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
157
158
use super::errors::BinNotFound;
use super::grid::Grid;
use ndarray::prelude::*;
use ndarray::Data;

/// Histogram data structure.
pub struct Histogram<A: Ord> {
    counts: ArrayD<usize>,
    grid: Grid<A>,
}

impl<A: Ord> Histogram<A> {
    /// Returns a new instance of Histogram given a [`Grid`].
    ///
    /// [`Grid`]: struct.Grid.html
    pub fn new(grid: Grid<A>) -> Self {
        let counts = ArrayD::zeros(grid.shape());
        Histogram { counts, grid }
    }

    /// Adds a single observation to the histogram.
    ///
    /// **Panics** if dimensions do not match: `self.ndim() != observation.len()`.
    ///
    /// # Example:
    /// ```
    /// use ndarray::array;
    /// use ndarray_stats::histogram::{Edges, Bins, Histogram, Grid};
    /// use noisy_float::types::n64;
    ///
    /// let edges = Edges::from(vec![n64(-1.), n64(0.), n64(1.)]);
    /// let bins = Bins::new(edges);
    /// let square_grid = Grid::from(vec![bins.clone(), bins.clone()]);
    /// let mut histogram = Histogram::new(square_grid);
    ///
    /// let observation = array![n64(0.5), n64(0.6)];
    ///
    /// histogram.add_observation(&observation)?;
    ///
    /// let histogram_matrix = histogram.counts();
    /// let expected = array![
    ///     [0, 0],
    ///     [0, 1],
    /// ];
    /// assert_eq!(histogram_matrix, expected.into_dyn());
    /// # Ok::<(), Box<std::error::Error>>(())
    /// ```
    pub fn add_observation<S>(&mut self, observation: &ArrayBase<S, Ix1>) -> Result<(), BinNotFound>
    where
        S: Data<Elem = A>,
    {
        match self.grid.index_of(observation) {
            Some(bin_index) => {
                self.counts[&*bin_index] += 1;
                Ok(())
            }
            None => Err(BinNotFound),
        }
    }

    /// Returns the number of dimensions of the space the histogram is covering.
    pub fn ndim(&self) -> usize {
        debug_assert_eq!(self.counts.ndim(), self.grid.ndim());
        self.counts.ndim()
    }

    /// Borrows a view on the histogram counts matrix.
    pub fn counts(&self) -> ArrayViewD<'_, usize> {
        self.counts.view()
    }

    /// Borrows an immutable reference to the histogram grid.
    pub fn grid(&self) -> &Grid<A> {
        &self.grid
    }
}

/// Extension trait for `ArrayBase` providing methods to compute histograms.
pub trait HistogramExt<A, S>
where
    S: Data<Elem = A>,
{
    /// Returns the [histogram](https://en.wikipedia.org/wiki/Histogram)
    /// for a 2-dimensional array of points `M`.
    ///
    /// Let `(n, d)` be the shape of `M`:
    /// - `n` is the number of points;
    /// - `d` is the number of dimensions of the space those points belong to.
    /// It follows that every column in `M` is a `d`-dimensional point.
    ///
    /// For example: a (3, 4) matrix `M` is a collection of 3 points in a
    /// 4-dimensional space.
    ///
    /// Important: points outside the grid are ignored!
    ///
    /// **Panics** if `d` is different from `grid.ndim()`.
    ///
    /// # Example:
    ///
    /// ```
    /// use ndarray::array;
    /// use ndarray_stats::{
    ///     HistogramExt,
    ///     histogram::{
    ///         Histogram, Grid, GridBuilder,
    ///         Edges, Bins,
    ///         strategies::Sqrt},
    /// };
    /// use noisy_float::types::{N64, n64};
    ///
    /// let observations = array![
    ///     [n64(1.), n64(0.5)],
    ///     [n64(-0.5), n64(1.)],
    ///     [n64(-1.), n64(-0.5)],
    ///     [n64(0.5), n64(-1.)]
    /// ];
    /// let grid = GridBuilder::<Sqrt<N64>>::from_array(&observations).unwrap().build();
    /// let expected_grid = Grid::from(
    ///     vec![
    ///         Bins::new(Edges::from(vec![n64(-1.), n64(0.), n64(1.), n64(2.)])),
    ///         Bins::new(Edges::from(vec![n64(-1.), n64(0.), n64(1.), n64(2.)])),
    ///     ]
    /// );
    /// assert_eq!(grid, expected_grid);
    ///
    /// let histogram = observations.histogram(grid);
    ///
    /// let histogram_matrix = histogram.counts();
    /// // Bins are left inclusive, right exclusive!
    /// let expected = array![
    ///     [1, 0, 1],
    ///     [1, 0, 0],
    ///     [0, 1, 0],
    /// ];
    /// assert_eq!(histogram_matrix, expected.into_dyn());
    /// ```
    fn histogram(&self, grid: Grid<A>) -> Histogram<A>
    where
        A: Ord;

    private_decl! {}
}

impl<A, S> HistogramExt<A, S> for ArrayBase<S, Ix2>
where
    S: Data<Elem = A>,
    A: Ord,
{
    fn histogram(&self, grid: Grid<A>) -> Histogram<A> {
        let mut histogram = Histogram::new(grid);
        for point in self.axis_iter(Axis(0)) {
            let _ = histogram.add_observation(&point);
        }
        histogram
    }

    private_impl! {}
}