using System;
using Unity.Mathematics;

/// <summary>
/// The Pulsar represents a type of neuron that operates based on
/// the product of its weighted inputs rather than the traditional summation. 
/// Drawing inspiration from the concept of pulsars in astrophysics
/// —highly magnetized rotating neutron stars that emit beams of radiation—
/// the Pulsar could symbolize dynamic, focused output based on the interaction of multiple factors.
/// </summary>
/// Multiplicative Functionality:
/// Instead of summing inputs, the Pulsar takes the weighted product of its inputs. 
/// This means that all inputs must be active (non-zero) for the neuron to "pulse" or activate.
/// Output Behavior:
/// The output could amplify or diminish depending on the magnitude of the inputs.
/// The product would be sensitive to small values,
/// which means that even a small input could significantly lower the overall output if multiplied.
/// Activation Mechanism:
/// The activation function can further refine the output from the product result.
/// For instance, a certain threshold could be used to determine if a pulse occurs.
/// Modeling Complex Interactions: 
/// The Pulsar could be particularly beneficial for modeling situations where interactions multiply rather than add.
/// This is useful in fields such as economics (e.g., compound growth models),
/// biology (e.g., interaction of hormones), and machine learning where multiplicative relationships exist.
[Serializable]
public class Pulsar : Neuron {
    public Pulsar(Cluster parent, string name) : base(parent, name) {
        // To prevent mistakes, bias one (instead of zero for standard neurons)
        this.bias = new float3(1, 1, 1);
    }
    public Pulsar(ClusterPrefab parent, string name) : base(parent, name) {
        // To prevent mistakes, bias one (instead of zero for standard neurons)
        this.bias = new float3(1, 1, 1);
    }

    public override Nucleus ShallowCloneTo(Cluster newParent) {
        Pulsar clone = new(newParent, this.name);
        CloneFields(clone);
        return clone;
    }

    public override void UpdateStateIsolated() {
        float3 product = this.bias;

        //Applying the weight factors
        foreach (Synapse synapse in this.synapses) {
            float3 input = synapse.weight * synapse.nucleus.outputValue;
            product *= input;
        }

        // Activation function
        this.outputValue = Activator(product);
    }
}