Merge cd3aa84e6019bad5492e9cbc6948caf6c0e0f726 into 7d0978e5fba98538de20d0fdb9d66e6cbf80d3f7

.github/htmldocs/index.html

@@ -58,14 +58,6 @@
         <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>
 
@@ -73,7 +65,8 @@
             🦀 <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://gitea.nulltech.uk/Magnus167/rustframe">Gitea mirror</a> -->
+            🐙 <a href="https://github.com/Magnus167/rustframe">GitHub</a> |
+            🌐 <a href="https://gitea.nulltech.uk/Magnus167/rustframe">Gitea mirror</a>
         </p>
     </main>
 </body>

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

@@ -153,6 +153,7 @@ 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/
@@ -165,30 +166,16 @@ 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 output/index.html
+          cp .github/htmldocs/index.html target/doc/index.html
-          cp .github/rustframe_logo.png output/rustframe_logo.png
+          cp .github/rustframe_logo.png target/doc/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

@@ -78,8 +78,3 @@ 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

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

README.md

@@ -1,12 +1,11 @@
 # 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) | 🌐 [Gitea mirror](https://gitea.nulltech.uk/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) -->
 
 [![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)
 
 ---
 
@@ -206,14 +205,3 @@ 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

@@ -1,7 +0,0 @@
-[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

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

docs/gen.sh

@@ -1,14 +0,0 @@
-#!/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

@@ -1,7 +0,0 @@
-# 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

@@ -1,222 +0,0 @@
-# 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

@@ -1,157 +0,0 @@
-# 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

@@ -1,40 +0,0 @@
-# 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

@@ -1,282 +0,0 @@
-# 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

@@ -1,63 +0,0 @@
-# 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!