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temporal_memory.py
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2014-2016, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero Public License version 3 as
# published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the GNU Affero Public License for more details.
#
# You should have received a copy of the GNU Affero Public License
# along with this program. If not, see http://www.gnu.org/licenses.
#
# http://numenta.org/licenses/
# ----------------------------------------------------------------------
"""
Temporal Memory implementation in Python.
The static methods in this file use the following parameter ordering convention:
1. Output / mutated params
2. Traditional parameters to the function, i.e. the ones that would still exist
if this function were a method on a class
3. Model state (not mutated)
4. Model parameters (including "learn")
"""
from collections import defaultdict
from operator import mul
from nupic.bindings.math import Random
from nupic.research.connections import Connections, binSearch
from nupic.support.group_by import groupby2
EPSILON = 0.00001 # constant error threshold to check equality of permanences to
# other floats
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 potential synapses active on a segment is at least this
threshold, it is said to be "matching" and is eligible for learning.
@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 segments are punished for incorrect predictions.
@param seed (int)
Seed for the random number generator.
@param maxSegmentsPerCell
The maximum number of segments per cell.
@param maxSynapsesPerSegment
The maximum number of synapses per segment.
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 cellsPerColumn <= 0:
raise ValueError("Number of cells per column must be greater than 0")
if minThreshold > activationThreshold:
raise ValueError(
"The min threshold can't be greater than the activation threshold")
# 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 = self.connectionsFactory(
self.numberOfCells(),
maxSegmentsPerCell=maxSegmentsPerCell,
maxSynapsesPerSegment=maxSynapsesPerSegment
)
self._random = Random(seed)
self.activeCells = []
self.winnerCells = []
self.activeSegments = []
self.matchingSegments = []
self.numActiveConnectedSynapsesForSegment = []
self.numActivePotentialSynapsesForSegment = []
@staticmethod
def connectionsFactory(*args, **kwargs):
""" Create a Connections instance. TemporalMemory subclasses may override
this method to choose a different Connections implementation, or to augment
the instance otherwise returned by the default Connections implementation.
See Connections for constructor signature and usage
@return: Connections instance
"""
return Connections(*args, **kwargs)
# ==============================
# Main functions
# ==============================
def compute(self, activeColumns, learn=True):
""" Feeds input record through TM, performing inference and learning.
@param activeColumns (iter)
Indices of active columns
@param learn (bool)
Whether or not learning is enabled
"""
self.activateCells(sorted(activeColumns), learn)
self.activateDendrites(learn)
def activateCells(self, activeColumns, learn=True):
""" Calculate the active cells, using the current active columns and
dendrite segments. Grow and reinforce synapses.
@param activeColumns (iter)
A sorted list of active column indices.
@param learn (bool)
If true, reinforce / punish / grow synapses.
Pseudocode:
for each column
if column is active and has active distal dendrite segments
call activatePredictedColumn
if column is active and doesn't have active distal dendrite segments
call burstColumn
if column is inactive and has matching distal dendrite segments
call punishPredictedColumn
"""
prevActiveCells = self.activeCells
prevWinnerCells = self.winnerCells
self.activeCells = []
self.winnerCells = []
segToCol = lambda segment: int(segment.cell / self.cellsPerColumn)
identity = lambda x: x
for columnData in groupby2(activeColumns, identity,
self.activeSegments, segToCol,
self.matchingSegments, segToCol):
(column,
activeColumns,
activeSegmentsOnCol,
matchingSegmentsOnCol) = columnData
if activeColumns is not None:
if activeSegmentsOnCol is not None:
cellsToAdd = self.activatePredictedColumn(
self.connections,
self._random,
activeSegmentsOnCol,
prevActiveCells,
prevWinnerCells,
self.numActivePotentialSynapsesForSegment,
self.maxNewSynapseCount,
self.initialPermanence,
self.permanenceIncrement,
self.permanenceDecrement,
learn)
self.activeCells += cellsToAdd
self.winnerCells += cellsToAdd
else:
(cellsToAdd,
winnerCell) = self.burstColumn(
self.connections,
self._random,
column,
matchingSegmentsOnCol,
prevActiveCells,
prevWinnerCells,
self.numActivePotentialSynapsesForSegment,
self.cellsPerColumn,
self.maxNewSynapseCount,
self.initialPermanence,
self.permanenceIncrement,
self.permanenceDecrement,
learn)
self.activeCells += cellsToAdd
self.winnerCells.append(winnerCell)
else:
if learn:
self.punishPredictedColumn(self.connections,
matchingSegmentsOnCol,
prevActiveCells,
self.predictedSegmentDecrement)
def activateDendrites(self, learn=True):
""" Calculate dendrite segment activity, using the current active cells.
