def dummyModelParams(perm):
 errScore = 50
 if (perm['modelParams']['sensorParams']['encoders']['address'] is not None):
  errScore -= 20
 if (perm['modelParams']['sensorParams']['encoders']['gym'] is not None):
  errScore -= 10
 if (perm['modelParams']['sensorParams']['encoders']['timestamp_dayOfWeek'] is not None):
  errScore += 30
 if (perm['modelParams']['sensorParams']['encoders']['timestamp_timeOfDay'] is not None):
  errScore += 40
 dummyModelParams = dict(metricValue=errScore, iterations=int(os.environ.get('NTA_TEST_numIterations', '1')), waitTime=None, sysExitModelRange=os.environ.get('NTA_TEST_sysExitModelRange', None), errModelRange=os.environ.get('NTA_TEST_errModelRange', None), jobFailErr=bool(os.environ.get('NTA_TEST_jobFailErr', False)))
 return dummyModelParams

def dummyModelParams ( perm ) : errScore = 50 if ( perm [ 'modelParams' ] [ 'sensorParams' ] [ 'encoders' ] [ 'address' ] is not None ) : errScore -= 20 if ( perm [ 'modelParams' ] [ 'sensorParams' ] [ 'encoders' ] [ 'gym' ] is not None ) : errScore -= 10 if ( perm [ 'modelParams' ] [ 'sensorParams' ] [ 'encoders' ] [ 'timestamp_dayOfWeek' ] is not None ) : errScore += 30 if ( perm [ 'modelParams' ] [ 'sensorParams' ] [ 'encoders' ] [ 'timestamp_timeOfDay' ] is not None ) : errScore += 40 dummyModelParams = dict ( metricValue = errScore , iterations = int ( os . environ . get ( 'NTA_TEST_numIterations' , '1' ) ) , waitTime = None , sysExitModelRange = os . environ . get ( 'NTA_TEST_sysExitModelRange' , None ) , errModelRange = os . environ . get ( 'NTA_TEST_errModelRange' , None ) , jobFailErr = bool ( os . environ . get ( 'NTA_TEST_jobFailErr' , False ) ) ) return dummyModelParams
this function can be used for hypersearch algorithm development .