def cell_affine_models(cell, step_sim, ntrain, ntest, tol, include_err):
"""cell_affine_models
Parameters
----------
cell : cell
step_sim : 1 time step (delta_t) simulator
tol : each abs state is split further into num_splits cells
in order to meet: modeling error < tol (module ntests samples)
Returns
-------
pwa.SubModel()
Notes
------
"""
# XXX: Generate different samples for each time step or reuse?
# Not clear!
sub_models = []
X, Y = getxy_ignoramous(cell, ntrain, step_sim)
rm = RegressionModel(X, Y)
X, Y = getxy_ignoramous(cell, ntest, step_sim)
e_pc = rm.error_pc(X, Y) # error %
if __debug__:
print('error%:', e_pc)
#error = np.linalg.norm(e_pc, 2)
# error exceeds tol in error_dims
error_dims = np.arange(len(e_pc))[np.where(e_pc >= tol)]
if len(error_dims) > 0:
err.warn('splitting on e%:{}, |e%|:{}'.format(
e_pc, np.linalg.norm(e_pc, 2)))
for split_cell in cell.split(axes=error_dims):
sub_models_ = cell_affine_models(
split_cell, step_sim, ntrain, ntest, tol, include_err)
sub_models.extend(sub_models_)
return sub_models
else:
#print('error%:', rm.error_pc(X, Y))
A, b = rm.Ab
C, d = cell.ival_constraints.poly()
e = rm.error(X, Y) if include_err else None
dmap = pwa.DiscreteAffineMap(A, b, e)
part = pwa.Partition(C, d, cell)
sub_model = pwa.SubModel(part, dmap)
if __debug__:
print('----------------Finalized------------------')
return [sub_model]
def cell_rel_affine_models(cell1, cell2, force, step_sim, ntrain, ntest, tol, include_err):
"""cell_affine_models
Parameters
----------
cell1 : source cell
cell2 : target cell
step_sim : 1 time step (delta_t) simulator
tol : each abs state is split further into num_splits cells
in order to meet: modeling error < tol (module ntests samples)
Returns
-------
pwa.SubModel()
Notes
------
"""
# XXX: Generate different samples for each time step or reuse?
# Not clear!
sub_models = []
X, Y = getxy_rel_ignoramous(cell1, cell2, force, ntrain, step_sim)
# No samples found => no model
training_samples_found = len(X) != 0
if not training_samples_found:
return [None]
rm = RegressionModel(X, Y)
X, Y = getxy_rel_ignoramous(cell1, cell2, True, ntest, step_sim)
testing_samples_found = len(X) != 0
# If valid samples are found, compute e_pc (error %) and dims
# where error % >= given tol
if testing_samples_found:
e_pc = rm.error_pc(X, Y)
error_dims = np.arange(len(e_pc))[np.where(e_pc >= tol)]
# Otherwise, forget it!
else:
e_pc = None
error_dims = []
if __debug__:
print('error%:', e_pc)
if len(error_dims) > 0:
err.warn('splitting on e%:{}, |e%|:{}'.format(
e_pc, np.linalg.norm(e_pc, 2)))
for split_cell1 in cell1.split(axes=error_dims):
sub_models_ = cell_rel_affine_models(
split_cell1, cell2, False, step_sim, ntrain, ntest, tol, include_err)
sub_models.extend(sub_models_)
return sub_models
else:
A, b = rm.Ab
C1, d1 = cell1.ival_constraints.poly()
C2, d2 = cell2.ival_constraints.poly()
e = rm.error(X, Y) if (include_err and testing_samples_found) else None
dmap = rel.DiscreteAffineMap(A, b, e)
part1 = rel.Partition(C1, d1, cell1)
part2 = rel.Partition(C2, d2, cell2)
sub_model = rel.Relation(part1, part2, dmap)
if __debug__:
print('----------------Finalized------------------')
return [sub_model]
