nengo-learncommunication_channel3

# https://www.nengo.ai/nengo/examples/learning/learn-communication-channel.html
import os
import ipdb # ipdb.set_trace()

import matplotlib.pyplot as plt
import numpy as np

import nengo
from nengo.processes import WhiteSignal
from nengo.solvers import LstsqL2

model = nengo.Network()
with model:
    inp = nengo.Node(WhiteSignal(60, high=5), size_out=1)
    #inp = nengo.Node(output=1.0, size_out=1)
    pre = nengo.Ensemble(60, dimensions=1)
    nengo.Connection(inp, pre)
    post = nengo.Ensemble(60, dimensions=1)
    conn = nengo.Connection(pre, post, function=lambda x: np.random.random(1))

    input_constant = nengo.Node(output=-1, size_out=1)
    input_constant_Ensemble = nengo.Ensemble(100, dimensions=1)
    nengo.Connection(input_constant, input_constant_Ensemble)

    inp_p = nengo.Probe(inp)
    pre_p = nengo.Probe(pre, synapse=0.01)
    post_p = nengo.Probe(post, synapse=0.01)

    # *********************
    # calculating error
    # *********************
    error = nengo.Ensemble(60, dimensions=1)
    error_p = nengo.Probe(error, synapse=0.03)

    # Error = actual - target = post - pre - 1
    nengo.Connection(post, error)
    nengo.Connection(pre, error, transform=-1)
    nengo.Connection(input_constant, error, transform=-1)

    # Add the learning rule to the connection
    conn.learning_rule_type = nengo.PES()

    # Connect the error into the learning rule
    nengo.Connection(error, conn.learning_rule)

with nengo.Simulator(model) as sim:
    sim.run(10.0)

plt.figure(figsize=(12, 8))
plt.subplot(2, 1, 1)
plt.plot(sim.trange(), sim.data[inp_p].T[0], c="k", label="Input")
plt.plot(sim.trange(), sim.data[pre_p].T[0], c="b", label="Pre")
plt.plot(sim.trange(), sim.data[post_p].T[0], c="r", label="Post")
plt.ylabel("Dimension 1")
plt.legend()
plt.show()