# Copyright (c) 2017 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 numpy import (
arccos, arcsin, arctan, arctan2, ceil, cos, cosh, exp, fabs, floor, fmod,
hypot, ldexp, log, log10, modf, power, sin, sinh, sqrt, tan, tanh, maximum,
minimum, e, pi)
from pyNN.random import NumpyRNG
from spinn_utilities.overrides import overrides
from spinn_utilities.safe_eval import SafeEval
from spynnaker.pyNN.utilities import utility_calls
from .abstract_connector import AbstractConnector
from .abstract_generate_connector_on_host import (
AbstractGenerateConnectorOnHost)
# support for arbitrary expression for the indices
_index_expr_context = SafeEval(math, numpy, arccos, arcsin, arctan, arctan2,
ceil, cos, cosh, exp, fabs, floor, fmod, hypot,
ldexp, log, log10, modf, power, sin, sinh, sqrt,
tan, tanh, maximum, minimum, e=e, pi=pi)
class IndexBasedProbabilityConnector(AbstractConnector,
AbstractGenerateConnectorOnHost):
"""
Make connections using a probability distribution which varies
dependent upon the indices of the pre- and post-populations.
"""
__slots = [
"__allow_self_connections",
"__index_expression",
"__probs",
"__rng"]
def __init__(
self, index_expression, allow_self_connections=True, rng=None,
safe=True, callback=None, verbose=False):
"""
:param str index_expression:
the right-hand side of a valid python expression for
probability, involving the indices of the pre- and
post-populations,
that can be parsed by `eval()`, that computes a probability
distribution;
the indices will be given as variables ``i`` and ``j`` when the
expression is evaluated.
: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 rng:
Seeded random number generator, or ``None`` to make one when
needed.
:type rng: ~pyNN.random.NumpyRNG or None
:param bool safe:
Whether to check that weights and delays have valid values.
If ``False``, this check is skipped.
:param callable callback:
if given, a callable that display a progress bar on the terminal.
.. note::
Not supported by sPyNNaker.
:param bool verbose:
Whether to output extra information about the connectivity to a
CSV file
"""
super().__init__(safe, callback, verbose)
self.__rng = rng or NumpyRNG()
self.__index_expression = index_expression
self.__allow_self_connections = allow_self_connections
self.__probs = None
def _update_probs_from_index_expression(self, synapse_info):
"""
:param SynapseInformation synapse_info:
"""
# note: this only needs to be done once
if self.__probs is None:
# numpy array of probabilities using the index_expression
self.__probs = numpy.fromfunction(
lambda i, j: _index_expr_context.eval(
self.__index_expression, i=i, j=j),
(synapse_info.n_pre_neurons, synapse_info.n_post_neurons))
[docs]
@overrides(AbstractConnector.get_delay_maximum)
def get_delay_maximum(self, synapse_info):
self._update_probs_from_index_expression(synapse_info)
n_connections = utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
numpy.amax(self.__probs))
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):
self._update_probs_from_index_expression(synapse_info)
n_connections = utility_calls.get_probable_minimum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
numpy.amax(self.__probs))
return self._get_delay_minimum(
synapse_info.delays, n_connections, 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):
self._update_probs_from_index_expression(synapse_info)
n_connections = utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
n_post_atoms, numpy.amax(self.__probs))
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):
self._update_probs_from_index_expression(synapse_info)
return utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info.n_pre_neurons, numpy.amax(self.__probs))
[docs]
@overrides(AbstractConnector.get_weight_maximum)
def get_weight_maximum(self, synapse_info):
self._update_probs_from_index_expression(synapse_info)
n_connections = utility_calls.get_probable_maximum_selected(
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
synapse_info.n_pre_neurons * synapse_info.n_post_neurons,
numpy.amax(self.__probs))
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):
# setup probs here
self._update_probs_from_index_expression(synapse_info)
probs = self.__probs[:, post_vertex_slice.as_slice].reshape(-1)
n_items = synapse_info.n_pre_neurons * post_vertex_slice.n_atoms
items = self.__rng.next(n_items)
# If self connections are not allowed, remove the possibility of self
# connections by setting the probability to a value of infinity
if not self.__allow_self_connections:
items[0:n_items:post_vertex_slice.n_atoms + 1] = numpy.inf
present = items < probs
ids = numpy.where(present)[0]
n_connections = numpy.sum(present)
block = numpy.zeros(
n_connections, dtype=AbstractConnector.NUMPY_SYNAPSES_DTYPE)
block["source"] = ids / post_vertex_slice.n_atoms
block["target"] = (
(ids % post_vertex_slice.n_atoms) + post_vertex_slice.lo_atom)
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"IndexBasedProbabilityConnector({self.__index_expression})"
@property
def allow_self_connections(self):
"""
When 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.
: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
def index_expression(self):
"""
The right-hand side of a valid python expression for probability,
involving the indices of the pre- and post-populations, that can
be parsed by `eval()`, that computes a probability distribution.
:rtype: str
"""
return self.__index_expression
@index_expression.setter
def index_expression(self, new_value):
self.__index_expression = new_value