Пример #1
0
    def __init__(
        self,
        inputDimensions=[32, 32],
        columnDimensions=[64, 64],
        potentialRadius=16,
        potentialPct=0.9,
        globalInhibition=True,
        localAreaDensity=-1.0,
        numActiveColumnsPerInhArea=20.0,
        stimulusThreshold=2,
        synPermInactiveDec=0.01,
        synPermActiveInc=0.03,
        synPermConnected=0.3,
        minPctOverlapDutyCycle=0.001,
        minPctActiveDutyCycle=0.001,
        dutyCyclePeriod=1000,
        maxBoost=1.0,
        seed=42,
        spVerbosity=0,
        wrapAround=True,
        # union_pooler.py parameters
        activeOverlapWeight=1.0,
        predictedActiveOverlapWeight=0.0,
        fixedPoolingActivationBurst=False,
        exciteFunction=None,
        decayFunction=None,
        maxUnionActivity=0.20,
    ):
        """
    Please see spatial_pooler.py in NuPIC for super class parameter
    descriptions.

    Class-specific parameters:
    -------------------------------------

    @param activeOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and active-cell input

    @param predictedActiveOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and predicted-active-cell input

    @param fixedPoolingActivationBurst: A Boolean, which, if True, has the
        Union Pooler grant a fixed amount of pooling activation to
        columns whenever they win the inhibition step. If False, columns'
        pooling activation is calculated based on their current overlap.

    @param exciteFunction: If fixedPoolingActivationBurst is False,
        this specifies the ExciteFunctionBase used to excite pooling
        activation.

    @param decayFunction: Specifies the DecayFunctionBase used to decay pooling
        activation.

    @param maxUnionActivity: Maximum number of active cells allowed in union SDR
        simultaneously in terms of the ratio between the number of active cells
        and the number of total cells
    """

        super(UnionPooler, self).__init__(
            inputDimensions,
            columnDimensions,
            potentialRadius,
            potentialPct,
            globalInhibition,
            localAreaDensity,
            numActiveColumnsPerInhArea,
            stimulusThreshold,
            synPermInactiveDec,
            synPermActiveInc,
            synPermConnected,
            minPctOverlapDutyCycle,
            minPctActiveDutyCycle,
            dutyCyclePeriod,
            maxBoost,
            seed,
            spVerbosity,
            wrapAround,
        )

        self._activeOverlapWeight = activeOverlapWeight
        self._predictedActiveOverlapWeight = predictedActiveOverlapWeight
        self._fixedPoolingActivationBurst = fixedPoolingActivationBurst
        self._maxUnionActivity = maxUnionActivity

        if exciteFunction is None:
            self._exciteFunction = LinearExciteFunction()
        else:
            self._exciteFunction = exciteFunction

        if decayFunction is None:
            self._decayFunction = NoDecayFunction()
        else:
            self._decayFunction = decayFunction

        # The maximum number of cells allowed in a single union SDR
        self._maxUnionCells = int(self._numColumns * self._maxUnionActivity)

        # Scalar activation of potential union SDR cells; most active cells become
        # the union SDR
        self._poolingActivation = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)

        # Current union SDR; the end product of the union pooler algorithm
        self._unionSDR = numpy.array([], dtype=INT_DTYPE)
Пример #2
0
    def __init__(
            self,
            inputDimensions=[32, 32],
            columnDimensions=[64, 64],
            potentialRadius=16,
            potentialPct=0.9,
            globalInhibition=True,
            localAreaDensity=-1.0,
            numActiveColumnsPerInhArea=20.0,
            stimulusThreshold=2,
            synPermInactiveDec=0.01,
            synPermActiveInc=0.03,
            synPermConnected=0.3,
            minPctOverlapDutyCycle=0.001,
            minPctActiveDutyCycle=0.001,
            dutyCyclePeriod=1000,
            maxBoost=1.0,
            seed=42,
            spVerbosity=0,
            wrapAround=True,

            # union_pooler.py parameters
            activeOverlapWeight=1.0,
            predictedActiveOverlapWeight=0.0,
            fixedPoolingActivationBurst=False,
            exciteFunction=None,
            decayFunction=None,
            maxUnionActivity=0.20):
        """
    Please see spatial_pooler.py in NuPIC for super class parameter
    descriptions.

