def __init__(self, cellsPerColumn=None):
self.inhibitionRadius = config.getint('init', 'inhibitionRadius')
if cellsPerColumn:
self.cellsPerColumn = cellsPerColumn
else:
self.cellsPerColumn = config.getint('init','cells_per_column')
self._inputCells = [[]] #a 2-d map of cells monitoring input
self._updateSegments = UpdateSegments()
def __init__(self, cellsPerColumn=None):
self.inhibitionRadius = config.getint('init', 'inhibitionRadius')
if cellsPerColumn:
self.cellsPerColumn = cellsPerColumn
else:
self.cellsPerColumn = config.getint('init', 'cells_per_column')
self._inputCells = [[]] #a 2-d map of cells monitoring input
self._updateSegments = UpdateSegments()
def testCreateSegment(self):
h = HTM()
h.initialize_input([[1, 1, 1], [1, 1, 1]])
cell = Cell()
startingSegments = config.getint("init", "segments_per_cell")
self.assertEqual(startingSegments, len(cell.segments))
cell.create_segment(h)
self.assertEqual(startingSegments + 1, len(cell.segments))
# make sure newly created segment has the right number of synapses
self.assertNotEqual(0, len(cell.segments[-1].synapses))
def testInit(self):
htm = self.htm
self._initialize()
self.assertEqual(htm.width, 2)
self.assertEqual(htm.length, 3)
cols = 0
for col in htm.columns:
cols += 1
self.assertEqual(len(col.synapses), config.getint('init', 'synapses_per_segment'))
self.assertEqual(cols, 6)
def testInit(self):
htm = self.htm
self._initialize()
self.assertEqual(htm.width, 2)
self.assertEqual(htm.length, 3)
cols = 0
for col in htm.columns:
cols += 1
self.assertEqual(len(col.synapses),
config.getint('init', 'synapses_per_segment'))
self.assertEqual(cols, 6)
def testCreateSegment(self):
htm = Mock()
learning = Mock()
learning.wasLearning = True
active = Mock()
active.wasLearning = False
active.wasActive = True
htm.cells = [learning, active, active]
cell = Cell()
self.assertNotEqual(cell, learning)
startingSegments = config.getint('init','segments_per_cell')
self.assertEqual(startingSegments, len(cell.segments))
cell.create_segment(htm, nextStep=False)
self.assertEqual(startingSegments+1, len(cell.segments))
cell.create_segment(htm, nextStep=True)
self.assertEqual(startingSegments+2, len(cell.segments))
#make sure newly created segment has the right number of synapses
self.assertNotEqual(0, len(cell.segments[-2].synapses))
self.assertNotEqual(0, len(cell.segments[-1].synapses))
'''
Created on Nov 26, 2010
@author: Numenta, Inc.
translated to python by Jason Carver
Reproduced from "HTM Cortical Learning Algorithms" v0.1.1 at:
http://www.numenta.com/htm-overview/education.php
'''
from carver.htm.config import config
from carver.htm.synapse import CONNECTED_CUTOFF
from carver.htm.segment import Segment
#one column out of n should fire:
desiredLocalActivity = config.getint('constants','desiredLocalActivity')
def pool_spatial(htm):
'''
A couple notable deviations:
*column overlap boost and cutoff are swapped from pseudocode, details inline
see _spatial_overlap
*time and inputData removed from code - used a data producer model, linked to htm
*getBestMatchingSegment now takes an argument for whether it is a nextStep segment or a sequence one
inspired by binarybarry on http://www.numenta.com/phpBB2/viewtopic.php?t=1403
'''
_spatial_overlap(htm)
activeColumns = _spatial_inhibition(htm)
'''
Created on Nov 27, 2010
@author: Jason
'''
from carver.htm.config import config
from carver.htm.segment import Segment
from carver.htm.synapse import MIN_THRESHOLD, SYNAPSES_PER_SEGMENT
import random
SEGMENTS_PER_CELL = config.getint('init', 'segments_per_cell')
class Cell(object):
'''
Cell in an HTM network
'''
def __init__(self, column=None, layer=None):
'''
@param layer the inner layer of the cell, so an HTM with 3 cells per column
would have cells with layers 0, 1 and 2
'''
self.column = column
self.layer = layer
self.active = False
self.__wasActive = False #read-only
self.predicting = False
self.__predicted = False #read-only
self.learning = False
self.__wasLearning = False #read-only
'''
Created on Nov 27, 2010
@author: Jason Carver
'''
from carver.htm.config import config
CONNECTED_CUTOFF = 0.2 #this is the permanence cutoff to be considered connected
PERMANENCE_INCREMENT = 0.04 #TODO: choose a reasonable number
PERMANENCE_DECREMENT = 0.04 #TODO: choose a reasonable number
MIN_THRESHOLD = config.getint('constants','min_synapses_per_segment_threshold')
SYNAPSES_PER_SEGMENT = config.getint('init', 'synapses_per_segment')
class Synapse(object):
'''
a single synapse between dendrite and axon
'''
def __init__(self, input, permanence = (CONNECTED_CUTOFF-.001)):
'''
@param input: the source of data coming into the synapse
synapses on distal dendrites will have a Cell as input
synapses on proximal dendrites will have an InputCell as input
'''
self.permanence = permanence
self.input = input
@property
def connected(self):
return self.permanence >= CONNECTED_CUTOFF
'''
Created on Dec 2, 2010
@author: Jason Carver
'''
from carver.htm.config import config
from carver.htm.synapse import Synapse
import random
#how many synapses need to fire to trigger a segment fire
FRACTION_SEGMENT_ACTIVATION_THRESHOLD = float(
config.getint('constants', 'percent_synapses_per_segment_threshold')) / 100
MAX_NEW_SYNAPSES = config.getint('constants', 'max_new_synapses')
class Segment(object):
'''
A group of synapses that act as a non-linear threshold detector.
