Merge cd3aa84e6019bad5492e9cbc6948caf6c0e0f726 into 2cb4e4621766466e995887c10892e8150d977c0a

Add user guide mdbook

.github/htmldocs/index.html

@@ -58,6 +58,14 @@
         <h2>A lightweight dataframe & math toolkit for Rust</h2>
         <hr style="border: 1px solid #d4d4d4; margin: 20px 0;">
         <p>
+
+            🐙 <a href="https://github.com/Magnus167/rustframe">GitHub</a>
+            <br><br>
+            
+            📖 <a href="https://magnus167.github.io/rustframe/user-guide">User Guide</a>
+            <br><br>
+            
+
             📚 <a href="https://magnus167.github.io/rustframe/docs">Docs</a> |
             📊 <a href="https://magnus167.github.io/rustframe/benchmark-report/">Benchmarks</a>
 
@@ -65,8 +73,7 @@
             🦀 <a href="https://crates.io/crates/rustframe">Crates.io</a> |
             🔖 <a href="https://docs.rs/rustframe/latest/rustframe/">docs.rs</a>
             <br><br>
-            🐙 <a href="https://github.com/Magnus167/rustframe">GitHub</a> |
+            <!-- 🌐 <a href="https://gitea.nulltech.uk/Magnus167/rustframe">Gitea mirror</a> -->
-            🌐 <a href="https://gitea.nulltech.uk/Magnus167/rustframe">Gitea mirror</a>
         </p>
     </main>
 </body>

.github/workflows/docs-and-testcov.yml

@@ -153,7 +153,6 @@ jobs:
 
           echo "<meta http-equiv=\"refresh\" content=\"0; url=../docs/index.html\">" > target/doc/rustframe/index.html
 
-          mkdir output
           cp tarpaulin-report.html target/doc/docs/
           cp tarpaulin-report.json target/doc/docs/
           cp tarpaulin-badge.json target/doc/docs/
@@ -166,16 +165,30 @@ jobs:
           # copy the benchmark report to the output directory
           cp -r benchmark-report target/doc/
 
+          mkdir output
+          cp -r target/doc/* output/
+
+      - name: Build user guide
+        run: |
+          cargo binstall mdbook
+          bash ./docs/build.sh
+
+      - name: Copy user guide to output directory
+        run: |
+          mkdir output/user-guide
+          cp -r docs/book/* output/user-guide/
+
       - name: Add index.html to output directory
         run: |
-          cp .github/htmldocs/index.html target/doc/index.html
+          cp .github/htmldocs/index.html output/index.html
-          cp .github/rustframe_logo.png target/doc/rustframe_logo.png
+          cp .github/rustframe_logo.png output/rustframe_logo.png
 
       - name: Upload Pages artifact
         # if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'
         uses: actions/upload-pages-artifact@v3
         with:
-          path: target/doc/
+          # path: target/doc/
+          path: output/
 
       - name: Deploy to GitHub Pages
         # if: github.event_name == 'push' || github.event_name == 'workflow_dispatch'

.github/workflows/run-unit-tests.yml

@@ -78,3 +78,8 @@ jobs:
         uses: codecov/test-results-action@v1
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
+
+      - name: Test build user guide
+        run: |
+          cargo binstall mdbook
+          bash ./docs/build.sh

.gitignore

@@ -17,3 +17,5 @@ data/
 tarpaulin-report.*
 
 .github/htmldocs/rustframe_logo.png
+
+docs/book/

README.md

@@ -1,11 +1,12 @@
 # rustframe
 
-📚 [Docs](https://magnus167.github.io/rustframe/) | 🐙 [GitHub](https://github.com/Magnus167/rustframe) | 🌐 [Gitea mirror](https://gitea.nulltech.uk/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) -->
 
