Struct DataSet 

pub struct DataSet<T>(pub Vec<T>);

A high-performance structure encapsulating a linear array of values designed for descriptive and bivariate statistical data mining.

Tuple Fields

0: Vec<T>

Implementations

impl DataSet<f64>

pub fn new(data: Vec<f64>) -> Self

Instantiates a new data frame context around a contiguous memory buffer.

Example

use scicdh::statistics::DataSet;
let ds = DataSet::new(vec![1.0, 2.0, 3.0]);

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

Computes the arithmetic mean ($\mu$) of the sample space.

$$\mu = \frac{\sum_{i=1}^n x_i}{n}$$

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

Computes the sample median using a non-destructive 0-indexed sorting pipeline. Handles both odd and even dataset lengths correctly.

pub fn mode(&self) -> CDHResult<Option<f64>>

Computes the statistical mode of the dataset in linear time $\mathcal{O}(n)$.

Uses low-level float bit-packing (to_bits()) to completely bypass hashing restrictions on f64. Returns Ok(None) if the distribution is completely uniform (no single clear majority frequency exists).

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

Calculates the algebraic range (Span) between the maximum and minimum parameters.

$$\text{Range} = X_{\text{max}} - X_{\text{min}}$$

pub fn has(&self, value: f64, other: Option<&[f64]>) -> CDHResult<bool>

Scans a target container or the internal dataset structure to check for the existence of a precise value.

Fixed Bug: Bypassed memory copying vulnerabilities from previous design.

pub fn sample_variation_x2(&self) -> CDHResult<f64>

Computes the sum of squared deviations for the primary variable ($SS_{xx}$).

$$SS_{xx} = \sum x^2 - \frac{(\sum x)^2}{n}$$

pub fn sample_variation_y2(&self, y: &[f64]) -> CDHResult<f64>

Computes the sum of squared deviations for an independent reference slice ($SS_{yy}$).

$$SS_{yy} = \sum y^2 - \frac{(\sum y)^2}{n}$$

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

Computes the unbiased sample variance ($s^2$) leveraging Bessel’s correction factor.

$$s^2 = \frac{SS_{xx}}{n - 1}$$

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

Computes the unbiased sample standard deviation ($s$).

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

Evaluates the dimensionless sample coefficient of variation ($CV$).

$$CV = \frac{s}{\bar{x}}$$

pub fn kth_central_moment(&self, k: f64) -> CDHResult<f64>

Generates the $k$-th mathematical population central moment ($m_k$).

$$\mu_k = \frac{\sum (x_i - \bar{x})^k}{n}$$

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

Measures the skewness ($\beta_3$), tracking distribution symmetry.

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

Computes excess kurtosis ($\beta_4$), tracking peak sharpness relative to standard normal shapes.

pub fn sample_variation_xy(&self, y: &[f64]) -> CDHResult<f64>

Computes the cross-variation sum of joint distribution observations ($SS_{xy}$).

$$SS_{xy} = \sum xy - \frac{(\sum x)(\sum y)}{n}$$

pub fn pearson_correlation_coeff(&self, y: &[f64]) -> CDHResult<f64>

Evaluates Pearson’s Product-Moment Correlation Coefficient ($r_{xy}$). Hardened against the textbook typo by using $SS_{yy}$ space arrays.

pub fn into_set(&self) -> CDHResult<Vec<f64>>

Deduplicates entries from the dataset to form a unique element mathematical set pool.

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

Calculates pure population variance ($\sigma^2$) relative to the exact population mean.

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

Computes the population standard deviation ($\sigma$).

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

Evaluates the peak extreme value bound present inside the dataset.

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

Evaluates the lowest value parameter bound present inside the dataset.

pub fn info(&self) -> CDHResult<()>

Dumps clean debug statements displaying the full metrics profile of the active dataset.