def learn_mag_circuit(learning_trials):
# build the initial network. This network consists of a unit marking PO units settling, three time-in-comparison-cycle units (two connected to the unit makring PO settling, the other a gating unit connected to the PO units and active when both POs are active), and three MLS semantics (just semantic units randomly connected to the time-in-comparison-cycle units that will come to code invariant "more", "less", and "same" after learning).
settling_unit = dataTypes.basicTimingNode('settling', [], [], [])
time_unit1, time_unit2 = dataTypes.basicTimingNode('A', [], [], []), dataTypes.basicTimingNode('B', [], [], [])
time_unit3 = dataTypes.basicGateNode('gate', [], [])
Local_inhib = dataTypes.localInhibitor()
semantic1, semantic2, semantic3 = dataTypes.MLS_sem('semantic1', [], []), dataTypes.MLS_sem('semantic2', [], []), dataTypes.MLS_sem('semantic3', [], [])
# set a POs flag indicating whether both POs are active after settling (to be used in learning magnitude vs. sameness; 1.0==both POs active after settling, 0.0==only one PO is active after settling).
POs = 0.0
# create links between settling unit and timing units. Weights are random values between .5 and 1.
link1 = dataTypes.basicLink(settling_unit, time_unit1, 0.8)
settling_unit.lower_connections.append(link1)
time_unit1.higher_connections.append(link1)
link2 = dataTypes.basicLink(settling_unit, time_unit2, 0.5)
settling_unit.lower_connections.append(link2)
time_unit2.higher_connections.append(link2)
time_unit3.input_nodes.append(POs)
# create links between timing units and semantics.
link3, link4, link5 = dataTypes.basicLink(time_unit1, semantic1, .5), dataTypes.basicLink(time_unit1, semantic2, .2), dataTypes.basicLink(time_unit1, semantic3, .2)
time_unit1.lower_connections.append(link3)
time_unit1.lower_connections.append(link4)
time_unit1.lower_connections.append(link5)
semantic1.higher_connections.append(link3)
semantic2.higher_connections.append(link4)
semantic3.higher_connections.append(link5)
link6, link7, link8 = dataTypes.basicLink(time_unit2, semantic1, .2), dataTypes.basicLink(time_unit2, semantic2, .5), dataTypes.basicLink(time_unit2, semantic3, .2)
time_unit2.lower_connections.append(link6)
time_unit2.lower_connections.append(link7)
time_unit2.lower_connections.append(link8)
semantic1.higher_connections.append(link6)
semantic2.higher_connections.append(link7)
semantic3.higher_connections.append(link8)
link9, link10, link11 = dataTypes.basicLink(time_unit3, semantic1, .2), dataTypes.basicLink(time_unit3, semantic2, .2), dataTypes.basicLink(time_unit3, semantic3, .5)
time_unit3.output_nodes.append(link9)
time_unit3.output_nodes.append(link10)
time_unit3.output_nodes.append(link11)
semantic1.higher_connections.append(link9)
semantic2.higher_connections.append(link10)
semantic3.higher_connections.append(link11)
# finally, set up lateral connections. These are not established by links, but rather any nodes in the same layer are placed in one-another's .lateral_connections field. Timing units and MLS semantics are all laterally inhibitory.
time_unit1.lateral_connections.append(time_unit2)
time_unit1.lateral_connections.append(time_unit3)
time_unit2.lateral_connections.append(time_unit1)
time_unit2.lateral_connections.append(time_unit3)
semantic1.lateral_connections.append(semantic2)
semantic1.lateral_connections.append(semantic3)
semantic2.lateral_connections.append(semantic1)
semantic2.lateral_connections.append(semantic3)
semantic3.lateral_connections.append(semantic1)
semantic3.lateral_connections.append(semantic2)
# put the units in arrays.
time_units = [time_unit1, time_unit2]
gate_units = [time_unit3]
MLS_semantics = [semantic1, semantic2, semantic3]
# set gamma and delta for leaky integrator activation function.
gamma, delta = 0.3, 0.1
# start the training process.
for train_instance in range(learning_trials):
# is this a trial when both POs are active or not?
rand_num = random.random()
if rand_num > .5:
same_flag = False
gate_units[0].input_nodes[0] = 0.0
else:
same_flag = True
gate_units[0].input_nodes[0] = 1.0
# activate unit indicating settling.
settling_unit.act = 1.0
# for n iterations, until the LI fires, run the network.
for iteration in range(110):
