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Similaire à Julia: The language for future
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Julia: The language for future
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- 4. python ★ 31.3k golang ★ 72.9k nodejs ★ 70.4k rust ★ 45.1k
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- 6. 6 Rapid development Production Readable & modifiable Performance
- 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> using Pkg julia> Pkg.update() julia> Pkg.add(“Foo”) julia> Pkg.rm(“Foo”) 36 Pkg mode: v(1.4) pkg> update V(1.4) pkg> add Foo v(1.4) 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 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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- 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
- 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/
- 69. Next: Machine Learning 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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