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,
level_no,
params=None,
previous_level_size=None,
debug=False):
self.level_no = level_no
self.debug = debug
if previous_level_size is None:
raise ValueError("previous_level_size must not be None.")
self.mode = 'learn' # learn or infer modes
self.nodes = [] # nodes in the current level
if params is not None:
params = utils.fill_default_params(params, default_params)
self.node_cloning = params['nodeCloning']
self.size = params['size'] # num. of nodes [width, height]
self.overlap = params['overlap']
self.sparsify = params['sparsify'] # TODO
# Create nodes
if self.node_cloning:
if self.debug:
print("Level %d: Creating one master node." %
self.level_no)
# create one master node
self.nodes.append(BaseNode(params, debug=self.debug))
else:
if self.debug:
print("Level %d: Creating %d nodes." %
(self.level_no, np.prod(params['size'])))
# create all nodes
for i in range(np.prod(params['size'])):
self.nodes.append(BaseNode(params, debug=self.debug))
else:
raise ValueError("params must not be of type None.")
# node_id: [list of node_ids from level-1]
self.links = {}
if self._is_first_level():
# in 1-to-1 correnspondence to image patches
for i in range(np.prod(self.size)):
self.links[i] = [i]
else:
# TODO for rectangular regions
# TODO factorize so it can be tested
# every node in this level gets linked with equal number of nodes
# in the previous level arraged in squares
#
# Example for [4, 4] -> [2,2]
# 12 13 14 15 0: [0, 1, 4, 5]
# 8 9 10 11 1: [2, 3, 6, 7]
# 4 5 6 7 -> 2: [8, 9, 12, 13]
# 0 1 2 3 3: [10, 11, 14, 15]
#
self.links = self._get_links(self.size, previous_level_size)
self.above_links = None # backwards mapping to self.link
def __init__(self, level_no, params=None, previous_level_size=None,
debug=False):
self.level_no = level_no
self.debug = debug
if previous_level_size is None:
raise ValueError("previous_level_size must not be None.")
self.mode = 'learn' # learn or infer modes
self.nodes = [] # nodes in the current level
if params is not None:
params = utils.fill_default_params(params, default_params)
self.node_cloning = params['nodeCloning']
self.size = params['size'] # num. of nodes [width, height]
self.overlap = params['overlap']
self.sparsify = params['sparsify'] # TODO
# Create nodes
if self.node_cloning:
if self.debug:
print("Level %d: Creating one master node." % self.level_no)
# create one master node
self.nodes.append(BaseNode(params, debug=self.debug))
else:
if self.debug:
print("Level %d: Creating %d nodes." %
(self.level_no, np.prod(params['size'])))
# create all nodes
for i in range(np.prod(params['size'])):
self.nodes.append(BaseNode(params, debug=self.debug))
else:
raise ValueError("params must not be of type None.")
# node_id: [list of node_ids from level-1]
self.links = {}
if self._is_first_level():
# in 1-to-1 correnspondence to image patches
for i in range(np.prod(self.size)):
self.links[i] = [i]
else:
# TODO for rectangular regions
# TODO factorize so it can be tested
# every node in this level gets linked with equal number of nodes
# in the previous level arraged in squares
#
# Example for [4, 4] -> [2,2]
# 12 13 14 15 0: [0, 1, 4, 5]
# 8 9 10 11 1: [2, 3, 6, 7]
# 4 5 6 7 -> 2: [8, 9, 12, 13]
# 0 1 2 3 3: [10, 11, 14, 15]
#
self.links = self._get_links(self.size, previous_level_size)
self.above_links = None # backwards mapping to self.link
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
