Updates

docs/weightmatrix.html

@@ -379,12 +379,12 @@ <h1 id="weight-matrices">Weight Matrices</h1>
 
         else: #Brute force knn search
 
-            knn_ind = np.array((n,k),dtype=int)
-            knn_dist = np.array((n,k))
+            knn_ind = np.zeros((n,k),dtype=int)
+            knn_dist = np.zeros((n,k))
             for i in range(n):
                 dist  = np.linalg.norm(X - X[i,:],axis=1) 
                 knn_ind[i,:] = np.argsort(dist)[:k]
-                knn_dist[i,:] = dist[knn_ind]
+                knn_dist[i,:] = dist[knn_ind[i,:]]
 
     elif method == &#39;annoy&#39;:
 
@@ -1127,12 +1127,12 @@ <h2 id="returns">Returns</h2>
 
         else: #Brute force knn search
 
-            knn_ind = np.array((n,k),dtype=int)
-            knn_dist = np.array((n,k))
+            knn_ind = np.zeros((n,k),dtype=int)
+            knn_dist = np.zeros((n,k))
             for i in range(n):
                 dist  = np.linalg.norm(X - X[i,:],axis=1) 
                 knn_ind[i,:] = np.argsort(dist)[:k]
-                knn_dist[i,:] = dist[knn_ind]
+                knn_dist[i,:] = dist[knn_ind[i,:]]
 
     elif method == &#39;annoy&#39;:
 

graphlearning/weightmatrix.py

@@ -346,12 +346,12 @@ def knnsearch(X, k, method=None, similarity='euclidean', dataset=None, metric='r
 
         else: #Brute force knn search
 
-            knn_ind = np.array((n,k),dtype=int)
-            knn_dist = np.array((n,k))
+            knn_ind = np.zeros((n,k),dtype=int)
+            knn_dist = np.zeros((n,k))
             for i in range(n):
                 dist  = np.linalg.norm(X - X[i,:],axis=1) 
                 knn_ind[i,:] = np.argsort(dist)[:k]
-                knn_dist[i,:] = dist[knn_ind]
+                knn_dist[i,:] = dist[knn_ind[i,:]]
 
     elif method == 'annoy':
 

pyproject.toml

@@ -9,7 +9,7 @@ packages = ['graphlearning']
 
 [project]
 name = "graphlearning"
-version = "1.5.3"
+version = "1.5.4"
 authors = [
   { name="Jeff Calder", email="[email protected]" },
 ]