# rustframe

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---

## Rustframe: _A lightweight dataframe & math toolkit for Rust_

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 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

- **Matrix operations** - Element-wise arithmetic, boolean logic, transpose, and more.
- **Math that reads like math** - element-wise `+`, `−`, `×`, `÷` on entire frames or scalars.
- **Frames** - Column major data structure for single-type data, with labeled columns and typed row indices.
- **Compute module** - Implements various statistical computations and machine learning models.

- **[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

### Heads up

- **Not memory‑efficient (yet)** - footprint needs work.
- **The feature set is still limited** - expect missing pieces.

### Somewhere down the line

- Optional GPU acceleration (Vulkan or similar) for heavier workloads.
- Straightforward Python bindings using `pyo3`.

---

## Quick start

```rust
use chrono::NaiveDate;
use rustframe::{
    frame::{Frame, RowIndex},
    matrix::{BoolOps, Matrix, SeriesOps},
    utils::{DateFreq, BDatesList},
};

let n_periods = 4;

// 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()
        .list().unwrap();

let col_names: Vec<String> = vec!["a".to_string(), "b".to_string()];

let ma: Matrix<f64> =
    Matrix::from_cols(vec![vec![1.0, 2.0, 3.0, 4.0], vec![5.0, 6.0, 7.0, 8.0]]);
let mb: Matrix<f64> =
    Matrix::from_cols(vec![vec![4.0, 3.0, 2.0, 1.0], vec![8.0, 7.0, 6.0, 5.0]]);

let fa: Frame<f64> = Frame::new(
    ma.clone(),
    col_names.clone(),
    Some(RowIndex::Date(dates.clone())),
);
let fb: Frame<f64> = Frame::new(mb, col_names, Some(RowIndex::Date(dates)));

// Math that reads like math
let result: Frame<f64> = &fa * &fb; // element‑wise multiply
let total: f64 = result.sum_vertical().iter().sum::<f64>();
assert_eq!(total, 184.0);

// broadcast & reduce
let result: Matrix<f64> = ma.clone() + 1.0; // add scalar
let result: Matrix<f64> = result + &ma - &ma; // add matrix
let result: Matrix<f64> = result - 1.0; // subtract scalar
let result: Matrix<f64> = result * 2.0; // multiply by scalar
let result: Matrix<f64> = result / 2.0; // divide by scalar

let check: bool = result.eq_elem(ma.clone()).all();
assert!(check);

// Alternatively:
let check: bool = (&(&(&(&ma + 1.0) - 1.0) * 2.0) / 2.0)
    .eq_elem(ma.clone())
    .all();
assert!(check);

// or even as:
let check: bool = ((((ma.clone() + 1.0) - 1.0) * 2.0) / 2.0)
    .eq_elem(ma.clone())
    .all();
assert!(check);

// Matrix multiplication
let mc: Matrix<f64> = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]);
let md: Matrix<f64> = Matrix::from_cols(vec![vec![5.0, 6.0], vec![7.0, 8.0]]);
let mul_result: Matrix<f64> = mc.matrix_mul(&md);
// Expected:
// 1*5 + 3*6 = 5 + 18 = 23
// 2*5 + 4*6 = 10 + 24 = 34
// 1*7 + 3*8 = 7 + 24 = 31
// 2*7 + 4*8 = 14 + 32 = 46
assert_eq!(mul_result.data(), &[23.0, 34.0, 31.0, 46.0]);

// Dot product (alias for matrix_mul for FloatMatrix)
let dot_result: Matrix<f64> = mc.dot(&md);
assert_eq!(dot_result, mul_result);

// Transpose
let original_matrix: Matrix<f64> = Matrix::from_cols(vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]]);
// Original:
// 1 4
// 2 5
// 3 6
let transposed_matrix: Matrix<f64> = original_matrix.transpose();
// Transposed:
// 1 2 3
// 4 5 6
assert_eq!(transposed_matrix.rows(), 2);
assert_eq!(transposed_matrix.cols(), 3);
assert_eq!(transposed_matrix.data(), &[1.0, 4.0, 2.0, 5.0, 3.0, 6.0]);

// Map
let matrix = Matrix::from_cols(vec![vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]]);
// Map function to double each value
let mapped_matrix = matrix.map(|x| x * 2.0);
// Expected data after mapping
// 2 8
// 4 10
// 6 12
assert_eq!(mapped_matrix.data(), &[2.0, 4.0, 6.0, 8.0, 10.0, 12.0]);

// Zip
let a = Matrix::from_cols(vec![vec![1.0, 2.0], vec![3.0, 4.0]]); // 2x2 matrix
let b = Matrix::from_cols(vec![vec![5.0, 6.0], vec![7.0, 8.0]]); // 2x2 matrix
                                                                   // Zip function to add corresponding elements
let zipped_matrix = a.zip(&b, |x, y| x + y);
// Expected data after zipping
// 6 10
// 8 12
assert_eq!(zipped_matrix.data(), &[6.0, 8.0, 10.0, 12.0]);
```

### More examples

See the [examples](./examples/) directory for some demonstrations of Rustframe's syntax and functionality.

To run the examples, use:

```bash
cargo run --example <example_name>
```

E.g. to run the `game_of_life` example:

```bash
cargo run --example game_of_life
```

More demos:

```bash
cargo run --example linear_regression
cargo run --example logistic_regression
cargo run --example k_means
cargo run --example pca
cargo run --example stats_overview
cargo run --example descriptive_stats
cargo run --example correlation
cargo run --example inferential_stats
cargo run --example distributions
```

To simply list all available examples, you can run:

```bash
# this technically raises an error, but it will list all examples
cargo run --example
```

Each demo runs a couple of mini-scenarios showcasing the APIs.

### Running benchmarks

To run the benchmarks, use:

```bash
cargo bench --features "bench"
```