PVS-Studio in 2021 - Error Examples
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![a = [1, 2, 3, 4, 5]
function square(x)
return x^2
end
for x in a
println(square(x))
end
8](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-8-2048.jpg?cb=1670795926)
![julia> @code_native add(1, 2)
.text
Filename: REPL[2]
pushq %rbp
movq %rsp, %rbp
Source line: 2
leaq (%rcx,%rdx), %rax
pop...](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-33-2048.jpg?cb=1670795926)
![julia> using DataFrames
julia> df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"])
4× 2 DataFrame
│ Row │ A │ B │
├─────┼───...](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-42-2048.jpg?cb=1670795926)
![julia> df[:A]
4-element Array{Int64,1}:
1
2
3
4
julia> df[2, :A]
2
56](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-43-2048.jpg?cb=1670795926)
![julia> names = DataFrame(ID = [1, 2], Name = ["John Doe",
"Jane Doe"])
julia> jobs = DataFrame(ID = [1, 2], Job = ["Lawyer...](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-45-2048.jpg?cb=1670795926)
![63
julia> data = DataFrame(X=[1,2,3], Y=[2,4,7])
3x2 DataFrame
|-------|---|---|
| Row # | X | Y |
| 1 | 1 | 2 |
| 2 | 2 |...](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-47-2048.jpg?cb=1670795926)
![65
julia> newX = DataFrame(X=[2,3,4]);
julia> predict(OLS, newX, :confint)
3× 3 Array{Float64,2}:
4.33333 1.33845 7.32821
...](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/75/the-language-for-futurejulia-49-2048.jpg?cb=1670795926)
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The Language for future-julia
- 1. 1
- 2. 2
- 3. 3
- 4. python ★ 29.2k golang ★ 68.7k nodejs ★ 67.6k rust ★ 38,548
- 5. 5
- 6. 6 Rapid development Production Readable & modifiable Performance
- 7. 7
- 8. a = [1, 2, 3, 4, 5] function square(x) return x^2 end for x in a println(square(x)) end 8
- 9. https://julialang.org/benchmarks/ 9
- 10. 10
- 11. https://juliacomputing.com/case-studies/laketide.html
- 12. https://juliacomputing.com/case-studies/mit-robotics.html
- 13. https://juliacomputing.com/case-studies/ny-fed.html 13 https://github.com/FRBNY-DSGE/DSGE.jl
- 14. https://juliacomputing.com/case-studies/rna.html
- 15. https://juliacomputing.com/case-studies/circuitscape.html http://maps.tnc.org/migrations-in-motion/
- 16. https://juliacomputing.com/case-studies/intel-astro.html 20
- 17. https://www.nature.com/articles/d41586-019-02310-3
- 18. https://github.com/JuliaRegistries/General/blob/master/Registry.toml 22
- 19. 23
- 20. 24 https://docs.juliatw.org/latest/
- 21. 25
- 22. 26
- 23. 27
- 24. 28
- 25. 29
- 26. 30 VimEmacsVscodeSublime IntelliJ
- 27. 31
- 28. 32 μ = 0 σ = 1 normal = Normal(μ, σ)
- 29. 33
- 30. 34
- 31. for i = 1:100_000 do_something() end Threads.@threads for i = 1:100_000 do_something() end 35
- 32. Julia mode: julia> using Pkg julia> Pkg.update() julia> Pkg.add(“Foo”) julia> Pkg.rm(“Foo”) 36 Pkg mode: v(1.3) pkg> update V(1.3) pkg> add Foo v(1.3) pkg> rm Foo
- 33. julia> @code_native add(1, 2) .text Filename: REPL[2] pushq %rbp movq %rsp, %rbp Source line: 2 leaq (%rcx,%rdx), %rax popq %rbp retq nopw (%rax,%rax) function add(a, b) return a+b end 37
- 34. julia> @code_llvm add(1, 2.0) ; Function Attrs: uwtable define double @julia_add_71636(i64, double) #0 { top: %2 = sitofp i64 %0 to double %3 = fadd double %2, %1 ret double %3 } function add(a, b) return a+b end 38
- 35. 48
- 36. 49 https://juliastats.org/
- 37. 50
- 38. 51
- 39. 52
- 40. 53
- 41. 54 Bootstrap CategoricalArrays Clustering CSV DataFrames Distances Distributions GLM HypothesisTests KernelDensity Loess MultivariateStats StatsBase TimeSeries
- 42. julia> using DataFrames julia> df = DataFrame(A = 1:4, B = ["M", "F", "F", "M"]) 4× 2 DataFrame │ Row │ A │ B │ ├─────┼───┼───┤ │ 1 │ 1 │ M │ │ 2 │ 2 │ F │ │ 3 │ 3 │ F │ │ 4 │ 4 │ M │ 55
