rmcmc 0.1.0

RMCMC is a Bayesian statistics toolkit with implements, among other algorithms, Markov-Chain-Monte-Carlo.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

extern crate rv;
use lens::*;
use rand::Rng;
use rv::traits::Rv;
use std::fmt;

pub struct Parameter<D, T, S>
where
    D: Rv<T> + Clone,
{
    // Name of parameter (must be unique)
    pub name: String,
    // Prior distribution
    pub prior: D,
    // Lens to update value
    pub lens: Lens<T, S>,
}

impl<D, T, S> fmt::Debug for Parameter<D, T, S>
where
    D: Rv<T> + Clone,
{
    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
        write!(f, "Parameter {{ name: {} }}", self.name)
    }
}

impl<D, T, S> Clone for Parameter<D, T, S>
where
    D: Rv<T> + Clone,
{
    fn clone(&self) -> Parameter<D, T, S> {
        Parameter {
            name: self.name.clone(),
            prior: self.prior.clone(),
            lens: self.lens.clone(),
        }
    }
}

/// Parameter Mapping
impl<D, T, S> Parameter<D, T, S>
where
    D: Rv<T> + Clone,
{
    pub fn new(name: String, prior: D, lens: Lens<T, S>) -> Self {
        Parameter {
            name,
            prior,
            lens,
        }
    }

    /// Create a new version of the struct with a randomly drawn value from the value's
    /// distribution.
    pub fn draw<R: Rng>(&self, s: &S, rng: &mut R) -> S {
        let new_value = self.prior.draw(rng);
        self.lens.set(s, new_value)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use rv::dist::Beta;

    #[test]
    fn new() {
        struct Foo {
            bar: f64,
        }

        let p = Parameter::new(
            "test".to_string(),
            Beta::jeffreys(),
            make_lens!(Foo, f64, bar),
        );
        assert!(p.name == "test".to_string());
    }
}