gc 이상탐색 모델도 추가해놓음

ai/build.gradle.kts

@@ -24,6 +24,8 @@ dependencies {
 
     implementation("com.github.haifengl:smile-core:2.6.0")
     implementation("com.github.haifengl:smile-plot:2.6.0")
+    implementation("com.github.haifengl:smile-data:2.6.0")
+    implementation("com.github.haifengl:smile-anomaly:2.6.0")
 
     testImplementation("org.springframework.boot:spring-boot-starter-test")
     testImplementation("org.jetbrains.kotlin:kotlin-test-junit5")

ai/src/main/kotlin/lab/Application.kt

@@ -3,7 +3,9 @@ package lab
 import org.springframework.boot.autoconfigure.SpringBootApplication
 import org.springframework.boot.runApplication
 
-@SpringBootApplication
+@SpringBootApplication(
+    scanBasePackages = ["lab.api", "lab.`ai-model`"]
+)
 class Application
 
 fun main(args: Array<String>) {

ai/src/main/kotlin/lab/ai-model/NormalizationUtil.kt

@@ -0,0 +1,16 @@
+package lab.`ai-model`
+
+object NormalizationUtil {
+    fun normalize(features: Array<DoubleArray>): Array<DoubleArray> {
+        val numFeatures = features.first().size
+        val minVals = DoubleArray(numFeatures) { idx -> features.minOf { it[idx] } }
+        val maxVals = DoubleArray(numFeatures) { idx -> features.maxOf { it[idx] } }
+
+        return features.map { f ->
+            DoubleArray(numFeatures) { i ->
+                if (maxVals[i] == minVals[i]) 0.0
+                else (f[i] - minVals[i]) / (maxVals[i] - minVals[i])
+            }
+        }.toTypedArray()
+    }
+}

ai/src/main/kotlin/lab/ai-model/gc/GcAnomalyDetector.kt

@@ -0,0 +1,74 @@
+package lab.`ai-model`.gc
+
+import lab.`ai-model`.NormalizationUtil
+import org.slf4j.LoggerFactory
+import org.springframework.stereotype.Component
+import smile.clustering.KMeans
+import smile.math.distance.EuclideanDistance
+import java.io.File
+import java.io.ObjectOutputStream
+
+@Component
+class GcAnomalyDetector {
+
+    private val extractor: GcFeatureExtractor by lazy { GcFeatureExtractor }
+    private val normalizationUtil: NormalizationUtil by lazy { NormalizationUtil }
+
+    private val projectRootDir: String = System.getProperty("user.dir")
+    private val modelDir = File("$projectRootDir/ai-models/gc-anomaly").apply { mkdirs() }
+
+    private lateinit var model: KMeans
+
+    private val log = LoggerFactory.getLogger(GcAnomalyDetector::class.java)
+
+    fun train(k: Int = 3) {
+        log.info("Start GcAnomalyDetector(KMeans) training...")
+
+        val dataList = getDataList()
+        if (dataList.isEmpty()) {
+            log.warn("No data available for training.")
+            return
+        }
+
+        val features = dataList.map { extractor.extract(it) }.toTypedArray()
+        val normalized = normalizationUtil.normalize(features)
+
+        // KMeans 학습
+        model = KMeans.fit(normalized, k)
+        log.info("✅ KMeans training completed with $k clusters.")
+        saveModel()
+    }
+
+    fun predict(data: GcTrainData): Double {
+        val dist = EuclideanDistance()
+        val features = extractor.extract(data)
+        val cluster = model.predict(features)
+        val centroid = model.centroids[cluster]
+        val distance = dist.d(features, centroid)
+        log.info("🔎 Distance to cluster center: %.4f".format(distance))
+        return distance
+    }
+
+    private fun saveModel() {
+        val file = File(modelDir, "gc_anomaly_kmeans.model")
+        ObjectOutputStream(file.outputStream().buffered()).use { oos ->
+            oos.writeObject(model)
+        }
+        log.info("💾 Saved KMeans anomaly model → ${file.absolutePath}")
+    }
+
+    private fun getDataList(isTestSet: Boolean = true): List<GcTrainData> {
+        return if(isTestSet) {
+            listOf(
+                GcTrainData(120, 500, 40, 1.2, 400_000, "G1", label = 1),
+                GcTrainData(200, 800, 350, 3.8, 1_200_000, "G1", label = 1),
+                GcTrainData(85, 260, 12, 0.9, 250_000, "Parallel", label = 1),
+                GcTrainData(600, 1500, 900, 6.8, 2_200_000, "G1", label = 1),
+                GcTrainData(95, 340, 18, 1.4, 370_000, "Serial", label = 1)
+            )
+        } else {
+            // TODO heesung feature
+            listOf()
+        }
+    }
+}

ai/src/main/kotlin/lab/ai-model/gc/GcTrainer.kt → ai/src/main/kotlin/lab/ai-model/gc/GcLogisticRegressionTrainer.kt

