# Copyright (c) 2014 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy
from spinn_utilities.overrides import overrides
from spinn_front_end_common.utilities.constants import BYTES_PER_WORD
from .abstract_connector import AbstractConnector
from .abstract_generate_connector_on_machine import (
AbstractGenerateConnectorOnMachine, ConnectorIDs)
from .abstract_generate_connector_on_host import (
AbstractGenerateConnectorOnHost)
class AllToAllConnector(AbstractGenerateConnectorOnMachine,
AbstractGenerateConnectorOnHost):
"""
Connects all cells in the presynaptic population to all cells in
the postsynaptic population.
"""
__slots__ = [
"__allow_self_connections"]
def __init__(self, allow_self_connections=True, safe=True,
verbose=None, callback=None):
"""
:param bool allow_self_connections:
if the connector is used to connect a Population to itself, this
flag determines whether a neuron is allowed to connect to itself,
or only to other neurons in the Population.
:param bool safe:
If ``True``, check that weights and delays have valid values.
If ``False``, this check is skipped.
:param bool verbose:
Whether to output extra information about the connectivity to a
CSV file
:param callable callback:
if given, a callable that display a progress bar on the terminal.
.. note::
Not supported by sPyNNaker.
"""
super().__init__(safe, callback, verbose)
self.__allow_self_connections = allow_self_connections
[docs]
@overrides(AbstractConnector.get_delay_maximum)
def get_delay_maximum(self, synapse_info):
return self._get_delay_maximum(
synapse_info.delays,
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info)
[docs]
@overrides(AbstractConnector.get_delay_minimum)
def get_delay_minimum(self, synapse_info):
return self._get_delay_minimum(
synapse_info.delays,
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info)
[docs]
@overrides(AbstractConnector.get_n_connections_from_pre_vertex_maximum)
def get_n_connections_from_pre_vertex_maximum(
self, n_post_atoms, synapse_info, min_delay=None,
max_delay=None):
# pylint: disable=too-many-arguments
if min_delay is None or max_delay is None:
return n_post_atoms
return self._get_n_connections_from_pre_vertex_with_delay_maximum(
synapse_info.delays,
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
n_post_atoms, min_delay, max_delay, synapse_info)
[docs]
@overrides(AbstractConnector.get_n_connections_to_post_vertex_maximum)
def get_n_connections_to_post_vertex_maximum(self, synapse_info):
return synapse_info.n_pre_neurons
[docs]
@overrides(AbstractConnector.get_weight_maximum)
def get_weight_maximum(self, synapse_info):
# pylint: disable=too-many-arguments
n_conns = synapse_info.n_pre_neurons * synapse_info.n_post_neurons
return self._get_weight_maximum(
synapse_info.weights, n_conns, synapse_info)
[docs]
@overrides(AbstractGenerateConnectorOnHost.create_synaptic_block)
def create_synaptic_block(
self, post_slices, post_vertex_slice, synapse_type, synapse_info):
# pylint: disable=too-many-arguments
n_connections = synapse_info.n_pre_neurons * post_vertex_slice.n_atoms
if not self.__allow_self_connections:
n_connections -= post_vertex_slice.n_atoms
block = numpy.zeros(
n_connections, dtype=AbstractConnector.NUMPY_SYNAPSES_DTYPE)
if not self.__allow_self_connections:
n_atoms = synapse_info.n_pre_neurons
block["source"] = numpy.where(numpy.diag(
numpy.repeat(1, n_atoms)) == 0)[0]
block["target"] = [block["source"][
((n_atoms * i) + (n_atoms - 1)) - j]
for j in range(n_atoms) for i in range(n_atoms - 1)]
block["target"] += post_vertex_slice.lo_atom
else:
block["source"] = numpy.repeat(numpy.arange(
0, synapse_info.n_pre_neurons), post_vertex_slice.n_atoms)
block["target"] = numpy.tile(numpy.arange(
post_vertex_slice.lo_atom, post_vertex_slice.hi_atom + 1),
synapse_info.n_pre_neurons)
block["weight"] = self._generate_weights(
block["source"], block["target"], n_connections, post_vertex_slice,
synapse_info)
block["delay"] = self._generate_delays(
block["source"], block["target"], n_connections, post_vertex_slice,
synapse_info)
block["synapse_type"] = synapse_type
return block
def __repr__(self):
return "AllToAllConnector()"
@property
def allow_self_connections(self):
"""
:rtype: bool
"""
return self.__allow_self_connections
@allow_self_connections.setter
def allow_self_connections(self, new_value):
self.__allow_self_connections = new_value
@property
@overrides(AbstractGenerateConnectorOnMachine.gen_connector_id)
def gen_connector_id(self):
return ConnectorIDs.ALL_TO_ALL_CONNECTOR.value
[docs]
@overrides(AbstractGenerateConnectorOnMachine.gen_connector_params)
def gen_connector_params(self):
return numpy.array([
int(self.__allow_self_connections)], dtype="uint32")
@property
@overrides(
AbstractGenerateConnectorOnMachine.gen_connector_params_size_in_bytes)
def gen_connector_params_size_in_bytes(self):
return BYTES_PER_WORD