feat: + cluster metrics

src/cluster/kmeans.rs

@@ -189,15 +189,18 @@ impl<T: RealNumber + Sum> KMeans<T> {
     /// Predict clusters for `x`
     /// * `x` - matrix with new data to transform of size _KxM_ , where _K_ is number of new samples and _M_ is number of features.
     pub fn predict<M: Matrix<T>>(&self, x: &M) -> Result<M::RowVector, Failed> {
-        let (n, _) = x.shape();
+        let (n, m) = x.shape();
         let mut result = M::zeros(1, n);
 
+        let mut row = vec![T::zero(); m];
+
         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]);
+                x.copy_row_as_vec(i, &mut row);
+                let dist = Euclidian::squared_distance(&row, &self.centroids[j]);
                 if dist < min_dist {
                     min_dist = dist;
                     best_cluster = j;
@@ -211,19 +214,22 @@ impl<T: RealNumber + Sum> KMeans<T> {
 
     fn kmeans_plus_plus<M: Matrix<T>>(data: &M, k: usize) -> Vec<usize> {
         let mut rng = rand::thread_rng();
-        let (n, _) = data.shape();
+        let (n, m) = 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];
 
+        let mut row = vec![T::zero(); m];
+
         // pick the next center
         for j in 1..k {
             // Loop over the samples and compare them to the most recent center.  Store
             // the distance from each sample to its closest center in scores.
             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);
+                data.copy_row_as_vec(i, &mut row);
+                let dist = Euclidian::squared_distance(&row, &centroid);
 
                 if dist < d[i] {
                     d[i] = dist;
@@ -237,20 +243,22 @@ impl<T: RealNumber + Sum> KMeans<T> {
             }
             let cutoff = T::from(rng.gen::<f64>()).unwrap() * sum;
             let mut cost = T::zero();
-            let index = 0;
-            for index in 0..n {
+            let mut index = 0;
+            while index < n {
                 cost = cost + d[index];
                 if cost >= cutoff {
                     break;
                 }
+                index += 1;
             }
 
-            centroid = data.get_row_as_vec(index);
+            data.copy_row_as_vec(index, &mut centroid);
         }
 
         for i in 0..n {
+            data.copy_row_as_vec(i, &mut row);
             // 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(&row, &centroid);
 
             if dist < d[i] {
                 d[i] = dist;