def compute(self, inputs, outputs):
    """
    Process one input sample.
    This method is called by the runtime engine.

    :param inputs: (dict) mapping region input names to numpy.array values
    :param outputs: (dict) mapping region output names to numpy.arrays that 
           should be populated with output values by this method
    """

    # This flag helps to prevent double-computation, in case the deprecated
    # customCompute() method is being called in addition to compute() called
    # when network.run() is called
    self._computeFlag = True

    patternNZ = inputs["bottomUpIn"].nonzero()[0]

    if self.learningMode:
      # An input can potentially belong to multiple categories.
      # If a category value is < 0, it means that the input does not belong to
      # that category.
      categories = [category for category in inputs["categoryIn"]
                    if category >= 0]

      if len(categories) > 0:
        # Allow to train on multiple input categories.
        bucketIdxList = []
        actValueList = []
        for category in categories:
          bucketIdxList.append(int(category))
          if "actValueIn" not in inputs:
            actValueList.append(int(category))
          else:
            actValueList.append(float(inputs["actValueIn"]))

        classificationIn = {"bucketIdx": bucketIdxList,
                            "actValue": actValueList}
      else:
        # If the input does not belong to a category, i.e. len(categories) == 0,
        # then look for bucketIdx and actValueIn.
        if "bucketIdxIn" not in inputs:
          raise KeyError("Network link missing: bucketIdxOut -> bucketIdxIn")
        if "actValueIn" not in inputs:
          raise KeyError("Network link missing: actValueOut -> actValueIn")

        classificationIn = {"bucketIdx": int(inputs["bucketIdxIn"]),
                            "actValue": float(inputs["actValueIn"])}
    else:
      # Use Dummy classification input, because this param is required even for
      # inference mode. Because learning is off, the classifier is not learning
      # this dummy input. Inference only here.
      classificationIn = {"actValue": 0, "bucketIdx": 0}

    # Perform inference if self.inferenceMode is True
    # Train classifier if self.learningMode is True
    clResults = self._sdrClassifier.compute(recordNum=self.recordNum,
                                            patternNZ=patternNZ,
                                            classification=classificationIn,
                                            learn=self.learningMode,
                                            infer=self.inferenceMode)

    # fill outputs with clResults
    if clResults is not None and len(clResults) > 0:
      outputs['actualValues'][:len(clResults["actualValues"])] = \
        clResults["actualValues"]

      for step in self.stepsList:
        stepIndex = self.stepsList.index(step)
        categoryOut = clResults["actualValues"][clResults[step].argmax()]
        outputs['categoriesOut'][stepIndex] = categoryOut

        # Flatten the rest of the output. For example:
        #   Original dict  {1 : [0.1, 0.3, 0.2, 0.7]
        #                   4 : [0.2, 0.4, 0.3, 0.5]}
        #   becomes: [0.1, 0.3, 0.2, 0.7, 0.2, 0.4, 0.3, 0.5]
        stepProbabilities = clResults[step]
        for categoryIndex in xrange(self.maxCategoryCount):
          flatIndex = categoryIndex + stepIndex * self.maxCategoryCount
          if categoryIndex < len(stepProbabilities):
            outputs['probabilities'][flatIndex] = \
              stepProbabilities[categoryIndex]
          else:
            outputs['probabilities'][flatIndex] = 0.0

    self.recordNum += 1
def compute(self, inputs, outputs):
    

    
    
    
    self._computeFlag = True

    patternNZ = inputs["bottomUpIn"].nonzero()[0]

    if self.learningMode:
      
      
      
      categories = [category for category in inputs["categoryIn"]
                    if category >= 0]

      if len(categories) > 0:
        
        bucketIdxList = []
        actValueList = []
        for category in categories:
          bucketIdxList.append(int(category))
          if "actValueIn" not in inputs:
            actValueList.append(int(category))
          else:
            actValueList.append(float(inputs["actValueIn"]))

        classificationIn = {"bucketIdx": bucketIdxList,
                            "actValue": actValueList}
      else:
        
        
        if "bucketIdxIn" not in inputs:
          raise KeyError("Network link missing: bucketIdxOut -> bucketIdxIn")
        if "actValueIn" not in inputs:
          raise KeyError("Network link missing: actValueOut -> actValueIn")

        classificationIn = {"bucketIdx": int(inputs["bucketIdxIn"]),
                            "actValue": float(inputs["actValueIn"])}
    else:
      
