# This controls how fast the classifier learns/forgets. Higher values # make it adapt faster and forget older patterns faster. 'alpha': 0.001, # This is set after the call to updateConfigFromSubConfig and is # computed from the aggregationInfo and predictAheadTime. 'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int( round( aggregationDivide(config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config)
'steps': '1', }, 'trainSPNetOnlyIfRequested': False, }, } # end of config dictionary # Adjust base config dictionary for any modifications if imported from a # sub-experiment updateConfigFromSubConfig(config) # Compute predictionSteps based on the predictAheadTime and the aggregation # period, which may be permuted over. if config['predictAheadTime'] is not None: predictionSteps = int(round(aggregationDivide( config['predictAheadTime'], config['aggregationInfo']))) assert (predictionSteps >= 1) config['modelParams']['clParams']['steps'] = str(predictionSteps) # Adjust config by applying ValueGetterBase-derived # futures. NOTE: this MUST be called after updateConfigFromSubConfig() in order # to support value-getter-based substitutions from the sub-experiment (if any) applyValueGettersToContainer(config)