https://github.com/vspaz/wls-zig
WLS, weighted linear regression in pure Zig w/o any 3d party dependencies or frameworks.
https://github.com/vspaz/wls-zig
least-square-fit least-square-method weighted-linear-regression weighted-regression wls zig ziglang
Last synced: 3 months ago
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WLS, weighted linear regression in pure Zig w/o any 3d party dependencies or frameworks.
- Host: GitHub
- URL: https://github.com/vspaz/wls-zig
- Owner: vspaz
- Created: 2025-01-19T17:46:01.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-03-06T23:45:47.000Z (4 months ago)
- Last Synced: 2025-03-07T00:23:31.922Z (4 months ago)
- Topics: least-square-fit, least-square-method, weighted-linear-regression, weighted-regression, wls, zig, ziglang
- Language: Zig
- Homepage:
- Size: 32.2 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# wls-zig
`wls-zig` is the implementation of weighted linear regression in pure **Zig** w/o any 3d party dependencies or frameworks.
## Installation
1. Run the following command inside your project:
```shell
zig fetch --save git+https://github.com/vspaz/wls-zig.git#main
```
it should add the following dependency to your project _build.zig.zon_ file, e.g.
```zig
.dependencies = .{
.wls = .{
.url = "git+https://github.com/vspaz/wls-zig.git?ref=main#f4dfc9a98f8f538d6d25d7d3e1e431fe77d54744",
.hash = "wls-0.1.0-NK6PrjMoAABYyveo97dzY5AugQxqcGgExHsaDVMxx1wm",
},
}
```
2. Navigate to _build.zig_ file located in the root directory and add the following 2 lines as shown below:
```zig
const exe = b.addExecutable(.{
.name = "your project name",
.root_module = exe_mod,
});
// add these 2 lines!
const wlszig = b.dependency("wls", .{});
exe.root_module.addImport("wls", wlszig.module("wls"));
```
3. Test the project build with `zig build`
There should be no error!
4. mport `wls-zig` lib in your code as follows:
```zig
const wls = @import("wls");
```
and you're good to go! :rocket:
## Examples
```zig
pub const Wls = @import("wls").Wls;
pub const asserts = @import("wls").asserts;
pub fn main() !void {
const x_points = [_]f64{ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0 };
const y_points = [_]f64{ 1.0, 3.0, 4.0, 5.0, 2.0, 3.0, 4.0 };
const weights = [_]f64{ 1.0, 2.0, 3.0, 1.0, 8.0, 1.0, 5.0 };
const wls_model = Wls.init(&x_points, &y_points, &weights);
const fitted_model = wls_model.fit_linear_regression();
if (fitted_model) |line| {
asserts.assert_almost_equal(2.14285714, line.intercept, 1.0e-6);
asserts.assert_almost_equal(0.150862, line.slope, 1.0e-6);
}
}
```
## Description
WLS is based on the OLS method and help solve problems of model inadequacy or violations of the basic regression assumptions.
Estimating a linear regression with WLS is can be hard w/o special stats packages, such as Python statsmodels or Pandas.
## References
- [Wikipedia: Weighted least squares](https://en.wikipedia.org/wiki/Weighted_least_squares)
- [Introduction to Linear Regression Analysis, 5th edition](https://tinyurl.com/y3clfnrs)
- [Least Squares Regression Analysis in Terms of Linear Algebra](https://tinyurl.com/y485qhlg)