Questions about using saved models

[Pegasus]

Hi, Taylor, I have been the save/load stage, now. And there are some questions:

@juanhorta

Do Nupic need a time to wake up? Maybe I’m wrong but I thought that anomaly detection based in a trained model would work since the first value.

1. Does the ‘deficiency’ mentioned in this topic has be repaired ?, if not, you mean use the module ‘pickle’, right? As I am not familiar with this module, could you give me some more information about how to save the likelihood by using pickle ?

2. I have got the right place to learn how to save/ load models: http://nupic.docs.numenta.org/1.0.0/guides/serialization.html
   However, I am a little confused with it. As I have read the code of htm_prediction ( and model_factory), I have find there are two ways to save/load model:

• save/load: inside the code, it take advantage of pickle module.

• readFromCheckpoint/writeToCheckpoint ( or loadFromCheckpoint of model_factory, which inner call readFromCheckPoint ): behind them is capnp

  Both of these have been deprecated, according to the page: “Serialization” (i.e., the url mentioned above). Since the methods are still in the the model, does it means I could still use them either ? then, I wonder the difference between these two ways with respect to the saved model ?

3. In the page mentioned above(i.e., the url above), writeToFile/readFromFile are the recommended methods, but in the htm_prediction model, I could not find the functions, does it means I can’t still use them? If they could be used now, does it work similarly to the methods mentioned above. Say I would like to save a model I can just work like this: model.writeToFile(dir) ? as the example just illustrates how to save SP, I don’t if it is the same to model.

4. I find a topic: “Load/Save model may lose precision?”
   Load/Save model may lose precision?
   Does this affect much to the result or model?

As last, I there is another question
5 . HTM loss its prediction ability - #13 by Pegasus
In this topic, I ask a question, could you give an answer ?

@Pegasus

The HTM as often the case fails to predict when anomaly occur, because the likelihoods of its predictions are ridiculous then abandoned, instead of the real real data at that time.

Do I get the point? I refer to the source code, there indeed are so called ‘best prediction’ but I couldn’t find the logic of mimicking raw data, and I wonder how the likelihoods of prediction( not the likelihood value of anomaly) are calculated ? Does the process of calculation take advantage of the real data in advance? Still, I have no idea where to get these information in the source code.

Thanks