663 lines
24 KiB
C#
663 lines
24 KiB
C#
using System;
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using System.Collections.Generic;
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using UnityEngine;
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using UnityEditor;
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#if UNITY_MATHEMATICS
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using Unity.Mathematics;
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using static Unity.Mathematics.math;
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#endif
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namespace NanoBrain {
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/// <summary>
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/// A neuron is a basic Nucleus
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/// </summary>
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/// A neuron combines the weighted input from other neurons and applies an activation function to it
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/// to compute the output value:
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/// \code
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/// Vector3 combination = NanoBrain::Neuron::Combinator(bias, synapses);
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/// Vector3 output = NanoBrain::Neuron::Activator(combination);
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/// \endcode
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/// The synapses are connections to other neurons.
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/// Each connection has a weight which is used to multiply the output of that other neuron
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/// before it is used by the combinator.
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[Serializable]
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public class Neuron : Nucleus {
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/// <summary>
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/// Create a new Neuron in a Cluster instance
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/// </summary>
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/// <param name="parent">The parent cluster in which the new Neuron should be created</param>
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/// <param name="name">The name of the new Neuron</param>
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public Neuron(Cluster parent, string name) {
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this.parent = parent;
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this.name = name;
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if (this.parent != null) {
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this.parent.nuclei ??= new();
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this.parent.nuclei.Add(this);
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}
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}
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#region Serialization
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/// <summary>
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/// The bias
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/// </summary>
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/// The bias which a value which is always added to the combined value of the neuron
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/// It does not have a synapse and therefore no weight of source nucleus
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//[HideInInspector]
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public Vector3 bias = Vector3.zero;
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#region Synapses
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[SerializeField]
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private List<Synapse> _synapses = new();
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/// <summary>
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/// The synapses of the nucleus
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/// </summary>
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public List<Synapse> synapses => _synapses;
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/// <summary>
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/// Add a new synapse to this nuclues
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/// </summary>
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/// <param name="sendingNucleus">The nucleus from which the signals may originate</param>
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/// <param name="weight">The weight applied to the input. Default value = 1</param>
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/// <returns>The created Synapse</returns>
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/// This will add a new input to this nucleus with the given weight.
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public Synapse AddSynapse(Neuron sendingNucleus, float weight = 1) {
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Synapse synapse = new(sendingNucleus, weight);
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this.synapses.Add(synapse);
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return synapse;
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}
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/// <summary>
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/// Find a synapse
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/// </summary>
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/// <param name="sender">The sender of the input to the Synapse</param>
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/// <returns>The found Synapse or null when the sender has no synapse to this nucleus.</returns>
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public Synapse GetSynapse(Nucleus sender) {
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foreach (Synapse synapse in this.synapses)
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if (synapse.neuron == sender)
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return synapse;
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return null;
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}
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/// <summary>
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/// Remove a synapse from a Nucleus
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/// </summary>
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/// <param name="sendingNucleus">Remote the synapse connecting to this Nucleus</param>
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public void RemoveSynapse(Nucleus sendingNucleus) {
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this.synapses.RemoveAll(synapse => synapse.neuron == sendingNucleus);
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}
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#endregion Synapses
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/// <summary>
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/// Set the bias, recalculate the output and update all Nuclei receiving from this Nucleus
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/// </summary>
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/// <param name="inputValue"></param>
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public virtual void SetBias(Vector3 inputValue) {
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this.bias = inputValue;
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this.lastUpdate = Time.time;
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this.parent?.UpdateFromNucleus(this);
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}
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/// <summary>
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/// The type of combinators
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/// </summary>
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/// A combinator combines the weighted values of the synapses to a single value
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public enum CombinatorType {
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/// Add the weighted values together
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Sum,
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/// Multiply the weighted values
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Product,
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}
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/// <summary>
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/// The type of combinator used for this Neuron
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/// </summary>
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[HideInInspector]
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public CombinatorType combinator = CombinatorType.Sum;
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/// <summary>
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/// The type of
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/// </summary>
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public enum ActivationType {
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Linear,
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Power,
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Sqrt,
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Reciprocal,
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Tanh,
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Binary,
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Normalized,
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Custom
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}
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/// <summary>
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/// The activation function
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/// </summary>
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[SerializeField]
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[HideInInspector]
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public ActivationType _activator;
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/// <summary>
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/// The activation funtion
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/// </summary>
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public ActivationType activator {
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get { return _activator; }
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set {
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_activator = value;
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//this.curve = GenerateCurve();
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}
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}
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#endregion Serialization
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#if UNITY_MATHEMATICS
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/// <summary>
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/// The output value of the neuron
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/// </summary>
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[HideInInspector]
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protected float3 _outputValue;
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/// <summary>
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/// The output value of the neuron
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/// </summary>
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public virtual float3 outputValue {
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get { return _outputValue; }
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set {
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_outputValue = value;
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if (this.isFiring)
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WhenFiring?.Invoke();
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}
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}
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/// <summary>
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/// The magnitude of the neuron output
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/// </summary>
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public float outputMagnitude => length(_outputValue);
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/// <summary>
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/// The squared magnitude of the neuron output
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/// </summary>
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public float outputSqrMagnitude => lengthsq(_outputValue);
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#else
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/// <summary>
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/// The output value of the neuron
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/// </summary>
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protected Vector3 _outputValue;
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/// <summary>
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/// The output value of the neuron
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/// </summary>
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public virtual Vector3 outputValue {
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get { return _outputValue; }
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set {
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_outputValue = value;
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if (this.isFiring)
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WhenFiring?.Invoke();
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}
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}
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/// <summary>
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/// The magnitude of the neuron output
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/// </summary>
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public float outputMagnitude => _outputValue.magnitude;
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/// <summary>
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/// The squared magnitude of the neuron output
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/// </summary>
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public float outputSqrMagnitude => _outputValue.sqrMagnitude;
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#endif
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/// <summary>
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/// True if the neuron have a positive value with magnitude > 0.5
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/// </summary>
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public bool isFiring => this.outputMagnitude > 0.5f;
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/// <summary>
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/// An action which is called every time the neuron is updated and is firing
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/// </summary>
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public Action WhenFiring;
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/// <summary>
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/// When true, the value will not be reset after timeToSleep.
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/// </summary>
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public bool persistOutput = false;
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/// <summary>
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/// True when the neuron is not persisting and has not be updated for timeToSleep seconds
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/// </summary>
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public virtual bool isSleeping => !persistOutput && (Time.time - this.lastUpdate > timeToSleep);
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/// <summary>
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/// Check if the neuron is sleeping.
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/// </summary>
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/// This will reset the output value if it is sleeping
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public void SleepCheck() {
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if (this.isSleeping && this.outputSqrMagnitude > 0) {
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#if UNITY_MATHEMATICS
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this._outputValue = new float3(0, 0, 0);
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#else
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this._outputValue = new Vector3(0,0,0);
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#endif
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}
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}
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/// <summary>
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/// The time at which the last update has been done
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/// </summary>
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[HideInInspector]
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public float lastUpdate = 0;
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/// <summary>
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/// Time in seconds after the last update the neuron can go to sleep
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/// </summary>
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public static readonly float timeToSleep = 0.5f;
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public bool breakOnUpdate = false;
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/// \copydoc NanoBrain::Nucleus::ShallowCloneTo
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public override Nucleus ShallowCloneTo(Cluster parent) {
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Neuron clone = new(parent, this.name) {
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// prefabNucleus = this
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};
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CloneFields(clone);
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return clone;
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}
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/// <summary>
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/// Copy relevant fields of this neuron to the given neuron
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/// </summary>
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/// <param name="clone"></param>
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protected virtual void CloneFields(Neuron clone) {
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clone.bias = this.bias;
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clone.persistOutput = this.persistOutput;
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clone.combinator = this.combinator;
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clone.activator = this.activator;
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clone.breakOnUpdate = this.breakOnUpdate;
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}
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/// <summary>
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/// Delete the give neuron
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/// </summary>
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/// <param name="nucleus">The neuron to delete</param>
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public static void Delete(Nucleus nucleus) {
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if (nucleus == null)
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return;
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if (nucleus is Neuron neuron) {
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foreach (Synapse synapse in neuron.synapses) {
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if (synapse.neuron is Neuron synapse_nucleus) {
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if (synapse_nucleus.receivers.Count > 1) {
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// there is another nucleus feeding into this input nucleus
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synapse_nucleus.receivers.RemoveAll(r => r == nucleus);
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}
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else {
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// No other links, delete it.
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Neuron.Delete(synapse_nucleus);
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}
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}
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}
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foreach (Nucleus receiver in neuron.receivers) {
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if (receiver is not Neuron receiverNeuron)
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continue;
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if (receiver != null && receiverNeuron.synapses != null)
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receiverNeuron.synapses.RemoveAll(s => s.neuron == nucleus);
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}
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}
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else if (nucleus is Cluster cluster) {
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// remove all receivers for this cluster
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foreach (Nucleus clusterNucleus in cluster.nuclei) {
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if (clusterNucleus is Neuron output) {
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foreach (Nucleus receiver in output.receivers) {
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if (receiver is not Neuron receiverNeuron)
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continue;
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receiverNeuron.synapses.RemoveAll(s => s.neuron == output);
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}
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}
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}
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}
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if (nucleus.parent.prefab != null) {
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nucleus.parent.nuclei.RemoveAll(n => n == nucleus);
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nucleus.parent.RefreshOutputs();
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}
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}
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/// \copydoc NanoBrain::Nucleus::UpdateStateIsolated
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public override void UpdateStateIsolated() {
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if (breakOnUpdate) {
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Debug.Break();
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}
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var combination = Combinator(this.bias, this.synapses);
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this.outputValue = Activator(combination);
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this.lastUpdate = Time.time;
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}
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#region Combinator
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#if UNITY_MATHEMATICS
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/// <summary>
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/// The combinator which combines the bias with the values from all synapses
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/// </summary>
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/// <param name="bias">The bias of the neuron</param>
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/// <param name="synapses">The synapses of the neuron</param>
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/// <returns></returns>
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protected float3 Combinator(float3 bias, List<Synapse> synapses) {
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switch (combinator) {
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case CombinatorType.Sum:
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return CombinatorSum(bias, synapses);
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case CombinatorType.Product:
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return CombinatorProduct(bias, synapses);
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default:
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return CombinatorSum(bias, synapses);
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}
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}
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/// <summary>
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/// Sum the bias and synpase outputs together
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/// </summary>
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/// <param name="bias">The bias of the neuron</param>
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/// <param name="synapses">The synapses of the neuron</param>
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/// <returns></returns>
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public static float3 CombinatorSum(float3 bias, List<Synapse> synapses) {
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float3 sum = bias;
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foreach (Synapse synapse in synapses) {
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synapse.neuron.SleepCheck();
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sum += synapse.weight * synapse.neuron.outputValue;
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}
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return sum;
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}
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/// <summary>
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/// Multiply the synapse outputs together
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/// </summary>
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/// <param name="bias">The bias of the neuron</param>
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/// <param name="synapses">The synapses of the neuron</param>
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/// <returns>The result of the multiplication</returns>
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public static float3 CombinatorProduct(float3 bias, List<Synapse> synapses) {
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float3 product = bias;
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foreach (Synapse synapse in synapses) {
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synapse.neuron.SleepCheck();
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product *= synapse.weight * synapse.neuron.outputValue;
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}
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return product;
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}
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#else
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/// <summary>
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/// The combinator which combines the bias with the values from all synapses
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/// </summary>
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/// <param name="bias">The bias of the neuron</param>
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/// <param name="synapses">The synapses of the neuron</param>
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/// <returns></returns>
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protected Vector3 Combinator(Vector3 bias, List<Synapse> synapses) {
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switch (combinator) {
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case CombinatorType.Sum: return CombinatorSum(bias, synapses);
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case CombinatorType.Product: return CombinatorProduct(bias, synapses);
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default: return CombinatorSum(bias, synapses);
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}
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}
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/// <summary>
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/// Sum the bias and synpase outputs together
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/// </summary>
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/// <param name="bias">The bias of the neuron</param>
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/// <param name="synapses">The synapses of the neuron</param>
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/// <returns></returns>
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public static Vector3 CombinatorSum(Vector3 bias, List<Synapse> synapses) {
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float3 sum = bias;
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foreach (Synapse synapse in synapses) {
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synapse.neuron.SleepCheck();
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sum += synapse.weight * synapse.neuron.outputValue;
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}
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return sum;
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}
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/// <summary>
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/// Multiply the synapse outputs together
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/// </summary>
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/// <param name="bias">The bias of the neuron</param>
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/// <param name="synapses">The synapses of the neuron</param>
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/// <returns>The result of the multiplication</returns>
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public static Vector3 CombinatorProduct(Vector3 bias, List<Synapse> synapses) {
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float3 product = bias;
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foreach (Synapse synapse in synapses) {
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synapse.neuron.SleepCheck();
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product *= synapse.weight * synapse.neuron.outputValue;
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}
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return product;
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}
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#endif
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#endregion Combinator
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#region Activator
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#if UNITY_MATHEMATICS
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/// <summary>
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/// Apply the activation function to the input
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/// </summary>
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/// <param name="inputValue"></param>
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/// <returns>The result of applying the activation function</returns>
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// This does not allocate memory and seems faster than a switch expression
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protected float3 Activator(float3 inputValue) {
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switch (activator) {
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case ActivationType.Linear:
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return ActivatorLinear(inputValue);
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case ActivationType.Sqrt:
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return ActivatorSqrt(inputValue);
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case ActivationType.Power:
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return ActivatorPower(inputValue);
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case ActivationType.Reciprocal:
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return ActivatorReciprocal(inputValue);
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case ActivationType.Tanh:
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return ActivatorTanh(inputValue);
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case ActivationType.Binary:
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return ActivatorBinary(inputValue);
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case ActivationType.Normalized:
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return ActivatorNormalized(inputValue);
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default:
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return ActivatorLinear(inputValue);
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}
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}
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/// <summary>
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/// Linear activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>The unchanged value</returns>
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protected float3 ActivatorLinear(float3 input) {
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return input;
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}
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/// <summary>
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/// Square root activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>The square root of the input</returns>
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protected float3 ActivatorSqrt(float3 input) {
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float3 result = normalize(input) * MathF.Sqrt(length(input));
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return result;
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}
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/// <summary>
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/// Power activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>The input to the power of 2</returns>
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protected float3 ActivatorPower(float3 input) {
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float3 result = normalize(input) * MathF.Pow(length(input), 2);
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return result;
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}
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/// <summary>
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/// Reciprocal activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>1/input value</returns>
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protected float3 ActivatorReciprocal(float3 input) {
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float magnitude = length(input);
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if (magnitude == 0)
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return new float3(0, 0, 0);
