def customCompute(self, recordNum, patternNZ, classification):
<EOL>if not hasattr(self, "<STR_LIT>"):<EOL><INDENT>self._computeFlag = False<EOL><DEDENT>if self._computeFlag:<EOL><INDENT>warnings.simplefilter('<STR_LIT:error>', DeprecationWarning)<EOL>warnings.warn("<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>",<EOL>DeprecationWarning)<EOL><DEDENT>return self._sdrClassifier.compute(recordNum,<EOL>patternNZ,<EOL>classification,<EOL>self.learningMode,<EOL>self.inferenceMode)<EOL>
Just return the inference value from one input sample. The actual
learning happens in compute() -- if, and only if learning is enabled --
which is called when you run the network.

.. warning:: This method is deprecated and exists only to maintain backward 
   compatibility. This method is deprecated, and will be removed. Use 
   :meth:`nupic.engine.Network.run` instead, which will call 
   :meth:`~nupic.regions.sdr_classifier_region.compute`.

:param recordNum: (int) Record number of the input sample.
:param patternNZ: (list) of the active indices from the output below
:param classification: (dict) of the classification information:

       * ``bucketIdx``: index of the encoder bucket
       * ``actValue``:  actual value going into the encoder

:returns: (dict) containing inference results, one entry for each step in
          ``self.steps``. The key is the number of steps, the value is an
          array containing the relative likelihood for each ``bucketIdx``
          starting from 0.

          For example:

          :: 

            {'actualValues': [0.0, 1.0, 2.0, 3.0]
              1 : [0.1, 0.3, 0.2, 0.7]
              4 : [0.2, 0.4, 0.3, 0.5]}