- 43. julia> df[:A] 4-element Array{Int64,1}: 1 2 3 4 julia> df[2, :A] 2 56
- 44. julia> using CSV julia> df = CSV.read("data.csv") julia> df = DataFrame(A = 1:10); julia> CSV.write("output.csv", df) 57
- 45. julia> names = DataFrame(ID = [1, 2], Name = ["John Doe", "Jane Doe"]) julia> jobs = DataFrame(ID = [1, 2], Job = ["Lawyer", "Doctor"]) julia> full = join(names, jobs, on = :ID) 2× 3 DataFrame │ Row │ ID │ Name │ Job │ ├─────┼────┼──────────┼────────┤ │ 1 │ 1 │ John Doe │ Lawyer │ │ 2 │ 2 │ Jane Doe │ Doctor │ 58
- 46. julia> q1 = @from i in df begin @where i.age > 40 @select {number_of_children=i.children, i.name} @collect DataFrame end 59
- 47. 63 julia> data = DataFrame(X=[1,2,3], Y=[2,4,7]) 3x2 DataFrame |-------|---|---| | Row # | X | Y | | 1 | 1 | 2 | | 2 | 2 | 4 | | 3 | 3 | 7 |
- 48. 64 julia> OLS = glm(@formula(Y ~ X), data, Normal(), IdentityLink()) DataFrameRegressionModel{GeneralizedLinearModel,Float64}: Coefficients: Estimate Std.Error z value Pr(>|z|) (Intercept) -0.666667 0.62361 -1.06904 0.2850 X 2.5 0.288675 8.66025 <1e-17
- 49. 65 julia> newX = DataFrame(X=[2,3,4]); julia> predict(OLS, newX, :confint) 3× 3 Array{Float64,2}: 4.33333 1.33845 7.32821 6.83333 2.09801 11.5687 9.33333 1.40962 17.257 # The columns of the matrix are prediction, 95% lower and upper confidence bounds
- 50. 66
- 51. 67 # initialize the attractor n = 1500 dt = 0.02 σ, ρ, β = 10., 28., 8/3 x, y, z = 1., 1., 1. # initialize a 3D plot with 1 empty series plt = plot3d(1, xlim=(-25,25), ylim=(-25,25), zlim=(0,50), xlab = "x", ylab = "y", zlab = "z", title = "Lorenz Attractor", marker = 1) # build an animated gif, saving every 10th frame @gif for i=1:n dx = σ*(y - x) ; x += dt * dx dy = x*(ρ - z) - y ; y += dt * dy dz = x*y - β*z ; z += dt * dz push!(plt, x, y, z) end every 10
- 52. JuliaStats 68
- 53. 69
- 54. 70 https://julialang.org/blog/2017/12/ml&pl-zh_tw
- 55. 71Ref: https://venturebeat.com/2019/02/18/facebooks-chief-ai-scientist-deep-learning-may-need-a-new-programming-language/ Pic: https://xconomy.com/boston/2017/11/01/as-facebook-fights-fake-news-lecun-sees-bigger-role-for-a-i/
- 56. 2019.2.20 10 a.m.
- 57. 73 https://github.com/FluxML/Zygote.jl
- 58. 74 julia> using Zygote julia> f(x) = 3x + 2 f (generic function with 1 method) julia> f(3.) 11.0 julia> f'(3.) 3.0
- 59. 75 julia> @code_llvm f'(3.) ; Function Attrs: uwtable define double @"julia_#34_17010"(double) #0 { top: ret double 3.000000e+00 }
- 60. 76
- 61. 77
- 62. 78 Pic: https://blog.algorithmia.com/introduction-to-loss-functions/ Loss function Pic: http://dsdeepdive.blogspot.com/2016/03/optimizations-of-gradient-descent.html Gradient
- 63. 79 for-loop, while-loop
- 64. 81 @model gdemo(x, y) = begin # Assumptions σ ~ InverseGamma(2,3) μ ~ Normal(0,sqrt(σ)) # Observations x ~ Normal(μ, sqrt(σ)) y ~ Normal(μ, sqrt(σ)) end https://turing.ml/dev/
- 65. 82 https://turing.ml/dev/
- 66. 83 https://github.com/alan-turing-institute/MLJ.jl Integrate 109 models
- 67. 84 https://github.com/alan-turing-institute/MLJ.jl
- 68. 85 https://github.com/alan-turing-institute/MLJ.jl
- 69. Next: Machine Learning and Deep Learning on Quantum Computing 86 https://github.com/QuantumBFS/Yao.jl
- 70. 87 https://github.com/JuliaGPU/CuArrays.jl
- 71. 88
- 72. 89
- 73. 90 http://www.stochasticlifestyle.com/co mparison-differential-equation-solver- suites-matlab-r-julia-python-c-fortran/
- 74. 91 Objective types • Linear • Convex Quadratic • Nonlinear (convex and nonconvex) Constraint types • Linear • Convex Quadratic • Second-order Conic • Semidefinite • Nonlinear (convex and nonconvex) Variable types • Continuous • Integer-valued • Semicontinuous • Semi-integer
- 75. 92
- 76. 93
- 77. 94
- 78. 95 https://mobile.twitter.com/KenoFischer/status/1158517084642582529
- 79. 96 https://juliacon.org/2020/
- 80. https://julialang.org/teaching/
- 81.
- 82. 101
