#region License Information
/* HeuristicLab
* Copyright (C) 2002-2008 Heuristic and Evolutionary Algorithms Laboratory (HEAL)
*
* This file is part of HeuristicLab.
*
* HeuristicLab is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* HeuristicLab is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with HeuristicLab. If not, see .
*/
#endregion
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using HeuristicLab.Core;
using HeuristicLab.DataAnalysis;
using HeuristicLab.Data;
using System.Xml;
using System.Globalization;
namespace HeuristicLab.Functions {
class LightWeightFunction {
public int arity = 0;
public IFunction functionType;
public List data = new List();
public LightWeightFunction Clone() {
LightWeightFunction clone = new LightWeightFunction();
clone.arity = arity;
clone.functionType = functionType;
clone.data.AddRange(data);
return clone;
}
}
class BakedFunctionTree : ItemBase, IFunctionTree {
private List linearRepresentation;
internal List LinearRepresentation {
get {
FlattenVariables();
FlattenTrees();
return linearRepresentation;
}
}
private bool treesExpanded = false;
private List subTrees;
private bool variablesExpanded = false;
private List variables;
private BakedTreeEvaluator evaluator;
public BakedFunctionTree() {
linearRepresentation = new List();
}
internal BakedFunctionTree(IFunction function)
: this() {
LightWeightFunction fun = new LightWeightFunction();
fun.functionType = function;
linearRepresentation.Add(fun);
treesExpanded = true;
subTrees = new List();
variables = new List();
variablesExpanded = true;
foreach(IVariableInfo variableInfo in function.VariableInfos) {
if(variableInfo.Local) {
variables.Add((IVariable)function.GetVariable(variableInfo.FormalName).Clone());
}
}
}
internal BakedFunctionTree(IFunctionTree tree)
: this() {
LightWeightFunction fun = new LightWeightFunction();
fun.functionType = tree.Function;
linearRepresentation.Add(fun);
foreach(IVariable variable in tree.LocalVariables) {
IItem value = variable.Value;
fun.data.Add(GetDoubleValue(value));
}
foreach(IFunctionTree subTree in tree.SubTrees) {
AddSubTree(new BakedFunctionTree(subTree));
}
}
private double GetDoubleValue(IItem value) {
if(value is DoubleData) {
return ((DoubleData)value).Data;
} else if(value is ConstrainedDoubleData) {
return ((ConstrainedDoubleData)value).Data;
} else if(value is IntData) {
return ((IntData)value).Data;
} else if(value is ConstrainedIntData) {
return ((ConstrainedIntData)value).Data;
} else throw new NotSupportedException("Invalid datatype of local variable for GP");
}
private int BranchLength(int branchRoot) {
int arity = linearRepresentation[branchRoot].arity;
int length = 1;
for(int i = 0; i < arity; i++) {
length += BranchLength(branchRoot + length);
}
return length;
}
private void FlattenTrees() {
if(treesExpanded) {
linearRepresentation[0].arity = subTrees.Count;
foreach(BakedFunctionTree subTree in subTrees) {
subTree.FlattenVariables();
subTree.FlattenTrees();
linearRepresentation.AddRange(subTree.linearRepresentation);
}
treesExpanded = false;
subTrees = null;
}
}
private void FlattenVariables() {
if(variablesExpanded) {
linearRepresentation[0].data.Clear();
foreach(IVariable variable in variables) {
linearRepresentation[0].data.Add(GetDoubleValue(variable.Value));
}
variablesExpanded = false;
variables = null;
}
}
public int Size {
get {
if(treesExpanded) {
int size = 1;
foreach(BakedFunctionTree tree in subTrees) {
size += tree.Size;
}
return size;
} else
return linearRepresentation.Count;
}
}
public int Height {
get {
if(treesExpanded) {
int height = 0;
foreach(IFunctionTree subTree in subTrees) {
int curHeight = subTree.Height;
if(curHeight > height) height = curHeight;
}
return height+1;
} else {
int nextBranchStart;
return BranchHeight(0, out nextBranchStart);
}
}
}
private int BranchHeight(int branchStart, out int nextBranchStart) {
LightWeightFunction f = linearRepresentation[branchStart];
int height = 0;
branchStart++;
for(int i = 0; i < f.arity; i++) {
int curHeight = BranchHeight(branchStart, out nextBranchStart);
if(curHeight > height) height = curHeight;
branchStart = nextBranchStart;
}
nextBranchStart = branchStart;
return height + 1;
}
public IList SubTrees {
get {
if(!treesExpanded) {
subTrees = new List();
int arity = linearRepresentation[0].arity;
int branchIndex = 1;
for(int i = 0; i < arity; i++) {
BakedFunctionTree subTree = new BakedFunctionTree();
int length = BranchLength(branchIndex);
for(int j = branchIndex; j < branchIndex + length; j++) {
subTree.linearRepresentation.Add(linearRepresentation[j]);
}
branchIndex += length;
subTrees.Add(subTree);
}
treesExpanded = true;
linearRepresentation.RemoveRange(1, linearRepresentation.Count - 1);
linearRepresentation[0].arity = 0;
}
