def multi_burgers(self, loadnamenpy):
fu_list = []
ICparams_list = []
grid_list = []
# Load simulation results
fu, gridvars, ICparams = self.datamanager.loadSolution(loadnamenpy)
difflearn = PDElearn(grid=PdfGrid(gridvars),
fu=fu,
ICparams=ICparams,
trainratio=self.trainratio,
debug=False,
verbose=False)
F = Features(scase=self.case,
option=self.feature_opt,
variableCoef=self.variableCoef,
variableCoefOrder=self.variableCoefOrder,
variableCoefBasis=self.variableCoefBasis)
featurelist, labels, featurenames = F.makeFeatures(
PdfGrid(gridvars), fu, ICparams)
Xtrain, ytrain, Xtest, ytest = difflearn.makeTTsets(featurelist,
labels,
shuffle=False)
# Fit data
lin, rem_feature_idx = difflearn.train_sindy(Xtrain, ytrain, \
RegCoef=self.RegCoef, maxiter=self.maxiter, tolerance=self.tolerance, sindy_iter=self.sindy_iter, sindy_alpha=self.sindy_alpha)
difflearn.print_full_report(lin, Xtrain, ytrain, Xtest, ytest,
rem_feature_idx, featurenames)
return difflearn, featurenames, Xtrain, ytrain, Xtest, ytest
def multiIC(self):
fuk_list = []
fu_list = []
ICparams_list = []
grid_list = []
# Load simulation results
for i in range(len(self.loadname_list)):
if i >= self.numexamples:
break
fuk, fu, gridvars, ICparams = self.datamanager.loadSolution(
self.loadname_list[i] + '.npy')
difflearn = PDElearn(fuk=fuk,
grid=PdfGrid(gridvars),
fu=fu,
ICparams=ICparams,
trainratio=self.trainratio,
debug=False,
verbose=False)
F = Features(scase=self.case,
option=self.feature_opt,
variableCoef=self.variableCoef,
variableCoefOrder=self.variableCoefOrder,
variableCoefBasis=self.variableCoefBasis)
featurelist, labels, featurenames = F.makeFeatures(
PdfGrid(gridvars), fu, ICparams)
Xtrain, ytrain, Xtest, ytest = difflearn.makeTTsets(featurelist,
labels,
shuffle=False)
#pdb.set_trace()
if i == 0:
Xall_train = Xtrain
Xall_test = Xtest
yall_train = ytrain
yall_test = ytest
else:
Xall_train = np.concatenate((Xall_train, Xtrain), axis=0)
yall_train = np.concatenate((yall_train, ytrain), axis=0)
Xall_test = np.concatenate((Xall_test, Xtest), axis=0)
yall_test = np.concatenate((yall_test, ytest), axis=0)
# Fit data
lin, rem_feature_idx = difflearn.train_sindy(Xall_train, yall_train, \
RegCoef=self.RegCoef, maxiter=self.maxiter, tolerance=self.tolerance, sindy_iter=self.sindy_iter, sindy_alpha=self.sindy_alpha)
difflearn.print_full_report(lin, Xall_train, yall_train, Xall_test,
yall_test, rem_feature_idx, featurenames)
return difflearn, featurenames, Xall_train, yall_train, Xall_test, yall_test
