This week 7/2020 - Julia

This post describing my first feel when I completed a Julia basic course.

I am experienced Java developer but I have also osculation with C/C++, Python and Octave languages. For me Julia has something from all those languages.

Linear Algebra support:

Octave:

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A = eye(1,2) %Diagonal Matrix % % 1 0 B = eye(3,2) %Diagonal Matrix % % 1 0 % 0 1 % 0 0 C = [A B] %C = % % 1 0 % 1 0 % 0 1 % 0 0

Julia:

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using LinearAlgebra A = 1* Matrix(I,1,2) # 1×2 Array{Int64,2}: # 1 0 B = 1* Matrix(I,3,2) # 3×2 Array{Int64,2}: # 1 0 # 0 1 # 0 0 C = [A B] # 4×2 Array{Int64,2}: # 1 0 # 1 0 # 0 1 # 0 0

There is also similarity when multiply matrixes. Julia support ex. operations f(A) = A*A and f.(A) whene every A[i,j] * A[i,j].

Syntax similarity to Python:

Julia:

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# for loop for i in 1:10, j in 1:20 println("Hi $i , $j") end for item in items println("Hi $item") end # function definition function power(x) # last element is returnes - the same as in Python x^2 end # other options to define function power(x) = x^2 power = x -> x^2 # immutable sorting sort(x) #mutable sorting sort!(x)

Overload operators:

Python:

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class String: def __init__(self, x=""): self.x = x def __add__(self, other): return self.x == other.x p1 = String("test1") p2 = String("test1") print(p1 + p2)

Julia:

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import Base: + +(x::String, y:: String) = x == y # returns boolean value x+y

Performance:

Benchmarks which I saw in course [3] shows that Julia has similar or a little better performance than C code and this about 2 orders of magnitude than Python.

Resources:

[1] https://julialang.org/

[2] Introduction to Julia (for programmers)

[3] Parallel Computing