csm-core-lib 0.3.8

Hyperdimensional computing kernel for chaotic_semantic_memory
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


#[test]
fn test_hvec_creation() {
    let vec = HVec10240::zero();
    assert_eq!(vec.data.iter().sum::<u128>(), 0);
}

#[test]
fn test_random_generation() {
    let vec1 = HVec10240::random();
    let vec2 = HVec10240::random();
    assert_ne!(vec1.data, vec2.data);
}

#[test]
fn test_self_similarity() {
    let vec = HVec10240::random();
    let similarity = vec.cosine_similarity(&vec);
    assert!(similarity > 0.99);
}

#[test]
fn test_binding() {
    let a = HVec10240::random();
    let b = HVec10240::random();
    let bound = a.bind(&b);
    let recovered = bound.bind(&b);
    let similarity = a.cosine_similarity(&recovered);
    assert!(similarity > 0.95);
}

#[test]
fn test_serialization() {
    let v = HVec10240::random();
    let bytes = v.to_bytes();
    assert_eq!(v.data, HVec10240::from_bytes(&bytes).unwrap().data);
}

#[test]
fn test_bundle() {
    let v: Vec<_> = (0..10).map(|_| HVec10240::random()).collect();
    assert_eq!(HVec10240::bundle(&v).unwrap().data.len(), 80);
}

#[test]
fn test_permute() {
    let v = HVec10240::random();
    assert_eq!(v, v.permute(0));
    let s = v.permute(128);
    for i in 0..80 {
        assert_eq!(s.data[i], v.data[(i + 1) % 80]);
    }
}

#[test]
fn test_json_serialize_is_base64() {
    let v = HVec10240::random();
    let json = serde_json::to_string(&v).unwrap();
    // Should be a base64 string, not an array
    assert!(json.starts_with('"'), "Expected string, got: {json}");
    assert!(
        !json.starts_with('['),
        "Expected base64 string, not array: {json}"
    );
    // Verify roundtrip
    let decoded: HVec10240 = serde_json::from_str(&json).unwrap();
    assert_eq!(v.data, decoded.data);
}

#[test]
fn test_json_array_deserialize_fallback() {
    // Legacy format: array of bytes (for backward compatibility)
    let v = HVec10240::random();
    let bytes = v.to_bytes();
    let array_json: String = serde_json::to_string(&bytes).unwrap();
    let decoded: HVec10240 = serde_json::from_str(&array_json).unwrap();
    assert_eq!(v.data, decoded.data);
}

#[test]
fn test_bundle_threshold_consistency() {
    // Test sizes that span sequential scalar, sequential SIMD, and parallel SIMD boundaries
    for n in [10, 255, 256, 1000] {
        let vectors: Vec<HVec10240> = (0..n).map(|i| HVec10240::new_seeded(i as u64)).collect();

        // 1. Get reference result using naive scalar majority (parity with sequential fallback)
        let mut expected = [0u128; 80];
        let threshold = n / 2 + 1;
        for i in 0..80 {
            let mut bit_counts = [0i32; 128];
            for v in &vectors {
                let word = v.data[i];
                for j in 0..128 {
                    if (word >> j) & 1 == 1 {
                        bit_counts[j] += 1;
                    }
                }
            }
            let mut word_res = 0u128;
            for j in 0..128 {
                if bit_counts[j] >= threshold {
                    word_res |= 1u128 << j;
                }
            }
            expected[i] = word_res;
        }

        // 2. Get actual result from bundle()
        let actual = HVec10240::bundle(&vectors).expect("bundling failed");

        assert_eq!(
            actual.data, expected,
            "Bundling inconsistency at N={n} vectors"
        );
    }
}