/// <summary>
///
/// </summary>
/// <param name="obj"></param>
/// <param name="xmlOut"></param>
public void Save(IEncogPersistedObject obj, WriteXML xmlOut)
{
PersistorUtil.BeginEncogObject(EncogPersistedCollection.TYPE_TRAINING,
xmlOut, obj, true);
INeuralDataSet set = (INeuralDataSet)obj;
StringBuilder builder = new StringBuilder();
foreach (INeuralDataPair pair in set)
{
xmlOut.BeginTag(BasicNeuralDataSetPersistor.TAG_ITEM);
NumberList.ToList(CSVFormat.EG_FORMAT, builder, pair.Input.Data);
xmlOut.AddProperty(BasicNeuralDataSetPersistor.TAG_INPUT, builder
.ToString());
if (pair.Ideal != null)
{
NumberList.ToList(CSVFormat.EG_FORMAT, builder, pair.Ideal.Data);
xmlOut.AddProperty(BasicNeuralDataSetPersistor.TAG_IDEAL, builder
.ToString());
}
xmlOut.EndTag();
}
xmlOut.EndTag();
}
/// <summary>
/// Save the specified Encog object to an XML writer.
/// </summary>
/// <param name="obj">The object to save.</param>
/// <param name="xmlOut">The XML writer to save to.</param>
public void Save(IEncogPersistedObject obj, WriteXML xmlOut)
{
PersistorUtil.BeginEncogObject(
EncogPersistedCollection.TYPE_BASIC_LAYER, xmlOut, obj, false);
BasicLayer layer = (BasicLayer)obj;
xmlOut.AddProperty(BasicLayerPersistor.PROPERTY_NEURONS, layer
.NeuronCount);
xmlOut.AddProperty(BasicLayerPersistor.PROPERTY_X, layer.X);
xmlOut.AddProperty(BasicLayerPersistor.PROPERTY_Y, layer.Y);
if (layer.HasBias)
{
StringBuilder result = new StringBuilder();
NumberList.ToList(CSVFormat.EG_FORMAT, result, layer.BiasWeights);
xmlOut.AddProperty(BasicLayerPersistor.PROPERTY_THRESHOLD, result
.ToString());
}
xmlOut.AddProperty(BasicLayerPersistor.PROPERTY_BIAS_ACTIVATION, layer.BiasActivation);
if (layer.ActivationFunction != null)
{
SaveActivationFunction(layer.ActivationFunction, xmlOut);
}
xmlOut.EndTag();
}
private void SaveRBF(WriteXML xmlout, RadialBasisFunctionLayer layer)
{
xmlout.BeginTag(RadialBasisFunctionLayerPersistor.PROPERTY_RBF);
foreach (IRadialBasisFunction rbf in layer.RadialBasisFunction)
{
xmlout.BeginTag(rbf.GetType().Name);
xmlout.AddProperty(PROPERTY_CENTERS, rbf.Centers, rbf.Centers.Length);
xmlout.AddProperty(PROPERTY_PEAK, rbf.Peak);
xmlout.AddProperty(PROPERTY_WIDTH, rbf.Width);
xmlout.EndTag();
}
xmlout.EndTag();
}
/// <summary>
/// Save a RBF layer.
/// </summary>
/// <param name="obj">The object to save.</param>
/// <param name="xmlout">XML stream to write to.</param>
public void Save(IEncogPersistedObject obj, WriteXML xmlout)
{
PersistorUtil.BeginEncogObject(
EncogPersistedCollection.TYPE_RADIAL_BASIS_LAYER, xmlout, obj,
false);
RadialBasisFunctionLayer layer = (RadialBasisFunctionLayer)obj;
xmlout.AddProperty(BasicLayerPersistor.PROPERTY_NEURONS, layer.NeuronCount);
xmlout.AddProperty(BasicLayerPersistor.PROPERTY_X, layer.X);
xmlout.AddProperty(BasicLayerPersistor.PROPERTY_Y, layer.Y);
SaveRBF(xmlout, layer);
xmlout.EndTag();
}
/// <summary>
/// Save a SVMNetwork.
/// </summary>
/// <param name="obj">The object to save.</param>
/// <param name="xmlout">Where to save it to.</param>
public void Save(IEncogPersistedObject obj, WriteXML xmlout)
{
PersistorUtil.BeginEncogObject(EncogPersistedCollection.TYPE_SVM, xmlout,
obj, true);
SVMNetwork net = (SVMNetwork)obj;
xmlout.AddProperty(SVMNetworkPersistor.TAG_INPUT, net.InputCount);
xmlout.AddProperty(SVMNetworkPersistor.TAG_OUTPUT, net.OutputCount);
xmlout.BeginTag(SVMNetworkPersistor.TAG_MODELS);
for (int i = 0; i < net.Models.Length; i++)
{
SaveModel(xmlout, net.Models[i]);
}
xmlout.EndTag();
xmlout.EndTag();
}
/// <summary>
/// Save a model.
/// </summary>
/// <param name="xmlout">Where to save a model to.</param>
/// <param name="model">The model to save to.</param>
public static void SaveModel(WriteXML xmlout, svm_model model)
{
if (model != null)
{
xmlout.BeginTag(SVMNetworkPersistor.TAG_MODEL);
svm_parameter param = model.param;
xmlout.AddProperty(SVMNetworkPersistor.TAG_TYPE_SVM,
svm_type_table[param.svm_type]);
xmlout.AddProperty(SVMNetworkPersistor.TAG_TYPE_KERNEL,
kernel_type_table[param.kernel_type]);
if (param.kernel_type == svm_parameter.POLY)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_DEGREE, param.degree);
}
if (param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.RBF ||
param.kernel_type == svm_parameter.SIGMOID)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_GAMMA, param.gamma);
}
if (param.kernel_type == svm_parameter.POLY ||
param.kernel_type == svm_parameter.SIGMOID)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_COEF0, param.coef0);
}
int nr_class = model.nr_class;
int l = model.l;
xmlout.AddProperty(SVMNetworkPersistor.TAG_NUMCLASS, nr_class);
xmlout.AddProperty(SVMNetworkPersistor.TAG_TOTALSV, l);
xmlout.AddProperty(SVMNetworkPersistor.TAG_RHO, model.rho, nr_class
* (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_LABEL, model.label,
nr_class);
xmlout.AddProperty(SVMNetworkPersistor.TAG_PROB_A, model.probA,
nr_class * (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_PROB_B, model.probB,
nr_class * (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_NSV, model.nSV, nr_class);
xmlout.BeginTag(SVMNetworkPersistor.TAG_DATA);
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
StringBuilder line = new StringBuilder();
for (int i = 0; i < l; i++)
{
line.Length = 0;
for (int j = 0; j < nr_class - 1; j++)
{
line.Append(sv_coef[j][i] + " ");
}
svm_node[] p = SV[i];
//if (param.kernel_type == svm_parameter.PRECOMPUTED)
//{
// line.Append("0:" + (int) (p[0].value));
//}
//else
for (int j = 0; j < p.Length; j++)
{
line.Append(p[j].index + ":" + p[j].value_Renamed + " ");
}
xmlout.AddProperty(SVMNetworkPersistor.TAG_ROW, line.ToString());
}
xmlout.EndTag();
xmlout.EndTag();
}
}