/// <summary>
/// Handle an item.
/// </summary>
/// <param name="xmlin">The XML input object.</param>
public void HandleItem(ReadXML xmlin)
{
String name = xmlin.LastTag.GetAttributeValue(
TrainingContinuationPersistor.ATTRIBUTE_NAME);
String str = xmlin.ReadTextToTag();
double[] list = NumberList.FromList(CSVFormat.EG_FORMAT, str);
this.current[name] = list;
}
/// <summary>
/// Load the specified Encog object from an XML reader.
/// </summary>
/// <param name="xmlIn">The XML reader to use.</param>
/// <returns>The loaded object.</returns>
public IEncogPersistedObject Load(ReadXML xmlIn)
{
int neuronCount = 0;
int x = 0;
int y = 0;
double biasActivation = 1;
String threshold = null;
IActivationFunction activation = null;
String end = xmlIn.LastTag.Name;
String context = null;
while (xmlIn.ReadToTag())
{
if (xmlIn.IsIt(BasicLayerPersistor.TAG_ACTIVATION, true))
{
xmlIn.ReadToTag();
String type = xmlIn.LastTag.Name;
activation = BasicLayerPersistor.LoadActivation(type, xmlIn);
}
else if (xmlIn.IsIt(BasicLayerPersistor.PROPERTY_NEURONS, true))
{
neuronCount = xmlIn.ReadIntToTag();
}
else if (xmlIn.IsIt(BasicLayerPersistor.PROPERTY_X, true))
{
x = xmlIn.ReadIntToTag();
}
else if (xmlIn.IsIt(BasicLayerPersistor.PROPERTY_Y, true))
{
y = xmlIn.ReadIntToTag();
}
else if (xmlIn.IsIt(BasicLayerPersistor.PROPERTY_THRESHOLD, true))
{
threshold = xmlIn.ReadTextToTag();
}
else if (xmlIn.IsIt(PROPERTY_CONTEXT, true))
{
context = xmlIn.ReadTextToTag();
}
else if (xmlIn.IsIt(BasicLayerPersistor.PROPERTY_BIAS_ACTIVATION, true))
{
biasActivation = double.Parse(xmlIn.ReadTextToTag());
}
else if (xmlIn.IsIt(end, false))
{
break;
}
}
if (neuronCount > 0)
{
ContextLayer layer;
if (threshold == null)
{
layer = new ContextLayer(activation, false, neuronCount);
}
else
{
double[] t = NumberList.FromList(CSVFormat.EG_FORMAT, threshold);
layer = new ContextLayer(activation, true, neuronCount);
for (int i = 0; i < t.Length; i++)
{
layer.BiasWeights[i] = t[i];
}
}
if (context != null)
{
double[] c = NumberList.FromList(CSVFormat.EG_FORMAT, context);
for (int i = 0; i < c.Length; i++)
{
layer.Context[i] = c[i];
}
}
layer.X = x;
layer.Y = y;
layer.BiasActivation = biasActivation;
return layer;
}
return null;
}
/// <summary>
/// Load the properties.
/// </summary>
/// <param name="xmlIn">The XML object.</param>
private void HandleProperties(ReadXML xmlIn)
{
String end = xmlIn.LastTag.Name;
while (xmlIn.ReadToTag())
{
if (xmlIn.IsIt(BasicNetworkPersistor.TAG_PROPERTY, true))
{
String name = xmlIn.LastTag.GetAttributeValue(
BasicNetworkPersistor.ATTRIBUTE_NAME);
String value = xmlIn.ReadTextToTag();
this.currentNetwork.SetProperty(name, value);
}
if (xmlIn.IsIt(end, false))
{
break;
}
}
}
/// <summary>
/// Load a matrix from the reader.
/// </summary>
/// <param name="xmlIn">The XML reader.</param>
/// <returns>The loaded matrix.</returns>
public static Matrix LoadMatrix(ReadXML xmlIn)
{
int rows = xmlIn.LastTag.GetAttributeInt(
PersistorUtil.ATTRIBUTE_MATRIX_ROWS);
int cols = xmlIn.LastTag.GetAttributeInt(
PersistorUtil.ATTRIBUTE_MATRIX_COLS);
Matrix matrix = new Matrix(rows, cols);
int row = 0;
String end = xmlIn.LastTag.Name;
while (xmlIn.ReadToTag())
{
if (xmlIn.IsIt(end, false))
{
break;
}
if (xmlIn.IsIt(PersistorUtil.ROW, true))
{
String str = xmlIn.ReadTextToTag();
double[] d = NumberList.FromList(CSVFormat.EG_FORMAT, str);
for (int col = 0; col < d.Length; col++)
{
matrix[row, col] = d[col];
}
row++;
}
}
return matrix;
}
/// <summary>
/// Load the neural logic object.