@@ -1,5 +1,6 @@
 package lab.`ai-model`.gc
 
+import lab.`ai-model`.NormalizationUtil
 import org.slf4j.LoggerFactory
 import org.springframework.stereotype.Component
 import smile.classification.LogisticRegression
@@ -12,14 +13,17 @@ import java.io.ObjectOutputStream
 import java.util.*
 
 @Component
-class GcTrainer {
+class GcLogisticRegressionTrainer {
     private val extractor: GcFeatureExtractor by lazy { GcFeatureExtractor }
-    private lateinit var model: LogisticRegression
-    private val log = LoggerFactory.getLogger(GcTrainer::class.java)
+    private val normalizationUtil: NormalizationUtil by lazy { NormalizationUtil }
 
     private val projectRootDir: String = System.getProperty("user.dir")
     private val modelDir = File("$projectRootDir/ai-models/gc-model").apply { mkdirs() }
 
+    private lateinit var model: LogisticRegression
+
+    private val log = LoggerFactory.getLogger(GcLogisticRegressionTrainer::class.java)
+
     fun train() {
         log.info("Start GcTrainer training...")
         val dataList = getDataList()
@@ -37,7 +41,7 @@ class GcTrainer {
         log.info("Sample training data: ${dataList.take(3)}")
 
         val features = dataList.map { extractor.extract(it) }.toTypedArray()
-        val normalizedFeatures = normalize(features)
+        val normalizedFeatures = normalizationUtil.normalize(features)
         val labels = dataList.map { it.label }.toIntArray()
 
         val df = DataFrame.of(
@@ -68,19 +72,6 @@ class GcTrainer {
         saveModel("test")
     }
 
-    private fun normalize(features: Array<DoubleArray>): Array<DoubleArray> {
-        val numFeatures = features.first().size
-        val minVals = DoubleArray(numFeatures) { idx -> features.minOf { it[idx] } }
-        val maxVals = DoubleArray(numFeatures) { idx -> features.maxOf { it[idx] } }
-
-        return features.map { f ->
-            DoubleArray(numFeatures) { i ->
-                if (maxVals[i] == minVals[i]) 0.0
-                else (f[i] - minVals[i]) / (maxVals[i] - minVals[i])
-            }
-        }.toTypedArray()
-    }
-
     private fun saveModel(key: String) {
         val m = model ?: run {
             log.error("Model not trained. Cannot save [$key].")
@@ -96,14 +87,18 @@ class GcTrainer {
         log.info("💾 Saved model [$key] → ${file.absolutePath}")
     }
 
-    private fun getDataList(): List<GcTrainData> {
-        // TODO - khope heesung이 만들어준 data get에서 가져와쓰는걸로 수정
-        return listOf(
-            GcTrainData(100, 400, 30, 1.2, 300_000, "G1", label = 1),
-            GcTrainData(150, 700, 300, 3.8, 1_000_000, "G1", label = 0),
-            GcTrainData(80, 250, 15, 0.8, 200_000, "Parallel", label = 1),
-            GcTrainData(400, 1200, 700, 6.2, 2_000_000, "G1", label = 0),
-            GcTrainData(90, 320, 20, 1.5, 350_000, "Serial", label = 1)
-        )
+    private fun getDataList(isTestSet: Boolean = true): List<GcTrainData> {
+        return if(isTestSet) {
+            listOf(
+                GcTrainData(120, 500, 40, 1.2, 400_000, "G1", label = 1),
+                GcTrainData(200, 800, 350, 3.8, 1_200_000, "G1", label = 1),
+                GcTrainData(85, 260, 12, 0.9, 250_000, "Parallel", label = 1),
+                GcTrainData(600, 1500, 900, 6.8, 2_200_000, "G1", label = 1),
+                GcTrainData(95, 340, 18, 1.4, 370_000, "Serial", label = 1)
+            )
+        } else {
+            // TODO heesung feature
+            listOf()
+        }
     }
 }

ai/src/main/kotlin/lab/api/ApiController.kt

@@ -1,16 +1,23 @@
 package lab.api
 
-import lab.`ai-model`.gc.GcTrainer
+import lab.`ai-model`.gc.GcAnomalyDetector
+import lab.`ai-model`.gc.GcLogisticRegressionTrainer
 import org.springframework.web.bind.annotation.GetMapping
+import org.springframework.web.bind.annotation.RequestParam
 import org.springframework.web.bind.annotation.RestController
 
 @RestController
 class ApiController(
-    private val gcTrainer: GcTrainer
+    private val gcLogisticRegressionTrainer: GcLogisticRegressionTrainer,
+    private val gcAnomalyDetector: GcAnomalyDetector,
 ) {
 
     @GetMapping("/api/train")
-    fun train() {
-        gcTrainer.train()
+    fun train(@RequestParam trainModelName: String?) {
+        when(trainModelName) {
+            "gc_logistic_regression" -> gcLogisticRegressionTrainer.train()
+            "gc_anomaly_detector" -> gcAnomalyDetector.train()
+            else -> throw IllegalArgumentException("Unknown model name: $trainModelName")
+        }
     }
 }