/// <summary>
/// Generate the RSOM network.
/// </summary>
///
/// <returns>The neural network.</returns>
public IMLMethod Generate()
{
var pnn = new BasicPNN(_kernel, _outmodel,
_inputNeurons, _outputNeurons);
return(pnn);
}
public void TestPersistSerial()
{
BasicPNN network = create();
SerializeObject.Save(SERIAL_FILENAME.ToString(), network);
BasicPNN network2 = (BasicPNN)SerializeObject.Load(SERIAL_FILENAME.ToString());
XOR.VerifyXOR(network2, 0.001);
}
public void TestPersistEG()
{
BasicPNN network = create();
EncogDirectoryPersistence.SaveObject((EG_FILENAME), network);
BasicPNN network2 = (BasicPNN)EncogDirectoryPersistence.LoadObject((EG_FILENAME));
XOR.VerifyXOR(network2, 0.001);
}
/// <summary>
/// Train a BasicPNN.
/// </summary>
///
/// <param name="network">The network to train.</param>
/// <param name="training">The training data.</param>
public TrainBasicPNN(BasicPNN network, IMLDataSet training) : base(TrainingImplementationType.OnePass)
{
_network = network;
_training = training;
_maxError = DefaultMaxError;
_minImprovement = DefaultMinImprovement;
_sigmaLow = DefaultSigmaLow;
_sigmaHigh = DefaultSigmaHigh;
_numSigmas = DefaultNumSigmas;
_samplesLoaded = false;
}
public void TestClassifyPNN()
{
PNNOutputMode mode = PNNOutputMode.Classification;
BasicPNN network = new BasicPNN(PNNKernelType.Gaussian, mode, 2, 2);
IMLDataSet trainingData = new BasicMLDataSet(XOR.XORInput, XOR.XORIdeal);
TrainBasicPNN train = new TrainBasicPNN(network, trainingData);
train.Iteration();
XOR.VerifyXOR(network, 0.01);
}
public BasicPNN create()
{
PNNOutputMode mode = PNNOutputMode.Regression;
BasicPNN network = new BasicPNN(PNNKernelType.Gaussian, mode, 2, 1);
BasicMLDataSet trainingSet = new BasicMLDataSet(XOR.XORInput,
XOR.XORIdeal);
TrainBasicPNN train = new TrainBasicPNN(network, trainingSet);
train.Iteration();
XOR.VerifyXOR(network, 0.001);
return(network);
}
/// <summary>
/// Create a PNN network.
/// </summary>
///
/// <param name="architecture">THe architecture string to use.</param>
/// <param name="input">The input count.</param>
/// <param name="output">The output count.</param>
/// <returns>The RBF network.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
IList <String> layers = ArchitectureParse.ParseLayers(architecture);
if (layers.Count != MaxLayers)
{
throw new EncogError(
"PNN Networks must have exactly three elements, "
+ "separated by ->.");
}
ArchitectureLayer inputLayer = ArchitectureParse.ParseLayer(
layers[0], input);
ArchitectureLayer pnnLayer = ArchitectureParse.ParseLayer(
layers[1], -1);
ArchitectureLayer outputLayer = ArchitectureParse.ParseLayer(
layers[2], output);
int inputCount = inputLayer.Count;
int outputCount = outputLayer.Count;
PNNKernelType kernel;
PNNOutputMode outmodel;
if (pnnLayer.Name.Equals("c", StringComparison.InvariantCultureIgnoreCase))
{
outmodel = PNNOutputMode.Classification;
}
else if (pnnLayer.Name.Equals("r", StringComparison.InvariantCultureIgnoreCase))
{
outmodel = PNNOutputMode.Regression;
}
else if (pnnLayer.Name.Equals("u", StringComparison.InvariantCultureIgnoreCase))
{
outmodel = PNNOutputMode.Unsupervised;
}
else
{
throw new NeuralNetworkError("Unknown model: " + pnnLayer.Name);
}
var holder = new ParamsHolder(pnnLayer.Params);
String kernelStr = holder.GetString("KERNEL", false, "gaussian");
if (kernelStr.Equals("gaussian", StringComparison.InvariantCultureIgnoreCase))
{
kernel = PNNKernelType.Gaussian;
}
else if (kernelStr.Equals("reciprocal", StringComparison.InvariantCultureIgnoreCase))
{
kernel = PNNKernelType.Reciprocal;
}
else
{
throw new NeuralNetworkError("Unknown kernel: " + kernelStr);
}
var result = new BasicPNN(kernel, outmodel, inputCount,
outputCount);
return(result);
}
/// <summary>
/// Read an object.
/// </summary>
public Object Read(Stream mask0)
{
var ins0 = new EncogReadHelper(mask0);
EncogFileSection section;
var samples = new BasicMLDataSet();
IDictionary <String, String> networkParams = null;
PNNKernelType kernel = default(PNNKernelType) /* was: null */;
PNNOutputMode outmodel = default(PNNOutputMode) /* was: null */;
int inputCount = 0;
int outputCount = 0;
double error = 0;
double[] sigma = null;
while ((section = ins0.ReadNextSection()) != null)
{
if (section.SectionName.Equals("PNN") &&
section.SubSectionName.Equals("PARAMS"))
{
networkParams = section.ParseParams();
}
if (section.SectionName.Equals("PNN") &&
section.SubSectionName.Equals("NETWORK"))
{
IDictionary <String, String> paras = section.ParseParams();
inputCount = EncogFileSection.ParseInt(paras,
PersistConst.InputCount);
outputCount = EncogFileSection.ParseInt(paras,
PersistConst.OutputCount);
kernel = StringToKernel(paras[PersistConst.Kernel]);
outmodel = StringToOutputMode(paras[PropertyOutputMode]);
error = EncogFileSection
.ParseDouble(paras, PersistConst.Error);
sigma = section.ParseDoubleArray(paras, PersistConst.Sigma);
}
if (section.SectionName.Equals("PNN") &&
section.SubSectionName.Equals("SAMPLES"))
{
foreach (String line in section.Lines)
{
IList <String> cols = EncogFileSection
.SplitColumns(line);
int index = 0;
var inputData = new BasicMLData(inputCount);
for (int i = 0; i < inputCount; i++)
{
inputData[i] =
CSVFormat.EgFormat.Parse(cols[index++]);
}
var idealData = new BasicMLData(inputCount);
idealData[0] = CSVFormat.EgFormat.Parse(cols[index++]);
IMLDataPair pair = new BasicMLDataPair(inputData,
idealData);
samples.Add(pair);
}
}
}
var result = new BasicPNN(kernel, outmodel, inputCount,
outputCount);
if (networkParams != null)
{
EngineArray.PutAll(networkParams, result.Properties);
}
result.Samples = samples;
result.Error = error;
if (sigma != null)
{
EngineArray.ArrayCopy(sigma, result.Sigma);
}
return(result);
}
