// Use this for initialization
IEnumerator LoadFromDb()
{
using (SqliteConnection conn = new SqliteConnection(GetConnectionString()))
{
conn.Open();
SqliteCommand command = new SqliteCommand(conn);
id3 = new DecisionTreeID3();
int iBatch = 0;
foreach (var info in decisionTrees)
{
string query = string.Format("SELECT * FROM {0}", info.tableName);
command.CommandText = query;
foreach (var split in info.outcomes)
{
var reader = command.ExecuteReader();
DataTable table = new DataTable(info.tableName);
for (int i = 0; i < reader.FieldCount; i++)
{
if (reader.GetName(i) == info.predictColumn)
{
table.Columns.Add(reader.GetName(i), typeof(bool));
}
else
{
table.Columns.Add(reader.GetName(i), reader.GetFieldType(i));
}
}
while (reader.Read())
{
var dr = table.NewRow();
for (int i = 0; i < reader.FieldCount; i++)
{
if (table.Columns[i].ColumnName == info.predictColumn)
{
if (!reader.IsDBNull(i))
{
switch (split.type)
{
case DecisionTreeOutcome.OutcomeType.Int:
dr[i] = (reader.GetInt32(i) == split.value);
break;
case DecisionTreeOutcome.OutcomeType.Text:
dr[i] = (reader.GetString(i) == split.textValue);
break;
}
}
}
else
{
dr[i] = reader[i];
}
}
table.Rows.Add(dr);
if (++iBatch % batchSize == 0)
{
yield return(new WaitForEndOfFrame());
}
}
table.AcceptChanges();
split.root = id3.MountTree(table, info.predictColumn, info.GetAttributes());
Hawk.Log(string.Format("DECISION TREE {0}{1}\n{2}", info.decisionTreeName, split.name, split.root.PrintNode("--->")));
foreach (var obj in eventListeners)
{
obj.SendMessage("OnDecisionTreeReady", split.root);
}
reader.Close();
}
}
}
}
private TreeNode InternalMountTree(DataTable samples, String targetCustomAttribute, CustomAttribute[] attributes)
{
if (AllSamplesPositives(samples, targetCustomAttribute) == true)
{
return(new TreeNode(new CustomAttribute(true)));
}
if (AllSamplesNegatives(samples, targetCustomAttribute) == true)
{
return(new TreeNode(new CustomAttribute(false)));
}
if (attributes.Length == 0)
{
return(new TreeNode(new CustomAttribute(GetMostCommonValue(samples, targetCustomAttribute))));
}
mTotal = samples.Rows.Count;
mTargetCustomAttribute = targetCustomAttribute;
mTotalPositives = CountTotalPositives(samples);
mEntropySet = CalcEntropy(mTotalPositives, mTotal - mTotalPositives);
CustomAttribute bestCustomAttribute = GetBestCustomAttribute(samples, attributes);
if (bestCustomAttribute == null)
{
return(new TreeNode(new CustomAttribute(false)));
}
TreeNode root = new TreeNode(bestCustomAttribute);
DataTable aSample = samples.Clone();
foreach (String value in bestCustomAttribute.values)
{
aSample.Rows.Clear();
DataRow[] rows = samples.Select(bestCustomAttribute.CustomAttributeName + " = " + "'" + value + "'");
foreach (DataRow row in rows)
{
aSample.Rows.Add(row.ItemArray);
}
ArrayList aCustomAttributes = new ArrayList(attributes.Length - 1);
for (int i = 0; i < attributes.Length; i++)
{
if (attributes[i].CustomAttributeName != bestCustomAttribute.CustomAttributeName)
{
aCustomAttributes.Add(attributes[i]);
}
}
if (aSample.Rows.Count == 0)
{
return(new TreeNode(new CustomAttribute(GetMostCommonValue(aSample, targetCustomAttribute))));
}
else
{
DecisionTreeID3 dc3 = new DecisionTreeID3();
TreeNode ChildNode = dc3.MountTree(aSample, targetCustomAttribute, (CustomAttribute[])aCustomAttributes.ToArray(typeof(CustomAttribute)));
root.AddTreeNode(ChildNode, value);
}
}
return(root);
}