zyx 0.15.6

Zyx machine learning library
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

// Copyright (C) 2025 zk4x
// SPDX-License-Identifier: LGPL-3.0-only

use zyx::{Scalar, Tensor, ZyxError};

#[test]
fn add() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    let z = x + y;
    let dataz: Vec<f32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        assert_eq!(x + y, z);
    }
    Ok(())
}

#[test]
fn add_2() -> Result<(), ZyxError> {
    let x = Tensor::from([2i32, 3, 5, 1, 6]);
    let y = Tensor::from([7i32, 2, 5, 1, 2]);
    let z = &x + y + &x + &x;
    assert_eq!(z, [13i32, 11, 20, 4, 20]);
    Ok(())
}

#[test]
fn sub() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    let z = x - y;
    //println!("{z}");
    let dataz: Vec<f32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        assert_eq!(x - y, z);
    }
    Ok(())
}

#[test]
fn mul() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    let z = x * y;
    let dataz: Vec<f32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        assert_eq!(x * y, z);
    }
    Ok(())
}

#[test]
fn div() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    let z = x / y;
    let dataz: Vec<f32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        assert!((x / y - z).abs() < 0.00001);
    }
    Ok(())
}

#[test]
fn pow() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    let z = x.pow(y)?;
    let dataz: Vec<f32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        //assert!((x.pow(y) - z).abs() < 0.00001);
        let x = x.pow(y);
        //println!("{x}, {z}");
        assert!(x.is_equal(z));
    }
    Ok(())
}

#[test]
fn maximum() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    let z = x.maximum(y)?;
    let dataz: Vec<f32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        assert_eq!(x.max(y), z);
    }
    Ok(())
}

#[test]
fn cmplt() -> Result<(), ZyxError> {
    let datax: [f32; 10] = [-3.285, 0.001, 1.780, 5.675, -8.521, -0.456, 1.215, -3.474, -4.128, -7.657];
    let datay: [f32; 10] = [2.772, -8.327, 1.945, 9.286, 3.989, 8.105, -5.307, 2.865, 3.106, 3.111];
    let x = Tensor::from(datax);
    let y = Tensor::from(datay);
    // We cast here since not all backends support bool dtype buffers.
    let z = x.cmplt(y)?.cast(zyx::DType::U32);
    let dataz: Vec<u32> = z.try_into()?;
    for ((x, y), z) in datax.iter().zip(datay).zip(dataz) {
        assert_eq!(x.cmplt(y) as u32, z);
    }
    Ok(())
}