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Palash Tyagi
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5
Cargo.toml
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@@ -1,12 +1,11 @@
[package]
name = "rustframe"
authors = ["Palash Tyagi (https://github.com/Magnus167)"]
version = "0.0.1-a.20250805"
version = "0.0.1-a.20250716"
edition = "2021"
license = "GPL-3.0-or-later"
readme = "README.md"
description = "A simple dataframe and math toolkit"
documentation = "https://magnus167.github.io/rustframe/"
description = "A simple dataframe library"
[lib]
name = "rustframe"

2
README.md
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# rustframe
🐙 [GitHub](https://github.com/Magnus167/rustframe) | 📚 [Docs](https://magnus167.github.io/rustframe/) | 📖 [User Guide](https://magnus167.github.io/rustframe/user-guide/) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
📚 [Docs](https://magnus167.github.io/rustframe/) | 🐙 [GitHub](https://github.com/Magnus167/rustframe) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
<!-- [![Last commit](https://img.shields.io/endpoint?url=https://magnus167.github.io/rustframe/rustframe/last-commit-date.json)](https://github.com/Magnus167/rustframe) -->

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docs/src/compute.md
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@@ -70,77 +70,6 @@ assert!((corr - 1.0).abs() < 1e-8);
assert!((cov - 2.5).abs() < 1e-8);
```
## Covariance
### `covariance`
Computes the population covariance between two equally sized matrices by flattening
their values.
```rust
# extern crate rustframe;
use rustframe::compute::stats::covariance;
use rustframe::matrix::Matrix;
let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let y = Matrix::from_vec(vec![2.0, 4.0, 6.0, 8.0], 2, 2);
let cov = covariance(&x, &y);
assert!((cov - 2.5).abs() < 1e-8);
```
### `covariance_vertical`
Evaluates covariance between columns (i.e. across rows) and returns a matrix of
column pair covariances.
```rust
# extern crate rustframe;
use rustframe::compute::stats::covariance_vertical;
use rustframe::matrix::Matrix;
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov = covariance_vertical(&m);
assert_eq!(cov.shape(), (2, 2));
assert!(cov.data().iter().all(|&v| (v - 1.0).abs() < 1e-8));
```
### `covariance_horizontal`
Computes covariance between rows (i.e. across columns) returning a matrix that
describes how each pair of rows varies together.
```rust
# extern crate rustframe;
use rustframe::compute::stats::covariance_horizontal;
use rustframe::matrix::Matrix;
let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
let cov = covariance_horizontal(&m);
assert_eq!(cov.shape(), (2, 2));
assert!(cov.data().iter().all(|&v| (v - 0.25).abs() < 1e-8));
```
### `covariance_matrix`
Builds a covariance matrix either between columns (`Axis::Col`) or rows
(`Axis::Row`). Each entry represents how two series co-vary.
```rust
# extern crate rustframe;
use rustframe::compute::stats::covariance_matrix;
use rustframe::matrix::{Axis, Matrix};
let data = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
// Covariance between columns
let cov_cols = covariance_matrix(&data, Axis::Col);
assert!((cov_cols.get(0, 0) - 2.0).abs() < 1e-8);
// Covariance between rows
let cov_rows = covariance_matrix(&data, Axis::Row);
assert!((cov_rows.get(0, 1) + 0.5).abs() < 1e-8);
```
## Distributions
Probability distribution helpers are available for common PDFs and CDFs.

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docs/src/introduction.md
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# Introduction
🐙 [GitHub](https://github.com/Magnus167/rustframe) | 📚 [Docs](https://magnus167.github.io/rustframe/) | 📖 [User Guide](https://magnus167.github.io/rustframe/user-guide/) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
📚 [Docs](https://magnus167.github.io/rustframe/) | 🐙 [GitHub](https://github.com/Magnus167/rustframe) | 🦀 [Crates.io](https://crates.io/crates/rustframe) | 🔖 [docs.rs](https://docs.rs/rustframe/latest/rustframe/)
Welcome to the **Rustframe User Guide**. Rustframe is a lightweight dataframe
and math toolkit for Rust written in 100% safe Rust. It focuses on keeping the

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docs/src/machine-learning.md
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@@ -41,6 +41,9 @@ let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
let cluster = model.predict(&new_point)[0];
```
For helper functions and upcoming modules, visit the
[utilities](./utilities.md) section.
