def mmGetDefaultMetrics(self, verbosity=1):
   resetsTrace = self.mmGetTraceResets()
   return ([Metric.createFromTrace(trace, excludeResets=resetsTrace)
             for trace in self.mmGetDefaultTraces()[:-3]] +
           [Metric.createFromTrace(trace)
             for trace in self.mmGetDefaultTraces()[-3:-1]] +
           [self.mmGetMetricSequencesPredictedActiveCellsPerColumn(),
            self.mmGetMetricSequencesPredictedActiveCellsShared()])
Ejemplo n.º 2
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 def mmGetDefaultMetrics(self, verbosity=1):
     resetsTrace = self.mmGetTraceResets()
     return ([
         Metric.createFromTrace(trace, excludeResets=resetsTrace)
         for trace in self.mmGetDefaultTraces()[:-3]
     ] + [
         Metric.createFromTrace(trace)
         for trace in self.mmGetDefaultTraces()[-3:-1]
     ])
 def mmGetDefaultMetrics(self, verbosity=1):
     resetsTrace = self.mmGetTraceResets()
     return ([
         Metric.createFromTrace(trace, excludeResets=resetsTrace)
         for trace in self.mmGetDefaultTraces()[:-3]
     ] + [
         Metric.createFromTrace(trace)
         for trace in self.mmGetDefaultTraces()[-3:-1]
     ] + [
         self.mmGetMetricSequencesPredictedActiveCellsPerColumn(),
         self.mmGetMetricSequencesPredictedActiveCellsShared()
     ])
Ejemplo n.º 4
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    def mmGetMetricDutyCycles(self):
        """
    @return (Metric) duty cycle metric
    """
        dutyCycles = self.mmGetDataDutyCycles()

        return Metric(self, "total column duty cycles", dutyCycles)
Ejemplo n.º 5
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 def testCreateFromTrace(self):
   metric = Metric.createFromTrace(self.trace)
   self.assertEqual(metric.title, self.trace.title)
   self.assertEqual(metric.min, 0)
   self.assertEqual(metric.max, 5)
   self.assertEqual(metric.sum, 15)
   self.assertEqual(metric.mean, 2.5)
   self.assertEqual(metric.standardDeviation, 1.707825127659933)
  def mmGetMetricBitlife(self):
    """
    See `mmGetDataBitlife` for description of bitlife.

    @return (Metric) bitlife metric
    """
    data = self._mmComputeBitLifeStats()
    return Metric(self, "Union SDR bitlife", data)
  def mmGetDefaultMetrics(self, verbosity=1):
    metrics = ([Metric.createFromTrace(trace)
                for trace in self.mmGetDefaultTraces()[:-1]])

    metrics += [self.mmGetMetricStabilityConfusion(),
                self.mmGetMetricDistinctnessConfusion()]

    return metrics
Ejemplo n.º 8
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    def mmGetDefaultMetrics(self, verbosity=1):
        metrics = ([
            Metric.createFromTrace(trace)
            for trace in self.mmGetDefaultTraces()
        ])
        metrics += [self.mmGetMetricDutyCycles()]

        return metrics
Ejemplo n.º 9
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    def mmGetMetricFromTrace(self, trace):
        """
    Convenience method to compute a metric over an indices trace, excluding
    resets.

    @param (IndicesTrace) Trace of indices

    @return (Metric) Metric over trace excluding resets
    """
        return Metric.createFromTrace(trace.makeCountsTrace(), excludeResets=self.mmGetTraceResets())
Ejemplo n.º 10
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 def testCreateFromTraceExcludeResets(self):
   resetTrace = BoolsTrace(self, "resets")
   resetTrace.data = [True, False, False, True, False, False]
   metric = Metric.createFromTrace(self.trace, excludeResets=resetTrace)
   self.assertEqual(metric.title, self.trace.title)
   self.assertEqual(metric.min, 0)
   self.assertEqual(metric.max, 5)
   self.assertEqual(metric.sum, 10)
   self.assertEqual(metric.mean, 2.5)
   self.assertEqual(metric.standardDeviation, 1.8027756377319946)
    def mmGetMetricFromTrace(self, trace):
        """
    Convenience method to compute a metric over an indices trace, excluding
    resets.

