Changed some comments and renamed variables

UnitTestBot · amandelpie · Jul 19, 2022 · Jul 5, 2022 · Jul 6, 2022 · Jul 6, 2022
commit c112184f0ab8a0bfbc44c7faf1245c0b23fae2aa
diff --git a/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/dbscan/DBSCANModel.kt b/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/dbscan/DBSCANModel.kt
@@ -1,15 +1,13 @@
 package org.utbot.summary.clustering.dbscan
 
-import org.utbot.summary.clustering.dbscan.neighbor.RangeQuery
-
 /**
  * Keeps the information about clusters produced by [DBSCANTrainer].
  *
- * @property [k] Number of clusters.
+ * @property [numberOfClusters] Number of clusters.
  * @property [clusterLabels] It contains labels of clusters in the range ```[0; k)```
  * or [Int.MIN_VALUE] if point could not be assigned to any cluster.
  */
 data class DBSCANModel(
-    val k: Int = 0,
+    val numberOfClusters: Int = 0,
     val clusterLabels: IntArray
 )
diff --git a/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/dbscan/DBSCANTrainer.kt b/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/dbscan/DBSCANTrainer.kt
@@ -36,44 +36,53 @@ class DBSCANTrainer<T>(val eps: Float, val minSamples: Int, val metric: Metric<T
             rangeQuery.metric = metric
         } // TODO: could be refactored if we add some new variants of RangeQuery
 
-        val labels = IntArray(data.size) { _ -> UNDEFINED }
-        var k = 0
+        val labels = IntArray(data.size) { UNDEFINED }
+
+        // It changes in the range [0; k), where k is a final number of clusters found by DBSCAN
+        var clusterLabel = 0
 
         for (i in data.indices) {
             if (labels[i] == UNDEFINED) {
                 val neighbors = rangeQuery.findNeighbors(data[i], eps).toMutableList()
                 if (neighbors.size < minSamples) {
                     labels[i] = NOISE
                 } else {
-                    labels[i] = k
-                    expandCluster(neighbors, labels, k)
-                    k++
+                    labels[i] = clusterLabel
+                    expandCluster(neighbors, labels, clusterLabel)
+
+                    // If the existing cluster can not be expanded, the cluster label is incremented.
+                    clusterLabel++
                 }
             }
         }
 
-        return DBSCANModel(k = k, clusterLabels = labels)
+        return DBSCANModel(numberOfClusters = clusterLabel, clusterLabels = labels)
     }
 
     private fun expandCluster(
         neighbors: MutableList<Neighbor<T>>,
         labels: IntArray,
         k: Int
     ) {
-        neighbors.forEach {  // Neighbors to expand.
+        // Neighbors to expand.
+        neighbors.forEach {
             if (labels[it.index] == UNDEFINED) {
-                labels[it.index] = CLUSTER_PART // All neighbors of a cluster point became cluster points.
+                // All neighbors of a cluster point became cluster points.
+                labels[it.index] = CLUSTER_PART
             }
         }
 
         // NOTE: the size of neighbors could grow from iteration to iteration and the classical for-loop in Kotlin could not be used
         var j = 0
-        while (j < neighbors.count()) // Process every seed point Q.
+
+        // Process every seed point Q.
+        while (j < neighbors.count())
         {
             val q = neighbors[j]
             val idx = q.index
 
-            if (labels[idx] == NOISE) { // Change Noise to border point.
+            // Change Noise to border point.
+            if (labels[idx] == NOISE) {
                 labels[idx] = k
             }
 
@@ -82,7 +91,7 @@ class DBSCANTrainer<T>(val eps: Float, val minSamples: Int, val metric: Metric<T
 
                 val qNeighbors = rangeQuery.findNeighbors(q.key, eps)
 
-                if (qNeighbors.size >= minSamples) { // Density check (if Q is a core point).
+                if (qNeighbors.size >= minSamples) {
                     mergeTwoGroupsInCluster(qNeighbors, labels, neighbors)
                 }
             }