 [![codecov](https://codecov.io/gh/Magnus167/rustframe/graph/badge.svg?token=J7ULJEFTVI)](https://codecov.io/gh/Magnus167/rustframe)
 [![Coverage](https://img.shields.io/endpoint?url=https://magnus167.github.io/rustframe/docs/tarpaulin-badge.json)](https://magnus167.github.io/rustframe/docs/tarpaulin-report.html)
+[![gitea-mirror](https://img.shields.io/badge/git_mirror-blue)](https://gitea.nulltech.uk/Magnus167/rustframe)
 
 ---
 
@@ -205,3 +206,14 @@ To run the benchmarks, use:
 ```bash
 cargo bench --features "bench"
 ```
+
+## Building the user-guide
+
+To build the user guide, use:
+
+```bash
+cargo binstall mdbook
+bash docs/build.sh
+```
+
+This will generate the user guide in the `docs/book` directory.

docs/book.toml

@@ -0,0 +1,7 @@
+[book]
+title = "Rustframe User Guide"
+authors = ["Palash Tyagi (https://github.com/Magnus167)"]
+description = "Guided journey through Rustframe capabilities."
+
+[build]
+build-dir = "book"

docs/build.sh

@@ -0,0 +1,7 @@
+#!/usr/bin/env sh
+# Build and test the Rustframe user guide using mdBook.
+set -e
+
+cd docs
+bash gen.sh "$@"
+cd ..

docs/gen.sh

@@ -0,0 +1,14 @@
+#!/usr/bin/env sh
+
+set -e
+
+cargo clean
+
+cargo build --manifest-path ../Cargo.toml
+
+mdbook test -L ../target/debug/deps "$@"
+
+mdbook build "$@"
+
+cargo build
+# cargo build --release

docs/src/SUMMARY.md

@@ -0,0 +1,7 @@
+# Summary
+
+- [Introduction](./introduction.md)
+- [Data Manipulation](./data-manipulation.md)
+- [Compute Features](./compute.md)
+- [Machine Learning](./machine-learning.md)
+- [Utilities](./utilities.md)

