cnetworks 0.7.0

A set of tools for creating and manipulating complex networks.
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

//! Test information source location.

use cnetworks::*;

fn setup_net(c: f64, seed: &str) -> Network {
    let mut net = Network::with_seed(10, Model::None, Weight::Constant { c }, seed);
    for (node, targets) in [
        (0, vec![1, 3, 4]),
        (1, vec![0, 2, 4, 5, 9]),
        (2, vec![1, 9, 8]),
        (3, vec![0, 4, 7]),
        (4, vec![0, 3, 6, 7, 5, 1]),
        (5, vec![1, 4, 7, 8, 9]),
        (6, vec![3, 7, 4]),
        (7, vec![4, 3, 6, 5, 8]),
        (8, vec![7, 5, 2]),
        (9, vec![1, 5, 2]),
    ] {
        for target in targets {
            net.link(node, target).unwrap();
        }
    }
    net
}

#[test]
fn test_pearson() {
    for c in [0.5, 0.75, 1.0] {
        let net = setup_net(c, "test seed");
        let mut observers = si::spread(&net, 5, 10).unwrap();
        observers.keep(&[0, 1, 2, 3]);
        let result = locate::pearson(&net, &observers).unwrap();
        let (most_likely, _score) = result.top().unwrap();
        dbg!(result);
        assert!(most_likely == 5);
    }
}

#[test]
fn test_pinto() {
    for c in [0.5, 0.75, 1.0] {
        let net = setup_net(c, "test seed");
        let mut observers = si::spread(&net, 5, 10).unwrap();
        observers.keep(&[0, 1, 2, 3]);
        let result = locate::lptv(&net, &observers).unwrap();
        let (most_likely, _score) = result.top().unwrap();
        assert!(most_likely == 5);
    }
}

#[test]
#[ignore]
fn test_pearson_stats() {
    let mut acc = 0.0;
    let size = 100.0;
    let p = 8.0 / size;
    for _ in 0..5000 {
        let net = Network::new(
            size as usize,
            Model::ER { p, whole: true },
            Weight::default(),
        );
        let mut observers = si::spread(&net, 5, 10).unwrap();
        let result = locate::pearson(
            &net,
            observers.keep_portion(0.1, &mut *net.rng_lock().unwrap()),
        )
        .unwrap();
        acc += result.precision();
    }
    acc /= 5000.0;
    assert!(acc > 0.95);
}

#[test]
#[ignore]
fn test_pinto_stats() {
    let mut acc = 0.0;
    let size = 100.0;
    let p = 8.0 / size;
    for _ in 0..5000 {
        let net = Network::new(
            size as usize,
            Model::ER { p, whole: true },
            Weight::default(),
        );
        let mut observers = si::spread(&net, 5, 10).unwrap();
        let result = locate::lptv(
            &net,
            observers.keep_portion(0.1, &mut *net.rng_lock().unwrap()),
        )
        .unwrap();
        acc += result.precision();
    }
    acc /= 5000.0;
    dbg!(acc);
    assert!(acc > 0.95);
}