player command parsing

data/model/sim/command_cat/fingerprint.pb

@@ -0,0 +1 @@
+д╢б÷Э²Ф⌡Ь
Зс▐зК╜·З═
╔в╔▓ИКЙG сМ╘И┐Ь▓б├
(≤≥╞юлЬ║╟·
2

data/semantic/rpg_command/test.csv

@@ -1,2 +0,0 @@
-"utility","transfer","query","imperative","sentence"
-1,0,0,0,"Hello"

data/semantic/rpg_command/train.csv

@@ -1,2 +0,0 @@
-"look","transfer","query","imperative","sentence"
-1,0,0,0,"Hello"

data/semantic/sim/rpg_command/test.csv

@@ -0,0 +1,25 @@
+help,look,walk,speak,sentence
+0,1,0,0, I look around.
+1,0,0,0,Help.
+0,0,1,0,I approach one of the merchants.
+0,0,1,0,I head towards the adventurer's guild.
+0,0,1,0,I walk up to the gnome.
+0,1,0,0,I look at the paper describing the trial quest.
+0,0,0,1,"I then ask, ""Where can I find the kobold caves?"""
+0,0,0,1,"""Thank you!"" I reply."
+0,0,0,1,"""Thank you again."" I say."
+0,0,1,0,I then follow his directions.
+0,1,0,0,I look around briefly.
+0,0,1,0,I follow the path up to the cave.
+0,1,0,0,I look around once I arrive.
+0,0,1,0,I enter the cave and begin to follow the footprints.
+0,0,1,0,I approach the sounds of battle cautiously.
+0,0,1,0,I sneak up behind a kobold.
+0,1,0,0,I try to assess whether the battle is still ongoing or has finished.
+0,0,0,1,"I ask them ""Are y'all ok?"""
+0,1,0,0,I inspect the human warrior.
+0,1,0,0,I inspect the halfling.
+0,1,0,0,I inspect the elf.
+0,0,1,0,I then head towards the entrance of the cave.
+0,0,1,0,I follow the human.
+0,0,1,0,I head towards the hot spring connected to the inn.

data/semantic/sim/rpg_command/train.csv

@@ -0,0 +1,25 @@
+help,look,walk,speak,sentence
+0,1,0,0, I look around.
+1,0,0,0,Help.
+0,0,1,0,I approach one of the merchants.
+0,0,1,0,I head towards the adventurer's guild.
+0,0,1,0,I walk up to the gnome.
+0,1,0,0,I look at the paper describing the trial quest.
+0,0,0,1,"I then ask, ""Where can I find the kobold caves?"""
+0,0,0,1,"""Thank you!"" I reply."
+0,0,0,1,"""Thank you again."" I say."
+0,0,1,0,I then follow his directions.
+0,1,0,0,I look around briefly.
+0,0,1,0,I follow the path up to the cave.
+0,1,0,0,I look around once I arrive.
+0,0,1,0,I enter the cave and begin to follow the footprints.
+0,0,1,0,I approach the sounds of battle cautiously.
+0,0,1,0,I sneak up behind a kobold.
+0,1,0,0,I try to assess whether the battle is still ongoing or has finished.
+0,0,0,1,"I ask them ""Are y'all ok?"""
+0,1,0,0,I inspect the human warrior.
+0,1,0,0,I inspect the halfling.
+0,1,0,0,I inspect the elf.
+0,0,1,0,I then head towards the entrance of the cave.
+0,0,1,0,I follow the human.
+0,0,1,0,I head towards the hot spring connected to the inn.

src/main/java/org/studiorailgun/interact/GameLoop.java

@@ -24,6 +24,7 @@ public class GameLoop {
      */
     public static void main(){
         Globals.init("web.json");
+        GameCommandParser.init();
         Globals.world = WorldGenerator.generateWorld();
         Globals.playerCharacter = PlayerCharSourcer.getPlayerCharacter();
 

src/main/java/org/studiorailgun/sim/character/Character.java

@@ -23,6 +23,12 @@ public class Character {
      */
     EmotionData emotions;
 
+    /**
+     * Simulates this character
+     */
+    public void simulate(){
+    }
+
     /**
      * Gets the appearance of the character
      * @return The appearance of the character

src/main/java/org/studiorailgun/sim/eval/GameCommandParser.java

@@ -1,16 +1,53 @@
 package org.studiorailgun.sim.eval;
 
