Myers Briggs Predictor

This could be a great skipping stone to your next stateful front-end project.

Please ⭐ (star) or 🍴 (fork) to show attribution or gratitude, as always.

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Origins

Heavily inspired by https://github.com/Skeletonxf/easy-ml-mnist-wasm-example.

Credit Skeletonxf for the original wasm example and classic WebWorker threading implementation.

From the above project's README.md:

# Easy ML MNIST Web Assembly Example

Simple MNIST Neural Network scaffold for demonstrating Rust code in the browser.

Uses `wasm-pack` to build the web assembly. The webpage can be accessed by
running the included Node.js server.

## About

This project is a template for doing machine learning in the browser via Rust
code loaded as WebAssembly. The code trains a simple feedforward neural network
on a subset of the MNIST data using [Easy ML](https://crates.io/crates/easy-ml)
with mini batching and automatic differentiation.

Myers Briggs Predictor Examples

This repository utilizes the above scaffold with a revamped frontend implemented in Vue.js. As the name suggests, instead of MNIST Number identification, Myers Briggs Type Indicators (MBTIs) are focused on.

Screenshot

Current State of Affairs

1. Procedurally generated 16x16 pixel heatmaps with validated labels have been provided
2. Network weights for a network trained to 7 epochs have been provided, 0% loss.
3. Vue Components replecate the scaffold's User Interface functionality with TypeScript
4. WebWorker and data fetching ported from JavaScript to TypeScript
5. Vue.js niceties such as prettier ensure that the code smell is reduced

Roadmap

1. Generate images procedurally in frontend from user text input
   • @see https://github.com/arosboro/mbti-random-forest/tree/arosboro-sandbox
2. Imutable Storage with Aleo Blockchain based contract deployed development node
3. Demonstrate ability to share Machine Learning statistics without exposing Text Input
4. Unit Tests
5. E2E Tests
6. Progressive Web Application (PWA) full support for offline-mode
7. Final implementation deployed to TestNet V3

Project setup

Recommended: Set prettier as the default formatter for saving the files in this project.

npm install

Compiles and hot-reloads for development

npm run serve

Compiles and minifies for production

npm run build

Run your unit tests

npm run test:unit

Run your end-to-end tests

npm run test:e2e

Lints and fixes files

npm run lint

Customize configuration

See Configuration Reference.