20190907 Julia the language for future
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The document discusses the Julia programming language. It provides information on Julia's popularity compared to other languages like Python and R. It highlights several use cases for Julia in fields like finance, science, and engineering. It also demonstrates basic Julia code for tasks like data analysis, plotting, and numerical computing. Overall, the document serves as an introduction to the Julia language and provides examples of its capabilities.
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20190907 Julia the language for future
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- 4. python ★ 26,156 golang ★ 62,354 nodejs ★ 63,620 rust ★ 38,548
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- 10. a = [1, 2, 3, 4, 5] function square(x) return x^2 end for x in a println(square(x)) end 10
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- 33. for i = 1:100000 do_something() end @parallel for i = 1:100000 do_something() end 37
- 34. Julia mode: julia> using Pkg julia> Pkg.update() julia> Pkg.add(“Foo”) julia> Pkg.rm(“Foo”) 38 Pkg mode: v(1.1) pkg> update v(1.1) pkg> add Foo v(1.1) pkg> rm Foo
- 35. 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 39
- 36. 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 40
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- 43. 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 │ 56
- 44. julia> df[:A] 4-element Array{Int64,1}: 1 2 3 4 julia> df[2, :A] 2 57
- 45. julia> df = readtable("data.csv") julia> df = DataFrame(A = 1:10); julia> writetable("output.csv", df) 58
- 46. 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 │ 59
- 47. julia> q1 = @from i in df begin @where i.age > 40 @select {number_of_children=i.children, i.name} @collect DataFrame end 60
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- 51. 64 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 |
- 52. 65 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
- 53. 66 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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- 55. 68 # 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
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- 64. 77 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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- 69. Next: Machine Learning and Deep Learning on Quantum Computing 82 https://github.com/QuantumBFS/Yao.jl
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- 73. 86 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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- 82. Lack of users and developers Lack of internet resources
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