+import org.studiorailgun.sim.eval.command.LookCommand;
+import org.studiorailgun.sim.eval.command.SpeakCommand;
+import org.studiorailgun.sim.eval.command.SystemCommand;
+import org.studiorailgun.sim.eval.command.WalkCommand;
+import org.studiorailgun.sim.eval.parse.CommandCategorizationModel;
+import org.studiorailgun.sim.eval.parse.CommandCategorizationModel.CommandType;
+
 /**
  * Parses game commands from the user
  */
 public class GameCommandParser {
+
+    /**
+     * Initializes the command parser
+     */
+    public static void init(){
+        CommandCategorizationModel.init();
+    }
     
     /**
      * Parses the player's input
      * @param input The player's input
      */
     public static void parse(String input){
-        
+        CommandType type = CommandCategorizationModel.categorize(input);
+        switch(type){
+
+            case HELP: {
+                SystemCommand.handle(input);
+            } break;
+
+            case LOOK: {
+                LookCommand.handle(input);
+            } break;
+
+            case WALK: {
+                WalkCommand.handle(input);
+            } break;
+
+            case SPEAK: {
+                SpeakCommand.handle(input);
+            } break;
+
+            default: {
+                String message = "Could not determine what the command was. \"" + input + "\" " + type;
+                throw new Error(message);
+            }
+        }
     }
 
 }

src/main/java/org/studiorailgun/sim/eval/Simulator.java

@@ -1,5 +1,8 @@
 package org.studiorailgun.sim.eval;
 
+import org.studiorailgun.Globals;
+import org.studiorailgun.sim.character.Character;
+
 /**
  * Simulates the game world
  */
@@ -9,9 +12,9 @@ public class Simulator {
      * Simulates a single frame of the game world
      */
     public static void simulate(){
-
-        
-
+        for(Character character : Globals.world.getCharacters()){
+            character.simulate();
+        }
     }
 
 }

src/main/java/org/studiorailgun/sim/eval/command/LookCommand.java

@@ -0,0 +1,15 @@
+package org.studiorailgun.sim.eval.command;
+
+/**
+ * Handler for look command
+ */
+public class LookCommand {
+    
+    /**
+     * Handles a look command
+     */
+    public static void handle(String input){
+        System.out.println("Look command..");
+    }
+
+}

src/main/java/org/studiorailgun/sim/eval/command/SpeakCommand.java

@@ -0,0 +1,16 @@
+package org.studiorailgun.sim.eval.command;
+
+/**
+ * Handles a speaking command
+ */
+public class SpeakCommand {
+    
+    /**
+     * Handles a speaking command
+     * @param input The raw command
+     */
+    public static void handle(String input){
+
+    }
+
+}

src/main/java/org/studiorailgun/sim/eval/command/SystemCommand.java

@@ -0,0 +1,15 @@
+package org.studiorailgun.sim.eval.command;
+
+/**
+ * Handles system commands
+ */
+public class SystemCommand {
+    
+    /**
+     * Handles a system command
+     */
+    public static void handle(String input){
+        System.out.println("System command.. " + input);
+    }
+
+}

src/main/java/org/studiorailgun/sim/eval/command/WalkCommand.java

@@ -0,0 +1,16 @@
+package org.studiorailgun.sim.eval.command;
+
+/**
+ * Handles a walk command
+ */
+public class WalkCommand {
+    
+    /**
+     * Handles a walk command
+     * @param input The input text
+     */
+    public static void handle(String input){
+        System.out.println("Walk command..");
+    }
+
+}