/// </summary>
/// <param name="xmlIn">The XML object.</param>
private void HandleLogic(ReadXML xmlIn)
{
String value = xmlIn.ReadTextToTag();
if (value.Equals("ART1Logic"))
{
this.currentNetwork.Logic = new ART1Logic();
}
else if (value.Equals("BAMLogic"))
{
this.currentNetwork.Logic = new BAMLogic();
}
else if (value.Equals("BoltzmannLogic"))
{
this.currentNetwork.Logic = new BoltzmannLogic();
}
else if (value.Equals("FeedforwardLogic"))
{
this.currentNetwork.Logic = new FeedforwardLogic();
}
else if (value.Equals("HopfieldLogic"))
{
this.currentNetwork.Logic = new HopfieldLogic();
}
else if (value.Equals("SimpleRecurrentLogic"))
{
this.currentNetwork.Logic = new SimpleRecurrentLogic();
}
else
{
INeuralLogic logic = (INeuralLogic)Assembly.GetExecutingAssembly().CreateInstance(value);
this.currentNetwork.Logic = logic;
}
}
/// <summary>
/// Load the data from a model.
/// </summary>
/// <param name="xmlin">Where to read the data from.</param>
/// <param name="model">The model to load data into.</param>
private void HandleData(ReadXML xmlin, svm_model model)
{
int i = 0;
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = EngineArray.AllocateDouble2D(m, l);
model.SV = new svm_node[l][];
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_ROW, true))
{
String line = xmlin.ReadTextToTag();
String[] st = xmlin.ReadTextToTag().Split(',');
for (int k = 0; k < m; k++)
model.sv_coef[k][i] = Double.Parse(st[i]);
int n = st.Length / 2;
model.SV[i] = new svm_node[n];
int idx = 0;
for (int j = 0; j < n; j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = int.Parse(st[idx++]);
model.SV[i][j].value_Renamed = Double.Parse(st[idx++]);
}
i++;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DATA, false))
{
break;
}
}
}
/// <summary>
/// Handle a model.
/// </summary>
/// <param name="xmlin">Where to read the model from.</param>
/// <param name="model">Where to load the model into.</param>
private void HandleModel(ReadXML xmlin, svm_model model)
{
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_TYPE_SVM, true))
{
int i = EngineArray.FindStringInArray(
SVMNetworkPersistor.svm_type_table, xmlin.ReadTextToTag());
model.param.svm_type = i;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DEGREE, true))
{
model.param.degree = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_GAMMA, true))
{
model.param.gamma = double.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_COEF0, true))
{
model.param.coef0 = double.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_NUMCLASS, true))
{
model.nr_class = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_TOTALSV, true))
{
model.l = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_RHO, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.rho = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.rho[i] = double.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_LABEL, true))
{
int n = model.nr_class;
model.label = new int[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.label[i] = int.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_PROB_A, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probA = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.probA[i] = Double.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_PROB_B, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probB = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.probB[i] = Double.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_NSV, true))
{
int n = model.nr_class;
model.nSV = new int[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.nSV[i] = int.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_TYPE_KERNEL, true))
{
int i = EngineArray.FindStringInArray(
SVMNetworkPersistor.kernel_type_table, xmlin
.ReadTextToTag());
model.param.kernel_type = i;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DATA, true))
{
HandleData(xmlin, model);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODEL, false))
{
break;
}
}
}
/// <summary>
/// Load the SVM network.
/// </summary>
/// <param name="xmlin">Where to read it from.</param>
/// <returns>The loaded object.</returns>
public IEncogPersistedObject Load(ReadXML xmlin)
{
SVMNetwork result = null;
int input = -1, output = -1;
String name = xmlin.LastTag.Attributes[
EncogPersistedCollection.ATTRIBUTE_NAME];
String description = xmlin.LastTag.Attributes[
EncogPersistedCollection.ATTRIBUTE_DESCRIPTION];
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_INPUT, true))
{
input = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_OUTPUT, true))
{
output = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODELS, true))
{
result = new SVMNetwork(input, output, false);
HandleModels(xmlin, result);
}
else if (xmlin.IsIt(EncogPersistedCollection.TYPE_SVM, false))
{
break;
}
}
result.Name = name;
result.Description = description;
return result;
}
private IRadialBasisFunction LoadRBF(String name, ReadXML xmlin)
{
String clazz = ReflectionUtil.ResolveEncogClass(name);
if (clazz == null)
{
throw new NeuralNetworkError("Unknown RBF function type: " + name);
}
IRadialBasisFunction result = (IRadialBasisFunction)ReflectionUtil.LoadObject(clazz);
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(name, false))
{
break;
}
else if (xmlin.IsIt(PROPERTY_CENTERS, true))
{
String str = xmlin.ReadTextToTag();
double[] centers = NumberList.FromList(CSVFormat.EG_FORMAT, str);
result.Centers = centers;
}
else if (xmlin.IsIt(PROPERTY_PEAK, true))
{
String str = xmlin.ReadTextToTag();
double d = Double.Parse(str);
result.Peak = d;
}
else if (xmlin.IsIt(PROPERTY_WIDTH, true))
{
String str = xmlin.ReadTextToTag();
double d = Double.Parse(str);
result.Width = d;
}
}
return result;
}
