def _remap(self):
"""Remaps the archive.
The boundaries are relocated to the percentage marks of the distribution
of solutions stored in the archive.
Also re-adds all of the solutions to the archive.
Returns:
tuple: The result of calling :meth:`ArchiveBase.add` on the last
item in the buffer.
"""
# Sort all behavior values along the axis of each bc.
sorted_bc = self._buffer.sorted_behavior_values
# Calculate new boundaries.
SlidingBoundariesArchive._remap_numba_helper(sorted_bc,
self._buffer.size,
self._boundaries,
self._behavior_dim,
self.dims)
index_columns = tuple(map(list, zip(*self._occupied_indices)))
old_sols = self._solutions[index_columns].copy()
old_objs = self._objective_values[index_columns].copy()
old_behs = self._behavior_values[index_columns].copy()
self._reset_archive()
for sol, obj, beh in zip(old_sols, old_objs, old_behs):
# Add solutions from old archive.
status, value = ArchiveBase.add(self, sol, obj, beh)
for sol, obj, beh in self._buffer:
# Add solutions from buffer.
status, value = ArchiveBase.add(self, sol, obj, beh)
return status, value
def add(self, solution, objective_value, behavior_values):
"""Attempts to insert a new solution into the archive.
This method remaps the archive after every ``self.remap_frequency``
solutions are added. Remapping involves changing the boundaries of the
archive to the percentage marks of the behavior values stored in the
buffer and re-adding all of the solutions stored in the buffer `and` the
current archive.
Args:
solution (array-like): See :meth:`ArchiveBase.add`
objective_value (float): See :meth:`ArchiveBase.add`
behavior_values (array-like): See :meth:`ArchiveBase.add`
Returns:
See :meth:`ArchiveBase.add`
"""
solution = np.asarray(solution)
behavior_values = np.asarray(behavior_values)
self._buffer.add(solution, objective_value, behavior_values)
self._total_num_sol += 1
if self._total_num_sol % self._remap_frequency == 0:
status, value = self._remap()
self._lower_bounds = np.array(
[bound[0] for bound in self._boundaries])
self._upper_bounds = np.array([
bound[dim] for bound, dim in zip(self._boundaries, self._dims)
])
else:
status, value = ArchiveBase.add(self, solution, objective_value,
behavior_values)
return status, value
def _remap(self):
"""Remaps the archive.
The boundaries are relocated to the percentage marks of the distribution
of solutions stored in the archive.
Also re-adds all of the solutions to the archive.
Returns:
tuple: The result of calling :meth:`ArchiveBase.add` on the last
item in the buffer.
"""
# Sort all behavior values along the axis of each bc.
sorted_bc = self._buffer.sorted_behavior_values
# Calculate new boundaries.
SlidingBoundariesArchive._remap_numba_helper(sorted_bc,
self._buffer.size,
self._boundaries,
self._behavior_dim,
self.dims)
# TODO (btjanaka): Add an option that allows adding solutions from the
# previous archive that are not in the buffer.
# Add all solutions to the new empty archive.
self._reset_archive()
for solution, objective_value, behavior_value in self._buffer:
status, value = ArchiveBase.add(self, solution, objective_value,
behavior_value)
return status, value
