# 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 math
import numpy
from pyNN.random import NumpyRNG
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 spynnaker.pyNN.utilities import utility_calls
from spynnaker.pyNN.exceptions import SpynnakerException
from .abstract_generate_connector_on_host import (
AbstractGenerateConnectorOnHost)
class FixedNumberPreConnector(AbstractGenerateConnectorOnMachine,
AbstractGenerateConnectorOnHost):
"""
Connects a fixed number of pre-synaptic neurons selected at random,
to all post-synaptic neurons.
"""
__slots__ = [
"__allow_self_connections",
"__n_pre",
"__pre_neurons",
"__pre_neurons_set",
"__with_replacement",
"__rng"]
def __init__(
self, n, allow_self_connections=True, safe=True, verbose=False,
with_replacement=False, rng=None, callback=None):
"""
:param int n:
number of random pre-synaptic neurons connected to output
: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:
Whether to 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 bool with_replacement:
this flag determines how the random selection of pre-synaptic
neurons is performed; if true, then every pre-synaptic neuron
can be chosen on each occasion, and so multiple connections
between neuron pairs are possible; if false, then once a
pre-synaptic neuron has been connected to a post-neuron, it
can't be connected again.
:param rng:
Seeded random number generator, or `None` to make one when needed
:type rng: ~pyNN.random.NumpyRNG or None
:param callable callback:
if given, a callable that display a progress bar on the terminal.
.. note::
Not supported by sPyNNaker.
"""
# :param ~pyNN.space.Space space:
# a Space object, needed if you wish to specify distance-dependent
# weights or delays - not implemented
super().__init__(safe, callback, verbose)
# We absolutely require an integer at this point!
self.__n_pre = self._roundsize(n, "FixedNumberPreConnector")
self.__allow_self_connections = allow_self_connections
self.__with_replacement = with_replacement
self.__pre_neurons_set = False
self.__pre_neurons = None
self.__rng = rng
[docs]
@overrides(AbstractConnector.get_delay_maximum)
def get_delay_maximum(self, synapse_info):
return self._get_delay_maximum(
synapse_info.delays, self.__n_pre * 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, self.__n_pre * synapse_info.n_post_neurons,
synapse_info)
def _get_pre_neurons(self, synapse_info):
"""
:param SynapseInformation synapse_info:
:rtype: list(~numpy.ndarray)
"""
rng = self.__rng or NumpyRNG()
# If we haven't set the array up yet, do it now
if not self.__pre_neurons_set:
self.__pre_neurons = [None] * synapse_info.n_post_neurons
self.__pre_neurons_set = True
# if verbose open a file to output the connectivity
if self.verbose:
filename = "{}_to_{}_fixednumberpre-conn.csv".format(
synapse_info.pre_population.label,
synapse_info.post_population.label)
with open(filename, 'w', encoding="utf-8") as file_handle:
numpy.savetxt(file_handle,
[(synapse_info.n_pre_neurons,
synapse_info.n_post_neurons,
self.__n_pre)],
fmt="%u,%u,%u")
# Loop over all the post neurons
for m in range(0, synapse_info.n_post_neurons):
# If the pre and post populations are the same
# then deal with allow_self_connections=False
if ((synapse_info.pre_population
is synapse_info.post_population)
and not self.__allow_self_connections):
# Exclude the current pre-neuron from the post-neuron
# list
no_self_pre_neurons = [
n for n in range(
synapse_info.n_pre_neurons) if n != m]
# Now use this list in the random choice
self.__pre_neurons[m] = rng.choice(
no_self_pre_neurons, self.__n_pre,
self.__with_replacement)
else:
self.__pre_neurons[m] = rng.choice(
synapse_info.n_pre_neurons, self.__n_pre,
self.__with_replacement)
# Sort the neurons now that we have them
self.__pre_neurons[m].sort()
# If verbose then output the list connected to this
# post-neuron
if self.verbose:
numpy.savetxt(
file_handle,
self.__pre_neurons[m][None, :],
fmt=("%u," * (self.__n_pre - 1) + "%u"))
return self.__pre_neurons
[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
prob_selection = 1.0 / float(synapse_info.n_pre_neurons)
n_connections_total = utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
self.__n_pre * synapse_info.n_post_neurons, prob_selection,
chance=1.0/10000.0)
prob_in_slice = min(
float(n_post_atoms) / float(synapse_info.n_post_neurons), 1.0)
n_connections = utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
n_connections_total, prob_in_slice, chance=1.0/100000.0)
if min_delay is None or max_delay is None:
return int(math.ceil(n_connections))
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_connections, 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):
# pylint: disable=too-many-arguments
return self.__n_pre
[docs]
@overrides(AbstractConnector.get_weight_maximum)
def get_weight_maximum(self, synapse_info):
# pylint: disable=too-many-arguments
return self._get_weight_maximum(
synapse_info.weights, self.__n_pre * synapse_info.n_post_neurons,
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
# Get lo and hi for the post vertex
lo = post_vertex_slice.lo_atom
hi = post_vertex_slice.hi_atom
pre_neurons = self._get_pre_neurons(synapse_info)
# Get number of connections
n_connections = self.__n_pre * post_vertex_slice.n_atoms
# Set up the block
block = numpy.zeros(
n_connections, dtype=AbstractConnector.NUMPY_SYNAPSES_DTYPE)
# Set up source and target
pre_neurons_in_slice = []
post_neurons_in_slice = []
post_vertex_array = numpy.arange(lo, hi + 1)
for n in range(lo, hi + 1):
for pn in pre_neurons[n]:
pre_neurons_in_slice.append(pn)
post_neurons_in_slice.append(post_vertex_array[n - lo])
block["source"] = pre_neurons_in_slice
block["target"] = post_neurons_in_slice
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 f"FixedNumberPreConnector({self.__n_pre})"
@property
def allow_self_connections(self):
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.FIXED_NUMBER_PRE_CONNECTOR.value
[docs]
@overrides(AbstractGenerateConnectorOnMachine.gen_connector_params)
def gen_connector_params(self):
return numpy.array([
int(self.__allow_self_connections),
int(self.__with_replacement),
self.__n_pre], dtype="uint32")
@property
@overrides(
AbstractGenerateConnectorOnMachine.gen_connector_params_size_in_bytes)
def gen_connector_params_size_in_bytes(self):
return 3 * BYTES_PER_WORD
[docs]
@overrides(AbstractConnector.validate_connection)
def validate_connection(self, application_edge, synapse_info):
if self.generate_on_machine(synapse_info.weights, synapse_info.delays):
utility_calls.check_rng(self.__rng, "FixedNumberPreConnector")