rotta_rs 0.0.5

a Deep Learning library with rust language
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

use crate::rotta_rs_module::arrayy::*;

pub fn sum_axis_arr(arr: &Arrayy, dims: &[i32]) -> Arrayy {
    let mut dims = dims
        .into_iter()
        .enumerate()
        .map(|(_, d)| {
            if *d >= 0 { *d as usize } else { ((arr.shape.len() as i32) + d) as usize }
        })
        .collect::<Vec<usize>>();
    dims.sort();

    let mut value = vec![];
    let mut _shape = arr.shape.clone();
    for d in &dims {
        let shape = &_shape;
        // sum axis negative indexing

        if *d >= shape.len() {
            panic!("array is {} dimension but will sum in dimension {}", shape.len(), d);
        }

        let len_items = (&shape[d + 1..]).multiple_sum();
        let count_loop = (&shape[..d + 1]).multiple_sum();
        let mut vec = vec![];
        let mut counter = 1;
        let mut vector = vec![];

        for i in 0..count_loop {
            let start = i * len_items;
            let stop = start + len_items;
            let slice = if value.is_empty() {
                &arr.value[start..stop]
            } else {
                // dbg!(&value);

                &value[start..stop]
            };

            if vector.is_empty() {
                vector.extend_from_slice(&slice);
            } else {
                let sum = vector
                    .iter()
                    .enumerate()
                    .map(|(i, v)| { v + slice[i] })
                    .collect::<Vec<f64>>();
                vector = sum;
            }

            if counter >= shape[*d] {
                counter = 1;

                vec.extend_from_slice(vector.as_slice());
                vector.clear();
            } else {
                counter += 1;
            }
        }

        _shape[*d] = 1;
        value = vec;
    }

    let mut new_shape = _shape;
    let mut i = 0;
    for d in dims {
        new_shape.remove(d - i);
        i += 1;
    }

    Arrayy::from_vector(new_shape, value)
}

pub fn sum_axis_keep_dim_arr(arr: &Arrayy, dims: &[i32]) -> Arrayy {
    let mut dims = dims
        .into_iter()
        .enumerate()
        .map(|(_, d)| {
            if *d >= 0 { *d as usize } else { ((arr.shape.len() as i32) + d) as usize }
        })
        .collect::<Vec<usize>>();
    dims.sort();

    let mut value = vec![];
    let mut _shape = arr.shape.clone();
    for d in &dims {
        let shape = &_shape;
        // sum axis negative indexing

        if *d >= shape.len() {
            panic!("array is {} dimension but will sum in dimension {}", shape.len(), d);
        }

        let len_items = (&shape[d + 1..]).multiple_sum();
        let count_loop = (&shape[..d + 1]).multiple_sum();
        let mut vec = vec![];
        let mut counter = 1;
        let mut vector = vec![];

        for i in 0..count_loop {
            let start = i * len_items;
            let stop = start + len_items;
            let slice = if value.is_empty() {
                &arr.value[start..stop]
            } else {
                &value[start..stop]
            };

            if vector.is_empty() {
                vector.extend_from_slice(&slice);
            } else {
                let sum = vector
                    .iter()
                    .enumerate()
                    .map(|(i, v)| { v + slice[i] })
                    .collect::<Vec<f64>>();
                vector = sum;
            }

            if counter >= shape[*d] {
                counter = 1;

                vec.extend_from_slice(vector.as_slice());
                vector.clear();
            } else {
                counter += 1;
            }
        }

        _shape[*d] = 1;
        value = vec;
    }

    Arrayy::from_vector(_shape, value)
}