def test_operators():
sig = Signal(np.array([0.0]), name="sig")
assert fnmatch(repr(TimeUpdate(sig, sig)), "<TimeUpdate at 0x*>")
assert fnmatch(repr(TimeUpdate(sig, sig, tag="tag")),
"<TimeUpdate 'tag' at 0x*>")
assert fnmatch(repr(Reset(sig)), "<Reset at 0x*>")
assert fnmatch(repr(Reset(sig, tag="tag")), "<Reset 'tag' at 0x*>")
assert fnmatch(repr(Copy(sig, sig)), "<Copy at 0x*>")
assert fnmatch(repr(Copy(sig, sig, tag="tag")), "<Copy 'tag' at 0x*>")
assert fnmatch(repr(ElementwiseInc(sig, sig, sig)),
"<ElementwiseInc at 0x*>")
assert fnmatch(repr(ElementwiseInc(sig, sig, sig, tag="tag")),
"<ElementwiseInc 'tag' at 0x*>")
assert fnmatch(repr(DotInc(sig, sig, sig)), "<DotInc at 0x*>")
assert fnmatch(repr(DotInc(sig, sig, sig, tag="tag")),
"<DotInc 'tag' at 0x*>")
assert fnmatch(repr(SimPyFunc(sig, lambda x: 0.0, True, sig)),
"<SimPyFunc at 0x*>")
assert fnmatch(
repr(SimPyFunc(sig, lambda x: 0.0, True, sig, tag="tag")),
"<SimPyFunc 'tag' at 0x*>",
)
assert fnmatch(repr(SimPES(sig, sig, sig, 0.1)), "<SimPES at 0x*>")
assert fnmatch(repr(SimPES(sig, sig, sig, 0.1, tag="tag")),
"<SimPES 'tag' at 0x*>")
assert fnmatch(repr(SimBCM(sig, sig, sig, sig, 0.1)), "<SimBCM at 0x*>")
assert fnmatch(repr(SimBCM(sig, sig, sig, sig, 0.1, tag="tag")),
"<SimBCM 'tag' at 0x*>")
assert fnmatch(repr(SimOja(sig, sig, sig, sig, 0.1, 1.0)),
"<SimOja at 0x*>")
assert fnmatch(repr(SimOja(sig, sig, sig, sig, 0.1, 1.0, tag="tag")),
"<SimOja 'tag' at 0x*>")
assert fnmatch(repr(SimVoja(sig, sig, sig, sig, 1.0, sig, 1.0)),
"<SimVoja at 0x*>")
assert fnmatch(
repr(SimVoja(sig, sig, sig, sig, 0.1, sig, 1.0, tag="tag")),
"<SimVoja 'tag' at 0x*>",
)
assert fnmatch(repr(SimRLS(sig, sig, sig, sig)), "<SimRLS at 0x*>")
assert fnmatch(
repr(SimRLS(sig, sig, sig, sig, tag="tag")),
"<SimRLS 'tag' at 0x*>",
)
assert fnmatch(repr(SimNeurons(LIF(), sig, {"sig": sig})),
"<SimNeurons at 0x*>")
assert fnmatch(
repr(SimNeurons(LIF(), sig, {"sig": sig}, tag="tag")),
"<SimNeurons 'tag' at 0x*>",
)
assert fnmatch(repr(SimProcess(WhiteNoise(), sig, sig, sig)),
"<SimProcess at 0x*>")
assert fnmatch(
repr(SimProcess(WhiteNoise(), sig, sig, sig, tag="tag")),
"<SimProcess 'tag' at 0x*>",
)
def build_node(model, node):
# input signal
if not is_array_like(node.output) and node.size_in > 0:
sig_in = Signal(np.zeros(node.size_in), name="%s.in" % node)
model.add_op(Reset(sig_in))
else:
sig_in = None
# Provide output
if node.output is None:
sig_out = sig_in
elif isinstance(node.output, Process):
sig_out = Signal(np.zeros(node.size_out), name="%s.out" % node)
model.build(node.output, sig_in, sig_out)
elif callable(node.output):
sig_out = (Signal(np.zeros(node.size_out), name="%s.out" %
node) if node.size_out > 0 else None)
model.add_op(
SimPyFunc(output=sig_out, fn=node.output, t=model.time, x=sig_in))
elif is_array_like(node.output):
