7 ms·

Julia: Come for the Syntax, Stay for the Speed

8 points by SriniK 7y ago

plg 7y ago
Something I always try with new (to me) languages: write a short script to (1) load a .txt file containing space-delimited columns of data; (2) fit a linear model in which one column is predicted by a linear combination of the others; (3) plot the predicted values again the actual values using dots and overlay a y=x line Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get something to plot. Has the Julia-verse changed? Is it easier now? I can do this in MATLAB in basically 3 or 4 lines of code. Python, not much more than that.
- ahurmazda 7y ago
  Not familiar with matlab but its not as terse as R ``` using GLM, CSV, Plots data = CSV.read("data.csv", header=["x","y"], types=[Float64, Float64]) #returns dataframe ols = lm(@formula(y ~ x), data) ypred = predict(ols) yall = Base.hcat(data.y, ypred) plot(data.x, yall, linewidth=2, title="Linear regression", label=["y", "ypred"], xlabel="x", ylabel="y") ```
  - deleted 7y ago
    [deleted]
  - cbkeller 7y ago
    The comment I was going to reply to disappeared, but for something closer in form to the Matlab example that used to be here: using Plots, DelimitedFiles d = readdlm("data.tsv",'\t') A = [ones(10,1) d[:,1:2]]; B = copy(d[:,3]); X = A\B plot(B, seriestype=:scatter, color=:blue); plot!(A*X, seriestype=:scatter, color=:red) I find Julia syntax feels closer to Matlab than to Python or R, just different enough to be frustrating for the first month or so (followed by a period of "oh, that's why Julia does it this way instead!")
    - DNF2 7y ago
      No need to `copy` from `d[:,3]`. Slicing already creates copies (so you're copying twice), but even if it made a view you could still just write `B = d[:, 3]`.
      - cbkeller 7y ago
        Ah, didn't realize slicing always made copies. A view/alias/whatever would have been fine for the example, but having multiple variable names referring to the same memory has caused me enough problems to try to avoid it by default.
- DNF2 7y ago
  That is strange. It is literally as easy as `using Plots; plot(...)`
jagtesh 7y ago
I've been following Julia for many years now and it hasn't quite picked up outside the Data Science/Research communities. I'm at a loss to understand why it hasn't gone mainstream. Technically, isn't this a superior language to everything out there if you are getting performance + readability? To start the discussion, maybe one factor could be the lack of a package manager like npm/crate. What else?
- cbkeller 7y ago
  The initial precompilation times may give some new users the wrong idea, but those have been for me a total non-issue once I'm using the language regularly.

8 points by SriniK 7y ago

plg 7y ago

Something I always try with new (to me) languages: write a short script to (1) load a .txt file containing space-delimited columns of data; (2) fit a linear model in which one column is predicted by a linear combination of the others; (3) plot the predicted values again the actual values using dots and overlay a y=x line Tried this with Julia a short while ago and basically gave up, couldn’t figure out how to get something to plot. Has the Julia-verse changed? Is it easier now? I can do this in MATLAB in basically 3 or 4 lines of code. Python, not much more than that.

ahurmazda 7y ago
Not familiar with matlab but its not as terse as R ``` using GLM, CSV, Plots data = CSV.read("data.csv", header=["x","y"], types=[Float64, Float64]) #returns dataframe ols = lm(@formula(y ~ x), data) ypred = predict(ols) yall = Base.hcat(data.y, ypred) plot(data.x, yall, linewidth=2, title="Linear regression", label=["y", "ypred"], xlabel="x", ylabel="y") ```
- deleted 7y ago
  [deleted]
- cbkeller 7y ago
  The comment I was going to reply to disappeared, but for something closer in form to the Matlab example that used to be here: using Plots, DelimitedFiles d = readdlm("data.tsv",'\t') A = [ones(10,1) d[:,1:2]]; B = copy(d[:,3]); X = A\B plot(B, seriestype=:scatter, color=:blue); plot!(A*X, seriestype=:scatter, color=:red) I find Julia syntax feels closer to Matlab than to Python or R, just different enough to be frustrating for the first month or so (followed by a period of "oh, that's why Julia does it this way instead!")
  - DNF2 7y ago
    No need to `copy` from `d[:,3]`. Slicing already creates copies (so you're copying twice), but even if it made a view you could still just write `B = d[:, 3]`.
    - cbkeller 7y ago
      Ah, didn't realize slicing always made copies. A view/alias/whatever would have been fine for the example, but having multiple variable names referring to the same memory has caused me enough problems to try to avoid it by default.
DNF2 7y ago
That is strange. It is literally as easy as `using Plots; plot(...)`

jagtesh 7y ago

I've been following Julia for many years now and it hasn't quite picked up outside the Data Science/Research communities. I'm at a loss to understand why it hasn't gone mainstream. Technically, isn't this a superior language to everything out there if you are getting performance + readability? To start the discussion, maybe one factor could be the lack of a package manager like npm/crate. What else?

cbkeller 7y ago
The initial precompilation times may give some new users the wrong idea, but those have been for me a total non-issue once I'm using the language regularly.