## Logistic Regression
```rust
@@ -69,7 +72,7 @@ let transformed = pca.transform(&data);
assert_eq!(transformed.cols(), 1);
```
## Gaussian Naive Bayes
### Gaussian Naive Bayes
Gaussian Naive Bayes classifier for continuous features:
@@ -98,7 +101,7 @@ let predictions = model.predict(&x);
assert_eq!(predictions.rows(), 4);
```
## Dense Neural Networks
### Dense Neural Networks
Simple fully connected neural network:
@@ -139,144 +142,5 @@ let predictions = model.predict(&x);
assert_eq!(predictions.rows(), 4);
```
## Real-world Examples
### Housing Price Prediction
```rust
# extern crate rustframe;
use rustframe::compute::models::linreg::LinReg;
use rustframe::matrix::Matrix;
// Features: square feet and bedrooms
let features = Matrix::from_rows_vec(vec![
2100.0, 3.0,
1600.0, 2.0,
2400.0, 4.0,
1400.0, 2.0,
], 4, 2);
// Sale prices
let target = Matrix::from_vec(vec![400_000.0, 330_000.0, 369_000.0, 232_000.0], 4, 1);
let mut model = LinReg::new(2);
model.fit(&features, &target, 1e-8, 10_000);
// Predict price of a new home
let new_home = Matrix::from_vec(vec![2000.0, 3.0], 1, 2);
let predicted_price = model.predict(&new_home);
println!("Predicted price: ${}", predicted_price.data()[0]);
```
### Spam Detection
```rust
# extern crate rustframe;
use rustframe::compute::models::logreg::LogReg;
use rustframe::matrix::Matrix;
// 20 e-mails × 5 features = 100 numbers (row-major, spam first)
let x = Matrix::from_rows_vec(
vec![
// ─────────── spam examples ───────────
2.0, 1.0, 1.0, 1.0, 1.0, // "You win a FREE offer - click for money-back bonus!"
1.0, 0.0, 1.0, 1.0, 0.0, // "FREE offer! Click now!"
0.0, 2.0, 0.0, 1.0, 1.0, // "Win win win - money inside, click…"
1.0, 1.0, 0.0, 0.0, 1.0, // "Limited offer to win easy money…"
1.0, 0.0, 1.0, 0.0, 1.0, // ...
0.0, 1.0, 1.0, 1.0, 0.0, // ...
2.0, 0.0, 0.0, 1.0, 1.0, // ...
0.0, 1.0, 1.0, 0.0, 1.0, // ...
1.0, 1.0, 1.0, 1.0, 0.0, // ...
1.0, 0.0, 0.0, 1.0, 1.0, // ...
// ─────────── ham examples ───────────
0.0, 0.0, 0.0, 0.0, 0.0, // "See you at the meeting tomorrow."
0.0, 0.0, 0.0, 1.0, 0.0, // "Here's the Zoom click-link."
0.0, 0.0, 0.0, 0.0, 1.0, // "Expense report: money attached."
0.0, 0.0, 0.0, 1.0, 1.0, // ...
0.0, 1.0, 0.0, 0.0, 0.0, // "Did we win the bid?"
0.0, 0.0, 0.0, 0.0, 0.0, // ...
0.0, 0.0, 0.0, 1.0, 0.0, // ...
1.0, 0.0, 0.0, 0.0, 0.0, // "Special offer for staff lunch."
0.0, 0.0, 0.0, 0.0, 0.0, // ...
0.0, 0.0, 0.0, 1.0, 0.0,
],
20,
5,
);
// Labels: 1 = spam, 0 = ham
let y = Matrix::from_vec(
vec![
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, // 10 spam
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, // 10 ham
],
20,
1,
);
// Train
let mut model = LogReg::new(5);
model.fit(&x, &y, 0.01, 5000);
// Predict
// e.g. "free money offer"
let email_data = vec![1.0, 0.0, 1.0, 0.0, 1.0];
let email = Matrix::from_vec(email_data, 1, 5);
let prob_spam = model.predict_proba(&email);
println!("Probability of spam: {:.4}", prob_spam.data()[0]);
```
### Iris Flower Classification
```rust
# extern crate rustframe;
use rustframe::compute::models::gaussian_nb::GaussianNB;
use rustframe::matrix::Matrix;
// Features: sepal length and petal length
let x = Matrix::from_rows_vec(vec![
5.1, 1.4, // setosa
4.9, 1.4, // setosa
6.2, 4.5, // versicolor
5.9, 5.1, // virginica
], 4, 2);
let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 2.0], 4, 1);
let names = vec!["setosa", "versicolor", "virginica"];
let mut model = GaussianNB::new(1e-9, true);
model.fit(&x, &y);
let sample = Matrix::from_vec(vec![5.0, 1.5], 1, 2);
let predicted_class = model.predict(&sample);
let class_name = names[predicted_class.data()[0] as usize];
println!("Predicted class: {} ({:?})", class_name, predicted_class.data()[0]);
```
### Customer Segmentation
```rust
# extern crate rustframe;
use rustframe::compute::models::k_means::KMeans;
use rustframe::matrix::Matrix;
// Each row: [age, annual_income]
let customers = Matrix::from_rows_vec(
vec![
25.0, 40_000.0, 34.0, 52_000.0, 58.0, 95_000.0, 45.0, 70_000.0,
],
4,
2,
);
let (model, labels) = KMeans::fit(&customers, 2, 20, 1e-4);
let new_customer = Matrix::from_vec(vec![30.0, 50_000.0], 1, 2);
let cluster = model.predict(&new_customer)[0];
println!("New customer belongs to cluster: {}", cluster);
println!("Cluster labels: {:?}", labels);
```
For helper functions and upcoming modules, visit the
[utilities](./utilities.md) section.