sig_out = Signal(node.output, name="%s.out" % node)
else:
raise BuildError("Invalid node output type %r" %
node.output.__class__.__name__)
model.sig[node]['in'] = sig_in
model.sig[node]['out'] = sig_out
model.params[node] = None
def build_delta_rule(model, delta_rule, rule):
conn = rule.connection
# Create input error signal
error = Signal(np.zeros(rule.size_in), name="DeltaRule:error")
model.add_op(Reset(error))
model.sig[rule]["in"] = error # error connection will attach here
# Multiply by post_fn output if necessary
post_fn = delta_rule.post_fn.function
post_tau = delta_rule.post_tau
post_target = delta_rule.post_target
if post_fn is not None:
post_sig = model.sig[conn.post_obj][post_target]
post_synapse = Lowpass(post_tau) if post_tau is not None else None
post_input = (post_sig if post_synapse is None else model.build(
post_synapse, post_sig))
post = Signal(np.zeros(post_input.shape), name="DeltaRule:post")
model.add_op(
SimPyFunc(post,
post_fn,
t=None,
x=post_input,
tag="DeltaRule:post_fn"))
model.sig[rule]["post"] = post
error0 = error
error = Signal(np.zeros(rule.size_in), name="DeltaRule:post_error")
model.add_op(Reset(error))
model.add_op(ElementwiseInc(error0, post, error))
# Compute: correction = -learning_rate * dt * error
correction = Signal(np.zeros(error.shape), name="DeltaRule:correction")
model.add_op(Reset(correction))
lr_sig = Signal(-delta_rule.learning_rate * model.dt,
name="DeltaRule:learning_rate")
model.add_op(DotInc(lr_sig, error, correction, tag="DeltaRule:correct"))
# delta_ij = correction_i * pre_j
pre_synapse = Lowpass(delta_rule.pre_tau)
pre = model.build(pre_synapse, model.sig[conn.pre_obj]["out"])
model.add_op(Reset(model.sig[rule]["delta"]))
model.add_op(
ElementwiseInc(
correction.reshape((-1, 1)),
pre.reshape((1, -1)),
model.sig[rule]["delta"],
tag="DeltaRule:Inc Delta",
))
# expose these for probes
model.sig[rule]["error"] = error
model.sig[rule]["correction"] = correction
model.sig[rule]["pre"] = pre
def test_planner_chain(planner):
# test a chain
a = dummies.Signal(label="a")
b = dummies.Signal(label="b")
c = dummies.Signal(label="c")
d = dummies.Signal(label="d")
operators = [Copy(a, b, inc=True) for _ in range(3)]
operators += [SimPyFunc(c, lambda x: x, None, b)]
operators += [Copy(c, d, inc=True) for _ in range(2)]
plan = planner(operators)
assert len(plan) == 3
assert len(plan[0]) == 3
assert len(plan[1]) == 1
assert len(plan[2]) == 2
def build_node(model, node):
"""Builds a `.Node` object into a model.
The node build function is relatively simple. It involves creating input
and output signals, and connecting them with an `.Operator` that depends
on the type of ``node.output``.
Parameters
----------
model : Model
The model to build into.
node : Node
The node to build.
Notes
-----
Sets ``model.params[node]`` to ``None``.
