Lmm/add seeds in more algorithms (#164)

* Provide better output in flaky tests

* feat: add seed parameter to multiple algorithms

* Update changelog

Co-authored-by: Luis Moreno <morenol@users.noreply.github.com>

src/cluster/kmeans.rs

@@ -52,10 +52,10 @@
 //! * ["An Introduction to Statistical Learning", James G., Witten D., Hastie T., Tibshirani R., 10.3.1 K-Means Clustering](http://faculty.marshall.usc.edu/gareth-james/ISL/)
 //! * ["k-means++: The Advantages of Careful Seeding", Arthur D., Vassilvitskii S.](http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf)
 
-use rand::Rng;
 use std::fmt::Debug;
 use std::iter::Sum;
 
+use ::rand::Rng;
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Serialize};
 
@@ -65,6 +65,7 @@ use crate::error::Failed;
 use crate::linalg::Matrix;
 use crate::math::distance::euclidian::*;
 use crate::math::num::RealNumber;
+use crate::rand::get_rng_impl;
 
 /// K-Means clustering algorithm
 #[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
@@ -108,6 +109,9 @@ pub struct KMeansParameters {
     pub k: usize,
     /// Maximum number of iterations of the k-means algorithm for a single run.
     pub max_iter: usize,
+    /// Determines random number generation for centroid initialization.
+    /// Use an int to make the randomness deterministic
+    pub seed: Option<u64>,
 }
 
 impl KMeansParameters {
@@ -128,6 +132,7 @@ impl Default for KMeansParameters {
         KMeansParameters {
             k: 2,
             max_iter: 100,
+            seed: None,
         }
     }
 }
@@ -238,7 +243,7 @@ impl<T: RealNumber + Sum> KMeans<T> {
         let (n, d) = data.shape();
 
         let mut distortion = T::max_value();
-        let mut y = KMeans::kmeans_plus_plus(data, parameters.k);
+        let mut y = KMeans::kmeans_plus_plus(data, parameters.k, parameters.seed);
         let mut size = vec![0; parameters.k];
         let mut centroids = vec![vec![T::zero(); d]; parameters.k];
 
@@ -311,8 +316,8 @@ impl<T: RealNumber + Sum> KMeans<T> {
         Ok(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, seed: Option<u64>) -> Vec<usize> {
+        let mut rng = get_rng_impl(seed);
         let (n, m) = data.shape();
         let mut y = vec![0; n];
         let mut centroid = data.get_row_as_vec(rng.gen_range(0..n));