def identify_nlss(data, linmodel, nlx, nly, nmax=25, info=2):
Rest = data.yest.shape[2]
T1 = np.r_[data.npp*data.Ntr, np.r_[0:(Rest-1)*data.npp+1:data.npp]]
model = NLSS(linmodel)
# model._cost_normalize = 1
model.add_nl(nlx=nlx, nly=nly)
model.set_signal(data.sig)
model.transient(T1)
model.optimize(lamb=100, weight=weight, nmax=nmax, info=info)
# get best model on validation data. Change Transient settings, as there is
# only one realization
nl_errvec = model.extract_model(data.yval, data.uval, T1=data.npp*data.Ntr,
info=info)
return model, nl_errvec
nly = NLS([pnlss1])
elif nlfunc.casefold() == 'tahn':
nlx = NLS([pnlss1, tahn1])
nlx = NLS([tahn1])
model = NLSS(linmodel)
model.add_nl(nlx=nlx, nly=nly)
model.set_signal(sig)
model.transient(T1)
model.optimize(lamb=100, weight=weight, nmax=25)
#raise SystemExit(0)
# get best model on validation data. Change Transient settings, as there is
# only one realization
nl_errvec = model.extract_model(yval, uval, T1=npp)
models = [linmodel, model]
descrip = [type(mod).__name__ for mod in models]
descrip = tuple(descrip) # convert to tuple for legend concatenation in figs
# simulation error
val = np.empty((*yval.shape, len(models)))
est = np.empty((*ym.shape, len(models)))
test = np.empty((*ytest.shape, len(models)))
for i, model in enumerate(models):
test[..., i] = model.simulate(utest, T1=npp * Ptr)[1]
val[..., i] = model.simulate(uval, T1=npp * Ptr)[1]
est[..., i] = model.simulate(um, T1=T1)[1]
# convenience inline functions
stack = lambda ydata, ymodel: \
def identify(data, nlx, nly, nmax=25, info=2, fnsi=False):
# transient: Add one period before the start of each realization. Note that
# this is for the signal averaged over periods
Rest = data.yest.shape[2]
T1 = np.r_[data.npp * data.Ntr,
np.r_[0:(Rest - 1) * data.npp + 1:data.npp]]
linmodel = Subspace(data.sig)
linmodel._cost_normalize = 1
linmodel.estimate(2, 5, weight=weight)
linmodel.optimize(weight=weight, info=info)
# estimate NLSS
model = NLSS(linmodel)
# model._cost_normalize = 1
model.add_nl(nlx=nlx, nly=nly)
model.set_signal(data.sig)
model.transient(T1)
model.optimize(lamb=100, weight=weight, nmax=nmax, info=info)
# get best model on validation data. Change Transient settings, as there is
# only one realization
nl_errvec = model.extract_model(data.yval,
data.uval,
T1=data.npp * data.Ntr,
info=info)
models = [linmodel, model]
descrip = [type(mod).__name__ for mod in models]
if fnsi:
# FNSI can only use 1 realization
sig = deepcopy(data.sig)
# This is stupid, but unfortunately nessecary
sig.y = sig.y[:, :, 0][:, :, None]
sig.u = sig.u[:, :, 0][:, :, None]
sig.R = 1
sig.average()
fnsi1 = FNSI()
fnsi1.set_signal(sig)
fnsi1.add_nl(nlx=nlx)
fnsi1.estimate(n=2, r=5, weight=weight)
fnsi1.transient(T1)
fnsi2 = deepcopy(fnsi1)
fnsi2.optimize(lamb=100, weight=weight, nmax=nmax, info=info)
models = models + [fnsi1, fnsi2]
descrip = descrip + ['FNSI', 'FNSI optimized']
descrip = tuple(descrip) # convert to tuple for legend concatenation
# simulation error
val = np.empty((*data.yval.shape, len(models)))
est = np.empty((*data.ym.shape, len(models)))
test = np.empty((*data.ytest.shape, len(models)))
for i, model in enumerate(models):
test[..., i] = model.simulate(data.utest, T1=data.npp * data.Ntr)[1]
val[..., i] = model.simulate(data.uval, T1=data.npp * data.Ntr)[1]
est[..., i] = model.simulate(data.um, T1=T1)[1]
Pest = data.yest.shape[3]
# convenience inline functions
def stack(ydata, ymodel): return \
np.concatenate((ydata[..., None], (ydata[..., None] - ymodel)), axis=2)
def rms(y):
return np.sqrt(np.mean(y**2, axis=0))
est_err = stack(data.ym, est) # (npp*R,p,nmodels)
val_err = stack(data.yval, val)
test_err = stack(data.ytest, test)
noise = np.abs(np.sqrt(Pest * data.covY.squeeze()))
print()
print(f"err for models: signal, {descrip}")
# print(f'rms error noise:\n{rms(noise)} \ndb: \n{db(rms(noise))} ')
# only print error for p = 0. Almost equal to p = 1
print(f'rms error est (db): \n{db(rms(est_err[:,0]))}')
print(f'rms error val (db): \n{db(rms(val_err[:,0]))}')
# print(f'rms error test: \n{rms(test_err)} \ndb: \n{db(rms(test_err))}')
return Result(est_err, val_err, test_err, noise, nl_errvec, descrip)