Reduce reservoir size in KNN and KDE

Author: MohsenTaheriShalmani

core/src/main/kotlin/org/evomaster/core/problem/rest/classifier/probabilistic/kde/KDE400Classifier.kt

@@ -9,7 +9,7 @@ class KDE400Classifier(
     warmup: Int,
     encoderType: EMConfig.EncoderType,
     metricType: EMConfig.AIClassificationMetrics,
-    private val maxStoredSamples: Int = 10_000,
+    private val maxStoredSamples: Int = 1000, // Values more than 1000 make the classification very time-consuming
     randomness: Randomness
 ) : AbstractProbabilistic400Classifier<KDE400EndpointModel>(
     warmup, encoderType, metricType, randomness) {

core/src/main/kotlin/org/evomaster/core/problem/rest/classifier/probabilistic/kde/KDE400EndpointModel.kt

@@ -28,7 +28,7 @@ class KDE400EndpointModel (
     dimension: Int? = null,
     encoderType: EMConfig.EncoderType,
     metricType: EMConfig.AIClassificationMetrics,
-    private val maxStoredSamples: Int = 10_000,
+    private val maxStoredSamples: Int = 1000, // Values more than 1000 make the classification very time-consuming
     randomness: Randomness
 ): AbstractProbabilistic400EndpointModel(
     endpoint, warmup, dimension, encoderType, metricType, randomness) {

core/src/main/kotlin/org/evomaster/core/problem/rest/classifier/probabilistic/knn/KNN400Classifier.kt

@@ -11,7 +11,7 @@ class KNN400Classifier(
     encoderType: EMConfig.EncoderType,
     metricType: EMConfig.AIClassificationMetrics,
     private val k: Int = 3,
-    private val maxStoredSamples: Int = 10000,
+    private val maxStoredSamples: Int = 1000, // Values more than 1000 make the classification very time-consuming
     randomness: Randomness
 ) : AbstractProbabilistic400Classifier<KNN400EndpointModel>(
     warmup, encoderType, metricType, randomness) {

core/src/main/kotlin/org/evomaster/core/problem/rest/classifier/probabilistic/knn/KNN400EndpointModel.kt

@@ -24,7 +24,7 @@ class KNN400EndpointModel (
     encoderType: EMConfig.EncoderType,
     metricType: EMConfig.AIClassificationMetrics,
     private val k: Int = 3,
-    private val maxStoredSamples: Int = 10000,
+    private val maxStoredSamples: Int = 1000, // Values more than 1000 make the classification very time-consuming
     randomness: Randomness
 ): AbstractProbabilistic400EndpointModel(
     endpoint, warmup, dimension, encoderType, metricType, randomness) {