def testNupicRandomPickling(self):
"""Test pickling / unpickling of NuPIC randomness."""
# Simple test: make sure that dumping / loading works...
r = Random(42)
pickledR = pickle.dumps(r)
test1 = [r.getUInt32() for _ in range(10)]
r = pickle.loads(pickledR)
test2 = [r.getUInt32() for _ in range(10)]
self.assertEqual(test1, test2,
"Simple NuPIC random pickle/unpickle failed.")
# A little tricker: dump / load _after_ some numbers have been generated
# (in the first test). Things should still work...
# ...the idea of this test is to make sure that the pickle code isn't just
# saving the initial seed...
pickledR = pickle.dumps(r)
test3 = [r.getUInt32() for _ in range(10)]
r = pickle.loads(pickledR)
test4 = [r.getUInt32() for _ in range(10)]
self.assertEqual(
test3, test4,
"NuPIC random pickle/unpickle didn't work for saving later state.")
self.assertNotEqual(test1, test3,
"NuPIC random gave the same result twice?!?")
def testNupicRandomPickling(self):
"""Test pickling / unpickling of NuPIC randomness."""
# Simple test: make sure that dumping / loading works...
r = Random(42)
pickledR = pickle.dumps(r)
test1 = [r.getUInt32() for _ in xrange(10)]
r = pickle.loads(pickledR)
test2 = [r.getUInt32() for _ in xrange(10)]
self.assertEqual(test1, test2,
"Simple NuPIC random pickle/unpickle failed.")
# A little tricker: dump / load _after_ some numbers have been generated
# (in the first test). Things should still work...
# ...the idea of this test is to make sure that the pickle code isn't just
# saving the initial seed...
pickledR = pickle.dumps(r)
test3 = [r.getUInt32() for _ in xrange(10)]
r = pickle.loads(pickledR)
test4 = [r.getUInt32() for _ in xrange(10)]
self.assertEqual(
test3, test4,
"NuPIC random pickle/unpickle didn't work for saving later state.")
self.assertNotEqual(test1, test3,
"NuPIC random gave the same result twice?!?")
def testEquals(self): r1 = Random(42) v1 = r1.getReal64() i1 = r1.getUInt32() r2 = Random(42) v2 = r2.getReal64() i2 = r2.getUInt32() self.assertEqual(v1, v2) self.assertEqual(r1, r2) self.assertEqual(i1, i2)
def testCapnpSerialization(self):
"""Test capnp serialization of NuPIC randomness."""
# Simple test: make sure that dumping / loading works...
r = Random(99)
builderProto = RandomProto.new_message()
r.write(builderProto)
readerProto = RandomProto.from_bytes(builderProto.to_bytes())
test1 = [r.getUInt32() for _ in range(10)]
r = Random(1)
r.read(readerProto)
self.assertEqual(r.getSeed(), 99)
test2 = [r.getUInt32() for _ in range(10)]
self.assertEqual(
test1, test2,
"Simple NuPIC random capnp serialization check failed.")
# A little tricker: dump / load _after_ some numbers have been generated
# (in the first test). Things should still work...
# ...the idea of this test is to make sure that the pickle code isn't just
# saving the initial seed...
builderProto = RandomProto.new_message()
r.write(builderProto)
readerProto = RandomProto.from_bytes(builderProto.to_bytes())
test3 = [r.getUInt32() for _ in range(10)]
r = Random()
r.read(readerProto)
self.assertEqual(r.getSeed(), 99)
test4 = [r.getUInt32() for _ in range(10)]
self.assertEqual(
test3, test4,
"NuPIC random capnp serialization check didn't work for saving later "
"state.")
self.assertNotEqual(
test1, test3,
"NuPIC random serialization test gave the same result twice?!?")
def _bitForCoordinate(cls, coordinate, n):
"""
Maps the coordinate to a bit in the SDR.
@param coordinate (numpy.array) Coordinate
@param n (int) The number of available bits in the SDR
@return (int) The index to a bit in the SDR
"""
seed = cls._hashCoordinate(coordinate)
rng = Random(seed)
return rng.getUInt32(n)
def _bitForCoordinate(cls, coordinate, n):
"""
Maps the coordinate to a bit in the SDR.
@param coordinate (numpy.array) Coordinate
@param n (int) The number of available bits in the SDR
@return (int) The index to a bit in the SDR
"""
seed = cls._hashCoordinate(coordinate)
rng = Random(seed)
return rng.getUInt32(n)
def testCapnpSerialization(self):
"""Test capnp serialization of NuPIC randomness."""
# Simple test: make sure that dumping / loading works...
r = Random(99)
builderProto = RandomProto.new_message()
r.write(builderProto)
readerProto = RandomProto.from_bytes(builderProto.to_bytes())
test1 = [r.getUInt32() for _ in xrange(10)]
r = Random(1);
r.read(readerProto)
self.assertEqual(r.getSeed(), 99)
test2 = [r.getUInt32() for _ in xrange(10)]
self.assertEqual(test1, test2,
"Simple NuPIC random capnp serialization check failed.")
# A little tricker: dump / load _after_ some numbers have been generated
# (in the first test). Things should still work...
# ...the idea of this test is to make sure that the pickle code isn't just
# saving the initial seed...
builderProto = RandomProto.new_message()
r.write(builderProto)
readerProto = RandomProto.from_bytes(builderProto.to_bytes())
test3 = [r.getUInt32() for _ in xrange(10)]
r = Random();
r.read(readerProto)
self.assertEqual(r.getSeed(), 99)
test4 = [r.getUInt32() for _ in xrange(10)]
self.assertEqual(
test3, test4,
"NuPIC random capnp serialization check didn't work for saving later "
"state.")
self.assertNotEqual(
test1, test3,
"NuPIC random serialization test gave the same result twice?!?")
def testSerialization(self):
"""Test serialization of NuPIC randomness."""
path = "RandomSerialization.stream"
# Simple test: make sure that dumping / loading works...
r = Random(99)
r.saveToFile(path)
test1 = [r.getUInt32() for _ in range(10)]
r = Random(1)
r.loadFromFile(path)
self.assertEqual(r.getSeed(), 99)
test2 = [r.getUInt32() for _ in range(10)]
self.assertEqual(test1, test2,
"Simple NuPIC random serialization check failed.")
# A little tricker: dump / load _after_ some numbers have been generated
# (in the first test). Things should still work...
# ...the idea of this test is to make sure that the pickle code isn't just
# saving the initial seed...
r.saveToFile(path)
test3 = [r.getUInt32() for _ in range(10)]
r = Random()
r.loadFromFile(path)
self.assertEqual(r.getSeed(), 99)
test4 = [r.getUInt32() for _ in range(10)]
self.assertEqual(
test3, test4,
"NuPIC random serialization check didn't work for saving later state."
)
self.assertNotEqual(
test1, test3,
"NuPIC random serialization test gave the same result twice?!?")
def testSerialization(self):
"""Test serialization of NuPIC randomness."""
path = "RandomSerialization.stream"
# Simple test: make sure that dumping / loading works...
r = Random(99)
r.saveToFile(path)
test1 = [r.getUInt32() for _ in range(10)]
r = Random(1);
r.loadFromFile(path)
self.assertEqual(r.getSeed(), 99)
test2 = [r.getUInt32() for _ in range(10)]
self.assertEqual(test1, test2,
"Simple NuPIC random serialization check failed.")
# A little tricker: dump / load _after_ some numbers have been generated
# (in the first test). Things should still work...
# ...the idea of this test is to make sure that the pickle code isn't just
# saving the initial seed...
r.saveToFile(path)
test3 = [r.getUInt32() for _ in range(10)]
r = Random();
r.loadFromFile(path)
self.assertEqual(r.getSeed(), 99)
test4 = [r.getUInt32() for _ in range(10)]
self.assertEqual(
test3, test4,
"NuPIC random serialization check didn't work for saving later state.")
self.assertNotEqual(
test1, test3,
"NuPIC random serialization test gave the same result twice?!?")
class PatternMachine(object):
"""
Base pattern machine class.
"""
def __init__(self, n, w, num=100, seed=42):
"""
@param n (int) Number of available bits in pattern
@param w (int/list) Number of on bits in pattern
If list, each pattern will have a `w` randomly
selected from the list.
@param num (int) Number of available patterns
"""
# Save member variables
self._n = n
self._w = w
self._num = num
# Initialize member variables
self._random = Random(seed)
self._patterns = dict()
self._generate()
def get(self, number):
"""
Return a pattern for a number.
@param number (int) Number of pattern
@return (set) Indices of on bits
"""
if not number in self._patterns:
raise IndexError("Invalid number")
return self._patterns[number]
def addNoise(self, bits, amount):
"""
Add noise to pattern.
@param bits (set) Indices of on bits
@param amount (float) Probability of switching an on bit with a random bit
@return (set) Indices of on bits in noisy pattern
"""
newBits = set()
for bit in bits:
if self._random.getReal64() < amount:
newBits.add(self._random.getUInt32(self._n))
else:
newBits.add(bit)
return newBits
def numbersForBit(self, bit):
"""
Return the set of pattern numbers that match a bit.
@param bit (int) Index of bit
@return (set) Indices of numbers
"""
if bit >= self._n:
raise IndexError("Invalid bit")
numbers = set()
for index, pattern in self._patterns.items():
if bit in pattern:
numbers.add(index)
return numbers
def numberMapForBits(self, bits):
"""
Return a map from number to matching on bits,
for all numbers that match a set of bits.
@param bits (set) Indices of bits
@return (dict) Mapping from number => on bits.
"""
numberMap = dict()
for bit in bits:
numbers = self.numbersForBit(bit)
for number in numbers:
if not number in numberMap:
numberMap[number] = set()
numberMap[number].add(bit)
return numberMap
def prettyPrintPattern(self, bits, verbosity=1):
"""
Pretty print a pattern.
@param bits (set) Indices of on bits
@param verbosity (int) Verbosity level
@return (string) Pretty-printed text
"""
numberMap = self.numberMapForBits(bits)
text = ""
numberList = []
numberItems = sorted(iter(numberMap.items()),
key=lambda number_bits: len(number_bits[1]),
reverse=True)
for number, bits in numberItems:
if verbosity > 2:
strBits = [str(n) for n in bits]
numberText = "{0} (bits: {1})".format(number,
",".join(strBits))
elif verbosity > 1:
numberText = "{0} ({1} bits)".format(number, len(bits))
else:
numberText = str(number)
numberList.append(numberText)
text += "[{0}]".format(", ".join(numberList))
return text
def _generate(self):
"""
Generates set of random patterns.
"""
candidates = np.array(list(range(self._n)), np.uint32)
for i in range(self._num):
self._random.shuffle(candidates)
pattern = candidates[0:self._getW()]
self._patterns[i] = set(pattern)
def _getW(self):
"""
Gets a value of `w` for use in generating a pattern.
"""
w = self._w
if type(w) is list:
return w[self._random.getUInt32(len(w))]
else:
return w
class TemporalMemory(object):
"""
Class implementing the Temporal Memory algorithm.
"""
def __init__(self,
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=13,
learningRadius=2048,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
seed=42):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected
synapses on a segment is at least
this threshold, the segment is
said to be active.
@param learningRadius (int) Radius around cell from which it can
sample to form distal dendrite
connections.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse
is greater than this value, it is said
to be connected.
@param minThreshold (int) If the number of synapses active on
a segment is at least this threshold,
it is selected as the best matching
cell in a bursing column.
@param maxNewSynapseCount (int) The maximum number of synapses added
to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses
are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses
are decremented during learning.
@param seed (int) Seed for the random number generator.
"""
# TODO: Validate all parameters (and add validation tests)
# Initialize member variables
self.connections = Connections(columnDimensions, cellsPerColumn)
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.activeSynapsesForSegment = dict()
self.winnerCells = set()
# Save member variables
self.activationThreshold = activationThreshold
self.learningRadius = learningRadius
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
"""
Feeds input record through TM, performing inference and learning.
Updates member variables with new state.
@param activeColumns (set) Indices of active columns in `t`
"""
(activeCells, winnerCells, activeSynapsesForSegment, activeSegments,
predictiveCells) = self.computeFn(activeColumns,
self.predictiveCells,
self.activeSegments,
self.activeSynapsesForSegment,
self.winnerCells,
self.connections,
learn=learn)
self.activeCells = activeCells
self.winnerCells = winnerCells
self.activeSynapsesForSegment = activeSynapsesForSegment
self.activeSegments = activeSegments
self.predictiveCells = predictiveCells
def computeFn(self,
activeColumns,
prevPredictiveCells,
prevActiveSegments,
prevActiveSynapsesForSegment,
prevWinnerCells,
connections,
learn=True):
"""
'Functional' version of compute.
Returns new state.
@param activeColumns (set) Indices of active columns
in `t`
@param prevPredictiveCells (set) Indices of predictive
cells in `t-1`
@param prevActiveSegments (set) Indices of active segments
in `t-1`
@param prevActiveSynapsesForSegment (dict) Mapping from segments to
active synapses in `t-1`,
see
`TM.computeActiveSynapses`
@param prevWinnerCells (set) Indices of winner cells
in `t-1`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`activeSynapsesForSegment` (dict),
`activeSegments` (set),
`predictiveCells` (set)
"""
activeCells = set()
winnerCells = set()
(_activeCells, _winnerCells,
predictedColumns) = self.activateCorrectlyPredictiveCells(
prevPredictiveCells, activeColumns, connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
(_activeCells, _winnerCells,
learningSegments) = self.burstColumns(activeColumns, predictedColumns,
prevActiveSynapsesForSegment,
connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
if learn:
self.learnOnSegments(prevActiveSegments, learningSegments,
prevActiveSynapsesForSegment, winnerCells,
prevWinnerCells, connections)
activeSynapsesForSegment = self.computeActiveSynapses(
activeCells, connections)
(activeSegments, predictiveCells) = self.computePredictiveCells(
activeSynapsesForSegment, connections)
return (activeCells, winnerCells, activeSynapsesForSegment,
activeSegments, predictiveCells)
def reset(self):
"""
Indicates the start of a new sequence. Resets sequence state of the TM.
"""
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.activeSynapsesForSegment = dict()
self.winnerCells = set()
# ==============================
# Phases
# ==============================
@staticmethod
def activateCorrectlyPredictiveCells(prevPredictiveCells, activeColumns,
connections):
"""
Phase 1: Activate the correctly predictive cells.
Pseudocode:
- for each prev predictive cell
- if in active column
- mark it as active
- mark it as winner cell
- mark column as predicted
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param activeColumns (set) Indices of active columns in `t`
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`predictedColumns` (set)
"""
activeCells = set()
winnerCells = set()
predictedColumns = set()
for cell in prevPredictiveCells:
column = connections.columnForCell(cell)
if column in activeColumns:
activeCells.add(cell)
winnerCells.add(cell)
predictedColumns.add(column)
return (activeCells, winnerCells, predictedColumns)
def burstColumns(self, activeColumns, predictedColumns,
prevActiveSynapsesForSegment, connections):
"""
Phase 2: Burst unpredicted columns.
Pseudocode:
- for each unpredicted active column
- mark all cells as active
- mark the best matching cell as winner cell
- (learning)
- if it has no matching segment
- (optimization) if there are prev winner cells
- add a segment to it
- mark the segment as learning
@param activeColumns (set) Indices of active columns
in `t`
@param predictedColumns (set) Indices of predicted
columns in `t`
@param prevActiveSynapsesForSegment (dict) Mapping from segments to
active synapses in `t-1`,
see
`TM.computeActiveSynapses`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`learningSegments` (set)
"""
activeCells = set()
winnerCells = set()
learningSegments = set()
unpredictedColumns = activeColumns - predictedColumns
for column in unpredictedColumns:
cells = connections.cellsForColumn(column)
activeCells.update(cells)
(bestCell, bestSegment) = self.getBestMatchingCell(
cells, prevActiveSynapsesForSegment, connections)
winnerCells.add(bestCell)
if bestSegment == None:
# TODO: (optimization) Only do this if there are prev winner cells
bestSegment = connections.createSegment(bestCell)
learningSegments.add(bestSegment)
return (activeCells, winnerCells, learningSegments)
def learnOnSegments(self, prevActiveSegments, learningSegments,
prevActiveSynapsesForSegment, winnerCells,
prevWinnerCells, connections):
"""
Phase 3: Perform learning by adapting segments.
Pseudocode:
- (learning) for each prev active or learning segment
- if learning segment or from winner cell
- strengthen active synapses
- weaken inactive synapses
- if learning segment
- add some synapses to the segment
- subsample from prev winner cells
@param prevActiveSegments (set) Indices of active segments
in `t-1`
@param learningSegments (set) Indices of learning
segments in `t`
@param prevActiveSynapsesForSegment (dict) Mapping from segments to
active synapses in `t-1`,
see
`TM.computeActiveSynapses`
@param winnerCells (set) Indices of winner cells
in `t`
@param prevWinnerCells (set) Indices of winner cells
in `t-1`
@param connections (Connections) Connectivity of layer
"""
for segment in prevActiveSegments | learningSegments:
isLearningSegment = segment in learningSegments
isFromWinnerCell = connections.cellForSegment(
segment) in winnerCells
activeSynapses = self.getConnectedActiveSynapsesForSegment(
segment, prevActiveSynapsesForSegment, 0, connections)
if isLearningSegment or isFromWinnerCell:
self.adaptSegment(segment, activeSynapses, connections)
if isLearningSegment:
n = self.maxNewSynapseCount - len(activeSynapses)
for sourceCell in self.pickCellsToLearnOn(
n, segment, prevWinnerCells, connections):
connections.createSynapse(segment, sourceCell,
self.initialPermanence)
def computePredictiveCells(self, activeSynapsesForSegment, connections):
"""
Phase 4: Compute predictive cells due to lateral input
on distal dendrites.
Pseudocode:
- for each distal dendrite segment with activity >= activationThreshold
- mark the segment as active
- mark the cell as predictive
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeSegments` (set),
`predictiveCells` (set)
"""
activeSegments = set()
predictiveCells = set()
for segment in activeSynapsesForSegment.keys():
synapses = self.getConnectedActiveSynapsesForSegment(
segment, activeSynapsesForSegment, self.connectedPermanence,
connections)
if len(synapses) >= self.activationThreshold:
activeSegments.add(segment)
predictiveCells.add(connections.cellForSegment(segment))
return (activeSegments, predictiveCells)
# ==============================
# Helper functions
# ==============================
@staticmethod
def computeActiveSynapses(activeCells, connections):
"""
Forward propagates activity from active cells to the synapses that touch
them, to determine which synapses are active.
@param activeCells (set) Indicies of active cells
@param connections (Connections) Connectivity of layer
@return (dict) Mapping from segment (int) to indices of
active synapses (set)
"""
activeSynapsesForSegment = dict()
for cell in activeCells:
for synapse in connections.synapsesForSourceCell(cell):
segment, _, _ = connections.dataForSynapse(synapse)
if not segment in activeSynapsesForSegment:
activeSynapsesForSegment[segment] = set()
activeSynapsesForSegment[segment].add(synapse)
return activeSynapsesForSegment
def getBestMatchingCell(self, cells, activeSynapsesForSegment,
connections):
"""
Gets the cell with the best matching segment
(see `TM.getBestMatchingSegment`) that has the largest number of active
synapses of all best matching segments.
If none were found, pick the least used cell (see `TM.getLeastUsedCell`).
@param cells (set) Indices of cells
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`cell` (int),
`bestSegment` (int)
"""
maxSynapses = 0
bestCell = None
bestSegment = None
for cell in cells:
(segment, connectedActiveSynapses) = self.getBestMatchingSegment(
cell, activeSynapsesForSegment, connections)
if segment != None and len(connectedActiveSynapses) > maxSynapses:
maxSynapses = len(connectedActiveSynapses)
bestCell = cell
bestSegment = segment
if bestCell == None:
bestCell = self.getLeastUsedCell(cells, connections)
return (bestCell, bestSegment)
def getBestMatchingSegment(self, cell, activeSynapsesForSegment,
connections):
"""
Gets the segment on a cell with the largest number of activate synapses,
including all synapses with non-zero permanences.
@param cell (int) Cell index
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`segment` (int),
`connectedActiveSynapses` (set)
"""
maxSynapses = self.minThreshold
bestSegment = None
connectedActiveSynapses = None
for segment in connections.segmentsForCell(cell):
synapses = self.getConnectedActiveSynapsesForSegment(
segment, activeSynapsesForSegment, 0, connections)
if len(synapses) >= maxSynapses:
maxSynapses = len(synapses)
bestSegment = segment
connectedActiveSynapses = set(synapses)
return (bestSegment, connectedActiveSynapses)
def getLeastUsedCell(self, cells, connections):
"""
Gets the cell with the smallest number of segments.