"""
# input signal
if not is_array_like(node.output) and node.size_in > 0:
sig_in = Signal(shape=node.size_in, name="%s.in" % node)
model.add_op(Reset(sig_in))
else:
sig_in = None
# Provide output
if node.output is None:
sig_out = sig_in
elif isinstance(node.output, Process):
sig_out = Signal(shape=node.size_out, name="%s.out" % node)
model.build(node.output, sig_in, sig_out, mode="set")
elif callable(node.output):
sig_out = (
Signal(shape=node.size_out, name="%s.out" % node)
if node.size_out > 0
else None
)
model.add_op(SimPyFunc(output=sig_out, fn=node.output, t=model.time, x=sig_in))
elif is_array_like(node.output):
sig_out = Signal(node.output.astype(rc.float_dtype), name="%s.out" % node)
else:
raise BuildError("Invalid node output type %r" % type(node.output).__name__)
model.sig[node]["in"] = sig_in
model.sig[node]["out"] = sig_out
model.params[node] = None
def build_FpgaPesEnsembleNetwork(model, network):
"""Builder to integrate FPGA network into Nengo
Add build steps like nengo?
"""
# Check if nengo_fpga.Simulator is being used to build this network
if not network.using_fpga_sim:
warn_str = "FpgaPesEnsembleNetwork not being built with nengo_fpga simulator."
logger.warning(warn_str)
print("WARNING: " + warn_str)
# Check if all of the requirements to use the FPGA board are met
if not (network.using_fpga_sim and network.config_found
and network.fpga_found):
# FPGA requirements not met...
# Build the dummy network instead of using FPGA-specific stuff
warn_str = "Building network with dummy (non-FPGA) ensemble."
logger.warning(warn_str)
print("WARNING: " + warn_str)
nengo.builder.network.build_network(model, network)
return
# Generate the ensemble and connection parameters and save them to file
extract_and_save_params(model, network)
# Build the nengo network using the network's udp_socket function
# Set up input/output signals
input_sig = Signal(np.zeros(network.input_dimensions), name="input")
model.sig[network.input]["in"] = input_sig
model.sig[network.input]["out"] = input_sig
model.add_op(Reset(input_sig))
input_sig = model.build(nengo.synapses.Lowpass(0), input_sig)
error_sig = Signal(np.zeros(network.output_dimensions), name="error")
model.sig[network.error]["in"] = error_sig
model.sig[network.error]["out"] = error_sig
model.add_op(Reset(error_sig))
error_sig = model.build(nengo.synapses.Lowpass(0), error_sig)
output_sig = Signal(np.zeros(network.output_dimensions), name="output")
model.sig[network.output]["out"] = output_sig
if network.connection.synapse is not None:
model.build(network.connection.synapse, output_sig)
# Set up udp_socket combined input signals
udp_socket_input_sig = Signal(
np.zeros(network.input_dimensions + network.output_dimensions),
name="udp_socket_input",
)
model.add_op(
Copy(
input_sig,
udp_socket_input_sig,
dst_slice=slice(0, network.input_dimensions),
))
model.add_op(
Copy(
error_sig,
udp_socket_input_sig,
dst_slice=slice(network.input_dimensions, None),
))
# Build udp socket function with Nengo SimPyFunc
model.add_op(
SimPyFunc(
output=output_sig,
fn=partial(udp_comm_func, net=network, dt=model.dt),
t=model.time,
x=udp_socket_input_sig,
))
def build_connection(model, conn):
"""Builds a `.Connection` object into a model.
A brief summary of what happens in the connection build process,
in order:
1. Solve for decoders.
2. Combine transform matrix with decoders to get weights.
3. Add operators for computing the function
or multiplying neural activity by weights.
4. Call build function for the synapse.
5. Call build function for the learning rule.
6. Add operator for applying learning rule delta to weights.
Some of these steps may be altered or omitted depending on the parameters
of the connection, in particular the pre and post types.
Parameters
----------
model : Model
The model to build into.
conn : Connection
The connection to build.
Notes
-----
Sets ``model.params[conn]`` to a `.BuiltConnection` instance.
