Using AI to Understand and Document a Complex Algorithm

A GPT-Assisted Approach

Introduction

Using AI models like GPT to explain and document complex coded algorithms can offer a variety of benefits:

1. Enhancing Understandability
   AI can break down complex algorithms into simpler terms, making them more understandable to a wider audience, including non-programmers or those unfamiliar with the specific algorithm.
2. Boosting Efficiency
   Documenting algorithms can be a time-consuming process. AI can automate this process, allowing developers to focus more on coding and problem-solving.
3. Maintaining Consistency
   AI can ensure consistent documentation across various algorithms, making them easier to reference and understand.
4. Improved Accessibility
   By generating clear and detailed explanations, AI can help make complex algorithms more accessible, inviting broader scrutiny and collaboration.
5. Facilitating Knowledge Transfer
   AI-generated explanations and documentation can be instrumental in onboarding new team members and facilitating knowledge transfer within a team or organization.
6. Supporting Best Practices
   Clear algorithm documentation is part of good coding practices. AI can help enforce this practice, leading to higher quality code and more maintainable software.
7. Learning Tool
   AI-generated explanations can serve as educational tools, helping students and novice programmers understand complex algorithms and how they're implemented in code.

Despite these benefits, human review is still critical to ensure the AI-generated explanations and documentation accurately represent the algorithm's logic and functionality. Moreover, AI should be considered a tool that assists human developers, not a replacement for human expertise and oversight.

Today, we're exploring a TypeScript function that is used by Logobean called WeightedEntry.

This function is quite interesting, as it incorporates elements of random selection and weighted decision-making, a mechanism commonly used in machine learning and AI.

GPT Prompt

Describe what the following typescript function does and create a fully documented version of the function:

const WeightedEntry = (entries: ITemplateEntry[], weights: IWeights[], rnd: number): TemplateFn | undefined => {
    const weightsSum: number = weights.reduce((acc, weightEntry) => acc + weightEntry.weight, 0);
    const rndWeighted: number = rnd * weightsSum;
    let accumulator: number = 0;

    for (let index = 0; index < entries.length; index++) {
        const templateEntry: ITemplateEntry = entries[index];
        const weight = weights.find(weightEntry => weightEntry.layout === templateEntry.layout)!.weight;
        accumulator += weight;

        if (rndWeighted < accumulator) {
            return templateEntry.fn;
        }
    }

    return undefined;
}

Prompt Overview

The WeightedEntry function is designed to choose a function from a list based on weighted probability.

Each entry in the list has an associated weight.

The function uses these weights to decide which entry to return, with entries of higher weight being more likely to be selected.

GPT Response 1 - Overview

1. In this TypeScript code, the WeightedEntry function goes through each entry in the entries array, finding the corresponding weight from the weights array.
2. It then cumulatively adds the weight to an accumulator.
3. If the accumulator becomes greater than a random number (scaled by the total sum of weights), the function from the current entry is returned. This way, entries with larger weights have a higher chance of being selected.

GPT Response 2 - Commented Code

interface ITemplateEntry {
    layout: string;
    fn: Function;
}

interface IWeights {
    layout: string;
    weight: number;
}

/**
 * Selects a function from a list of template entries based on weighted probability.
 *
 * @param entries - An array of template entries. Each entry includes a layout identifier and a function.
 * @param weights - An array of weights, each associated with a layout identifier.
 * @param rnd - A random number between 0 and 1.
 * @returns A function from the list of entries. Entries with a higher weight are more likely to be selected.
 */
const WeightedEntry = (entries: ITemplateEntry[], weights: IWeights[], rnd: number): TemplateFn | undefined => {
    // Calculate the total sum of weights
    const weightsSum: number = weights.reduce((acc, weightEntry) => acc + weightEntry.weight, 0);

    // Adjust the random number by the sum of weights
    const rndWeighted: number = rnd * weightsSum;

    let accumulator: number = 0;

    // Iterate over the entries array
    for (let index = 0; index < entries.length; index++) {
        const templateEntry: ITemplateEntry = entries[index];

        // Find the weight associated with the current entry
        const weight = weights.find(weightEntry => weightEntry.layout === templateEntry.layout)!.weight;

        // Accumulate the weight
        accumulator += weight;

        // If the accumulated weight is greater than the random number, 
        // return the current entry's function
        if (rndWeighted < accumulator) {
            return templateEntry.fn;
        }
    }

    // If no entry is selected (which should not normally happen if weights and entries 
    // are properly defined), return undefined
    return undefined;
}

Benefits and Conclusion

The WeightedEntry function provides an efficient way to make a weighted selection from a list. This is useful in various scenarios where decisions need to be made based on certain probabilities, such as in machine learning algorithms or simulations.

Was This Helpful?

If you found this blog post helpful, feel free to check out our other blog posts on using AI in software development at the Logobean Blog!