mirror of
https://github.com/mii443/independent_randomized_projections.git
synced 2025-08-22 07:55:45 +00:00
first commit
This commit is contained in:
mii443
1
.gitignore
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1
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vendored
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333
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generated
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333
Cargo.lock
generated
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@ -0,0 +1,333 @@
|
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10
Cargo.toml
Normal file
10
Cargo.toml
Normal file
@ -0,0 +1,10 @@
|
||||
[package]
|
||||
name = "independent_randomized_projections"
|
||||
version = "0.1.0"
|
||||
edition = "2024"
|
||||
|
||||
[dependencies]
|
||||
nalgebra = "0.33.2"
|
||||
rand = "0.9.1"
|
||||
rand_chacha = "0.9.0"
|
||||
rand_distr = "0.5.1"
|
||||
44
src/client.rs
Normal file
44
src/client.rs
Normal file
@ -0,0 +1,44 @@
|
||||
use nalgebra::SMatrix;
|
||||
|
||||
use crate::{
|
||||
common,
|
||||
constant::{GRID_SIZE, NUM_OF_PROJECTIONS},
|
||||
};
|
||||
|
||||
pub struct Client {
|
||||
pub i: usize,
|
||||
pub projection_matrix: Vec<SMatrix<f32, GRID_SIZE, 1>>,
|
||||
}
|
||||
|
||||
impl Client {
|
||||
pub fn new(i: usize, projection_matrix: Vec<SMatrix<f32, GRID_SIZE, 1>>) -> Self {
|
||||
Client {
|
||||
i,
|
||||
projection_matrix,
|
||||
}
|
||||
}
|
||||
|
||||
pub fn observe(&mut self, current_grid: (usize, usize)) -> (usize, f32) {
|
||||
let index = common::grid_to_index(current_grid);
|
||||
let projection = &self.projection_matrix[self.i];
|
||||
|
||||
let observation = projection[(index, 0)];
|
||||
|
||||
let result = (self.i, observation);
|
||||
|
||||
if self.i < NUM_OF_PROJECTIONS {
|
||||
self.i += 1;
|
||||
} else {
|
||||
self.i = 0;
|
||||
}
|
||||
|
||||
result
|
||||
}
|
||||
|
||||
pub fn observe_quantized(&mut self, current_grid: (usize, usize)) -> (usize, i8) {
|
||||
let normal_observation = self.observe(current_grid);
|
||||
let quantized_observation = common::quantize(normal_observation.1);
|
||||
|
||||
(normal_observation.0, quantized_observation)
|
||||
}
|
||||
}
|
||||
24
src/common.rs
Normal file
24
src/common.rs
Normal file
@ -0,0 +1,24 @@
|
||||
use crate::constant::{GRID_SIZE, GRID_WIDTH};
|
||||
|
||||
pub fn quantize(value: f32) -> i8 {
|
||||
let std_dev = 1.0 / (GRID_SIZE as f32).sqrt();
|
||||
|
||||
let three_sigma = 3.0 * std_dev;
|
||||
|
||||
let scale_factor = 120.0 / three_sigma;
|
||||
|
||||
let scaled = value * scale_factor;
|
||||
let clamped = scaled.clamp(-128.0, 127.0);
|
||||
|
||||
clamped.round() as i8
|
||||
}
|
||||
|
||||
pub fn grid_to_index(grid: (usize, usize)) -> usize {
|
||||
grid.0 * GRID_WIDTH + grid.1
|
||||
}
|
||||
|
||||
pub fn index_to_grid(index: usize) -> (usize, usize) {
|
||||
let x = index / GRID_WIDTH;
|
||||
let y = index % GRID_WIDTH;
|
||||
(x, y)
|
||||
}
|
||||
5
src/constant.rs
Normal file
5
src/constant.rs
Normal file
@ -0,0 +1,5 @@
|
||||
pub const GRID_HEIGHT: usize = 500;
|
||||
pub const GRID_WIDTH: usize = 500;
|
||||
pub const GRID_SIZE: usize = GRID_HEIGHT * GRID_WIDTH;
|
||||
|
||||
pub const NUM_OF_PROJECTIONS: usize = 256;
|
||||
55
src/main.rs
Normal file
55
src/main.rs
Normal file
@ -0,0 +1,55 @@
|
||||
use std::collections::HashMap;
|
||||
|
||||
use rand::seq::IndexedRandom;
|
||||
|
||||
use crate::constant::NUM_OF_PROJECTIONS;
|
||||
|
||||
pub mod client;
|
||||
pub mod common;
|
||||
pub mod constant;
|
||||
pub mod server;
|
||||
|
||||
fn main() {
|
||||
println!("Generating projections...");
|
||||
let projections = server::generate_projection_matrix();
|
||||
|
||||
println!("Generating fingerprint database...");
|
||||
let fingerprint_database = server::generate_fingerprint_database(projections.clone());
|
||||
|
||||
let mut client = client::Client::new(0, projections);
|