Break ties randomly.
@param cells (set) Indices of cells
@param connections (Connections) Connectivity of layer
@return (int) Cell index
"""
leastUsedCells = set()
minNumSegments = float("inf")
for cell in cells:
numSegments = len(connections.segmentsForCell(cell))
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = set()
if numSegments == minNumSegments:
leastUsedCells.add(cell)
i = self._random.getUInt32(len(leastUsedCells))
return sorted(leastUsedCells)[i]
@staticmethod
def getConnectedActiveSynapsesForSegment(segment, activeSynapsesForSegment,
permanenceThreshold, connections):
"""
Returns the synapses on a segment that are active due to lateral input
from active cells.
@param segment (int) Segment index
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param permanenceThreshold (float) Minimum threshold for
permanence for synapse to
be connected
@param connections (Connections) Connectivity of layer
@return (set) Indices of active synapses on segment
"""
connectedSynapses = set()
if not segment in activeSynapsesForSegment:
return connectedSynapses
# TODO: (optimization) Can skip this logic if permanenceThreshold = 0
for synapse in activeSynapsesForSegment[segment]:
(_, _, permanence) = connections.dataForSynapse(synapse)
if permanence >= permanenceThreshold:
connectedSynapses.add(synapse)
return connectedSynapses
def adaptSegment(self, segment, activeSynapses, connections):
"""
Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param segment (int) Segment index
@param activeSynapses (set) Indices of active synapses
@param connections (Connections) Connectivity of layer
"""
for synapse in connections.synapsesForSegment(segment):
(_, _, permanence) = connections.dataForSynapse(synapse)
if synapse in activeSynapses:
permanence += self.permanenceIncrement
else:
permanence -= self.permanenceDecrement
# Keep permanence within min/max bounds
permanence = max(0.0, min(1.0, permanence))
connections.updateSynapsePermanence(synapse, permanence)
def pickCellsToLearnOn(self, n, segment, winnerCells, connections):
"""
Pick cells to form distal connections to.
TODO: Respect topology and learningRadius
@param n (int) Number of cells to pick
@param segment (int) Segment index
@param winnerCells (set) Indices of winner cells in `t`
@param connections (Connections) Connectivity of layer
@return (set) Indices of cells picked
"""
candidates = set(winnerCells)
# Remove cells that are already synapsed on by this segment
for synapse in connections.synapsesForSegment(segment):
(_, sourceCell, _) = connections.dataForSynapse(synapse)
if sourceCell in candidates:
candidates.remove(sourceCell)
n = min(n, len(candidates))
candidates = sorted(candidates)
cells = set()
# Pick n cells randomly
for _ in range(n):
i = self._random.getUInt32(len(candidates))
cells.add(candidates[i])
del candidates[i]
return cells
class ColumnPooler(object):
"""
This class constitutes a temporary implementation for a cross-column pooler.
The implementation goal of this class is to prove basic properties before
creating a cleaner implementation.
"""
def __init__(self,
inputWidth,
numActiveColumnsPerInhArea=40,
synPermProximalInc=0.1,
synPermProximalDec=0.001,
initialProximalPermanence=0.6,
columnDimensions=(2048,),
activationThreshold=13,
minThreshold=10,
initialPermanence=0.41,
connectedPermanence=0.50,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=255,
maxSynapsesPerSegment=255,
seed=42):
"""
This classes uses an ExtendedTemporalMemory internally to keep track of
distal segments. Please see ExtendedTemporalMemory for descriptions of
constructor parameters not defined below.
Parameters:
----------------------------
@param inputWidth (int)
The number of proximal inputs into this layer
@param numActiveColumnsPerInhArea (int)
Target number of active cells
@param synPermProximalInc (float)
Permanence increment for proximal synapses
@param synPermProximalDec (float)
Permanence decrement for proximal synapses
@param initialProximalPermanence (float)
Initial permanence value for proximal segments
"""
self.inputWidth = inputWidth
self.numActiveColumnsPerInhArea = numActiveColumnsPerInhArea
self.synPermProximalInc = synPermProximalInc
self.synPermProximalDec = synPermProximalDec
self.initialProximalPermanence = initialProximalPermanence
self.connectedPermanence = connectedPermanence
self.maxNewSynapseCount = maxNewSynapseCount
self.minThreshold = minThreshold
self.activeCells = set()
self._random = Random(seed)
# Create our own instance of extended temporal memory to handle distal
# segments.
self.tm = createModel(
modelName="extendedCPP",
columnDimensions=columnDimensions,
cellsPerColumn=1,
activationThreshold=activationThreshold,
initialPermanence=initialPermanence,
connectedPermanence=connectedPermanence,
minThreshold=minThreshold,
maxNewSynapseCount=maxNewSynapseCount,
permanenceIncrement=permanenceIncrement,
permanenceDecrement=permanenceDecrement,
predictedSegmentDecrement=predictedSegmentDecrement,
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment,
seed=seed,
learnOnOneCell=False,
)
# These sparse matrices will hold the synapses for each proximal segment.
#
# proximalPermanences - SparseMatrix with permanence values
# proximalConnections - SparseBinaryMatrix of connected synapses
self.proximalPermanences = SparseMatrix(self.numberOfColumns(),
inputWidth)
self.proximalConnections = SparseBinaryMatrix(inputWidth)
self.proximalConnections.resize(self.numberOfColumns(), inputWidth)
def compute(self,
feedforwardInput=None,
activeExternalCells=None,
learn=True):
"""
Parameters:
----------------------------
@param feedforwardInput (set)
Indices of active input bits
@param activeExternalCells (set)
Indices of active cells that will form connections to distal
segments.
@param learn (bool)
If True, we are learning a new object
"""
if activeExternalCells is None:
activeExternalCells = set()
if learn:
self._computeLearningMode(feedforwardInput=feedforwardInput,
lateralInput=activeExternalCells)
else:
self._computeInferenceMode(feedforwardInput=feedforwardInput,
lateralInput=activeExternalCells)
def _computeLearningMode(self, feedforwardInput, lateralInput):
"""
Learning mode: we are learning a new object. If there is no prior
activity, we randomly activate 2% of cells and create connections to
incoming input. If there was prior activity, we maintain it.
These cells will represent the object and learn distal connections to
lateral cortical columns.
Parameters:
----------------------------
@param feedforwardInput (set)
Indices of active input bits
@param lateralInput (set)
Indices of active cells from neighboring columns.
"""
# If there are no previously active cells, select random subset of cells
if len(self.activeCells) == 0:
self.activeCells = set(self._random.shuffle(
numpy.array(range(self.numberOfCells()),
dtype="uint32"))[0:self.numActiveColumnsPerInhArea])
# else we maintain previous activity, nothing to do.
# Those cells that remain active will learn on their proximal and distal
# dendrites as long as there is some input. If there are no
# cells active, no learning happens. This only happens in the very
# beginning if there has been no bottom up activity at all.
if len(self.activeCells) > 0:
# Learn on proximal dendrite if appropriate
if len(feedforwardInput) > 0:
self._learnProximal(feedforwardInput, self.activeCells,
self.maxNewSynapseCount, self.proximalPermanences,
self.proximalConnections,
self.initialProximalPermanence,
self.synPermProximalInc, self.synPermProximalDec,
self.connectedPermanence)
# Learn on distal dendrites if appropriate
self.tm.compute(activeColumns=self.activeCells,
activeExternalCells=lateralInput,
formInternalConnections=False,
learn=True)
def _computeInferenceMode(self, feedforwardInput, lateralInput):
"""
Inference mode: if there is some feedforward activity, perform
spatial pooling on it to recognize previously known objects. If there
is no feedforward activity, maintain previous activity.
Parameters:
----------------------------
@param feedforwardInput (set)
Indices of active input bits
@param lateralInput (list of lists)
A list of list of active cells from neighboring columns.
len(lateralInput) == number of connected neighboring cortical
columns.
"""
# Figure out which cells are active due to feedforward proximal inputs
# In order to form unions, we keep all cells that are over threshold
inputVector = numpy.zeros(self.numberOfInputs(), dtype=realDType)
inputVector[list(feedforwardInput)] = 1
overlaps = numpy.zeros(self.numberOfColumns(), dtype=realDType)
self.proximalConnections.rightVecSumAtNZ_fast(inputVector.astype(realDType),
overlaps)
overlaps[overlaps < self.minThreshold] = 0
bottomUpActivity = set(overlaps.nonzero()[0])
# If there is insufficient current bottom up activity, we incorporate all
# previous activity. We set their overlaps so they are sure to win.
if len(bottomUpActivity) < self.numActiveColumnsPerInhArea:
bottomUpActivity = bottomUpActivity.union(self.activeCells)
maxOverlap = overlaps.max()
overlaps[self.getActiveCells()] = maxOverlap+1
# Narrow down list of active cells based on lateral activity
self.activeCells = self._winnersBasedOnLateralActivity(
bottomUpActivity,
self.getPredictiveCells(),
overlaps,
self.numActiveColumnsPerInhArea
)
# Update predictive cells for next time step
self.tm.compute(activeColumns=self.activeCells,
activeExternalCells=lateralInput,
formInternalConnections=False,
learn=False)
def numberOfInputs(self):
"""
Returns the number of inputs into this layer
"""
return self.inputWidth
def numberOfColumns(self):
"""
Returns the number of columns in this layer.
@return (int) Number of columns
"""
return self.tm.numberOfColumns()
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.tm.numberOfCells()
def getActiveCells(self):
"""
Returns the indices of the active cells.
@return (set) Indices of active cells.
"""
return self.getCellIndices(self.activeCells)
@classmethod
def getCellIndices(cls, cells):
return [cls.getCellIndex(c) for c in cells]
@staticmethod
def getCellIndex(cell):
return cell
def numberOfConnectedSynapses(self, cells=None):
"""
Returns the number of proximal connected synapses on these cells.
Parameters:
----------------------------
@param cells (set or list)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
n += self.proximalConnections.nNonZerosOnRow(cell)
return n
def numberOfSynapses(self, cells=None):
"""
Returns the number of proximal synapses with permanence>0 on these cells.
Parameters:
----------------------------
@param cells (set or list)
Indices of the cells. If None return count for all cells.
"""
if cells is None:
cells = xrange(self.numberOfCells())
n = 0
for cell in cells:
n += self.proximalPermanences.nNonZerosOnRow(cell)
return n
def numberOfDistalSegments(self, cells):
"""
Returns the total number of distal segments for these cells.
Parameters:
----------------------------
@param cells (set or list)
Indices of the cells
"""
n = 0
for cell in cells:
n += len(self.tm.connections.segmentsForCell(cell))
return n
def numberOfDistalSynapses(self, cells):
"""
Returns the total number of distal synapses for these cells.
Parameters:
----------------------------
@param cells (set or list)
Indices of the cells
"""
n = 0
for cell in cells:
segments = self.tm.connections.segmentsForCell(cell)
for segment in segments:
n += len(self.tm.connections.synapsesForSegment(segment))
return n
def reset(self):
"""
Reset internal states. When learning this signifies we are to learn a
unique new object.
"""
self.activeCells = set()
self.tm.reset()
def getPredictiveCells(self):
"""
Get the set of distally predictive cells as a set.
@return (list) A list containing indices of the current distally predicted
cells.
"""
return self.tm.getPredictiveCells()
def getPredictedActiveCells(self):
"""
Get the set of cells that were predicted previously then became active
@return (set) A set containing indices.
"""
return self.tm.predictedActiveCellsIndices()
def getConnections(self):
"""
Get the Connections structure associated with our TM. Beware of using
this as it is implementation specific and may change.
@return (object) A Connections object
"""
return self.tm.connections
def _learnProximal(self,
activeInputs, activeCells, maxNewSynapseCount, proximalPermanences,
proximalConnections, initialPermanence, synPermProximalInc,
synPermProximalDec, connectedPermanence):
"""
Learn on proximal dendrites of active cells. Updates proximalPermanences
"""
for cell in activeCells:
cellPermanencesDense = proximalPermanences.getRow(cell)
cellNonZeroIndices, _ = proximalPermanences.rowNonZeros(cell)
cellNonZeroIndices = list(cellNonZeroIndices)
# Get new and existing connections for this segment
newInputs, existingInputs = self._pickProximalInputsToLearnOn(
maxNewSynapseCount, activeInputs, cellNonZeroIndices
)
# Adjust existing connections appropriately
# First we decrement all existing permanences
if len(cellNonZeroIndices) > 0:
cellPermanencesDense[cellNonZeroIndices] -= synPermProximalDec
# Then we add inc + dec to existing active synapses
if len(existingInputs) > 0:
cellPermanencesDense[existingInputs] += synPermProximalInc + synPermProximalDec
# Add new connections
if len(newInputs) > 0:
cellPermanencesDense[newInputs] += initialPermanence
# Update proximalPermanences and proximalConnections
proximalPermanences.setRowFromDense(cell, cellPermanencesDense)
newConnected = numpy.where(cellPermanencesDense >= connectedPermanence)[0]
proximalConnections.replaceSparseRow(cell, newConnected)
def _pickProximalInputsToLearnOn(self, newSynapseCount, activeInputs,
cellNonZeros):
"""
Pick inputs to form proximal connections to a particular cell. We just
randomly subsample from activeInputs, regardless of whether they are already
connected.
We return a list of up to newSynapseCount input indices from activeInputs
that are valid new connections for this cell. We also return a list
containing all inputs in activeInputs that are already connected to this
cell.
Parameters:
----------------------------
@param newSynapseCount (int) Number of inputs to pick
@param cell (int) Cell index
@param activeInputs (set) Indices of active inputs
@param cellNonZeros (list) Indices of inputs input this cell with
non-zero permanences.
@return (list, list) Indices of new inputs to connect to, inputs already
connected
"""
candidates = []
alreadyConnected = []
# Collect inputs that already have synapses and list of new candidate inputs
for inputIdx in activeInputs:
if inputIdx in cellNonZeros:
alreadyConnected += [inputIdx]
else:
candidates += [inputIdx]
# Select min(newSynapseCount, len(candidates)) new inputs to connect to
if newSynapseCount >= len(candidates):
return candidates, alreadyConnected
else:
# Pick newSynapseCount cells randomly
# TODO: we could maybe implement this more efficiently with shuffle.
inputs = []
for _ in range(newSynapseCount):
i = self._random.getUInt32(len(candidates))
inputs += [candidates[i]]
candidates.remove(candidates[i])
return inputs, alreadyConnected
def _winnersBasedOnLateralActivity(self,
activeCells,
predictiveCells,
overlaps,
targetActiveCells):
"""
Given the set of cells active due to feedforward input, narrow down the
list of active cells based on predictions due to previous lateralInput.
Parameters:
----------------------------
@param activeCells (set)
Indices of cells activated by bottom-up input.
@param predictiveCells (set)
Indices of cells that are laterally predicted.
@param overlaps (numpy array)
Bottom up overlap scores for each proximal segment. This is used
to select additional cells if the narrowed down list contains less
than targetActiveCells.
@param targetActiveCells (int)
The number of active cells we want to have active.
@return (set) List of new winner cell indices
"""
# No TM accessors that return set so access internal member directly
predictedActiveCells = activeCells.intersection(predictiveCells)
# If predicted cells don't intersect at all with active cells, we go with
# bottom up input. In these cases we can stick with existing active cells
# and skip the overlap sorting
if len(predictedActiveCells) == 0:
predictedActiveCells = activeCells
# We want to keep all cells that were predicted and currently active due to
# feedforward input. This set could be larger than our target number of
# active cells due to unions, which is ok. However if there are insufficient
# cells active after this intersection, we fill in with those currently
# active cells that have highest overlap.
elif len(predictedActiveCells) < targetActiveCells:
# Don't want to consider cells already chosen
overlaps[list(predictedActiveCells)] = 0
# Add in the desired number of cells with highest activity
numActive = targetActiveCells - len(predictedActiveCells)
winnerIndices = numpy.argsort(overlaps, kind='mergesort')
sortedWinnerIndices = winnerIndices[-numActive:][::-1]
predictedActiveCells = predictedActiveCells.union(set(sortedWinnerIndices))
return predictedActiveCells
class TemporalMemory(object):
"""
Class implementing the Temporal Memory algorithm.
"""
def __init__(self,
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=13,
learningRadius=2048,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
seed=42):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected synapses on a segment is at least this threshold, the segment is said to be active.
@param learningRadius (int) Radius around cell from which it can sample to form distal dendrite connections.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse is greater than this value, it is said to be connected.
@param minThreshold (int) If the number of synapses active on a segment is at least this threshold, it is selected as the best matching cell in a bursting column.
@param maxNewSynapseCount (int) The maximum number of synapses added to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses are decremented during learning.
@param seed (int) Seed for the random number generator.
"""
# Error checking
if not len(columnDimensions):
raise ValueError(
"Number of column dimensions must be greater than 0")
if not cellsPerColumn > 0:
raise ValueError(
"Number of cells per column must be greater than 0")
# TODO: Validate all parameters (and add validation tests)
# Save member variables
self.columnDimensions = columnDimensions
self.cellsPerColumn = cellsPerColumn
self.activationThreshold = activationThreshold
self.learningRadius = learningRadius
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
# Initialize member variables
self.connections = Connections(self.numberOfCells())
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
"""
Feeds input record through TM, performing inference and learning.
Updates member variables with new state.
@param activeColumns (set) Indices of active columns in `t`
"""
(activeCells, winnerCells, activeSegments, predictiveCells,
predictedColumns) = self.computeFn(activeColumns,
self.predictiveCells,
self.activeSegments,
self.activeCells,
self.winnerCells,
self.connections,
learn=learn)
self.activeCells = activeCells
self.winnerCells = winnerCells
self.activeSegments = activeSegments
self.predictiveCells = predictiveCells
def computeFn(self,
activeColumns,
prevPredictiveCells,
prevActiveSegments,
prevActiveCells,
prevWinnerCells,
connections,
learn=True):
"""
'Functional' version of compute.
Returns new state.
@param activeColumns (set) Indices of active columns in `t`
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param prevActiveSegments (set) Indices of active segments in `t-1`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@param learn (bool) Whether or not learning is enabled
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`activeSegments` (set),
`predictiveCells` (set)
"""
activeCells = set()
winnerCells = set()
(_activeCells, _winnerCells,
predictedColumns) = self.activateCorrectlyPredictiveCells(
prevPredictiveCells, activeColumns)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
(_activeCells, _winnerCells,
learningSegments) = self.burstColumns(activeColumns, predictedColumns,
prevActiveCells,
prevWinnerCells, connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
if learn:
self.learnOnSegments(prevActiveSegments, learningSegments,
prevActiveCells, winnerCells, prevWinnerCells,
connections)
(activeSegments, predictiveCells) = self.computePredictiveCells(
activeCells, connections)
return (activeCells, winnerCells, activeSegments, predictiveCells,
predictedColumns)
def reset(self):
"""
Indicates the start of a new sequence. Resets sequence state of the TM.
"""
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
# ==============================
# Phases
# ==============================
def activateCorrectlyPredictiveCells(self, prevPredictiveCells,
activeColumns):
"""
Phase 1: Activate the correctly predictive cells.
Pseudocode:
- for each prev predictive cell
- if in active column
- mark it as active
- mark it as winner cell
- mark column as predicted
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param activeColumns (set) Indices of active columns in `t`
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`predictedColumns` (set)
"""
activeCells = set()
winnerCells = set()
predictedColumns = set()
for cell in prevPredictiveCells:
column = self.columnForCell(cell)
if column in activeColumns:
activeCells.add(cell)
winnerCells.add(cell)
predictedColumns.add(column)
return activeCells, winnerCells, predictedColumns
def burstColumns(self, activeColumns, predictedColumns, prevActiveCells,
prevWinnerCells, connections):
"""
Phase 2: Burst unpredicted columns.
Pseudocode:
- for each unpredicted active column
- mark all cells as active
- mark the best matching cell as winner cell
- (learning)
- if it has no matching segment
- (optimization) if there are prev winner cells
- add a segment to it
- mark the segment as learning
@param activeColumns (set) Indices of active columns in `t`
@param predictedColumns (set) Indices of predicted columns in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`learningSegments` (set)
"""
activeCells = set()
winnerCells = set()
learningSegments = set()
unpredictedColumns = activeColumns - predictedColumns
for column in unpredictedColumns:
cells = self.cellsForColumn(column)
activeCells.update(cells)
(bestCell,
bestSegment) = self.bestMatchingCell(cells, prevActiveCells,
connections)
winnerCells.add(bestCell)
if bestSegment is None and len(prevWinnerCells):
bestSegment = connections.createSegment(bestCell)
if bestSegment is not None:
learningSegments.add(bestSegment)
return activeCells, winnerCells, learningSegments
def learnOnSegments(self, prevActiveSegments, learningSegments,
prevActiveCells, winnerCells, prevWinnerCells,
connections):
"""
Phase 3: Perform learning by adapting segments.
