Struct RandomVector 

pub struct RandomVector<T> {
    pub variables: Vec<RandomVariable>,
    pub probability_matrix: JointProbability<T>,
}

A multivariate structure tracking pairs or collections of intersecting random processes over a discrete matrix space.

Fields

variables: Vec<RandomVariable>

probability_matrix: JointProbability<T>

Implementations

impl RandomVector<f64>

pub fn new(x: Vec<RandomVariable>, px: JointProbability<f64>) -> CDHResult<Self>

Instantiates a new multivariate distribution vector matrix frame context.

Errors

Returns an Err if the initial input array length drops below 2 or tracking configurations mismatch inside the vectors.

pub fn get_jointprobability(&self) -> CDHResult<JointProbability<f64>>

Evaluates the joint operational probability layout grid configurations.

pub fn marginal_x2(&self, index: usize) -> CDHResult<f64>

Extracts the marginal probability distribution value matching a specific index coordinate for $X_2$.

pub fn marginal_x1(&self, index: usize) -> CDHResult<f64>

Extracts the marginal probability distribution value matching a specific index coordinate for $X_1$.

pub fn conditional_x1(&self, index_x1: usize, index_x2: usize) -> CDHResult<f64>

Computes the conditional probability allocation metric context $P(X_1 \mid X_2)$.

pub fn conditional_x2(&self, index_x1: usize, index_x2: usize) -> CDHResult<f64>

Computes the conditional probability allocation metric context $P(X_2 \mid X_1)$.

pub fn conditional_expectation_x1<H>( &self, index_x2: usize, h: H, ) -> CDHResult<f64>

where H: Fn(f64) -> f64,

Calculates the conditional expectation variable limit $E[h(X_1) \mid X_2 = x_2]$.

pub fn conditional_expectation_x2<H>( &self, index_x1: usize, h: H, ) -> CDHResult<f64>

where H: Fn(f64) -> f64,

Calculates the conditional expectation variable limit $E[h(X_2) \mid X_1 = x_1]$.

pub fn joint_expectation<F>(&self, g: F) -> CDHResult<f64>

where F: Fn(f64, f64) -> f64,

Evaluates joint expectation transformations $E[g(X, Y)]$ over the combined sample domains.

pub fn covariance(&self) -> CDHResult<f64>

2. THE COVARIANCE CALCULATOR Uses your exact definitional formula: E[(X1 - E(X1))(X2 - E(X2))] Computes the true covariance spatial scaling link metric between two random processes.

$$\text{Cov}(X,Y) = E[(X - E[X])(Y - E[Y])]$$

pub fn correlation(&self) -> CDHResult<f64>

Evaluates Pearson’s joint population correlation coefficient ($\rho$) containing zero-variance protections.

Trait Implementations

impl<T: Debug> Debug for RandomVector<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.