Here is the CPU example (5 step inference). https://github.com/numenta/nupic/blob/master/examples/opf/clients/cpu/cpu.py
You basically just do prediction = result.inferences["multiStepBestPredictions"][5] at run time. Another thing to note, HTMs don't care about whether it's predictions are correct or not. If we just fed in data for step No. 40, then it will split out predictions for No. 50, while it updates its look up table for step No. 10. This is quite different from back-propagation. On Tue, Dec 1, 2015 at 2:58 AM, Michael Lam <[email protected]> wrote: > When evaluating prediction models, the bottom line method of measuring > performance is to compare the predicted values NuPIC is returning to the > actual values in the data *when they occur*. So if you are predicting 10 > steps ahead, you'll not know how well the predictions really are until you > get to 10 steps in the future. While I understand how to set predictionSteps > in swarming, I am not sure how to use that results to actually generate a > future prediction, e.g. 10 steps ahead. Take the run.py in your hot gym > example, it seems to only do prediction 1 step ahead and only runs until the > actual data runs out. Can you show me, preferably with an example based on > run.py, how to do predictions 10 steps ahead? I tried to do this: > > _METRIC_SPECS = ( > > MetricSpec(field='price', metric='multiStep', > > inferenceElement='multiStepBestPredictions', > > params={'errorMetric': 'aae', 'window': 1000, 'steps': 10}), > > MetricSpec(field='price', metric='trivial', > > inferenceElement='prediction', > > params={'errorMetric': 'aae', 'window': 1000, 'steps': 10}), > > MetricSpec(field='price', metric='multiStep', > > inferenceElement='multiStepBestPredictions', > > params={'errorMetric': 'altMAPE', 'window': 1000, 'steps': > 10}), > > MetricSpec(field='price', metric='trivial', > > inferenceElement='prediction', > > params={'errorMetric': 'altMAPE', 'window': 1000, 'steps': > 10}), > > ) > > But I don’t see any difference than if I set ‘steps’ to 1. Also I am not > sure what this do: > > prediction = result.inferences["multiStepBestPredictions"][1] > > Thanks. > >
