What is the equivalent of numpy random choice for generating a non uniform random sample based on a user supplied probability

In Julia, the equivalent of numpy’s random choice for generating a non-uniform random sample based on a user-supplied probability can be achieved in different ways. Let’s explore three options to solve this problem.

Option 1: Using the `rand` function with weights

The `rand` function in Julia can be used to generate random numbers between 0 and 1. By using the `weights` argument, we can specify the probability distribution for the random sample.

function nonUniformRandomSample(choices, probabilities, n)
    sample = []
    for _ in 1:n
        index = rand(choices, weights(probabilities))
        push!(sample, choices[index])
    end
    return sample
end

In this code, the `choices` argument represents the possible values to choose from, the `probabilities` argument represents the corresponding probabilities for each choice, and `n` represents the number of samples to generate.

Option 2: Using the `Distributions` package

The `Distributions` package in Julia provides a wide range of probability distributions. We can use the `Categorical` distribution to generate a non-uniform random sample based on user-supplied probabilities.

using Distributions

function nonUniformRandomSample(choices, probabilities, n)
    dist = Categorical(probabilities)
    sample = rand(dist, n)
    return choices[sample]
end

In this code, the `choices` argument represents the possible values to choose from, the `probabilities` argument represents the corresponding probabilities for each choice, and `n` represents the number of samples to generate.

Option 3: Using the `StatsBase` package

The `StatsBase` package in Julia provides additional functionality for statistical computations. We can use the `sample` function from this package to generate a non-uniform random sample based on user-supplied probabilities.

using StatsBase

function nonUniformRandomSample(choices, probabilities, n)
    sample = sample(choices, probabilities, n)
    return sample
end

In this code, the `choices` argument represents the possible values to choose from, the `probabilities` argument represents the corresponding probabilities for each choice, and `n` represents the number of samples to generate.

Among these three options, the best choice depends on the specific requirements of your project. Option 1 provides a simple and straightforward solution using built-in Julia functions. Option 2 utilizes the `Distributions` package, which offers a wide range of probability distributions. Option 3 utilizes the `StatsBase` package, which provides additional statistical functionality. Consider the specific needs of your project and choose the option that best suits your requirements.