/// <summary>
/// Construct a gradient worker.
/// </summary>
///
/// <param name="theNetwork">The network to train.</param>
/// <param name="theOwner">The owner that is doing the training.</param>
/// <param name="theTraining">The training data.</param>
/// <param name="theLow">The low index to use in the training data.</param>
/// <param name="theHigh">The high index to use in the training data.</param>
/// <param name="theFlatSpots">Holds an array of flat spot constants.</param>
public GradientWorker(FlatNetwork theNetwork,
TrainFlatNetworkProp theOwner, IMLDataSet theTraining,
int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
{
_errorCalculation = new ErrorCalculation();
_network = theNetwork;
_training = theTraining;
_low = theLow;
_high = theHigh;
_owner = theOwner;
_flatSpot = theFlatSpots;
_layerDelta = new double[_network.LayerOutput.Length];
_gradients = new double[_network.Weights.Length];
_actual = new double[_network.OutputCount];
_weights = _network.Weights;
_layerIndex = _network.LayerIndex;
_layerCounts = _network.LayerCounts;
_weightIndex = _network.WeightIndex;
_layerOutput = _network.LayerOutput;
_layerSums = _network.LayerSums;
_layerFeedCounts = _network.LayerFeedCounts;
_ef = ef;
_pair = BasicMLDataPair.CreatePair(_network.InputCount,
_network.OutputCount);
}
public GradientWorker(FlatNetwork theNetwork, TrainFlatNetworkProp theOwner, IMLDataSet theTraining, int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
{
goto Label_0155;
Label_0114:
this._x071bde1041617fce = theOwner;
this._x0ba854627e1326f9 = theFlatSpots;
this._x58c3d5da5c5c72db = new double[this._x87a7fc6a72741c2e.LayerOutput.Length];
this._xe05127febf8b7904 = new double[this._x87a7fc6a72741c2e.Weights.Length];
this._xd505507cf33ae543 = new double[this._x87a7fc6a72741c2e.OutputCount];
if (0 == 0)
{
this._x2f33d779e5a20b28 = this._x87a7fc6a72741c2e.Weights;
if ((((uint) theHigh) + ((uint) theLow)) <= uint.MaxValue)
{
this._xb25095f37f20a1c1 = this._x87a7fc6a72741c2e.LayerIndex;
if (((uint) theLow) <= uint.MaxValue)
{
this._xe05f7b8f952f0ba4 = this._x87a7fc6a72741c2e.LayerCounts;
this._x7d5bf19d36074a85 = this._x87a7fc6a72741c2e.WeightIndex;
this._x5e72e5e601f79c78 = this._x87a7fc6a72741c2e.LayerOutput;
this._x59e01312f2f4aa96 = this._x87a7fc6a72741c2e.LayerSums;
this._xc99b49dd213196ca = this._x87a7fc6a72741c2e.LayerFeedCounts;
this._x2cb049236d33bbda = ef;
}
}
}
this._x61830ac74d65acc3 = BasicMLDataPair.CreatePair(this._x87a7fc6a72741c2e.InputCount, this._x87a7fc6a72741c2e.OutputCount);
if (0 == 0)
{
return;
}
Label_0155:
this._x84e81691256999b2 = new ErrorCalculation();
this._x87a7fc6a72741c2e = theNetwork;
this._x823a2b9c8bf459c5 = theTraining;
if (0xff == 0)
{
return;
}
do
{
if ((((uint) theHigh) + ((uint) theLow)) > uint.MaxValue)
{
goto Label_0114;
}
this._xd12d1dba8a023d95 = theLow;
}
while (0 != 0);
this._x628ea9b89457a2a9 = theHigh;
goto Label_0114;
}
/// <summary>
/// Construct a gradient worker.
/// </summary>
///
/// <param name="theNetwork">The network to train.</param>
/// <param name="theOwner">The owner that is doing the training.</param>
/// <param name="theTraining">The training data.</param>
/// <param name="theLow">The low index to use in the training data.</param>
/// <param name="theHigh">The high index to use in the training data.</param>
/// <param name="theFlatSpots">Holds an array of flat spot constants.</param>
public GradientWorker(FlatNetwork theNetwork,
TrainFlatNetworkProp theOwner, IMLDataSet theTraining,
int theLow, int theHigh, double[] theFlatSpots, IErrorFunction ef)
{
_errorCalculation = new ErrorCalculation();
_network = theNetwork;
_training = theTraining;
_low = theLow;
_high = theHigh;
_owner = theOwner;
_flatSpot = theFlatSpots;
_layerDelta = new double[_network.LayerOutput.Length];
_gradients = new double[_network.Weights.Length];
_actual = new double[_network.OutputCount];
_weights = _network.Weights;
_layerIndex = _network.LayerIndex;
_layerCounts = _network.LayerCounts;
_weightIndex = _network.WeightIndex;
_layerOutput = _network.LayerOutput;
_layerSums = _network.LayerSums;
_layerFeedCounts = _network.LayerFeedCounts;
_ef = ef;
_pair = BasicMLDataPair.CreatePair(_network.InputCount,
_network.OutputCount);
}