    @param (IndicesTrace) Trace of indices

    @return (Metric) Metric over trace excluding resets
    """
        return Metric.createFromTrace(trace.makeCountsTrace(),
                                      excludeResets=self.mmGetTraceResets())
    def mmGetMetricEntropy(self):
        """
    @return (Metric) entropy 
    """
        dutyCycles = self.mmGetDataDutyCycles()
        MIN_ACTIVATION_PROB = 0.000001

        dutyCycles[dutyCycles < MIN_ACTIVATION_PROB] = MIN_ACTIVATION_PROB
        dutyCycles = dutyCycles / numpy.sum(dutyCycles)

        entropy = -numpy.dot(dutyCycles, numpy.log2(dutyCycles))
        return Metric(self, "entropy", entropy)
Ejemplo n.º 13
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    def mmGetMetricDistinctnessConfusion(self):
        """
    For each iteration that doesn't follow a reset, looks at every other
    iteration for every other world that doesn't follow a reset, and computes
    the number of bits that show up in both sets of active cells those that
    iteration. This metric returns the distribution of those numbers.

    @return (Metric) Distinctness confusion metric
    """
        self._mmComputeSequenceRepresentationData()
        numbers = self._mmData["distinctnessConfusion"]
        return Metric(self, "distinctness confusion", numbers)
Ejemplo n.º 14
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    def mmGetMetricStabilityConfusion(self):
        """
    For each iteration that doesn't follow a reset, looks at every other
    iteration for the same world that doesn't follow a reset, and computes the
    number of bits that show up in one or the other set of active cells for
    that iteration, but not both. This metric returns the distribution of those
    numbers.

    @return (Metric) Stability confusion metric
    """
        self._mmComputeSequenceRepresentationData()
        numbers = self._mmData["stabilityConfusion"]
        return Metric(self, "stability confusion", numbers)
  def mmGetMetricDistinctnessConfusion(self):
    """
    Returns metric made of values from the distinctness confusion matrix
    (excluding diagonals). (See `mmGetDataDistinctnessConfusion`.)

    @return (Metric) Distinctness confusion metric
    """
    data, _ = self.mmGetDataDistinctnessConfusion()
    numbers = []

    for i in xrange(len(data)):
      for j in xrange(len(data[i])):
        if i != j:  # Ignoring diagonal
          numbers.append(data[i][j])

    return Metric(self, "distinctness confusion", numbers)
    def mmGetDefaultMetrics(self, verbosity=1):
        metrics = [Metric.createFromTrace(trace) for trace in self.mmGetDefaultTraces()[:-2]]

        connectionsPerColumnMetricIntial = self._mmTraces["connectionsPerColumnMetric"].data[0].copy()
        connectionsPerColumnMetricIntial.title += " (initial)"
        connectionsPerColumnMetricFinal = self._mmTraces["connectionsPerColumnMetric"].data[-1].copy()
        connectionsPerColumnMetricFinal.title += " (final)"

        metrics += [
            self.mmGetMetricStabilityConfusion(),
            self.mmGetMetricDistinctnessConfusion(),
            connectionsPerColumnMetricIntial,
            connectionsPerColumnMetricFinal,
        ]

        return metrics
  def mmGetMetricStabilityConfusion(self):
    """
    Returns metric made of values from the stability confusion matrix.
    (See `mmGetDataStabilityConfusion`.)

    @return (Metric) Stability confusion metric
    """
    data = self.mmGetDataStabilityConfusion()
    numbers = []

    for matrix in data.values():
      for i in xrange(len(matrix)):
        for j in xrange(len(matrix[i])):
          if i != j:  # Ignoring diagonal
            numbers.append(matrix[i][j])

    return Metric(self, "stability confusion", numbers)
  def mmGetDefaultMetrics(self, verbosity=1):
    metrics = ([Metric.createFromTrace(trace)
                for trace in self.mmGetDefaultTraces()[:-2]])

    connectionsPerColumnMetricIntial = (
      self._mmTraces["connectionsPerColumnMetric"].data[0].copy())
    connectionsPerColumnMetricIntial.title += " (initial)"
    connectionsPerColumnMetricFinal = (
      self._mmTraces["connectionsPerColumnMetric"].data[-1].copy())
    connectionsPerColumnMetricFinal.title += " (final)"

    metrics += [self.mmGetMetricStabilityConfusion(),
                self.mmGetMetricDistinctnessConfusion(),
                connectionsPerColumnMetricIntial,
                connectionsPerColumnMetricFinal]

    return metrics
    def mmGetMetricSequencesPredictedActiveCellsPerColumn(self):
        """
    Metric for number of predicted => active cells per column for each sequence