docs/src/compute.md

@@ -0,0 +1,222 @@
+# Compute Features
+
+The `compute` module hosts numerical routines for exploratory data analysis.
+It covers descriptive statistics, correlations, probability distributions and
+some basic inferential tests.
+
+## Basic Statistics
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::stats::{mean, mean_horizontal, mean_vertical, stddev, median, population_variance, percentile};
+use rustframe::matrix::Matrix;
+
+let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+assert_eq!(mean(&m), 2.5);
+assert_eq!(stddev(&m), 1.118033988749895);
+assert_eq!(median(&m), 2.5);
+assert_eq!(population_variance(&m), 1.25);
+assert_eq!(percentile(&m, 50.0), 3.0);
+// column averages returned as 1 x n matrix
+let row_means = mean_horizontal(&m);
+assert_eq!(row_means.data(), &[2.0, 3.0]);
+let col_means = mean_vertical(&m);
+assert_eq!(col_means.data(), & [1.5, 3.5]);
+```
+
+### Axis-specific Operations
+
+Operations can be applied along specific axes (rows or columns):
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::stats::{mean_vertical, mean_horizontal, stddev_vertical, stddev_horizontal};
+use rustframe::matrix::Matrix;
+
+// 3x2 matrix
+let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 3, 2);
+
+// Mean along columns (vertical) - returns 1 x cols matrix
+let col_means = mean_vertical(&m);
+assert_eq!(col_means.shape(), (1, 2));
+assert_eq!(col_means.data(), &[3.0, 4.0]); // [(1+3+5)/3, (2+4+6)/3]
+
+// Mean along rows (horizontal) - returns rows x 1 matrix
+let row_means = mean_horizontal(&m);
+assert_eq!(row_means.shape(), (3, 1));
+assert_eq!(row_means.data(), &[1.5, 3.5, 5.5]); // [(1+2)/2, (3+4)/2, (5+6)/2]
+
+// Standard deviation along columns
+let col_stddev = stddev_vertical(&m);
+assert_eq!(col_stddev.shape(), (1, 2));
+
+// Standard deviation along rows
+let row_stddev = stddev_horizontal(&m);
+assert_eq!(row_stddev.shape(), (3, 1));
+```
+
+## Correlation
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::stats::{pearson, 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 corr = pearson(&x, &y);
+let cov = covariance(&x, &y);
+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.
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::stats::distributions::normal_pdf;
+use rustframe::matrix::Matrix;
+
+let x = Matrix::from_vec(vec![0.0, 1.0], 1, 2);
+let pdf = normal_pdf(x, 0.0, 1.0);
+assert_eq!(pdf.data().len(), 2);
+```
+
+### Additional Distributions
+
+Rustframe provides several other probability distributions:
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::stats::distributions::{normal_cdf, binomial_pmf, binomial_cdf, poisson_pmf};
+use rustframe::matrix::Matrix;
+
+// Normal distribution CDF
+let x = Matrix::from_vec(vec![0.0, 1.0], 1, 2);
+let cdf = normal_cdf(x, 0.0, 1.0);
+assert_eq!(cdf.data().len(), 2);
+
+// Binomial distribution PMF
+// Probability of k successes in n trials with probability p
+let k = Matrix::from_vec(vec![0_u64, 1, 2, 3], 1, 4);
+let pmf = binomial_pmf(3, k.clone(), 0.5);
+assert_eq!(pmf.data().len(), 4);
+
+// Binomial distribution CDF
+let cdf = binomial_cdf(3, k, 0.5);
+assert_eq!(cdf.data().len(), 4);
+
+// Poisson distribution PMF
+// Probability of k events with rate parameter lambda
+let k = Matrix::from_vec(vec![0_u64, 1, 2], 1, 3);
+let pmf = poisson_pmf(2.0, k);
+assert_eq!(pmf.data().len(), 3);
+```
+
+### Inferential Statistics
+
+Rustframe provides several inferential statistical tests:
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::Matrix;
+use rustframe::compute::stats::inferential::{t_test, chi2_test, anova};
+
+// Two-sample t-test
+let sample1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
+let sample2 = Matrix::from_vec(vec![6.0, 7.0, 8.0, 9.0, 10.0], 1, 5);
+let (t_statistic, p_value) = t_test(&sample1, &sample2);
+assert!((t_statistic + 5.0).abs() < 1e-5);
+assert!(p_value > 0.0 && p_value < 1.0);
+
+// Chi-square test of independence
+let observed = Matrix::from_vec(vec![12.0, 5.0, 8.0, 10.0], 2, 2);
+let (chi2_statistic, p_value) = chi2_test(&observed);
+assert!(chi2_statistic > 0.0);
+assert!(p_value > 0.0 && p_value < 1.0);
+
+// One-way ANOVA
+let group1 = Matrix::from_vec(vec![1.0, 2.0, 3.0], 1, 3);
+let group2 = Matrix::from_vec(vec![2.0, 3.0, 4.0], 1, 3);
+let group3 = Matrix::from_vec(vec![3.0, 4.0, 5.0], 1, 3);
+let groups = vec![&group1, &group2, &group3];
+let (f_statistic, p_value) = anova(groups);
+assert!(f_statistic > 0.0);
+assert!(p_value > 0.0 && p_value < 1.0);
+```
+
+With the basics covered, explore predictive models in the
+[machine learning](./machine-learning.md) chapter.