||||
|
||||
let grid_candidates: Vec<(usize, usize)> = vec![(5, 5), (120, 50), (200, 200), (10, 5)];
|
||||
|
||||
let mut rng = rand::rng();
|
||||
|
||||
let mut count: HashMap<usize, usize> = HashMap::new();
|
||||
for i in 0..NUM_OF_PROJECTIONS {
|
||||
let current_grid = *grid_candidates.choose(&mut rng).unwrap();
|
||||
|
||||
let (index, observation) = client.observe_quantized(current_grid);
|
||||
println!("Observation {i}: index = {index}, observation = {observation}");
|
||||
let predictions = server::predict_location_from_database_quantized(
|
||||
&fingerprint_database,
|
||||
(index, observation),
|
||||
);
|
||||
for location in predictions.iter() {
|
||||
count.entry(*location).and_modify(|e| *e += 1).or_insert(1);
|
||||
}
|
||||
}
|
||||
|
||||
let sorted = count.iter().collect::<Vec<_>>();
|
||||
let mut sorted = sorted
|
||||
.into_iter()
|
||||
.map(|(k, v)| (*k, *v))
|
||||
.collect::<Vec<_>>();
|
||||
sorted.sort_by(|a, b| b.1.cmp(&a.1));
|
||||
|
||||
println!("Top 10 locations:");
|
||||
for (i, (location, count)) in sorted.iter().take(10).enumerate() {
|
||||
println!(
|
||||
"Rank {}: Location: {:?}, Count: {count}",
|
||||
i + 1,
|
||||
common::index_to_grid(*location)
|
||||
);
|
||||
}
|
||||
}
|
||||
79
src/server.rs
Normal file
79
src/server.rs
Normal file
@ -0,0 +1,79 @@
|
||||
use std::collections::HashMap;
|
||||
|
||||
use nalgebra::SMatrix;
|
||||
use rand_chacha::{rand_core::SeedableRng, ChaCha20Rng};
|
||||
use rand_distr::{Distribution, Normal};
|
||||
|
||||
use crate::{
|
||||
common,
|
||||
constant::{GRID_SIZE, NUM_OF_PROJECTIONS},
|
||||
};
|
||||
|
||||
pub fn generate_projection_matrix() -> Vec<SMatrix<f32, GRID_SIZE, 1>> {
|
||||
let mut rng = ChaCha20Rng::from_rng(&mut rand::rng());
|
||||
|
||||
let std_dev = 1.0 / (GRID_SIZE as f64).sqrt();
|
||||
let normal = Normal::new(0.0, std_dev).unwrap();
|
||||
|
||||
(0..NUM_OF_PROJECTIONS)
|
||||
.map(|_| {
|
||||
let mut projection = SMatrix::<f32, GRID_SIZE, 1>::zeros();
|
||||
for i in 0..GRID_SIZE {
|
||||
projection[(i, 0)] = normal.sample(&mut rng) as f32;
|
||||
}
|
||||
projection
|
||||
})
|
||||
.collect()
|
||||
}
|
||||
|
||||
pub fn generate_fingerprint_database(
|
||||
projections: Vec<SMatrix<f32, GRID_SIZE, 1>>,
|
||||
) -> HashMap<usize, (usize, Vec<f32>)> {
|
||||
let mut fingerprint_database = HashMap::new();
|
||||
|
||||
for (i, projection) in projections.iter().enumerate() {
|
||||
let mut fingerprint = Vec::with_capacity(GRID_SIZE);
|
||||
for j in 0..GRID_SIZE {
|
||||
let observation = projection[(j, 0)];
|
||||
fingerprint.push(observation);
|
||||
}
|
||||
fingerprint_database.insert(i, (i, fingerprint));
|
||||
}
|
||||
|
||||
fingerprint_database
|
||||
}
|
||||
|
||||
pub fn predict_location_from_database(
|
||||
fingerprint_database: &HashMap<usize, (usize, Vec<f32>)>,
|
||||
observation: (usize, f32),
|
||||
) -> usize {
|
||||
let ideal_observation = fingerprint_database.get(&observation.0).unwrap();
|
||||
let mut min_distance = f32::MAX;
|
||||
let mut predicted_location = None;
|
||||
for (i, ideal) in ideal_observation.1.iter().enumerate() {
|
||||
let distance = (ideal - observation.1).abs();
|
||||
if distance < min_distance {
|
||||
min_distance = distance;
|
||||
predicted_location = Some(i);
|
||||
}
|
||||
}
|
||||
|
||||
predicted_location.unwrap()
|
||||
}
|
||||
|
||||
pub fn predict_location_from_database_quantized(
|
||||
fingerprint_database: &HashMap<usize, (usize, Vec<f32>)>,
|
||||
observation: (usize, i8),
|
||||
) -> Vec<usize> {
|
||||
let ideal_observation = fingerprint_database.get(&observation.0).unwrap();
|
||||
let locations: Vec<usize> = ideal_observation
|
||||
.1
|
||||
.iter()
|
||||
.map(|o| common::quantize(*o))
|
||||
.enumerate()
|
||||
.filter(|(_, quantized)| *quantized == observation.1)
|
||||
.map(|(i, _)| i)
|
||||
.collect();
|
||||
|
||||
locations
|
||||
}
|
||||
Reference in New Issue
Block a user