Esempi in C# (CSharp) per Encog.Neural.Networks.Training.PNN DeriveMinimum.Calculate

Calculate() public method

Derive the minimum, using a conjugate gradient method.

public Calculate ( int maxIterations, double maxError, double eps, double tol, ICalculationCriteria network, int n, double x, double ystart, double bs, double direc, double g, double h, double deriv2 ) : double
maxIterations	int	The max iterations.
maxError	double	Stop at this error rate.
eps	double	The machine's precision.
tol	double	The convergence tolerance.
network	ICalculationCriteria	The network to get the error from.
n	int	The number of variables.
x	double	The independent variable.
ystart	double	The start for y.
bs	double	Work vector, must have n elements.
direc	double	Work vector, must have n elements.
g	double	Work vector, must have n elements.
h	double	Work vector, must have n elements.
deriv2	double	Work vector, must have n elements.
return	double

Esempio n. 1

File: TrainBasicPNN.cs Progetto: benw408701/MLHCTransactionPredictor

        /// <summary>
        /// 
        /// </summary>
        ///
        public override sealed void Iteration()
        {
            if (!_samplesLoaded)
            {
                _network.Samples = new BasicMLDataSet(_training);
                _samplesLoaded = true;
            }

            var globalMinimum = new GlobalMinimumSearch();
            var dermin = new DeriveMinimum();

            int k;

            if (_network.OutputMode == PNNOutputMode.Classification)
            {
                k = _network.OutputCount;
            }
            else
            {
                k = _network.OutputCount + 1;
            }

            _dsqr = new double[_network.InputCount];
            _v = new double[_network.InputCount*k];
            _w = new double[_network.InputCount*k];

            var x = new double[_network.InputCount];
            var bs = new double[_network.InputCount];
            var direc = new double[_network.InputCount];
            var g = new double[_network.InputCount];
            var h = new double[_network.InputCount];
            var dwk2 = new double[_network.InputCount];

            if (_network.Trained)
            {
                for (int i = 0; i < _network.InputCount; i++)
                {
                    x[i] = _network.Sigma[i];
                }
                globalMinimum.Y2 = 1.0e30d;
            }
            else
            {
                globalMinimum.FindBestRange(_sigmaLow, _sigmaHigh,
                                            _numSigmas, true, _maxError, this);

                for (int i = 0; i < _network.InputCount; i++)
                {
                    x[i] = globalMinimum.X2;
                }
            }

            double d = dermin.Calculate(32767, _maxError, 1.0e-8d,
                                        _minImprovement, this, _network.InputCount, x,
                                        globalMinimum.Y2, bs, direc, g, h, dwk2);
            globalMinimum.Y2 = d;

            for (int i = 0; i < _network.InputCount; i++)
            {
                _network.Sigma[i] = x[i];
            }

            _network.Error = Math.Abs(globalMinimum.Y2);
            _network.Trained = true; // Tell other routines net is trained

            return;
        }

Esempio n. 2

        /// <summary>
        ///
        /// </summary>
        ///
        public override sealed void Iteration()
        {
            if (!_samplesLoaded)
            {
                _network.Samples = new BasicMLDataSet(_training);
                _samplesLoaded   = true;
            }

            var globalMinimum = new GlobalMinimumSearch();
            var dermin        = new DeriveMinimum();

            int k;

            if (_network.OutputMode == PNNOutputMode.Classification)
            {
                k = _network.OutputCount;
            }
            else
            {
                k = _network.OutputCount + 1;
            }

            _dsqr = new double[_network.InputCount];
            _v    = new double[_network.InputCount * k];
            _w    = new double[_network.InputCount * k];

            var x     = new double[_network.InputCount];
            var bs    = new double[_network.InputCount];
            var direc = new double[_network.InputCount];
            var g     = new double[_network.InputCount];
            var h     = new double[_network.InputCount];
            var dwk2  = new double[_network.InputCount];

            if (_network.Trained)
            {
                for (int i = 0; i < _network.InputCount; i++)
                {
                    x[i] = _network.Sigma[i];
                }
                globalMinimum.Y2 = 1.0e30d;
            }
            else
            {
                globalMinimum.FindBestRange(_sigmaLow, _sigmaHigh,
                                            _numSigmas, true, _maxError, this);

                for (int i = 0; i < _network.InputCount; i++)
                {
                    x[i] = globalMinimum.X2;
                }
            }

            double d = dermin.Calculate(32767, _maxError, 1.0e-8d,
                                        _minImprovement, this, _network.InputCount, x,
                                        globalMinimum.Y2, bs, direc, g, h, dwk2);

            globalMinimum.Y2 = d;

            for (int i = 0; i < _network.InputCount; i++)
            {
                _network.Sigma[i] = x[i];
            }

            _network.Error   = Math.Abs(globalMinimum.Y2);
            _network.Trained = true; // Tell other routines net is trained

            return;
        }