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#[allow(unused)]
mod metrics;
#[cfg(test)]
#[cfg(any(target_arch = "x86", target_arch = "x86_64"))]
mod tests {
use quantization::encoded_vectors::{DistanceType, EncodedVectors, VectorParameters};
use quantization::encoded_vectors_u8::EncodedVectorsU8;
use rand::{Rng, SeedableRng};
use crate::metrics::{dot_similarity, l1_similarity, l2_similarity};
#[test]
fn test_dot_sse() {
let vectors_count = 129;
let vector_dim = 65;
let error = vector_dim as f32 * 0.1;
//let mut rng = rand::thread_rng();
let mut rng = rand::rngs::StdRng::seed_from_u64(42);
let mut vector_data: Vec<Vec<f32>> = Vec::new();
for _ in 0..vectors_count {
let vector: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
vector_data.push(vector);
}
let query: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
let encoded = EncodedVectorsU8::encode(
vector_data.iter(),
Vec::<u8>::new(),
&VectorParameters {
dim: vector_dim,
count: vectors_count,
distance_type: DistanceType::Dot,
invert: false,
},
None,
|| false,
)
.unwrap();
let query_u8 = encoded.encode_query(&query);
for (index, vector) in vector_data.iter().enumerate() {
let score = encoded.score_point_sse(&query_u8, index as u32);
let orginal_score = dot_similarity(&query, vector);
assert!((score - orginal_score).abs() < error);
}
}
#[test]
fn test_l2_sse() {
let vectors_count = 129;
let vector_dim = 65;
let error = vector_dim as f32 * 0.1;
//let mut rng = rand::thread_rng();
let mut rng = rand::rngs::StdRng::seed_from_u64(42);
let mut vector_data: Vec<Vec<f32>> = Vec::new();
for _ in 0..vectors_count {
let vector: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
vector_data.push(vector);
}
let query: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
let encoded = EncodedVectorsU8::encode(
vector_data.iter(),
Vec::<u8>::new(),
&VectorParameters {
dim: vector_dim,
count: vectors_count,
distance_type: DistanceType::L2,
invert: false,
},
None,
|| false,
)
.unwrap();
let query_u8 = encoded.encode_query(&query);
for (index, vector) in vector_data.iter().enumerate() {
let score = encoded.score_point_sse(&query_u8, index as u32);
let orginal_score = l2_similarity(&query, vector);
assert!((score - orginal_score).abs() < error);
}
}
#[test]
fn test_l1_sse() {
let vectors_count = 129;
let vector_dim = 65;
let error = vector_dim as f32 * 0.1;
//let mut rng = rand::thread_rng();
let mut rng = rand::rngs::StdRng::seed_from_u64(42);
let mut vector_data: Vec<Vec<f32>> = Vec::new();
for _ in 0..vectors_count {
let vector: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
vector_data.push(vector);
}
let query: Vec<f32> = (0..vector_dim).map(|_| rng.gen()).collect();
let encoded = EncodedVectorsU8::encode(
vector_data.iter(),
Vec::<u8>::new(),
&VectorParameters {
dim: vector_dim,
count: vectors_count,
distance_type: DistanceType::L1,
invert: false,
},
None,
|| false,
)
.unwrap();
let query_u8 = encoded.encode_query(&query);
for (index, vector) in vector_data.iter().enumerate() {
let score = encoded.score_point_sse(&query_u8, index as u32);
let orginal_score = l1_similarity(&query, vector);
assert!((score - orginal_score).abs() < error);
}
}
}
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