Hi John,
I'm not sure I know all the details, but here's a stab at it:
The OPF provides a set of API's for creating and running a particular type
of CLA model designed for streaming in temporal datasets.
The code in examples/opf/clients/hotgym is a simple example of using that
API to create a model, feed in data, and retrieve results. It currently
doesn't save predictions to a file, but it could be easily modified to do
that.
In this code the result object contains the result for each prediction as
it's being made. If you want to see this, insert the following print
statement into the code right after "model.run":
print "timestamp:",modelInput['timestamp'],
"actual:",modelInput['consumption'],\
"1-step
prediction",result.inferences['multiStepBestPredictions'][1],\
"5-step
prediction",result.inferences['multiStepBestPredictions'][5]
You could also write the results to a CSV file if you want. You should also
change the global _NUM_RECORDS to a large value, like 10000 so that you see
the whole dataset.
OpfRunExperiment.py is a much more complex wrapper around the OPF for
running experiments and evaluating results on different datasets. For
convenience, one of the things it does is *shift* the predictions after the
fact so that the actual and predictions line up in the CSV file. This makes
it easy to use a spreadsheet and display actual vs predicted values in a
chart.
In this case multiStepBestPredictions.1 shows one step predictions and is
shifted down by one row. multiStepBestPredictions.5 shows 5 step-ahead
predictions and is shifted down by 5 rows.
Hope this helps,
--Subutai
On Tue, Feb 18, 2014 at 2:54 PM, John Blackburn
<[email protected]>wrote:
> I have recently installed NuPIC and am trying the hotgym example which
> seems to be the simplest. I have tried running python client hotgym.py and
> would like to ask a few questions if I may.
>
> First, am I right in saying that the client loads data from
>
> nupic/examples/prediction/data/extra/hotgym/rec-centre-hourly.csv ?
>
> (despite the client being located in nupic/examples/opf/clients/hotgym)
>
> When I run the client itself, it simply produces numbers at stdout.
> However, if I run it as an experiment, using
>
> python $NUPIC/examples/opf/bin/OpfRunExperiment.py
> $NUPIC/examples/opf/experiments/multistep/hotgym/
>
> It outputs a CSV file "DefaultTask..predictionLog.csv" which I can view in a
> spreadsheet. I want to know exactly what the various colums in this
> spreadsheet mean. I notice there is a column marked
> "multiStepBestPredictions.1". Is this meant to be predicting the next value
> of the time series? Also there is "multiStepBestPredictions.5", is this meant
> to be predicting 5 steps in advance?
>
> After about 10 timesteps, I notice the multiStepBestPredictions.1 seems to
> shadow the actual input but is one step *behind*, eg,
>
> actual multiStepBestPredictions.1
>
> 45.4 47.5
>
> 46.1 45.4
>
> 41.5 46.1
>
> It seems to me this is the opposite of what we want! ie
> multiStepBestPredictions.1 is "predicting" *after* the input has occurred
> rather than before it. multiStepBestPredictions.5 seems to have the same
> problem.
>
> So when is the prediction actually being made? How can I tell if the HTM got
> it right?
>
> Ideally I would like a quick example so I can see that it works and convince
> my colleageus in a presentation tomorrow! (GMT time). If hotgym is not
> appropriate is there another example I could show them?
>
>
> Many thanks for your help and sorry for the rush!
>
>
> John.
>
>
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>
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