"""
# Create random number generator
rng = np.random.RandomState(model.seeds[conn])
# Get input and output connections from pre and post
def get_prepost_signal(is_pre):
target = conn.pre_obj if is_pre else conn.post_obj
key = 'out' if is_pre else 'in'
if target not in model.sig:
raise BuildError("Building %s: the %r object %s is not in the "
"model, or has a size of zero." %
(conn, 'pre' if is_pre else 'post', target))
if key not in model.sig[target]:
raise BuildError(
"Building %s: the %r object %s has a %r size of zero." %
(conn, 'pre' if is_pre else 'post', target, key))
return model.sig[target][key]
model.sig[conn]['in'] = get_prepost_signal(is_pre=True)
model.sig[conn]['out'] = get_prepost_signal(is_pre=False)
weights = None
eval_points = None
solver_info = None
signal_size = conn.size_out
post_slice = conn.post_slice
# Sample transform if given a distribution
transform = get_samples(conn.transform,
conn.size_out,
d=conn.size_mid,
rng=rng)
# Figure out the signal going across this connection
in_signal = model.sig[conn]['in']
if isinstance(conn.pre_obj,
Node) or (isinstance(conn.pre_obj, Ensemble)
and isinstance(conn.pre_obj.neuron_type, Direct)):
# Node or Decoded connection in directmode
weights = transform
sliced_in = slice_signal(model, in_signal, conn.pre_slice)
if conn.function is None:
in_signal = sliced_in
elif isinstance(conn.function, np.ndarray):
raise BuildError("Cannot use function points in direct connection")
else:
in_signal = Signal(np.zeros(conn.size_mid), name='%s.func' % conn)
model.add_op(SimPyFunc(in_signal, conn.function, None, sliced_in))
elif isinstance(conn.pre_obj, Ensemble): # Normal decoded connection
eval_points, weights, solver_info = model.build(
conn.solver, conn, rng, transform)
if conn.solver.weights:
model.sig[conn]['out'] = model.sig[conn.post_obj.neurons]['in']
signal_size = conn.post_obj.neurons.size_in
post_slice = None # don't apply slice later
else:
weights = transform
in_signal = slice_signal(model, in_signal, conn.pre_slice)
# Add operator for applying weights
model.sig[conn]['weights'] = Signal(weights,
name="%s.weights" % conn,
readonly=True)
signal = Signal(np.zeros(signal_size), name="%s.weighted" % conn)
model.add_op(Reset(signal))
if isinstance(conn.transform, Conv2D):
assert not isinstance(conn.pre_obj, Ensemble)
model.add_op(
Conv2DInc(model.sig[conn]["weights"], in_signal, signal,
conn.transform))
else:
op = ElementwiseInc if weights.ndim < 2 else DotInc
model.add_op(
op(model.sig[conn]['weights'],
in_signal,
signal,
tag="%s.weights_elementwiseinc" % conn))
# Add operator for filtering
if conn.synapse is not None:
signal = model.build(conn.synapse, signal)
# Store the weighted-filtered output in case we want to probe it
model.sig[conn]['weighted'] = signal
if isinstance(conn.post_obj, Neurons):
# Apply neuron gains (we don't need to do this if we're connecting to
# an Ensemble, because the gains are rolled into the encoders)
gains = Signal(model.params[conn.post_obj.ensemble].gain[post_slice],
name="%s.gains" % conn)
model.add_op(
ElementwiseInc(gains,
signal,
model.sig[conn]['out'][post_slice],
tag="%s.gains_elementwiseinc" % conn))
else:
# Copy to the proper slice
model.add_op(
Copy(signal,
model.sig[conn]['out'],
dst_slice=post_slice,
inc=True,
tag="%s" % conn))
# Build learning rules
if conn.learning_rule is not None:
