Snap.svg VS scikit-learn

Snap SVG : The JavaScript SVG library for the modern web

It allows users to create tasks, change their schedule and duration with drag-and-drop, add dependency lines, and review extra information on tasks via tooltips. You can add multiple timescales to the chart. The list of possible options includes Quarter Day, Half Day, Full Day, Week, and Month. There are also some customization opportunities such as changing the tooltip content with custom HTML, modifying the appearance of task bars and dependency lines, and setting the default timescale. It should be noted that Frappe Gantt has two dependencies: momentjs and snapsvg.

Inspecting the buttons suggests that it's made with SVG animations too. It says "made with snap" http://snapsvg.io/

Why’d you steal Snap.svg’s logo though? That’s an immediate and major red flag to me. http://snapsvg.io/

Animated SVG http://snapsvg.io is best. Tiny file size, good performance. Resolution independent.

SNAP SVG

Building the SVG by hand is an option, but I suggest you look at dedicated SVG JS libraries to make the graphic easier to build and maintain. Try SVG.js or Snap.svg - both have good reputations.

Need someone who can make an extremely high-quality interactive single web page with SVG / CSS / JS / HTML. And the page is already done, so just someone who can make an interactive portion of the header. Possibly with something like: http://snapsvg.io/, https://www.svgator.com/, or just raw CSS/JS skills.

Canvas2SVG - I have a feeling I'll get to know this library well someday. Apache Batik - I used it quite a bit in the early days but it never took root in my toolchain. SVGJS It offers compelling shortcuts, I'm just a fan of vanilla JS. This also goes for SNAP SVG

In my research to solve the CSS animation issue I branched out and one of my findings was SVGs (Scalable Vector Graphics). Having already gained some proficiency with Adobe Illustrator in my previous career, I’m going to experiment and create some SVG’s soon, perhaps for some buttons or landing page graphics. The low file size and clarity of the graphics is really appealing, and I think they could make my future portfolio really pop! The SVG Snap library looks interesting.

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.

Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!

I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.

As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

[1] https://en.wikipedia.org/wiki/Platt_scaling

[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

Scikit-learn Documentation

Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.

The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole.