using System;
using UnityEngine;
using Unity.Mathematics;

namespace NanoBrain {

    /// <summary>
    /// A Synapse connects the ouput of a Neuron to another Neuron
    /// </summary>
    [Serializable]
    public class Synapse {
        /// <summary>
        /// The neuron from which input is received
        /// </summary>
        [SerializeReference]
        [HideInInspector]
        public Neuron neuron;

        /// <summary>
        /// The weight value to apply to the Neuron input
        /// </summary>
        public float weight;

        /// <summary>
        /// Indicator whether the weight can be trained
        /// </summary>
        public bool trainable = false;

        /// <summary>
        /// Create a new Synapse
        /// </summary>
        /// <param name="nucleus">The neuron from which input is received</param>
        /// <param name="weight">The weight value to apply to the Neuron input</param>
        public Synapse(Neuron nucleus, float weight = 1.0f) {
            this.neuron = nucleus;
            this.weight = weight;
        }

        public virtual void BackPropagation(Neuron receiver, float error, float learningRate) {
            float derivative = error;

            switch (receiver.activator) {
                case Neuron.ActivationType.Linear:
                    derivative *= 1;
                    break;
                case Neuron.ActivationType.Power:
                    // untested
                    derivative *= 2 * math.length(this.neuron.combination);
                    break;
                case Neuron.ActivationType.Reciprocal:
                    // untested
                    derivative *= -1 / Mathf.Pow(math.length(this.neuron.combination), 2);
                    break;
                default:
                    Debug.Log("other activator");
                    break;
            }

            this.neuron.BackPropagation2(derivative * this.weight, learningRate);

            derivative *= math.length(this.neuron.activation);

            if (this.trainable) {
                float deltaWeight = learningRate * derivative;
                this.weight += deltaWeight;
            }
        }

        public virtual void BackPropagation(Neuron receiver, Vector3 derivative, float learningRate) {
            //Vector3 derivative = error;

            switch (receiver.activator) {
                case Neuron.ActivationType.Linear:
                    derivative *= 1;
                    break;
                case Neuron.ActivationType.Power:
                    // untested
                    derivative *= 2 * math.length(this.neuron.combination);
                    break;
                case Neuron.ActivationType.Reciprocal:
                    // untested
                    derivative *= -1 / Mathf.Pow(math.length(this.neuron.combination), 2);
                    break;
                default:
                    Debug.Log("other activator");
                    break;               
            }

            this.neuron.BackPropagation3D(derivative * this.weight, learningRate);

            derivative = Vector3.Scale(derivative, this.neuron.activation);

            if (this.trainable) {
                // Compared to the 1D solution, this does not decrease the weight.
                // direction and sign are different....
                // Perhaps the sign is determine by the direction of the derivative and the neuron activation?
                // When they are oppositie, the sign is negative? (or the other way round...)
                this.weight += learningRate * derivative.magnitude;
            }
        }
    }

}