assert conn.transform is not Conv2D
rule = conn.learning_rule
rule = [rule] if not is_iterable(rule) else rule
targets = []
for r in rule.values() if isinstance(rule, dict) else rule:
model.build(r)
targets.append(r.modifies)
if 'encoders' in targets:
encoder_sig = model.sig[conn.post_obj]['encoders']
encoder_sig.readonly = False
if 'decoders' in targets or 'weights' in targets:
if weights.ndim < 2:
raise BuildError(
"'transform' must be a 2-dimensional array for learning")
model.sig[conn]['weights'].readonly = False
model.params[conn] = BuiltConnection(eval_points=eval_points,
solver_info=solver_info,
transform=transform,
weights=weights)
def test_mergeable():
# anything is mergeable with an empty list
assert mergeable(None, [])
# ops with different numbers of sets/incs/reads/updates are not mergeable
assert not mergeable(dummies.Op(sets=[dummies.Signal()]), [dummies.Op()])
assert not mergeable(dummies.Op(incs=[dummies.Signal()]), [dummies.Op()])
assert not mergeable(dummies.Op(reads=[dummies.Signal()]), [dummies.Op()])
assert not mergeable(dummies.Op(updates=[dummies.Signal()]), [dummies.Op()])
assert mergeable(dummies.Op(sets=[dummies.Signal()]),
[dummies.Op(sets=[dummies.Signal()])])
# check matching dtypes
assert not mergeable(dummies.Op(sets=[dummies.Signal(dtype=np.float32)]),
[dummies.Op(sets=[dummies.Signal(dtype=np.float64)])])
# shape mismatch
assert not mergeable(dummies.Op(sets=[dummies.Signal(shape=(1, 2))]),
[dummies.Op(sets=[dummies.Signal(shape=(1, 3))])])
# display shape mismatch
assert not mergeable(
dummies.Op(sets=[dummies.Signal(base_shape=(2, 2), shape=(4, 1))]),
[dummies.Op(sets=[dummies.Signal(base_shape=(2, 2), shape=(1, 4))])])
# first dimension mismatch
assert mergeable(dummies.Op(sets=[dummies.Signal(shape=(3, 2))]),
[dummies.Op(sets=[dummies.Signal(shape=(4, 2))])])
# Copy (inc must match)
assert mergeable(Copy(dummies.Signal(), dummies.Signal(), inc=True),
[Copy(dummies.Signal(), dummies.Signal(), inc=True)])
assert not mergeable(Copy(dummies.Signal(), dummies.Signal(), inc=True),
[Copy(dummies.Signal(), dummies.Signal(), inc=False)])
# elementwise (first dimension must match)
assert mergeable(
ElementwiseInc(dummies.Signal(), dummies.Signal(), dummies.Signal()),
[ElementwiseInc(dummies.Signal(), dummies.Signal(), dummies.Signal())])
assert mergeable(
ElementwiseInc(dummies.Signal(shape=(1,)), dummies.Signal(), dummies.Signal()),
[ElementwiseInc(dummies.Signal(shape=()), dummies.Signal(), dummies.Signal())])
assert not mergeable(
ElementwiseInc(dummies.Signal(shape=(3,)), dummies.Signal(), dummies.Signal()),
[ElementwiseInc(dummies.Signal(shape=(2,)), dummies.Signal(),
dummies.Signal())])
# simpyfunc (t input must match)
time = dummies.Signal()
assert mergeable(SimPyFunc(None, None, time, None),
[SimPyFunc(None, None, time, None)])
assert mergeable(SimPyFunc(None, None, None, dummies.Signal()),
[SimPyFunc(None, None, None, dummies.Signal())])
assert not mergeable(SimPyFunc(None, None, dummies.Signal(), None),
[SimPyFunc(None, None, None, dummies.Signal())])
# simneurons
# check matching TF_NEURON_IMPL
assert mergeable(SimNeurons(LIF(), dummies.Signal(), dummies.Signal()),
[SimNeurons(LIF(), dummies.Signal(), dummies.Signal())])
assert not mergeable(SimNeurons(LIF(), dummies.Signal(), dummies.Signal()),
[SimNeurons(LIFRate(), dummies.Signal(), dummies.Signal())])