    Class-specific parameters:
    -------------------------------------

    @param activeOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and active-cell input

    @param predictedActiveOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and predicted-active-cell input

    @param fixedPoolingActivationBurst: A Boolean, which, if True, has the
        Union Pooler grant a fixed amount of pooling activation to
        columns whenever they win the inhibition step. If False, columns'
        pooling activation is calculated based on their current overlap.

    @param exciteFunction: If fixedPoolingActivationBurst is False,
        this specifies the ExciteFunctionBase used to excite pooling
        activation.

    @param decayFunction: Specifies the DecayFunctionBase used to decay pooling
        activation.

    @param maxUnionActivity: Maximum number of active cells allowed in union SDR
        simultaneously in terms of the ratio between the number of active cells
        and the number of total cells
    """

        super(UnionPooler, self).__init__(
            inputDimensions, columnDimensions, potentialRadius, potentialPct,
            globalInhibition, localAreaDensity, numActiveColumnsPerInhArea,
            stimulusThreshold, synPermInactiveDec, synPermActiveInc,
            synPermConnected, minPctOverlapDutyCycle, minPctActiveDutyCycle,
            dutyCyclePeriod, maxBoost, seed, spVerbosity, wrapAround)

        self._activeOverlapWeight = activeOverlapWeight
        self._predictedActiveOverlapWeight = predictedActiveOverlapWeight
        self._fixedPoolingActivationBurst = fixedPoolingActivationBurst
        self._maxUnionActivity = maxUnionActivity

        if exciteFunction is None:
            self._exciteFunction = LinearExciteFunction()
        else:
            self._exciteFunction = exciteFunction

        if decayFunction is None:
            self._decayFunction = NoDecayFunction()
        else:
            self._decayFunction = decayFunction

        # The maximum number of cells allowed in a single union SDR
        self._maxUnionCells = int(self._numColumns * self._maxUnionActivity)

        # Scalar activation of potential union SDR cells; most active cells become
        # the union SDR
        self._poolingActivation = numpy.zeros(self._numColumns,
                                              dtype=REAL_DTYPE)

        # Current union SDR; the end product of the union pooler algorithm
        self._unionSDR = numpy.array([], dtype=INT_DTYPE)
Пример #3
0
class UnionPooler(SpatialPooler):
    """
  Experimental Union Pooler Python implementation. The Union Pooler builds a
  "union SDR" of the most recent sets of active columns. It is driven by
  active-cell input and, more strongly, by predictive-active cell input. The
  latter is more likely to produce active columns. Such winning columns will
  also tend to persist longer in the union SDR.
  """

    def __init__(
        self,
        inputDimensions=[32, 32],
        columnDimensions=[64, 64],
        potentialRadius=16,
        potentialPct=0.9,
        globalInhibition=True,
        localAreaDensity=-1.0,
        numActiveColumnsPerInhArea=20.0,
        stimulusThreshold=2,
        synPermInactiveDec=0.01,
        synPermActiveInc=0.03,
        synPermConnected=0.3,
        minPctOverlapDutyCycle=0.001,
        minPctActiveDutyCycle=0.001,
        dutyCyclePeriod=1000,
        maxBoost=1.0,
        seed=42,
        spVerbosity=0,
        wrapAround=True,
        # union_pooler.py parameters
        activeOverlapWeight=1.0,
        predictedActiveOverlapWeight=0.0,
        fixedPoolingActivationBurst=False,
        exciteFunction=None,
        decayFunction=None,
        maxUnionActivity=0.20,
    ):
        """
    Please see spatial_pooler.py in NuPIC for super class parameter
    descriptions.

    Class-specific parameters:
    -------------------------------------

    @param activeOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and active-cell input

    @param predictedActiveOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and predicted-active-cell input

    @param fixedPoolingActivationBurst: A Boolean, which, if True, has the
        Union Pooler grant a fixed amount of pooling activation to
        columns whenever they win the inhibition step. If False, columns'
        pooling activation is calculated based on their current overlap.

    @param exciteFunction: If fixedPoolingActivationBurst is False,
        this specifies the ExciteFunctionBase used to excite pooling
        activation.

    @param decayFunction: Specifies the DecayFunctionBase used to decay pooling
        activation.

