def test_kwta_presentation():
"""Tests one kwta presentation to half of the units, followed by
another presentation to the second half."""
s = InputSample(8, 8, [[1] * 8] * 4 + [[0] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 2
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[4:8], rates[0:4])
s = InputSample(8, 8, [[0] * 8] * 4 + [[1] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 4
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[0:4], rates[4:8])
def test_kwta_presentation():
"""Tests one kwta presentation to half of the units, followed by
another presentation to the second half."""
s = InputSample(8, 8, [[1] * 8] * 4 + [[0] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 2
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[4:8], rates[0:4])
s = InputSample(8, 8, [[0] * 8] * 4 + [[1] * 8] * 4)
kwta_presentation(Tns.p2, Tns.p1, s, 2)
assert get_current_time() == 4
rates = get_rate_encoder(Tns.p1).get_rates()
assert_array_less(rates[0:4], rates[4:8])
def kwta_epoch(trained_population,
input_population,
projection,
input_samples,
num_winners,
neighbourhood_fn,
presentation_duration,
learning_rule,
learning_rate,
max_weight_value,
trained_pop_max_rate=None,
input_pop_max_rate=None):
if trained_pop_max_rate == None:
try:
trained_pop_max_rate = trained_population.max_unit_rate
except AttributeError:
raise SimulationError("Could not find the trained population's max "
"expected spiking rate per unit. It should be "
"set as population.max_unit_rate.")
if input_pop_max_rate == None:
try:
input_pop_max_rate = input_population.max_unit_rate
except AttributeError:
raise SimulationError("Could not find the input population's max "
"expected spiking rate per unit. It should be "
"set as population.max_unit_rate.")
rate_enc = get_rate_encoder(trained_population)
if neighbourhood_fn == None:
neighbourhood_fn = lambda _, u : [(u, 1)]
max_deltaw = 0
for s in input_samples:
weights = get_weights(projection, max_weight=max_weight_value)
kwta_presentation(trained_population, input_population, s, presentation_duration)
argwinners = select_kwta_winners(trained_population, num_winners, presentation_duration)
for argwin in argwinners:
main_unit = rate_enc[argwin[0]][argwin[1]][1]
# Adapt the weights for winner w and any activated neighbour
for unit, factor in neighbourhood_fn(trained_population, main_unit):
unit_index = trained_population.id_to_index(unit)
# weight vector to the unit
wv = weights.get_normalized_weights_vector(unit_index)
# input to the unit (normalized, TODO: sigmoidal contrast-enhancement?)
pre_syn_out = presynaptic_outputs(unit, projection, t=presentation_duration)
pre_syn_out /= input_pop_max_rate
# output of the unit (normalized, TODO: sigmoidal contrast-enhancement?)
post_syn_act = rate_enc.get_rate_for_unit_index(unit_index,
t=presentation_duration)
post_syn_act /= trained_pop_max_rate
# calculate and apply the new weight vector
new_wv = learning_rule(pre_syn_out,
post_syn_act,
wv,
learning_rate * factor)
weights.set_normalized_weights_vector(unit_index, new_wv)
# calculate the max synaptic weight delta
max_deltaw = max(max_deltaw, max(numpy.abs(new_wv - wv)))
set_weights(projection, weights)
return max_deltaw
def schedule_output_rate_calculation(population, start_t=None, duration=None):
"""Schedules the recurrent calculation of the output rate of the given
population. A new RectilinearOutputRateEncoderis created with default
parameters if none is registered for this population. If no start_t is
given, the current simulation time is used as start time. If no duration is
given, the output rate encoder is active during the whole simulation."""
rore = get_rate_encoder(population)
if start_t == None:
start_t = get_current_time()
end_t = None
if duration != None:
end_t = duration + start_t
_schedule_output_rate_encoder(rore, start_t, end_t)
def schedule_output_rate_calculation(population, start_t=None, duration=None):
"""Schedules the recurrent calculation of the output rate of the given
population. A new RectilinearOutputRateEncoderis created with default
parameters if none is registered for this population. If no start_t is
given, the current simulation time is used as start time. If no duration is
given, the output rate encoder is active during the whole simulation."""