    @return (Metric) metric
    """
        self._mmComputeTransitionTraces()

        numCellsPerColumn = []

        for predictedActiveCells in (
                self._mmData["predictedActiveCellsForSequence"].values()):
            cellsForColumn = self.mapCellsToColumns(predictedActiveCells)
            numCellsPerColumn += [len(x) for x in cellsForColumn.values()]

        return Metric(
            self, "# predicted => active cells per column for each sequence",
            numCellsPerColumn)
    def mmGetMetricSequencesPredictedActiveCellsShared(self):
        """
    Metric for number of sequences each predicted => active cell appears in

    Note: This metric is flawed when it comes to high-order sequences.

    @return (Metric) metric
    """
        self._mmComputeTransitionTraces()

        numSequencesForCell = defaultdict(lambda: 0)

        for predictedActiveCells in (
                self._mmData["predictedActiveCellsForSequence"].values()):
            for cell in predictedActiveCells:
                numSequencesForCell[cell] += 1

        return Metric(self,
                      "# sequences each predicted => active cells appears in",
                      numSequencesForCell.values())
  def compute(self, *args, **kwargs):
    sequenceLabel = None
    if "sequenceLabel" in kwargs:
      sequenceLabel = kwargs["sequenceLabel"]
      del kwargs["sequenceLabel"]

    activeColumns = super(TemporalPoolerMonitorMixin, self).compute(*args,
                                                                    **kwargs)
    activeColumns = set(activeColumns)
    activeCells = activeColumns  # TODO: Update when moving to a cellular TP

    self._mmTraces["activeCells"].data.append(activeCells)
    self._mmTraces["sequenceLabels"].data.append(sequenceLabel)

    self._mmTraces["resets"].data.append(self._mmResetActive)
    self._mmResetActive = False

    self._mmTraces["connectionsPerColumnMetric"].data.append(
      Metric(self, "connections per column", self._connectedCounts.tolist()))

    self._sequenceRepresentationDataStale = True
  def compute(self, *args, **kwargs):
    sequenceLabel = kwargs.pop("sequenceLabel", None)

    unionSDR = super(UnionTemporalPoolerMonitorMixin, self).compute(*args,
                                                                    **kwargs)

    ### From spatial pooler
    # total number of connections
    connectedCounts = numpy.zeros(self.getNumColumns(), dtype=uintType)
    self.getConnectedCounts(connectedCounts)
    numConnections = numpy.sum(connectedCounts)

    self._mmTraces["unionSDR"].data.append(set(unionSDR))
    self._mmTraces["numConnections"].data.append(numConnections)
    self._mmTraces["sequenceLabels"].data.append(sequenceLabel)
    self._mmTraces["resets"].data.append(self._mmResetActive)
    self._mmResetActive = False
    self._mmTraces["connectionsPerColumnMetric"].data.append(
      Metric(self, "connections per column", self._connectedCounts.tolist()))

    self._sequenceRepresentationDataStale = True
    self._mmUpdateDutyCycles()
Ejemplo n.º 23
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 def mmGetDefaultMetrics(self, verbosity=1):
   resetsTrace = self.mmGetTraceResets()
   return ([Metric.createFromTrace(trace, excludeResets=resetsTrace)
             for trace in self.mmGetDefaultTraces()[:-3]] +
           [Metric.createFromTrace(trace)
             for trace in self.mmGetDefaultTraces()[-3:-1]])
  def mmGetDefaultMetrics(self, verbosity=1):
    metrics = ([Metric.createFromTrace(trace)
                for trace in self.mmGetDefaultTraces()])
    metrics += [self.mmGetMetricDutyCycles()]

    return metrics
 def mmGetMetricConnectedCounts(self):
   data = self.mmGetDataConnectedCounts()
   return Metric(self, "Connected synapse counts", data)
 def mmGetMetricUnionSDRDutyCycle(self):
   """
   @return (Metric) duty cycle metric for union SDR bits
   """
   data = self.mmGetDataUnionSDRDutyCycle()
   return Metric(self, "Union SDR duty cycle", data)
 def mmGetMetricPersistenceDutyCycle(self):
   """
   @return (Metric) duty cycle metric for persistences
   """
   data = self.mmGetDataPersistenceDutyCycle()
   return Metric(self, "Persistence duty cycle", data)