# Examples
Rust
example [more info (opens new window)]
use hora::core::ann_index::ANNIndex;
use rand::{thread_rng, Rng};
use rand_distr::{Distribution, Normal};
pub fn demo() {
let n = 1000;
let dimension = 64;
// make sample points
let mut samples = Vec::with_capacity(n);
let normal = Normal::new(0.0, 10.0).unwrap();
for _i in 0..n {
let mut sample = Vec::with_capacity(dimension);
for _j in 0..dimension {
sample.push(normal.sample(&mut rand::thread_rng()));
}
samples.push(sample);
}
// init index
let mut index = hora::index::hnsw_idx::HNSWIndex::<f32, usize>::new(
dimension,
&hora::index::hnsw_params::HNSWParams::<f32>::default(),
);
for (i, sample) in samples.iter().enumerate().take(n) {
// add point
index.add(sample, i).unwrap();
}
index.build(hora::core::metrics::Metric::Euclidean).unwrap();
let mut rng = thread_rng();
let target: usize = rng.gen_range(0..n);
// 523 has neighbors: [523, 762, 364, 268, 561, 231, 380, 817, 331, 246]
println!(
"{:?} has neighbors: {:?}",
target,
index.search(&samples[target], 10) // search for k nearest neighbors
);
}
Python
exmaple [more info (opens new window)]
import numpy as np
from horapy import HNSWIndex
dimension = 50
n = 1000
# init index instance
index = HNSWIndex(dimension, "usize")
samples = np.float32(np.random.rand(n, dimension))
for i in range(0, len(samples)):
# add node
index.add(np.float32(samples[i]), i)
index.build("euclidean") # build index
target = np.random.randint(0, n)
# 410 in Hora ANNIndex <HNSWIndexUsize> (dimension: 50, dtype: usize, max_item: 1000000, n_neigh: 32, n_neigh0: 64, ef_build: 20, ef_search: 500, has_deletion: False)
# has neighbors: [410, 736, 65, 36, 631, 83, 111, 254, 990, 161]
print("{} in {} \nhas neighbors: {}".format(
target, index, index.search(samples[target], 10))) # search
Javascript
example [more info (opens new window)]
const demo = () => {
const dimension = 50;
var bf_idx = hora_wasm.BruteForceIndexUsize.new(dimension);
for (var i = 0; i < 1000; i++) {
var feature = [];
for (var j = 0; j < dimension; j++) {
feature.push(Math.random());
}
bf_idx.add(feature, i); // add point
}
bf_idx.build("euclidean"); // build index
var feature = [];
for (var j = 0; j < dimension; j++) {
feature.push(Math.random());
}
console.log("bf result", .search(feature, 10)); //bf result Uint32Array(10) [704, 113, 358, 835, 408, 379, 117, 414, 808, 826]
}
Java
examples [more info (opens new window)]
public void demo() {
final int dimension = 2;
final float variance = 2.0f;
Random fRandom = new Random();
BruteForceIndex bruteforce_idx = new BruteForceIndex(dimension); // init index instance
List<float[]> tmp = new ArrayList<>();
for (int i = 0; i < 5; i++) {
for (int p = 0; p < 10; p++) {
float[] features = new float[dimension];
for (int j = 0; j < dimension; j++) {
features[j] = getGaussian(fRandom, (float) (i * 10), variance);
}
bruteforce_idx.add("bf", features, i * 10 + p); // add point
tmp.add(features);
}
}
bruteforce_idx.build("bf", "euclidean"); // build index
int search_index = fRandom.nextInt(tmp.size());
// nearest neighbor search
int[] result = bruteforce_idx.search("bf", 10, tmp.get(search_index));
// [main] INFO com.hora.app.ANNIndexTest - demo bruteforce_idx[7, 8, 0, 5, 3, 9, 1, 6, 4, 2]
log.info("demo bruteforce_idx" + Arrays.toString(result));
}
private static float getGaussian(Random fRandom, float aMean, float variance) {
float r = (float) fRandom.nextGaussian();
return aMean + r * variance;
}