public PruneIncremental(IMLDataSet training, INeuralNetworkPattern pattern, int iterations, int weightTries, int numTopResults, IStatusReportable report)
: base(report)
{
goto Label_008E;
Label_0031:
this._x7890c8b3a33b26e2 = new double[numTopResults];
return;
Label_008E:
this._x0b03741e8f17a9f7 = false;
this._xab3ddaff42dd298a = new List<HiddenLayerParams>();
this._x823a2b9c8bf459c5 = training;
if ((((uint) numTopResults) - ((uint) weightTries)) < 0)
{
goto Label_0031;
}
this._x49d5b7c4ad0e0bdd = pattern;
if ((((uint) iterations) - ((uint) iterations)) <= uint.MaxValue)
{
this._xdbf51c857aeb8093 = iterations;
this._x64343a0786fb9a3f = report;
this._xe009ad1bd0a8245a = weightTries;
this._xc5f756e0b4a83af0 = new BasicNetwork[numTopResults];
goto Label_0031;
}
goto Label_008E;
}
public PostureRecognition(PatternType patternType, DataTrainingType dataTrainingType, int iterations)
{
this.networkPattern = PatternResolver <InputDataType, OutputDataType> .ResolvePattern(patternType, dataTrainingType);
var activationFunction = new ActivationFunction();
activationFunction.InitializeSigmodeFunction(1.0);
network = new NeuronNetwork(activationFunction, iterations);
}
/**
* Construct an object to determine the optimal number of hidden layers and
* neurons for the specified training data and pattern.
*
* @param training
* The training data to use.
* @param pattern
* The network pattern to use to solve this data.
* @param iterations
* How many iterations to try per network.
* @param report
* Object used to report status to.
*/
public PruneIncremental(INeuralDataSet training,
INeuralNetworkPattern pattern, int iterations,
IStatusReportable report)
: base(report)
{
this.training = training;
this.pattern = pattern;
this.iterations = iterations;
}
/// <summary>
/// Construct an object to determine the optimal number of hidden layers and
/// neurons for the specified training data and pattern.
/// </summary>
///
/// <param name="training">The training data to use.</param>
/// <param name="pattern">The network pattern to use to solve this data.</param>
/// <param name="iterations">How many iterations to try per network.</param>
/// <param name="weightTries">The number of random weights to use.</param>
/// <param name="numTopResults"></param>
/// <param name="report">Object used to report status to.</param>
public PruneIncremental(IMLDataSet training,
INeuralNetworkPattern pattern, int iterations,
int weightTries, int numTopResults,
IStatusReportable report) : base(report)
{
_done = false;
_hidden = new List <HiddenLayerParams>();
_training = training;
_pattern = pattern;
_iterations = iterations;
_report = report;
_weightTries = weightTries;
_topNetworks = new BasicNetwork[numTopResults];
_topErrors = new double[numTopResults];
}
/// <summary>
/// Construct an object to determine the optimal number of hidden layers and
/// neurons for the specified training data and pattern.
/// </summary>
///
/// <param name="training">The training data to use.</param>
/// <param name="pattern">The network pattern to use to solve this data.</param>
/// <param name="iterations">How many iterations to try per network.</param>
/// <param name="weightTries">The number of random weights to use.</param>
/// <param name="numTopResults"></param>
/// <param name="report">Object used to report status to.</param>
public PruneIncremental(IMLDataSet training,
INeuralNetworkPattern pattern, int iterations,
int weightTries, int numTopResults,
IStatusReportable report)
: base(report)
{
_done = false;
_hidden = new List<HiddenLayerParams>();
_training = training;
_pattern = pattern;
_iterations = iterations;
_report = report;
_weightTries = weightTries;
_topNetworks = new BasicNetwork[numTopResults];
_topErrors = new double[numTopResults];
}
/**
* Construct an object to determine the optimal number of hidden layers and
* neurons for the specified training data and pattern.
*
* @param training
* The training data to use.
* @param pattern
* The network pattern to use to solve this data.
* @param iterations
* How many iterations to try per network.
* @param report
* Object used to report status to.
*/
public PruneIncremental(INeuralDataSet training,
INeuralNetworkPattern pattern, int iterations,
IStatusReportable report)
: base(report)
{
this.training = training;
this.pattern = pattern;
this.iterations = iterations;
}
