Download as PDF, PPTX
![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-200208074218/85/The-Language-for-future-julia-8-320.jpg)
![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](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/85/The-Language-for-future-julia-33-320.jpg)
![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](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/85/The-Language-for-future-julia-42-320.jpg)
![julia> df[:A]
4-element Array{Int64,1}:
1
2
3
4
julia> df[2, :A]
2
56](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/85/The-Language-for-future-julia-43-320.jpg)
![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](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/85/The-Language-for-future-julia-45-320.jpg)
![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 |](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/85/The-Language-for-future-julia-47-320.jpg)
![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](https://image.slidesharecdn.com/20200208thelanguageforfuturejulia-200208074218/85/The-Language-for-future-julia-49-320.jpg)
Uploaded by岳華 杜
The Language for future-julia
This document provides information about the Julia programming language. It discusses Julia's performance, use cases in different industries, available packages and tools, and ongoing development work. Key highlights include Julia's speed, its use in fields like robotics, quantitative finance, and science, and recent improvements to its machine learning and quantum computing capabilities.
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The Language for future-julia
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- 4. python ★ 29.2k golang ★ 68.7k nodejs ★67.6k rust ★ 38,548
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- 6. 6 Rapid development Production Readable& modifiable Performance
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- 8. a = [1,2, 3, 4, 5] function square(x) return x^2 end for x in a println(square(x)) end 8
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- 28. 32 μ = 0 σ= 1 normal = Normal(μ, σ)
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- 31. for i =1:100_000 do_something() end Threads.@threads for i = 1:100_000 do_something() end 35
- 32. Julia mode: julia> usingPkg 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
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- 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
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- 51. 67 # initialize theattractor 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
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- 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 }
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- 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
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- 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/
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- 69. Next: MachineLearning and Deep Learning on Quantum Computing 86 https://github.com/QuantumBFS/Yao.jl
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- 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
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