iclassifier | reference pytorch code for intent classification | Machine Learning library

I am writing a Visual Studio extension and currently adding margin glyphs to the editor. I have started with the walk-through example from MS documentation: https://docs.microsoft.com/en-us/visualstudio/extensibility/walkthrough-creating-a-margin-glyph?view=vs-2017.

My logic is a bit different from the example since I don't analyze the file content given by the spans parameter: I already get a set of results with defects locations from an analysis process before. And the rationale is simply to generate a tag if and only if it has not been rendered yet.

Indeed, I want to avoid a new glyph is falsely be rendered if the user adds a new line the line before an existing flag. Example: if there is a glyph on line 42, and cursor is on line 41, and user types a new line, then a new glyph is generated on line 42 (because the GetTags method is called again) while previous glyph is moved to line 43.

My code is:

I'm writing a simple extension for visual studio that should provide syntax highlighting. It works fine for contextless scenarios where I just use regex to identify text span class:

I'm developing a project in which I need to do gesture recognition. After searching for a way to do this I came across dynamic time warping. To try this idea, and since my project is in C#, I decided to try Accord.NET. Before I tried this out in my project, I created a clean project and modified the example on Accord.NET's documentation, that can be found here:

http://accord-framework.net/docs/html/T_Accord_Statistics_Kernels_DynamicTimeWarping.htm

So, what I'm trying right now, is to train my algorithm with a set of learning data (composed of the gestures swipe right, swipe left and double tap) and then, use the same examples in the learning data to see if the algorithm is identifying the correct gesture. The values are just an exemple, not the real deal.

The documentation is not very clear on how to do this, since the Decide method used on the example only returns a boolean. I've searched the documentation for a way to identify the correct class but to no avail.

The only thing I've found was the following line, but what it returns is an int value of 0 or 1, instead of a boolean:

var res1 = ((IClassifier)svm.ToMulticlass()).Decide(sequences[0]);

Can anyone point me in the right direction on how to correctly identify the gesture? This is my first attempt at machine learning and Accord.NET, so this is all absolutelly new to me. The example code can be found bellow.