docs/src/data-manipulation.md

@@ -0,0 +1,157 @@
+# Data Manipulation
+
+Rustframe's `Frame` type couples tabular data with
+column labels and a typed row index. Frames expose a familiar API for loading
+data, selecting rows or columns and performing aggregations.
+
+## Creating a Frame
+
+```rust
+# extern crate rustframe;
+use rustframe::frame::{Frame, RowIndex};
+use rustframe::matrix::Matrix;
+
+let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
+let frame = Frame::new(data, vec!["A", "B"], None);
+assert_eq!(frame["A"], vec![1.0, 2.0]);
+```
+
+## Indexing Rows
+
+Row labels can be integers, dates or a default range. Retrieving a row returns a
+view that lets you inspect values by column name or position.
+
+```rust
+# extern crate rustframe;
+# extern crate chrono;
+use chrono::NaiveDate;
+use rustframe::frame::{Frame, RowIndex};
+use rustframe::matrix::Matrix;
+
+let d = |y, m, d| NaiveDate::from_ymd_opt(y, m, d).unwrap();
+let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
+let index = RowIndex::Date(vec![d(2024, 1, 1), d(2024, 1, 2)]);
+let mut frame = Frame::new(data, vec!["A", "B"], Some(index));
+assert_eq!(frame.get_row_date(d(2024, 1, 2))["B"], 4.0);
+
+// mutate by row key
+frame.get_row_date_mut(d(2024, 1, 1)).set_by_index(0, 9.0);
+assert_eq!(frame.get_row_date(d(2024, 1, 1))["A"], 9.0);
+```
+
+## Column operations
+
+Columns can be inserted, renamed, removed or reordered in place.
+
+```rust
+# extern crate rustframe;
+use rustframe::frame::{Frame, RowIndex};
+use rustframe::matrix::Matrix;
+
+let data = Matrix::from_cols(vec![vec![1, 2], vec![3, 4]]);
+let mut frame = Frame::new(data, vec!["X", "Y"], Some(RowIndex::Range(0..2)));
+
+frame.add_column("Z", vec![5, 6]);
+frame.rename("Y", "W");
+let removed = frame.delete_column("X");
+assert_eq!(removed, vec![1, 2]);
+frame.sort_columns();
+assert_eq!(frame.columns(), &["W", "Z"]);
+```
+
+## Aggregations
+
+Any numeric aggregation available on `Matrix` is forwarded to `Frame`.
+
+```rust
+# extern crate rustframe;
+use rustframe::frame::Frame;
+use rustframe::matrix::{Matrix, SeriesOps};
+
+let frame = Frame::new(Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]), vec!["A", "B"], None);
+assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
+assert_eq!(frame.sum_horizontal(), vec![4.0, 6.0]);
+```
+
+## Matrix Operations
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::Matrix;
+
+let data1 = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let data2 = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
+
+let sum = data1.clone() + data2.clone();
+assert_eq!(sum.data(), vec![6.0, 8.0, 10.0, 12.0]);
+
+let product = data1.clone() * data2.clone();
+assert_eq!(product.data(), vec![5.0, 12.0, 21.0, 32.0]);
+
+let scalar_product = data1.clone() * 2.0;
+assert_eq!(scalar_product.data(), vec![2.0, 4.0, 6.0, 8.0]);
+
+let equals = data1 == data1.clone();
+assert_eq!(equals, true);
+```
+
+### Advanced Matrix Operations
+
+Matrices support a variety of advanced operations:
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::{Matrix, SeriesOps};
+
+// Matrix multiplication (dot product)
+let a = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let b = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
+let product = a.matrix_mul(&b);
+assert_eq!(product.data(), vec![23.0, 34.0, 31.0, 46.0]);
+
+// Transpose
+let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let transposed = m.transpose();
+assert_eq!(transposed.data(), vec![1.0, 3.0, 2.0, 4.0]);
+
+// Map function over all elements
+let m = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let squared = m.map(|x| x * x);
+assert_eq!(squared.data(), vec![1.0, 4.0, 9.0, 16.0]);
+
+// Zip two matrices with a function
+let a = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let b = Matrix::from_vec(vec![5.0, 6.0, 7.0, 8.0], 2, 2);
+let zipped = a.zip(&b, |x, y| x + y);
+assert_eq!(zipped.data(), vec![6.0, 8.0, 10.0, 12.0]);
+```
+
+### Matrix Reductions
+
+Matrices support various reduction operations:
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::{Matrix, SeriesOps};
+
+let m = Matrix::from_rows_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0], 3, 2);
+
+// Sum along columns (vertical)
+let col_sums = m.sum_vertical();
+assert_eq!(col_sums, vec![9.0, 12.0]); // [1+3+5, 2+4+6]
+
+// Sum along rows (horizontal)
+let row_sums = m.sum_horizontal();
+assert_eq!(row_sums, vec![3.0, 7.0, 11.0]); // [1+2, 3+4, 5+6]
+
+// Cumulative sum along columns
+let col_cumsum = m.cumsum_vertical();
+assert_eq!(col_cumsum.data(), vec![1.0, 4.0, 9.0, 2.0, 6.0, 12.0]);
+
+// Cumulative sum along rows
+let row_cumsum = m.cumsum_horizontal();
+assert_eq!(row_cumsum.data(), vec![1.0, 3.0, 5.0, 3.0, 7.0, 11.0]);
+```
+
+With the basics covered, continue to the [compute features](./compute.md)
+chapter for statistics and analytics.