Pseudocode:
- (learning) for each prev active or learning segment
- if learning segment or from winner cell
- strengthen active synapses
- weaken inactive synapses
- if learning segment
- add some synapses to the segment
- subsample from prev winner cells
@param prevActiveSegments (set) Indices of active segments in `t-1`
@param learningSegments (set) Indices of learning segments in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param winnerCells (set) Indices of winner cells in `t`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
"""
for segment in prevActiveSegments | learningSegments:
isLearningSegment = segment in learningSegments
isFromWinnerCell = connections.cellForSegment(
segment) in winnerCells
activeSynapses = self.activeSynapsesForSegment(
segment, prevActiveCells, connections)
if isLearningSegment or isFromWinnerCell:
self.adaptSegment(segment, activeSynapses, connections)
if isLearningSegment:
n = self.maxNewSynapseCount - len(activeSynapses)
for presynapticCell in self.pickCellsToLearnOn(
n, segment, prevWinnerCells, connections):
connections.createSynapse(segment, presynapticCell,
self.initialPermanence)
def computePredictiveCells(self, activeCells, connections):
"""
Phase 4: Compute predictive cells due to lateral input
on distal dendrites.
Pseudocode:
- for each distal dendrite segment with activity >= activationThreshold
- mark the segment as active
- mark the cell as predictive
Forward propagates activity from active cells to the synapses that touch
them, to determine which synapses are active.
@param activeCells (set) Indices of active cells in `t`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeSegments` (set),
`predictiveCells` (set)
"""
numActiveConnectedSynapsesForSegment = defaultdict(lambda: 0)
activeSegments = set()
predictiveCells = set()
for cell in activeCells:
for synapseData in connections.synapsesForPresynapticCell(
cell).values():
segment = synapseData.segment
permanence = synapseData.permanence
if permanence >= self.connectedPermanence:
numActiveConnectedSynapsesForSegment[segment] += 1
if (numActiveConnectedSynapsesForSegment[segment] >=
self.activationThreshold):
activeSegments.add(segment)
predictiveCells.add(
connections.cellForSegment(segment))
return activeSegments, predictiveCells
# ==============================
# Helper functions
# ==============================
def bestMatchingCell(self, cells, activeCells, connections):
"""
Gets the cell with the best matching segment
(see `TM.bestMatchingSegment`) that has the largest number of active
synapses of all best matching segments.
If none were found, pick the least used cell (see `TM.leastUsedCell`).
@param cells (set) Indices of cells
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`cell` (int),
`bestSegment` (int)
"""
maxSynapses = 0
bestCell = None
bestSegment = None
for cell in cells:
segment, numActiveSynapses = self.bestMatchingSegment(
cell, activeCells, connections)
if segment is not None and numActiveSynapses > maxSynapses:
maxSynapses = numActiveSynapses
bestCell = cell
bestSegment = segment
if bestCell is None:
bestCell = self.leastUsedCell(cells, connections)
return bestCell, bestSegment
def bestMatchingSegment(self, cell, activeCells, connections):
"""
Gets the segment on a cell with the largest number of activate synapses,
including all synapses with non-zero permanences.
@param cell (int) Cell index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`segment` (int),
`connectedActiveSynapses` (set)
"""
maxSynapses = self.minThreshold
bestSegment = None
bestNumActiveSynapses = None
for segment in connections.segmentsForCell(cell):
numActiveSynapses = 0
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if synapseData.presynapticCell in activeCells:
numActiveSynapses += 1
if numActiveSynapses >= maxSynapses:
maxSynapses = numActiveSynapses
bestSegment = segment
bestNumActiveSynapses = numActiveSynapses
return bestSegment, bestNumActiveSynapses
def leastUsedCell(self, cells, connections):
"""
Gets the cell with the smallest number of segments.
Break ties randomly.
@param cells (set) Indices of cells
@param connections (Connections) Connectivity of layer
@return (int) Cell index
"""
leastUsedCells = set()
minNumSegments = float("inf")
for cell in cells:
numSegments = len(connections.segmentsForCell(cell))
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = set()
if numSegments == minNumSegments:
leastUsedCells.add(cell)
i = self._random.getUInt32(len(leastUsedCells))
return sorted(leastUsedCells)[i]
@staticmethod
def activeSynapsesForSegment(segment, activeCells, connections):
"""
Returns the synapses on a segment that are active due to lateral input
from active cells.
@param segment (int) Segment index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (set) Indices of active synapses on segment
"""
synapses = set()
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if synapseData.presynapticCell in activeCells:
synapses.add(synapse)
return synapses
def adaptSegment(self, segment, activeSynapses, connections):
"""
Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param segment (int) Segment index
@param activeSynapses (set) Indices of active synapses
@param connections (Connections) Connectivity of layer
"""
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
permanence = synapseData.permanence
if synapse in activeSynapses:
permanence += self.permanenceIncrement
else:
permanence -= self.permanenceDecrement
# Keep permanence within min/max bounds
permanence = max(0.0, min(1.0, permanence))
connections.updateSynapsePermanence(synapse, permanence)
def pickCellsToLearnOn(self, n, segment, winnerCells, connections):
"""
Pick cells to form distal connections to.
TODO: Respect topology and learningRadius
@param n (int) Number of cells to pick
@param segment (int) Segment index
@param winnerCells (set) Indices of winner cells in `t`
@param connections (Connections) Connectivity of layer
@return (set) Indices of cells picked
"""
candidates = set(winnerCells)
# Remove cells that are already synapsed on by this segment
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
presynapticCell = synapseData.presynapticCell
if presynapticCell in candidates:
candidates.remove(presynapticCell)
n = min(n, len(candidates))
candidates = sorted(candidates)
cells = set()
# Pick n cells randomly
for _ in range(n):
i = self._random.getUInt32(len(candidates))
cells.add(candidates[i])
del candidates[i]
return cells
def columnForCell(self, cell):
"""
Returns the index of the column that a cell belongs to.
@param cell (int) Cell index
@return (int) Column index
"""
self._validateCell(cell)
return int(cell / self.cellsPerColumn)
def cellsForColumn(self, column):
"""
Returns the indices of cells that belong to a column.
@param column (int) Column index
@return (set) Cell indices
"""
self._validateColumn(column)
start = self.cellsPerColumn * column
end = start + self.cellsPerColumn
return set([cell for cell in range(start, end)])
def numberOfColumns(self):
"""
Returns the number of columns in this layer.
@return (int) Number of columns
"""
return reduce(mul, self.columnDimensions, 1)
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.numberOfColumns() * self.cellsPerColumn
def mapCellsToColumns(self, cells):
"""
Maps cells to the columns they belong to
@param cells (set) Cells
@return (dict) Mapping from columns to their cells in `cells`
"""
cellsForColumns = defaultdict(set)
for cell in cells:
column = self.columnForCell(cell)
cellsForColumns[column].add(cell)
return cellsForColumns
def _validateColumn(self, column):
"""
Raises an error if column index is invalid.
@param column (int) Column index
"""
if column >= self.numberOfColumns() or column < 0:
raise IndexError("Invalid column")
def _validateCell(self, cell):
"""
Raises an error if cell index is invalid.
@param cell (int) Cell index
"""
if cell >= self.numberOfCells() or cell < 0:
raise IndexError("Invalid cell")
@classmethod
def getCellIndices(cls, cells):
return [cls.getCellIndex(c) for c in cells]
@staticmethod
def getCellIndex(cell):
return cell.idx
class RandomDistributedScalarEncoder(Encoder):
"""
A scalar encoder encodes a numeric (floating point) value into an array
of bits.
This class maps a scalar value into a random distributed representation that
is suitable as scalar input into the spatial pooler. The encoding scheme is
designed to replace a simple ScalarEncoder. It preserves the important
properties around overlapping representations. Unlike ScalarEncoder the min
and max range can be dynamically increased without any negative effects. The
only required parameter is resolution, which determines the resolution of
input values.
Scalar values are mapped to a bucket. The class maintains a random distributed
encoding for each bucket. The following properties are maintained by
RandomDistributedEncoder:
1) Similar scalars should have high overlap. Overlap should decrease smoothly
as scalars become less similar. Specifically, neighboring bucket indices must
overlap by a linearly decreasing number of bits.
2) Dissimilar scalars should have very low overlap so that the SP does not
confuse representations. Specifically, buckets that are more than w indices
apart should have at most maxOverlap bits of overlap. We arbitrarily (and
safely) define "very low" to be 2 bits of overlap or lower.
Properties 1 and 2 lead to the following overlap rules for buckets i and j:
If abs(i-j) < w then:
overlap(i,j) = w - abs(i-j)
else:
overlap(i,j) <= maxOverlap
3) The representation for a scalar must not change during the lifetime of
the object. Specifically, as new buckets are created and the min/max range
is extended, the representation for previously in-range sscalars and
previously created buckets must not change.
"""
def __init__(self, resolution, w=21, n=400, name=None, offset=None,
seed=42, verbosity=0):
"""Constructor
@param resolution A floating point positive number denoting the resolution
of the output representation. Numbers within
[offset-resolution/2, offset+resolution/2] will fall into
the same bucket and thus have an identical representation.
Adjacent buckets will differ in one bit. resolution is a
required parameter.
@param w Number of bits to set in output. w must be odd to avoid centering
problems. w must be large enough that spatial pooler
columns will have a sufficiently large overlap to avoid
false matches. A value of w=21 is typical.
@param n Number of bits in the representation (must be > w). n must be
large enough such that there is enough room to select
new representations as the range grows. With w=21 a value
of n=400 is typical. The class enforces n > 6*w.
@param name An optional string which will become part of the description.
@param offset A floating point offset used to map scalar inputs to bucket
indices. The middle bucket will correspond to numbers in the
range [offset - resolution/2, offset + resolution/2). If set
to None, the very first input that is encoded will be used
to determine the offset.
@param seed The seed used for numpy's random number generator. If set to -1
the generator will be initialized without a fixed seed.
@param verbosity An integer controlling the level of debugging output. A
value of 0 implies no output. verbosity=1 may lead to
one-time printouts during construction, serialization or
deserialization. verbosity=2 may lead to some output per
encode operation. verbosity>2 may lead to significantly
more output.
"""
# Validate inputs
if (w <= 0) or (w%2 == 0):
raise ValueError("w must be an odd positive integer")
if resolution <= 0:
raise ValueError("resolution must be a positive number")
if (n <= 6*w) or (not isinstance(n, int)):
raise ValueError("n must be an int strictly greater than 6*w. For "
"good results we recommend n be strictly greater "
"than 11*w")
self.encoders = None
self.verbosity = verbosity
self.w = w
self.n = n
self.resolution = float(resolution)
# The largest overlap we allow for non-adjacent encodings
self._maxOverlap = 2
# initialize the random number generators
self._seed(seed)
# Internal parameters for bucket mapping
self.minIndex = None
self.maxIndex = None
self._offset = None
self._initializeBucketMap(INITIAL_BUCKETS, offset)
# A name used for debug printouts
if name is not None:
self.name = name
else:
self.name = "[%s]" % (self.resolution)
if self.verbosity > 0:
self.dump()
def __setstate__(self, state):
self.__dict__.update(state)
# Initialize self.random as an instance of NupicRandom derived from the
# previous numpy random state
randomState = state["random"]
if isinstance(randomState, numpy.random.mtrand.RandomState):
self.random = NupicRandom(randomState.randint(sys.maxint))
def _seed(self, seed=-1):
"""
Initialize the random seed
"""
if seed != -1:
self.random = NupicRandom(seed)
else:
self.random = NupicRandom()
def getDecoderOutputFieldTypes(self):
""" See method description in base.py """
return (FieldMetaType.float, )
def getWidth(self):
""" See method description in base.py """
return self.n
def getDescription(self):
return [(self.name, 0)]
def getBucketIndices(self, x):
""" See method description in base.py """
if ((isinstance(x, float) and math.isnan(x)) or
x == SENTINEL_VALUE_FOR_MISSING_DATA):
return [None]
if self._offset is None:
self._offset = x
bucketIdx = (
(self._maxBuckets/2) + int(round((x - self._offset) / self.resolution))
)
if bucketIdx < 0:
bucketIdx = 0
elif bucketIdx >= self._maxBuckets:
bucketIdx = self._maxBuckets-1
return [bucketIdx]
def mapBucketIndexToNonZeroBits(self, index):
"""
Given a bucket index, return the list of non-zero bits. If the bucket
index does not exist, it is created. If the index falls outside our range
we clip it.
@param index The bucket index to get non-zero bits for.
@returns numpy array of indices of non-zero bits for specified index.
"""
if index < 0:
index = 0
if index >= self._maxBuckets:
index = self._maxBuckets-1
if not self.bucketMap.has_key(index):
if self.verbosity >= 2:
print "Adding additional buckets to handle index=", index
self._createBucket(index)
return self.bucketMap[index]
def encodeIntoArray(self, x, output):
""" See method description in base.py """
if x is not None and not isinstance(x, numbers.Number):
raise TypeError(
"Expected a scalar input but got input of type %s" % type(x))
# Get the bucket index to use
bucketIdx = self.getBucketIndices(x)[0]
# None is returned for missing value in which case we return all 0's.
output[0:self.n] = 0
if bucketIdx is not None:
output[self.mapBucketIndexToNonZeroBits(bucketIdx)] = 1
def _createBucket(self, index):
"""
Create the given bucket index. Recursively create as many in-between
bucket indices as necessary.
"""
if index < self.minIndex:
if index == self.minIndex - 1:
# Create a new representation that has exactly w-1 overlapping bits
# as the min representation
self.bucketMap[index] = self._newRepresentation(self.minIndex,
index)
self.minIndex = index
else:
# Recursively create all the indices above and then this index
self._createBucket(index+1)
self._createBucket(index)
else:
if index == self.maxIndex + 1:
# Create a new representation that has exactly w-1 overlapping bits
# as the max representation
self.bucketMap[index] = self._newRepresentation(self.maxIndex,
index)
self.maxIndex = index
else:
# Recursively create all the indices below and then this index
self._createBucket(index-1)
self._createBucket(index)
def _newRepresentation(self, index, newIndex):
"""
Return a new representation for newIndex that overlaps with the
representation at index by exactly w-1 bits
"""
newRepresentation = self.bucketMap[index].copy()
# Choose the bit we will replace in this representation. We need to shift
# this bit deterministically. If this is always chosen randomly then there
# is a 1 in w chance of the same bit being replaced in neighboring
# representations, which is fairly high
ri = newIndex % self.w
# Now we choose a bit such that the overlap rules are satisfied.
newBit = self.random.getUInt32(self.n)
newRepresentation[ri] = newBit
while newBit in self.bucketMap[index] or \
not self._newRepresentationOK(newRepresentation, newIndex):
self.numTries += 1
newBit = self.random.getUInt32(self.n)
newRepresentation[ri] = newBit
return newRepresentation
def _newRepresentationOK(self, newRep, newIndex):
"""
Return True if this new candidate representation satisfies all our overlap
rules. Since we know that neighboring representations differ by at most
one bit, we compute running overlaps.
"""
if newRep.size != self.w:
return False
if (newIndex < self.minIndex-1) or (newIndex > self.maxIndex+1):
raise ValueError("newIndex must be within one of existing indices")
# A binary representation of newRep. We will use this to test containment
newRepBinary = numpy.array([False]*self.n)
newRepBinary[newRep] = True
# Midpoint
midIdx = self._maxBuckets/2
# Start by checking the overlap at minIndex
runningOverlap = self._countOverlap(self.bucketMap[self.minIndex], newRep)
if not self._overlapOK(self.minIndex, newIndex, overlap=runningOverlap):
return False
# Compute running overlaps all the way to the midpoint
for i in range(self.minIndex+1, midIdx+1):
# This is the bit that is going to change
newBit = (i-1)%self.w
# Update our running overlap
if newRepBinary[self.bucketMap[i-1][newBit]]:
runningOverlap -= 1
if newRepBinary[self.bucketMap[i][newBit]]:
runningOverlap += 1
# Verify our rules
if not self._overlapOK(i, newIndex, overlap=runningOverlap):
return False
# At this point, runningOverlap contains the overlap for midIdx
# Compute running overlaps all the way to maxIndex
for i in range(midIdx+1, self.maxIndex+1):
# This is the bit that is going to change
newBit = i%self.w
# Update our running overlap
if newRepBinary[self.bucketMap[i-1][newBit]]:
runningOverlap -= 1
if newRepBinary[self.bucketMap[i][newBit]]:
runningOverlap += 1
# Verify our rules
if not self._overlapOK(i, newIndex, overlap=runningOverlap):
return False
return True
def _countOverlapIndices(self, i, j):
"""
Return the overlap between bucket indices i and j
"""
if self.bucketMap.has_key(i) and self.bucketMap.has_key(j):
iRep = self.bucketMap[i]
jRep = self.bucketMap[j]
return self._countOverlap(iRep, jRep)
else:
raise ValueError("Either i or j don't exist")
@staticmethod
def _countOverlap(rep1, rep2):
"""
Return the overlap between two representations. rep1 and rep2 are lists of
non-zero indices.
"""
overlap = 0
for e in rep1:
if e in rep2:
overlap += 1
return overlap
def _overlapOK(self, i, j, overlap=None):
"""
Return True if the given overlap between bucket indices i and j are
acceptable. If overlap is not specified, calculate it from the bucketMap
"""
if overlap is None:
overlap = self._countOverlapIndices(i, j)
if abs(i-j) < self.w:
if overlap == (self.w - abs(i-j)):
return True
else:
return False
else:
if overlap <= self._maxOverlap:
return True
else:
return False
def _initializeBucketMap(self, maxBuckets, offset):
"""
Initialize the bucket map assuming the given number of maxBuckets.
"""
# The first bucket index will be _maxBuckets / 2 and bucket indices will be
# allowed to grow lower or higher as long as they don't become negative.
# _maxBuckets is required because the current CLA Classifier assumes bucket
# indices must be non-negative. This normally does not need to be changed
# but if altered, should be set to an even number.
self._maxBuckets = maxBuckets
self.minIndex = self._maxBuckets / 2
self.maxIndex = self._maxBuckets / 2
# The scalar offset used to map scalar values to bucket indices. The middle
# bucket will correspond to numbers in the range
# [offset-resolution/2, offset+resolution/2).
# The bucket index for a number x will be:
# maxBuckets/2 + int( round( (x-offset)/resolution ) )
self._offset = offset
# This dictionary maps a bucket index into its bit representation
# We initialize the class with a single bucket with index 0
self.bucketMap = {}
def _permutation(n):
r = numpy.arange(n, dtype=numpy.uint32)
self.random.shuffle(r)
return r
self.bucketMap[self.minIndex] = _permutation(self.n)[0:self.w]
# How often we need to retry when generating valid encodings
self.numTries = 0
def dump(self):
print "RandomDistributedScalarEncoder:"
print " minIndex: %d" % self.minIndex
print " maxIndex: %d" % self.maxIndex
print " w: %d" % self.w
print " n: %d" % self.getWidth()
print " resolution: %g" % self.resolution
print " offset: %s" % str(self._offset)
print " numTries: %d" % self.numTries
print " name: %s" % self.name
if self.verbosity > 2:
print " All buckets: "
pprint.pprint(self.bucketMap)
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.resolution = proto.resolution
encoder.w = proto.w
encoder.n = proto.n
encoder.name = proto.name
encoder._offset = proto.offset
encoder.random = NupicRandom()
encoder.random.read(proto.random)
encoder.resolution = proto.resolution
encoder.verbosity = proto.verbosity
encoder.minIndex = proto.minIndex
encoder.maxIndex = proto.maxIndex
encoder.encoders = None
encoder._maxBuckets = INITIAL_BUCKETS
encoder.bucketMap = {x.key: numpy.array(x.value, dtype=numpy.uint32)
for x in proto.bucketMap}
return encoder
def write(self, proto):
proto.resolution = self.resolution
proto.w = self.w
proto.n = self.n
proto.name = self.name
proto.offset = self._offset
self.random.write(proto.random)
proto.verbosity = self.verbosity
proto.minIndex = self.minIndex
proto.maxIndex = self.maxIndex
proto.bucketMap = [{"key": key, "value": value.tolist()}
for key, value in self.bucketMap.items()]
class PatternMachine(object):
"""
Base pattern machine class.
