Fit a smoothing spline to collection of data points
npm install @umn-latis/simple-smoothing-spline> Fit a smoothing spline to collection of data points
``js
const data = [
{ x: 1, y: 0.5 },
{ x: 2, y: 3 },
{ x: 3, y: 8.5 },
{ x: 4, y: 20 },
{ x: 1, y: 1 },
{ x: 2, y: 5 },
{ x: 3, y: 11 },
{ x: 4, y: 15 },
];
const spline = await simpleSmoothingSpline(data, { lambda: 1000 });
// spline.points is a collection of {x, y} values
// between the min and max the x values in the data set
graphItWithYourOwnFunction(spline.points);
// spline.fn is a function that can return a y for a given x
const myY = spline.fn(2.5);
// 6.25
`
`console`
npm install @umn-latis/simple-smoothing-spline
`console`
yarn add @umn-latis/simple-smoothing-spline
Added this before your main script:
`html`
async
>
Then, in your scripts, you can call a global simpleSmoothingSpline() function.
Parameters:
- data - an array of data points in the form of {x: 1, y: 2}.opts.lambda = 1000
- - lambda parameter for Ridge regression. This is the tuning parameter for the regression function. The higher the lambda, the smoother the spline will be. By default, this is 1000.opts.type = smooth (default) | cubic
- - type of spline to use for regression.
Example:
`js`
const cubicSpline = await simpleSmoothingSpline(data, { type: "cubic" });
Returns a Promise for::
- spline.points - An array of {x, y} points on the smoothing spline in the range of the data (between min(x) and max(x)).spline.fn
- - A function to get an arbitrary f(x) for a given x.
Example:
`js``
const spline = await simpleSmoothingSpline(data, { lambda: 2 ** 8 });
const y = spline.fn(3);
// y is value on the spline when x = 3
An example using Plotly JS can be found at
- Background notes and references
- Maintainers
- Contributors
MIT