Python MovingAverage.next方法代码示例

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]
  def testMovingAverageInstance(self):
    """
    Test that the (internal) moving average maintains the averages correctly,
    even for null initial condition and when the number of values goes over
    windowSize.  Pass in integers and floats.
    this is for the instantce method next()
    """
    ma = MovingAverage(windowSize=3)

    newAverage = ma.next(3)
    self.assertEqual(newAverage, 3.0)
    self.assertListEqual(ma.getSlidingWindow(), [3.0])
    self.assertEqual(ma.total, 3.0)

    newAverage = ma.next(4)
    self.assertEqual(newAverage, 3.5)
    self.assertListEqual(ma.getSlidingWindow(), [3.0, 4.0])
    self.assertEqual(ma.total, 7.0)

    newAverage = ma.next(5)
    self.assertEqual(newAverage, 4.0)
    self.assertListEqual(ma.getSlidingWindow(), [3.0, 4.0, 5.0])
    self.assertEqual(ma.total, 12.0)

    # Ensure the first value gets popped
    newAverage = ma.next(6)
    self.assertEqual(newAverage, 5.0)
    self.assertListEqual(ma.getSlidingWindow(), [4.0, 5.0, 6.0])
    self.assertEqual(ma.total, 15.0)

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]
  def testSerialization(self):
    """serialization using pickle"""
    ma = MovingAverage(windowSize=3)

    ma.next(3)
    ma.next(4)
    ma.next(5)

    stored = pickle.dumps(ma)
    restored = pickle.loads(stored)
    self.assertEqual(restored, ma) 
    self.assertEqual(ma.next(6), restored.next(6))

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]
  def testEquals(self):
    ma = MovingAverage(windowSize=3)
    maP = MovingAverage(windowSize=3)
    self.assertEqual(ma, maP)
    
    maN = MovingAverage(windowSize=10)
    self.assertNotEqual(ma, maN)

    ma = MovingAverage(windowSize=2, existingHistoricalValues=[3.0, 4.0, 5.0])
    maP = MovingAverage(windowSize=2, existingHistoricalValues=[3.0, 4.0, 5.0])
    self.assertEqual(ma, maP)
    maP.next(6)
    self.assertNotEqual(ma, maP)
    ma.next(6)
    self.assertEqual(ma, maP)

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]
  def testMovingAverageReadWrite(self):
    ma = MovingAverage(windowSize=3)

    ma.next(3)
    ma.next(4)
    ma.next(5)

    proto1 = MovingAverageProto.new_message()
    ma.write(proto1)

    # Write the proto to a temp file and read it back into a new proto
    with tempfile.TemporaryFile() as f:
      proto1.write(f)
      f.seek(0)
      proto2 = MovingAverageProto.read(f)

    resurrectedMa = MovingAverage.read(proto2)

    newAverage = ma.next(6)
    self.assertEqual(newAverage, resurrectedMa.next(6))
    self.assertListEqual(ma.getSlidingWindow(),
                         resurrectedMa.getSlidingWindow())
    self.assertEqual(ma.total, resurrectedMa.total)

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]
class Anomaly(object):
  """Utility class for generating anomaly scores in different ways.

  Supported modes:
    MODE_PURE - the raw anomaly score as computed by computeRawAnomalyScore
    MODE_LIKELIHOOD - uses the AnomalyLikelihood class on top of the raw
        anomaly scores
    MODE_WEIGHTED - multiplies the likelihood result with the raw anomaly score
        that was used to generate the likelihood
  """


  # anomaly modes supported
  MODE_PURE = "pure"
  MODE_LIKELIHOOD = "likelihood"
  MODE_WEIGHTED = "weighted"
  _supportedModes = (MODE_PURE, MODE_LIKELIHOOD, MODE_WEIGHTED)


  def __init__(self, slidingWindowSize = None, mode=MODE_PURE):
    """
    @param slidingWindowSize (optional) - how many elements are summed up;
        enables moving average on final anomaly score; int >= 0
    @param mode (optional) - (string) how to compute anomaly;
        possible values are:
          - "pure" - the default, how much anomal the value is;
              float 0..1 where 1=totally unexpected
          - "likelihood" - uses the anomaly_likelihood code;
              models probability of receiving this value and anomalyScore
          - "weighted" - "pure" anomaly weighted by "likelihood"
              (anomaly * likelihood)
    """
    self._mode = mode
    if slidingWindowSize is not None:
      self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
    else:
      self._movingAverage = None

    if self._mode == Anomaly.MODE_LIKELIHOOD:
      self._likelihood = AnomalyLikelihood() # probabilistic anomaly
    if not self._mode in Anomaly._supportedModes:
      raise ValueError("Invalid anomaly mode; only supported modes are: "
                       "Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
                       "Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)


  def compute(self, activeColumns, predictedColumns, 
				inputValue=None, timestamp=None):
    """Compute the anomaly score as the percent of active columns not predicted.

