causal_hub/datasets/table/gaussian/
weighted.rs

1use ndarray::prelude::*;
2
3use crate::{
4    datasets::{Dataset, GaussSample, GaussTable},
5    models::Labelled,
6    types::Labels,
7};
8
9/// A type alias for a Gaussian weighted sample.
10pub type GaussWtdSample = (GaussSample, f64);
11
12/// A multivariate Gaussian weighted dataset.
13#[derive(Clone, Debug)]
14pub struct GaussWtdTable {
15    dataset: GaussTable,
16    weights: Array1<f64>,
17}
18
19impl Labelled for GaussWtdTable {
20    #[inline]
21    fn labels(&self) -> &Labels {
22        self.dataset.labels()
23    }
24}
25
26impl GaussWtdTable {
27    /// Creates a new Gaussian weighted dataset.
28    ///
29    /// # Arguments
30    ///
31    /// * `dataset` - The Gaussian dataset.
32    /// * `weights` - The weights of the samples.
33    ///
34    /// # Panics
35    ///
36    /// * Panics if the number of weights is not equal to the number of samples.
37    /// * Panics if any weight is not in the range [0, 1].
38    ///
39    /// # Returns
40    ///
41    /// A new Gaussian weighted dataset instance.
42    ///
43    pub fn new(dataset: GaussTable, weights: Array1<f64>) -> Self {
44        assert_eq!(
45            dataset.values().nrows(),
46            weights.len(),
47            "The number of weights must be equal to the number of samples."
48        );
49        assert!(
50            weights.iter().all(|&w| (0.0..=1.0).contains(&w)),
51            "All weights must be in the range [0, 1]."
52        );
53
54        Self { dataset, weights }
55    }
56
57    /// Returns the weights of the samples in the Gaussian distribution.
58    ///
59    /// # Returns
60    ///
61    /// A reference to the array of weights.
62    ///
63    #[inline]
64    pub const fn weights(&self) -> &Array1<f64> {
65        &self.weights
66    }
67}
68
69impl Dataset for GaussWtdTable {
70    type Values = GaussTable;
71
72    #[inline]
73    fn values(&self) -> &Self::Values {
74        &self.dataset
75    }
76
77    #[inline]
78    fn sample_size(&self) -> f64 {
79        self.weights.sum()
80    }
81}
causal_hub/datasets/table/gaussian/weighted.rs

causal_hub/datasets/table/gaussian/
weighted.rs
