Added basic interfaces

UnitTestBot · amandelpie · Jul 19, 2022 · Jul 5, 2022 · Jul 6, 2022 · Jul 6, 2022
commit d7c570ea48c15c5b13abb67919b7c6218b6b721a
diff --git a/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/ExecutionDistance.kt b/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/ExecutionDistance.kt
@@ -1,13 +1,9 @@
 package org.utbot.summary.clustering
 
 import org.utbot.framework.plugin.api.Step
-import smile.math.distance.Distance
+import org.utbot.summary.clustering.dbscan.Distance
 
 class ExecutionDistance : Distance<Iterable<Step>> {
-    override fun d(x: Iterable<Step>, y: Iterable<Step>): Double {
-        return compareTwoPaths(x, y)
-    }
-
     /**
      * Minimum Edit Distance
      */
@@ -31,4 +27,8 @@ class ExecutionDistance : Distance<Iterable<Step>> {
     private fun distance(stmt1: Step, stmt2: Step): Int {
         return if (stmt1 == stmt2) 0 else 2
     }
+
+    override fun compute(object1: Iterable<Step>, object2: Iterable<Step>): Double {
+        return compareTwoPaths(object1, object2)
+    }
 }
diff --git a/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/MatrixUniqueness.kt b/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/MatrixUniqueness.kt
@@ -4,6 +4,7 @@ import org.utbot.framework.plugin.api.Step
 import org.utbot.framework.plugin.api.UtExecution
 import org.utbot.framework.plugin.api.UtTestCase
 import org.utbot.summary.UtSummarySettings
+import org.utbot.summary.clustering.dbscan.DBSCANTrainer
 import smile.clustering.dbscan
 
 class MatrixUniqueness {
@@ -89,8 +90,11 @@ class MatrixUniqueness {
             radius: Double = UtSummarySettings.RADIUS_DBSCAN
         ): Map<Int, List<UtExecution>> {
             val executionPaths = methodExecutions.map { it.path.asIterable() }.toTypedArray()
-            val cluster = dbscan(executionPaths, ExecutionDistance(), minPts, radius)
-            return methodExecutions.withIndex().groupBy({ cluster.y[it.index] }, { it.value })
+            val dbscan = DBSCANTrainer(eps = radius.toFloat(), minSamples = minPts, metric = ExecutionDistance())
+            val dbscanModel = dbscan.fit(executionPaths)
+            val clusterLabels = dbscanModel.clusterLabels
+            //val cluster = dbscan(executionPaths, ExecutionDistance(), minPts, radius)
+            return methodExecutions.withIndex().groupBy({ clusterLabels[it.index] }, { it.value })
         }
     }
 }

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
@@ -0,0 +1,10 @@
+package org.utbot.summary.clustering.dbscan
+
+/**
+ * @property k
+ * @property clusterLabels
+ * @property clusterSizes  The number of observations in each cluster.
+ */
+class DBSCANModel(val k: Int = 0, val clusterLabels: IntArray, val clusterSizes: 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
@@ -0,0 +1,16 @@
+package org.utbot.summary.clustering.dbscan
+
+class DBSCANTrainer<T>(val eps: Float, val minSamples: Int, val metric: Distance<T>) {
+    init {
+        require(minSamples > 0) { "MinSamples parameter should be more than 0: $minSamples" }
+        require(eps > 0.0f) { "Eps parameter should be more than 0: $eps" }
+    }
+
+
+    fun fit(data: Array<T>): DBSCANModel {
+        val numberOfClusters = 0
+        val clusterLabels = IntArray(data.size)
+        val clusterSizes = IntArray(numberOfClusters)
+        return DBSCANModel(k = numberOfClusters, clusterLabels = clusterLabels, clusterSizes = clusterSizes)
+    }
+}
diff --git a/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/dbscan/Distance.kt b/utbot-summary/src/main/kotlin/org/utbot/summary/clustering/dbscan/Distance.kt
@@ -0,0 +1,6 @@
+package org.utbot.summary.clustering.dbscan
+
+
+interface Distance<T> {
+    fun compute(object1: T, object2: T): Double
+}