    @param activeColumns: array of active column indices
    @param predictedColumns: array of columns indices predicted in this step
                             (used for anomaly in step T+1)
    @param inputValue: (optional) value of current input to encoders 
				(eg "cat" for category encoder)
                              	(used in anomaly-likelihood)
    @param timestamp: (optional) date timestamp when the sample occured
                              	(used in anomaly-likelihood)
    @return the computed anomaly score; float 0..1
    """
    # Start by computing the raw anomaly score.
    anomalyScore = computeRawAnomalyScore(activeColumns, predictedColumns)

    # Compute final anomaly based on selected mode.
    if self._mode == Anomaly.MODE_PURE:
      score = anomalyScore
    elif self._mode == Anomaly.MODE_LIKELIHOOD:
      if inputValue is None:
        raise ValueError("Selected anomaly mode 'Anomaly.MODE_LIKELIHOOD' "
                 "requires 'inputValue' as parameter to compute() method. ")

      probability = self._likelihood.anomalyProbability(
          inputValue, anomalyScore, timestamp)
      # low likelihood -> hi anomaly
      score = 1 - probability
    elif self._mode == Anomaly.MODE_WEIGHTED:
      probability = self._likelihood.anomalyProbability(
          inputValue, anomalyScore, timestamp)
      score = anomalyScore * (1 - probability)

    # Last, do moving-average if windowSize was specified.
    if self._movingAverage is not None:
      score = self._movingAverage.next(score)

    return score

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]

#.........这里部分代码省略.........
               binaryAnomalyThreshold=None):
    self._mode = mode
    if slidingWindowSize is not None:
      self._movingAverage = MovingAverage(windowSize=slidingWindowSize)
    else:
      self._movingAverage = None

    if (self._mode == Anomaly.MODE_LIKELIHOOD or
        self._mode == Anomaly.MODE_WEIGHTED):
      self._likelihood = AnomalyLikelihood() # probabilistic anomaly
    else:
      self._likelihood = None

    if not self._mode in self._supportedModes:
      raise ValueError("Invalid anomaly mode; only supported modes are: "
                       "Anomaly.MODE_PURE, Anomaly.MODE_LIKELIHOOD, "
                       "Anomaly.MODE_WEIGHTED; you used: %r" % self._mode)

    self._binaryThreshold = binaryAnomalyThreshold
    if binaryAnomalyThreshold is not None and (
          not isinstance(binaryAnomalyThreshold, float) or
          binaryAnomalyThreshold >= 1.0  or
          binaryAnomalyThreshold <= 0.0 ):
      raise ValueError("Anomaly: binaryAnomalyThreshold must be from (0,1) "
                       "or None if disabled.")


  def compute(self, activeColumns, predictedColumns,
              inputValue=None, timestamp=None):
    """Compute the anomaly score as the percent of active columns not predicted.

    :param activeColumns: array of active column indices
    :param predictedColumns: array of columns indices predicted in this step
                             (used for anomaly in step T+1)
    :param inputValue: (optional) value of current input to encoders
                                  (eg "cat" for category encoder)
                                  (used in anomaly-likelihood)
    :param timestamp: (optional) date timestamp when the sample occured
                                 (used in anomaly-likelihood)
    :returns: the computed anomaly score; float 0..1
    """
    # Start by computing the raw anomaly score.
    anomalyScore = computeRawAnomalyScore(activeColumns, predictedColumns)

    # Compute final anomaly based on selected mode.
    if self._mode == Anomaly.MODE_PURE:
      score = anomalyScore
    elif self._mode == Anomaly.MODE_LIKELIHOOD:
      if inputValue is None:
        raise ValueError("Selected anomaly mode 'Anomaly.MODE_LIKELIHOOD' "
                 "requires 'inputValue' as parameter to compute() method. ")

      probability = self._likelihood.anomalyProbability(
          inputValue, anomalyScore, timestamp)
      # low likelihood -> hi anomaly
      score = 1 - probability
    elif self._mode == Anomaly.MODE_WEIGHTED:
      probability = self._likelihood.anomalyProbability(
          inputValue, anomalyScore, timestamp)
      score = anomalyScore * (1 - probability)

    # Last, do moving-average if windowSize was specified.
    if self._movingAverage is not None:
      score = self._movingAverage.next(score)

