In [29]:

    

import nengo
import nengo.spa as spa
import numpy as np

dimensions = 64
vocab = spa.Vocabulary(dimensions)
model = spa.SPA(label='RadiusTest')

with model:    
    model.mem = spa.Memory(
        dimensions=dimensions, subdimensions=16, 
        neurons_per_dimension=50, vocab=vocab)
    
    for ens in model.mem.all_ensembles:
        ens.radius = np.sqrt(float(ens.dimensions)/dimensions)

    model.input = spa.Input(mem=lambda t: 'TEST' if t<0.5 else '0') 
      
    pMem = nengo.Probe(model.mem.state.output, synapse=0.03)

sim = nengo.Simulator(model)
sim.run(1)

plt.figure()
plt.plot(sim.trange(), spa.similarity(sim.data, pMem))
plt.legend(pMem.target.vocab.keys, fontsize=12)
plt.show()


    

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