def __init__(self, params=None, debug=False):
"""
Args:
params: dict with parameters for spatial and temporal poolers
debug: whether to print debug messages
"""
params = utils.fill_default_params(params, default_params)
self.debug = debug
assert (params['maxCoincidenceCount'] > params['requestedGroupsCount'])
if params['spatialPoolerAlgorithm'] == 'gaussian':
assert (params['sigma'] >= 0)
assert (params['maxDistance'] >= 0)
self.sp = SpatialPooler(
algorithm=params['spatialPoolerAlgorithm'],
max_distance=params['maxDistance'],
sigma=params['sigma'],
max_coincidence_count=params['maxCoincidenceCount'],
rare_coincidence_threshold=params['rareCoincidenceThreshold'],
ignore_background_pattern=params['ignoreBackgroundPattern'],
background_color=params['backgroundColor'],
debug=self.debug)
self.tp = TemporalPooler(
algorithm=params['temporalPoolerAlgorithm'],
transition_memory=params['transitionMemory'],
requested_group_count=params['requestedGroupsCount'],
symmetrizeTAM=params['symmetrizeTAM'],
debug=self.debug)
self.tp_first_run = True
self.y = None # spatial inference in fw direction
self.fw_mesage = None # forward output of the node
def __init__(self, params=None, debug=False):
"""
Args:
params: dict with parameters for spatial and temporal poolers
debug: whether to print debug messages
"""
params = utils.fill_default_params(params, default_params)
self.debug = debug
assert(params['maxCoincidenceCount'] > params['requestedGroupsCount'])
if params['spatialPoolerAlgorithm'] == 'gaussian':
assert(params['sigma'] >= 0)
assert(params['maxDistance'] >= 0)
self.sp = SpatialPooler(
algorithm=params['spatialPoolerAlgorithm'],
max_distance=params['maxDistance'],
sigma=params['sigma'],
max_coincidence_count=params['maxCoincidenceCount'],
rare_coincidence_threshold=params['rareCoincidenceThreshold'],
ignore_background_pattern=params['ignoreBackgroundPattern'],
background_color=params['backgroundColor'],
debug=self.debug
)
self.tp = TemporalPooler(
algorithm=params['temporalPoolerAlgorithm'],
transition_memory=params['transitionMemory'],
requested_group_count=params['requestedGroupsCount'],
symmetrizeTAM=params['symmetrizeTAM'],
debug=self.debug
)
self.tp_first_run = True
self.y = None # spatial inference in fw direction
self.fw_mesage = None # forward output of the node
class BaseNode:
def __init__(self, params=None, debug=False):
"""
Args:
params: dict with parameters for spatial and temporal poolers
debug: whether to print debug messages
"""
params = utils.fill_default_params(params, default_params)
self.debug = debug
assert(params['maxCoincidenceCount'] > params['requestedGroupsCount'])
if params['spatialPoolerAlgorithm'] == 'gaussian':
assert(params['sigma'] >= 0)
assert(params['maxDistance'] >= 0)
self.sp = SpatialPooler(
algorithm=params['spatialPoolerAlgorithm'],
max_distance=params['maxDistance'],
sigma=params['sigma'],
max_coincidence_count=params['maxCoincidenceCount'],
rare_coincidence_threshold=params['rareCoincidenceThreshold'],
ignore_background_pattern=params['ignoreBackgroundPattern'],
background_color=params['backgroundColor'],
debug=self.debug
)
self.tp = TemporalPooler(
algorithm=params['temporalPoolerAlgorithm'],
transition_memory=params['transitionMemory'],
requested_group_count=params['requestedGroupsCount'],
symmetrizeTAM=params['symmetrizeTAM'],
debug=self.debug
)
self.tp_first_run = True
self.y = None # spatial inference in fw direction
self.fw_mesage = None # forward output of the node
def enable_debug(self):
self.debug = True
def do_spatial_learning(self, pattern):
self.sp.learn(pattern)
def do_temporal_learning(self, pattern, is_reset=False):
if self.tp_first_run:
self.sp.finalize_learning()
self.tp.coincidences_stats = self.sp.coincidences_stats
self.tp.coincidences_count = len(self.sp.coincidences_stats)
self.tp_first_run = False
self.tp.learn(self.sp.infer(pattern, is_reset), is_reset)
def finalize_learning(self):
self.tp.finalize_learning()
if self.debug:
print(" Codebook size after learning: %d"
% self.tp.coincidences_count)
print(" Temporal group size after learning: %d"
% len(self.tp.temporal_groups))
def infer(self, pattern):
self.y = self.sp.infer(pattern)
self.fw_mesage = self.tp.infer(self.y) # feed-forward message
return self.fw_mesage
def print_status(self):
print("Status of a HTM node")
print("Receptive field: [%d,%d]" % (self.receptive_field[0],
self.receptive_field[1]))
print("# of coincidences", len(self.coincidences))
print(self.coincidences_stats)