    @param maxUnionActivity: Maximum number of active cells allowed in union SDR
        simultaneously in terms of the ratio between the number of active cells
        and the number of total cells
    """

        super(UnionPooler, self).__init__(
            inputDimensions,
            columnDimensions,
            potentialRadius,
            potentialPct,
            globalInhibition,
            localAreaDensity,
            numActiveColumnsPerInhArea,
            stimulusThreshold,
            synPermInactiveDec,
            synPermActiveInc,
            synPermConnected,
            minPctOverlapDutyCycle,
            minPctActiveDutyCycle,
            dutyCyclePeriod,
            maxBoost,
            seed,
            spVerbosity,
            wrapAround,
        )

        self._activeOverlapWeight = activeOverlapWeight
        self._predictedActiveOverlapWeight = predictedActiveOverlapWeight
        self._fixedPoolingActivationBurst = fixedPoolingActivationBurst
        self._maxUnionActivity = maxUnionActivity

        if exciteFunction is None:
            self._exciteFunction = LinearExciteFunction()
        else:
            self._exciteFunction = exciteFunction

        if decayFunction is None:
            self._decayFunction = NoDecayFunction()
        else:
            self._decayFunction = decayFunction

        # The maximum number of cells allowed in a single union SDR
        self._maxUnionCells = int(self._numColumns * self._maxUnionActivity)

        # Scalar activation of potential union SDR cells; most active cells become
        # the union SDR
        self._poolingActivation = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)

        # Current union SDR; the end product of the union pooler algorithm
        self._unionSDR = numpy.array([], dtype=INT_DTYPE)

    def reset(self):
        """
    Reset the state of the Union Pooler.
    """

        # Reset Union Pooler fields
        self._poolingActivation = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)
        self._unionSDR = []

        # Reset Spatial Pooler fields
        self._overlapDutyCycles = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)
        self._activeDutyCycles = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)
        self._minOverlapDutyCycles = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)
        self._minActiveDutyCycles = numpy.zeros(self._numColumns, dtype=REAL_DTYPE)
        self._boostFactors = numpy.ones(self._numColumns, dtype=REAL_DTYPE)

    def compute(self, activeInput, predictedActiveInput, learn):
        """
    Computes one cycle of the Union Pooler algorithm.
    """
        assert numpy.size(activeInput) == self._numInputs
        assert numpy.size(predictedActiveInput) == self._numInputs
        self._updateBookeepingVars(learn)

        # Compute proximal dendrite overlaps with active and active-predicted inputs
        overlapsActive = self._calculateOverlap(activeInput)
        overlapsPredictedActive = self._calculateOverlap(predictedActiveInput)
        totalOverlap = (
            overlapsActive * self._activeOverlapWeight + overlapsPredictedActive * self._predictedActiveOverlapWeight
        )

        if learn:
            boostedOverlaps = self._boostFactors * totalOverlap
        else:
            boostedOverlaps = totalOverlap

        activeCells = self._inhibitColumns(boostedOverlaps)

        if learn:
            self._adaptSynapses(activeInput, activeCells)
            self._updateDutyCycles(totalOverlap, activeCells)
            self._bumpUpWeakColumns()
            self._updateBoostFactors()
            if self._isUpdateRound():
                self._updateInhibitionRadius()
                self._updateMinDutyCycles()

        # Decrement pooling activation of all cells
        self._decayPoolingActivation()

        # Reset the poolingActivation of current active Union Pooler cells
        if self._fixedPoolingActivationBurst:
            # Increase is based on fixed parameter
            tieBreaker = [random.random() * _TIE_BREAKER_FACTOR for _ in xrange(len(activeCells))]
            self._poolingActivation[activeCells] = self._poolingActivationBurst + tieBreaker
        else:
            # PoolingActivation update is based on active & predicted-active overlap
            self._addToPoolingActivation(activeCells, overlapsActive)
            self._addToPoolingActivation(activeCells, overlapsPredictedActive)

        return self._getMostActiveCells()

    def _decayPoolingActivation(self):
        """
    Decrements pooling activation of all cells
    """
        self._poolingActivation = self._decayFunction.decay(self._poolingActivation, 1)
        self._poolingActivation[self._poolingActivation < 0] = 0
        return self._poolingActivation

    def _addToPoolingActivation(self, activeCells, overlaps):
        """
    Adds overlaps from specified active cells to cells' pooling
    activation.
    :param activeCells: Indices of those cells winning the inhibition step
    :param overlaps: A current set of overlap values for each cell
    """
        cellIndices = numpy.where(overlaps[activeCells] > 0)[0]
        subset = activeCells[cellIndices]
        self._poolingActivation[subset] = self._exciteFunction.excite(self._poolingActivation[subset], overlaps[subset])
        return self._poolingActivation