@param learn (bool)
If true, segment activations will be recorded. This information is used
during segment cleanup.
Pseudocode:
for each distal dendrite segment with activity >= activationThreshold
mark the segment as active
for each distal dendrite segment with unconnected activity >= minThreshold
mark the segment as matching
"""
(numActiveConnected,
numActivePotential) = self.connections.computeActivity(
self.activeCells,
self.connectedPermanence)
activeSegments = (
self.connections.segmentForFlatIdx(i)
for i in xrange(len(numActiveConnected))
if numActiveConnected[i] >= self.activationThreshold
)
matchingSegments = (
self.connections.segmentForFlatIdx(i)
for i in xrange(len(numActivePotential))
if numActivePotential[i] >= self.minThreshold
)
maxSegmentsPerCell = self.connections.maxSegmentsPerCell
segmentKey = lambda segment: (segment.cell * maxSegmentsPerCell
+ segment.idx)
self.activeSegments = sorted(activeSegments, key = segmentKey)
self.matchingSegments = sorted(matchingSegments, key = segmentKey)
self.numActiveConnectedSynapsesForSegment = numActiveConnected
self.numActivePotentialSynapsesForSegment = numActivePotential
if learn:
for segment in self.activeSegments:
self.connections.recordSegmentActivity(segment)
self.connections.startNewIteration()
def reset(self):
""" Indicates the start of a new sequence and resets the sequence
state of the TM. """
self.activeCells = []
self.winnerCells = []
self.activeSegments = []
self.matchingSegments = []
@classmethod
def activatePredictedColumn(cls, connections, random, columnActiveSegments,
prevActiveCells, prevWinnerCells,
numActivePotentialSynapsesForSegment,
maxNewSynapseCount,
initialPermanence, permanenceIncrement,
permanenceDecrement, learn):
""" Determines which cells in a predicted column should be added to winner
cells list, and learns on the segments that correctly predicted this column.
@param connections (Object)
Connections for the TM. Gets mutated.
@param random (Object)
Random number generator. Gets mutated.
@param columnActiveSegments (iter)
Active segments in this column.
@param prevActiveCells (list)
Active cells in `t-1`.
@param prevWinnerCells (list)
Winner cells in `t-1`.
@param numActivePotentialSynapsesForSegment (list)
Number of active potential synapses per segment, indexed by the segment's
flatIdx.
@param maxNewSynapseCount (int)
The maximum number of synapses added to a segment during learning
@param initialPermanence (float)
Initial permanence of a new synapse.
@permanenceIncrement (float)
Amount by which permanences of synapses are incremented during learning.
@permanenceDecrement (float)
Amount by which permanences of synapses are decremented during learning.
@param learn (bool)
If true, grow and reinforce synapses.
@return cellsToAdd (list)
A list of predicted cells that will be added to active cells and winner
cells.
Pseudocode:
for each cell in the column that has an active distal dendrite segment
mark the cell as active
mark the cell as a winner cell
(learning) for each active distal dendrite segment
strengthen active synapses
weaken inactive synapses
grow synapses to previous winner cells
"""
cellsToAdd = []
previousCell = None
for segment in columnActiveSegments:
if segment.cell != previousCell:
cellsToAdd.append(segment.cell)
previousCell = segment.cell
if learn:
cls.adaptSegment(connections, segment, prevActiveCells,
permanenceIncrement, permanenceDecrement)
active = numActivePotentialSynapsesForSegment[segment.flatIdx]
nGrowDesired = maxNewSynapseCount - active
if nGrowDesired > 0:
cls.growSynapses(connections, random, segment, nGrowDesired,
prevWinnerCells, initialPermanence)
return cellsToAdd
@classmethod
def burstColumn(cls, connections, random, column, columnMatchingSegments,
prevActiveCells, prevWinnerCells,
numActivePotentialSynapsesForSegment, cellsPerColumn,
maxNewSynapseCount, initialPermanence, permanenceIncrement,
permanenceDecrement, learn):
""" Activates all of the cells in an unpredicted active column, chooses a
winner cell, and, if learning is turned on, learns on one segment, growing a
new segment if necessary.
@param connections (Object)
Connections for the TM. Gets mutated.
@param random (Object)
Random number generator. Gets mutated.
@param column (int)
Index of bursting column.
@param columnMatchingSegments (iter)
Matching segments in this column.
@param prevActiveCells (list)
Active cells in `t-1`.
@param prevWinnerCells (list)
Winner cells in `t-1`.
@param numActivePotentialSynapsesForSegment (list)
Number of active potential synapses per segment, indexed by the segment's
flatIdx.
@param cellsPerColumn (int)
Number of cells per column.
@param maxNewSynapseCount (int)
The maximum number of synapses added to a segment during learning.