src/main/java/org/studiorailgun/sim/eval/parse/CommandCategorizationModel.java

@@ -0,0 +1,141 @@
+package org.studiorailgun.sim.eval.parse;
+
+import java.util.HashMap;
+import java.util.Map;
+
+import org.tensorflow.Result;
+import org.tensorflow.SavedModelBundle;
+import org.tensorflow.Tensor;
+import org.tensorflow.ndarray.FloatNdArray;
+import org.tensorflow.ndarray.NdArray;
+import org.tensorflow.ndarray.NdArrays;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.types.TFloat32;
+import org.tensorflow.types.TString;
+
+/**
+ * Categorizes player commands
+ */
+public class CommandCategorizationModel {
+    
+    /**
+     * The type of the command
+     */
+    public static enum CommandType {
+        /**
+         * Asking the system for help
+         */
+        HELP,
+
+        /**
+         * Looking at something
+         */
+        LOOK,
+
+        /**
+         * Walk towards something
+         */
+        WALK,
+
+        /**
+         * Speak at something
+         */
+        SPEAK,
+    }
+
+    /**
+     * The number of different command types
+     */
+    static final int NUM_COMMAND_TYPES = 4;
+
+    /**
+     * Cutoff after which we say the command was not classified successfully
+     */
+    static final float CLASSIFICAITON_CUTOFF = 0.5f;
+
+    /**
+     * The model
+     */
+    static SavedModelBundle model;
+
+    /**
+     * Initializes the categorization model
+     */
+    public static void init(){
+        model = SavedModelBundle.load("./data/model/sim/command_cat");
+
+        //describe the model
+        // System.out.println(model.functions());
+        // System.out.println(model.functions().get(0).signature());
+        // System.out.println(model.functions().get(1).signature());
+        // System.out.println(model.functions().get(2).signature());
+        // System.out.println(model.functions().get(0).signature().getInputs());
+        // System.out.println(model.functions().get(0).signature().getInputs().values().iterator().next().name);
+        // System.out.println(model.functions().get(0).signature().getInputs().values().iterator().next().shape);
+    }
+
+    /**
+     * Categorizes the sentence by function
+     * @param input The input string
+     * @return The function of the sentence
+     */
+    public static CommandType categorize(String input){
+        //construct input
+        TString inputTensor = TString.scalarOf(input);
+        inputTensor.shape().append(1);
+        NdArray<String> stringArr = NdArrays.ofObjects(String.class, Shape.of(1, 1));
+        stringArr.setObject(input);
+        inputTensor = TString.tensorOf(stringArr);
+
+        Map<String,Tensor> inputMap = new HashMap<String,Tensor>();
+        inputMap.put("keras_tensor",inputTensor);
+
+        //call
+        Result result = model.function("serve").call(inputMap);
+
+        //parse results
+        TFloat32 resultFloats = (TFloat32)result.get(0);
+        FloatNdArray floatArr = resultFloats.get(0);
+        float[] classification = new float[NUM_COMMAND_TYPES];
+        for(int i = 0; i < NUM_COMMAND_TYPES; i++){
+            classification[i] = floatArr.getFloat(i);
+        }
+
+        //figure out highest value category
+        int maxAt = 0;
+        for(int i = 0; i < classification.length; i++){
+            if(classification[i] > classification[maxAt]){
+                maxAt = i;
+            }
+        }
+
+        //throw an error if not confident in the classification
+        if(classification[maxAt] < CLASSIFICAITON_CUTOFF){
+            String message = "Failed to classify command definitively! \n";
+            for(int i = 0; i < NUM_COMMAND_TYPES; i++){
+                message = message + "class[" + i + "] " + classification[i] + "\n";
+            }
+            throw new Error(message);
+        }
+
+        //convert to enum of command types
+        switch(maxAt){
+            case 0: {
+                return CommandType.HELP;
+            }
+            case 1: {
+                return CommandType.LOOK;
+            }
+            case 2: {
+                return CommandType.WALK;
+            }
+            case 3: {
+                return CommandType.SPEAK;
+            }
+            default: {
+                throw new Error("Failed to categorize!");
+            }
+        }
+    }
+
+}

src/main/java/org/studiorailgun/sim/space/World.java

@@ -1,5 +1,10 @@
 package org.studiorailgun.sim.space;
 
+import java.util.LinkedList;
+import java.util.List;
+
+import org.studiorailgun.sim.character.Character;
+
 /**
  * Top level spatial container
  */
@@ -10,6 +15,11 @@ public class World {
      */
     Region region;
 
+    /**
+     * The list of characters
+     */
+    List<Character> characters = new LinkedList<Character>();
+
     /**
      * Gets the top level region of this world
      * @return The top level region of this world
@@ -26,4 +36,22 @@ public class World {
         this.region = region;
     }
 
+    /**
+     * Gets the list of characters in this world
+     * @return The list of characters in this world
+     */
+    public List<Character> getCharacters() {
+        return characters;
+    }
+
+    /**
+     * Sets the list of characters in this world
+     * @param characters The list of characters in this world
+     */
+    public void setCharacters(List<Character> characters) {
+        this.characters = characters;
+    }
+
+    
+
 }