# three units (marking time-in-comparison-cycle) compete to become active.
# update the input of time-in-cycle and semantic units.
for unit in time_units:
unit.update_input()
for unit in gate_units:
unit.update_input()
for unit in MLS_semantics:
unit.update_input()
# get max_sem_input.
max_sem_input = 0.0
for semantic in MLS_semantics:
if semantic.input > max_sem_input:
max_sem_input = semantic.input
# subtractive normalisation.
for semantic in MLS_semantics:
if semantic.input < max_sem_input:
semantic.input -= max_sem_input
# then updat max_sem_input for divisive normalisation.
for semantic in MLS_semantics:
semantic.set_max_sem_input(max_sem_input)
# update the activation and clear the input of time-in-cycle and semantic units.
for unit in time_units:
unit.update_act(gamma, delta)
print 'input = ', str(unit.input), '\tact = ', str(unit.act), '\n'
unit.clear_input()
for unit in gate_units:
unit.update_act()
unit.clear_input()
for unit in MLS_semantics:
unit.update_act()
print unit.name, ' ', str(unit.act)
unit.clear_input()
# update weights from time-in-comparison-cycle units to MLS semantics if iteration >= 3.
if iteration >= 3:
for unit in time_units:
unit.adjust_links()
for unit in gate_units:
unit.adjust_links()
# after n iterations, if same_flag is False, then fire the local inhibitor, which inhibits time-in-comparison-cycle units and MLS semantics.
if not same_flag:
active_unit = None
max_act = 0.0
for unit in time_units:
if unit.act > max_act:
active_unit = unit
active_unit.time_since_fired = 1
for unit in time_units:
unit.clear_inputandact()
for semantic in MLS_semantics:
semantic.clear_all()
# for n further iterations, run the network.
for iteration in range(110):
# three units (marking time-in-comparison-cycle) compete to become active.
# update the input of time-in-cycle and semantic units.
for unit in time_units:
unit.update_input()
for unit in gate_units:
unit.update_input()
for unit in MLS_semantics:
unit.update_input()
# get max sem input.
max_sem_input = 0.0
for semantic in MLS_semantics:
if semantic.input > max_sem_input:
max_sem_input = semantic.input
# subtractive normalisation.
for semantic in MLS_semantics:
if semantic.input < max_sem_input:
semantic.input -= max_sem_input
# then update max_sem_input for divisive normalisation.
for semantic in MLS_semantics:
semantic.set_max_sem_input(max_sem_input)
# update the activation and clear the input of time-in-cycle and semantic units.
for unit in time_units:
unit.update_act(gamma, delta)
print 'input = ', str(unit.input), '\tact = ', str(unit.act), '\n'
unit.clear_input()
for unit in gate_units:
unit.update_act()
unit.clear_input()
for unit in MLS_semantics:
unit.update_act()
print unit.name, ' ', str(unit.act)
unit.clear_input()
# update weights from time-in-comparison-cycle units to MLS semantics if iteration >= 3.
if iteration >= 3:
for unit in time_units:
unit.adjust_links()
for unit in gate_units:
unit.adjust_links()
# after n iterations, end the learning cycle and clear all units.
for unit in time_units:
unit.clear_all()
for unit in gate_units:
unit.clear_all()
for unit in MLS_semantics:
unit.clear_all()
# END. The semantic unit connected to the time-in-comparison-cycle unit that fires first with activation to the settling unit is the "more" semantic, the semantic unit connected to the time-in-comparison-cycle unit that fires second with activation to the settling unit is the "less" semantic, and the semantic unit connected to the gating time-in-comparison-cycle unit is the "same" semantic.
# print settling unit's connections.
for link in settling_unit.lower_connections:
print 'Settling unit to ', link.mylowernode.name, ' weight = ', str(link.weight), '\n'
print '\n'
# print timing units' connections.
for unit in time_units:
for link in unit.lower_connections:
print unit.name, ' to ', link.mylowernode.name, ' weight = ', str(link.weight), '\n'
print '\n'
# print gating unit's connections.
for link in gate_units[0].output_nodes:
print gate_units[0].name, ' to ', link.mylowernode.name, ' weight = ', str(link.weight), '\n'
print '\n'
def learn_mag_circuit_neural(learning_trials):
