Merge cd3aa84e6019bad5492e9cbc6948caf6c0e0f726 into 27e9eab02829523c98744bdf9f7d780ebc352b48

Updating README

Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "rustframe"
-version = "0.0.1-a.0"
+version = "0.0.1-a.20250716"
 edition = "2021"
 license = "GPL-3.0-or-later"
 readme = "README.md"
@@ -19,9 +19,6 @@ rand = "^0.9.1"
 [features]
 bench = ["dep:criterion"]
 
-# [dev-dependencies]
-# criterion = { version = "0.5", features = ["html_reports"], optional = true }
-
 [[bench]]
 name = "benchmarks"
 harness = false

README.md

@@ -2,7 +2,7 @@
 
 <!-- # <img align="center" alt="Rustframe" src=".github/rustframe_logo.png" height="50px" /> rustframe -->
 
-<!-- though the centre tag doesn't work as it would noramlly, it achieves the desired effect -->
+<!-- though the centre tag doesn't work as it would normally, it achieves the desired effect -->
 
 📚 [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/)
 
@@ -15,27 +15,62 @@
 
 ## Rustframe: _A lightweight dataframe & math toolkit for Rust_
 
-Rustframe provides intuitive dataframe, matrix, and series operations small-to-mid scale data analysis and manipulation.
+Rustframe provides intuitive dataframe, matrix, and series operations for data analysis and manipulation.
 
-Rustframe keeps things simple, safe, and readable. It is handy for quick numeric experiments and small analytical tasks, but it is **not** meant to compete with powerhouse crates like `polars` or `ndarray`.
+Rustframe keeps things simple, safe, and readable. It is handy for quick numeric experiments and small analytical tasks as well as for educational purposes. It is designed to be easy to use and understand, with a clean API implemented in 100% safe Rust.
+
+Rustframe is an educational project, and is not intended for production use. It is **not** meant to compete with powerhouse crates like `polars` or `ndarray`. It is a work in progress, and the API is subject to change. There are no guarantees of stability or performance, and it is not optimized for large datasets or high-performance computing.
 
 ### What it offers
 
-- **Math that reads like math** - element‑wise `+`, `−`, `×`, `÷` on entire frames or scalars.
+- **Matrix operations** - Element-wise arithmetic, boolean logic, transpose, and more.
-- **Broadcast & reduce** - sum, product, any/all across rows or columns without boilerplate.
+- **Math that reads like math** - element-wise `+`, `−`, `×`, `÷` on entire frames or scalars.
-- **Boolean masks made simple** - chain comparisons, combine with `&`/`|`, get a tidy `BoolMatrix` back.
+- **Frames** - Column major data structure for single-type data, with labeled columns and typed row indices.
-- **Date‑centric row index** - business‑day ranges and calendar slicing built in.
+- **Compute module** - Implements various statistical computations and machine learning models.
-- **Pure safe Rust** - 100 % safe, zero `unsafe`.
+
+- **[Coming Soon]** _DataFrame_ - Multi-type data structure for heterogeneous data, with labeled columns and typed row indices.
+
+- **[Coming Soon]** _Random number utils_ - Random number generation utilities for statistical sampling and simulations. (Currently using the [`rand`](https://crates.io/crates/rand) crate.)
+
+#### Matrix and Frame functionality
+
+- **Matrix operations** - Element-wise arithmetic, boolean logic, transpose, and more.
+- **Frame operations** - Column manipulation, sorting, and more.
+
+#### Compute Module
+
+The `compute` module provides implementations for various statistical computations and machine learning models.
+
+**Statistics, Data Analysis, and Machine Learning:**
+
+- Correlation analysis
+- Descriptive statistics
+- Distributions
+- Inferential statistics
+
+- Dense Neural Networks
+- Gaussian Naive Bayes
+- K-Means Clustering
+- Linear Regression
+- Logistic Regression
+- Principal Component Analysis
+
+### Coming soon
+
+- **CSV I/O** - read/write CSV files with a simple API.
+- **Date Utils** - date math, calendar slicing, indexing, and more.
+- **More math** - more math functions and aggregations.
 
 ### Heads up
 
 - **Not memory‑efficient (yet)** - footprint needs work.
-- **Feature set still small** - expect missing pieces.
+- **The feature set is still limited** - expect missing pieces.
 
-### On the horizon
+### Somewhere down the line
 
-- Optional GPU help (Vulkan or similar) for heavier workloads.
+- Optional GPU acceleration (Vulkan or similar) for heavier workloads.
 - Straightforward Python bindings using `pyo3`.
+- Integration with common ML libraries, or introduce simple ML features.
 
 ---
 
@@ -51,7 +86,7 @@ use rustframe::{
 
 let n_periods = 4;
 
-// Four business days starting 2024‑01‑02
+// Four business days starting 2024-01-02
 let dates: Vec<NaiveDate> =
     BDatesList::from_n_periods("2024-01-02".to_string(), DateFreq::Daily, n_periods)
         .unwrap()
@@ -86,13 +121,13 @@ let result: Matrix<f64> = result / 2.0; // divide by scalar
 let check: bool = result.eq_elem(ma.clone()).all();
 assert!(check);
 
-// The above math can also be written as:
+// Alternatively:
 let check: bool = (&(&(&(&ma + 1.0) - 1.0) * 2.0) / 2.0)
     .eq_elem(ma.clone())
     .all();
 assert!(check);
 
-// The above math can also be written as:
+// or even as:
 let check: bool = ((((ma.clone() + 1.0) - 1.0) * 2.0) / 2.0)
     .eq_elem(ma.clone())
     .all();
@@ -163,3 +198,11 @@ E.g. to run the `game_of_life` example:
 ```bash
 cargo run --example game_of_life
 ```
+
+### Running benchmarks
+
+To run the benchmarks, use:
+
+```bash
+cargo bench --features "bench"
+```