Optimize prediction

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

@@ -34,9 +34,9 @@ public class Main {
         System.out.println(network.synCount());
 
         Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(0.001F, 2000, network, dataset);
+        trainer.train(0.01F, 2000, network, dataset);
 
-        plotGraph(dataset, network);
+        //plotGraph(dataset, network);
     }
 
     private static FullyConnectedNetwork createNetwork(int[] neuronPerLayer, int nbrInput){
@@ -83,8 +83,8 @@ public class Main {
         float step = 0.03F;
         for (float x = min; x < max; x+=step){
             for (float y = min; y < max; y+=step){
-                List<Float> predictions = network.predict(List.of(new Input(x), new Input(y)));
-                visualizer.addPoint(Float.toString(Math.round(predictions.getFirst())), x, y);
+                float[] predictions = network.predict(new float[]{x, y});
+                visualizer.addPoint(Float.toString(Math.round(predictions[0])), x, y);
             }
         }
 

src/main/java/com/naaturel/ANN/domain/abstraction/Model.java

@@ -15,5 +15,5 @@ public interface Model {
     //void forEachSynapse(Consumer<Synapse> consumer);
     void forEachOutputNeurons(Consumer<Neuron> consumer);
     void forEachNeuronConnectedTo(Neuron n, Consumer<Neuron> consumer);
-    List<Float> predict(List<Input> inputs);
+    float[] predict(float[] inputs);
 }

src/main/java/com/naaturel/ANN/domain/abstraction/TrainingContext.java

@@ -11,7 +11,7 @@ public abstract class TrainingContext {
     public DataSetEntry currentEntry;
 
     public List<Float> expectations;
-    public List<Float> predictions;
+    public float[] predictions;
     public float[] deltas;
 
     public float globalLoss;

src/main/java/com/naaturel/ANN/domain/model/neuron/FullyConnectedNetwork.java

@@ -24,13 +24,12 @@ public class FullyConnectedNetwork implements Model {
     }
 
     @Override
-    public List<Float> predict(List<Input> inputs) {
-        List<Input> previousLayerOutputs = new ArrayList<>(inputs);
-        for(Layer layer : this.layers){
-            List<Float> currentLayerOutputs = layer.predict(previousLayerOutputs);
-            previousLayerOutputs = currentLayerOutputs.stream().map(Input::new).toList();
+    public float[] predict(float[] inputs) {
+        float[] previousLayerOutputs = inputs;
+        for (Layer layer : layers) {
+            previousLayerOutputs = layer.predict(previousLayerOutputs);
         }
-        return previousLayerOutputs.stream().map(Input::getValue).toList();
+        return previousLayerOutputs;
     }
 
     @Override

src/main/java/com/naaturel/ANN/domain/model/neuron/Layer.java

@@ -19,11 +19,10 @@ public class Layer implements Model {
     }
 
     @Override
-    public List<Float> predict(List<Input> inputs) {
-        List<Float> result = new ArrayList<>();
-        for(Neuron neuron : this.neurons){
-            List<Float> res = neuron.predict(inputs);
-            result.addAll(res);
+    public float[] predict(float[] inputs) {
+        float[] result = new float[neurons.length];
+        for (int i = 0; i < neurons.length; i++) {
+            result[i] = neurons[i].predict(inputs)[0];
         }
         return result;
     }

src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java

@@ -17,6 +17,7 @@ public class Neuron implements Model {
         this.id = id;
         this.activationFunction = func;
 
+        output = 0;
         weights = new float[synapses.length+1]; //takes the bias into account
         inputs = new float[synapses.length+1]; //takes the bias into account
 
@@ -44,10 +45,6 @@ public class Neuron implements Model {
         return this.activationFunction;
     }
 
-    public float getOutput(){
-        return this.output;
-    }
-
     public float calculateWeightedSum() {
         int count = synCount();
         float weightedSum = 0F;
@@ -61,6 +58,10 @@ public class Neuron implements Model {
         return this.id;
     }
 
+    public float getOutput() {
+        return this.output;
+    }
+
     @Override
     public int synCount() {
         return this.weights.length;
@@ -77,10 +78,10 @@ public class Neuron implements Model {
     }
 
     @Override
-    public List<Float> predict(List<Input> inputs) {
+    public float[] predict(float[] inputs) {
         this.setInputs(inputs);
-        this.output = activationFunction.accept(this);
-        return List.of(output);
+        output = activationFunction.accept(this);
+        return new float[] {output};
     }
 
     @Override
@@ -98,10 +99,8 @@ public class Neuron implements Model {
         throw new UnsupportedOperationException("Neurons have no connection with themselves");
     }
 
-    private void setInputs(List<Input> values){
-        for(int i = 0;  i < values.size(); i++){
-            inputs[i+1] = values.get(i).getValue();
-        }
+    private void setInputs(float[] values){
+        System.arraycopy(values, 0, inputs, 1, values.length);
     }
 
 }

src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java

@@ -101,7 +101,7 @@ public class TrainingPipeline {
 
             if(this.verbose && ctx.epoch % this.verboseDelay == 0) {
                 System.out.printf("Epoch : %d, ", ctx.epoch);
-                System.out.printf("predicted : %s, ", Arrays.toString(ctx.predictions.toArray()));
+                System.out.printf("predicted : %s, ", Arrays.toString(ctx.predictions));
                 System.out.printf("expected : %s, ", Arrays.toString(ctx.expectations.toArray()));
                 System.out.printf("delta : %s, ", Arrays.toString(ctx.deltas));
                 System.out.printf("loss : %.5f\n", ctx.localLoss);

src/main/java/com/naaturel/ANN/implementation/simplePerceptron/SimpleDeltaStep.java

@@ -21,11 +21,11 @@ public class SimpleDeltaStep implements AlgorithmStep {
     public void run() {
         DataSet dataSet = context.dataset;
         DataSetEntry entry = context.currentEntry;
-        List<Float> predicted = context.predictions;
+        float[] predicted = context.predictions;
         List<Float> expected = dataSet.getLabelsAsFloat(entry);
 
-        for (int i = 0; i < predicted.size(); i++) {
-            context.deltas[i] = expected.get(i) - predicted.get(i);
+        for (int i = 0; i < predicted.length; i++) {
+            context.deltas[i] = expected.get(i) - predicted[0];
         }
     }
 

src/main/java/com/naaturel/ANN/implementation/simplePerceptron/SimplePredictionStep.java

@@ -2,6 +2,9 @@ package com.naaturel.ANN.implementation.simplePerceptron;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.TrainingContext;
+import com.naaturel.ANN.domain.model.neuron.Input;
+
+import java.util.List;
 
 public class SimplePredictionStep implements AlgorithmStep {
 
@@ -13,6 +16,11 @@ public class SimplePredictionStep implements AlgorithmStep {
 
     @Override
     public void run() {
-        context.predictions = context.model.predict(context.currentEntry.getData());
+        List<Input> data = context.currentEntry.getData();
+        float[] flatData = new float[data.size()];
+        for (int i = 0; i < data.size(); i++) {
+            flatData[i] = data.get(i).getValue();
+        }
+        context.predictions = context.model.predict(flatData);
     }
 }