What is the crowd's opinion of Orange?

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> Yes, cones of fire (or deviation, as the game calls it) are fully accounted for. If you saw the actual positions the aimbot was aiming at the camera would be very shaky and kind of annoying to watch.

Perhaps you could help me with something: I OCR'ed a bunch of stats someone put in an infographic to a spreadsheet, and I'm thinking of making a weapon-comparison visualization. Possibly with Processing.js, or at least something vaguely interactive through a web-browser. (I'm using Orange and Excel to poke at it for now.)

What I'd like to do is present people with weapon comparisons that take into account the achievable DPS of various weapons assuming you fire them with the best rate of fire. (And what that rate-of-fire is for a given distance.)

So do you happen to know:

1. Are both the base deviation and the per-shot deviation measured in degrees? (Anecdotally I'd guess they're scaled up to something a bit more than that.)

2. At what rate does the per-shot deviation decay back to the base level level? I'm assuming it's a linear decay based on time.

3. In in-game meters, about how tall and wide are players' hit-boxes as seen from the front?

For reference, I've got the vanilla LMGs down with a standing base deviation of 2.5 and a per-shot deviation ranging from 0.5 to 0.7.

> it's interesting but technically difficult for a number of reasons

Isn't that the best kind of side-project? ;)

If you're fresh with python I'd suggest trying out orange.

It uses a visual editor so you can start right away. Also, check out Kaggle.com for walkthroughs on how you'd actually manipulate data.

Produktbeskrivning.

Högst spekulativt, såklart, men inhämtninsstyrningen, eller metodiken, för FRA i detta fall borde vara att använda den passiva tap(ethernet) enheten, från din ISP, och lagra denna trafik-/paket-data i pcap format i databasen.

Skulle jag bygga ett sådant system skulle jag därefter koppla på något maskininlärningsverktyg, likt orange för att segmentera, eller klassificera innehållet av olika typer av nätverksdata.

Check out Orange Data Mining. While it isn't necessarily an R Language tool, it is certainly worth having in your data science toolkit. Specifically relevant is the interactive decision tred visualization widget.

Just tried the latest Orange 3.3 release and following their Ubuntu 14.04 setup script instructions, Orange can be compiled successful on Pi3 for Python 3. http://orange.biolab.si/download/linux/

However, since it is running under Python 3, the SimpleCV module is only supported up to Python 2.7. This disparity has limited my current SimpleCV project to use the latest Orange 3.3 release.

Hi, I don't know if this is what you're looking for. But there was a course on data mining and visualization at my school. And the students taking it where using this application. I personally didn't use it, but I heard nothing but good things about it. I think it also support Python scripting.

Alright, that makes sense. I'm not familiar with any of these (they have been in my bookmarks in the rubric "check out later" for months...), but maybe they offer some of the functionality that you're looking for:

• pentaho

• Orange Data Mining

• RapidMiner

I'm not sure if I remember correctly, but Orange and RapidMiner should be open-source whereas pentaho probably costs money. Either way, have a look, and good luck in finding what you're looking for!

I actually discovered Orange after I'd started developing Pathomx. It is similar, but when I looked was focused more on machine learning rather than data processing. That architecture isn't linear passing, making tools more complicated than simple scripts. The internal data table format also didn't seem itself to the NMR data I was processing.

I would have preferred to implement the analysis on an existing platform if I could, to save on all the GUI programming!

One advantage of Pathomx is that it is based on standard Python libraries like numpy and pandas - it just passes variables between scripts and displays and viewable outputs. That means a lower learning curve to write tools.