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fix: cargo fmt

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This commit is contained in:
Volodymyr Orlov
2020-06-05 17:52:03 -07:00
parent 685be04488
commit a2784d6345
52 changed files with 3342 additions and 2829 deletions
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src/cluster/kmeans.rs
+54 -57
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@@ -1,40 +1,41 @@
extern crate rand;
use rand::Rng;
use std::iter::Sum;
use std::fmt::Debug;
use std::iter::Sum;
use serde::{Serialize, Deserialize};
use serde::{Deserialize, Serialize};
use crate::math::num::FloatExt;
use crate::algorithm::neighbour::bbd_tree::BBDTree;
use crate::linalg::Matrix;
use crate::math::distance::euclidian::*;
use crate::algorithm::neighbour::bbd_tree::BBDTree;
use crate::math::num::FloatExt;
#[derive(Serialize, Deserialize, Debug)]
pub struct KMeans<T: FloatExt> {
pub struct KMeans<T: FloatExt> {
k: usize,
y: Vec<usize>,
size: Vec<usize>,
distortion: T,
centroids: Vec<Vec<T>>
centroids: Vec<Vec<T>>,
}
impl<T: FloatExt> PartialEq for KMeans<T> {
impl<T: FloatExt> PartialEq for KMeans<T> {
fn eq(&self, other: &Self) -> bool {
if self.k != other.k ||
self.size != other.size ||
self.centroids.len() != other.centroids.len() {
if self.k != other.k
|| self.size != other.size
|| self.centroids.len() != other.centroids.len()
{
false
} else {
let n_centroids = self.centroids.len();
for i in 0..n_centroids{
if self.centroids[i].len() != other.centroids[i].len(){
return false
for i in 0..n_centroids {
if self.centroids[i].len() != other.centroids[i].len() {
return false;
}
for j in 0..self.centroids[i].len() {
if (self.centroids[i][j] - other.centroids[i][j]).abs() > T::epsilon() {
return false
return false;
}
}
}
@@ -44,21 +45,18 @@ impl<T: FloatExt> PartialEq for KMeans<T> {
}
#[derive(Debug, Clone)]
pub struct KMeansParameters {
pub max_iter: usize
pub struct KMeansParameters {
pub max_iter: usize,
}
impl Default for KMeansParameters {
fn default() -> Self {
KMeansParameters {
max_iter: 100
}
}
fn default() -> Self {
KMeansParameters { max_iter: 100 }
}
}
impl<T: FloatExt + Sum> KMeans<T>{
impl<T: FloatExt + Sum> KMeans<T> {
pub fn new<M: Matrix<T>>(data: &M, k: usize, parameters: KMeansParameters) -> KMeans<T> {
let bbd = BBDTree::new(data);
if k < 2 {
@@ -66,11 +64,14 @@ impl<T: FloatExt + Sum> KMeans<T>{
}
if parameters.max_iter <= 0 {
panic!("Invalid maximum number of iterations: {}", parameters.max_iter);
panic!(
"Invalid maximum number of iterations: {}",
parameters.max_iter
);
}
let (n, d) = data.shape();
let mut distortion = T::max_value();
let mut y = KMeans::kmeans_plus_plus(data, k);
let mut size = vec![0; k];
@@ -90,10 +91,10 @@ impl<T: FloatExt + Sum> KMeans<T>{
for j in 0..d {
centroids[i][j] = centroids[i][j] / T::from(size[i]).unwrap();
}
}
}
let mut sums = vec![vec![T::zero(); d]; k];
for _ in 1..= parameters.max_iter {
for _ in 1..=parameters.max_iter {
let dist = bbd.clustering(&centroids, &mut sums, &mut size, &mut y);
for i in 0..k {
if size[i] > 0 {
@@ -108,48 +109,46 @@ impl<T: FloatExt + Sum> KMeans<T>{
} else {
distortion = dist;
}
}
}
KMeans{
KMeans {