docs/src/introduction.md

@@ -0,0 +1,40 @@
+# 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/)
+
+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
+API approachable while offering handy features for small analytical or
+educational projects.
+
+Rustframe bundles:
+
+- column‑labelled frames built on a fast column‑major matrix
+- familiar element‑wise math and aggregation routines
+- a growing `compute` module for statistics and machine learning
+- utilities for dates and random numbers
+
+```rust
+# extern crate rustframe;
+use rustframe::{frame::Frame, matrix::{Matrix, SeriesOps}};
+
+let data = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
+let frame = Frame::new(data, vec!["A", "B"], None);
+
+// Perform column wise aggregation
+assert_eq!(frame.sum_vertical(), vec![3.0, 7.0]);
+```
+
+## Resources
+
+- [GitHub repository](https://github.com/Magnus167/rustframe)
+- [Crates.io](https://crates.io/crates/rustframe) & [API docs](https://docs.rs/rustframe)
+- [Code coverage](https://codecov.io/gh/Magnus167/rustframe)
+
+This guide walks through the main building blocks of the library. Each chapter
+contains runnable snippets so you can follow along:
+
+1. [Data manipulation](./data-manipulation.md) for loading and transforming data
+2. [Compute features](./compute.md) for statistics and analytics
+3. [Machine learning](./machine-learning.md) for predictive models
+4. [Utilities](./utilities.md) for supporting helpers and upcoming modules