    def _getMostActiveCells(self):
        """
    Gets the most active cells in the Union SDR having at least non-zero
    activation in sorted order.
    :return: a list of cell indices
    """
        potentialUnionSDR = numpy.argsort(self._poolingActivation)[::-1][: len(self._poolingActivation)]

        topCells = potentialUnionSDR[0 : self._maxUnionCells]
        nonZeroTopCells = self._poolingActivation[topCells] > 0
        self._unionSDR = numpy.sort(topCells[nonZeroTopCells]).astype(INT_DTYPE)
        return self._unionSDR

    def getUnionSDR(self):
        return self._unionSDR
Пример #4
0
class UnionPooler(SpatialPooler):
    """
  Experimental Union Pooler Python implementation. The Union Pooler builds a
  "union SDR" of the most recent sets of active columns. It is driven by
  active-cell input and, more strongly, by predictive-active cell input. The
  latter is more likely to produce active columns. Such winning columns will
  also tend to persist longer in the union SDR.
  """
    def __init__(
            self,
            inputDimensions=[32, 32],
            columnDimensions=[64, 64],
            potentialRadius=16,
            potentialPct=0.9,
            globalInhibition=True,
            localAreaDensity=-1.0,
            numActiveColumnsPerInhArea=20.0,
            stimulusThreshold=2,
            synPermInactiveDec=0.01,
            synPermActiveInc=0.03,
            synPermConnected=0.3,
            minPctOverlapDutyCycle=0.001,
            minPctActiveDutyCycle=0.001,
            dutyCyclePeriod=1000,
            maxBoost=1.0,
            seed=42,
            spVerbosity=0,
            wrapAround=True,

            # union_pooler.py parameters
            activeOverlapWeight=1.0,
            predictedActiveOverlapWeight=0.0,
            fixedPoolingActivationBurst=False,
            exciteFunction=None,
            decayFunction=None,
            maxUnionActivity=0.20):
        """
    Please see spatial_pooler.py in NuPIC for super class parameter
    descriptions.

    Class-specific parameters:
    -------------------------------------

    @param activeOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and active-cell input

    @param predictedActiveOverlapWeight: A multiplicative weight applied to
        the overlap between connected synapses and predicted-active-cell input

    @param fixedPoolingActivationBurst: A Boolean, which, if True, has the
        Union Pooler grant a fixed amount of pooling activation to
        columns whenever they win the inhibition step. If False, columns'
        pooling activation is calculated based on their current overlap.

    @param exciteFunction: If fixedPoolingActivationBurst is False,
        this specifies the ExciteFunctionBase used to excite pooling
        activation.

    @param decayFunction: Specifies the DecayFunctionBase used to decay pooling
        activation.

    @param maxUnionActivity: Maximum number of active cells allowed in union SDR
        simultaneously in terms of the ratio between the number of active cells
        and the number of total cells
    """

        super(UnionPooler, self).__init__(
            inputDimensions, columnDimensions, potentialRadius, potentialPct,
            globalInhibition, localAreaDensity, numActiveColumnsPerInhArea,
            stimulusThreshold, synPermInactiveDec, synPermActiveInc,
            synPermConnected, minPctOverlapDutyCycle, minPctActiveDutyCycle,
            dutyCyclePeriod, maxBoost, seed, spVerbosity, wrapAround)

        self._activeOverlapWeight = activeOverlapWeight
        self._predictedActiveOverlapWeight = predictedActiveOverlapWeight
        self._fixedPoolingActivationBurst = fixedPoolingActivationBurst
        self._maxUnionActivity = maxUnionActivity

        if exciteFunction is None:
            self._exciteFunction = LinearExciteFunction()
        else:
            self._exciteFunction = exciteFunction

        if decayFunction is None:
            self._decayFunction = NoDecayFunction()
        else:
            self._decayFunction = decayFunction

        # The maximum number of cells allowed in a single union SDR
        self._maxUnionCells = int(self._numColumns * self._maxUnionActivity)

        # Scalar activation of potential union SDR cells; most active cells become
        # the union SDR
        self._poolingActivation = numpy.zeros(self._numColumns,
                                              dtype=REAL_DTYPE)