"""
def __init__(self,
n,
w,
num=100,
seed=42):
"""
@param n (int) Number of available bits in pattern
@param w (int/list) Number of on bits in pattern
If list, each pattern will have a `w` randomly
selected from the list.
@param num (int) Number of available patterns
"""
# Save member variables
self._n = n
self._w = w
self._num = num
# Initialize member variables
self._random = Random(seed)
self._patterns = dict()
self._generate()
def get(self, number):
"""
Return a pattern for a number.
@param number (int) Number of pattern
@return (set) Indices of on bits
"""
if not number in self._patterns:
raise IndexError("Invalid number")
return self._patterns[number]
def addNoise(self, bits, amount):
"""
Add noise to pattern.
@param bits (set) Indices of on bits
@param amount (float) Probability of switching an on bit with a random bit
@return (set) Indices of on bits in noisy pattern
"""
newBits = set()
for bit in bits:
if self._random.getReal64() < amount:
newBits.add(self._random.getUInt32(self._n))
else:
newBits.add(bit)
return newBits
def numbersForBit(self, bit):
"""
Return the set of pattern numbers that match a bit.
@param bit (int) Index of bit
@return (set) Indices of numbers
"""
if bit >= self._n:
raise IndexError("Invalid bit")
numbers = set()
for index, pattern in self._patterns.iteritems():
if bit in pattern:
numbers.add(index)
return numbers
def numberMapForBits(self, bits):
"""
Return a map from number to matching on bits,
for all numbers that match a set of bits.
@param bits (set) Indices of bits
@return (dict) Mapping from number => on bits.
"""
numberMap = dict()
for bit in bits:
numbers = self.numbersForBit(bit)
for number in numbers:
if not number in numberMap:
numberMap[number] = set()
numberMap[number].add(bit)
return numberMap
def prettyPrintPattern(self, bits, verbosity=1):
"""
Pretty print a pattern.
@param bits (set) Indices of on bits
@param verbosity (int) Verbosity level
@return (string) Pretty-printed text
"""
numberMap = self.numberMapForBits(bits)
text = ""
numberList = []
numberItems = sorted(numberMap.iteritems(),
key=lambda (number, bits): len(bits),
reverse=True)
for number, bits in numberItems:
if verbosity > 2:
strBits = [str(n) for n in bits]
numberText = "{0} (bits: {1})".format(number, ",".join(strBits))
elif verbosity > 1:
numberText = "{0} ({1} bits)".format(number, len(bits))
else:
numberText = str(number)
numberList.append(numberText)
text += "[{0}]".format(", ".join(numberList))
return text
def _generate(self):
"""
Generates set of random patterns.
"""
candidates = np.array(range(self._n), np.uint32)
for i in xrange(self._num):
self._random.shuffle(candidates)
pattern = candidates[0:self._getW()]
self._patterns[i] = set(pattern)
def _getW(self):
"""
Gets a value of `w` for use in generating a pattern.
"""
w = self._w
if type(w) is list:
return w[self._random.getUInt32(len(w))]
else:
return w
class TemporalMemory(object):
"""
Class implementing the Temporal Memory algorithm.
"""
def __init__(self,
columnDimensions=(2048,),
cellsPerColumn=32,
activationThreshold=13,
learningRadius=2048,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
seed=42):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected synapses on a segment is at least this threshold, the segment is said to be active.
@param learningRadius (int) Radius around cell from which it can sample to form distal dendrite connections.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse is greater than this value, it is said to be connected.
@param minThreshold (int) If the number of synapses active on a segment is at least this threshold, it is selected as the best matching cell in a bursting column.
@param maxNewSynapseCount (int) The maximum number of synapses added to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses are decremented during learning.
@param seed (int) Seed for the random number generator.
"""
# Error checking
if not len(columnDimensions):
raise ValueError("Number of column dimensions must be greater than 0")
if not cellsPerColumn > 0:
raise ValueError("Number of cells per column must be greater than 0")
# TODO: Validate all parameters (and add validation tests)
# Save member variables
self.columnDimensions = columnDimensions
self.cellsPerColumn = cellsPerColumn
self.activationThreshold = activationThreshold
self.learningRadius = learningRadius
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
# Initialize member variables
self.connections = Connections(self.numberOfCells())
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
"""
Feeds input record through TM, performing inference and learning.
Updates member variables with new state.
@param activeColumns (set) Indices of active columns in `t`
"""
(activeCells,
winnerCells,
activeSegments,
predictiveCells,
predictedColumns) = self.computeFn(activeColumns,
self.predictiveCells,
self.activeSegments,
self.activeCells,
self.winnerCells,
self.connections,
learn=learn)
self.activeCells = activeCells
self.winnerCells = winnerCells
self.activeSegments = activeSegments
self.predictiveCells = predictiveCells
def computeFn(self,
activeColumns,
prevPredictiveCells,
prevActiveSegments,
prevActiveCells,
prevWinnerCells,
connections,
learn=True):
"""
'Functional' version of compute.
Returns new state.
@param activeColumns (set) Indices of active columns in `t`
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param prevActiveSegments (set) Indices of active segments in `t-1`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@param learn (bool) Whether or not learning is enabled
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`activeSegments` (set),
`predictiveCells` (set)
"""
activeCells = set()
winnerCells = set()
(_activeCells,
_winnerCells,
predictedColumns) = self.activateCorrectlyPredictiveCells(
prevPredictiveCells,
activeColumns)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
(_activeCells,
_winnerCells,
learningSegments) = self.burstColumns(activeColumns,
predictedColumns,
prevActiveCells,
prevWinnerCells,
connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
if learn:
self.learnOnSegments(prevActiveSegments,
learningSegments,
prevActiveCells,
winnerCells,
prevWinnerCells,
connections)
(activeSegments,
predictiveCells) = self.computePredictiveCells(activeCells, connections)
return (activeCells,
winnerCells,
activeSegments,
predictiveCells,
predictedColumns)
def reset(self):
"""
Indicates the start of a new sequence. Resets sequence state of the TM.
"""
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
# ==============================
# Phases
# ==============================
def activateCorrectlyPredictiveCells(self,
prevPredictiveCells,
activeColumns):
"""
Phase 1: Activate the correctly predictive cells.
Pseudocode:
- for each prev predictive cell
- if in active column
- mark it as active
- mark it as winner cell
- mark column as predicted
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param activeColumns (set) Indices of active columns in `t`
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`predictedColumns` (set)
"""
activeCells = set()
winnerCells = set()
predictedColumns = set()
for cell in prevPredictiveCells:
column = self.columnForCell(cell)
if column in activeColumns:
activeCells.add(cell)
winnerCells.add(cell)
predictedColumns.add(column)
return activeCells, winnerCells, predictedColumns
def burstColumns(self,
activeColumns,
predictedColumns,
prevActiveCells,
prevWinnerCells,
connections):
"""
Phase 2: Burst unpredicted columns.
Pseudocode:
- for each unpredicted active column
- mark all cells as active
- mark the best matching cell as winner cell
- (learning)
- if it has no matching segment
- (optimization) if there are prev winner cells
- add a segment to it
- mark the segment as learning
@param activeColumns (set) Indices of active columns in `t`
@param predictedColumns (set) Indices of predicted columns in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`learningSegments` (set)
"""
activeCells = set()
winnerCells = set()
learningSegments = set()
unpredictedColumns = activeColumns - predictedColumns
for column in unpredictedColumns:
cells = self.cellsForColumn(column)
activeCells.update(cells)
(bestCell,
bestSegment) = self.bestMatchingCell(cells,
prevActiveCells,
connections)
winnerCells.add(bestCell)
if bestSegment is None and len(prevWinnerCells):
bestSegment = connections.createSegment(bestCell)
if bestSegment is not None:
learningSegments.add(bestSegment)
return activeCells, winnerCells, learningSegments
def learnOnSegments(self,
prevActiveSegments,
learningSegments,
prevActiveCells,
winnerCells,
prevWinnerCells,
connections):
"""
Phase 3: Perform learning by adapting segments.
Pseudocode:
- (learning) for each prev active or learning segment
- if learning segment or from winner cell
- strengthen active synapses
- weaken inactive synapses
- if learning segment
- add some synapses to the segment
- subsample from prev winner cells
@param prevActiveSegments (set) Indices of active segments in `t-1`
@param learningSegments (set) Indices of learning segments in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param winnerCells (set) Indices of winner cells in `t`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
"""
for segment in prevActiveSegments | learningSegments:
isLearningSegment = segment in learningSegments
isFromWinnerCell = connections.cellForSegment(segment) in winnerCells
activeSynapses = self.activeSynapsesForSegment(
segment, prevActiveCells, connections)
if isLearningSegment or isFromWinnerCell:
self.adaptSegment(segment, activeSynapses, connections)
if isLearningSegment:
n = self.maxNewSynapseCount - len(activeSynapses)
for presynapticCell in self.pickCellsToLearnOn(n,
segment,
prevWinnerCells,
connections):
connections.createSynapse(segment,
presynapticCell,
self.initialPermanence)
def computePredictiveCells(self, activeCells, connections):
"""
Phase 4: Compute predictive cells due to lateral input
on distal dendrites.
Pseudocode:
- for each distal dendrite segment with activity >= activationThreshold
- mark the segment as active
- mark the cell as predictive
Forward propagates activity from active cells to the synapses that touch
them, to determine which synapses are active.
@param activeCells (set) Indices of active cells in `t`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeSegments` (set),
`predictiveCells` (set)
"""
numActiveConnectedSynapsesForSegment = defaultdict(lambda: 0)
activeSegments = set()
predictiveCells = set()
for cell in activeCells:
for synapseData in connections.synapsesForPresynapticCell(cell).values():
segment = synapseData.segment
permanence = synapseData.permanence
if permanence >= self.connectedPermanence:
numActiveConnectedSynapsesForSegment[segment] += 1
if (numActiveConnectedSynapsesForSegment[segment] >=
self.activationThreshold):
activeSegments.add(segment)
predictiveCells.add(connections.cellForSegment(segment))
return activeSegments, predictiveCells
# ==============================
# Helper functions
# ==============================
def bestMatchingCell(self, cells, activeCells, connections):
"""
Gets the cell with the best matching segment
(see `TM.bestMatchingSegment`) that has the largest number of active
synapses of all best matching segments.
If none were found, pick the least used cell (see `TM.leastUsedCell`).
@param cells (set) Indices of cells
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`cell` (int),
`bestSegment` (int)
"""
maxSynapses = 0
bestCell = None
bestSegment = None
for cell in cells:
segment, numActiveSynapses = self.bestMatchingSegment(
cell, activeCells, connections)
if segment is not None and numActiveSynapses > maxSynapses:
maxSynapses = numActiveSynapses
bestCell = cell
bestSegment = segment
if bestCell is None:
bestCell = self.leastUsedCell(cells, connections)
return bestCell, bestSegment
def bestMatchingSegment(self, cell, activeCells, connections):
"""
Gets the segment on a cell with the largest number of activate synapses,
including all synapses with non-zero permanences.
@param cell (int) Cell index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`segment` (int),
`connectedActiveSynapses` (set)
"""
maxSynapses = self.minThreshold
bestSegment = None
bestNumActiveSynapses = None
for segment in connections.segmentsForCell(cell):
numActiveSynapses = 0
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if synapseData.presynapticCell in activeCells:
numActiveSynapses += 1
if numActiveSynapses >= maxSynapses:
maxSynapses = numActiveSynapses
bestSegment = segment
bestNumActiveSynapses = numActiveSynapses
return bestSegment, bestNumActiveSynapses
def leastUsedCell(self, cells, connections):
"""
Gets the cell with the smallest number of segments.
Break ties randomly.
@param cells (set) Indices of cells
@param connections (Connections) Connectivity of layer
@return (int) Cell index
"""
leastUsedCells = set()
minNumSegments = float("inf")
for cell in cells:
numSegments = len(connections.segmentsForCell(cell))
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = set()
if numSegments == minNumSegments:
leastUsedCells.add(cell)
i = self._random.getUInt32(len(leastUsedCells))
return sorted(leastUsedCells)[i]
@staticmethod
def activeSynapsesForSegment(segment, activeCells, connections):
"""
Returns the synapses on a segment that are active due to lateral input
from active cells.
@param segment (int) Segment index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (set) Indices of active synapses on segment
"""
synapses = set()
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if synapseData.presynapticCell in activeCells:
synapses.add(synapse)
return synapses
def adaptSegment(self, segment, activeSynapses, connections):
"""
Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param segment (int) Segment index
@param activeSynapses (set) Indices of active synapses
@param connections (Connections) Connectivity of layer
"""
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
permanence = synapseData.permanence
if synapse in activeSynapses:
permanence += self.permanenceIncrement
else:
permanence -= self.permanenceDecrement
# Keep permanence within min/max bounds
permanence = max(0.0, min(1.0, permanence))
connections.updateSynapsePermanence(synapse, permanence)
def pickCellsToLearnOn(self, n, segment, winnerCells, connections):
"""
Pick cells to form distal connections to.
TODO: Respect topology and learningRadius
@param n (int) Number of cells to pick
@param segment (int) Segment index
@param winnerCells (set) Indices of winner cells in `t`
@param connections (Connections) Connectivity of layer
@return (set) Indices of cells picked
"""
candidates = set(winnerCells)
# Remove cells that are already synapsed on by this segment
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
presynapticCell = synapseData.presynapticCell
if presynapticCell in candidates:
candidates.remove(presynapticCell)
n = min(n, len(candidates))
candidates = sorted(candidates)
cells = set()
# Pick n cells randomly
for _ in range(n):
i = self._random.getUInt32(len(candidates))
cells.add(candidates[i])
del candidates[i]
return cells
def columnForCell(self, cell):
"""
Returns the index of the column that a cell belongs to.
@param cell (int) Cell index
@return (int) Column index
"""
self._validateCell(cell)
return int(cell / self.cellsPerColumn)
def cellsForColumn(self, column):
"""
Returns the indices of cells that belong to a column.
@param column (int) Column index
@return (set) Cell indices
"""
self._validateColumn(column)
start = self.cellsPerColumn * column
end = start + self.cellsPerColumn
return set([cell for cell in range(start, end)])
def numberOfColumns(self):
"""
Returns the number of columns in this layer.
@return (int) Number of columns
"""
return reduce(mul, self.columnDimensions, 1)
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.numberOfColumns() * self.cellsPerColumn
def mapCellsToColumns(self, cells):
"""
Maps cells to the columns they belong to
@param cells (set) Cells
@return (dict) Mapping from columns to their cells in `cells`
"""
cellsForColumns = defaultdict(set)
for cell in cells:
column = self.columnForCell(cell)
cellsForColumns[column].add(cell)
return cellsForColumns
def _validateColumn(self, column):
"""
Raises an error if column index is invalid.
@param column (int) Column index
"""
if column >= self.numberOfColumns() or column < 0:
raise IndexError("Invalid column")
def _validateCell(self, cell):
"""
Raises an error if cell index is invalid.
@param cell (int) Cell index
"""
if cell >= self.numberOfCells() or cell < 0:
raise IndexError("Invalid cell")
@classmethod
def getCellIndices(cls, cells):
return [cls.getCellIndex(c) for c in cells]
@staticmethod
def getCellIndex(cell):
return cell.idx
class SMSequences(object):
"""
Class generates sensorimotor sequences
"""
def __init__(
self,
sensoryInputElements,
spatialConfig,
sensoryInputElementsPool=list("ABCDEFGHIJKLMNOPQRSTUVWXYZ" "abcdefghijklmnopqrstuvwxyz0123456789"),
minDisplacement=1,
maxDisplacement=1,
numActiveBitsSensoryInput=9,
numActiveBitsMotorInput=9,
seed=42,
verbosity=False,
useRandomEncoder=False,
):
"""
@param sensoryInputElements (list)
Strings or numbers representing the sensory elements that exist in your
world. Elements can be repeated if multiple of the same exist.
@param spatialConfig (numpy.array)
Array of size: (1, len(sensoryInputElements), dimension). It has a
coordinate for every element in sensoryInputElements.
@param sensoryInputElementsPool (list)
List of strings representing a readable version of all possible sensory
elements in this world. Elements don't need to be in any order and there
should be no duplicates. By default this contains the set of
alphanumeric characters.
@param maxDisplacement (int)
Maximum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param minDisplacement (int)
Minimum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param numActiveBitsSensoryInput (int)
Number of active bits for each sensory input.
@param numActiveBitsMotorInput (int)
Number of active bits for each dimension of the motor input.
@param seed (int)
Random seed for nupic.bindings.Random.
@param verbosity (int)
Verbosity
@param useRandomEncoder (boolean)
if True, use the random encoder SDRCategoryEncoder. If False,
use CategoryEncoder. CategoryEncoder encodes categories using contiguous
non-overlapping bits for each category, which makes it easier to debug.
"""
# ---------------------------------------------------------------------------------
# Store creation parameters
self.sensoryInputElements = sensoryInputElements
self.sensoryInputElementsPool = sensoryInputElementsPool
self.spatialConfig = spatialConfig.astype(int)
self.spatialLength = len(spatialConfig)
self.maxDisplacement = maxDisplacement
self.minDisplacement = minDisplacement
self.numActiveBitsSensoryInput = numActiveBitsSensoryInput
self.numActiveBitsMotorInput = numActiveBitsMotorInput
self.verbosity = verbosity
self.seed = seed
self.initialize(useRandomEncoder)
def initialize(self, useRandomEncoder):
"""
Initialize the various data structures.
"""
self.setRandomSeed(self.seed)
self.dim = numpy.shape(self.spatialConfig)[-1]
self.spatialMap = dict(zip(map(tuple, list(self.spatialConfig)), self.sensoryInputElements))
self.lengthMotorInput1D = (2 * self.maxDisplacement + 1) * self.numActiveBitsMotorInput
uniqueSensoryElements = list(set(self.sensoryInputElementsPool))
if useRandomEncoder:
self.sensoryEncoder = SDRCategoryEncoder(
n=1024, w=self.numActiveBitsSensoryInput, categoryList=uniqueSensoryElements, forced=True
)
self.lengthSensoryInput = self.sensoryEncoder.getWidth()
else:
self.lengthSensoryInput = (len(self.sensoryInputElementsPool) + 1) * self.numActiveBitsSensoryInput
self.sensoryEncoder = CategoryEncoder(
w=self.numActiveBitsSensoryInput, categoryList=uniqueSensoryElements, forced=True
)
motorEncoder1D = ScalarEncoder(
n=self.lengthMotorInput1D,
w=self.numActiveBitsMotorInput,
minval=-self.maxDisplacement,
maxval=self.maxDisplacement,
clipInput=True,
forced=True,
)
self.motorEncoder = VectorEncoder(length=self.dim, encoder=motorEncoder1D)
def generateSensorimotorSequence(self, sequenceLength):
"""
Generate sensorimotor sequences of length sequenceLength.
@param sequenceLength (int)
Length of the sensorimotor sequence.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
currentEyeLoc = self.nupicRandomChoice(self.spatialConfig)
for i in xrange(sequenceLength):
currentSensoryInput = self.spatialMap[tuple(currentEyeLoc)]
nextEyeLoc, currentEyeV = self.getNextEyeLocation(currentEyeLoc)
if self.verbosity:
print "sensory input = ", currentSensoryInput, "eye location = ", currentEyeLoc, " motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
currentEyeLoc = nextEyeLoc
return (sensorySequence, motorSequence, sensorimotorSequence)
def encodeSensorimotorSequence(self, eyeLocs):
"""
Encode sensorimotor sequence given the eye movements. Sequence will have
length len(eyeLocs) - 1 because only the differences of eye locations can be
used to encoder motor commands.
@param eyeLocs (list)
Numpy coordinates describing where the eye is looking.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
sequenceLength = len(eyeLocs) - 1
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
for i in xrange(sequenceLength):
currentEyeLoc = eyeLocs[i]
nextEyeLoc = eyeLocs[i + 1]
currentSensoryInput = self.spatialMap[currentEyeLoc]
currentEyeV = nextEyeLoc - currentEyeLoc
if self.verbosity:
print "sensory input = ", currentSensoryInput, "eye location = ", currentEyeLoc, " motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
return (sensorySequence, motorSequence, sensorimotorSequence)
def getNextEyeLocation(self, currentEyeLoc):
"""
Generate next eye location based on current eye location.
@param currentEyeLoc (numpy.array)
Current coordinate describing the eye location in the world.
