# to give you trouble) and then use "pip install scipy --no-use-wheel". Or, if you can figure out how to get PyCharm
# to ignore warnings, that's fine too.
#
# More info here: https://stackoverflow.com/questions/40845304/runtimewarning-numpy-dtype-size-changed-may-indicate
# -binary-incompatibility
import psyneulink as pnl
import numpy as np
### building the LeabraMechanism
n_input = 4 # don't change this!
n_output = 2 # don't change this!
n_hidden = 0
Leab = pnl.LeabraMechanism(input_size=n_input,
output_size=n_output,
hidden_layers=n_hidden,
hidden_sizes=None,
training_flag=True,
quarter_size=20)
### building the PsyNeuLink network
T_input = pnl.TransferMechanism(size=n_input)
T_target = pnl.TransferMechanism(size=n_output)
# target_projection connects T_target to the TARGET InputPort of Leab
target_projection = pnl.MappingProjection(sender=T_target,
receiver=Leab.input_ports[1])
p_input = pnl.Process(pathway=[T_input, Leab])
p_target = pnl.Process(pathway=[T_target, target_projection, Leab])
sys = pnl.System(processes=[p_input, p_target])
train_flag = False # should the LeabraMechanism and leabra network learn?
# NOTE: there is currently a bug with training, in which the output may differ between trials, randomly
# ending up in one of two possible outputs. Running this script repeatedly will make this behavior clear.
# The leabra network and LeabraMechanism experience this bug equally.
# NOTE: The reason TransferMechanisms are used below is because there is currently a bug where LeabraMechanism
# (and other `Mechanism`s with multiple input states) cannot be used as origin Mechanisms for a System. If you desire
# to use a LeabraMechanism as an origin Mechanism, you can work around this bug by creating two `TransferMechanism`s
# as origin Mechanisms instead, and have these two TransferMechanisms pass their output to the InputStates of
# the LeabraMechanism.
# create a LeabraMechanism in PsyNeuLink
L = pnl.LeabraMechanism(input_size=input_size,
output_size=output_size,
hidden_layers=hidden_layers,
hidden_sizes=hidden_sizes,
name='L',
training_flag=train_flag)
T1 = pnl.TransferMechanism(name='T1', size=input_size, function=pnl.Linear)
T2 = pnl.TransferMechanism(name='T2', size=output_size, function=pnl.Linear)
p1 = pnl.Process(pathway=[T1, L])
proj = pnl.MappingProjection(sender=T2, receiver=L.input_states[1])
p2 = pnl.Process(pathway=[T2, proj, L])
s = pnl.System(processes=[p1, p2])
print('Running Leabra in PsyNeuLink...')
start_time = time.process_time()
outputs = s.run(inputs={T1: input_pattern.copy(), T2: training_pattern.copy()})
end_time = time.process_time()