/// <summary>
/// Construct a BasicPNN network.
/// </summary>
///
/// <param name="kernel">The kernel to use.</param>
/// <param name="outmodel">The output model for this network.</param>
/// <param name="inputCount">The number of inputs in this network.</param>
/// <param name="outputCount">The number of outputs in this network.</param>
public BasicPNN(PNNKernelType kernel, PNNOutputMode outmodel,
int inputCount, int outputCount) : base(kernel, outmodel, inputCount, outputCount)
{
SeparateClass = false;
_sigma = new double[inputCount];
}
/// <summary>
/// Construct a BasicPNN network.
/// </summary>
///
/// <param name="kernel">The kernel to use.</param>
/// <param name="outmodel">The output model for this network.</param>
/// <param name="inputCount">The number of inputs in this network.</param>
/// <param name="outputCount">The number of outputs in this network.</param>
public BasicPNN(PNNKernelType kernel, PNNOutputMode outmodel,
int inputCount, int outputCount) : base(kernel, outmodel, inputCount, outputCount)
{
SeparateClass = false;
_sigma = new double[inputCount];
}
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);
}
public static string OutputModeToString(PNNOutputMode mode)
{
switch (mode)
{
case PNNOutputMode.Unsupervised:
return "unsupervised";
case PNNOutputMode.Regression:
return "regression";
case PNNOutputMode.Classification:
return "classification";
}
return null;
}
/// <summary>
/// Convert output mode to string.
/// </summary>
///
/// <param name="mode">The output mode.</param>
/// <returns>The string.</returns>
public static String OutputModeToString(PNNOutputMode mode)
{
switch (mode)
{
case PNNOutputMode.Regression:
return("regression");
case PNNOutputMode.Unsupervised:
return("unsupervised");
case PNNOutputMode.Classification:
return("classification");
default:
return(null);
}
}
protected AbstractPNN(PNNKernelType kernel, PNNOutputMode outputMode, int inputCount, int outputCount)
{
while ((((uint) inputCount) + ((uint) inputCount)) >= 0)
{
this._kernel = kernel;
this._outputMode = outputMode;
this._inputCount = inputCount;
if (((uint) inputCount) < 0)
{
goto Label_0020;
}
this._outputCount = outputCount;
this.Trained = false;
this.Error = double.MinValue;
if (0 == 0)
{
this._confusion = null;
goto Label_006C;
}
}
return;
Label_0020:
if (((((uint) inputCount) | 0xfffffffe) != 0) && (((uint) inputCount) >= 0))
{
if ((((uint) outputCount) & 0) == 0)
{
return;
}
goto Label_006C;
}
Label_0038:
if (this._outputMode == PNNOutputMode.Classification)
{
this._confusion = new int[this._outputCount + 1];
goto Label_0020;
}
return;
Label_006C:
this.Exclude = -1;
this._deriv = new double[inputCount];
this._deriv2 = new double[inputCount];
goto Label_0038;
}
/// <summary>
/// Constructor.
/// </summary>
///
/// <param name="kernel">The kernel type to use.</param>
/// <param name="outputMode">The output mode to use.</param>
/// <param name="inputCount">The input count.</param>
/// <param name="outputCount">The output count.</param>
protected AbstractPNN(PNNKernelType kernel,
PNNOutputMode outputMode, int inputCount,
int outputCount)
{
_kernel = kernel;
_outputMode = outputMode;
_inputCount = inputCount;
_outputCount = outputCount;
Trained = false;
Error = -1000;
_confusion = null;
Exclude = -1;
_deriv = new double[inputCount];
_deriv2 = new double[inputCount];
if (_outputMode == PNNOutputMode.Classification)
{
_confusion = new int[_outputCount + 1];
}
}
/// <summary>
/// Constructor.
/// </summary>
///
/// <param name="kernel">The kernel type to use.</param>
/// <param name="outputMode">The output mode to use.</param>
/// <param name="inputCount">The input count.</param>
/// <param name="outputCount">The output count.</param>
protected AbstractPNN(PNNKernelType kernel,
PNNOutputMode outputMode, int inputCount,
int outputCount)
{
_kernel = kernel;
_outputMode = outputMode;
_inputCount = inputCount;
_outputCount = outputCount;
Trained = false;
Error = -1000;
_confusion = null;
Exclude = -1;
_deriv = new double[inputCount];
_deriv2 = new double[inputCount];
if (_outputMode == PNNOutputMode.Classification)
{
_confusion = new int[_outputCount + 1];
}
}
/// <summary>
/// Construct the object.
/// </summary>
public PNNPattern()
{
_kernel = PNNKernelType.Gaussian;
_outmodel = PNNOutputMode.Regression;
}
/// <summary>
/// Construct the object.
/// </summary>
public PNNPattern()
{
_kernel = PNNKernelType.Gaussian;
_outmodel = PNNOutputMode.Regression;
}
/// <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);
}