def run_param_set(self, n_neurons, d, seed, trial):
spaopt.optimization.SubvectorRadiusOptimizer.Simulator = \
nengo_spinnaker.Simulator
rng = np.random.RandomState(seed)
ctx = nengo.spa.SemanticPointer(d, rng)
ctx.make_unitary()
model = nengo.Network(seed=get_seed(rng))
with model:
step = SignalGenerator(self.duration).make_step(0, d, .001, rng)
in_a = nengo.Node(step, size_out=d)
a = nengo.Node(size_in=d)
b = nengo.Node(size_in=d)
c = nengo.Node(size_in=d)
nengo.Connection(in_a, a)
nengo.Connection(a, b, synapse=None)
nengo.Connection(b, c, synapse=None)
old_repr = nengo.networks.EnsembleArray(n_neurons, d)
nengo.Connection(in_a, old_repr.input)
repr_ = spaopt.UnitEA(n_neurons, d, d)
nengo.Connection(in_a, repr_.input)
in_probe = nengo.Probe(c, synapse=0.005)
old_probe = nengo.Probe(old_repr.output, synapse=0.005)
probe = nengo.Probe(repr_.output, synapse=0.005)
nengo_spinnaker.add_spinnaker_params(model.config)
model.config[in_a].function_of_time = True
sim = nengo_spinnaker.Simulator(model)
sim.run(self.duration)
sim.close()
return {
't': sim.trange(),
'default': rmse(sim.data[old_probe], sim.data[in_probe], axis=1),
'optimized': rmse(sim.data[probe], sim.data[in_probe], axis=1)
}
import sys
sys.path.insert(0, os.path.dirname(__file__))
import platform
import numpy as np
from psyrun import Param
from psyrun.scheduler import Sqsub
from benchmarks import get_seed, EmpiricalError
rng = np.random.RandomState(29873)
n_trials = 20
seeds = [get_seed(rng) for i in range(n_trials)]
pspace = ((Param(dimensions=64, N=200, n_eval_points=750, reg=[0.01, 0.25], m=1) +
Param(dimensions=64, N=200, n_eval_points=2000, reg=[0.1], m=1) +
Param(dimensions=256, N=200, n_eval_points=750, reg=[0.1], m=4)) *
Param(r=np.linspace(0.01, 1, 11)) *
Param(seed=seeds, trial=range(n_trials)))
min_items = 64
if platform.node().startswith('bul') or platform.node().startswith('saw'):
workdir = '/work/jgosmann/spaopt'
scheduler = Sqsub(workdir)
scheduler_args = {
'timelimit': '15m',