@param initialPermanence (float)
Initial permanence of a new synapse.
@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 learn (bool)
Whether or not learning is enabled.
@return (tuple) Contains:
`cells` (iter),
`winnerCell` (int),
Pseudocode:
mark all cells as active
if there are any matching distal dendrite segments
find the most active matching segment
mark its cell as a winner cell
(learning)
grow and reinforce synapses to previous winner cells
else
find the cell with the least segments, mark it as a winner cell
(learning)
(optimization) if there are prev winner cells
add a segment to this winner cell
grow synapses to previous winner cells
"""
start = cellsPerColumn * column
cells = xrange(start, start + cellsPerColumn)
if columnMatchingSegments is not None:
numActive = lambda s: numActivePotentialSynapsesForSegment[s.flatIdx]
bestMatchingSegment = max(columnMatchingSegments, key=numActive)
winnerCell = bestMatchingSegment.cell
if learn:
cls.adaptSegment(connections, bestMatchingSegment, prevActiveCells,
permanenceIncrement, permanenceDecrement)
nGrowDesired = maxNewSynapseCount - numActive(bestMatchingSegment)
if nGrowDesired > 0:
cls.growSynapses(connections, random, bestMatchingSegment,
nGrowDesired, prevWinnerCells, initialPermanence)
else:
winnerCell = cls.leastUsedCell(random, cells, connections)
if learn:
nGrowExact = min(maxNewSynapseCount, len(prevWinnerCells))
if nGrowExact > 0:
segment = connections.createSegment(winnerCell)
cls.growSynapses(connections, random, segment, nGrowExact,
prevWinnerCells, initialPermanence)
return cells, winnerCell
@classmethod
def punishPredictedColumn(cls, connections, columnMatchingSegments,
prevActiveCells, predictedSegmentDecrement):
"""Punishes the Segments that incorrectly predicted a column to be active.
@param connections (Object)
Connections for the TM. Gets mutated.
@param columnMatchingSegments (iter)
Matching segments for this column.
@param prevActiveCells (list)
Active cells in `t-1`.
@param predictedSegmentDecrement (float)
Amount by which segments are punished for incorrect predictions.
Pseudocode:
for each matching segment in the column
weaken active synapses
"""
if predictedSegmentDecrement > 0.0 and columnMatchingSegments is not None:
for segment in columnMatchingSegments:
cls.adaptSegment(connections, segment, prevActiveCells,
-predictedSegmentDecrement, 0.0)
# ==============================
# Helper functions
# ==============================
@classmethod
def leastUsedCell(cls, random, cells, connections):
""" Gets the cell with the smallest number of segments.
Break ties randomly.
@param random (Object)
Random number generator. Gets mutated.
@param cells (list)
Indices of cells.
@param connections (Object)
Connections instance for the TM.
@return (int) Cell index.
"""
leastUsedCells = []
minNumSegments = float("inf")
for cell in cells:
numSegments = connections.numSegments(cell)
if numSegments < minNumSegments:
minNumSegments = numSegments
leastUsedCells = []
if numSegments == minNumSegments:
leastUsedCells.append(cell)
i = random.getUInt32(len(leastUsedCells))
return leastUsedCells[i]
@classmethod
def growSynapses(cls, connections, random, segment, nDesiredNewSynapes,
prevWinnerCells, initialPermanence):
""" Creates nDesiredNewSynapes synapses on the segment passed in if
possible, choosing random cells from the previous winner cells that are
not already on the segment.
@param connections (Object) Connections instance for the tm
@param random (Object) Tm object used to generate random
numbers
@param segment (int) Segment to grow synapses on.
@params nDesiredNewSynapes (int) Desired number of synapses to grow
@params prevWinnerCells (list) Winner cells in `t-1`
@param initialPermanence (float) Initial permanence of a new synapse.
Notes: The process of writing the last value into the index in the array
that was most recently changed is to ensure the same results that we get
in the c++ implentation using iter_swap with vectors.
"""
candidates = list(prevWinnerCells)
eligibleEnd = len(candidates) - 1
for synapse in connections.synapsesForSegment(segment):
try:
index = candidates[:eligibleEnd + 1].index(synapse.presynapticCell)
except ValueError:
index = -1
if index != -1:
candidates[index] = candidates[eligibleEnd]
eligibleEnd -= 1
candidatesLength = eligibleEnd + 1
nActual = min(nDesiredNewSynapes, candidatesLength)
for _ in range(nActual):
rand = random.getUInt32(candidatesLength)
connections.createSynapse(segment, candidates[rand],
initialPermanence)
candidates[rand] = candidates[candidatesLength - 1]
candidatesLength -= 1
@classmethod
def adaptSegment(cls, connections, segment, prevActiveCells,
permanenceIncrement, permanenceDecrement):
""" Updates synapses on segment.
