linfa-kernel 0.8.1

Kernel methods for non-linear algorithms
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
141
142
143
144
145
146

use linfa::Float;
use ndarray::prelude::*;
use ndarray::Data;
use sprs::{CsMat, CsMatView};
use std::ops::Mul;

/// Specifies the methods an inner matrix of a kernel must
/// be able to provide
pub trait Inner {
    type Elem: Float;

    fn dot(&self, rhs: &ArrayView2<Self::Elem>) -> Array2<Self::Elem>;
    fn sum(&self) -> Array1<Self::Elem>;
    fn size(&self) -> usize;
    fn column(&self, i: usize) -> Vec<Self::Elem>;
    fn to_upper_triangle(&self) -> Vec<Self::Elem>;
    fn is_dense(&self) -> bool;
    fn diagonal(&self) -> Array1<Self::Elem>;
}

/// Allows a kernel to have either a dense or a sparse inner
/// matrix in a way that is transparent to the user
#[derive(Debug, Clone, PartialEq)]
pub enum KernelInner<K1: Inner, K2: Inner> {
    Dense(K1),
    Sparse(K2),
}

impl<F: Float, D: Data<Elem = F>> Inner for ArrayBase<D, Ix2> {
    type Elem = F;

    fn dot(&self, rhs: &ArrayView2<F>) -> Array2<F> {
        self.dot(rhs)
    }
    fn sum(&self) -> Array1<F> {
        self.sum_axis(Axis(1))
    }
    fn size(&self) -> usize {
        self.ncols()
    }
    fn column(&self, i: usize) -> Vec<F> {
        self.column(i).to_vec()
    }
    fn to_upper_triangle(&self) -> Vec<F> {
        self.indexed_iter()
            .filter(|((row, col), _)| col > row)
            .map(|(_, val)| *val)
            .collect()
    }

    fn diagonal(&self) -> Array1<F> {
        self.diag().to_owned()
    }

    fn is_dense(&self) -> bool {
        true
    }
}

impl<F: Float> Inner for CsMat<F> {
    type Elem = F;

    fn dot(&self, rhs: &ArrayView2<F>) -> Array2<F> {
        self.mul(rhs)
    }
    fn sum(&self) -> Array1<F> {
        let mut sum = Array1::zeros(self.cols());
        for (val, i) in self.iter() {
            let (_, col) = i;
            sum[col] += *val;
        }

        sum
    }
    fn size(&self) -> usize {
        self.cols()
    }
    fn column(&self, i: usize) -> Vec<F> {
        (0..self.size())
            .map(|j| *self.get(j, i).unwrap_or(&F::neg_zero()))
            .collect::<Vec<_>>()
    }
    fn to_upper_triangle(&self) -> Vec<F> {
        let mat = self.to_dense();
        mat.indexed_iter()
            .filter(|((row, col), _)| col > row)
            .map(|(_, val)| *val)
            .collect()
    }

    fn diagonal(&self) -> Array1<F> {
        let diag_sprs = self.diag();
        let mut diag = Array1::zeros(diag_sprs.dim());
        for (sparse_i, sparse_elem) in diag_sprs.iter() {
            diag[sparse_i] = *sparse_elem;
        }
        diag
    }

    fn is_dense(&self) -> bool {
        false
    }
}

impl<F: Float> Inner for CsMatView<'_, F> {
    type Elem = F;

    fn dot(&self, rhs: &ArrayView2<F>) -> Array2<F> {
        self.mul(rhs)
    }
    fn sum(&self) -> Array1<F> {
        let mut sum = Array1::zeros(self.cols());
        for (val, i) in self.iter() {
            let (_, col) = i;
            sum[col] += *val;
        }

        sum
    }
    fn size(&self) -> usize {
        self.cols()
    }
    fn column(&self, i: usize) -> Vec<F> {
        (0..self.size())
            .map(|j| *self.get(j, i).unwrap_or(&F::neg_zero()))
            .collect::<Vec<_>>()
    }
    fn to_upper_triangle(&self) -> Vec<F> {
        let mat = self.to_dense();
        mat.indexed_iter()
            .filter(|((row, col), _)| col > row)
            .map(|(_, val)| *val)
            .collect()
    }
    fn diagonal(&self) -> Array1<F> {
        let diag_sprs = self.diag();
        let mut diag = Array1::zeros(diag_sprs.dim());
        for (sparse_i, sparse_elem) in diag_sprs.iter() {
            diag[sparse_i] = *sparse_elem;
        }
        diag
    }
    fn is_dense(&self) -> bool {
        false
    }
}