@return (tuple) Contains:
nextEyeLoc (numpy.array)
Coordinate of the next eye location.
eyeDiff (numpy.array)
Vector describing change from currentEyeLoc to nextEyeLoc.
"""
possibleEyeLocs = []
for loc in self.spatialConfig:
shift = abs(max(loc - currentEyeLoc))
if self.minDisplacement <= shift <= self.maxDisplacement:
possibleEyeLocs.append(loc)
nextEyeLoc = self.nupicRandomChoice(possibleEyeLocs)
eyeDiff = nextEyeLoc - currentEyeLoc
return nextEyeLoc, eyeDiff
def setRandomSeed(self, seed):
"""
Reset the nupic random generator. This is necessary to reset random seed to
generate new sequences.
@param seed (int)
Seed for nupic.bindings.Random.
"""
self.seed = seed
self._random = Random()
self._random.setSeed(seed)
def nupicRandomChoice(self, array):
"""
Chooses a random element from an array using the nupic random number
generator.
@param array (list or numpy.array)
Array to choose random element from.
@return (element)
Element chosen at random.
"""
return array[self._random.getUInt32(len(array))]
def encodeMotorInput(self, motorInput):
"""
Encode motor command to bit vector.
@param motorInput (1D numpy.array)
Motor command to be encoded.
@return (1D numpy.array)
Encoded motor command.
"""
if not hasattr(motorInput, "__iter__"):
motorInput = list([motorInput])
return self.motorEncoder.encode(motorInput)
def decodeMotorInput(self, motorInputPattern):
"""
Decode motor command from bit vector.
@param motorInputPattern (1D numpy.array)
Encoded motor command.
@return (1D numpy.array)
Decoded motor command.
"""
key = self.motorEncoder.decode(motorInputPattern)[0].keys()[0]
motorCommand = self.motorEncoder.decode(motorInputPattern)[0][key][1][0]
return motorCommand
def encodeSensoryInput(self, sensoryInputElement):
"""
Encode sensory input to bit vector
@param sensoryElement (1D numpy.array)
Sensory element to be encoded.
@return (1D numpy.array)
Encoded sensory element.
"""
return self.sensoryEncoder.encode(sensoryInputElement)
def decodeSensoryInput(self, sensoryInputPattern):
"""
Decode sensory input from bit vector.
@param sensoryInputPattern (1D numpy.array)
Encoded sensory element.
@return (1D numpy.array)
Decoded sensory element.
"""
return self.sensoryEncoder.decode(sensoryInputPattern)[0]["category"][1]
def printSensoryCodingScheme(self):
"""
Print sensory inputs along with their encoded versions.
"""
print "\nsensory coding scheme: "
for loc in self.spatialConfig:
sensoryElement = self.spatialMap[tuple(loc)]
print sensoryElement, "%s : " % loc,
printSequence(self.encodeSensoryInput(sensoryElement))
def printMotorCodingScheme(self):
"""
Print motor commands (displacement vector) along with their encoded
versions.
"""
print "\nmotor coding scheme: "
self.build(self.dim, [])
def build(self, n, vec):
"""
Recursive function to help print motor coding scheme.
"""
for i in range(-self.maxDisplacement, self.maxDisplacement + 1):
next = vec + [i]
if n == 1:
print "{:>5}\t".format(next), " = ",
printSequence(self.encodeMotorInput(next))
else:
self.build(n - 1, next)
class TemporalMemory(object):
"""
Class implementing the Temporal Memory algorithm.
"""
def __init__(self,
columnDimensions=(2048,),
cellsPerColumn=32,
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=255,
maxSynapsesPerSegment=255,
seed=42,
**kwargs):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected synapses on a segment is at least this threshold, the segment is said to be active.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse is greater than this value, it is said to be connected.
@param minThreshold (int) If the number of synapses active on a segment is at least this threshold, it is selected as the best matching cell in a bursting column.
@param maxNewSynapseCount (int) The maximum number of synapses added to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses are decremented during learning.
@param predictedSegmentDecrement (float) Amount by which active permanences of synapses of previously predicted but inactive segments are decremented.
@param seed (int) Seed for the random number generator.
Notes:
predictedSegmentDecrement: A good value is just a bit larger than
(the column-level sparsity * permanenceIncrement). So, if column-level
sparsity is 2% and permanenceIncrement is 0.01, this parameter should be
something like 4% * 0.01 = 0.0004).
"""
# Error checking
if not len(columnDimensions):
raise ValueError("Number of column dimensions must be greater than 0")
if not cellsPerColumn > 0:
raise ValueError("Number of cells per column must be greater than 0")
# TODO: Validate all parameters (and add validation tests)
# Save member variables
self.columnDimensions = columnDimensions
self.cellsPerColumn = cellsPerColumn
self.activationThreshold = activationThreshold
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
self.predictedSegmentDecrement = predictedSegmentDecrement
# Initialize member variables
self.connections = Connections(self.numberOfCells(),
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment)
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
self.matchingSegments = set()
self.matchingCells = set()
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
"""
Feeds input record through TM, performing inference and learning.
@param activeColumns (set) Indices of active columns
@param learn (bool) Whether or not learning is enabled
Updates member variables:
- `activeCells` (set)
- `winnerCells` (set)
- `activeSegments` (set)
- `predictiveCells` (set)
- `matchingSegments`(set)
- `matchingCells` (set)
"""
prevPredictiveCells = self.predictiveCells
prevActiveSegments = self.activeSegments
prevActiveCells = self.activeCells
prevWinnerCells = self.winnerCells
prevMatchingSegments = self.matchingSegments
prevMatchingCells = self.matchingCells
activeCells = set()
winnerCells = set()
(_activeCells,
_winnerCells,
predictedActiveColumns,
predictedInactiveCells) = self.activateCorrectlyPredictiveCells(
prevPredictiveCells,
prevMatchingCells,
activeColumns)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
(_activeCells,
_winnerCells,
learningSegments) = self.burstColumns(activeColumns,
predictedActiveColumns,
prevActiveCells,
prevWinnerCells,
self.connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
if learn:
self.learnOnSegments(prevActiveSegments,
learningSegments,
prevActiveCells,
winnerCells,
prevWinnerCells,
self.connections,
predictedInactiveCells,
prevMatchingSegments)
(activeSegments,
predictiveCells,
matchingSegments,
matchingCells) = self.computePredictiveCells(activeCells, self.connections)
self.activeCells = activeCells
self.winnerCells = winnerCells
self.activeSegments = activeSegments
self.predictiveCells = predictiveCells
self.matchingSegments = matchingSegments
self.matchingCells = matchingCells
def reset(self):
"""
Indicates the start of a new sequence. Resets sequence state of the TM.
"""
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
# ==============================
# Phases
# ==============================
def activateCorrectlyPredictiveCells(self,
prevPredictiveCells,
prevMatchingCells,
activeColumns):
"""
Phase 1: Activate the correctly predictive cells.
Pseudocode:
- for each prev predictive cell
- if in active column
- mark it as active
- mark it as winner cell
- mark column as predicted => active
- if not in active column
- mark it as an predicted but inactive cell
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param activeColumns (set) Indices of active columns in `t`
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`predictedActiveColumns` (set),
`predictedInactiveCells` (set)
"""
activeCells = set()
winnerCells = set()
predictedActiveColumns = set()
predictedInactiveCells = set()
for cell in prevPredictiveCells:
column = self.columnForCell(cell)
if column in activeColumns:
activeCells.add(cell)
winnerCells.add(cell)
predictedActiveColumns.add(column)
if self.predictedSegmentDecrement > 0:
for cell in prevMatchingCells:
column = self.columnForCell(cell)
if column not in activeColumns:
predictedInactiveCells.add(cell)
return (activeCells,
winnerCells,
predictedActiveColumns,
predictedInactiveCells)
def burstColumns(self,
activeColumns,
predictedActiveColumns,
prevActiveCells,
prevWinnerCells,
connections):
"""
Phase 2: Burst unpredicted columns.
Pseudocode:
- for each unpredicted active column
- mark all cells as active
- mark the best matching cell as winner cell
- (learning)
- if it has no matching segment
- (optimization) if there are prev winner cells
- add a segment to it
- mark the segment as learning
@param activeColumns (set) Indices of active columns in `t`
@param predictedActiveColumns (set) Indices of predicted => active columns in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`learningSegments` (set)
"""
activeCells = set()
winnerCells = set()
learningSegments = set()
unpredictedActiveColumns = activeColumns - predictedActiveColumns
# Sort unpredictedActiveColumns before iterating for compatibility with C++
for column in sorted(unpredictedActiveColumns):
cells = self.cellsForColumn(column)
activeCells.update(cells)
(bestCell,
bestSegment) = self.bestMatchingCell(cells,
prevActiveCells,
connections)
winnerCells.add(bestCell)
if bestSegment is None and len(prevWinnerCells):
bestSegment = connections.createSegment(bestCell)
if bestSegment is not None:
learningSegments.add(bestSegment)
return activeCells, winnerCells, learningSegments
def learnOnSegments(self,
prevActiveSegments,
learningSegments,
prevActiveCells,
winnerCells,
prevWinnerCells,
connections,
predictedInactiveCells,
prevMatchingSegments):
"""
Phase 3: Perform learning by adapting segments.
Pseudocode:
- (learning) for each prev active or learning segment
- if learning segment or from winner cell
- strengthen active synapses
- weaken inactive synapses
- if learning segment
- add some synapses to the segment
- subsample from prev winner cells
- if predictedSegmentDecrement > 0
- for each previously matching segment
- if cell is a predicted inactive cell
- weaken active synapses but don't touch inactive synapses
@param prevActiveSegments (set) Indices of active segments in `t-1`
@param learningSegments (set) Indices of learning segments in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param winnerCells (set) Indices of winner cells in `t`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@param predictedInactiveCells (set) Indices of predicted inactive cells
@param prevMatchingSegments (set) Indices of segments with
"""
segments = prevActiveSegments | learningSegments
# Sort segments before iterating for compatibility with C++
# Sort with primary key = cell idx, secondary key = segment idx
segments = sorted(
segments,
key=lambda segment: (connections.cellForSegment(segment), segment))
for segment in segments:
isLearningSegment = segment in learningSegments
isFromWinnerCell = connections.cellForSegment(segment) in winnerCells
activeSynapses = self.activeSynapsesForSegment(
segment, prevActiveCells, connections)
if isLearningSegment or isFromWinnerCell:
self.adaptSegment(segment, activeSynapses, connections,
self.permanenceIncrement,
self.permanenceDecrement)
if isLearningSegment:
n = self.maxNewSynapseCount - len(activeSynapses)
# Fix for NUP #3268 is commented out for now until test failures are
# addressed.
# n = min(self.maxNewSynapseCount,
# connections.maxSynapsesPerSegment
# - len(connections.synapsesForSegment(segment)))
for presynapticCell in self.pickCellsToLearnOn(n,
segment,
prevWinnerCells,
connections):
connections.createSynapse(segment,
presynapticCell,
self.initialPermanence)
if self.predictedSegmentDecrement > 0:
for segment in prevMatchingSegments:
isPredictedInactiveCell = connections.cellForSegment(segment) in predictedInactiveCells
activeSynapses = self.activeSynapsesForSegment(
segment, prevActiveCells, connections)
if isPredictedInactiveCell:
self.adaptSegment(segment, activeSynapses, connections,
-self.predictedSegmentDecrement,
0.0)
def computePredictiveCells(self, activeCells, connections):
"""
Phase 4: Compute predictive cells due to lateral input
on distal dendrites.
Pseudocode:
- for each distal dendrite segment with activity >= activationThreshold
- mark the segment as active
- mark the cell as predictive
- if predictedSegmentDecrement > 0
- for each distal dendrite segment with unconnected
activity >= minThreshold
- mark the segment as matching
- mark the cell as matching
Forward propagates activity from active cells to the synapses that touch
them, to determine which synapses are active.
@param activeCells (set) Indices of active cells in `t`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeSegments` (set),
`predictiveCells` (set),
`matchingSegments` (set),
`matchingCells` (set)
"""
numActiveConnectedSynapsesForSegment = defaultdict(int)
numActiveSynapsesForSegment = defaultdict(int)
activeSegments = set()
predictiveCells = set()
matchingSegments = set()
matchingCells = set()
for cell in activeCells:
for synapseData in connections.synapsesForPresynapticCell(cell).values():
segment = synapseData.segment
permanence = synapseData.permanence
if permanence >= self.connectedPermanence:
numActiveConnectedSynapsesForSegment[segment] += 1
if (numActiveConnectedSynapsesForSegment[segment] >=
self.activationThreshold):
activeSegments.add(segment)
predictiveCells.add(connections.cellForSegment(segment))
if permanence > 0 and self.predictedSegmentDecrement > 0:
numActiveSynapsesForSegment[segment] += 1
if numActiveSynapsesForSegment[segment] >= self.minThreshold:
matchingSegments.add(segment)
matchingCells.add(connections.cellForSegment(segment))
return activeSegments, predictiveCells, matchingSegments, matchingCells
# ==============================
# Helper functions
# ==============================
def bestMatchingCell(self, cells, activeCells, connections):
"""
Gets the cell with the best matching segment
(see `TM.bestMatchingSegment`) that has the largest number of active
synapses of all best matching segments.
If none were found, pick the least used cell (see `TM.leastUsedCell`).
@param cells (set) Indices of cells
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`cell` (int),
`bestSegment` (int)
"""
maxSynapses = 0
bestCell = None
bestSegment = None
for cell in cells:
segment, numActiveSynapses = self.bestMatchingSegment(
cell, activeCells, connections)
if segment is not None and numActiveSynapses > maxSynapses:
maxSynapses = numActiveSynapses
bestCell = cell
bestSegment = segment
if bestCell is None:
bestCell = self.leastUsedCell(cells, connections)
return bestCell, bestSegment
def bestMatchingSegment(self, cell, activeCells, connections):
"""
Gets the segment on a cell with the largest number of activate synapses,
including all synapses with non-zero permanences.
@param cell (int) Cell index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`segment` (int),
`connectedActiveSynapses` (set)
"""
maxSynapses = self.minThreshold
bestSegment = None
bestNumActiveSynapses = None
for segment in connections.segmentsForCell(cell):
numActiveSynapses = 0
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if ( (synapseData.presynapticCell in activeCells) and
synapseData.permanence > 0):
numActiveSynapses += 1
if numActiveSynapses >= maxSynapses:
maxSynapses = numActiveSynapses
bestSegment = segment
bestNumActiveSynapses = numActiveSynapses
return bestSegment, bestNumActiveSynapses
def leastUsedCell(self, cells, connections):
"""
Gets the cell with the smallest number of segments.
Break ties randomly.
@param cells (set) Indices of cells
@param connections (Connections) Connectivity of layer
@return (int) Cell index
"""
leastUsedCells = set()
minNumSegments = float("inf")
for cell in cells:
numSegments = len(connections.segmentsForCell(cell))
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = set()
if numSegments == minNumSegments:
leastUsedCells.add(cell)
i = self._random.getUInt32(len(leastUsedCells))
return sorted(leastUsedCells)[i]
@staticmethod
def activeSynapsesForSegment(segment, activeCells, connections):
"""
Returns the synapses on a segment that are active due to lateral input
from active cells.
@param segment (int) Segment index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (set) Indices of active synapses on segment
"""
synapses = set()
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if synapseData.presynapticCell in activeCells:
synapses.add(synapse)
return synapses
def adaptSegment(self, segment, activeSynapses, connections,
permanenceIncrement, permanenceDecrement):
"""
Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param segment (int) Segment index
@param activeSynapses (set) Indices of active synapses
@param connections (Connections) Connectivity of layer
@param permanenceIncrement (float) Amount to increment active synapses
@param permanenceDecrement (float) Amount to decrement inactive synapses
"""
# Need to copy synapses for segment set below because it will be modified
# during iteration by `destroySynapse`
for synapse in set(connections.synapsesForSegment(segment)):
synapseData = connections.dataForSynapse(synapse)
permanence = synapseData.permanence
if synapse in activeSynapses:
permanence += permanenceIncrement
else:
permanence -= permanenceDecrement
# Keep permanence within min/max bounds
permanence = max(0.0, min(1.0, permanence))
if (permanence < EPSILON):
connections.destroySynapse(synapse)
else:
connections.updateSynapsePermanence(synapse, permanence)
def pickCellsToLearnOn(self, n, segment, winnerCells, connections):
"""
Pick cells to form distal connections to.
TODO: Respect topology and learningRadius
@param n (int) Number of cells to pick
@param segment (int) Segment index
@param winnerCells (set) Indices of winner cells in `t`
@param connections (Connections) Connectivity of layer
@return (set) Indices of cells picked
"""
candidates = set(winnerCells)
# Remove cells that are already synapsed on by this segment
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
presynapticCell = synapseData.presynapticCell
if presynapticCell in candidates:
candidates.remove(presynapticCell)
n = min(n, len(candidates))
candidates = sorted(candidates)
cells = set()
# Pick n cells randomly
for _ in range(n):
i = self._random.getUInt32(len(candidates))
cells.add(candidates[i])
del candidates[i]
return cells
def columnForCell(self, cell):
"""
Returns the index of the column that a cell belongs to.
@param cell (int) Cell index
@return (int) Column index
"""
self._validateCell(cell)
return int(cell / self.cellsPerColumn)
def cellsForColumn(self, column):
"""
Returns the indices of cells that belong to a column.
@param column (int) Column index
@return (set) Cell indices
"""
self._validateColumn(column)
start = self.cellsPerColumn * self.getCellIndex(column)
end = start + self.cellsPerColumn
return set(xrange(start, end))
def numberOfColumns(self):
"""
Returns the number of columns in this layer.
@return (int) Number of columns
"""
return reduce(mul, self.columnDimensions, 1)
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.numberOfColumns() * self.cellsPerColumn
def getActiveCells(self):
"""
Returns the indices of the active cells.
@return (list) Indices of active cells.
"""
return self.getCellIndices(self.activeCells)
def getPredictiveCells(self):
"""
Returns the indices of the predictive cells.
@return (list) Indices of predictive cells.
"""
return self.getCellIndices(self.predictiveCells)
def getWinnerCells(self):
"""
Returns the indices of the winner cells.
@return (list) Indices of winner cells.
"""
return self.getCellIndices(self.winnerCells)
def getMatchingCells(self):
"""
Returns the indices of the matching cells.
@return (list) Indices of matching cells.
"""
return self.getCellIndices(self.matchingCells)
def getColumnDimensions(self):
"""
Returns the dimensions of the columns in the region.
@return (tuple) Column dimensions
"""
return self.columnDimensions
def getCellsPerColumn(self):
"""
Returns the number of cells per column.
@return (int) The number of cells per column.
"""
return self.cellsPerColumn
def getActivationThreshold(self):
"""
Returns the activation threshold.
@return (int) The activation threshold.
"""
return self.activationThreshold
def setActivationThreshold(self, activationThreshold):
"""
Sets the activation threshold.
@param activationThreshold (int) activation threshold.
"""
self.activationThreshold = activationThreshold
def getInitialPermanence(self):
"""
Get the initial permanence.
@return (float) The initial permanence.
"""
return self.initialPermanence
def setInitialPermanence(self, initialPermanence):
"""
Sets the initial permanence.
@param initialPermanence (float) The initial permanence.
"""
self.initialPermanence = initialPermanence
def getMinThreshold(self):
"""
Returns the min threshold.
@return (int) The min threshold.
"""
return self.minThreshold
def setMinThreshold(self, minThreshold):
"""
Sets the min threshold.
@param minThreshold (int) min threshold.
"""
self.minThreshold = minThreshold
def getMaxNewSynapseCount(self):
"""
Returns the max new synapse count.
@return (int) The max new synapse count.
"""
return self.maxNewSynapseCount
def setMaxNewSynapseCount(self, maxNewSynapseCount):
"""
Sets the max new synapse count.
@param maxNewSynapseCount (int) Max new synapse count.
"""
self.maxNewSynapseCount = maxNewSynapseCount
def getPermanenceIncrement(self):
"""
Get the permanence increment.
@return (float) The permanence increment.
"""
return self.permanenceIncrement
def setPermanenceIncrement(self, permanenceIncrement):
"""
Sets the permanence increment.
@param permanenceIncrement (float) The permanence increment.
"""
self.permanenceIncrement = permanenceIncrement
def getPermanenceDecrement(self):
"""
Get the permanence decrement.
@return (float) The permanence decrement.
"""
return self.permanenceDecrement
def setPermanenceDecrement(self, permanenceDecrement):
"""
Sets the permanence decrement.
@param permanenceDecrement (float) The permanence decrement.