Strengthens active synapses; weakens inactive synapses.
@param connections (Object) Connections instance for the tm
@param segment (int) Segment to adapt
@param prevActiveCells (list) Active cells in `t-1`
@param permanenceIncrement (float) Amount to increment active synapses
@param permanenceDecrement (float) Amount to decrement inactive synapses
"""
for synapse in connections.synapsesForSegment(segment):
permanence = synapse.permanence
if binSearch(prevActiveCells, synapse.presynapticCell) != -1:
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)
if connections.numSynapses(segment) == 0:
connections.destroySegment(segment)
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 (list) Cell indices
"""
self._validateColumn(column)
start = self.cellsPerColumn * column
end = start + self.cellsPerColumn
return 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 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.
"""
previousCell = None
predictiveCells = []
for segment in self.activeSegments:
if segment.cell != previousCell:
predictiveCells.append(segment.cell)
previousCell = segment.cell
return predictiveCells
def getWinnerCells(self):
""" Returns the indices of the winner cells.
@return (list) Indices of winner cells.
"""
return self.getCellIndices(self.winnerCells)
def getCellsPerColumn(self):
""" Returns the number of cells per column.
@return (int) The number of cells per column.
"""
return self.cellsPerColumn
def getColumnDimensions(self):
"""
Returns the dimensions of the columns in the region.
@return (tuple) Column dimensions
"""
return self.columnDimensions
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 write(self, proto):
""" Writes serialized data to proto object
@param proto (DynamicStructBuilder) Proto object
"""
# capnp fails to save a tuple. Let's force columnDimensions to list.
proto.columnDimensions = list(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.winnerCells = list(self.winnerCells)
activeSegmentOverlaps = \
proto.init('activeSegmentOverlaps', len(self.activeSegments))
for i, segment in enumerate(self.activeSegments):
activeSegmentOverlaps[i].cell = segment.cell
activeSegmentOverlaps[i].segment = segment.idx
activeSegmentOverlaps[i].overlap = (
self.numActiveConnectedSynapsesForSegment[segment.flatIdx]
)
matchingSegmentOverlaps = \
proto.init('matchingSegmentOverlaps', len(self.matchingSegments))
for i, segment in enumerate(self.matchingSegments):
matchingSegmentOverlaps[i].cell = segment.cell
matchingSegmentOverlaps[i].segment = segment.idx
matchingSegmentOverlaps[i].overlap = (
self.numActivePotentialSynapsesForSegment[segment.flatIdx]
)
@classmethod
def read(cls, proto):
""" Reads deserialized data from proto object
@param proto (DynamicStructBuilder) Proto object
@return (TemporalMemory) TemporalMemory instance
"""
tm = object.__new__(cls)
# capnp fails to save a tuple, so proto.columnDimensions was forced to
# serialize as a list. We prefer a tuple, however, because columnDimensions
# should be regarded as immutable.
tm.columnDimensions = tuple(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)
#pylint: disable=W0212
tm._random = Random()
tm._random.read(proto.random)
#pylint: enable=W0212
tm.activeCells = [int(x) for x in proto.activeCells]
tm.winnerCells = [int(x) for x in proto.winnerCells]
flatListLength = tm.connections.segmentFlatListLength()
tm.numActiveConnectedSynapsesForSegment = [0] * flatListLength
tm.numActivePotentialSynapsesForSegment = [0] * flatListLength
tm.activeSegments = []
tm.matchingSegments = []
for i in xrange(len(proto.activeSegmentOverlaps)):
protoSegmentOverlap = proto.activeSegmentOverlaps[i]
segment = tm.connections.getSegment(protoSegmentOverlap.cell,
protoSegmentOverlap.segment)
tm.activeSegments.append(segment)
overlap = protoSegmentOverlap.overlap
tm.numActiveConnectedSynapsesForSegment[segment.flatIdx] = overlap
for i in xrange(len(proto.matchingSegmentOverlaps)):
protoSegmentOverlap = proto.matchingSegmentOverlaps[i]
segment = tm.connections.getSegment(protoSegmentOverlap.cell,
protoSegmentOverlap.segment)
tm.matchingSegments.append(segment)
overlap = protoSegmentOverlap.overlap
tm.numActivePotentialSynapsesForSegment[segment.flatIdx] = overlap
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.winnerCells != other.winnerCells:
return False
if self.matchingSegments != other.matchingSegments:
return False
if self.activeSegments != other.activeSegments:
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):
""" Returns the indices of the cells passed in.
@param cells (list) cells to find the indices of
"""
return [cls.getCellIndex(c) for c in cells]
@staticmethod
def getCellIndex(cell):
""" Returns the index of the cell
@param cell (int) cell to find the index of
"""
return cell
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