package com.naaturel.ANN;

import com.naaturel.ANN.domain.model.neuron.Neuron;
import com.naaturel.ANN.domain.abstraction.Trainer;
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.dataset.DataSet;
import com.naaturel.ANN.infrastructure.dataset.DataSetEntry;
import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
import com.naaturel.ANN.domain.model.neuron.*;
import com.naaturel.ANN.infrastructure.graph.GraphVisualizer;

import java.util.*;

public class Main {

    public static void main(String[] args){

        int nbrClass = 1;

        DataSet dataset = new DatasetExtractor()
                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_12.csv", nbrClass);

        int[] neuronPerLayer = new int[]{10, dataset.getNbrLabels()};
        int nbrInput = dataset.getNbrInputs();

        List<Layer> layers = new ArrayList<>();
        for (int i = 0; i < neuronPerLayer.length; i++){

            List<Neuron> neurons = new ArrayList<>();
            for (int j = 0; j < neuronPerLayer[i]; j++){

                int nbrSyn = i == 0 ? nbrInput: neuronPerLayer[i-1];

                List<Synapse> syns = new ArrayList<>();
                for (int k=0; k < nbrSyn; k++){
                    syns.add(new Synapse(new Input(0), new Weight()));
                }

                Bias bias =  new Bias(new Weight());

                Neuron n = new Neuron(syns, bias, new Sigmoid(2));
                neurons.add(n);
            }
            Layer layer = new Layer(neurons);
            layers.add(layer);
        }

        FullyConnectedNetwork network = new FullyConnectedNetwork(layers);

        Trainer trainer = new GradientBackpropagationTraining();
        trainer.train(0.5F, network, dataset);

        /*GraphVisualizer visualizer = new GraphVisualizer();

        for (DataSetEntry entry : dataset) {
            List<Float> label = dataset.getLabelsAsFloat(entry);
            visualizer.addPoint("Label " + label.getFirst(), entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
        }

        float min = -2F;
        float max = 2F;
        float step = 0.01F;
        for (float x = min; x < max; x+=step){
            for (float y = min; y < max; y+=step){
                float prediction = network.predict(List.of(new Input(x), new Input(y))).getFirst();
                float predSeries = prediction > 0.5F ? 1 : 0;
                visualizer.addPoint(Float.toString(predSeries), x, y);
            }
        }


        visualizer.buildScatterGraph();*/
    }

}