Add JSON config loading

config.json

@@ -0,0 +1,14 @@
+{
+  "model": {
+    "new": false,
+    "parameters": [25, 50, 1],
+    "path": "C:/Users/Laurent/Desktop/ANN-framework/snapshot.json"
+  },
+  "training" : {
+    "learning_rate" : 0.01,
+    "max_epoch" : 2000
+  },
+  "dataset" : {
+    "path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_10.csv"
+  }
+}

src/main/java/com/naaturel/ANN/Main.java

@@ -8,6 +8,8 @@ import com.naaturel.ANN.implementation.gradientDescent.Linear;
 import com.naaturel.ANN.implementation.multiLayers.Sigmoid;
 import com.naaturel.ANN.implementation.multiLayers.TanH;
 import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
+import com.naaturel.ANN.infrastructure.config.ConfigDto;
+import com.naaturel.ANN.infrastructure.config.ConfigLoader;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.infrastructure.dataset.DataSetEntry;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
@@ -22,27 +24,37 @@ public class Main {
 
     public static void main(String[] args) throws Exception {
 
+        ConfigDto config = ConfigLoader.load("C:/Users/Laurent/Desktop/ANN-framework/config.json");
+
+        boolean newModel = config.getModelProperty("new", Boolean.class);
+        int[] modelParameters = config.getModelProperty("parameters", int[].class);
+        String modelPath = config.getModelProperty("path", String.class);
+        int maxEpoch = config.getTrainingProperty("max_epoch", Integer.class);
+        float learningRate = config.getTrainingProperty("learning_rate", Double.class).floatValue();
+        String datasetPath = config.getDatasetProperty("path", String.class);
+
         int nbrClass = 1;
-
-        DataSet dataset = new DatasetExtractor()
-                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_10.csv", nbrClass);
-
-        int[] neuronPerLayer = new int[]{2, 3, dataset.getNbrLabels()};
+        DataSet dataset = new DatasetExtractor().extract(datasetPath, nbrClass);
         int nbrInput = dataset.getNbrInputs();
 
-        FullyConnectedNetwork network = createNetwork(neuronPerLayer, nbrInput);
 
-        System.out.println(network.synCount());
+        ModelSnapshot snapshot;
 
-        Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(0.01F, 2000, network, dataset);
+        Model network;
+        if(newModel){
+            network = createNetwork(modelParameters, nbrInput);
+            snapshot = new ModelSnapshot(network);
+            System.out.println("Parameters: " + network.synCount());
+            Trainer trainer = new GradientBackpropagationTraining();
+            trainer.train(learningRate, maxEpoch, network, dataset);
+        } else {
+            snapshot = new ModelSnapshot();
+            snapshot.loadFromFile(modelPath);
+            network = snapshot.getModel();
+        }
 
-        //plotGraph(dataset, network);
-        ModelSnapshot snapshot = new ModelSnapshot(network);
-        snapshot.saveToFile("C:/Users/Laurent/Desktop/MASI4-RNA/snapshot.json");
-        snapshot.loadFromFile("C:/Users/Laurent/Desktop/MASI4-RNA/snapshot.json");
-        Model m = snapshot.getModel();
-        System.out.println();
+        plotGraph(dataset, network);
+        snapshot.saveToFile(modelPath);
     }
 
     private static FullyConnectedNetwork createNetwork(int[] neuronPerLayer, int nbrInput){

src/main/java/com/naaturel/ANN/infrastructure/config/ConfigDto.java

@@ -0,0 +1,54 @@
+package com.naaturel.ANN.infrastructure.config;
+
+import com.fasterxml.jackson.annotation.JsonProperty;
+
+import java.util.List;
+import java.util.Map;
+
+public class ConfigDto {
+
+    @JsonProperty("model")
+    private Map<String, Object> modelConfig;
+
+    @JsonProperty("training")
+    private Map<String, Object> trainingConfig;
+
+    @JsonProperty("dataset")
+    private Map<String, Object> datasetConfig;
+
+    public <T> T getModelProperty(String key, Class<T> type) {
+        Object value = find(key, this.modelConfig);
+        if (value instanceof List<?> list && type.isArray()) {
+            int[] arr = new int[list.size()];
+            for (int i = 0; i < list.size(); i++) {
+                arr[i] = ((Number) list.get(i)).intValue();
+            }
+            return type.cast(arr);
+        }
+        if (!type.isInstance(value)) {
+            throw new RuntimeException("Property '" + key + "' is not of type " + type.getSimpleName());
+        }
+        return type.cast(value);
+    }
+
+    public <T> T getTrainingProperty(String key, Class<T> type) {
+        Object value = find(key, this.trainingConfig);
+        if (!type.isInstance(value)) {
+            throw new RuntimeException("Property '" + key + "' is not of type " + type.getSimpleName());
+        }
+        return type.cast(value);
+    }
+
+    public <T> T getDatasetProperty(String key, Class<T> type) {
+        Object value = find(key, this.datasetConfig);
+        if (!type.isInstance(value)) {
+            throw new RuntimeException("Property '" + key + "' is not of type " + type.getSimpleName());
+        }
+        return type.cast(value);
+    }
+
+    private Object find(String key, Map<String, Object> config){
+        if(!config.containsKey(key)) throw new RuntimeException("Unable to find property for key '" + key + "'");
+        return config.get(key);
+    }
+}

src/main/java/com/naaturel/ANN/infrastructure/config/ConfigLoader.java

@@ -0,0 +1,22 @@
+package com.naaturel.ANN.infrastructure.config;
+
+import com.fasterxml.jackson.databind.ObjectMapper;
+
+import java.io.File;
+
+public class ConfigLoader {
+
+
+    public static ConfigDto load(String path) throws Exception {
+        try {
+
+            ObjectMapper mapper = new ObjectMapper();
+            ConfigDto config = mapper.readValue(new File("config.json"), ConfigDto.class);
+
+            return config;
+        } catch (Exception e){
+            throw new Exception("Unable to load config : " + e.getMessage());
+        }
+    }
+
+}