Merge 9e6e22fc37 into 7d0978e5fb

Cargo.toml

@@ -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"
+description = "A simple dataframe library"
-documentation = "https://magnus167.github.io/rustframe/"
 
 [lib]
 name = "rustframe"

README.md

@@ -1,6 +1,6 @@
 # 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) -->
 

docs/src/introduction.md

@@ -1,6 +1,6 @@
 # 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

docs/src/machine-learning.md

@@ -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.