print('---------------------') print('\tMCcount = ', MCcount) print('---------------------') # BUILD PDF MCprocess = MCprocessing(savenameMC, case=case) savenamepdf = MCprocess.buildKDE(nu, distribution=distribution, MCcount=MCcount, save=save, u_margin=u_margin, bandwidth=bandwidth) # LEARN dataman = DataIO(case, directory=PDFDIR) fu, gridvars, ICparams = dataman.loadSolution(savenamepdf, array_opt='marginal') adjustgrid = {'mu':mu, 'mx':mx, 'mt':mt, 'pu':pu, 'px':px, 'pt':pt} grid = PdfGrid(gridvars) fu = grid.adjust(fu, adjustgrid) difflearn = PDElearn(grid=grid, fu=fu, ICparams=ICparams, scase=case, trainratio=trainratio, verbose=printlearning) filename = difflearn.fit_sparse(feature_opt=feature_opt, variableCoef=variableCoef, variableCoefBasis=variableCoefBasis, \ variableCoefOrder=coeforder, use_rfe=use_rfe, rfe_alpha=rfe_alpha, nzthresh=nzthresh, maxiter=maxiter, \ LassoType=LassoType, RegCoef=RegCoef, cv=cv, criterion=criterion, print_rfeiter=print_rfeiter, shuffle=shuffle, \ basefile=savenamepdf, adjustgrid=adjustgrid, save=save, normalize=normalize, comments=comments) # Save Learning D = DataIO(case, directory=LEARNDIR) output, metadata = D.readLearningResults(filename) output_vec.append(output) metadata_vec.append(metadata) filename_vec.append(filename)
feature_opt = '1storder' coeforder = 2 sindy_alpha = 0.01 nzthresh = 1e-190 RegCoef = 0.000004 maxiter = 10000 if "savenamepdf" not in locals(): # Check if there is already a loadfile (if not load it) savenamepdf = 'advection_reaction_analytical_717_944.npy' dataman = DataIO(case) fu, gridvars, ICparams = dataman.loadSolution(savenamepdf, array_opt='marginal') grid = PdfGrid(gridvars) fu = grid.adjust(fu, aparams) if plot: s = 10 V = Visualize(grid) V.plot_fu3D(fu) V.plot_fu(fu, dim='t', steps=s) V.plot_fu(fu, dim='x', steps=s) V.show() difflearn = PDElearn(grid=grid, fu=fu, ICparams=ICparams, scase=case, trainratio=0.8, debug=False,