Recently, I published a small example project to utilize the htm.core AI algorithm by consuming its REST API via C#. As the API also transfers serialized multi-dimensional NumPy arrays, I was looking for an easy way to get them back into C# objects. I’ve tried out a couple of approaches and finally decided on using the NumSharp library, as I wanted a solution that works on multiple platforms.
I am really honored by the fact, that a lot of people seem to use my .NET for Apache Spark docker image to explore how C# and Apache Spark can work together, for example.
Additionally, I am getting a lot of request lately, asking whether I would be willing to share the code for creating the images.
And finally, after tidying it up a bit (e.g. removing the experimental Windows support), it is now available on GitHub.
So thanks to everyone who made this image such a success and of course you are very welcome to … more
If you are interested, check out the official resources, or one of the following articles.
- .NET for Apache Spark ForeachWriter & PostgreSQL
- .NET for Apache Spark – VSCode with Docker on Linux and df.Collect()
- Happy New Year & the answer to the Christmas puzzle
- .NET for Apache Spark – UDF, VS2019, Docker for Windows and a Christmas Puzzle
- Debug .NET for Apache Spark with Visual Studio and docker
Before we do that, I want to show you one last single order memory example however.
Single Order Sequence Memory Recap
As you might remember from the last post, these were the settings for our htm.core temporal memory (aka sequence memory).
columns = 8 inputSDR = SDR( columns ) cellsPerColumn = 1 tm = TM(columnDimensions = (inputSDR.size,), cellsPerColumn = cellsPerColumn,… more
To allow the htm.core temporal memory to learn sequences effectively, it is import to understand the impact of the different parameters in more detail.
In this part I will introduce
Temporal Memory – Previously on this blog…
Part 1 just covered enough basics of htm.core to get us started, and we actually saw how the single order memory got trained.
A cycle of encoded increasing numbers from 0 to 9 was very easy to predict, as there was always just one specific value that could follow the … more