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

use std::cmp;

use crate::base::allocator::Allocator;
use crate::base::default_allocator::DefaultAllocator;
use crate::base::dimension::{Const, Dim, DimAdd, DimDiff, DimSub, DimSum};
use crate::storage::Storage;
use crate::{zero, OVector, RealField, Vector, U1};

impl<T: RealField, D1: Dim, S1: Storage<T, D1>> Vector<T, D1, S1> {
    /// Returns the convolution of the target vector and a kernel.
    ///
    /// # Arguments
    ///
    /// * `kernel` - A Vector with size > 0
    ///
    /// # Errors
    /// Inputs must satisfy `vector.len() >= kernel.len() > 0`.
    ///
    pub fn convolve_full<D2, S2>(
        &self,
        kernel: Vector<T, D2, S2>,
    ) -> OVector<T, DimDiff<DimSum<D1, D2>, U1>>
    where
        D1: DimAdd<D2>,
        D2: DimAdd<D1, Output = DimSum<D1, D2>>,
        DimSum<D1, D2>: DimSub<U1>,
        S2: Storage<T, D2>,
        DefaultAllocator: Allocator<DimDiff<DimSum<D1, D2>, U1>>,
    {
        let vec = self.len();
        let ker = kernel.len();

        if ker == 0 || ker > vec {
            panic!("convolve_full expects `self.len() >= kernel.len() > 0`, received {} and {} respectively.", vec, ker);
        }

        let result_len = self
            .data
            .shape()
            .0
            .add(kernel.shape_generic().0)
            .sub(Const::<1>);
        let mut conv = OVector::zeros_generic(result_len, Const::<1>);

        for i in 0..(vec + ker - 1) {
            let u_i = if i > vec { i - ker } else { 0 };
            let u_f = cmp::min(i, vec - 1);

            if u_i == u_f {
                conv[i] += self[u_i].clone() * kernel[i - u_i].clone();
            } else {
                for u in u_i..(u_f + 1) {
                    if i - u < ker {
                        conv[i] += self[u].clone() * kernel[i - u].clone();
                    }
                }
            }
        }
        conv
    }
    /// Returns the convolution of the target vector and a kernel.
    ///
    /// The output convolution consists only of those elements that do not rely on the zero-padding.
    /// # Arguments
    ///
    /// * `kernel` - A Vector with size > 0
    ///
    ///
    /// # Errors
    /// Inputs must satisfy `self.len() >= kernel.len() > 0`.
    ///
    pub fn convolve_valid<D2, S2>(
        &self,
        kernel: Vector<T, D2, S2>,
    ) -> OVector<T, DimDiff<DimSum<D1, U1>, D2>>
    where
        D1: DimAdd<U1>,
        D2: Dim,
        DimSum<D1, U1>: DimSub<D2>,
        S2: Storage<T, D2>,
        DefaultAllocator: Allocator<DimDiff<DimSum<D1, U1>, D2>>,
    {
        let vec = self.len();
        let ker = kernel.len();

        if ker == 0 || ker > vec {
            panic!("convolve_valid expects `self.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
        }

        let result_len = self
            .data
            .shape()
            .0
            .add(Const::<1>)
            .sub(kernel.shape_generic().0);
        let mut conv = OVector::zeros_generic(result_len, Const::<1>);

        for i in 0..(vec - ker + 1) {
            for j in 0..ker {
                conv[i] += self[i + j].clone() * kernel[ker - j - 1].clone();
            }
        }
        conv
    }

    /// Returns the convolution of the target vector and a kernel.
    ///
    /// The output convolution is the same size as vector, centered with respect to the ‘full’ output.
    /// # Arguments
    ///
    /// * `kernel` - A Vector with size > 0
    ///
    /// # Errors
    /// Inputs must satisfy `self.len() >= kernel.len() > 0`.
    #[must_use]
    pub fn convolve_same<D2, S2>(&self, kernel: Vector<T, D2, S2>) -> OVector<T, D1>
    where
        D2: Dim,
        S2: Storage<T, D2>,
        DefaultAllocator: Allocator<D1>,
    {
        let vec = self.len();
        let ker = kernel.len();

        if ker == 0 || ker > vec {
            panic!("convolve_same expects `self.len() >= kernel.len() > 0`, received {} and {} respectively.",vec,ker);
        }

        let mut conv = OVector::zeros_generic(self.shape_generic().0, Const::<1>);

        for i in 0..vec {
            for j in 0..ker {
                let val = if i + j < 1 || i + j >= vec + 1 {
                    zero::<T>()
                } else {
                    self[i + j - 1].clone()
                };
                conv[i] += val * kernel[ker - j - 1].clone();
            }
        }

        conv
    }
}