# make the E nodes.
Enode1 = dataTypes.reg_E_Node('E1', [], [], [])
Enode2 = dataTypes.reg_E_Node('E2', [], [], [])
Enode3 = dataTypes.gaussian_E_Node('EG', [], [], [])
Enodes = [Enode1, Enode2, Enode3]
# make the A nodes.
Anodes = []
for i in range(3):
Anode = dataTypes.reg_A_Node(str(i), [], [], [])
Anodes.append(Anode)
# make the semantics.
semantics = []
for i in range(10):
new_sem = dataTypes.MLS_sem(str(i), [], [])
semantics.append(new_sem)
# make stand-in POs.
PO1 = dataTypes.basicTimingNode('PO1', [], [], [])
PO2 = dataTypes.basicTimingNode('PO2', [], [], [])
POs = [PO1, PO2]
# connect stand-in POs to E nodes.
Links = []
for PO in POs:
for Enode in Enodes:
new_link = dataTypes.basicLink(PO, Enode, 1.0)
Enode.higher_connections.append(new_link)
# connect E nodes to A nodes.
for Enode in Enodes:
Enode.lateral_connections = Enodes
for Anode in Anodes:
new_link = dataTypes.basicLink(Enode, Anode, random.uniform(.1, 1.0))
Enode.lower_connections.append(new_link)
Anode.higher_connections.append(new_link)
Links.append(new_link)
# connect the A nodes to semantics.
for Anode in Anodes:
Anode.lateral_connections = Anodes
for semantic in semantics:
new_link = dataTypes.basicLink(Anode, semantic, random.random())
Anode.lower_connections.append(new_link)
semantic.higher_connections.append(new_link)
Links.append(new_link)
learning_trials = 10
for i in range(learning_trials):
# set activation of POs.
same_trial = random.random()
if same_trial > .5:
PO1.act = 1.0
same_trial = False
else:
PO1.act, PO2.act = 0.5, 0.5
same_trial = True
# until the local inhibitor fires (i.e., while the first PO is active), update inputs, activations, and weights.
for i in range(110):
# update inputs to all units.
for Enode in Enodes:
Enode.update_input()
for Anode in Anodes:
Anode.update_input()
for semantic in semantics:
semantic.update_input()
#pdb.set_trace()
# get max_sem_input.
max_sem_input = 0.0
for semantic in semantics:
if semantic.input > max_sem_input:
max_sem_input = semantic.input
# subtractive normalisation.
for semantic in semantics:
if semantic.input < max_sem_input:
semantic.input -= max_sem_input
# then update max_sem_input for divisive normalisation.
for semantic in semantics:
semantic.set_max_sem_input(max_sem_input)
# update activation and clear the input of all units.
for Enode in Enodes:
Enode.update_act()
Enode.clear_input()
for Anode in Anodes:
Anode.update_act()
Anode.clear_input()
for semantic in semantics:
semantic.update_act()
semantic.clear_input()
# learning.
if i >= 20:
for Enode in Enodes:
Enode.adjust_links()
for Anode in Anodes:
Anode.adjust_links()
# after n iterations, if same_trial is False, then fire the local inhibitor, which inhibits all units, and start time since last fired of the most active A unit.
if not same_trial:
active_unit = None
max_act = 0.0
for Anode in Anodes:
if Anode.act > max_act:
active_unit = Anode
active_unit.time_since_fired = 1
for Enode in Enodes:
Enode.clear_input_and_act()
for Anode in Anodes:
Anode.clear_input_and_act
for semantic in semantics:
semantic.clear_all()
# until the global inhibitor fires (i.e., while second PO is active), update inputs, activations, and weights.
for i in range(110):
# update inputs to all units.
for Enode in Enodes:
Enode.update_input()
for Anode in Anodes:
Anode.update_input()
for semantic in semantics:
semantic.update_input()
# get max_sem_input.
max_sem_input = 0.0
for semantic in semantics:
if semantic.input > max_sem_input:
max_sem_input = semantic.input
# subtractive normalisation.
for semantic in semantics:
if semantic.input < max_sem_input:
semantic.input -= max_sem_input
# then update max_sem_input for divisive normalisation.
for semantic in semantics:
semantic.set_max_sem_input(max_sem_input)
# update activation of all units.
for Enode in Enodes:
Enode.update_act()
for Anode in Anodes:
Anode.update_act()
for semantic in semantics:
semantic.update_act()
# learning.
if i >= 20:
for Enode in Enodes:
Enode.adjust_links()
for Anode in Anodes:
Anode.adjust_links()
def learn_energy_circuit(learning_trials):