After a certain number of synapses fire, the segment
Note: a typical threshold for how many synapses on a dendrite need to fire
for a spike is 15 (not sure how many synapses total) source 0.1.1 HTM doc,
Appendix A, distal dendrites, page 55.
'''
def __init__(self, distal=True, nextStep=False):
'''
@param nextStep: boolean for whether this segment indicates predicted
firing in the very next time step
'''
self.synapses = []
self.distal = distal
'''
Created on Dec 2, 2010
@author: Jason Carver
'''
from carver.htm.config import config
from carver.htm.synapse import Synapse
import random
#how many synapses need to fire to trigger a segment fire
FRACTION_SEGMENT_ACTIVATION_THRESHOLD = float(config.getint('constants','percent_synapses_per_segment_threshold'))/100
MAX_NEW_SYNAPSES = config.getint('constants','max_new_synapses')
class Segment(object):
'''
A group of synapses that act as a non-linear threshold detector.
After a certain number of synapses fire, the segment
Note: a typical threshold for how many synapses on a dendrite need to fire
for a spike is 15 (not sure how many synapses total) source 0.1.1 HTM doc,
Appendix A, distal dendrites, page 55.
'''
def __init__(self, distal=True, nextStep=False):
'''
@param nextStep: boolean for whether this segment indicates predicted
firing in the very next time step
'''
self.synapses = []
self.distal = distal
def __init__(self, cellsPerColumn=None):
self.inhibitionRadius = config.getint('init', 'inhibitionRadius')
if cellsPerColumn:
self.cellsPerColumn = cellsPerColumn
else:
self.cellsPerColumn = config.getint('init','cells_per_column')
'''
Created on Nov 27, 2010
@author: Jason
'''
from carver.htm.config import config
from carver.htm.segment import Segment
from carver.htm.synapse import MIN_THRESHOLD, SYNAPSES_PER_SEGMENT
import random
SEGMENTS_PER_CELL = config.getint('init','segments_per_cell')
class Cell(object):
'''
Cell in an HTM network
'''
def __init__(self):
'''
'''
self.active = False
self.__wasActive = False #read-only
self.predicting = False
self.__predicted = False #read-only
self.learning = False
self.__wasLearning = False #read-only
self.segments = [Segment() for i in xrange(SEGMENTS_PER_CELL)]
'''
Created on Nov 27, 2010
@author: Jason Carver
'''
from carver.htm.config import config
CONNECTED_CUTOFF = 0.2 #this is the permanence cutoff to be considered connected
PERMANENCE_INCREMENT = 0.04 #TODO: choose a reasonable number
PERMANENCE_DECREMENT = 0.04 #TODO: choose a reasonable number
MIN_THRESHOLD = config.getint('constants',
'min_synapses_per_segment_threshold')
SYNAPSES_PER_SEGMENT = config.getint('init', 'synapses_per_segment')
class Synapse(object):
'''
a single synapse between dendrite and axon
'''
def __init__(self, input, permanence=(CONNECTED_CUTOFF - .001)):
'''
@param input: the source of data coming into the synapse
synapses on distal dendrites will have a Cell as input
synapses on proximal dendrites will have an InputCell as input
'''
self.permanence = permanence
self.input = input
@property
def connected(self):
return self.permanence >= CONNECTED_CUTOFF
"""
Created on Nov 26, 2010
@author: Numenta, Inc.
translated to python by Jason Carver
Reproduced from "HTM Cortical Learning Algorithms" v0.1.1 at:
http://www.numenta.com/htm-overview/education.php
"""
from carver.htm.config import config
from carver.htm.synapse import CONNECTED_CUTOFF
from carver.htm.column import Column
# one column out of n should fire:
desiredLocalActivity = config.getint("constants", "desiredLocalActivity")
def pool_spatial(htm):
"""
A couple notable deviations:
*column overlap boost and cutoff are swapped from pseudocode, details inline
see _spatial_overlap
*time and inputData removed from code - used a data producer model, linked to htm
"""
_spatial_overlap(htm)
activeColumns = _spatial_inhibition(htm)
inhibitionRadius = _spatial_learning(htm, activeColumns)
'''
Created on Nov 26, 2010
@author: Numenta, Inc.
translated to python by Jason Carver
Reproduced from "HTM Cortical Learning Algorithms" v0.1.1 at:
http://www.numenta.com/htm-overview/education.php
'''
from carver.htm.config import config
from carver.htm.synapse import CONNECTED_CUTOFF
from carver.htm.segment import Segment
#one column out of n should fire:
desiredLocalActivity = config.getint('constants', 'desiredLocalActivity')
def pool_spatial(htm):
'''
A couple notable deviations:
*column overlap boost and cutoff are swapped from pseudocode, details inline
see _spatial_overlap
*time and inputData removed from code - used a data producer model, linked to htm
*getBestMatchingSegment now takes an argument for whether it is a nextStep segment or a sequence one
inspired by binarybarry on http://www.numenta.com/phpBB2/viewtopic.php?t=1403
'''
_spatial_overlap(htm)
activeColumns = _spatial_inhibition(htm)
def __init__(self):
self.inhibitionRadius = config.getint('init', 'inhibitionRadius')
self.cellsPerColumn = config.getint('init','cells_per_column')
