johnmyleswhite

14 ms·

91.
by johnmyleswhite 12y ago
To echo what I said in the post, I think using Nullable as the representation of missing values is the right strategy. I think it achieves sufficiently strong performance while also providing a lot of other desirable features. As for other
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by johnmyleswhite 12y ago
I am definitely going to use embedded SQLite in some of future prototypes. The only problem is that SQLite doesn't, to my knowledge, support all of the features I'd like Julia to support -- including columns that contain arrays, d
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by johnmyleswhite 12y ago
I'd encourage you to read the post again, since it's primarily focused on describing design patterns that allow one to support missing values without taking a performance hit.
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by johnmyleswhite 12y ago
We will probably be pushing people very heavily towards always using specialized arrays for working with nullable objects. I'm a little surprised by your comment about sufficiently smart compilers, since Julia seems to have taken the e
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by johnmyleswhite 12y ago
We'll keep re-evaluating this over time, but I'm currently not sure it's that serious a problem. For many numerically intensive computations, you'll want to move over to a pure array of 64-bit floating point values befor
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by johnmyleswhite 12y ago
I didn't mean to imply I thought you would guess the wrong answer, but rather that most R users do not understand delayed evaluation well enough to understand how it will impact their code. Sorry if my comment seemed rude.
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by johnmyleswhite 12y ago
Thankfully, the solution paths you're enumerating aren't the only viable solutions to the problem of representing missing values. Because Julia has value types (called immutables) and because Julia can compile functions based on t
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by johnmyleswhite 12y ago
That's true if Rational means Rational{BigInt}, but the default in Julia is for Rational to mean Rational{Int}, which can potentially produce less reliable answers that using Float64.
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by johnmyleswhite 12y ago
My experience asking people about that snippet is that very few people can correctly predict the results of that function without actually executing the code. What interests me about the question is that many people's mental model of h
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by johnmyleswhite 12y ago
I think it would be great to develop support for generic sets in a separate package that could eventually be merged into Distributions. I'm not sure that I think all of the issues you've linked to are related to that problem, thou
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by johnmyleswhite 12y ago
A fun test to see if R users understand how R works is to ask them to predict the behavior of the following snippet of code: foo <- function(a, b) { if (a == 0) { return(1) } else { return(2) } } foo(0, stop("Error!&qu
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by johnmyleswhite 12y ago
I fully share that viewpoint, but there are many R users who believe that R's idiosyncratic evaluation rules are essential to writing "readable" code.
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by johnmyleswhite 12y ago
I often say this, but I think if you have doubts about whether you should learn Julia or Python, then you should certainly learn Python.
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by johnmyleswhite 12y ago
Looks like the growth curve starts before then: https://books.google.com/ngrams/graph?content=software+ecosy...
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by johnmyleswhite 12y ago
Right now, DataFrames is handicapped by problems with how DataArrays was designed. With some revisions coming in Julia 0.4, it should be possible to fix a lot of the worst problems (esp. performance) with DataFrames.
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by johnmyleswhite 12y ago
Yeah, it's a huge bummer that this leaked before it could be set up for a heavy load.
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by johnmyleswhite 12y ago
Resolving this issue is one of the main goals for the 0.4 release. There is already a way to get much better performance for frequently used packages, but it involves a level of tinkering that's prohibitive for most users.
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by johnmyleswhite 12y ago
I think MCMC.jl is pretty immature compared with Stan. But there is work on a Stan wrapper. Personally, I mostly write MCMC code by hand these days.
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by johnmyleswhite 12y ago
Personally, I don't see Julia replacing R for a while. As you note, the R community has much of what it needs already. When I initially switched from R to Julia, it was because R wasn't really usable for the kind of work I do. It&
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by johnmyleswhite 13y ago
No, it is not.
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by johnmyleswhite 14y ago
DataFrames are in-memory. It sounds like you're describing what we're calling DataStreams, which are still a work in progress, but do already exist. And we're also building SGD descent for doing things like OLS incrementally.
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by johnmyleswhite 17y ago
I have a friend who was an intern with Siemens in computer vision while preparing for Ph.D. programs in machine learning. I would guess that other companies that build medical imaging tools have similar divisions.
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Predict This Year's Supreme Court Rulings(fantasyscotus.net)
2 points by johnmyleswhite 17y ago|0 comments
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by johnmyleswhite 17y ago
If your math background is weak enough that you don't feel totally at home using calculus and linear algebra, I'd recommend "Principles of Data Mining", which they use for the intro to ML class at Princeton. The ratio of English to equation
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by johnmyleswhite 17y ago
This is a fun analysis to see. Here's a suggestion for cleaning up the stats, though it won't help you get around people noting that there's a selection bias for the poll results themselves. If you're interested in showing that the frequen
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by johnmyleswhite 17y ago
It sounds like you're interested in statistics from the words "a few interesting stats-heavy compsci papers". The MacKay book, "Information Theory, Inference and Learning Algorithms," already referenced by benm is quite good, but I'd sugges