package com.naaturel.ANN;

import com.naaturel.ANN.domain.abstraction.Neuron;
import com.naaturel.ANN.domain.model.dataset.DataSet;
import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
import com.naaturel.ANN.domain.model.dataset.Label;
import com.naaturel.ANN.domain.model.neuron.Bias;
import com.naaturel.ANN.domain.model.neuron.Input;
import com.naaturel.ANN.domain.model.neuron.Synapse;
import com.naaturel.ANN.domain.model.neuron.Weight;
import com.naaturel.ANN.implementation.activationFunction.Heaviside;
import com.naaturel.ANN.implementation.activationFunction.Linear;
import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
import com.naaturel.ANN.implementation.training.GradientDescentTraining;

import java.util.*;

public class Main {

    public static void main(String[] args){

        DataSet orDataSet = new DataSet(Map.ofEntries(
                Map.entry(new DataSetEntry(List.of(0.0F, 0.0F)), new Label(0.0F)),
                Map.entry(new DataSetEntry(List.of(0.0F, 1.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(1.0F, 0.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(1.0F, 1.0F)), new Label(1.0F))
        ));

        DataSet andDataSet = new DataSet(Map.ofEntries(
                Map.entry(new DataSetEntry(List.of(0.0F, 0.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(0.0F, 1.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(1.0F, 0.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(1.0F, 1.0F)), new Label(1.0F))
        ));

        DataSet dataSet = new DataSet(Map.ofEntries(
                Map.entry(new DataSetEntry(List.of(1.0F, 6.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(7.0F, 9.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(1.0F, 9.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(7.0F, 10.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(2.0F, 5.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(2.0F, 7.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(2.0F, 8.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(6.0F, 8.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(6.0F, 9.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(3.0F, 5.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(3.0F, 6.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(3.0F, 8.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(3.0F, 9.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(5.0F, 7.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(5.0F, 8.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(5.0F, 10.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(5.0F, 11.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(4.0F, 6.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(4.0F, 7.0F)), new Label(-1.0F)),
                Map.entry(new DataSetEntry(List.of(4.0F, 9.0F)), new Label(1.0F)),
                Map.entry(new DataSetEntry(List.of(4.0F, 10.0F)), new Label(1.0F))
        ));

        List<Synapse> syns = new ArrayList<>();
        syns.add(new Synapse(new Input(0), new Weight(0)));
        syns.add(new Synapse(new Input(0), new Weight(0)));

        Bias bias =  new Bias(new Weight(0));

        Neuron n = new SimplePerceptron(syns, bias, new Linear());
        GradientDescentTraining st = new GradientDescentTraining();

        long start = System.currentTimeMillis();

        st.train(n, 0.2F, andDataSet);

        long end = System.currentTimeMillis();
        System.out.printf("Training completed in %.2f s%n", (end - start) / 1000.0);
    }
}