def _constructClassificationRecord(self, inputs):
<EOL>allSPColumns = inputs["<STR_LIT>"]<EOL>activeSPColumns = allSPColumns.nonzero()[<NUM_LIT:0>]<EOL>score = anomaly.computeRawAnomalyScore(activeSPColumns,<EOL>self._prevPredictedColumns)<EOL>spSize = len(allSPColumns)<EOL>allTPCells = inputs['<STR_LIT>']<EOL>tpSize = len(inputs['<STR_LIT>'])<EOL>classificationVector = numpy.array([])<EOL>if self.classificationVectorType == <NUM_LIT:1>:<EOL><INDENT>classificationVector = numpy.zeros(tpSize)<EOL>activeCellMatrix = inputs["<STR_LIT>"].reshape(tpSize, <NUM_LIT:1>)<EOL>activeCellIdx = numpy.where(activeCellMatrix > <NUM_LIT:0>)[<NUM_LIT:0>]<EOL>if activeCellIdx.shape[<NUM_LIT:0>] > <NUM_LIT:0>:<EOL><INDENT>classificationVector[numpy.array(activeCellIdx, dtype=numpy.uint16)] = <NUM_LIT:1><EOL><DEDENT><DEDENT>elif self.classificationVectorType == <NUM_LIT:2>:<EOL><INDENT>classificationVector = numpy.zeros(spSize+spSize)<EOL>if activeSPColumns.shape[<NUM_LIT:0>] > <NUM_LIT:0>:<EOL><INDENT>classificationVector[activeSPColumns] = <NUM_LIT:1.0><EOL><DEDENT>errorColumns = numpy.setdiff1d(self._prevPredictedColumns,<EOL>activeSPColumns)<EOL>if errorColumns.shape[<NUM_LIT:0>] > <NUM_LIT:0>:<EOL><INDENT>errorColumnIndexes = ( numpy.array(errorColumns, dtype=numpy.uint16) +<EOL>spSize )<EOL>classificationVector[errorColumnIndexes] = <NUM_LIT:1.0><EOL><DEDENT><DEDENT>else:<EOL><INDENT>raise TypeError("<STR_LIT>"<EOL>"<STR_LIT>" % (self.classificationVectorType))<EOL><DEDENT>numPredictedCols = len(self._prevPredictedColumns)<EOL>predictedColumns = allTPCells.nonzero()[<NUM_LIT:0>]<EOL>self._prevPredictedColumns = copy.deepcopy(predictedColumns)<EOL>if self._anomalyVectorLength is None:<EOL><INDENT>self._anomalyVectorLength = len(classificationVector)<EOL><DEDENT>result = _CLAClassificationRecord(<EOL>ROWID=self._iteration, <EOL>anomalyScore=score,<EOL>anomalyVector=classificationVector.nonzero()[<NUM_LIT:0>].tolist(),<EOL>anomalyLabel=[]<EOL>)<EOL>return result<EOL>
Construct a _HTMClassificationRecord based on the state of the model
passed in through the inputs.

Types for self.classificationVectorType:
  1 - TM active cells in learn state
  2 - SP columns concatenated with error from TM column predictions and SP