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float3 result = normalize(input) * (1 / magnitude);
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return result;
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}
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/// <summary>
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/// Tanh activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>Tanh(input value)</returns>
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protected float3 ActivatorTanh(float3 input) {
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float magnitude = length(input);
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float3 result = normalize(input) * MathF.Tanh(magnitude);
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return result;
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}
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/// <summary>
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/// Binary activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>An uniform vector with magnitude between 0 and 1</returns>
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protected float3 ActivatorBinary(float3 input) {
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float magnitude = length(input);
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float value = Mathf.Clamp01(magnitude);
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return float3(value, value, value);
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}
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/// <summary>
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/// Normalize activation function
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/// </summary>
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/// <param name="input">Input value</param>
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/// <returns>The normalized vector</returns>
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protected float3 ActivatorNormalized(float3 input) {
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if (lengthsq(input) == 0)
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return input;
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float3 result = normalize(input);
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return result;
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}
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#else
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/// <summary>
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/// Apply the activation function to the input
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/// </summary>
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/// <param name="inputValue"></param>
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/// <returns>The result of applying the activation function</returns>
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// This does not allocate memory and seems faster than a switch expression
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protected Vector3 Activator(Vector3 inputValue) {
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switch (activator) {
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case ActivationType.Linear: return ActivatorLinear(inputValue);
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case ActivationType.Sqrt: return ActivatorSqrt(inputValue);
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case ActivationType.Power: return ActivatorPower(inputValue);
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case ActivationType.Reciprocal: return ActivatorReciprocal(inputValue);
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// case ActivationType.Tanh: return ActivatorTanh(inputValue);
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|
// case ActivationType.Binary: return ActivatorBinary(inputValue);
|
|
// case ActivationType.Normalized: return ActivatorNormalized(inputValue);
|
|
default: return ActivatorLinear(inputValue);
|
|
}
|
|
}
|
|
|
|
/// <summary>
|
|
/// Linear activation function
|
|
/// </summary>
|
|
/// <param name="input">Input value</param>
|
|
/// <returns>The unchanged value</returns>
|
|
protected Vector3 ActivatorLinear(Vector3 input) {
|
|
return input;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Square root activation function
|
|
/// </summary>
|
|
/// <param name="input">Input value</param>
|
|
/// <returns>The square root of the input</returns>
|
|
protected Vector3 ActivatorSqrt(Vector3 input) {
|
|
Vector3 result = input.normalized * System.MathF.Sqrt(input.magnitude);
|
|
return result;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Power activation function
|
|
/// </summary>
|
|
/// <param name="input">Input value</param>
|
|
/// <returns>The input to the power of 2</returns>
|
|
protected Vector3 ActivatorPower(Vector3 input) {
|
|
Vector3 result = input.normalized * System.MathF.Pow(input.magnitude, 2);
|
|
return result;
|
|
}
|
|
|
|
/// <summary>
|
|
/// Reciprocal activation function
|
|
/// </summary>
|
|
/// <param name="input">Input value</param>
|
|
/// <returns>1/input value</returns>
|
|
protected Vector3 ActivatorReciprocal(Vector3 input) {
|
|
float magnitude = input.magnitude;
|
|
if (magnitude == 0)
|
|
return new Vector3(0, 0, 0);
|
|
|
|
Vector3 result = input.normalized * (1 / magnitude);
|
|
return result;
|
|
}
|
|
|
|
#endif
|
|
|
|
#endregion Activator
|
|
|
|
#region Receivers
|
|
|
|
/// <summary>
|
|
/// The nuclei which have a synapse to this neuron
|
|
/// </summary>
|
|
[SerializeReference]
|
|
[HideInInspector]
|
|
private List<Nucleus> _receivers = new();
|
|
/// <summary>
|
|
/// The nuclei which have a synapse to this neuron
|
|
/// </summary>
|
|
public virtual List<Nucleus> receivers {
|
|
get { return _receivers; }
|
|
set { _receivers = value; }
|
|
}
|
|
|
|
/// <summary>
|
|
/// Add a new receiver to this neuron
|
|
/// </summary>
|
|
/// <param name="receiverToAdd">The receiver to add</param>
|
|
/// <param name="weight">The weight to use for the synapse to his neuron</param>
|
|
public virtual void AddReceiver(Nucleus receiverToAdd, float weight = 1) {
|
|
if (receiverToAdd is not Neuron receiverNeuron)
|
|
return;
|
|
this._receivers.Add(receiverNeuron);
|
|
receiverNeuron.AddSynapse(this, weight);
|
|
//Debug.Log($"Add synapse {this.clusterPrefab.name}.{this.name} -> {receiverToAdd.name} --- [{this.receivers.Count}]");
|
|
|
|
}
|
|
|
|
/// <summary>
|
|
/// Remove a receiver to this neuron
|
|
/// </summary>
|
|
/// <param name="receiverToRemove">The receiver to remove</param>
|
|
public virtual void RemoveReceiver(Nucleus receiverToRemove) {
|
|
if (receiverToRemove is not Neuron receiverNeuron)
|
|
return;
|
|
this._receivers.RemoveAll(receiver => receiver == receiverNeuron);
|
|
receiverNeuron.synapses.RemoveAll(synapse => synapse.neuron == this);
|
|
|
|
// Nucleus prefabReceiver = receiverToRemove.prefabNucleus;
|
|
// if (this.prefabNucleus is Neuron prefabNeuron && prefabReceiver != null) {
|
|
// prefabNeuron.receivers.RemoveAll(receiver => receiver == prefabReceiver);
|
|
// prefabReceiver.synapses.RemoveAll(synapse => synapse.neuron == prefabNeuron);
|
|
// }
|
|
}
|
|
|
|
|
|
#endregion Receivers
|
|
|
|
/// <summary>
|
|
/// Process an external stimulus
|
|
/// </summary>
|
|
/// <param name="inputValue">The value of the stimulus</param>
|
|
public virtual void ProcessStimulus(Vector3 inputValue) {
|
|
this.lastUpdate = Time.time;
|
|
this.bias = inputValue;
|
|
this.parent?.UpdateFromNucleus(this);
|
|
}
|
|
}
|
|
|
|
} |