"""
self.permanenceDecrement = permanenceDecrement
def getPredictedSegmentDecrement(self):
"""
Get the predicted segment decrement.
@return (float) The predicted segment decrement.
"""
return self.predictedSegmentDecrement
def setPredictedSegmentDecrement(self, predictedSegmentDecrement):
"""
Sets the predicted segment decrement.
@param predictedSegmentDecrement (float) The predicted segment decrement.
"""
self.predictedSegmentDecrement = predictedSegmentDecrement
def getConnectedPermanence(self):
"""
Get the connected permanence.
@return (float) The connected permanence.
"""
return self.connectedPermanence
def setConnectedPermanence(self, connectedPermanence):
"""
Sets the connected permanence.
@param connectedPermanence (float) The connected permanence.
"""
self.connectedPermanence = connectedPermanence
def mapCellsToColumns(self, cells):
"""
Maps cells to the columns they belong to
@param cells (set) Cells
@return (dict) Mapping from columns to their cells in `cells`
"""
cellsForColumns = defaultdict(set)
for cell in cells:
column = self.columnForCell(cell)
cellsForColumns[column].add(cell)
return cellsForColumns
def write(self, proto):
"""
Writes serialized data to proto object
@param proto (DynamicStructBuilder) Proto object
"""
proto.columnDimensions = self.columnDimensions
proto.cellsPerColumn = self.cellsPerColumn
proto.activationThreshold = self.activationThreshold
proto.initialPermanence = self.initialPermanence
proto.connectedPermanence = self.connectedPermanence
proto.minThreshold = self.minThreshold
proto.maxNewSynapseCount = self.maxNewSynapseCount
proto.permanenceIncrement = self.permanenceIncrement
proto.permanenceDecrement = self.permanenceDecrement
proto.predictedSegmentDecrement = self.predictedSegmentDecrement
self.connections.write(proto.connections)
self._random.write(proto.random)
proto.activeCells = list(self.activeCells)
proto.predictiveCells = list(self.predictiveCells)
proto.activeSegments = list(self.activeSegments)
proto.winnerCells = list(self.winnerCells)
proto.matchingSegments = list(self.matchingSegments)
proto.matchingCells = list(self.matchingCells)
@classmethod
def read(cls, proto):
"""
Reads deserialized data from proto object
@param proto (DynamicStructBuilder) Proto object
@return (TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
tm.columnDimensions = list(proto.columnDimensions)
tm.cellsPerColumn = int(proto.cellsPerColumn)
tm.activationThreshold = int(proto.activationThreshold)
tm.initialPermanence = proto.initialPermanence
tm.connectedPermanence = proto.connectedPermanence
tm.minThreshold = int(proto.minThreshold)
tm.maxNewSynapseCount = int(proto.maxNewSynapseCount)
tm.permanenceIncrement = proto.permanenceIncrement
tm.permanenceDecrement = proto.permanenceDecrement
tm.predictedSegmentDecrement = proto.predictedSegmentDecrement
tm.connections = Connections.read(proto.connections)
tm._random = Random()
tm._random.read(proto.random)
tm.activeCells = set([int(x) for x in proto.activeCells])
tm.predictiveCells = set([int(x) for x in proto.predictiveCells])
tm.activeSegments = set([int(x) for x in proto.activeSegments])
tm.winnerCells = set([int(x) for x in proto.winnerCells])
tm.matchingSegments = set([int(x) for x in proto.matchingSegments])
tm.matchingCells = set([int(x) for x in proto.matchingCells])
return tm
def __eq__(self, other):
"""
Equality operator for TemporalMemory instances.
Checks if two instances are functionally identical
(might have different internal state).
@param other (TemporalMemory) TemporalMemory instance to compare to
"""
if self.columnDimensions != other.columnDimensions: return False
if self.cellsPerColumn != other.cellsPerColumn: return False
if self.activationThreshold != other.activationThreshold: return False
if abs(self.initialPermanence - other.initialPermanence) > EPSILON:
return False
if abs(self.connectedPermanence - other.connectedPermanence) > EPSILON:
return False
if self.minThreshold != other.minThreshold: return False
if self.maxNewSynapseCount != other.maxNewSynapseCount: return False
if abs(self.permanenceIncrement - other.permanenceIncrement) > EPSILON:
return False
if abs(self.permanenceDecrement - other.permanenceDecrement) > EPSILON:
return False
if abs(self.predictedSegmentDecrement - other.predictedSegmentDecrement) > EPSILON:
return False
if self.connections != other.connections: return False
if self.activeCells != other.activeCells: return False
if self.predictiveCells != other.predictiveCells: return False
if self.winnerCells != other.winnerCells: return False
if self.matchingSegments != other.matchingSegments: return False
if self.matchingCells != other.matchingCells: return False
return True
def __ne__(self, other):
"""
Non-equality operator for TemporalMemory instances.
Checks if two instances are not functionally identical
(might have different internal state).
@param other (TemporalMemory) TemporalMemory instance to compare to
"""
return not self.__eq__(other)
def _validateColumn(self, column):
"""
Raises an error if column index is invalid.
@param column (int) Column index
"""
if column >= self.numberOfColumns() or column < 0:
raise IndexError("Invalid column")
def _validateCell(self, cell):
"""
Raises an error if cell index is invalid.
@param cell (int) Cell index
"""
if cell >= self.numberOfCells() or cell < 0:
raise IndexError("Invalid cell")
@classmethod
def getCellIndices(cls, cells):
return [cls.getCellIndex(c) for c in cells]
@staticmethod
def getCellIndex(cell):
return cell
class TemporalMemory(object):
"""
Class implementing the Temporal Memory algorithm.
"""
def __init__(self,
columnDimensions=(2048,),
cellsPerColumn=32,
activationThreshold=13,
learningRadius=2048,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
seed=42):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected
synapses on a segment is at least
this threshold, the segment is
said to be active.
@param learningRadius (int) Radius around cell from which it can
sample to form distal dendrite
connections.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse
is greater than this value, it is said
to be connected.
@param minThreshold (int) If the number of synapses active on
a segment is at least this threshold,
it is selected as the best matching
cell in a bursing column.
@param maxNewSynapseCount (int) The maximum number of synapses added
to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses
are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses
are decremented during learning.
@param seed (int) Seed for the random number generator.
"""
# TODO: Validate all parameters (and add validation tests)
# Initialize member variables
self.connections = Connections(columnDimensions, cellsPerColumn)
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.activeSynapsesForSegment = dict()
self.winnerCells = set()
# Save member variables
self.activationThreshold = activationThreshold
self.learningRadius = learningRadius
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
"""
Feeds input record through TM, performing inference and learning.
Updates member variables with new state.
@param activeColumns (set) Indices of active columns in `t`
"""
(activeCells,
winnerCells,
activeSynapsesForSegment,
activeSegments,
predictiveCells) = self.computeFn(activeColumns,
self.predictiveCells,
self.activeSegments,
self.activeSynapsesForSegment,
self.winnerCells,
self.connections,
learn=learn)
self.activeCells = activeCells
self.winnerCells = winnerCells
self.activeSynapsesForSegment = activeSynapsesForSegment
self.activeSegments = activeSegments
self.predictiveCells = predictiveCells
def computeFn(self,
activeColumns,
prevPredictiveCells,
prevActiveSegments,
prevActiveSynapsesForSegment,
prevWinnerCells,
connections,
learn=True):
"""
'Functional' version of compute.
Returns new state.
@param activeColumns (set) Indices of active columns
in `t`
@param prevPredictiveCells (set) Indices of predictive
cells in `t-1`
@param prevActiveSegments (set) Indices of active segments
in `t-1`
@param prevActiveSynapsesForSegment (dict) Mapping from segments to
active synapses in `t-1`,
see
`TM.computeActiveSynapses`
@param prevWinnerCells (set) Indices of winner cells
in `t-1`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`activeSynapsesForSegment` (dict),
`activeSegments` (set),
`predictiveCells` (set)
"""
activeCells = set()
winnerCells = set()
(_activeCells,
_winnerCells,
predictedColumns) = self.activateCorrectlyPredictiveCells(
prevPredictiveCells,
activeColumns,
connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
(_activeCells,
_winnerCells,
learningSegments) = self.burstColumns(activeColumns,
predictedColumns,
prevActiveSynapsesForSegment,
connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
if learn:
self.learnOnSegments(prevActiveSegments,
learningSegments,
prevActiveSynapsesForSegment,
winnerCells,
prevWinnerCells,
connections)
activeSynapsesForSegment = self.computeActiveSynapses(activeCells,
connections)
(activeSegments,
predictiveCells) = self.computePredictiveCells(activeSynapsesForSegment,
connections)
return (activeCells,
winnerCells,
activeSynapsesForSegment,
activeSegments,
predictiveCells)
def reset(self):
"""
Indicates the start of a new sequence. Resets sequence state of the TM.
"""
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.activeSynapsesForSegment = dict()
self.winnerCells = set()
# ==============================
# Phases
# ==============================
@staticmethod
def activateCorrectlyPredictiveCells(prevPredictiveCells,
activeColumns,
connections):
"""
Phase 1: Activate the correctly predictive cells.
Pseudocode:
- for each prev predictive cell
- if in active column
- mark it as active
- mark it as winner cell
- mark column as predicted
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param activeColumns (set) Indices of active columns in `t`
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`predictedColumns` (set)
"""
activeCells = set()
winnerCells = set()
predictedColumns = set()
for cell in prevPredictiveCells:
column = connections.columnForCell(cell)
if column in activeColumns:
activeCells.add(cell)
winnerCells.add(cell)
predictedColumns.add(column)
return (activeCells, winnerCells, predictedColumns)
def burstColumns(self,
activeColumns,
predictedColumns,
prevActiveSynapsesForSegment,
connections):
"""
Phase 2: Burst unpredicted columns.
Pseudocode:
- for each unpredicted active column
- mark all cells as active
- mark the best matching cell as winner cell
- (learning)
- if it has no matching segment
- (optimization) if there are prev winner cells
- add a segment to it
- mark the segment as learning
@param activeColumns (set) Indices of active columns
in `t`
@param predictedColumns (set) Indices of predicted
columns in `t`
@param prevActiveSynapsesForSegment (dict) Mapping from segments to
active synapses in `t-1`,
see
`TM.computeActiveSynapses`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`learningSegments` (set)
"""
activeCells = set()
winnerCells = set()
learningSegments = set()
unpredictedColumns = activeColumns - predictedColumns
for column in unpredictedColumns:
cells = connections.cellsForColumn(column)
activeCells.update(cells)
(bestCell,
bestSegment) = self.getBestMatchingCell(column,
prevActiveSynapsesForSegment,
connections)
winnerCells.add(bestCell)
if bestSegment == None:
# TODO: (optimization) Only do this if there are prev winner cells
bestSegment = connections.createSegment(bestCell)
learningSegments.add(bestSegment)
return (activeCells, winnerCells, learningSegments)
def learnOnSegments(self,
prevActiveSegments,
learningSegments,
prevActiveSynapsesForSegment,
winnerCells,
prevWinnerCells,
connections):
"""
Phase 3: Perform learning by adapting segments.
Pseudocode:
- (learning) for each prev active or learning segment
- if learning segment or from winner cell
- strengthen active synapses
- weaken inactive synapses
- if learning segment
- add some synapses to the segment
- subsample from prev winner cells
@param prevActiveSegments (set) Indices of active segments
in `t-1`
@param learningSegments (set) Indices of learning
segments in `t`
@param prevActiveSynapsesForSegment (dict) Mapping from segments to
active synapses in `t-1`,
see
`TM.computeActiveSynapses`
@param winnerCells (set) Indices of winner cells
in `t`
@param prevWinnerCells (set) Indices of winner cells
in `t-1`
@param connections (Connections) Connectivity of layer
"""
for segment in prevActiveSegments | learningSegments:
isLearningSegment = segment in learningSegments
isFromWinnerCell = connections.cellForSegment(segment) in winnerCells
activeSynapses = self.getConnectedActiveSynapsesForSegment(
segment,
prevActiveSynapsesForSegment,
0,
connections)
if isLearningSegment or isFromWinnerCell:
self.adaptSegment(segment, activeSynapses, connections)
if isLearningSegment:
n = self.maxNewSynapseCount - len(activeSynapses)
for sourceCell in self.pickCellsToLearnOn(n,
segment,
prevWinnerCells,
connections):
connections.createSynapse(segment, sourceCell, self.initialPermanence)
def computePredictiveCells(self, activeSynapsesForSegment, connections):
"""
Phase 4: Compute predictive cells due to lateral input
on distal dendrites.
Pseudocode:
- for each distal dendrite segment with activity >= activationThreshold
- mark the segment as active
- mark the cell as predictive
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeSegments` (set),
`predictiveCells` (set)
"""
activeSegments = set()
predictiveCells = set()
for segment in activeSynapsesForSegment.keys():
synapses = self.getConnectedActiveSynapsesForSegment(
segment,
activeSynapsesForSegment,
self.connectedPermanence,
connections)
if len(synapses) >= self.activationThreshold:
activeSegments.add(segment)
predictiveCells.add(connections.cellForSegment(segment))
return (activeSegments, predictiveCells)
# ==============================
# Helper functions
# ==============================
@staticmethod
def computeActiveSynapses(activeCells, connections):
"""
Forward propagates activity from active cells to the synapses that touch
them, to determine which synapses are active.
@param activeCells (set) Indicies of active cells
@param connections (Connections) Connectivity of layer
@return (dict) Mapping from segment (int) to indices of
active synapses (set)
"""
activeSynapsesForSegment = dict()
for cell in activeCells:
for synapse in connections.synapsesForSourceCell(cell):
segment, _, _ = connections.dataForSynapse(synapse)
if not segment in activeSynapsesForSegment:
activeSynapsesForSegment[segment] = set()
activeSynapsesForSegment[segment].add(synapse)
return activeSynapsesForSegment
def getBestMatchingCell(self, column, activeSynapsesForSegment, connections):
"""
Gets the cell with the best matching segment
(see `TM.getBestMatchingSegment`) that has the largest number of active
synapses of all best matching segments.
If none were found, pick the least used cell (see `TM.getLeastUsedCell`).
@param column (int) Column index
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`cell` (int),
`bestSegment` (int)
"""
maxSynapses = 0
bestCell = None
bestSegment = None
cells = connections.cellsForColumn(column)
for cell in cells:
(
segment,
connectedActiveSynapses
) = self.getBestMatchingSegment(cell,
activeSynapsesForSegment,
connections)
if segment != None and len(connectedActiveSynapses) > maxSynapses:
maxSynapses = len(connectedActiveSynapses)
bestCell = cell
bestSegment = segment
if bestCell == None:
bestCell = self.getLeastUsedCell(column, connections)
return (bestCell, bestSegment)
def getBestMatchingSegment(self, cell, activeSynapsesForSegment, connections):
"""
Gets the segment on a cell with the largest number of activate synapses,
including all synapses with non-zero permanences.
@param cell (int) Cell index
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`segment` (int),
`connectedActiveSynapses` (set)
"""
maxSynapses = self.minThreshold
bestSegment = None
connectedActiveSynapses = None
for segment in connections.segmentsForCell(cell):
synapses = self.getConnectedActiveSynapsesForSegment(
segment,
activeSynapsesForSegment,
0,
connections)
if len(synapses) >= maxSynapses:
maxSynapses = len(synapses)
bestSegment = segment
connectedActiveSynapses = set(synapses)
return (bestSegment, connectedActiveSynapses)
def getLeastUsedCell(self, column, connections):
"""
Gets the cell with the smallest number of segments.
Break ties randomly.
@param column (int) Column index
@param connections (Connections) Connectivity of layer
@return (int) Cell index
"""
cells = connections.cellsForColumn(column)
leastUsedCells = set()
minNumSegments = float("inf")
for cell in cells:
numSegments = len(connections.segmentsForCell(cell))
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = set()
if numSegments == minNumSegments:
leastUsedCells.add(cell)
i = self._random.getUInt32(len(leastUsedCells))
return sorted(leastUsedCells)[i]
@staticmethod
def getConnectedActiveSynapsesForSegment(segment,
activeSynapsesForSegment,
permanenceThreshold,
connections):
"""
Returns the synapses on a segment that are active due to lateral input
from active cells.
@param segment (int) Segment index
@param activeSynapsesForSegment (dict) Mapping from segments to
active synapses (see
`TM.computeActiveSynapses`)
@param permanenceThreshold (float) Minimum threshold for
permanence for synapse to
be connected
@param connections (Connections) Connectivity of layer
@return (set) Indices of active synapses on segment
"""
connectedSynapses = set()
if not segment in activeSynapsesForSegment:
return connectedSynapses
# TODO: (optimization) Can skip this logic if permanenceThreshold = 0
for synapse in activeSynapsesForSegment[segment]:
(_, _, permanence) = connections.dataForSynapse(synapse)
if permanence >= permanenceThreshold:
connectedSynapses.add(synapse)
return connectedSynapses
def adaptSegment(self, segment, activeSynapses, connections):
"""
Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param segment (int) Segment index
@param activeSynapses (set) Indices of active synapses
@param connections (Connections) Connectivity of layer
"""
for synapse in connections.synapsesForSegment(segment):
(_, _, permanence) = connections.dataForSynapse(synapse)
if synapse in activeSynapses:
permanence += self.permanenceIncrement
else:
permanence -= self.permanenceDecrement
# Keep permanence within min/max bounds
permanence = max(0.0, min(1.0, permanence))
connections.updateSynapsePermanence(synapse, permanence)
def pickCellsToLearnOn(self, n, segment, winnerCells, connections):
"""
Pick cells to form distal connections to.
TODO: Respect topology and learningRadius
@param n (int) Number of cells to pick
@param segment (int) Segment index
@param winnerCells (set) Indices of winner cells in `t`
@param connections (Connections) Connectivity of layer
@return (set) Indices of cells picked
"""
candidates = set(winnerCells)
# Remove cells that are already synapsed on by this segment
for synapse in connections.synapsesForSegment(segment):
(_, sourceCell, _) = connections.dataForSynapse(synapse)
if sourceCell in candidates:
candidates.remove(sourceCell)
n = min(n, len(candidates))
candidates = sorted(candidates)
cells = set()
# Pick n cells randomly
for _ in range(n):
i = self._random.getUInt32(len(candidates))
cells.add(candidates[i])
del candidates[i]
return cells
class RandomDistributedScalarEncoder(Encoder):
"""
A scalar encoder encodes a numeric (floating point) value into an array
of bits.
This class maps a scalar value into a random distributed representation that
is suitable as scalar input into the spatial pooler. The encoding scheme is
designed to replace a simple ScalarEncoder. It preserves the important
properties around overlapping representations. Unlike ScalarEncoder the min
and max range can be dynamically increased without any negative effects. The
only required parameter is resolution, which determines the resolution of
input values.
Scalar values are mapped to a bucket. The class maintains a random distributed
encoding for each bucket. The following properties are maintained by
RandomDistributedEncoder:
1) Similar scalars should have high overlap. Overlap should decrease smoothly
as scalars become less similar. Specifically, neighboring bucket indices must
overlap by a linearly decreasing number of bits.
2) Dissimilar scalars should have very low overlap so that the SP does not
confuse representations. Specifically, buckets that are more than w indices
apart should have at most maxOverlap bits of overlap. We arbitrarily (and
safely) define "very low" to be 2 bits of overlap or lower.
Properties 1 and 2 lead to the following overlap rules for buckets i and j:
If abs(i-j) < w then:
overlap(i,j) = w - abs(i-j)
else:
overlap(i,j) <= maxOverlap
3) The representation for a scalar must not change during the lifetime of
the object. Specifically, as new buckets are created and the min/max range
is extended, the representation for previously in-range sscalars and
previously created buckets must not change.
"""
def __init__(self,
resolution,
w=21,
n=400,
name=None,
offset=None,
seed=42,
verbosity=0):
"""Constructor
@param resolution A floating point positive number denoting the resolution
of the output representation. Numbers within
[offset-resolution/2, offset+resolution/2] will fall into
the same bucket and thus have an identical representation.
Adjacent buckets will differ in one bit. resolution is a
required parameter.
@param w Number of bits to set in output. w must be odd to avoid centering
problems. w must be large enough that spatial pooler
columns will have a sufficiently large overlap to avoid
false matches. A value of w=21 is typical.
@param n Number of bits in the representation (must be > w). n must be
large enough such that there is enough room to select
new representations as the range grows. With w=21 a value
of n=400 is typical. The class enforces n > 6*w.
@param name An optional string which will become part of the description.
