fix(ll4j): Thread safety model implementation

src/main/java/huzpsb/ll4j/minrt/JFuncModelScript.java

@@ -0,0 +1,60 @@
+package huzpsb.ll4j.minrt;
+
+import java.util.function.Function;
+
+public class JFuncModelScript {
+    public static Function<double[], double[]> compile(String[] script) {
+        Function<double[], double[]> current = (input) -> {
+            double[] copied = new double[input.length];
+            System.arraycopy(input, 0, copied, 0, input.length);
+            return copied;
+        };
+
+        for (String str : script) {
+            if (str.length() < 2) {
+                continue;
+            }
+            String[] tokens = str.split(" ");
+            switch (tokens[0]) {
+                case "D":
+                    int ic = Integer.parseInt(tokens[1]);
+                    int oc = Integer.parseInt(tokens[2]);
+                    double[] weights = new double[ic * oc];
+                    for (int i = 0; i < oc; i++) {
+                        for (int j = 0; j < ic; j++) {
+                            weights[i + j * oc] = Double.parseDouble(tokens[3 + i + j * oc]);
+                        }
+                    }
+                    current = current.andThen((input) -> {
+                        if (input.length != ic) {
+                            throw new RuntimeException("Wrong input size for Dense layer (expected " + ic + ", got " + input.length + ")");
+                        }
+                        double[] tmp = new double[oc];
+                        for (int i = 0; i < oc; i++) {
+                            double sum = 0;
+                            for (int j = 0; j < ic; j++) {
+                                sum += input[j] * weights[i + j * oc];
+                            }
+                            tmp[i] = sum;
+                        }
+                        return tmp;
+                    });
+                    break;
+                case "L":
+                    int n = Integer.parseInt(tokens[1]);
+                    current = current.andThen((input) -> {
+                        if (input.length != n) {
+                            throw new RuntimeException("Wrong input size for LeakyRelu layer (expected " + n + ", got " + input.length + ")");
+                        }
+                        double[] tmp = new double[n];
+                        for (int i = 0; i < n; i++) {
+                            tmp[i] = input[i] > 0 ? input[i] : input[i] * 0.01;
+                        }
+                        return tmp;
+                    });
+                    break;
+            }
+        }
+        return current;
+    }
+}

src/main/kotlin/ltd/guimc/lgzbot/command/LGZBotCommand.kt

@@ -129,7 +129,7 @@ object LGZBotCommand : CompositeCommand(
     @Description("让模型学习一段文本")
     suspend fun CommandSender.iI1I1i1iIi1I(type: Int, string: String) {
         LL4JUtils.learn(type, string)
-        sendMessage("Done.")
+        sendMessage("Deprecated")
     }
 
     @SubCommand("downloadModel")

src/main/kotlin/ltd/guimc/lgzbot/utils/LL4JUtils.kt

@@ -1,5 +1,6 @@
 package ltd.guimc.lgzbot.utils
 
+import huzpsb.ll4j.minrt.JFuncModelScript
 import huzpsb.ll4j.model.Model
 import huzpsb.ll4j.nlp.token.Tokenizer
 import huzpsb.ll4j.utils.data.DataSet
@@ -8,7 +9,7 @@ import ltd.guimc.lgzbot.utils.TextUtils.removeInterference
 import ltd.guimc.lgzbot.utils.TextUtils.removeNonVisible
 
 object LL4JUtils {
-    lateinit var model: Model
+    lateinit var model: (DoubleArray) -> DoubleArray
     lateinit var tokenizer: Tokenizer
     var version = "FEB25"
 
@@ -17,23 +18,25 @@ object LL4JUtils {
         val modelFile = LL4JUtils.javaClass.getResourceAsStream("/anti-ad.model")!!
         tokenizer = Tokenizer.load(tokenizerFile.bufferedReader(Charsets.UTF_8))
         modelFile.bufferedReader(Charsets.UTF_8).use {
-            model = Model.read(it)
+            val compiled = JFuncModelScript.compile(it.readLines().toTypedArray())
+            model = { arr -> compiled.apply(arr) } // to kotlin style lambda (wtf)
         }
     }
 
     fun predict(string: String): Boolean =
-        model.predictDebug(
+        model(
             tokenizer.tokenize(
                 0,
                 string.replace("\n", "").replace("live.bilibili.com", "")
             ).values
-        ).first == 1
+        ).let { it[1] > it[0] }
 
-    fun predictDebug(string: String): Pair<Int, Double> =
-        model.predictDebug(tokenizer.tokenize(0, string.replace("\n", "").replace("live.bilibili.com", "")).values)
+    // [unused]
+//    fun predictDebug(string: String): Pair<Int, Double> =
+//        model.predictDebug(tokenizer.tokenize(0, string.replace("\n", "").replace("live.bilibili.com", "")).values)
 
     fun predictAllResult(string: String): DoubleArray =
-        model.predictAllResult(
+        model(
             tokenizer.tokenize(
                 0, sbc2dbcCase(string.replace("\n", "").replace("live.bilibili.com", ""))
                     .lowercase()
@@ -42,15 +45,16 @@ object LL4JUtils {
             ).values
         )
 
+    // [deprecated]
     fun learn(type: Int, string: String) {
         val dataSet = DataSet()
         dataSet.split.add(tokenizer.tokenize(type, string.replace("\n", "")))
-        model.trainOn(dataSet)
+//        model.trainOn(dataSet)
     }
 
     fun downloadModel() {
         try {
-            model = Model.read(HttpUtils.getResponse("https://raw.githubusercontent.com/siuank/ADDetector4J/main/anti-ad.model"))
+//            model = Model.read(HttpUtils.getResponse("https://raw.githubusercontent.com/siuank/ADDetector4J/main/anti-ad.model"))
             tokenizer = Tokenizer.load(HttpUtils.getResponse("https://raw.githubusercontent.com/siuank/ADDetector4J/main/t1.tokenized.txt").reader())
             val time = GithubUtils.getLastCommit("siuank/ADDetector4J").commitTime
             version = "${time.month.name.substring(0..3)}${time.dayOfMonth}"