src/main/python/sim/command_cat.py

@@ -0,0 +1,220 @@
+import tensorflow as tf
+keras = tf.keras
+from tensorflow import Tensor
+from keras.api.layers import TextVectorization, Embedding, LSTM, Dense, Bidirectional
+from keras.api.models import Sequential
+import numpy as np
+import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
+
+
+
+#
+#
+#  Description: The purpose of this model is to categorize the command a player inputs
+#
+#    - "HELP" - Asking the system for help (ie for what commands are available)
+#
+#    - "LOOK" - Looks at something
+#
+#    - "WALK" - Walks towards something
+#
+#    - "SPEAK" - Speaks at something
+#
+
+
+
+#
+#
+# MODEL CONSTANTS
+#
+#
+
+# this is the maximum allowed size of the vocabulary
+max_features: int = 20000
+
+# the dimension of the output from the embedding layer
+embedding_dim: int = 128
+
+# The number of epochs to train for
+epochs: int = 50
+
+# Maximum size of the vocab for this layer
+max_tokens: int = 5000
+
+# (Only valid in INT mode) If set, the output will have its time dimension padded or truncated to exactly output_sequence_length values
+output_sequence_length: int = 4
+
+# The number of classes we're training for
+num_classes: int = 4
+
+# Path to the data
+data_path: str = './data/semantic/sim/rpg_command'
+
+# Path to export the model to
+export_path: str = './data/model/sim/command_cat'
+
+
+
+
+#
+#
+# LOAD DATA
+#
+#
+
+
+# read training sentences
+train_csv_path: str = data_path + '/train.csv'
+train_csv_raw: DataFrame = pd.read_csv(open(train_csv_path), dtype={
+    "help": "int64",
+    "look": "int64",
+    "walk": "int64",
+    "speak": "int64",
+    "sentence": "string",
+})
+train_data_raw: DataFrame = train_csv_raw[["sentence"]]
+train_data_split: DataFrame = train_data_raw
+vocab: list[str] = train_data_raw.to_numpy()
+train_labels: DataFrame = train_csv_raw[["help", "look", "walk", "speak"]]
+
+# read testing sentences
+test_csv_path: str = data_path + '/test.csv'
+test_csv_raw: DataFrame = pd.read_csv(open(test_csv_path), dtype={
+    "help": "int64",
+    "look": "int64",
+    "walk": "int64",
+    "speak": "int64",
+    "sentence": "string",
+})
+test_data_raw: DataFrame = test_csv_raw[["sentence"]]
+test_data_split: DataFrame = test_data_raw
+test_labels: DataFrame = test_csv_raw[["help", "look", "walk", "speak"]]
+
+
+
+#
+#
+# CREATE VECTORIZER
+#
+#
+
+
+# init vectorizer
+textVec: TextVectorization = TextVectorization(
+    max_tokens=max_tokens,
+    output_mode='int',
+    output_sequence_length=output_sequence_length,
+    pad_to_max_tokens=True
+)
+
+# Add the vocab to the tokenizer
+textVec.adapt(vocab) 
+input_data: list[str] = train_data_split
+train_data: Tensor = textVec.call(input_data)
+
+
+
+#
+#
+# CREATE MODEL
+#
+#
+
+
+# construct model
+model: Sequential = Sequential([
+    keras.Input(shape=(1,), dtype=tf.string),
+    textVec,
+    Embedding(max_features + 1, embedding_dim),
+    Bidirectional(LSTM(256)),
+    Dense(64, activation='relu'),
+    Dense(num_classes, activation='sigmoid')
+])
+
+#compile the model
+# model.build(keras.Input(shape=(None,), dtype="int64"))
+model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
+
+
+
+#
+#
+# TRAIN MODEL
+#
+#
+
+# Final formatting of data
+npTrainData = train_data_split.to_numpy(dtype=object).flatten()
+npTrainLabel: npt.NDArray[np.complex64] = train_labels.to_numpy()
+if(npTrainData.shape[0] != npTrainLabel.shape[0]):
+    print("Data label size mismatch!")
+    print(npTrainData.shape)
+    print(npTrainLabel.shape)
+
+# try fitting data
+print("Training..")
+model.fit(npTrainData,npTrainLabel,epochs=epochs)
+
+
+
+#
+#
+# EVALUATE MODEL
+#
+#
+
+
+# evaluate here
+npTestData: npt.NDArray = test_data_split.to_numpy(dtype=object).flatten()
+npTestLabel: npt.NDArray[np.complex64] = test_labels.to_numpy()
+if(npTestData.shape[0] != npTestLabel.shape[0]):
+    print("Data label size mismatch!")
+    print(npTestData.shape)
+    print(npTestLabel.shape)
+
+print("Evaluating..")
+model.evaluate(npTestData,npTestLabel)
+
+
+
+#
+#
+# PREDICT
+#
+#
+
+# predictTargetRaw: Tensor = tf.constant(['Hello'])
+# npPredict: npt.NDArray = np.array(predictTargetRaw, dtype=object)
+# print("Prediction test..")
+# result: list[int] = model.predict(predictTargetRaw)
+# print("predict result:")
+# print(predictTargetRaw)
+# print(result)
+# print(train_data)
+# print(train_labels)
+
+
+
+#
+#
+# SAVE (DEVELOPMENT) MODEL
+#
+#
+
+# save the model so keras can reload
+# savePath: str = export_path
+# model.save(savePath)
+
+
+#
+#
+# SAVE (PRODUCTION) MODEL
+#
+#
+
+# export the model so java can leverage it
+print("Saving..")
+model.export(export_path)
+