@param offset A floating point offset used to map scalar inputs to bucket
indices. The middle bucket will correspond to numbers in the
range [offset - resolution/2, offset + resolution/2). If set
to None, the very first input that is encoded will be used
to determine the offset.
@param seed The seed used for numpy's random number generator. If set to -1
the generator will be initialized without a fixed seed.
@param verbosity An integer controlling the level of debugging output. A
value of 0 implies no output. verbosity=1 may lead to
one-time printouts during construction, serialization or
deserialization. verbosity=2 may lead to some output per
encode operation. verbosity>2 may lead to significantly
more output.
"""
# Validate inputs
if (w <= 0) or (w % 2 == 0):
raise ValueError("w must be an odd positive integer")
if resolution <= 0:
raise ValueError("resolution must be a positive number")
if (n <= 6 * w) or (not isinstance(n, int)):
raise ValueError("n must be an int strictly greater than 6*w. For "
"good results we recommend n be strictly greater "
"than 11*w")
self.encoders = None
self.verbosity = verbosity
self.w = w
self.n = n
self.resolution = float(resolution)
# The largest overlap we allow for non-adjacent encodings
self._maxOverlap = 2
# initialize the random number generators
self._seed(seed)
# Internal parameters for bucket mapping
self.minIndex = None
self.maxIndex = None
self._offset = None
self._initializeBucketMap(INITIAL_BUCKETS, offset)
# A name used for debug printouts
if name is not None:
self.name = name
else:
self.name = "[%s]" % (self.resolution)
if self.verbosity > 0:
self.dump()
def __setstate__(self, state):
self.__dict__.update(state)
# Initialize self.random as an instance of NupicRandom derived from the
# previous numpy random state
randomState = state["random"]
if isinstance(randomState, numpy.random.mtrand.RandomState):
self.random = NupicRandom(randomState.randint(sys.maxint))
def _seed(self, seed=-1):
"""
Initialize the random seed
"""
if seed != -1:
self.random = NupicRandom(seed)
else:
self.random = NupicRandom()
def getDecoderOutputFieldTypes(self):
""" See method description in base.py """
return (FieldMetaType.float, )
def getWidth(self):
""" See method description in base.py """
return self.n
def getDescription(self):
return [(self.name, 0)]
def getBucketIndices(self, x):
""" See method description in base.py """
if ((isinstance(x, float) and math.isnan(x))
or x == SENTINEL_VALUE_FOR_MISSING_DATA):
return [None]
if self._offset is None:
self._offset = x
bucketIdx = ((self._maxBuckets / 2) +
int(round((x - self._offset) / self.resolution)))
if bucketIdx < 0:
bucketIdx = 0
elif bucketIdx >= self._maxBuckets:
bucketIdx = self._maxBuckets - 1
return [bucketIdx]
def mapBucketIndexToNonZeroBits(self, index):
"""
Given a bucket index, return the list of non-zero bits. If the bucket
index does not exist, it is created. If the index falls outside our range
we clip it.
"""
if index < 0:
index = 0
if index >= self._maxBuckets:
index = self._maxBuckets - 1
if not self.bucketMap.has_key(index):
if self.verbosity >= 2:
print "Adding additional buckets to handle index=", index
self._createBucket(index)
return self.bucketMap[index]
def encodeIntoArray(self, x, output):
""" See method description in base.py """
if x is not None and not isinstance(x, numbers.Number):
raise TypeError(
"Expected a scalar input but got input of type %s" % type(x))
# Get the bucket index to use
bucketIdx = self.getBucketIndices(x)[0]
# None is returned for missing value in which case we return all 0's.
output[0:self.n] = 0
if bucketIdx is not None:
output[self.mapBucketIndexToNonZeroBits(bucketIdx)] = 1
def _createBucket(self, index):
"""
Create the given bucket index. Recursively create as many in-between
bucket indices as necessary.
"""
if index < self.minIndex:
if index == self.minIndex - 1:
# Create a new representation that has exactly w-1 overlapping bits
# as the min representation
self.bucketMap[index] = self._newRepresentation(
self.minIndex, index)
self.minIndex = index
else:
# Recursively create all the indices above and then this index
self._createBucket(index + 1)
self._createBucket(index)
else:
if index == self.maxIndex + 1:
# Create a new representation that has exactly w-1 overlapping bits
# as the max representation
self.bucketMap[index] = self._newRepresentation(
self.maxIndex, index)
self.maxIndex = index
else:
# Recursively create all the indices below and then this index
self._createBucket(index - 1)
self._createBucket(index)
def _newRepresentation(self, index, newIndex):
"""
Return a new representation for newIndex that overlaps with the
representation at index by exactly w-1 bits
"""
newRepresentation = self.bucketMap[index].copy()
# Choose the bit we will replace in this representation. We need to shift
# this bit deterministically. If this is always chosen randomly then there
# is a 1 in w chance of the same bit being replaced in neighboring
# representations, which is fairly high
ri = newIndex % self.w
# Now we choose a bit such that the overlap rules are satisfied.
newBit = self.random.getUInt32(self.n)
newRepresentation[ri] = newBit
while newBit in self.bucketMap[index] or \
not self._newRepresentationOK(newRepresentation, newIndex):
self.numTries += 1
newBit = self.random.getUInt32(self.n)
newRepresentation[ri] = newBit
return newRepresentation
def _newRepresentationOK(self, newRep, newIndex):
"""
Return True if this new candidate representation satisfies all our overlap
rules. Since we know that neighboring representations differ by at most
one bit, we compute running overlaps.
"""
if newRep.size != self.w:
return False
if (newIndex < self.minIndex - 1) or (newIndex > self.maxIndex + 1):
raise ValueError("newIndex must be within one of existing indices")
# A binary representation of newRep. We will use this to test containment
newRepBinary = numpy.array([False] * self.n)
newRepBinary[newRep] = True
# Midpoint
midIdx = self._maxBuckets / 2
# Start by checking the overlap at minIndex
runningOverlap = self._countOverlap(self.bucketMap[self.minIndex],
newRep)
if not self._overlapOK(self.minIndex, newIndex,
overlap=runningOverlap):
return False
# Compute running overlaps all the way to the midpoint
for i in range(self.minIndex + 1, midIdx + 1):
# This is the bit that is going to change
newBit = (i - 1) % self.w
# Update our running overlap
if newRepBinary[self.bucketMap[i - 1][newBit]]:
runningOverlap -= 1
if newRepBinary[self.bucketMap[i][newBit]]:
runningOverlap += 1
# Verify our rules
if not self._overlapOK(i, newIndex, overlap=runningOverlap):
return False
# At this point, runningOverlap contains the overlap for midIdx
# Compute running overlaps all the way to maxIndex
for i in range(midIdx + 1, self.maxIndex + 1):
# This is the bit that is going to change
newBit = i % self.w
# Update our running overlap
if newRepBinary[self.bucketMap[i - 1][newBit]]:
runningOverlap -= 1
if newRepBinary[self.bucketMap[i][newBit]]:
runningOverlap += 1
# Verify our rules
if not self._overlapOK(i, newIndex, overlap=runningOverlap):
return False
return True
def _countOverlapIndices(self, i, j):
"""
Return the overlap between bucket indices i and j
"""
if self.bucketMap.has_key(i) and self.bucketMap.has_key(j):
iRep = self.bucketMap[i]
jRep = self.bucketMap[j]
return self._countOverlap(iRep, jRep)
else:
raise ValueError("Either i or j don't exist")
@staticmethod
def _countOverlap(rep1, rep2):
"""
Return the overlap between two representations. rep1 and rep2 are lists of
non-zero indices.
"""
overlap = 0
for e in rep1:
if e in rep2:
overlap += 1
return overlap
def _overlapOK(self, i, j, overlap=None):
"""
Return True if the given overlap between bucket indices i and j are
acceptable. If overlap is not specified, calculate it from the bucketMap
"""
if overlap is None:
overlap = self._countOverlapIndices(i, j)
if abs(i - j) < self.w:
if overlap == (self.w - abs(i - j)):
return True
else:
return False
else:
if overlap <= self._maxOverlap:
return True
else:
return False
def _initializeBucketMap(self, maxBuckets, offset):
"""
Initialize the bucket map assuming the given number of maxBuckets.
"""
# The first bucket index will be _maxBuckets / 2 and bucket indices will be
# allowed to grow lower or higher as long as they don't become negative.
# _maxBuckets is required because the current CLA Classifier assumes bucket
# indices must be non-negative. This normally does not need to be changed
# but if altered, should be set to an even number.
self._maxBuckets = maxBuckets
self.minIndex = self._maxBuckets / 2
self.maxIndex = self._maxBuckets / 2
# The scalar offset used to map scalar values to bucket indices. The middle
# bucket will correspond to numbers in the range
# [offset-resolution/2, offset+resolution/2).
# The bucket index for a number x will be:
# maxBuckets/2 + int( round( (x-offset)/resolution ) )
self._offset = offset
# This dictionary maps a bucket index into its bit representation
# We initialize the class with a single bucket with index 0
self.bucketMap = {}
def _permutation(n):
r = numpy.arange(n, dtype=numpy.uint32)
self.random.shuffle(r)
return r
self.bucketMap[self.minIndex] = _permutation(self.n)[0:self.w]
# How often we need to retry when generating valid encodings
self.numTries = 0
def dump(self):
print "RandomDistributedScalarEncoder:"
print " minIndex: %d" % self.minIndex
print " maxIndex: %d" % self.maxIndex
print " w: %d" % self.w
print " n: %d" % self.getWidth()
print " resolution: %g" % self.resolution
print " offset: %s" % str(self._offset)
print " numTries: %d" % self.numTries
print " name: %s" % self.name
if self.verbosity > 2:
print " All buckets: "
pprint.pprint(self.bucketMap)
@classmethod
def read(cls, proto):
encoder = object.__new__(cls)
encoder.resolution = proto.resolution
encoder.w = proto.w
encoder.n = proto.n
encoder.name = proto.name
encoder._offset = proto.offset
encoder.random = NupicRandom()
encoder.random.read(proto.random)
encoder.resolution = proto.resolution
encoder.verbosity = proto.verbosity
encoder.minIndex = proto.minIndex
encoder.maxIndex = proto.maxIndex
encoder.encoders = None
encoder._maxBuckets = INITIAL_BUCKETS
encoder.bucketMap = {
x.key: numpy.array(x.value, dtype=numpy.uint32)
for x in proto.bucketMap
}
return encoder
def write(self, proto):
proto.resolution = self.resolution
proto.w = self.w
proto.n = self.n
proto.name = self.name
proto.offset = self._offset
self.random.write(proto.random)
proto.verbosity = self.verbosity
proto.minIndex = self.minIndex
proto.maxIndex = self.maxIndex
proto.bucketMap = [{
"key": key,
"value": value.tolist()
} for key, value in self.bucketMap.items()]
class SMSequences(object):
"""
Class generates sensorimotor sequences
"""
def __init__(self,
sensoryInputElements,
spatialConfig,
sensoryInputElementsPool=list(
"ABCDEFGHIJKLMNOPQRSTUVWXYZ"
"abcdefghijklmnopqrstuvwxyz0123456789"),
minDisplacement=1,
maxDisplacement=1,
numActiveBitsSensoryInput=9,
numActiveBitsMotorInput=9,
seed=42,
verbosity=False,
useRandomEncoder=False):
"""
@param sensoryInputElements (list)
Strings or numbers representing the sensory elements that exist in your
world. Elements can be repeated if multiple of the same exist.
@param spatialConfig (numpy.array)
Array of size: (1, len(sensoryInputElements), dimension). It has a
coordinate for every element in sensoryInputElements.
@param sensoryInputElementsPool (list)
List of strings representing a readable version of all possible sensory
elements in this world. Elements don't need to be in any order and there
should be no duplicates. By default this contains the set of
alphanumeric characters.
@param maxDisplacement (int)
Maximum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param minDisplacement (int)
Minimum `distance` for a motor command. Distance is defined by the
largest difference along any coordinate dimension.
@param numActiveBitsSensoryInput (int)
Number of active bits for each sensory input.
@param numActiveBitsMotorInput (int)
Number of active bits for each dimension of the motor input.
@param seed (int)
Random seed for nupic.bindings.Random.
@param verbosity (int)
Verbosity
@param useRandomEncoder (boolean)
if True, use the random encoder SDRCategoryEncoder. If False,
use CategoryEncoder. CategoryEncoder encodes categories using contiguous
non-overlapping bits for each category, which makes it easier to debug.
"""
#---------------------------------------------------------------------------------
# Store creation parameters
self.sensoryInputElements = sensoryInputElements
self.sensoryInputElementsPool = sensoryInputElementsPool
self.spatialConfig = spatialConfig.astype(int)
self.spatialLength = len(spatialConfig)
self.maxDisplacement = maxDisplacement
self.minDisplacement = minDisplacement
self.numActiveBitsSensoryInput = numActiveBitsSensoryInput
self.numActiveBitsMotorInput = numActiveBitsMotorInput
self.verbosity = verbosity
self.seed = seed
self.initialize(useRandomEncoder)
def initialize(self, useRandomEncoder):
"""
Initialize the various data structures.
"""
self.setRandomSeed(self.seed)
self.dim = numpy.shape(self.spatialConfig)[-1]
self.spatialMap = dict(
zip(map(tuple, list(self.spatialConfig)),
self.sensoryInputElements))
self.lengthMotorInput1D = (2*self.maxDisplacement + 1) * \
self.numActiveBitsMotorInput
uniqueSensoryElements = list(set(self.sensoryInputElementsPool))
if useRandomEncoder:
self.sensoryEncoder = SDRCategoryEncoder(
n=1024,
w=self.numActiveBitsSensoryInput,
categoryList=uniqueSensoryElements,
forced=True)
self.lengthSensoryInput = self.sensoryEncoder.getWidth()
else:
self.lengthSensoryInput = (len(self.sensoryInputElementsPool)+1) * \
self.numActiveBitsSensoryInput
self.sensoryEncoder = CategoryEncoder(
w=self.numActiveBitsSensoryInput,
categoryList=uniqueSensoryElements,
forced=True)
motorEncoder1D = ScalarEncoder(n=self.lengthMotorInput1D,
w=self.numActiveBitsMotorInput,
minval=-self.maxDisplacement,
maxval=self.maxDisplacement,
clipInput=True,
forced=True)
self.motorEncoder = VectorEncoder(length=self.dim,
encoder=motorEncoder1D)
def generateSensorimotorSequence(self, sequenceLength):
"""
Generate sensorimotor sequences of length sequenceLength.
@param sequenceLength (int)
Length of the sensorimotor sequence.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
currentEyeLoc = self.nupicRandomChoice(self.spatialConfig)
for i in xrange(sequenceLength):
currentSensoryInput = self.spatialMap[tuple(currentEyeLoc)]
nextEyeLoc, currentEyeV = self.getNextEyeLocation(currentEyeLoc)
if self.verbosity:
print "sensory input = ", currentSensoryInput, \
"eye location = ", currentEyeLoc, \
" motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
currentEyeLoc = nextEyeLoc
return (sensorySequence, motorSequence, sensorimotorSequence)
def encodeSensorimotorSequence(self, eyeLocs):
"""
Encode sensorimotor sequence given the eye movements. Sequence will have
length len(eyeLocs) - 1 because only the differences of eye locations can be
used to encoder motor commands.
@param eyeLocs (list)
Numpy coordinates describing where the eye is looking.
@return (tuple) Contains:
sensorySequence (list)
Encoded sensory input for whole sequence.
motorSequence (list)
Encoded motor input for whole sequence.
sensorimotorSequence (list)
Encoder sensorimotor input for whole sequence. This is useful
when you want to give external input to temporal memory.
"""
sequenceLength = len(eyeLocs) - 1
motorSequence = []
sensorySequence = []
sensorimotorSequence = []
for i in xrange(sequenceLength):
currentEyeLoc = eyeLocs[i]
nextEyeLoc = eyeLocs[i + 1]
currentSensoryInput = self.spatialMap[currentEyeLoc]
currentEyeV = nextEyeLoc - currentEyeLoc
if self.verbosity:
print "sensory input = ", currentSensoryInput, \
"eye location = ", currentEyeLoc, \
" motor command = ", currentEyeV
sensoryInput = self.encodeSensoryInput(currentSensoryInput)
motorInput = self.encodeMotorInput(list(currentEyeV))
sensorimotorInput = numpy.concatenate((sensoryInput, motorInput))
sensorySequence.append(sensoryInput)
motorSequence.append(motorInput)
sensorimotorSequence.append(sensorimotorInput)
return (sensorySequence, motorSequence, sensorimotorSequence)
def getNextEyeLocation(self, currentEyeLoc):
"""
Generate next eye location based on current eye location.
@param currentEyeLoc (numpy.array)
Current coordinate describing the eye location in the world.
@return (tuple) Contains:
nextEyeLoc (numpy.array)
Coordinate of the next eye location.
eyeDiff (numpy.array)
Vector describing change from currentEyeLoc to nextEyeLoc.
"""
possibleEyeLocs = []
for loc in self.spatialConfig:
shift = abs(max(loc - currentEyeLoc))
if self.minDisplacement <= shift <= self.maxDisplacement:
possibleEyeLocs.append(loc)
nextEyeLoc = self.nupicRandomChoice(possibleEyeLocs)
eyeDiff = nextEyeLoc - currentEyeLoc
return nextEyeLoc, eyeDiff
def setRandomSeed(self, seed):
"""
Reset the nupic random generator. This is necessary to reset random seed to
generate new sequences.
@param seed (int)
Seed for nupic.bindings.Random.
"""
self.seed = seed
self._random = Random()
self._random.setSeed(seed)
def nupicRandomChoice(self, array):
"""
Chooses a random element from an array using the nupic random number
generator.
@param array (list or numpy.array)
Array to choose random element from.
@return (element)
Element chosen at random.
"""
return array[self._random.getUInt32(len(array))]
def encodeMotorInput(self, motorInput):
"""
Encode motor command to bit vector.
@param motorInput (1D numpy.array)
Motor command to be encoded.
@return (1D numpy.array)
Encoded motor command.
"""
if not hasattr(motorInput, "__iter__"):
motorInput = list([motorInput])
return self.motorEncoder.encode(motorInput)
def decodeMotorInput(self, motorInputPattern):
"""
Decode motor command from bit vector.
@param motorInputPattern (1D numpy.array)
Encoded motor command.
@return (1D numpy.array)
Decoded motor command.
"""
key = self.motorEncoder.decode(motorInputPattern)[0].keys()[0]
motorCommand = self.motorEncoder.decode(
motorInputPattern)[0][key][1][0]
return motorCommand
def encodeSensoryInput(self, sensoryInputElement):
"""
Encode sensory input to bit vector
@param sensoryElement (1D numpy.array)
Sensory element to be encoded.
@return (1D numpy.array)
Encoded sensory element.
"""
return self.sensoryEncoder.encode(sensoryInputElement)
def decodeSensoryInput(self, sensoryInputPattern):
"""
Decode sensory input from bit vector.
@param sensoryInputPattern (1D numpy.array)
Encoded sensory element.
@return (1D numpy.array)
Decoded sensory element.
"""
return self.sensoryEncoder.decode(
sensoryInputPattern)[0]['category'][1]
def printSensoryCodingScheme(self):
"""
Print sensory inputs along with their encoded versions.
"""
print "\nsensory coding scheme: "
for loc in self.spatialConfig:
sensoryElement = self.spatialMap[tuple(loc)]
print sensoryElement, "%s : " % loc,
printSequence(self.encodeSensoryInput(sensoryElement))
def printMotorCodingScheme(self):
"""
Print motor commands (displacement vector) along with their encoded
versions.
"""
print "\nmotor coding scheme: "
self.build(self.dim, [])
def build(self, n, vec):
"""
Recursive function to help print motor coding scheme.
"""
for i in range(-self.maxDisplacement, self.maxDisplacement + 1):
next = vec + [i]
if n == 1:
print '{:>5}\t'.format(next), " = ",
printSequence(self.encodeMotorInput(next))
else:
self.build(n - 1, next)
def testPlatformSame(self): r = Random(42) [r.getUInt32() for _ in range(80085)] v = r.getUInt32() self.assertEqual(v, 1651991554)
class TemporalMemory(object):
"""
Class implementing the Temporal Memory algorithm.
"""
def __init__(self,
columnDimensions=(2048, ),
cellsPerColumn=32,
activationThreshold=13,
initialPermanence=0.21,
connectedPermanence=0.50,
minThreshold=10,
maxNewSynapseCount=20,
permanenceIncrement=0.10,
permanenceDecrement=0.10,
predictedSegmentDecrement=0.0,
maxSegmentsPerCell=255,
maxSynapsesPerSegment=255,
seed=42,
**kwargs):
"""
@param columnDimensions (list) Dimensions of the column space
@param cellsPerColumn (int) Number of cells per column
@param activationThreshold (int) If the number of active connected synapses on a segment is at least this threshold, the segment is said to be active.
