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Downloads   0
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Last Updated   11/15/2023
License   MIT License
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Version   1.1.2
Date   11/15/2023
Status   Stable Stable software is believed to be stable and ready for production use.

This software is open source. You can obtain the latest source code from the GitHub repository or browse the releases for the source code associated with a specific release. If you make any changes which you feel improves this application, please let us know via our Contact Page.


?? Be sure to check out the NuGet pacakge: https://www.nuget.org/packages/NTDLS.Determinet

Determinet is versatile multilayer perception neural network designed for extendibility and genetic-style mutations to allow forward propagation of the network variants.

Below is a simple example of using the network to navigate a maze or other obstacles for a simple simulation. You can find more advanced examples as well as working models of this in the AIVolution project: https://github.com/NTDLS/AIVolution

If you are in a fighting mood, you can also battle it out against some of these trained models in the Space Flight Shooter Game: https://github.com/NTDLS/NebulaSiege

public enum AIInputs

public enum AIOutputs

static void Main()

    //Note that if you want to use the model in different threads, you will need to
    //  make a clone since the values of the input nodes are altered when making decisions.
    //  Fortunately, this can be easily accomplished with a call to Clone();
    var network = LoadSavedModel("./TestHarness.json");

    var decidingFactors = GatherInputs(); //Get decision inputs.

    var decisions = network.FeedForward(decidingFactors); //Make decisions.

    //Handle the speed decisions.
    var shouldAdjustSpeed = decisions.Get(AIOutputs.AdjustSpeed);
    if (shouldAdjustSpeed > 0.8)
        //Adjust speed up.
    else if (shouldAdjustSpeed < 0.2)
        //Adjust speed down.

    //Handle the heading direction.
    var shouldMoveAway = decisions.Get(AIOutputs.MoveAway);
    if (shouldMoveAway > 0.9)
        //Change heading. Maybe just turn around completely?

/// <summary>
/// Get the input values we need to make a decision.
/// </summary>
/// <returns></returns>
static DniNamedInterfaceParameters GatherInputs()
    //Here we are just using some dummy values, in this hypothetical situation
    //  we would be getting the distance from a wall and the angle to it.

    double idealMaxDistance = 1000;
    double distanceFromObstacle = 500;

    double percentageOfCloseness = (distanceFromObstacle / idealMaxDistance);
    double angleToObstacleInDecimalDegrees = 0.8;

    var aiParams = new DniNamedInterfaceParameters();
    aiParams.Set(AIInputs.DistanceFromObstacle, percentageOfCloseness);
    aiParams.Set(AIInputs.AngleToObstacleInDecimalDegrees, angleToObstacleInDecimalDegrees);
    return aiParams;

static DniNeuralNetwork LoadSavedModel(string fileName)
    var network = DniNeuralNetwork.LoadFromFile(fileName);

    if (network == null)
        throw new Exception("Failed to load the network from file.");

    return network;

static void TrainAndSaveModel(string fileName)
    var Network = new DniNeuralNetwork
        LearningRate = 0.01

    //Add input layer
        new object[] {

    //Add a intermediate "hidden" layer. You can add more if you like.
    Network.Layers.AddIntermediate(ActivationType.Sigmoid, 8);

    //Add the output layer.
        new object[] {

    //Train the model with some input scenarios. Look at TrainingScenerio() and TrainingDecision()
    //  to see that these ominous looking numbers are actualy just named inouts. Its pretty simple really.
    for (int epoch = 0; epoch < 5000; epoch++)
        //Very close to observed object, slow way down and get away
        Network.BackPropagate(TrainingScenerio(0, 0), TrainingDecision(1, 0));
        Network.BackPropagate(TrainingScenerio(0, -1), TrainingDecision(1, 0));
        Network.BackPropagate(TrainingScenerio(0, 1), TrainingDecision(1, 0));
        Network.BackPropagate(TrainingScenerio(0, 0.5), TrainingDecision(1, 0));
        Network.BackPropagate(TrainingScenerio(0, -0.5), TrainingDecision(1, 0));

        //Pretty close to observed object, slow down a bit and get away.
        Network.BackPropagate(TrainingScenerio(0.25, 0), TrainingDecision(1, 0.2));
        Network.BackPropagate(TrainingScenerio(0.25, -1), TrainingDecision(1, 0.2));
        Network.BackPropagate(TrainingScenerio(0.25, 1), TrainingDecision(1, 0.2));
        Network.BackPropagate(TrainingScenerio(0.25, 0.5), TrainingDecision(1, 0.2));
        Network.BackPropagate(TrainingScenerio(0.25, -0.5), TrainingDecision(1, 0.2));

        //Very far from observed object, speed up and maintain heading.
        Network.BackPropagate(TrainingScenerio(1, 0), TrainingDecision(0, 1));
        Network.BackPropagate(TrainingScenerio(1, -1), TrainingDecision(0, 1));
        Network.BackPropagate(TrainingScenerio(1, 1), TrainingDecision(0, 1));
        Network.BackPropagate(TrainingScenerio(1, 0.5), TrainingDecision(0, 1));
        Network.BackPropagate(TrainingScenerio(1, -0.5), TrainingDecision(0, 1));

        //Pretty far from observed object, maintain heading but don't change speed.
        Network.BackPropagate(TrainingScenerio(0.75, 0), TrainingDecision(0, 0.5));
        Network.BackPropagate(TrainingScenerio(0.75, -1), TrainingDecision(0, 0.5));
        Network.BackPropagate(TrainingScenerio(0.75, 1), TrainingDecision(0, 0.5));
        Network.BackPropagate(TrainingScenerio(0.75, 0.5), TrainingDecision(0, 0.5));
        Network.BackPropagate(TrainingScenerio(0.75, -0.5), TrainingDecision(0, 0.5));

    static DniNamedInterfaceParameters TrainingScenerio(double distanceFromObstacle, double angleToObstacleInDecimalDegrees)
        var param = new DniNamedInterfaceParameters();
        param.Set(AIInputs.DistanceFromObstacle, distanceFromObstacle);
        param.Set(AIInputs.AngleToObstacleInDecimalDegrees, angleToObstacleInDecimalDegrees);
        return param;

    static DniNamedInterfaceParameters TrainingDecision(double moveAway, double adjustSpeed)
        var param = new DniNamedInterfaceParameters();

        param.Set(AIOutputs.MoveAway, moveAway);
        param.Set(AIOutputs.AdjustSpeed, adjustSpeed);
        return param;

    //Save the network to a file. This is only done here for examples sake.



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