return subTrees;
}
}
public ICollection LocalVariables {
get {
if(!variablesExpanded) {
variables = new List();
IFunction function = Function;
int localVariableIndex = 0;
foreach(IVariableInfo variableInfo in function.VariableInfos) {
if(variableInfo.Local) {
IVariable clone = (IVariable)function.GetVariable(variableInfo.FormalName).Clone();
IItem value = clone.Value;
if(value is ConstrainedDoubleData) {
((ConstrainedDoubleData)value).Data = linearRepresentation[0].data[localVariableIndex];
} else if(value is ConstrainedIntData) {
((ConstrainedIntData)value).Data = (int)linearRepresentation[0].data[localVariableIndex];
} else if(value is DoubleData) {
((DoubleData)value).Data = linearRepresentation[0].data[localVariableIndex];
} else if(value is IntData) {
((IntData)value).Data = (int)linearRepresentation[0].data[localVariableIndex];
} else throw new NotSupportedException("Invalid local variable type for GP.");
variables.Add(clone);
localVariableIndex++;
}
}
variablesExpanded = true;
linearRepresentation[0].data.Clear();
}
return variables;
}
}
public IFunction Function {
get { return linearRepresentation[0].functionType; }
}
public IVariable GetLocalVariable(string name) {
foreach(IVariable var in LocalVariables) {
if(var.Name == name) return var;
}
return null;
}
public void AddVariable(IVariable variable) {
throw new NotSupportedException();
}
public void RemoveVariable(string name) {
throw new NotSupportedException();
}
public void AddSubTree(IFunctionTree tree) {
if(!treesExpanded) throw new InvalidOperationException();
subTrees.Add(tree);
}
public void InsertSubTree(int index, IFunctionTree tree) {
if(!treesExpanded) throw new InvalidOperationException();
subTrees.Insert(index, tree);
}
public void RemoveSubTree(int index) {
// sanity check
if(!treesExpanded) throw new InvalidOperationException();
subTrees.RemoveAt(index);
}
public IEvaluator CreateEvaluator() {
return new BakedTreeEvaluator();
}
public override XmlNode GetXmlNode(string name, XmlDocument document, IDictionary persistedObjects) {
FlattenVariables();
FlattenTrees();
XmlNode node = base.GetXmlNode(name, document, persistedObjects);
XmlNode linearRepresentationNode = document.CreateElement("LinearRepresentation");
foreach(LightWeightFunction f in linearRepresentation) {
XmlNode entryNode = PersistenceManager.Persist("FunctionType", f.functionType, document, persistedObjects);
XmlAttribute arityAttribute = document.CreateAttribute("Arity");
arityAttribute.Value = f.arity+"";
entryNode.Attributes.Append(arityAttribute);
if(f.data.Count > 0) {
XmlAttribute dataAttribute = document.CreateAttribute("Data");
dataAttribute.Value = GetString(f.data);
entryNode.Attributes.Append(dataAttribute);
}
linearRepresentationNode.AppendChild(entryNode);
}
node.AppendChild(linearRepresentationNode);
return node;
}
public override void Populate(XmlNode node, IDictionary restoredObjects) {
base.Populate(node, restoredObjects);
XmlNode linearRepresentationNode = node.SelectSingleNode("LinearRepresentation");
foreach(XmlNode entryNode in linearRepresentationNode.ChildNodes) {
LightWeightFunction f = new LightWeightFunction();
f.arity = int.Parse(entryNode.Attributes["Arity"].Value, CultureInfo.InvariantCulture);
if(entryNode.Attributes["Data"]!=null)
f.data = GetList(entryNode.Attributes["Data"].Value, s => double.Parse(s, CultureInfo.InvariantCulture));
f.functionType = (IFunction)PersistenceManager.Restore(entryNode, restoredObjects);
linearRepresentation.Add(f);
}
treesExpanded = false;
variablesExpanded = false;
}
private string GetString(IEnumerable xs) {
StringBuilder builder = new StringBuilder();
foreach(double x in xs) {
builder.Append(x.ToString("r", CultureInfo.InvariantCulture) + "; ");
}
if(builder.Length > 0) builder.Remove(builder.Length - 2, 2);
return builder.ToString();
}
private List GetList(string s, Converter converter) {
List result = new List();
string[] tokens = s.Split(new char[] { ';', ' ' }, StringSplitOptions.RemoveEmptyEntries);
foreach(string token in tokens) {
T x = converter(token.Trim());
result.Add(x);
}
return result;
}
public override object Clone(IDictionary clonedObjects) {
BakedFunctionTree clone = new BakedFunctionTree();
// in case the user (de)serialized the tree between evaluation and selection we have to flatten the tree again.
if(treesExpanded) FlattenTrees();
if(variablesExpanded) FlattenVariables();
foreach(LightWeightFunction f in linearRepresentation) {
clone.linearRepresentation.Add(f.Clone());
}
return clone;
}
public override IView CreateView() {
return new FunctionTreeView(this);
}
public override string ToString() {
SymbolicExpressionExporter exporter = new SymbolicExpressionExporter();
exporter.Visit(this);
return exporter.GetStringRepresentation();
}
}
}