QuantEcon/QuantEcon.jl

random_stochastic_matrix and random_markov_chain support sparse output

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#102 opened on Mar 17, 2016

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 (0 comments) (0 reactions) (0 assignees)Julia (495 stars) (300 forks)batch import
enhancementhelp wanted

Description

This might look like this:

random_markov_chain{T}(n::Int, k::Int=n, ::Type{Matrix{T}})  # ==> return dense array
random_markov_chain{T}(n::Int, k::Int=n, ::Type{SparseMatrixCSC{Int,T}})  # ==> return sparse array

Also, having Union{Int,Void} shouldn't be necessary here as it is in python because default arguments can depend on one another. (e.g. def f(n, k=n): is n't possible in python but function f(n, k=n) is possible in Julia)

Contributor guide

Tech stack
None
Domain
dataperformance
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Julia fundamentalsSparse matrix conceptsQuantEcon.jl codebase
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75
Research direction
Identify the current implementation of random stochastic matrix and random markov chain in the codebase (likely in src/random.jl). Extend each function by adding a method that accepts a type parameter for sparse output, e.g., ::Type{SparseMatrixCSC{Int,T}}. Use sparse constructors to build the output. Remove any unnecessary Union{Int,Void} types in function signatures. Test the new functionality with both dense and sparse output types.