/// <summary>
/// Destroy internal network object
/// </summary>
public override void Destroy()
{
try
{
LibsannInvoke.MlpDelete(_network);
} catch (Exception e) { throw e; }
}
/// <summary>
/// Training the network
/// </summary>
/// <param name="inputPatterns">Input patterns set</param>
/// <param name="outputPatterns">Output patterns set</param>
/// <param name="errorTarget">Target error</param>
/// <param name="maxEpochs">Maximum number of epochs</param>
/// <param name="mode">Weights initialization mode</param>
/// <param name="learningRate">Learning rate</param>
/// <returns>Returns true if the training is successful, otherwise it returns false</returns>
public bool Training(
double[][] inputPatterns,
double[][] outputPatterns,
double errorTarget = 0.01,
uint maxEpochs = 100000,
SynInitMode mode = SynInitMode.FAN_IN,
double learningRate = 0.25
)
{
IntPtr p_in;
IntPtr p_out;
// Conversion to unmanaged types
LibsannInvoke.MatrixConversion(inputPatterns, out p_in);
LibsannInvoke.MatrixConversion(outputPatterns, out p_out);
_currentError = LibsannInvoke.mAdelineTrainingWithWindrowHoff(
_network, p_in, p_out, (uint)inputPatterns.Count(), _inputSize, _outputSize, errorTarget, maxEpochs, learningRate, mode);
if (errorTarget < _currentError)
{
return(false);
}
else
{
return(true);
}
}
/// <summary>
/// Instantiate structure of the network
/// </summary>
public override void Instantiate()
{
try
{
_network = LibsannInvoke.MlpInstantiate(_inputSize, _hiddenLayers, _hiddenUnits, _outputSize, useBias, _transferFunction);
} catch (Exception e) { throw e; }
}
/// <summary>
/// Build internal stats listener
/// </summary>
public void BuildStatsListener()
{
try
{
stats = LibsannInvoke.BuildStatsListener(trainAlg);
} catch (Exception e) { throw e; }
}
public void InstantiateBackPropagationAlgorithm(
double errorTarget = 0.01,
uint maxEpochs = 100000,
double learningRate = 0.2,
double beta = 0.2)
{
trainAlg = LibsannInvoke.InstantiateBackPropagationTraininglgorithm(_network, errorTarget, maxEpochs, learningRate, beta);
}
/// <summary>
/// Destroy the stats listener
/// </summary>
public void DestroyStatsListener()
{
try
{
LibsannInvoke.DestroyPropStatsListener(trainAlg);
stats = null;
} catch (Exception e) { throw e; }
}
/// <summary>
/// Set synapses weights
/// </summary>
/// <param name="vector">Weights values</param>
public void SetSynapsesWeights(List <double> vector)
{
try
{
LibsannInvoke.MlpLoadWeights(_network, vector.ToArray(), vector.Count);
} catch (Exception e)
{
throw e;
}
}
public void InstantiateResilientPropagationAlgorithm(
double errorTarget = 0.01,
uint maxEpochs = 100000,
double maxUpdateValue = 50.0,
double minUpdateValue = 0.001,
double growthFactor = 1.2,
double decreaseFactor = 0.5
)
{
trainAlg = LibsannInvoke.InstantiateResilientPropagationTrainingAlgorithm(_network, errorTarget, maxEpochs, maxUpdateValue, minUpdateValue, growthFactor, decreaseFactor);
}
/// <summary>
/// Network execution, with 'pattern' inputs. Return the output values
/// </summary>
/// <param name="pattern">Input values</param>
/// <returns>Network output values</returns>
public virtual double[] Exec(double[] pattern)
{
// Set input
LibsannInvoke.NetSetInput(_network, pattern, pattern.Count());
// Execution
IntPtr result = LibsannInvoke.NetExec(_network, _outputSize);
// Pass output to unmanaged to managed array
double[] ret = new double[_outputSize];
Marshal.Copy(result, ret, 0, ret.Count());
return(ret);
}
/// <summary>
/// Get number of synapses of the network
/// </summary>
/// <returns>Weights values</returns>
public double[] GetSynapsesWeights()
{
try
{
IntPtr result = LibsannInvoke.MlpGetWeights(_network);
double[] ret = new double[LibsannInvoke.MlpGetWeightsNumber(_network)];
Marshal.Copy(result, ret, 0, ret.Count());
return(ret);
} catch (Exception e)
{
throw e;
}
}
/// <summary>
/// Get all output values of the current training session
/// </summary>
/// <returns>Output values through time</returns>
public List <double[][]> GetOutputValues()
{
try
{
if (stats == null)
{
throw new Exception("Stats listener not initialized.");
}
int patterns_count;
IntPtr outputs = LibsannInvoke.GetPropOutputValues(stats.Value, out patterns_count);
int cols = Convert.ToInt32(_outputSize);
List <double[][]> results;
LibsannInvoke.MatrixConversion(outputs, patterns_count, 2, cols, out results);
return(results);
} catch (Exception e) { throw e; }
}
/// <summary>
/// Training the network
/// </summary>
/// <param name="inputPatterns">Input patterns set</param>
/// <param name="outputPatterns">Output patterns set</param>
/// <param name="errorTarget">Target error</param>
/// <param name="mode">Initialization mode</param>
/// <returns>Returns true if the training is successful, otherwise it returns false</returns>
public bool Training(
double[][] inputPatterns,
double[][] outputPatterns,
double errorTarget,
SynInitMode mode = SynInitMode.NGUYEN_WINDROW
)
{
IntPtr p_in;
IntPtr p_out;
try
{
if (trainAlg == null)
{
throw new Exception("Training algorithm not initialized.");
}
if (stats != null)
{
LibsannInvoke.ResetPropStats(stats.Value);
}
// Conversion to unmanaged types
LibsannInvoke.MatrixConversion(inputPatterns, out p_in);
LibsannInvoke.MatrixConversion(outputPatterns, out p_out);
_currentError = LibsannInvoke.MlpTrainingWithPropagation(
_network, p_in, p_out, (uint)inputPatterns.Count(), _inputSize, _outputSize, trainAlg, mode);
if (errorTarget < _currentError)
{
return(false);
}
else
{
return(true);
}
} catch (Exception e) { throw e; }
}
/// <summary>
/// Get error values through time
/// </summary>
/// <returns>Error values</returns>
public double[] GetErrorValues()
{
try
{
if (stats == null)
{
throw new Exception("Stats listener not initialized.");
}
int size;
IntPtr errors = LibsannInvoke.GetPropErrorValue(stats.Value, out size);
double[] results = new double[size];
if (errors.ToInt32() != 0)
{
Marshal.Copy(errors, results, 0, results.Count());
}
return(results);
} catch (Exception e)
{
throw e;
}
}
/// <summary>
/// Destroy internal network object
/// </summary>
public override void Destroy()
{
LibsannInvoke.EMlpDelete(_network);
}
/// <summary>
/// Instantiate structure of the network
/// </summary>
public override void Instantiate()
{
_network = LibsannInvoke.EMlpInstantiate(_inputSize, _hiddenLayers, _hiddenUnits, _outputSize, useBias, _transferFunction);
}
/// <summary>
/// Destroy internal network object
/// </summary>
public override void Destroy()
{
LibsannInvoke.mAdelineDelete(_network);
}
/// <summary>
/// Instantiate structure of the network
/// </summary>
public override void Instantiate()
{
_network = LibsannInvoke.mAdelineInstantiate(_inputSize, _outputSize, useBias, _transferFunction);
}
