def analyze(self, adjust=False, plot=False, learn=False, adjustparams={}, learnparams={'feature_opt':'1storder', 'coeforder':1}):
dataman = DataIO(self.case)
fu, gridvars, ICparams = dataman.loadSolution(self.loadnamenpy, array_opt='marginal')
##Make fu smaller (in time)
if adjust:
fu, gridvars = self.adjust(fu, gridvars, adjustparams)
grid = PdfGrid(gridvars)
if plot:
V = Visualize(grid)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=5)
V.plot_fu(fu, dim='x', steps=5)
V.show()
if learn:
t0 = time.time()
print('fu dimension: ', fu.shape)
print('fu num elem.: ', np.prod(fu.shape))
feature_opt = learnparams['feature_opt']
coeforder = learnparams['coeforder']
sindy_alpha = learnparams['sindy_alpha']
RegCoef = learnparams['RegCoef']
nzthresh = learnparams['nzthresh']
# Learn
difflearn = PDElearn(grid=grid, fu=fu, ICparams=ICparams, scase=self.case, trainratio=0.8, debug=False, verbose=True)
difflearn.fit_sparse(feature_opt=feature_opt, variableCoef=True, variableCoefBasis='simple_polynomial', \
variableCoefOrder=coeforder, use_sindy=True, sindy_alpha=sindy_alpha, RegCoef=RegCoef, nzthresh=nzthresh)
print('learning took t = ', str(t0 - time.time()))
def advection():
#loadnamenpy = 'advection_marginal_7397.npy'
loadnamenpy = 'advection_marginal_6328.npy'
loadnamenpy = 'advection_marginal_8028.npy'
loadnamenpy = 'advection_marginal_5765.npy'
#loadnamenpy = 'advection_marginal_4527.npy'
case = '_'.join(loadnamenpy.split('_')[:2])
dataman = DataIO(case)
fuk, fu, gridvars, ICparams = dataman.loadSolution(loadnamenpy)
grid = PdfGrid(gridvars)
V = Visualize(grid)
V.plot_fuk3D(fuk)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=5)
V.plot_fu(fu, dim='x', steps=5)
V.show()
# Learn
difflearn = PDElearn(fuk,
grid,
fu=fu,
ICparams=ICparams,
scase=case,
trainratio=0.8,
debug=False,
verbose=True)
difflearn.fit_sparse(feature_opt='2ndorder',
variableCoef=True,
variableCoefBasis='simple_polynomial',
variableCoefOrder=3,
use_sindy=True,
sindy_alpha=0.001)
def plot(self):
dataman = DataIO(self.case)
fu, gridvars, ICparams = dataman.loadSolution(self.loadnamenpy,
array_opt='marginal')
grid = PdfGrid(gridvars)
V = Visualize(grid)
V.plot_fu3D(fu)
V.plot_fu(fu, dim='t', steps=5)
V.plot_fu(fu, dim='x', steps=5)
V.show()
criterion = 'bic'
if "savenamepdf" not in locals():
# Check if there is already a loadfile (if not load it)
savenamepdf = 'advection_reaction_analytical_388_128.npy'
dataman = DataIO(case)
fu, gridvars, ICparams = dataman.loadSolution(savenamepdf, array_opt='marginal')
grid = PdfGrid(gridvars)
fu = grid.adjust(fu, adjustgrid)
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=trainratio, verbose=True)
output = 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)
d = DataIO(case, directory=LEARNDIR)
learndata, pdfdata, mcdata = d.readLearningResults(savenamepdf.split('.')[0]+'.txt', PDFdata=True, MCdata=True, display=False)
else:
# Train model. Comment out if unneeded
logging.info("Beginning training")
lda = Model(num_categories=NUM_CATEGORIES)
ldamodel = lda.create_model(train_doc_matrix,
train_term_dictionary,
ROOT,
language=lang)
logging.info('Model created')
# Displays topics with top words
logging.info('TOP WORDS OF EACH CATEGORY FOR FINAL MODEL')
for i in ldamodel.print_topics():
for j in i:
logging.info(j)
if WRITE:
# Cluster information to csv
test_clusters = p.cluster_data(doc_matrix=test_doc_matrix,
ldamodel=ldamodel,
to_csv=True,
keywords=test_keywords,
filenames=test_filenames,
num_categories=NUM_CATEGORIES)
if VISUALIZE:
# Visualize model
visualize = Visualize(num_categories=NUM_CATEGORIES, language=lang)
visualize.visualize(ldamodel=ldamodel,
doc_matrix=test_doc_matrix,
raw_documents=test_docs)
p["alpha"], p["gamma"] = .15, .30
p["c_rr"], p["c_bb"] = .01, .05
p["c_rb"], p["c_br"] = .20, .15
create_artificial_data(p)
elif run_type == 'v':
"""
Run interactive visualisation of model
Second argument is the timestep-interval
"""
# set-up models
models = initiate_models(params_model)
# set-up visualisation
data.assign_data(models)
visualisation = Visualize(models)
# run models
for t in data.daterange():
print("Timestep %i" % data.get_timestep(t))
for model in models:
if t in data.measurements:
model.collect_data_fit()
model.step(visualize=True)
if data.get_timestep(t) % vis_steps == 0:
visualisation.update()
res = input(
"Press enter to continue simulation, type 'q' to stop current run:\n\t"
)
if res == 'q':
break