/// <summary>
/// Construct a Manhattan propagation training object.
/// </summary>
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="profile">The learning rate.</param>
/// <param name="learnRate">The OpenCL profile to use, null for CPU.</param>
public ManhattanPropagation(BasicNetwork network,
INeuralDataSet training, OpenCLTrainingProfile profile, double learnRate)
: base(network, training)
{
if (profile == null)
{
FlatTraining = new TrainFlatNetworkManhattan(
network.Structure.Flat,
this.Training,
learnRate);
}
#if !SILVERLIGHT
else
{
TrainFlatNetworkOpenCL rpropFlat = new TrainFlatNetworkOpenCL(
network.Structure.Flat, this.Training,
profile);
rpropFlat.LearnManhattan(learnRate);
this.FlatTraining = rpropFlat;
}
#endif
}
/// <summary>
/// Construct a Manhattan propagation training object.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data to use.</param>
/// <param name="learnRate">The learning rate.</param>
public ManhattanPropagation(IContainsFlat network,
IMLDataSet training, double learnRate) : base(network, training)
{
FlatTraining = new TrainFlatNetworkManhattan(network.Flat,
Training, learnRate);
}
