public void calc_gain()
{
setLengthOfDS();
setNumberofFeatures();
calc_feature_list();
calc_unique_feature_values();
calcclassCounts();
calc_class_list();
setMajorityClass();
getPureClass();
calc_entropy();
if (Len_dataset > 0)
{
if (Info_gain_features_map == null)
{
Info_gain_features_map = new Dictionary <string, double>();
}
foreach (string feature_name in Features_list)
{
if (Featurestoexclude == null)
{
Featurestoexclude = new List <string>();
}
if (!Featurestoexclude.Contains(feature_name) && feature_name != (Number_of_features - 1).ToString())
{
Info_gain_features_map[feature_name] = set_gains(feature_name);
}
}
}
}//info gain <-- dont consider max if it is in the excluded features list
public void removeFeatures(List <string> features_to_remove)
{
if (Featurestoexclude == null)
{
Featurestoexclude = new List <string>();
}
if (features_to_remove != null)
{
foreach (string fe in features_to_remove)
{
if (!Featurestoexclude.Contains(fe))
{
Featurestoexclude.Add(fe);
}
}
}
}
public void calc_unique_feature_values()
{
if (Len_dataset > 0)
{
foreach (string feat in Features_list)
{
List <string> u_list = new List <string>();
bool goAhead = false;
if (Featurestoexclude == null)
{
goAhead = true;
}
else
if (!Featurestoexclude.Contains(feat))
{
goAhead = true;
}
if (goAhead)
{
foreach (DataRow datarow in Dataset.Rows)
{
string val = Convert.ToString(datarow.ItemArray[int.Parse(feat)]);
if (!u_list.Contains(val))
{
u_list.Add(val);
}
}
}
if (Feature_uniqueValues == null)
{
Feature_uniqueValues = new Dictionary <string, List <string> >();
}
Feature_uniqueValues[feat] = u_list;
}
}
}