k: k,
y: y,
size: size,
distortion: distortion,
centroids: centroids
centroids: centroids,
}
}
pub fn predict<M: Matrix<T>>(&self, x: &M) -> M::RowVector {
let (n, _) = x.shape();
let mut result = M::zeros(1, n);
let (n, _) = x.shape();
let mut result = M::zeros(1, n);
for i in 0..n {
let mut min_dist = T::max_value();
let mut best_cluster = 0;
for j in 0..self.k {
let dist = Euclidian::squared_distance(&x.get_row_as_vec(i), &self.centroids[j]);
let dist = Euclidian::squared_distance(&x.get_row_as_vec(i), &self.centroids[j]);
if dist < min_dist {
min_dist = dist;
best_cluster = j;
}
}
}
result.set(0, i, T::from(best_cluster).unwrap());
}
result.to_row_vector()
}
fn kmeans_plus_plus<M: Matrix<T>>(data: &M, k: usize) -> Vec<usize>{
let mut rng = rand::thread_rng();
fn kmeans_plus_plus<M: Matrix<T>>(data: &M, k: usize) -> Vec<usize> {
let mut rng = rand::thread_rng();
let (n, _) = data.shape();
let mut y = vec![0; n];
let mut centroid = data.get_row_as_vec(rng.gen_range(0, n));
let mut d = vec![T::max_value(); n];
// pick the next center
for j in 1..k {
// Loop over the samples and compare them to the most recent center. Store
@@ -157,7 +156,7 @@ impl<T: FloatExt + Sum> KMeans<T>{
for i in 0..n {
// compute the distance between this sample and the current center
let dist = Euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
if dist < d[i] {
d[i] = dist;
y[i] = j - 1;
@@ -165,7 +164,7 @@ impl<T: FloatExt + Sum> KMeans<T>{
}
let mut sum: T = T::zero();
for i in d.iter(){
for i in d.iter() {
sum = sum + *i;
}
let cutoff = T::from(rng.gen::<f64>()).unwrap() * sum;
@@ -183,8 +182,8 @@ impl<T: FloatExt + Sum> KMeans<T>{
for i in 0..n {
// compute the distance between this sample and the current center
let dist = Euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
let dist = Euclidian::squared_distance(&data.get_row_as_vec(i), &centroid);
if dist < d[i] {
d[i] = dist;
y[i] = k - 1;
@@ -193,17 +192,15 @@ impl<T: FloatExt + Sum> KMeans<T>{
y
}
}
#[cfg(test)]
mod tests {
use super::*;
use super::*;
use crate::linalg::naive::dense_matrix::DenseMatrix;
#[test]
fn fit_predict_iris() {
fn fit_predict_iris() {
let x = DenseMatrix::from_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
@@ -224,7 +221,8 @@ mod tests {
&[6.3, 3.3, 4.7, 1.6],
&[4.9, 2.4, 3.3, 1.0],
&[6.6, 2.9, 4.6, 1.3],
&[5.2, 2.7, 3.9, 1.4]]);
&[5.2, 2.7, 3.9, 1.4],
]);
let kmeans = KMeans::new(&x, 2, Default::default());
@@ -232,12 +230,11 @@ mod tests {
for i in 0..y.len() {
assert_eq!(y[i] as usize, kmeans.y[i]);
}
}
}
#[test]
fn serde() {
fn serde() {
let x = DenseMatrix::from_array(&[
&[5.1, 3.5, 1.4, 0.2],
&[4.9, 3.0, 1.4, 0.2],
@@ -258,14 +255,14 @@ mod tests {
&[6.3, 3.3, 4.7, 1.6],
&[4.9, 2.4, 3.3, 1.0],
&[6.6, 2.9, 4.6, 1.3],
&[5.2, 2.7, 3.9, 1.4]]);
&[5.2, 2.7, 3.9, 1.4],
]);
let kmeans = KMeans::new(&x, 2, Default::default());
let deserialized_kmeans: KMeans<f64> = serde_json::from_str(&serde_json::to_string(&kmeans).unwrap()).unwrap();
let deserialized_kmeans: KMeans<f64> =
serde_json::from_str(&serde_json::to_string(&kmeans).unwrap()).unwrap();
assert_eq!(kmeans, deserialized_kmeans);
assert_eq!(kmeans, deserialized_kmeans);
}
}
}