def testTemporalPerformance(self, testModel, Xall, Yall, Lall, XtestAll, YtestAll, LtestAll, verbose):
# Initial window to kick-off free simulation
x_start = XtestAll[0, :][:, None].T
# Free simulation
ygp, varygp = utils.gp_narx(testModel[0].SAMObject.model, x_start, YtestAll.shape[0], LtestAll, self.windowSize)
return 1000
def testTemporalPerformance(self, testModel, Xall, Yall, Lall, XtestAll,
YtestAll, LtestAll, verbose):
# Initial window to kick-off free simulation
x_start = XtestAll[0, :][:, None].T
# Free simulation
ygp, varygp = utils.gp_narx(testModel[0].SAMObject.model, x_start,
YtestAll.shape[0], LtestAll,
self.windowSize)
return 1000
def testTemporalPerformance(self, testModel, Xall, Yall, Lall, XtestAll, YtestAll, LtestAll, verbose):
# Initial window to kick-off free simulation
x_start = XtestAll[0, :][:, None].T
# Free simulation
ygp, varygp = utils.gp_narx(testModel[0].SAMObject.model, x_start, YtestAll.shape[0], LtestAll, self.windowSize)
pb.figure()
pb.plot(YtestAll, 'x-')
pb.plot(ygp, 'ro-')
pb.legend(('True', 'Pred'))
pb.title('NARX-with-exogenous')
err = np.sum((YtestAll - ygp)**2)
return err
def testTemporalPerformance(self, testModel, Xall, Yall, Lall, XtestAll,
YtestAll, LtestAll, verbose):
# Initial window to kick-off free simulation
x_start = XtestAll[0, :][:, None].T
# Free simulation
ygp, varygp = utils.gp_narx(testModel[0].SAMObject.model, x_start,
YtestAll.shape[0], LtestAll,
self.windowSize)
pb.figure()
pb.plot(YtestAll, 'x-')
pb.plot(ygp, 'ro-')
pb.legend(('True', 'Pred'))
pb.title('NARX-with-exogenous')
err = np.sum((YtestAll - ygp)**2)
return err
Yts = yy[Ntr:, :]
Utr = uu[0:Ntr, :]
Uts = uu[Ntr:, :]
# ---------- Train autoregressive model with additional (exogenous) inputs
m_autoreg = GPy.models.SparseGPRegression(np.hstack((Xtr, Utr)),
Ytr,
num_inducing=num_inducing)
m_autoreg.optimize('bfgs', max_iters=1000, messages=True)
print m_autoreg
# Initial window to kick-off free simulation
x_start = Xts[0, :][:, None].T
# Free simulation
ygp, varygp = autoregressive.gp_narx(m_autoreg, x_start, Yts.shape[0], Uts, ws)
pb.figure()
pb.plot(Yts, 'x-')
pb.plot(ygp, 'ro-')
pb.legend(('True', 'Pred'))
pb.title('NARX-with-exogenous')
#--------------------------------------------
# #---------- Train autoregressive model with no (exogenous) inputs
m_autoreg2 = GPy.models.SparseGPRegression(Xtr, Ytr, num_inducing=num_inducing)
print m_autoreg2
# Initial window to kick-off free simulation
x_start = Xts[0, :][:, None].T
# Free simulation