      
      
      classificationIn = {"actValue": 0, "bucketIdx": 0}

    
    
    clResults = self._sdrClassifier.compute(recordNum=self.recordNum,
                                            patternNZ=patternNZ,
                                            classification=classificationIn,
                                            learn=self.learningMode,
                                            infer=self.inferenceMode)

    
    if clResults is not None and len(clResults) > 0:
      outputs['actualValues'][:len(clResults["actualValues"])] = \
        clResults["actualValues"]

      for step in self.stepsList:
        stepIndex = self.stepsList.index(step)
        categoryOut = clResults["actualValues"][clResults[step].argmax()]
        outputs['categoriesOut'][stepIndex] = categoryOut

        
        
        
        
        stepProbabilities = clResults[step]
        for categoryIndex in xrange(self.maxCategoryCount):
          flatIndex = categoryIndex + stepIndex * self.maxCategoryCount
          if categoryIndex < len(stepProbabilities):
            outputs['probabilities'][flatIndex] = \
              stepProbabilities[categoryIndex]
          else:
            outputs['probabilities'][flatIndex] = 0.0

    self.recordNum += 1
def compute ( self , inputs , outputs ) : # This flag helps to prevent double-computation, in case the deprecated # customCompute() method is being called in addition to compute() called # when network.run() is called self . _computeFlag = True patternNZ = inputs [ "bottomUpIn" ] . nonzero ( ) [ 0 ] if self . learningMode : # An input can potentially belong to multiple categories. # If a category value is < 0, it means that the input does not belong to # that category. categories = [ category for category in inputs [ "categoryIn" ] if category >= 0 ] if len ( categories ) > 0 : # Allow to train on multiple input categories. bucketIdxList = [ ] actValueList = [ ] for category in categories : bucketIdxList . append ( int ( category ) ) if "actValueIn" not in inputs : actValueList . append ( int ( category ) ) else : actValueList . append ( float ( inputs [ "actValueIn" ] ) ) classificationIn = { "bucketIdx" : bucketIdxList , "actValue" : actValueList } else : # If the input does not belong to a category, i.e. len(categories) == 0, # then look for bucketIdx and actValueIn. if "bucketIdxIn" not in inputs : raise KeyError ( "Network link missing: bucketIdxOut -> bucketIdxIn" ) if "actValueIn" not in inputs : raise KeyError ( "Network link missing: actValueOut -> actValueIn" ) classificationIn = { "bucketIdx" : int ( inputs [ "bucketIdxIn" ] ) , "actValue" : float ( inputs [ "actValueIn" ] ) } else : # Use Dummy classification input, because this param is required even for # inference mode. Because learning is off, the classifier is not learning # this dummy input. Inference only here. classificationIn = { "actValue" : 0 , "bucketIdx" : 0 } # Perform inference if self.inferenceMode is True # Train classifier if self.learningMode is True clResults = self . _sdrClassifier . compute ( recordNum = self . recordNum , patternNZ = patternNZ , classification = classificationIn , learn = self . learningMode , infer = self . inferenceMode ) # fill outputs with clResults if clResults is not None and len ( clResults ) > 0 : outputs [ 'actualValues' ] [ : len ( clResults [ "actualValues" ] ) ] = clResults [ "actualValues" ] for step in self . stepsList : stepIndex = self . stepsList . index ( step ) categoryOut = clResults [ "actualValues" ] [ clResults [ step ] . argmax ( ) ] outputs [ 'categoriesOut' ] [ stepIndex ] = categoryOut # Flatten the rest of the output. For example: #   Original dict  {1 : [0.1, 0.3, 0.2, 0.7] #                   4 : [0.2, 0.4, 0.3, 0.5]} #   becomes: [0.1, 0.3, 0.2, 0.7, 0.2, 0.4, 0.3, 0.5] stepProbabilities = clResults [ step ] for categoryIndex in xrange ( self . maxCategoryCount ) : flatIndex = categoryIndex + stepIndex * self . maxCategoryCount if categoryIndex < len ( stepProbabilities ) : outputs [ 'probabilities' ] [ flatIndex ] = stepProbabilities [ categoryIndex ] else : outputs [ 'probabilities' ] [ flatIndex ] = 0.0 self . recordNum += 1
Process one input sample.
    This method is called by the runtime engine.

    :param inputs: (dict) mapping region input names to numpy.array values
    :param outputs: (dict) mapping region output names to numpy.arrays that 
           should be populated with output values by this method
Process one input sample . This method is called by the runtime engine .