output, metadata = D.readLearningResults(filename)
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
print('files = [')
for f in filename_vec:
print("\'"+f+"\',")
print(']')
## PLOT
A = Analyze()
savename = 'Burgers_MC_CDF'
A.plotRMSEandCoefs(output_vec, MCcountvec, '$N_{MC}$, Number of Realizations', threshold=0.01, set_grid=False, cdf=True, invert_sign=True, savename='Burgers_MC_CDF')
##############
##Function of Regularization - For MC = num_realizations (MAX)
## WTF??
# fig, ax = plt.subplots(1, 2)
# alphas, mse = A.getRegMseDependence_single(output_vec[-1])
# ax[0].plot(alphas, mse)
# ax[0].set_xlabel('Regularization Coefficient')
# ax[0].set_ylabel('MSE')
# alphas, coefficients, feats = A.getCoefRegDependence(output_vec[-1], threshold=0.0)
# for i in range(len(feats)):
# ax[1].plot(alphas, coefficients[i])
# ax[1].set_xlabel('Regularization Coefficient')
# READ Learning
D = DataIO(case, directory=LEARNDIR)
output, metadata = D.readLearningResults(filename)
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
print('files = [')
for f in filename_vec:
print("\'" + f + "\',")
print(']')
## PLOT
A = Analyze()
savename = 'advectreact_rde'
A.plotRMSEandCoefs(output_vec,
rfe_alpha_vec,
'Percentage Distance from Boundary',
threshold=0.01,
invert_sign=True,
savename=savename)
# Plot boundary
s = 8
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()
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
print('files = [')
for f in filename_vec:
print("\'" + f + "\',")
print(']')
## PLOT
A = Analyze()
savename = 'advectreact_MC' + "_" + LassoType + "_" + str(mu[0]).split('.')[1]
A.plotRMSEandCoefs(output_vec,
MCcountvec,
'Number of Realizations',
threshold=0.01,
invert_sign=True,
use_logx=False,
set_grid=True,
savename=savename)
## PLOT 3D
# s = 8
# 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()
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)
# READ Learning
D = DataIO(case, directory=LEARNDIR)
output, metadata = D.readLearningResults(filename)
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
## PLOT
# Error function of MC
A = Analyze()
savename = 'Burgers_rfe'
A.plotRMSEandCoefs(output_vec,
rfe_alpha_vec,
'RFE Threshold',
threshold=0.01,
invert_sign=True,
savename=savename)
plt.show()
# Plot Coefficients as a function of t0
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
## PLOT
# Error function of MC
# fig = plt.figure()
A = Analyze()
savename = 'Burgers_shock'
portion = [(t[1] - 0.5) / (t[1] - t[0]) for t in mtvec]
A.plotRMSEandCoefs(output_vec,
portion,
'$p_s$, Time Portion in Shock Region',
threshold=0.01,
invert_sign=True,
cdf=True,
set_grid=False,
savename='Burgers_shock')
# trainRMSE, testRMSE = A.getTrainTestDependence(output_vec)
# t0 = [t[0] for t in mtvec]
# plt.plot(t0, testRMSE, linwidth=3)
# plt.plot(t0, trainRMSE, linwidth=3)
# plt.xlabel('Initial Time (0.5 duration)', fontsize=14)
# plt.ylabel('Test Error', fontsize=14)
# plt.legend(['Test Error', 'Train Error'], fontsize=14)
# # Plot Coefficients as a function of t0
# fig = plt.figure()
output, metadata = D.readLearningResults(filename)
output_vec.append(output)
metadata_vec.append(metadata)
filename_vec.append(filename)
print('files = [')
for f in filename_vec:
print("\'" + f + "\',")
print(']')
## PLOT
A = Analyze()
savename = 'advectreact_rfe' + "_" + feature_opt + "_" + LassoType + "_" + str(
coeforder)
A.plotRMSEandCoefs(output_vec,
rfe_alpha_vec,
'RFE Threshold',
threshold=0.001,
use_logx=True,
set_grid=True,
invert_sign=True,
savename=savename,
show=True)
except:
print("\n\n\n************************\n\n\n")
print("Exception Happened for ", feature_opt, " ", LassoType,
" ", rfe_alpha)
print("\n\n\n************************\n\n\n")