        # Current union SDR; the end product of the union pooler algorithm
        self._unionSDR = numpy.array([], dtype=INT_DTYPE)

    def reset(self):
        """
    Reset the state of the Union Pooler.
    """

        # Reset Union Pooler fields
        self._poolingActivation = numpy.zeros(self._numColumns,
                                              dtype=REAL_DTYPE)
        self._unionSDR = []

        # Reset Spatial Pooler fields
        self._overlapDutyCycles = numpy.zeros(self._numColumns,
                                              dtype=REAL_DTYPE)
        self._activeDutyCycles = numpy.zeros(self._numColumns,
                                             dtype=REAL_DTYPE)
        self._minOverlapDutyCycles = numpy.zeros(self._numColumns,
                                                 dtype=REAL_DTYPE)
        self._minActiveDutyCycles = numpy.zeros(self._numColumns,
                                                dtype=REAL_DTYPE)
        self._boostFactors = numpy.ones(self._numColumns, dtype=REAL_DTYPE)

    def compute(self, activeInput, predictedActiveInput, learn):
        """
    Computes one cycle of the Union Pooler algorithm.
    """
        assert numpy.size(activeInput) == self._numInputs
        assert numpy.size(predictedActiveInput) == self._numInputs
        self._updateBookeepingVars(learn)

        # Compute proximal dendrite overlaps with active and active-predicted inputs
        overlapsActive = self._calculateOverlap(activeInput)
        overlapsPredictedActive = self._calculateOverlap(predictedActiveInput)
        totalOverlap = (
            overlapsActive * self._activeOverlapWeight +
            overlapsPredictedActive * self._predictedActiveOverlapWeight)

        if learn:
            boostedOverlaps = self._boostFactors * totalOverlap
        else:
            boostedOverlaps = totalOverlap

        activeCells = self._inhibitColumns(boostedOverlaps)

        if learn:
            self._adaptSynapses(activeInput, activeCells)
            self._updateDutyCycles(totalOverlap, activeCells)
            self._bumpUpWeakColumns()
            self._updateBoostFactors()
            if self._isUpdateRound():
                self._updateInhibitionRadius()
                self._updateMinDutyCycles()

        # Decrement pooling activation of all cells
        self._decayPoolingActivation()

        # Reset the poolingActivation of current active Union Pooler cells
        if self._fixedPoolingActivationBurst:
            # Increase is based on fixed parameter
            tieBreaker = [
                random.random() * _TIE_BREAKER_FACTOR
                for _ in xrange(len(activeCells))
            ]
            self._poolingActivation[activeCells] = (
                self._poolingActivationBurst + tieBreaker)
        else:
            # PoolingActivation update is based on active & predicted-active overlap
            self._addToPoolingActivation(activeCells, overlapsActive)
            self._addToPoolingActivation(activeCells, overlapsPredictedActive)

        return self._getMostActiveCells()

    def _decayPoolingActivation(self):
        """
    Decrements pooling activation of all cells
    """
        self._poolingActivation = self._decayFunction.decay(
            self._poolingActivation, 1)
        self._poolingActivation[self._poolingActivation < 0] = 0
        return self._poolingActivation

    def _addToPoolingActivation(self, activeCells, overlaps):
        """
    Adds overlaps from specified active cells to cells' pooling
    activation.
    :param activeCells: Indices of those cells winning the inhibition step
    :param overlaps: A current set of overlap values for each cell
    """
        cellIndices = numpy.where(overlaps[activeCells] > 0)[0]
        subset = activeCells[cellIndices]
        self._poolingActivation[subset] = self._exciteFunction.excite(
            self._poolingActivation[subset], overlaps[subset])
        return self._poolingActivation

    def _getMostActiveCells(self):
        """
    Gets the most active cells in the Union SDR having at least non-zero
    activation in sorted order.
    :return: a list of cell indices
    """
        potentialUnionSDR = numpy.argsort(
            self._poolingActivation)[::-1][:len(self._poolingActivation)]

        topCells = potentialUnionSDR[0:self._maxUnionCells]
        nonZeroTopCells = self._poolingActivation[topCells] > 0
        self._unionSDR = numpy.sort(
            topCells[nonZeroTopCells]).astype(INT_DTYPE)
        return self._unionSDR

    def getUnionSDR(self):
        return self._unionSDR