Hi all. I'm executing the examples that come with nupic installation and was wondering if there is some documentation that explain better the description.py files and the inference output files.
For example, how should i interpret all the columns of the CSV output file and the last few lines of the description file of hotgym example? I had some clues from here https://github.com/numenta/nupic/wiki/Spatial-Classification and here https://github.com/numenta/nupic/wiki/Online-Prediction-Framework but it isn't enough. #hotgym description.py # Logged Metrics: A sequence of regular expressions that specify which of # the metrics from the Inference Specifications section MUST be logged for # every prediction. The regex's correspond to the automatically generated # metric labels. This is similar to the way the optimization metric is # specified in permutations.py. 'loggedMetrics': ['.*aae.*'], } # Add multi-step prediction metrics for steps in config['predictionSteps']: control['metrics'].append( MetricSpec(field=config['predictedField'], metric='multiStep', inferenceElement='multiStepBestPredictions', params={'errorMetric': 'aae', 'window': 1000, 'steps': steps})) control['metrics'].append( MetricSpec(field=config['predictedField'], metric='trivial', inferenceElement='prediction', params={'errorMetric': 'aae', 'window': 1000, 'steps': steps})) control['metrics'].append( MetricSpec(field=config['predictedField'], metric='multiStep', inferenceElement='multiStepBestPredictions', params={'errorMetric': 'altMAPE', 'window': 1000, 'steps': steps})) control['metrics'].append( MetricSpec(field=config['predictedField'], metric='trivial', inferenceElement='prediction', params={'errorMetric': 'altMAPE', 'window': 1000, 'steps': steps}))
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