MathOps is a Rust library providing mathematical and statistical operations on vectors. It supports both f32 and f64 types and includes methods for common statistical measures, normalization, sorting, and vector arithmetic.
- Statistical Methods:
mean,median,variance,standard deviation,quantile,interquartile range (IQR)andcumulative sum. - Normalization: Min-max normalization and standardization (mean 0, standard deviation 1).
- Sorting Methods:
sortedandsort_in_place. - Vector Arithmetic: Addition, subtraction, multiplication, division, and modulus with both vectors and scalars using explicit methods.
- Operator Overloading: Supports
Vector<T> + Vector<T>,Vector<T> - Vector<T>, etc., using operator overloading. - Type Conversion: Convert between integer types and
f32/f64, and betweenf32andf64. - Summary Statistics: Generate a neatly formatted summary of key statistical measures.
- Seamless Conversion: Easily wrap and unwrap between
Vec<T>andVector<T>.
Wrapping Vec<T> into a local Vector<T> struct allows us to implement custom traits and methods without violating Rust's orphan rules. This approach is a zero-cost abstraction:
- No Overhead: The
Vector<T>struct is a simple wrapper aroundVec<T>, introducing no runtime overhead. - Seamless Integration: By implementing
DerefandDerefMut,Vector<T>can be used just like aVec<T>, supporting indexing, iteration, and other common operations. - Enhanced Functionality: Enables the implementation of custom traits for mathematical and statistical operations directly on
Vector<T>.
use math_ops::{
Normalize,
SortOps,
Statistics,
SummaryOps,
UnwrapToVec,
Vector,
VectorOps,
WrapAsVector,
};
fn main() {
// Sample data with NaN values
let data_f64 = vec![1.0_f64, 2.0, f64::NAN, 4.0, 5.0].wrap_as_vector();
// Statistical operations
println!("=== Statistical Operations ===");
println!("Mean (f64): {:?}", data_f64.mean());
println!("Median (f64): {:?}", data_f64.median());
println!("Variance (f64): {:?}", data_f64.var());
println!("Standard Deviation (f64): {:?}", data_f64.stddev());
println!("IQR (f64): {:?}", data_f64.iqr());
println!("Quantile(25%) (f64): {:?}", data_f64.quantile(0.25));
println!("Quantile(95%) (f64): {:?}", data_f64.quantile(0.95));
// Cumulative Sum
println!("Cumulative Sum (f64): {:?}", data_f64.cumsum());
// Summary
println!("\n=== Summary ===");
let summary = data_f64.summary();
println!("{}", summary);
// Normalization
println!("\n=== Normalization ===");
let normalized = data_f64.min_max_normalize();
println!("Min-Max Normalized: {:?}", normalized);
let standardized = data_f64.standardize();
println!("Standardized: {:?}", standardized);
// Sorting
println!("\n=== Sorting ===");
let sorted = data_f64.sorted();
println!("Sorted: {:?}", sorted);
// Arithmetic Operations with Vectors
println!("\n=== Arithmetic Operations with Vectors ===");
let data2 = vec![5.0_f64, 4.0, 3.0, 2.0, 1.0].wrap_as_vector();
let sum_vec = &data_f64 + &data2;
println!("Vector Addition: {:?}", sum_vec);
let sub_vec = &data_f64 - &data2;
println!("Vector Subtraction: {:?}", sub_vec);
let mul_vec = &data_f64 * &data2;
println!("Vector Multiplication: {:?}", mul_vec);
let div_vec = &data_f64 / &data2;
println!("Vector Division: {:?}", div_vec);
let rem_vec = &data_f64 % &data2;
println!("Vector Modulus: {:?}", rem_vec);
// Arithmetic Operations with Scalars
println!("\n=== Arithmetic Operations with Scalars ===");
let scalar = 2.0_f64;
let added = data_f64.add_scalar(scalar);
println!("Added Scalar: {:?}", added);
let subtracted = data_f64.sub_scalar(scalar);
println!("Subtracted Scalar: {:?}", subtracted);
let multiplied = data_f64.mul_scalar(scalar);
println!("Multiplied by Scalar: {:?}", multiplied);
let divided = data_f64.div_scalar(scalar);
println!("Divided by Scalar: {:?}", divided);
let modulus = data_f64.rem_scalar(scalar);
println!("Modulus with Scalar: {:?}", modulus);
// Type Conversion
println!("\n=== Type Conversion ===");
let int_data = vec![1, 2, 3, 4, 5];
let float_data_f64: Vector<f64> = int_data
.iter()
.map(|&x| x as f64)
.collect::<Vec<_>>()
.wrap_as_vector();
let float_data_f32: Vector<f32> = int_data
.iter()
.map(|&x| x as f32)
.collect::<Vec<_>>()
.wrap_as_vector();
println!("Converted to f64: {:?}", float_data_f64);
println!("Converted to f32: {:?}", float_data_f32);
// Unwrap to Vec
let original_vec: Vec<f64> = float_data_f64.unwrap_to_vec();
println!("Unwrapped to Vec<f64>: {:?}", original_vec);
}