public NeuralRobot(BasicNetwork network, bool track, Position source, Position destination) { _hStats = new NormalizedField(NormalizationAction.Normalize, "Heading", 359, 0, .9, -.9); _CanGoStats = new NormalizedField(NormalizationAction.Normalize, "CanGo", 1, 0, 0.9, -0.9); _track = track; _network = network; sim = new RobotSimulator(source, destination); }
/// <summary> /// Construct the training generator. /// </summary> /// <param name="thePath">The path to use.</param> public GenerateTraining(string thePath) { _path = thePath; _trainingFile = FileUtil.CombinePath(new FileInfo(_path), Config.FilenameTrain); _fieldDifference = new NormalizedField(NormalizationAction.Normalize, "diff", Config.DiffRange, -Config.DiffRange, 1, -1); _fieldOutcome = new NormalizedField(NormalizationAction.Normalize, "out", Config.PipRange, -Config.PipRange, 1, -1); }
public NeuralPilot(BasicNetwork network, bool track) { _fuelStats = new NormalizedField(NormalizationAction.Normalize, "fuel", 200, 0, -0.9, 0.9); _altitudeStats = new NormalizedField(NormalizationAction.Normalize, "altitude", 10000, 0, -0.9, 0.9); _velocityStats = new NormalizedField(NormalizationAction.Normalize, "velocity", LanderSimulator.TerminalVelocity, -LanderSimulator.TerminalVelocity, -0.9, 0.9); _track = track; _network = network; }
public void Execute(IExampleInterface app) { // Normalize values with an actual range of (0 to 100) to (-1 to 1) var norm = new NormalizedField(NormalizationAction.Normalize, null, 100, 0, 1, -1); double x = 5; double y = norm.Normalize(x); Console.WriteLine(x + @" normalized is " + y); double z = norm.DeNormalize(y); Console.WriteLine(y + @" denormalized is " + z); }
public double[] Process(double[] inputArray) { double num; double[] numArray; int num2; int num3; this._x24d1ebc88ca906aa = new NormalizedField(); goto Label_0097; Label_0010: if (num2 >= inputArray.Length) { goto Label_0085; } Label_0023: numArray[num2] = this._x24d1ebc88ca906aa.Normalize(inputArray[num2]); num2++; if ((((uint) num2) - ((uint) num3)) <= uint.MaxValue) { goto Label_0010; } Label_0085: if (((uint) num3) <= uint.MaxValue) { return numArray; } Label_0097: this._x24d1ebc88ca906aa.NormalizedHigh = this._x891507b50bbab0f9; if ((((uint) num) - ((uint) num)) > uint.MaxValue) { goto Label_0023; } this._x24d1ebc88ca906aa.NormalizedLow = this._x136bfff0efb12047; double[] numArray2 = inputArray; num3 = 0; while (true) { if (num3 >= numArray2.Length) { numArray = new double[inputArray.Length]; num2 = 0; goto Label_0010; } num = numArray2[num3]; this._x24d1ebc88ca906aa.Analyze(num); num3++; } }
/// <summary> /// Construct the indicator. /// </summary> /// <param name="theMethod">The machine learning method to use.</param> /// <param name="thePath">The path to use.</param> public MyInd(IMLRegression theMethod, string thePath) : base(theMethod != null) { _method = theMethod; _path = thePath; RequestData("CLOSE[1]"); RequestData("SMA(10)[" + Config.InputWindow + "]"); RequestData("SMA(25)[" + Config.InputWindow + "]"); _fieldDifference = new NormalizedField(NormalizationAction.Normalize, "diff", Config.DiffRange, -Config.DiffRange, 1, -1); _fieldOutcome = new NormalizedField(NormalizationAction.Normalize, "out", Config.PipRange, -Config.PipRange, 1, -1); }