How to normalize the nengo_spa.State?

I want to ask you the way to normalize the norm of the vector which nengo_spa.State represents.

import nengo_spa as spa

def agent_input(t):
    return 'Dog'

def verb_input(t):
    return 'Chase'

def obj_input(t):
    return 'Boy'

d = 16
model = spa.Network()

with model:
    agent_inp = spa.Transcode(agent_input, output_vocab=d)
    verb_inp = spa.Transcode(verb_input, output_vocab=d)
    object_inp = spa.Transcode(object_input, output_vocab=d)
    sentence = spa.State(d)
    agent = spa.State(d)
    verb = spa.State(d)
    object = spa.State(d)
    agent_inp >> agent
    verb_inp >> verb
    object_inp >> object
    spa.sym.Agent*agent + spa.sym.Verb*verb + spa.sym.Object * object >> sentence

In this case, the norm of the vector in sentence represents exceeds 1.
What is the way to normalize this?

In SPA models we generally rely on the neuron saturation behaviour to provide a soft normalization. Because LIF neurons “flatten out” for higher and higher input values, trying to represent vectors with magnitude significantly greater than 1 will all tend to result in the same representation (with magnitude ~1). You can change where that saturation happens by playing with the Ensemble’s radius parameter.

If you want an exact normalization you can use nengo.Node (e.g. nengo.Node(lambda t, x: x / np.linalg.norm(x))). The output of that node will always be exactly normalized (but it’s not being done in a biologically plausible way, if that is important to you).

A quick addendum: the semantic pointer (SP) class has a normalize method and a unitary method. It’s also useful to know that any unitary vector bound with another unitary vector will maintain unit length after binding; this property can be leveraged to help control the lengths of vectors.