# architecture is 4 E nodes, 6 A nodes, and 10 semantics.
# make the guassian E nodes.
Enode1 = dataTypes.gaussian_E_Node('E1', [], [], [], 1, .3)
Enode2 = dataTypes.gaussian_E_Node('E2', [], [], [], 1, .1)
Enode3 = dataTypes.gaussian_E_Node('E1', [], [], [], 2, .3)
Enode4 = dataTypes.gaussian_E_Node('E2', [], [], [], 2, .1)
Enodes = [Enode1, Enode2, Enode3, Enode4]
# make the semantics.
semantics = []
for i in range(10):
new_sem = dataTypes.MLS_sem(str(i), [], [])
semantics.append(new_sem)
# make proxy POs.
PO1 = dataTypes.basicTimingNode('PO1', [], [], [])
PO2 = dataTypes.basicTimingNode('PO2', [], [], [])
POs = [PO1, PO2]
# connect proxy POs to E nodes.
for PO in POs:
for Enode in Enodes:
new_link = dataTypes.basicLink(PO, Enode, 1.0)
Enode.higher_connections.append(new_link)
# connect the E nodes to semantics and update lateral_connections for E nodes (for lateral inhibition).
for Enode in Enodes:
Enode.lateral_connections = Enodes
for semantic in semantics:
new_link = dataTypes.basicLink(Enode, semantic, random.random())
Enode.lower_connections.append(new_link)
semantic.higher_connections.append(new_link)
# leaning.
for i in range(learning_trials):
# set activation of PO proxys.
same_trial = random.random()
if same_trial > .5:
PO1.act = 1.0
same_trial = False
else:
PO1.act, PO2.act = 1.0, 1.0
same_trial = True
#pdb.set_trace()
# until the local inhibitor fires (i.e., while the first PO is active), update inputs, activations, and weights.
for i in range(110):
# update inputs to all units.
for Enode in Enodes:
Enode.update_input()
for semantic in semantics:
semantic.update_input()
# get max_sem_input.
max_sem_input = 0.0
for semantic in semantics:
if semantic.input > max_sem_input:
max_sem_input = semantic.input
# subtractive normalisation.
for semantic in semantics:
if semantic.input < max_sem_input:
semantic.input -= max_sem_input
# then update max_sem_input for divisive normalisation.
for semantic in semantics:
semantic.set_max_sem_input(max_sem_input)
#pdb.set_trace()
# update activation and clear the input of all units.
for Enode in Enodes:
Enode.update_act()
Enode.clear_input()
for semantic in semantics:
semantic.update_act()
semantic.clear_input()
#pdb.set_trace()
# learning.
if i >= 20:
for Enode in Enodes:
Enode.adjust_links()
# after n iterations, if same_trial is False, then fire the local inhibitor, which inhibits all units, and start time since last fired of the most active A unit.
if not same_trial:
active_unit = None
max_act = 0.0
for Enode in Enodes:
if Enode.act > max_act:
active_unit = Enode
active_unit.time_since_fired = 1
for Enode in Enodes:
Enode.clear_input_and_act()
for semantic in semantics:
semantic.clear_all()
# until the global inhibitor fires (i.e., while second PO is active), update inputs, activations, and weights.
for i in range(110):
# update inputs to all units.
for Enode in Enodes:
Enode.update_input()
for semantic in semantics:
semantic.update_input()
# get max_sem_input.
max_sem_input = 0.0
for semantic in semantics:
if semantic.input > max_sem_input:
max_sem_input = semantic.input
# subtractive normalisation.
for semantic in semantics:
if semantic.input < max_sem_input:
semantic.input -= max_sem_input
# then update max_sem_input for divisive normalisation.
for semantic in semantics:
semantic.set_max_sem_input(max_sem_input)
#pdb.set_trace()
# update activation of all units.
for Enode in Enodes:
Enode.update_act()
Enode.clear_input()
for semantic in semantics:
semantic.update_act()
semantic.clear_input()
#pdb.set_trace()
# learning.
if i >= 20:
for Enode in Enodes:
Enode.adjust_links()
# now fire global inhibitor.
for Enode in Enodes:
Enode.clear_all()
for semantic in semantics:
semantic.clear_all()
# returns.
return [POs, Enodes, semantics]