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`grid-search` is a small, simple node module that can be used to generate an array of parameters to use during a machine learning grid search. It will generate every possible combination of parameters based on user input.
This package additionally ships with a small `range` method that can be used to generate a range of values between two numbers.
# Installation
Install `grid-search` using npm.
```bash
npm i grid-search
```
# Example Use
## Basic grid search parameter generation
```javascript
const { gridSearch } = require("grid-search");
const params = {
iterations: [10, 20, 30],
objective: "binaryCrossentropy",
dropout: [0.4, 0.5, 0.6]
};
const search = gridSearch(params);
console.log(search);
```
Your output will be:
```javascript
[
{ iterations: 10, objective: "binaryCrossentropy", dropout: 0.4 },
{ iterations: 10, objective: "binaryCrossentropy", dropout: 0.5 },
{ iterations: 10, objective: "binaryCrossentropy", dropout: 0.6 },
{ iterations: 20, objective: "binaryCrossentropy", dropout: 0.4 },
{ iterations: 20, objective: "binaryCrossentropy", dropout: 0.5 },
{ iterations: 20, objective: "binaryCrossentropy", dropout: 0.6 },
{ iterations: 30, objective: "binaryCrossentropy", dropout: 0.4 },
{ iterations: 30, objective: "binaryCrossentropy", dropout: 0.5 },
{ iterations: 30, objective: "binaryCrossentropy", dropout: 0.6 }
];
```
## Using range
The previous example can use the `range` function to specify the `iterations` and `dropout` parameters and achieve the exact same result.
`range(start, finish, step)`
```javascript
const { range } = require("grid-search");
const params = {
iterations: range(10, 30, 10),
objective: "binaryCrossentropy",
dropout: range(0.4, 0.6, 0.1)
};
```
# Contributing
Contributions welcome! Please open an issue in the [Github repository](https://github.com/nas5w/grid-search) describing what changes you would like to see (or to contribute yourself).