -248,8 +248,7 @@ if __name__ == "__main__":
BayesOpts.MCMCnwalkers = 300
# BayesOpts.MCMCinitSamples = np.zeros((1,10)) + 1e-3 * np.random.randn(200, 10)
# BayesOpts.MCMCnSteps = 1000
- import emcee
- BayesOpts.MCMCmoves = emcee.moves.KDEMove()
+ # import emcee
# BayesOpts.MCMCmoves = [(emcee.moves.DEMove(), 0.2),
# (emcee.moves.WalkMove(), 0.3),
# (emcee.moves.KDEMove(), 0.3),
diff --git a/BayesValidRox/tests/PollutionTest/Pollution_Test.py b/BayesValidRox/tests/PollutionTest/Pollution_Test.py
index b5c7a458b50465e8a49c0c1242a4a05902e999d6..888d43121ccf6b626991a54b40d835e9c2ff76eb 100644
--- a/BayesValidRox/tests/PollutionTest/Pollution_Test.py
+++ b/BayesValidRox/tests/PollutionTest/Pollution_Test.py
@@ -106,7 +106,7 @@ if __name__ == "__main__":
MetaModelOpts = Metamodel(Inputs)
# Select if you want to preserve the spatial/temporal depencencies
- MetaModelOpts.DimRedMethod = 'PCA'
+ # MetaModelOpts.DimRedMethod = 'PCA'
# MetaModelOpts.varPCAThreshold = 99.5 #99.999
# Select your metamodel method
@@ -131,13 +131,13 @@ if __name__ == "__main__":
# error (or the highest score=1-LOO)estimator is chosen as the final
# metamodel.
MetaModelOpts.MinPceDegree = 1
- MetaModelOpts.MaxPceDegree = 8 #12
+ MetaModelOpts.MaxPceDegree = 10 #8
# q-quasi-norm 0<q<1 (default=1)
- # MetaModelOpts.q = 0.75 #np.linspace(0.3,0.8, 3)
+ MetaModelOpts.q = 0.95 #np.linspace(0.3,0.8, 3)
# Print summary of the regression results
- MetaModelOpts.DisplayFlag = True
+ # MetaModelOpts.DisplayFlag = True
# ------ Experimental Design --------
# Generate an experimental design of size NrExpDesign based on a latin
@@ -145,8 +145,8 @@ if __name__ == "__main__":
MetaModelOpts.addExpDesign()
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.Method = 'normal'
- NrofInitSamples = 100 #200
+ MetaModelOpts.ExpDesign.Method = 'sequential'
+ NrofInitSamples = 20 #200
MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
@@ -264,13 +264,13 @@ if __name__ == "__main__":
# Select the inference method
BayesOpts.SamplingMethod = 'MCMC'
- BayesOpts.MCMCnwalkers = 100
+ BayesOpts.MCMCnwalkers = 300
# BayesOpts.MCMCnSteps = 500
- import emcee
- BayesOpts.MCMCmoves = [(emcee.moves.DEMove(), 0.4),
- (emcee.moves.WalkMove(), 0.1),
- (emcee.moves.KDEMove(), 0.1),
- (emcee.moves.DESnookerMove(), 0.4),]
+ # import emcee
+ # BayesOpts.MCMCmoves = [(emcee.moves.KDEMove(), 1.0)]
+ # (emcee.moves.WalkMove(), 0.1),
+ # (emcee.moves.DEMove(), 0.1),
+ # (emcee.moves.DESnookerMove(), 0.4),]
BayesOpts.PlotPostDist = True
BayesOpts.PlotPostPred = True