# check custom with non-custom implementation
assert not mergeable(SimNeurons(LIF(), dummies.Signal(), dummies.Signal()),
[SimNeurons(Izhikevich(), dummies.Signal(),
dummies.Signal())])
# check non-custom matching
assert not mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal()),
[SimNeurons(AdaptiveLIF(), dummies.Signal(), dummies.Signal())])
assert not mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(dtype=np.float32)]),
[SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(dtype=np.int32)])])
assert mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(3,))]),
[SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(2,))])])
assert not mergeable(
SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(2, 1))]),
[SimNeurons(Izhikevich(), dummies.Signal(), dummies.Signal(),
states=[dummies.Signal(shape=(2, 2))])])
# simprocess
# mode must match
assert not mergeable(
SimProcess(Lowpass(0), None, dummies.Signal(), dummies.Signal(),
mode="inc"),
[SimProcess(Lowpass(0), None, dummies.Signal(), dummies.Signal(),
mode="set")])
# check that lowpass match
assert mergeable(SimProcess(Lowpass(0), None, None, dummies.Signal()),
[SimProcess(Lowpass(0), None, None, dummies.Signal())])
# check that lowpass and linear don't match
assert not mergeable(SimProcess(Lowpass(0), None, None, dummies.Signal()),
[SimProcess(Alpha(0), None, None, dummies.Signal())])
# check that two linear do match
assert mergeable(
SimProcess(Alpha(0.1), dummies.Signal(), None, dummies.Signal()),
[SimProcess(LinearFilter([1], [1, 1, 1]), dummies.Signal(), None,
dummies.Signal())])
# check custom and non-custom don't match
assert not mergeable(SimProcess(Triangle(0), None, None, dummies.Signal()),
[SimProcess(Alpha(0), None, None, dummies.Signal())])
# check non-custom matching
assert mergeable(SimProcess(Triangle(0), None, None, dummies.Signal()),
[SimProcess(Triangle(0), None, None, dummies.Signal())])
# simtensornode
a = SimTensorNode(None, dummies.Signal(), None, dummies.Signal())
assert not mergeable(a, [a])
# learning rules
a = SimBCM(dummies.Signal((4,)), dummies.Signal(), dummies.Signal(), dummies.Signal(),
dummies.Signal())
b = SimBCM(dummies.Signal((5,)), dummies.Signal(), dummies.Signal(), dummies.Signal(),
dummies.Signal())
assert not mergeable(a, [b])
def test_mergeable():
# anything is mergeable with an empty list
assert mergeable(None, [])
# ops with different numbers of sets/incs/reads/updates are not mergeable
assert not mergeable(DummyOp(sets=[DummySignal()]), [DummyOp()])
assert not mergeable(DummyOp(incs=[DummySignal()]), [DummyOp()])
assert not mergeable(DummyOp(reads=[DummySignal()]), [DummyOp()])
assert not mergeable(DummyOp(updates=[DummySignal()]), [DummyOp()])
assert mergeable(DummyOp(sets=[DummySignal()]),
[DummyOp(sets=[DummySignal()])])
# check matching dtypes
assert not mergeable(DummyOp(sets=[DummySignal(dtype=np.float32)]),
[DummyOp(sets=[DummySignal(dtype=np.float64)])])
# shape mismatch
assert not mergeable(DummyOp(sets=[DummySignal(shape=(1, 2))]),
[DummyOp(sets=[DummySignal(shape=(1, 3))])])
# display shape mismatch
assert not mergeable(
DummyOp(sets=[DummySignal(base_shape=(2, 2), shape=(4, 1))]),
[DummyOp(sets=[DummySignal(base_shape=(2, 2), shape=(1, 4))])])
# first dimension mismatch
assert mergeable(DummyOp(sets=[DummySignal(shape=(3, 2))]),
