# 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)
N_GEN_PARAMS = 8
class FixedNumberPostConnector(AbstractGenerateConnectorOnMachine,
AbstractGenerateConnectorOnHost):
"""
Connects a fixed number of post-synaptic neurons selected at random,
to all pre-synaptic neurons.
"""
__slots__ = [
"__allow_self_connections",
"__n_post",
"__post_neurons",
"__post_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 post-synaptic neurons connected to pre-neurons.
: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 post-synaptic
neurons is performed; if ``True``, then every post-synaptic neuron
can be chosen on each occasion, and so multiple connections
between neuron pairs are possible; if ``False``, then once a
post-synaptic neuron has been connected to a pre-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.
"""
super().__init__(safe, callback, verbose)
self.__n_post = self._roundsize(n, "FixedNumberPostConnector")
self.__allow_self_connections = allow_self_connections
self.__with_replacement = with_replacement
self.__post_neurons = None
self.__post_neurons_set = False
self.__rng = rng
[docs]
def set_projection_information(self, synapse_info):
super().set_projection_information(synapse_info)
if (not self.__with_replacement and
self.__n_post > synapse_info.n_post_neurons):
raise SpynnakerException(
"FixedNumberPostConnector will not work when "
"with_replacement=False and n > n_post_neurons")
if (not self.__with_replacement and
not self.__allow_self_connections and
self.__n_post == synapse_info.n_post_neurons):
raise SpynnakerException(
"FixedNumberPostConnector will not work when "
"with_replacement=False, allow_self_connections=False "
"and n = n_post_neurons")
[docs]
@overrides(AbstractConnector.get_delay_maximum)
def get_delay_maximum(self, synapse_info):
n_connections = synapse_info.n_pre_neurons * self.__n_post
return self._get_delay_maximum(
synapse_info.delays, n_connections, synapse_info)
[docs]
@overrides(AbstractConnector.get_delay_minimum)
def get_delay_minimum(self, synapse_info):
n_connections = synapse_info.n_pre_neurons * self.__n_post
return self._get_delay_minimum(
synapse_info.delays, n_connections, synapse_info)
def _get_post_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.__post_neurons_set:
self.__post_neurons = [None] * synapse_info.n_pre_neurons
self.__post_neurons_set = True
# if verbose open a file to output the connectivity
if self.verbose:
filename = "{}_to_{}_fixednumberpost-conn.csv".format(
synapse_info.pre_population.label,
synapse_info.post_population.label)
print('Output post-connectivity to ', filename)
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_post)],
fmt="%u,%u,%u")
# Loop over all the pre neurons
for m in range(0, synapse_info.n_pre_neurons):
if self.__post_neurons[m] is None:
# 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):
# Create a list without the pre_neuron in it
no_self_post_neurons = numpy.concatenate(
[numpy.arange(0, m),
numpy.arange(m + 1,
synapse_info.n_post_neurons)])
# Now use this list in the random choice
self.__post_neurons[m] = rng.choice(
no_self_post_neurons, self.__n_post,
self.__with_replacement)
else:
self.__post_neurons[m] = rng.choice(
synapse_info.n_post_neurons, self.__n_post,
self.__with_replacement)
# If verbose then output the list connected to this
# pre-neuron
if self.verbose:
numpy.savetxt(
file_handle,
self.__post_neurons[m][None, :],
fmt=("%u," * (self.__n_post - 1) + "%u"))
return self.__post_neurons
def _post_neurons_in_slice(self, post_vertex_slice, n, synapse_info):
"""
:param ~pacman.model.graphs.common.Slice post_vertex_slice:
:param int n:
:param SynapseInformation synapse_info:
:rtype: ~numpy.ndarray
"""
post_neurons = self._get_post_neurons(synapse_info)
# Get the nth array and get the bits we need for
# this post-vertex slice
this_post_neuron_array = post_neurons[n]
return this_post_neuron_array[numpy.logical_and(
post_vertex_slice.lo_atom <= this_post_neuron_array,
this_post_neuron_array <= post_vertex_slice.hi_atom)]
def _n_post_neurons_in_slice(self, post_vertex_slice, n, synapse_info):
"""
Count the number of post neurons in the slice.
Faster than ``len(_post_neurons_in_slice(...))``.
:param ~pacman.model.graphs.common.Slice post_vertex_slice:
:param int n:
:param SynapseInformation synapse_info:
:rtype: int
"""
post_neurons = self._get_post_neurons(synapse_info)
# Get the nth array and get the bits we need for
# this post-vertex slice
this_post_neuron_array = post_neurons[n]
return numpy.count_nonzero(numpy.logical_and(
post_vertex_slice.lo_atom <= this_post_neuron_array,
this_post_neuron_array <= post_vertex_slice.hi_atom))
[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_in_slice = min(
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,
self.__n_post, 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
selection_prob = 1.0 / float(synapse_info.n_post_neurons)
n_connections = utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info.n_pre_neurons, selection_prob,
chance=1.0/100000.0)
return int(math.ceil(n_connections))
[docs]
@overrides(AbstractConnector.get_weight_maximum)
def get_weight_maximum(self, synapse_info):
n_connections = synapse_info.n_pre_neurons * self.__n_post
return self._get_weight_maximum(
synapse_info.weights, n_connections, 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 pre vertex
lo = 0
hi = synapse_info.n_pre_neurons - 1
# Get number of connections
n_connections = sum(
self._n_post_neurons_in_slice(post_vertex_slice, n, synapse_info)
for n in range(lo, hi + 1))
# 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 = []
pre_vertex_array = numpy.arange(lo, hi + 1)
for n in range(lo, hi + 1):
for pn in self._post_neurons_in_slice(
post_vertex_slice, n, synapse_info):
post_neurons_in_slice.append(pn)
pre_neurons_in_slice.append(pre_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"FixedNumberPostConnector({self.__n_post})"
@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_POST_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_post], 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, "FixedNumberPostConnector")