Merge branch 'main' into release/a20250805

Update package version and enhance description in Cargo.toml

Cargo.toml

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

README.md

@@ -1,6 +1,6 @@
 # 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/)
+🐙 [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/)
 
 <!-- [![Last commit](https://img.shields.io/endpoint?url=https://magnus167.github.io/rustframe/rustframe/last-commit-date.json)](https://github.com/Magnus167/rustframe) -->
 

docs/src/compute.md

@@ -70,6 +70,77 @@ 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.

docs/src/introduction.md

@@ -1,6 +1,6 @@
 # Introduction
 
-📚 [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/)
+🐙 [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/)
 
 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

docs/src/machine-learning.md

@@ -41,9 +41,6 @@ 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
@@ -72,7 +69,7 @@ let transformed = pca.transform(&data);
 assert_eq!(transformed.cols(), 1);
 ```
 
-### Gaussian Naive Bayes
+## Gaussian Naive Bayes
 
 Gaussian Naive Bayes classifier for continuous features:
 
@@ -101,7 +98,7 @@ let predictions = model.predict(&x);
 assert_eq!(predictions.rows(), 4);
 ```
 
-### Dense Neural Networks
+## Dense Neural Networks
 
 Simple fully connected neural network:
 
@@ -142,5 +139,144 @@ 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.