docs/src/machine-learning.md

@@ -0,0 +1,282 @@
+# Machine Learning
+
+The `compute::models` module bundles several learning algorithms that operate on
+`Matrix` structures. These examples highlight the basic training and prediction
+APIs. For more end‑to‑end walkthroughs see the examples directory in the
+repository.
+
+Currently implemented models include:
+
+- Linear and logistic regression
+- K‑means clustering
+- Principal component analysis (PCA)
+- Gaussian Naive Bayes
+- Dense neural networks
+
+## Linear Regression
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::linreg::LinReg;
+use rustframe::matrix::Matrix;
+
+let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
+let y = Matrix::from_vec(vec![2.0, 3.0, 4.0, 5.0], 4, 1);
+let mut model = LinReg::new(1);
+model.fit(&x, &y, 0.01, 100);
+let preds = model.predict(&x);
+assert_eq!(preds.rows(), 4);
+```
+
+## K-means Walkthrough
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::k_means::KMeans;
+use rustframe::matrix::Matrix;
+
+let data = Matrix::from_vec(vec![1.0, 1.0, 5.0, 5.0], 2, 2);
+let (model, _labels) = KMeans::fit(&data, 2, 10, 1e-4);
+let new_point = Matrix::from_vec(vec![0.0, 0.0], 1, 2);
+let cluster = model.predict(&new_point)[0];
+```
+
+## Logistic Regression
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::logreg::LogReg;
+use rustframe::matrix::Matrix;
+
+let x = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 4, 1);
+let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
+let mut model = LogReg::new(1);
+model.fit(&x, &y, 0.1, 200);
+let preds = model.predict_proba(&x);
+assert_eq!(preds.rows(), 4);
+```
+
+## Principal Component Analysis
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::pca::PCA;
+use rustframe::matrix::Matrix;
+
+let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0], 2, 2);
+let pca = PCA::fit(&data, 1, 0);
+let transformed = pca.transform(&data);
+assert_eq!(transformed.cols(), 1);
+```
+
+## Gaussian Naive Bayes
+
+Gaussian Naive Bayes classifier for continuous features:
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::gaussian_nb::GaussianNB;
+use rustframe::matrix::Matrix;
+
+// Training data with 2 features
+let x = Matrix::from_rows_vec(vec![
+    1.0, 2.0,
+    2.0, 3.0,
+    3.0, 4.0,
+    4.0, 5.0
+], 4, 2);
+
+// Class labels (0 or 1)
+let y = Matrix::from_vec(vec![0.0, 0.0, 1.0, 1.0], 4, 1);
+
+// Train the model
+let mut model = GaussianNB::new(1e-9, true);
+model.fit(&x, &y);
+
+// Make predictions
+let predictions = model.predict(&x);
+assert_eq!(predictions.rows(), 4);
+```
+
+## Dense Neural Networks
+
+Simple fully connected neural network:
+
+```rust
+# extern crate rustframe;
+use rustframe::compute::models::dense_nn::{DenseNN, DenseNNConfig, ActivationKind, InitializerKind, LossKind};
+use rustframe::matrix::Matrix;
+
+// Training data with 2 features
+let x = Matrix::from_rows_vec(vec![
+    0.0, 0.0,
+    0.0, 1.0,
+    1.0, 0.0,
+    1.0, 1.0
+], 4, 2);
+
+// XOR target outputs
+let y = Matrix::from_vec(vec![0.0, 1.0, 1.0, 0.0], 4, 1);
+
+// Create a neural network with 2 hidden layers
+let config = DenseNNConfig {
+    input_size: 2,
+    hidden_layers: vec![4, 4],
+    output_size: 1,
+    activations: vec![ActivationKind::Sigmoid, ActivationKind::Sigmoid, ActivationKind::Sigmoid],
+    initializer: InitializerKind::Uniform(0.5),
+    loss: LossKind::MSE,
+    learning_rate: 0.1,
+    epochs: 1000,
+};
+let mut model = DenseNN::new(config);
+
+// Train the model
+model.train(&x, &y);
+
+// Make predictions
+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.

docs/src/utilities.md

@@ -0,0 +1,63 @@
+# Utilities
+
+Utilities provide handy helpers around the core library. Existing tools
+include:
+
+- Date utilities for generating calendar sequences and business‑day sets
+- Random number generators for simulations and testing
+
+## Date Helpers
+
+```rust
+# extern crate rustframe;
+use rustframe::utils::dateutils::{BDatesList, BDateFreq, DatesList, DateFreq};
+
+// Calendar sequence
+let list = DatesList::new("2024-01-01".into(), "2024-01-03".into(), DateFreq::Daily);
+assert_eq!(list.count().unwrap(), 3);
+
+// Business days starting from 2024‑01‑02
+let bdates = BDatesList::from_n_periods("2024-01-02".into(), BDateFreq::Daily, 3).unwrap();
+assert_eq!(bdates.list().unwrap().len(), 3);
+```
+
+## Random Numbers
+
+The `random` module offers deterministic and cryptographically secure RNGs.
+
+```rust
+# extern crate rustframe;
+use rustframe::random::{Prng, Rng};
+
+let mut rng = Prng::new(42);
+let v1 = rng.next_u64();
+let v2 = rng.next_u64();
+assert_ne!(v1, v2);
+```
+
+## Stats Functions
+
+```rust
+# extern crate rustframe;
+use rustframe::matrix::Matrix;
+use rustframe::compute::stats::descriptive::{mean, median, stddev};
+
+let data = Matrix::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0], 1, 5);
+
+let mean_value = mean(&data);
+assert_eq!(mean_value, 3.0);
+
+let median_value = median(&data);
+assert_eq!(median_value, 3.0);
+
+let std_value = stddev(&data);
+assert_eq!(std_value, 2.0_f64.sqrt());
+```
+
+Upcoming utilities will cover:
+
+- Data import/export helpers
+- Visualization adapters
+- Streaming data interfaces
+
+Contributions to these sections are welcome!