Statistical and Vector Operations Library

This Rust library provides statistical operations and vector algebra functionalities, working with both f64 and i64 data types. It also includes methods for calculating common statistical measures (mean, median, mode, variance, standard deviation) and operations on vectors (addition, dot product, magnitude).

Modules

1. Mean and Statistical Functions

The Mean struct includes statistical functions for both f64 and i64 types:

• Mean:

  • mean_f: Calculates the mean of a list of f64 numbers.
  • mean_i: Calculates the mean of a list of i64 numbers.

• Weighted Average:

  • weighted_average_f: Calculates the weighted average for f64 numbers.
  • weighted_average_i: Calculates the weighted average for i64 numbers.

• Median:

  • median_f: Finds the median for f64 numbers.
  • median_i: Finds the median for i64 numbers.

• Mode:

  • mode: Calculates the mode for any Hash, Ord, and Clone types.
  • mode_f64: Specialized mode calculation for f64 numbers.

• Variance and Standard Deviation:

  • variance_f: Computes variance for f64 numbers.
  • variance_i: Computes variance for i64 numbers.
  • standard_deviation_f: Computes standard deviation for f64 numbers.
  • standard_deviation_i: Computes standard deviation for i64 numbers.

2. CalculusError for Error Calculation

The CalculusError struct provides error calculation methods for both f64 and i64 values:

• Absolute Error:

  • absolute_error_f: Calculates the absolute error for f64.
  • absolute_error_i: Calculates the absolute error for i64.

• Relative Error:

  • relative_error_f: Calculates the relative error for f64.
  • relative_error_i: Calculates the relative error for i64.

3. Vector Operations

The Vector struct is implemented for both i64 and f64 types and supports basic vector algebra:

• Creation:

  • new: Initializes a new vector with i64 or f64 components.

• Magnitude:

  • magnitude: Computes the magnitude (Euclidean length) of the vector.

• Addition:

  • add: Adds two i64 vectors (mutates the first vector).
  • add_f: Adds two f64 vectors (mutates the first vector).
  • add_i: Adds an i64 vector to an f64 vector (mutates the f64 vector).

• Dot Product:

  • dot_product: Computes the dot product of two i64 vectors.
  • dot_product_f: Computes the dot product of two f64 vectors.
  • dot_product_i: Computes the dot product between an f64 and i64 vector.

• Scalar Multiplication:

  • Overloaded multiplication (Mul trait) for both i64 and f64 scalar multiplication.

4. VectorError for Error Handling

The VectorError enum handles errors during vector operations:

• DimensionMismatch: Error raised when vectors of different dimensions are used in an operation.

Tests

The library is fully tested with unit tests, covering:

• Calculation of mean, weighted average, median, mode, variance, and standard deviation for both f64 and i64.
• Error handling for mismatched vector dimensions.
• Vector addition, scalar multiplication, and dot product operations.
• Magnitude computation for vectors.

How to Use

1. Add to your Cargo.toml:

[dependencies]

datas = "0.1.6"

2. Example Usage:

use datas::Mean;
use datas::vector::Vector;

// Statistical calculations
let data = vec![1.0, 2.0, 3.0, 4.0];
let mean = Mean::mean_f(&data);
println!("Mean: {}", mean);

// Vector operations
let vec1 = Vector::<i64>::new(vec![1, 2, 3]);
let vec2 = Vector::<i64>::new(vec![4, 5, 6]);
let dot_product = vec1.dot_product(&vec2).unwrap();
println!("Dot product: {}", dot_product);

This library offers efficient and flexible handling of common statistical and vector-based operations, making it a useful tool for mathematical computation in Rust projects. Still, it needs much work and improvements such as a more generic approach, so feel free to contribute.