private static void write_about_rows(XmlWriter writer, c_samples_set samplesSet)
{
writer.WriteStartElement("Rows");
writer.WriteAttributeString("Count", XmlConvert.ToString(samplesSet.Data_Rows.Count()));
for (int i = 0; i < samplesSet.Data_Rows.Count; i++)
{
writer.WriteStartElement("Row");
for (int j = 0; j < samplesSet.Count_Vars; j++)
{
if (samplesSet.Input_Attribute(j).labels_values.Count() > 0)
{
writer.WriteElementString(samplesSet.Input_Attribute(j).Name,
samplesSet.Data_Rows[i].Input_Attribute_String[j]);
}
else
{
writer.WriteElementString(samplesSet.Input_Attribute(j).Name,
XmlConvert.ToString(samplesSet.Data_Rows[i].Input_Attribute_Value[j]));
}
}
writer.WriteElementString(samplesSet.Output_Attributes.Name, samplesSet.Data_Rows[i].Class_Label);
writer.WriteEndElement();
}
writer.WriteEndElement();
}
private static void write_about_table(XmlWriter writer, c_samples_set samplesSet, c_Fuzzy_System Classifier)
{
writer.WriteStartElement("Table");
writer.WriteAttributeString("Name", samplesSet.File_Name.Remove(samplesSet.File_Name.Length - 4, 4));
if (samplesSet == Classifier.Learn_Samples_set)
{
writer.WriteAttributeString("Type", "Training");
}
else
{
writer.WriteAttributeString("Type", "Testing");
}
writer.WriteAttributeString("Output", samplesSet.Output_Attributes.Name);
writer.WriteStartElement("Attributes");
writer.WriteAttributeString("Count", XmlConvert.ToString(samplesSet.Count_Vars));
for (int i = 0; i < samplesSet.Count_Vars; i++)
{
write_about_attribute(writer, samplesSet.Input_Attribute(i));
}
write_about_attribute(writer, samplesSet.Output_Attributes);
writer.WriteEndElement();
write_about_rows(writer, samplesSet);
writer.WriteEndElement();
}
public static c_samples_set Load_test_from_UFS(this c_samples_set table_set, string file_name)
{ //init
c_samples_set temp_set = null;
List <c_samples_set.Attribune_Info> input_Attribute = new List <c_samples_set.Attribune_Info>();
List <c_samples_set.Row_Sample> data_Row = new List <c_samples_set.Row_Sample>();
c_samples_set.Attribune_Info output_Attribute = new c_samples_set.Attribune_Info();
string opened_dataset;
//parse_start
XmlDocument Source = new XmlDocument();
Source.Load(file_name);
XmlNode table_node = Source.DocumentElement.SelectSingleNode("descendant::Table[@Type='Testing'] "); //We get learning table
if (table_node == null)
{
throw new System.FormatException("В файле нет таблиц данных");
}
opened_dataset = table_node.Attributes.GetNamedItem("Name").Value;
output_Attribute.Name = table_node.Attributes.GetNamedItem("Output").Value;
XmlNode attrib_node = table_node.SelectSingleNode("Attributes"); //We get atribute's tags
int count_attribs = XmlConvert.ToInt32(attrib_node.Attributes.GetNamedItem("Count").Value);
for (int k = 0; k <= count_attribs; k++)
{
c_samples_set.Attribune_Info temp_attib = new c_samples_set.Attribune_Info();
temp_attib.Name = attrib_node.ChildNodes[k].Attributes.GetNamedItem("Name").Value; // We get one attribute tag
foreach (XmlNode Value in attrib_node.ChildNodes[k].ChildNodes)
{
double temp_double = XmlConvert.ToDouble(Value.InnerXml);
switch (Value.Name)
{
case "Min": { temp_attib.Min = temp_double; break; }
case "Max": { temp_attib.Max = temp_double; break; }
}
}
if (temp_attib.Name.Equals(output_Attribute.Name, StringComparison.OrdinalIgnoreCase))
{
output_Attribute = temp_attib;
}
else
{
input_Attribute.Add(temp_attib);
}
}
XmlNode rows_node = table_node.SelectSingleNode("Rows"); //We get data rows
int count_rows = XmlConvert.ToInt32(rows_node.Attributes.GetNamedItem("Count").Value);
for (int r = 0; r < count_rows; r++)
{
string Classifier_value;
double[] double_value = new double[count_attribs];
string[] string_value = new string[count_attribs];
for (int a = 0; a < count_attribs; a++)
{
XmlNode value = rows_node.ChildNodes[r].SelectSingleNode(input_Attribute[a].Name);
try { double_value[a] = XmlConvert.ToDouble(value.InnerXml); }
catch
{
string_value[a] = value.InnerText;
}
}
XmlNode outvalue = rows_node.ChildNodes[r].SelectSingleNode(output_Attribute.Name);
Classifier_value = outvalue.InnerText;
c_samples_set.Row_Sample temp_rows = new c_samples_set.Row_Sample(double_value, string_value, Classifier_value);
data_Row.Add(temp_rows);
}
temp_set = new c_samples_set(opened_dataset, data_Row, input_Attribute, output_Attribute);
GC.Collect();
return(temp_set);
}