[DummyOp(sets=[DummySignal(shape=(4, 2))])])
# Copy (inc must match)
assert mergeable(Copy(DummySignal(), DummySignal(), inc=True),
[Copy(DummySignal(), DummySignal(), inc=True)])
assert not mergeable(Copy(DummySignal(), DummySignal(), inc=True),
[Copy(DummySignal(), DummySignal(), inc=False)])
# elementwise (first dimension must match)
assert mergeable(
ElementwiseInc(DummySignal(), DummySignal(), DummySignal()),
[ElementwiseInc(DummySignal(), DummySignal(), DummySignal())])
assert mergeable(
ElementwiseInc(DummySignal(shape=(1,)), DummySignal(), DummySignal()),
[ElementwiseInc(DummySignal(shape=()), DummySignal(), DummySignal())])
assert not mergeable(
ElementwiseInc(DummySignal(shape=(3,)), DummySignal(), DummySignal()),
[ElementwiseInc(DummySignal(shape=(2,)), DummySignal(),
DummySignal())])
# simpyfunc (t input must match)
time = DummySignal()
assert mergeable(SimPyFunc(None, None, time, None),
[SimPyFunc(None, None, time, None)])
assert mergeable(SimPyFunc(None, None, None, DummySignal()),
[SimPyFunc(None, None, None, DummySignal())])
assert not mergeable(SimPyFunc(None, None, DummySignal(), None),
[SimPyFunc(None, None, None, DummySignal())])
# simneurons
# check matching TF_NEURON_IMPL
assert mergeable(SimNeurons(LIF(), DummySignal(), DummySignal()),
[SimNeurons(LIF(), DummySignal(), DummySignal())])
assert not mergeable(SimNeurons(LIF(), DummySignal(), DummySignal()),
[SimNeurons(LIFRate(), DummySignal(), DummySignal())])
# check custom with non-custom implementation
assert not mergeable(SimNeurons(LIF(), DummySignal(), DummySignal()),
[SimNeurons(Izhikevich(), DummySignal(),
DummySignal())])
# check non-custom matching
assert not mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal()),
[SimNeurons(AdaptiveLIF(), DummySignal(), DummySignal())])
assert not mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(dtype=np.float32)]),
[SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(dtype=np.int32)])])
assert mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(3,))]),
[SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(2,))])])
assert not mergeable(
SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(2, 1))]),
[SimNeurons(Izhikevich(), DummySignal(), DummySignal(),
states=[DummySignal(shape=(2, 2))])])
# simprocess
# mode must match
assert not mergeable(
SimProcess(Lowpass(0), None, None, DummySignal(), mode="inc"),
[SimProcess(Lowpass(0), None, None, DummySignal(), mode="set")])
# check matching TF_PROCESS_IMPL
# note: we only have one item in TF_PROCESS_IMPL at the moment, so no
# such thing as a mismatch
assert mergeable(SimProcess(Lowpass(0), None, None, DummySignal()),
[SimProcess(Lowpass(0), None, None, DummySignal())])
# check custom vs non custom
assert not mergeable(SimProcess(Lowpass(0), None, None, DummySignal()),
[SimProcess(Alpha(0), None, None, DummySignal())])
# check non-custom matching
assert mergeable(SimProcess(Triangle(0), None, None, DummySignal()),
[SimProcess(Alpha(0), None, None, DummySignal())])
# simtensornode
a = SimTensorNode(None, DummySignal(), None, DummySignal())
assert not mergeable(a, [a])
# learning rules
a = SimBCM(DummySignal((4,)), DummySignal(), DummySignal(), DummySignal(),
DummySignal())
b = SimBCM(DummySignal((5,)), DummySignal(), DummySignal(), DummySignal(),
DummySignal())
assert not mergeable(a, [b])
