prodef 0.2.0

A simple Rust crate for handling probability distributions, primarily intended for use with Bayesian inference.
Documentation 
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140

use crate::{
    multivariate::{
        ConstantDensity, CosineDensity, LogUniformDensity, NormalDensity, UniformDensity,
        UnivariateDensity,
    },
    pytypes::Float,
};
use pyo3::{PyResult, exceptions::PyValueError, prelude::*, types::PyType};

/// A univariate density for use in Python.
#[derive(Clone)]
#[pyclass(from_py_object, name = "Univariate")]
pub struct PyUnivariate {
    #[pyo3(get)]
    name: String,
    density: UnivariateDensity<Float>,
}

impl PyUnivariate {
    /// Return a reference to the underlying [`UnivariateDensity`].
    pub fn density(&self) -> &UnivariateDensity<Float> {
        &self.density
    }

    /// Return the parameter name.
    pub fn name(&self) -> &str {
        &self.name
    }
}

#[pymethods]
impl PyUnivariate {
    /// Create a new [`PyUnivariate`] with a constant density.
    #[classmethod]
    pub fn constant(_cls: &Bound<PyType>, name: String, value: Float) -> PyResult<Self> {
        Ok(Self {
            name,
            density: ConstantDensity::new(value).into(),
        })
    }

    /// Create a new [`PyUnivariate`] with a cosine density.
    #[classmethod]
    pub fn cosine(
        _cls: &Bound<PyType>,
        name: String,
        minimum: Float,
        maximum: Float,
    ) -> PyResult<Self> {
        match CosineDensity::new(minimum, maximum) {
            Some(value) => Ok(Self {
                name,
                density: value.into(),
            }),
            None => Err(PyValueError::new_err("invalid domain")),
        }
    }

    /// Return the domain of the univariate density.
    pub fn domain(&self) -> (Option<Float>, Option<Float>) {
        match &self.density {
            UnivariateDensity::Constant(constant) => {
                (Some(constant.constant()), Some(constant.constant()))
            }
            UnivariateDensity::Cosine(cosine) => (Some(cosine.minimum()), Some(cosine.maximum())),
            UnivariateDensity::Loguniform(log_uniform) => {
                (Some(log_uniform.minimum()), Some(log_uniform.maximum()))
            }
            UnivariateDensity::Normal(normal) => (normal.minimum(), normal.maximum()),
            UnivariateDensity::Uniform(uniform) => {
                (Some(uniform.minimum()), Some(uniform.maximum()))
            }
        }
    }

    /// Create a new [`PyUnivariate`] with a normal density.
    #[classmethod]
    #[pyo3(signature = (name, mean, std_dev, opt_minimum = None, opt_maximum = None))]
    pub fn normal(
        _cls: &Bound<PyType>,
        name: String,
        mean: Float,
        std_dev: Float,
        opt_minimum: Option<Float>,
        opt_maximum: Option<Float>,
    ) -> PyResult<Self> {
        match NormalDensity::new(mean, std_dev, opt_minimum, opt_maximum) {
            Some(value) => Ok(Self {
                name,
                density: value.into(),
            }),
            None => Err(PyValueError::new_err("invalid domain")),
        }
    }

    /// Create a new [`PyUnivariate`] with a log-uniform density.
    #[classmethod]
    pub fn loguniform(
        _cls: &Bound<PyType>,
        name: String,
        minimum: Float,
        maximum: Float,
    ) -> PyResult<Self> {
        match LogUniformDensity::new(minimum, maximum) {
            Some(value) => Ok(Self {
                name,
                density: value.into(),
            }),
            None => Err(PyValueError::new_err("invalid domain")),
        }
    }

    /// Create a new [`PyUnivariate`] with a uniform density.
    #[classmethod]
    pub fn uniform(
        _cls: &Bound<PyType>,
        name: String,
        minimum: Float,
        maximum: Float,
    ) -> PyResult<Self> {
        match UniformDensity::new(minimum, maximum) {
            Some(value) => Ok(Self {
                name,
                density: value.into(),
            }),
            None => Err(PyValueError::new_err("invalid domain")),
        }
    }

    /// Return the type name of the univariate density.
    pub fn typename(&self) -> &str {
        match &self.density {
            UnivariateDensity::Constant(_) => "constant",
            UnivariateDensity::Cosine(_) => "cosine",
            UnivariateDensity::Loguniform(_) => "loguniform",
            UnivariateDensity::Normal(_) => "normal",
            UnivariateDensity::Uniform(_) => "uniform",
        }
    }
}