Julia，多次运行函数，将结果保存在数组中 [英] Julia, run function multiple times, save results in array

问题描述

我正在Julia建立一个微观模拟模型。我已经建立了我的功能结构，并且对于1个人来说它运行得非常好。我希望编写脚本来通过模型运行100000多人，并将结果保存在一个位置。

I am building a microsimulation model in Julia. I have built the structure of my function and it runs great for for 1 "person". I'd like to write the script to run 100000+ people through the model and save the results in one location.

最后，我想要并行执行此操作。

Eventually I'd like to execute this in parallel.

下面我已经包含了一个带有虚拟概率的代码的简单工作版本。

Below I have included a simple working version of the code with dummy probabilities.

using Distributions

# Microsim function
function  MicroSim(start_age, stages)
  stage = 0
  age = start_age

  # Set all trackers to 0
  Death_tracker = 0
  Disease_tracker = 0

  # While loop
  while stage <= stages
    age = age

    ###########################################################
    # Probability of Death
    pD = 0.02

    if age == 100
      pD = 1.0
    else
      pD = pD
    end

    # Coin flip
    dist_pD = Bernoulli(pD)
    Died = rand(dist_pD, 1)

    if Died == [1]
      Death_tracker = 1
      # death tracker loop break
      if Death_tracker == 1
        # println("I died")
        break
      end
    else
      Death_tracker = Death_tracker
    end
    ###########################################################

  # Update age and stage
  age = age + 1
  stage = stage + 1

  end

return age, Death_tracker

end


MicroSim(18,100)

推荐答案

您正在寻找函数 map 和 pmap （用于并行化）。我简化了你的功能，给出了一个更简单的工作示例。 （未来，请参阅此链接，以获取关于在您的问题中创建最小示例的指导）。

You are looking for the functions map and pmap (for parallelization). I've simplified your function to give a more minimal working example. (in the future, please see this link for guidance on creating such minimal examples in your questions).

map 采用一个函数（您指定）并将其应用于数组中的所有元素。如果你的函数需要多个参数（就像你的那样），那么你只需提供 map 多个连续数组。 map 然后返回一个包含所有函数结果的新数组。

map takes a function (that you specify) and applies it to all of the elements in an array. If your function takes multiple arguments (as yours does), then you simply feed map multiple successive arrays. map then returns a new array with the results of all your functions.

function MicroSim(start_age, stages)
    return rand(start_age), rand(stages)
end

Start_Ages = [10, 20, 15]
Stages = [1, 4, 5]

Results = map(MicroSim, Start_Ages, Stages)

如果你想平行化，只需要三个简单的调整。 1.使用 addprocs（）函数添加所需的许多其他进程。 2.在声明你的函数时使用 @everywhere 宏，这样你的工作进程也可以访问它。 3.使用函数 pmap 而不是 map ：

If you want to parallelize things, there are just three simple adjustments. 1. use the addprocs() function to add however many additional processes you want. 2. use the @everywhere macro when declaring your function so that your worker processes also have access to it. 3. use the function pmap instead of map:

addprocs(2)

@everywhere begin
    function MicroSim(start_age, stages)
        return rand(start_age), rand(stages)
    end
end

Results = pmap(MicroSim, Start_Ages, Stages)

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