open-index/open-npm huggingface dataset:
npm package: @hycord/image
Description: This is a custom package I wrote to make interfacing with canvas in node easier.
Latest version: 0.0.1

# @hycord/image

This is a custom package I wrote to make interfacing with canvas in node easier.

## In practice

The main value this library provides is the `Viewport` class which contains a number of utility functions for holding a virtual viewport.
This allws for easy panning, and zooming.
Drawing to the canvas requires a call to the `shape` function which is the only way to access the context of the underlying canvas. The shape function automatically handles saving and restoring the context.

# Usage

```typescript
import { Viewport } from '@hycord/image';

const view = new Viewport({
  width: 128,
  height: 128,
  backgroundColor: 'rgb(150, 150, 150)', // Light gray background
});

view.shape((ctx) => {
  ctx.fillStyle = 'black';
  ctx.fillRect(0, 0, 100, 100);
});

// There is now a black square at the top left corner of the canvas.

const imageBuffer = view.canvas.encodeSync('png');
Bun.write('./image.png', imageBuffer);
```

The above code will result in the following image being written to the root of your project (Assuming you're using Bun. There are other methods for writing image buffers and you do not need to use bun when writing the output from the canvas.):

![Square image with gray background and a black square in top left](public/image.png)
npm package: @hycord/logger
Description: ```typescript import Logger from '@hycord/logger'; const logger = new Logger();
Latest version: 0.1.1

# Usage

```typescript
import Logger from '@hycord/logger';
const logger = new Logger();

// The following code produxes the image below
logger.log('Log, Default Title');
logger.log('Log, Custom Title', 'Custom Title');
logger.log('Log, Date title', 'date');
logger.data('Data, Default Title');
logger.data('Data, Custom Title', 'Custom Title');
logger.data('Data, Date title', 'date');
logger.success('Success, Default Title');
logger.success('Success, Custom Title', 'Custom Title');
logger.success('Success, Date title', 'date');
logger.error('Error, Default Title');
logger.error('Error, Custom Title', 'Custom Title');
logger.error('Error, Date title', 'date');
logger.warn('Warn, Default Title');
logger.warn('Warn, Custom Title', 'Custom Title');
logger.warn('Warn, Date title', 'date');
```

![Example Logging](/public/example.png 'Example Logging')

## Customization

You may also use `logger._log()` to pass a custom title and colors to the logger. The function is defined as follows, where the first array is an array of chalk style options (I.E. `chalk.bold`) and the first array is applied to the brackets and title, while the second is just applied to the title.:

```ts
_log(
    [color = chalk, style = chalk]: Chalk[],
    title = 'LOG',
    message: string,
  )
```
npm package: @hycord/neural
Description: This package is used to easily create and interface with Neural Networks
Latest version: 0.0.1

# @hycord/neural

This package is used to easily create and interface with Neural Networks


## Usage

- `FeedForwardNeuralNetwork` is the main class you will interact with.
 - You pass an array of numbers corrosponding to the number of neurons in each layer at that index with index 0 being the input layer and the last index being the output layer.
- `TrainingData` is a helper interface that is exported to make it easier to interface with the data expected by the network and trainer.
- `FeedForwardNeuralNetworkTrainer` is a helper class used to train a network with arbitrary training data.
 - It uses back-propogation and gradient-descent to train the network

```typescript
import {
  FeedForwardNeuralNetwork,
  FeedForwardNeuralNetworkTrainer,
  TrainingData,
} from '@hycord/neural';

const xorData: TrainingData[] = [
  { inputs: [0, 0], expectedOutput: [0] },
  { inputs: [0, 1], expectedOutput: [1] },
  { inputs: [1, 0], expectedOutput: [1] },
  { inputs: [1, 1], expectedOutput: [0] },
];

const network = new FeedForwardNeuralNetwork([2, 5, 1]);

const trainer = new FeedForwardNeuralNetworkTrainer(network);

trainer.train(xorData, 1_000, 0.1);
trainer.test(xorData);
```

The code above will genrate and test an example neural network for simulating an XOR gate.

The images below are examples of 100,000 training epochs (top image) and 1,000 training epochs (bottom image)
The epochs are adjusted in the second paramater of the `FeedForwardNeuralNetworkTrainer#train` function. In the code above it is set to 1,000.

You can optionally pass false as the second argument to `FeedForwardNeuralNetworkTrainer#test` and it will not log a summary in the console. This can be useful if you would like to display your own summary with many tests and don't want to clog the console.

![100,000 Epochs, 5 Internal Neurons](/public/100k.png)

![1,000 Epochs, 5 Internal Neurons](/public/1k.png)
npm package: @hydapps/create-matchstick-app
Description: This project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 17.3.8.
Author: Joseph Nwachukwu
Latest version: 17.2.1

# Matchstick

This project was generated with [Angular CLI](https://github.com/angular/angular-cli) version 17.3.8.

## Development server

Run `ng serve` for a dev server. Navigate to `http://localhost:4200/`. The application will automatically reload if you change any of the source files.

## Code scaffolding

Run `ng generate component component-name` to generate a new component. You can also use `ng generate directive|pipe|service|class|guard|interface|enum|module`.

## Build

Run `ng build` to build the project. The build artifacts will be stored in the `dist/` directory.

## Running unit tests

Run `ng test` to execute the unit tests via [Karma](https://karma-runner.github.io).

## Running end-to-end tests

Run `ng e2e` to execute the end-to-end tests via a platform of your choice. To use this command, you need to first add a package that implements end-to-end testing capabilities.

## Further help

To get more help on the Angular CLI use `ng help` or go check out the [Angular CLI Overview and Command Reference](https://angular.io/cli) page.