@param initialPermanence (float) Initial permanence of a new synapse.
@param connectedPermanence (float) If the permanence value for a synapse is greater than this value, it is said to be connected.
@param minThreshold (int) If the number of synapses active on a segment is at least this threshold, it is selected as the best matching cell in a bursting column.
@param maxNewSynapseCount (int) The maximum number of synapses added to a segment during learning.
@param permanenceIncrement (float) Amount by which permanences of synapses are incremented during learning.
@param permanenceDecrement (float) Amount by which permanences of synapses are decremented during learning.
@param predictedSegmentDecrement (float) Amount by which active permanences of synapses of previously predicted but inactive segments are decremented.
@param seed (int) Seed for the random number generator.
Notes:
predictedSegmentDecrement: A good value is just a bit larger than
(the column-level sparsity * permanenceIncrement). So, if column-level
sparsity is 2% and permanenceIncrement is 0.01, this parameter should be
something like 4% * 0.01 = 0.0004).
"""
# Error checking
if not len(columnDimensions):
raise ValueError(
"Number of column dimensions must be greater than 0")
if not cellsPerColumn > 0:
raise ValueError(
"Number of cells per column must be greater than 0")
# TODO: Validate all parameters (and add validation tests)
# Save member variables
self.columnDimensions = columnDimensions
self.cellsPerColumn = cellsPerColumn
self.activationThreshold = activationThreshold
self.initialPermanence = initialPermanence
self.connectedPermanence = connectedPermanence
self.minThreshold = minThreshold
self.maxNewSynapseCount = maxNewSynapseCount
self.permanenceIncrement = permanenceIncrement
self.permanenceDecrement = permanenceDecrement
self.predictedSegmentDecrement = predictedSegmentDecrement
# Initialize member variables
self.connections = Connections(
self.numberOfCells(),
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment)
self._random = Random(seed)
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
self.matchingSegments = set()
self.matchingCells = set()
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
"""
Feeds input record through TM, performing inference and learning.
@param activeColumns (set) Indices of active columns
@param learn (bool) Whether or not learning is enabled
Updates member variables:
- `activeCells` (set)
- `winnerCells` (set)
- `activeSegments` (set)
- `predictiveCells` (set)
- `matchingSegments`(set)
- `matchingCells` (set)
"""
prevPredictiveCells = self.predictiveCells
prevActiveSegments = self.activeSegments
prevActiveCells = self.activeCells
prevWinnerCells = self.winnerCells
prevMatchingSegments = self.matchingSegments
prevMatchingCells = self.matchingCells
activeCells = set()
winnerCells = set()
(_activeCells, _winnerCells, predictedActiveColumns,
predictedInactiveCells) = self.activateCorrectlyPredictiveCells(
prevPredictiveCells, prevMatchingCells, activeColumns)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
(_activeCells, _winnerCells, learningSegments) = self.burstColumns(
activeColumns, predictedActiveColumns, prevActiveCells,
prevWinnerCells, self.connections)
activeCells.update(_activeCells)
winnerCells.update(_winnerCells)
if learn:
self.learnOnSegments(prevActiveSegments, learningSegments,
prevActiveCells, winnerCells, prevWinnerCells,
self.connections, predictedInactiveCells,
prevMatchingSegments)
(activeSegments, predictiveCells, matchingSegments,
matchingCells) = self.computePredictiveCells(activeCells,
self.connections)
self.activeCells = activeCells
self.winnerCells = winnerCells
self.activeSegments = activeSegments
self.predictiveCells = predictiveCells
self.matchingSegments = matchingSegments
self.matchingCells = matchingCells
def reset(self):
"""
Indicates the start of a new sequence. Resets sequence state of the TM.
"""
self.activeCells = set()
self.predictiveCells = set()
self.activeSegments = set()
self.winnerCells = set()
# ==============================
# Phases
# ==============================
def activateCorrectlyPredictiveCells(self, prevPredictiveCells,
prevMatchingCells, activeColumns):
"""
Phase 1: Activate the correctly predictive cells.
Pseudocode:
- for each prev predictive cell
- if in active column
- mark it as active
- mark it as winner cell
- mark column as predicted => active
- if not in active column
- mark it as an predicted but inactive cell
@param prevPredictiveCells (set) Indices of predictive cells in `t-1`
@param activeColumns (set) Indices of active columns in `t`
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`predictedActiveColumns` (set),
`predictedInactiveCells` (set)
"""
activeCells = set()
winnerCells = set()
predictedActiveColumns = set()
predictedInactiveCells = set()
for cell in prevPredictiveCells:
column = self.columnForCell(cell)
if column in activeColumns:
activeCells.add(cell)
winnerCells.add(cell)
predictedActiveColumns.add(column)
if self.predictedSegmentDecrement > 0:
for cell in prevMatchingCells:
column = self.columnForCell(cell)
if column not in activeColumns:
predictedInactiveCells.add(cell)
return (activeCells, winnerCells, predictedActiveColumns,
predictedInactiveCells)
def burstColumns(self, activeColumns, predictedActiveColumns,
prevActiveCells, prevWinnerCells, connections):
"""
Phase 2: Burst unpredicted columns.
Pseudocode:
- for each unpredicted active column
- mark all cells as active
- mark the best matching cell as winner cell
- (learning)
- if it has no matching segment
- (optimization) if there are prev winner cells
- add a segment to it
- mark the segment as learning
@param activeColumns (set) Indices of active columns in `t`
@param predictedActiveColumns (set) Indices of predicted => active columns in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeCells` (set),
`winnerCells` (set),
`learningSegments` (set)
"""
activeCells = set()
winnerCells = set()
learningSegments = set()
unpredictedActiveColumns = activeColumns - predictedActiveColumns
for column in unpredictedActiveColumns:
cells = self.cellsForColumn(column)
activeCells.update(cells)
(bestCell,
bestSegment) = self.bestMatchingCell(cells, prevActiveCells,
connections)
winnerCells.add(bestCell)
if bestSegment is None and len(prevWinnerCells):
bestSegment = connections.createSegment(bestCell)
if bestSegment is not None:
learningSegments.add(bestSegment)
return activeCells, winnerCells, learningSegments
def learnOnSegments(self, prevActiveSegments, learningSegments,
prevActiveCells, winnerCells, prevWinnerCells,
connections, predictedInactiveCells,
prevMatchingSegments):
"""
Phase 3: Perform learning by adapting segments.
Pseudocode:
- (learning) for each prev active or learning segment
- if learning segment or from winner cell
- strengthen active synapses
- weaken inactive synapses
- if learning segment
- add some synapses to the segment
- subsample from prev winner cells
- if predictedSegmentDecrement > 0
- for each previously matching segment
- if cell is a predicted inactive cell
- weaken active synapses but don't touch inactive synapses
@param prevActiveSegments (set) Indices of active segments in `t-1`
@param learningSegments (set) Indices of learning segments in `t`
@param prevActiveCells (set) Indices of active cells in `t-1`
@param winnerCells (set) Indices of winner cells in `t`
@param prevWinnerCells (set) Indices of winner cells in `t-1`
@param connections (Connections) Connectivity of layer
@param predictedInactiveCells (set) Indices of predicted inactive cells
@param prevMatchingSegments (set) Indices of segments with
"""
for segment in prevActiveSegments | learningSegments:
isLearningSegment = segment in learningSegments
isFromWinnerCell = connections.cellForSegment(
segment) in winnerCells
activeSynapses = self.activeSynapsesForSegment(
segment, prevActiveCells, connections)
if isLearningSegment or isFromWinnerCell:
self.adaptSegment(segment, activeSynapses, connections,
self.permanenceIncrement,
self.permanenceDecrement)
if isLearningSegment:
n = self.maxNewSynapseCount - len(activeSynapses)
for presynapticCell in self.pickCellsToLearnOn(
n, segment, prevWinnerCells, connections):
connections.createSynapse(segment, presynapticCell,
self.initialPermanence)
if self.predictedSegmentDecrement > 0:
for segment in prevMatchingSegments:
isPredictedInactiveCell = connections.cellForSegment(
segment) in predictedInactiveCells
activeSynapses = self.activeSynapsesForSegment(
segment, prevActiveCells, connections)
if isPredictedInactiveCell:
self.adaptSegment(segment, activeSynapses, connections,
-self.predictedSegmentDecrement, 0.0)
def computePredictiveCells(self, activeCells, connections):
"""
Phase 4: Compute predictive cells due to lateral input
on distal dendrites.
Pseudocode:
- for each distal dendrite segment with activity >= activationThreshold
- mark the segment as active
- mark the cell as predictive
- if predictedSegmentDecrement > 0
- for each distal dendrite segment with unconnected
activity >= minThreshold
- mark the segment as matching
- mark the cell as matching
Forward propagates activity from active cells to the synapses that touch
them, to determine which synapses are active.
@param activeCells (set) Indices of active cells in `t`
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`activeSegments` (set),
`predictiveCells` (set),
`matchingSegments` (set),
`matchingCells` (set)
"""
numActiveConnectedSynapsesForSegment = defaultdict(int)
numActiveSynapsesForSegment = defaultdict(int)
activeSegments = set()
predictiveCells = set()
matchingSegments = set()
matchingCells = set()
for cell in activeCells:
for synapseData in connections.synapsesForPresynapticCell(
cell).values():
segment = synapseData.segment
permanence = synapseData.permanence
if permanence >= self.connectedPermanence:
numActiveConnectedSynapsesForSegment[segment] += 1
if (numActiveConnectedSynapsesForSegment[segment] >=
self.activationThreshold):
activeSegments.add(segment)
predictiveCells.add(
connections.cellForSegment(segment))
if permanence > 0 and self.predictedSegmentDecrement > 0:
numActiveSynapsesForSegment[segment] += 1
if numActiveSynapsesForSegment[
segment] >= self.minThreshold:
matchingSegments.add(segment)
matchingCells.add(connections.cellForSegment(segment))
return activeSegments, predictiveCells, matchingSegments, matchingCells
# ==============================
# Helper functions
# ==============================
def bestMatchingCell(self, cells, activeCells, connections):
"""
Gets the cell with the best matching segment
(see `TM.bestMatchingSegment`) that has the largest number of active
synapses of all best matching segments.
If none were found, pick the least used cell (see `TM.leastUsedCell`).
@param cells (set) Indices of cells
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`cell` (int),
`bestSegment` (int)
"""
maxSynapses = 0
bestCell = None
bestSegment = None
for cell in cells:
segment, numActiveSynapses = self.bestMatchingSegment(
cell, activeCells, connections)
if segment is not None and numActiveSynapses > maxSynapses:
maxSynapses = numActiveSynapses
bestCell = cell
bestSegment = segment
if bestCell is None:
bestCell = self.leastUsedCell(cells, connections)
return bestCell, bestSegment
def bestMatchingSegment(self, cell, activeCells, connections):
"""
Gets the segment on a cell with the largest number of activate synapses,
including all synapses with non-zero permanences.
@param cell (int) Cell index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (tuple) Contains:
`segment` (int),
`connectedActiveSynapses` (set)
"""
maxSynapses = self.minThreshold
bestSegment = None
bestNumActiveSynapses = None
for segment in connections.segmentsForCell(cell):
numActiveSynapses = 0
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if ((synapseData.presynapticCell in activeCells)
and synapseData.permanence > 0):
numActiveSynapses += 1
if numActiveSynapses >= maxSynapses:
maxSynapses = numActiveSynapses
bestSegment = segment
bestNumActiveSynapses = numActiveSynapses
return bestSegment, bestNumActiveSynapses
def leastUsedCell(self, cells, connections):
"""
Gets the cell with the smallest number of segments.
Break ties randomly.
@param cells (set) Indices of cells
@param connections (Connections) Connectivity of layer
@return (int) Cell index
"""
leastUsedCells = set()
minNumSegments = float("inf")
for cell in cells:
numSegments = len(connections.segmentsForCell(cell))
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = set()
if numSegments == minNumSegments:
leastUsedCells.add(cell)
i = self._random.getUInt32(len(leastUsedCells))
return sorted(leastUsedCells)[i]
@staticmethod
def activeSynapsesForSegment(segment, activeCells, connections):
"""
Returns the synapses on a segment that are active due to lateral input
from active cells.
@param segment (int) Segment index
@param activeCells (set) Indices of active cells
@param connections (Connections) Connectivity of layer
@return (set) Indices of active synapses on segment
"""
synapses = set()
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
if synapseData.presynapticCell in activeCells:
synapses.add(synapse)
return synapses
def adaptSegment(self, segment, activeSynapses, connections,
permanenceIncrement, permanenceDecrement):
"""
Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param segment (int) Segment index
@param activeSynapses (set) Indices of active synapses
@param connections (Connections) Connectivity of layer
@param permanenceIncrement (float) Amount to increment active synapses
@param permanenceDecrement (float) Amount to decrement inactive synapses
"""
# Need to copy synapses for segment set below because it will be modified
# during iteration by `destroySynapse`
for synapse in set(connections.synapsesForSegment(segment)):
synapseData = connections.dataForSynapse(synapse)
permanence = synapseData.permanence
if synapse in activeSynapses:
permanence += permanenceIncrement
else:
permanence -= permanenceDecrement
# Keep permanence within min/max bounds
permanence = max(0.0, min(1.0, permanence))
if (abs(permanence) < EPSILON):
connections.destroySynapse(synapse)
else:
connections.updateSynapsePermanence(synapse, permanence)
def pickCellsToLearnOn(self, n, segment, winnerCells, connections):
"""
Pick cells to form distal connections to.
TODO: Respect topology and learningRadius
@param n (int) Number of cells to pick
@param segment (int) Segment index
@param winnerCells (set) Indices of winner cells in `t`
@param connections (Connections) Connectivity of layer
@return (set) Indices of cells picked
"""
candidates = set(winnerCells)
# Remove cells that are already synapsed on by this segment
for synapse in connections.synapsesForSegment(segment):
synapseData = connections.dataForSynapse(synapse)
presynapticCell = synapseData.presynapticCell
if presynapticCell in candidates:
candidates.remove(presynapticCell)
n = min(n, len(candidates))
candidates = sorted(candidates)
cells = set()
# Pick n cells randomly
for _ in range(n):
i = self._random.getUInt32(len(candidates))
cells.add(candidates[i])
del candidates[i]
return cells
def columnForCell(self, cell):
"""
Returns the index of the column that a cell belongs to.
@param cell (int) Cell index
@return (int) Column index
"""
self._validateCell(cell)
return int(cell / self.cellsPerColumn)
def cellsForColumn(self, column):
"""
Returns the indices of cells that belong to a column.
@param column (int) Column index
@return (set) Cell indices
"""
self._validateColumn(column)
start = self.cellsPerColumn * self.getCellIndex(column)
end = start + self.cellsPerColumn
return set(xrange(start, end))
def numberOfColumns(self):
"""
Returns the number of columns in this layer.
@return (int) Number of columns
"""
return reduce(mul, self.columnDimensions, 1)
def numberOfCells(self):
"""
Returns the number of cells in this layer.
@return (int) Number of cells
"""
return self.numberOfColumns() * self.cellsPerColumn
def getActiveCells(self):
"""
Returns the indices of the active cells.
@return (list) Indices of active cells.
"""
return self.getCellIndices(self.activeCells)
def getPredictiveCells(self):
"""
Returns the indices of the predictive cells.
@return (list) Indices of predictive cells.
"""
return self.getCellIndices(self.predictiveCells)
def getWinnerCells(self):
"""
Returns the indices of the winner cells.
@return (list) Indices of winner cells.
"""
return self.getCellIndices(self.winnerCells)
def getMatchingCells(self):
"""
Returns the indices of the matching cells.
@return (list) Indices of matching cells.
"""
return self.getCellIndices(self.matchingCells)
def mapCellsToColumns(self, cells):
"""
Maps cells to the columns they belong to
@param cells (set) Cells
@return (dict) Mapping from columns to their cells in `cells`
"""
cellsForColumns = defaultdict(set)
for cell in cells:
column = self.columnForCell(cell)
cellsForColumns[column].add(cell)
return cellsForColumns
def write(self, proto):
"""
Writes serialized data to proto object
@param proto (DynamicStructBuilder) Proto object
"""
proto.columnDimensions = self.columnDimensions
proto.cellsPerColumn = self.cellsPerColumn
proto.activationThreshold = self.activationThreshold
proto.initialPermanence = self.initialPermanence
proto.connectedPermanence = self.connectedPermanence
proto.minThreshold = self.minThreshold
proto.maxNewSynapseCount = self.maxNewSynapseCount
proto.permanenceIncrement = self.permanenceIncrement
proto.permanenceDecrement = self.permanenceDecrement
proto.predictedSegmentDecrement = self.predictedSegmentDecrement
self.connections.write(proto.connections)
self._random.write(proto.random)
proto.activeCells = list(self.activeCells)
proto.predictiveCells = list(self.predictiveCells)
proto.activeSegments = list(self.activeSegments)
proto.winnerCells = list(self.winnerCells)
proto.matchingSegments = list(self.matchingSegments)
proto.matchingCells = list(self.matchingCells)
@classmethod
def read(cls, proto):
"""
Reads deserialized data from proto object
@param proto (DynamicStructBuilder) Proto object
@return (TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
tm.columnDimensions = list(proto.columnDimensions)
tm.cellsPerColumn = int(proto.cellsPerColumn)
tm.activationThreshold = int(proto.activationThreshold)
tm.initialPermanence = proto.initialPermanence
tm.connectedPermanence = proto.connectedPermanence
tm.minThreshold = int(proto.minThreshold)
tm.maxNewSynapseCount = int(proto.maxNewSynapseCount)
tm.permanenceIncrement = proto.permanenceIncrement
tm.permanenceDecrement = proto.permanenceDecrement
tm.predictedSegmentDecrement = proto.predictedSegmentDecrement
tm.connections = Connections.read(proto.connections)
tm._random = Random()
tm._random.read(proto.random)
tm.activeCells = set([int(x) for x in proto.activeCells])
tm.predictiveCells = set([int(x) for x in proto.predictiveCells])
tm.activeSegments = set([int(x) for x in proto.activeSegments])
tm.winnerCells = set([int(x) for x in proto.winnerCells])
tm.matchingSegments = set([int(x) for x in proto.matchingSegments])
tm.matchingCells = set([int(x) for x in proto.matchingCells])
return tm
def __eq__(self, other):
"""
Equality operator for TemporalMemory instances.
Checks if two instances are functionally identical
(might have different internal state).
@param other (TemporalMemory) TemporalMemory instance to compare to
"""
if self.columnDimensions != other.columnDimensions: return False
if self.cellsPerColumn != other.cellsPerColumn: return False
if self.activationThreshold != other.activationThreshold: return False
if abs(self.initialPermanence - other.initialPermanence) > EPSILON:
return False
if abs(self.connectedPermanence - other.connectedPermanence) > EPSILON:
return False
if self.minThreshold != other.minThreshold: return False
if self.maxNewSynapseCount != other.maxNewSynapseCount: return False
if abs(self.permanenceIncrement - other.permanenceIncrement) > EPSILON:
return False
if abs(self.permanenceDecrement - other.permanenceDecrement) > EPSILON:
return False
if abs(self.predictedSegmentDecrement -
other.predictedSegmentDecrement) > EPSILON:
return False
if self.connections != other.connections: return False
if self.activeCells != other.activeCells: return False
if self.predictiveCells != other.predictiveCells: return False
if self.winnerCells != other.winnerCells: return False
if self.matchingSegments != other.matchingSegments: return False
if self.matchingCells != other.matchingCells: return False
return True
def __ne__(self, other):
"""
Non-equality operator for TemporalMemory instances.
Checks if two instances are not functionally identical
(might have different internal state).
@param other (TemporalMemory) TemporalMemory instance to compare to
"""
return not self.__eq__(other)
def _validateColumn(self, column):
"""
Raises an error if column index is invalid.
@param column (int) Column index
"""
if column >= self.numberOfColumns() or column < 0:
raise IndexError("Invalid column")
def _validateCell(self, cell):
"""
Raises an error if cell index is invalid.
@param cell (int) Cell index
"""
if cell >= self.numberOfCells() or cell < 0:
raise IndexError("Invalid cell")
@classmethod
def getCellIndices(cls, cells):
return [cls.getCellIndex(c) for c in cells]
@staticmethod
def getCellIndex(cell):
return cell
