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
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156

//! Library for **one-dimensional data classification** and **simple statistics**,
//! especially methods used for cartographic purposes such as "quantiles breaks"
//! or "Jenks natural breaks".
//!
//! This library allows to computed break values according to a few methods (listed in
//! the [`Classification`] Enum).
//! Theses break values can be computed by creating a new [`BoundsInfo`] struct.
//!
//! If the input values are already sorted and you don't care about error checking
//! you can directly use the `get_{jenks|quantiles|etc...}_breaks` functions.
//!
//! This library also provide a few basic statistical functionnalities, allowing to
//! compute mean value, kurtosis value, standard deviation, variance, root of mean square, etc.
//!
//! [`Classification`]: enum.Classification.html
//! [`BoundsInfo`]: struct.BoundsInfo.html

extern crate num_traits;

/// Basic statistical functionnalities: mean, standard deviation, kurtosis, variance, etc.
pub mod stats;

mod jenks;
mod classif;

pub use classif::{Classification, BoundsInfo};
pub use jenks::get_jenks_breaks;
pub use classif::{get_quantiles, get_equal_interval, get_head_tail_breaks, get_tail_head_breaks,
                  get_arithmetic_breaks};

#[cfg(test)]
mod tests {
    use ::*;
    fn get_test_values() -> [f64; 76] {
        [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0,
         3.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 12.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,
         10.0, 11.0, 5.0, 6.0, 7.0, 6.0, 5.0, 6.0, 7.0, 8.0, 8.0, 9.0, 8.0, 7.0, 6.0, 7.0, 8.0,
         9.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 3.0,
         2.0, 2.0, 2.0, 1.0, 1.0, 1.0]
    }
    #[test]
    fn test_head_tail() {
        let mut values = get_test_values();
        let b = BoundsInfo::new(4, &values, Classification::HeadTail).unwrap();
        assert_eq!(b.bounds.as_slice(), [1., 7., 9.090909090909092, 11., 12.]);
        values.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let breaks = get_head_tail_breaks(&mut values);
        assert_eq!(breaks.as_slice(), [1., 7., 9.090909090909092, 11., 12.]);
    }

    #[test]
    fn test_jenks_breaks() {
        let mut values = get_test_values();
        let b = BoundsInfo::new(5, &values, Classification::JenksNaturalBreaks).unwrap();
        assert_eq!(b.bounds.as_slice(), [1.0, 2.0, 4.0, 7.0, 9.0, 12.0]);
        values.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let breaks = get_jenks_breaks(&values, 5);
        assert_eq!(breaks.as_slice(), [1.0, 2.0, 4.0, 7.0, 9.0, 12.0]);
    }

    #[test]
    fn test_quantiles_breaks() {
        let mut values = get_test_values();
        let b = BoundsInfo::new(4, &values, Classification::Quantiles).unwrap();
        assert_eq!(b.bounds.as_slice(), [1., 2., 3., 6., 12.]);
        values.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let breaks = get_quantiles(&values, 4);
        assert_eq!(breaks.as_slice(), [1., 2., 3., 6., 12.]);
    }

    #[test]
    fn test_equal_interval_breaks() {
        let mut values = get_test_values();
        let b = BoundsInfo::new(4, &values, Classification::EqualInterval).unwrap();
        assert_eq!(b.bounds.as_slice(), [1., 3.75, 6.5, 9.25, 12.]);
        values.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let breaks = get_equal_interval(&values, 4);
        assert_eq!(breaks.as_slice(), [1., 3.75, 6.5, 9.25, 12.]);
    }

    #[test]
    fn test_arithmetic_breaks() {
        let mut values = get_test_values();
        let b = BoundsInfo::new(6, &values, Classification::Arithmetic).unwrap();
        assert_eq!(b.bounds.as_slice(),
                   [1.,
                    1.5238095238095237,
                    2.571428571428571,
                    4.142857142857142,
                    6.238095238095237,
                    8.857142857142856,
                    12.]);
        values.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let breaks = get_arithmetic_breaks(&values, 6);
        assert_eq!(breaks.as_slice(),
                   [1.,
                    1.5238095238095237,
                    2.571428571428571,
                    4.142857142857142,
                    6.238095238095237,
                    8.857142857142856,
                    12.]);
    }

    #[test]
    fn test_get_class_index() {
        let values = get_test_values();
        let b = BoundsInfo::new(4, &values, Classification::EqualInterval).unwrap();
        assert_eq!(b.bounds.as_slice(), [1., 3.75, 6.5, 9.25, 12.]);
        // 0.1 is below the minimum so it belongs in no class, None is expected:
        assert_eq!(b.get_class_index(0.1), None);
        // 2.0 is between 1.0 and 3.75 so 2.0 should belong in the first class, with index 0:
        assert_eq!(b.get_class_index(2.), Some(0));
        // And so on...
        assert_eq!(b.get_class_index(4.), Some(1));
        assert_eq!(b.get_class_index(7.), Some(2));
        assert_eq!(b.get_class_index(10.), Some(3));
        // 15.0 is larger than the maximum so it belongs in no class, None is expected:
        assert_eq!(b.get_class_index(15.0), None);
    }

    #[test]
    fn test_kurtosis() {
        let values = get_test_values();
        let kv = stats::kurtosis(&values);
        assert_eq!(kv, 0.042107329018970074);
    }

    #[test]
    fn test_variance() {
        let values = get_test_values();
        let r = stats::sum_pow_deviations(&values, 2);
        assert_eq!(r, 608.631578947369);
        let v = stats::variance(&values);
        assert_eq!(v, 8.008310249307486);
    }

    #[test]
    fn test_root_mean_square() {
        let values = [-1., 1., -1., 1.];
        let v = stats::rootmeansquare(&values);
        assert_eq!(v, 1.);
    }

    #[test]
    fn test_median() {
        // Number of values is odd:
        let values = [1., 3., 3., 6., 7., 8., 9.];
        let median = stats::median(&values);
        assert_eq!(median, 6.);
        // Number of values is even:
        let values = [1., 2., 3., 4., 5., 6., 8., 9.];
        let median = stats::median(&values);
        assert_eq!(median, 4.5);
    }
}