Make euclidean distance be double for float as well and use that intel to reduce code.

Co-authored-by: Florentin Dörre <florentin.dorre@neotechnology.com>
Co-authored-by: Veselin Nikolov <veselin.nikolov@neotechnology.com>

Author: 3 people

algo/src/main/java/org/neo4j/gds/kmeans/ClusterManager.java

@@ -55,14 +55,28 @@ void initializeCenters(List<Long> initialCenterIds) {
         }
     }
 
-    abstract int findClosestCenter(long nodeId);
 
     static ClusterManager createClusterManager(NodePropertyValues values, int dimensions, int k) {
         if (values.valueType() == ValueType.FLOAT_ARRAY) {
             return new FloatClusterManager(values, dimensions, k);
         }
         return new DoubleClusterManager(values, dimensions, k);
     }
+
+    public abstract double euclidean(long nodeId, int centerId);
+
+    public int findClosestCenter(long nodeId) {
+        int community = 0;
+        double smallestDistance = Double.MAX_VALUE;
+        for (int centerId = 0; centerId < k; ++centerId) {
+            double distance = euclidean(nodeId, centerId);
+            if (Double.compare(distance, smallestDistance) < 0) {
+                smallestDistance = distance;
+                community = centerId;
+            }
+        }
+        return community;
+    }
 }
 
 class FloatClusterManager extends ClusterManager {
@@ -107,6 +121,13 @@ public void updateFromTask(KmeansTask task) {
         }
     }
 
+    @Override
+    public double euclidean(long nodeId, int centerId) {
+        float[] left = nodePropertyValues.floatArrayValue(nodeId);
+        float[] right = clusterCenters[centerId];
+        return Math.sqrt(Intersections.sumSquareDelta(left, right, right.length));
+    }
+
     private float floatEuclidean(float[] left, float[] right) {
         return (float) Math.sqrt(Intersections.sumSquareDelta(left, right, right.length));
     }
@@ -164,28 +185,18 @@ public void updateFromTask(KmeansTask task) {
     }
 
     @Override
-    public void initialAssignCluster(int i, long id) {
-        double[] cluster = nodePropertyValues.doubleArrayValue(id);
-        System.arraycopy(cluster, 0, clusterCenters[i], 0, cluster.length);
-    }
-
-    private double doubleEuclidean(double[] left, double[] right) {
+    public double euclidean(long nodeId, int centerId) {
+        double[] left = nodePropertyValues.doubleArrayValue(nodeId);
+        double[] right = clusterCenters[centerId];
         return Math.sqrt(Intersections.sumSquareDelta(left, right, right.length));
+
     }
 
     @Override
-    public int findClosestCenter(long nodeId) {
-        var property = nodePropertyValues.doubleArrayValue(nodeId);
-        int community = 0;
-        double smallestDistance = Double.MAX_VALUE;
-        for (int centerId = 0; centerId < k; ++centerId) {
-            double distance = doubleEuclidean(property, clusterCenters[centerId]);
-            if (Double.compare(distance, smallestDistance) < 0) {
-                smallestDistance = distance;
-                community = centerId;
-            }
-        }
-        return community;
+    public void initialAssignCluster(int i, long id) {
+        double[] cluster = nodePropertyValues.doubleArrayValue(id);
+        System.arraycopy(cluster, 0, clusterCenters[i], 0, cluster.length);
     }
 
+
 }