rore = get_rate_encoder(population)
if start_t == None:
start_t = get_current_time()
end_t = None
if duration != None:
end_t = duration + start_t
_schedule_output_rate_encoder(rore, start_t, end_t)
def select_kwta_winners(population, k, presentation_duration):
"""Returns the list of coordinates of the k most active units in
the population for the the presentation duration to the current
simulator time. Ties are broken using uniform random selection."""
argwinners = []
if k > 0:
rate_enc = get_rate_encoder(population)
rates = list(itertools.izip(splice(rate_enc.get_rates(t=presentation_duration)),
infinite_xrange()))
# we need to shuffle to randomize ties resolution
numpy.random.shuffle(rates)
winners = rates[0:k]
heapq.heapify(winners)
for r in rates[k:]:
if r[0] > winners[0][0]:
heapq.heapreplace(winners, r)
argwinners = [(w[1] / rate_enc.shape[0], w[1] % rate_enc.shape[0])
for w in winners]
return argwinners
def select_kwta_winners(population, k, presentation_duration):
"""Returns the list of coordinates of the k most active units in
the population for the the presentation duration to the current
simulator time. Ties are broken using uniform random selection."""
argwinners = []
if k > 0:
rate_enc = get_rate_encoder(population)
rates = list(
itertools.izip(splice(rate_enc.get_rates(t=presentation_duration)),
infinite_xrange()))
# we need to shuffle to randomize ties resolution
numpy.random.shuffle(rates)
winners = rates[0:k]
heapq.heapify(winners)
for r in rates[k:]:
if r[0] > winners[0][0]:
heapq.heapreplace(winners, r)
argwinners = [(w[1] / rate_enc.shape[0], w[1] % rate_enc.shape[0])
for w in winners]
return argwinners
def kwta_epoch(trained_population,
input_population,
projection,
input_samples,
num_winners,
neighbourhood_fn,
presentation_duration,
learning_rule,
learning_rate,
max_weight_value,
trained_pop_max_rate=None,
input_pop_max_rate=None):
if trained_pop_max_rate == None:
try:
trained_pop_max_rate = trained_population.max_unit_rate
except AttributeError:
raise SimulationError(
"Could not find the trained population's max "
"expected spiking rate per unit. It should be "
"set as population.max_unit_rate.")
if input_pop_max_rate == None:
try:
input_pop_max_rate = input_population.max_unit_rate
except AttributeError:
raise SimulationError(
"Could not find the input population's max "
"expected spiking rate per unit. It should be "
"set as population.max_unit_rate.")
rate_enc = get_rate_encoder(trained_population)
if neighbourhood_fn == None:
neighbourhood_fn = lambda _, u: [(u, 1)]
max_deltaw = 0
for s in input_samples:
weights = get_weights(projection, max_weight=max_weight_value)
kwta_presentation(trained_population, input_population, s,
presentation_duration)
argwinners = select_kwta_winners(trained_population, num_winners,
presentation_duration)
for argwin in argwinners:
main_unit = rate_enc[argwin[0]][argwin[1]][1]
# Adapt the weights for winner w and any activated neighbour
for unit, factor in neighbourhood_fn(trained_population,
main_unit):
unit_index = trained_population.id_to_index(unit)
# weight vector to the unit
wv = weights.get_normalized_weights_vector(unit_index)
# input to the unit (normalized, TODO: sigmoidal contrast-enhancement?)
pre_syn_out = presynaptic_outputs(unit,
projection,
t=presentation_duration)
pre_syn_out /= input_pop_max_rate
# output of the unit (normalized, TODO: sigmoidal contrast-enhancement?)
post_syn_act = rate_enc.get_rate_for_unit_index(
unit_index, t=presentation_duration)
post_syn_act /= trained_pop_max_rate
# calculate and apply the new weight vector
new_wv = learning_rule(pre_syn_out, post_syn_act, wv,
learning_rate * factor)
weights.set_normalized_weights_vector(unit_index, new_wv)
# calculate the max synaptic weight delta
max_deltaw = max(max_deltaw, max(numpy.abs(new_wv - wv)))
set_weights(projection, weights)
return max_deltaw