    # apply binary discretization if required
    if self._binaryThreshold is not None:
      if score >= self._binaryThreshold:
        score = 1.0
      else:
        score = 0.0

    return score


  def __str__(self):
    windowSize = 0
    if self._movingAverage is not None:
      windowSize = self._movingAverage.windowSize
    return "Anomaly:\tmode=%s\twindowSize=%r" % (self._mode, windowSize)


  def __eq__(self, other):
    return (isinstance(other, Anomaly) and
            other._mode == self._mode and
            other._binaryThreshold == self._binaryThreshold and
            other._movingAverage == self._movingAverage and
            other._likelihood == self._likelihood)


  def __setstate__(self, state):
    """deserialization"""
    self.__dict__.update(state)

    if not hasattr(self, '_mode'):
      self._mode = Anomaly.MODE_PURE
    if not hasattr(self, '_movingAverage'):
      self._movingAverage = None
    if not hasattr(self, '_binaryThreshold'):
      self._binaryThreshold = None

# 需要导入模块: from nupic.utils import MovingAverage [as 别名]
# 或者: from nupic.utils.MovingAverage import next [as 别名]
class AdaptiveScalarEncoder(ScalarEncoder):
  """
  This is an implementation of the scalar encoder that adapts the min and
  max of the scalar encoder dynamically. This is essential to the streaming
  model of the online prediction framework.

  Initialization of an adapive encoder using resolution or radius is not supported;
  it must be intitialized with n. This n is kept constant while the min and max of the
  encoder changes.

  The adaptive encoder must be have periodic set to false.

  The adaptive encoder may be initialized with a minval and maxval or with `None`
  for each of these. In the latter case, the min and max are set as the 1st and 99th
  percentile over a window of the past 100 records.

  **Note:** the sliding window may record duplicates of the values in the dataset,
  and therefore does not reflect the statistical distribution of the input data
  and may not be used to calculate the median, mean etc.
  """


  def __init__(self, w, minval=None, maxval=None, periodic=False, n=0, radius=0,
                resolution=0, name=None, verbosity=0, clipInput=True, forced=False):
    """
    [overrides nupic.encoders.scalar.ScalarEncoder.__init__]
    """
    self._learningEnabled = True
    if periodic:
      #Adaptive scalar encoders take non-periodic inputs only
      raise Exception('Adaptive scalar encoder does not encode periodic inputs')
    assert n!=0           #An adaptive encoder can only be intialized using n

    super(AdaptiveScalarEncoder, self).__init__(w=w, n=n, minval=minval, maxval=maxval,
                                clipInput=True, name=name, verbosity=verbosity, forced=forced)
    self.recordNum=0    #how many inputs have been sent to the encoder?
    self.slidingWindow = MovingAverage(300)


  def _setEncoderParams(self):
    """
    Set the radius, resolution and range. These values are updated when minval
    and/or maxval change.
    """

    self.rangeInternal = float(self.maxval - self.minval)

    self.resolution = float(self.rangeInternal) / (self.n - self.w)
    self.radius = self.w * self.resolution
    self.range = self.rangeInternal + self.resolution

    # nInternal represents the output area excluding the possible padding on each side
    self.nInternal = self.n - 2 * self.padding

    # Invalidate the bucket values cache so that they get recomputed
    self._bucketValues = None


  def setFieldStats(self, fieldName, fieldStats):
    """
    TODO: document
    """
    #If the stats are not fully formed, ignore.
    if fieldStats[fieldName]['min'] is None or \
      fieldStats[fieldName]['max'] is None:
        return
    self.minval = fieldStats[fieldName]['min']
    self.maxval = fieldStats[fieldName]['max']
    if self.minval == self.maxval:
      self.maxval+=1
    self._setEncoderParams()


  def _setMinAndMax(self, input, learn):
    """
    Potentially change the minval and maxval using input.
    **The learn flag is currently not supported by cla regions.**
    """

    self.slidingWindow.next(input)

    if self.minval is None and self.maxval is None:
      self.minval = input
      self.maxval = input+1   #When the min and max and unspecified and only one record has been encoded
      self._setEncoderParams()

    elif learn:
      sorted = self.slidingWindow.getSlidingWindow()
      sorted.sort()

      minOverWindow = sorted[0]
      maxOverWindow = sorted[len(sorted)-1]

      if minOverWindow < self.minval:
        #initialBump = abs(self.minval-minOverWindow)*(1-(min(self.recordNum, 200.0)/200.0))*2      #decrement minval more aggressively in the beginning
        if self.verbosity >= 2:
          print "Input {0!s}={1:.2f} smaller than minval {2:.2f}. Adjusting minval to {3:.2f}".format(self.name, input, self.minval, minOverWindow)
        self.minval = minOverWindow       #-initialBump
        self._setEncoderParams()

#.........这里部分代码省略.........