#region License Information
/* HeuristicLab
* Copyright (C) 2002-2015 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 HeuristicLab.Common;
using HeuristicLab.Core;
using HeuristicLab.Data;
using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding;
using HeuristicLab.Parameters;
using HeuristicLab.Persistence.Default.CompositeSerializers.Storable;
namespace HeuristicLab.Problems.DataAnalysis.Symbolic {
[StorableClass]
[Item("SymbolicDataAnalysisExpressionTreeInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.")]
public class SymbolicDataAnalysisExpressionTreeInterpreter : ParameterizedNamedItem, ISymbolicDataAnalysisExpressionTreeInterpreter {
private const string CheckExpressionsWithIntervalArithmeticParameterName = "CheckExpressionsWithIntervalArithmetic";
private const string EvaluatedSolutionsParameterName = "EvaluatedSolutions";
public override bool CanChangeName { get { return false; } }
public override bool CanChangeDescription { get { return false; } }
#region parameter properties
public IValueParameter CheckExpressionsWithIntervalArithmeticParameter {
get { return (IValueParameter)Parameters[CheckExpressionsWithIntervalArithmeticParameterName]; }
}
public IValueParameter EvaluatedSolutionsParameter {
get { return (IValueParameter)Parameters[EvaluatedSolutionsParameterName]; }
}
#endregion
#region properties
public BoolValue CheckExpressionsWithIntervalArithmetic {
get { return CheckExpressionsWithIntervalArithmeticParameter.Value; }
set { CheckExpressionsWithIntervalArithmeticParameter.Value = value; }
}
public IntValue EvaluatedSolutions {
get { return EvaluatedSolutionsParameter.Value; }
set { EvaluatedSolutionsParameter.Value = value; }
}
#endregion
[StorableConstructor]
protected SymbolicDataAnalysisExpressionTreeInterpreter(bool deserializing) : base(deserializing) { }
protected SymbolicDataAnalysisExpressionTreeInterpreter(SymbolicDataAnalysisExpressionTreeInterpreter original, Cloner cloner) : base(original, cloner) { }
public override IDeepCloneable Clone(Cloner cloner) {
return new SymbolicDataAnalysisExpressionTreeInterpreter(this, cloner);
}
public SymbolicDataAnalysisExpressionTreeInterpreter()
: base("SymbolicDataAnalysisExpressionTreeInterpreter", "Interpreter for symbolic expression trees including automatically defined functions.") {
Parameters.Add(new ValueParameter(CheckExpressionsWithIntervalArithmeticParameterName, "Switch that determines if the interpreter checks the validity of expressions with interval arithmetic before evaluating the expression.", new BoolValue(false)));
Parameters.Add(new ValueParameter(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
}
protected SymbolicDataAnalysisExpressionTreeInterpreter(string name, string description)
: base(name, description) {
Parameters.Add(new ValueParameter(CheckExpressionsWithIntervalArithmeticParameterName, "Switch that determines if the interpreter checks the validity of expressions with interval arithmetic before evaluating the expression.", new BoolValue(false)));
Parameters.Add(new ValueParameter(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
}
[StorableHook(HookType.AfterDeserialization)]
private void AfterDeserialization() {
if (!Parameters.ContainsKey(EvaluatedSolutionsParameterName))
Parameters.Add(new ValueParameter(EvaluatedSolutionsParameterName, "A counter for the total number of solutions the interpreter has evaluated", new IntValue(0)));
}
#region IStatefulItem
public void InitializeState() {
EvaluatedSolutions.Value = 0;
}
public void ClearState() {
}
#endregion
public IEnumerable GetSymbolicExpressionTreeValues(ISymbolicExpressionTree tree, IDataset dataset, IEnumerable rows) {
if (CheckExpressionsWithIntervalArithmetic.Value)
throw new NotSupportedException("Interval arithmetic is not yet supported in the symbolic data analysis interpreter.");
lock (EvaluatedSolutions) {
EvaluatedSolutions.Value++; // increment the evaluated solutions counter
}
var state = PrepareInterpreterState(tree, dataset);
foreach (var rowEnum in rows) {
int row = rowEnum;
yield return Evaluate(dataset, ref row, state);
state.Reset();
}
}
private static InterpreterState PrepareInterpreterState(ISymbolicExpressionTree tree, IDataset dataset) {
Instruction[] code = SymbolicExpressionTreeCompiler.Compile(tree, OpCodes.MapSymbolToOpCode);
int necessaryArgStackSize = 0;
foreach (Instruction instr in code) {
if (instr.opCode == OpCodes.Variable) {
var variableTreeNode = (VariableTreeNode)instr.dynamicNode;
instr.data = dataset.GetReadOnlyDoubleValues(variableTreeNode.VariableName);
} else if (instr.opCode == OpCodes.LagVariable) {
var laggedVariableTreeNode = (LaggedVariableTreeNode)instr.dynamicNode;
instr.data = dataset.GetReadOnlyDoubleValues(laggedVariableTreeNode.VariableName);
} else if (instr.opCode == OpCodes.VariableCondition) {
var variableConditionTreeNode = (VariableConditionTreeNode)instr.dynamicNode;
instr.data = dataset.GetReadOnlyDoubleValues(variableConditionTreeNode.VariableName);
} else if (instr.opCode == OpCodes.Call) {
necessaryArgStackSize += instr.nArguments + 1;
}
}
return new InterpreterState(code, necessaryArgStackSize);
}
public virtual double Evaluate(IDataset dataset, ref int row, InterpreterState state) {
Instruction currentInstr = state.NextInstruction();
switch (currentInstr.opCode) {
case OpCodes.Add: {
double s = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
s += Evaluate(dataset, ref row, state);
}
return s;
}
case OpCodes.Sub: {
double s = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
s -= Evaluate(dataset, ref row, state);
}
if (currentInstr.nArguments == 1) s = -s;
return s;
}
case OpCodes.Mul: {
double p = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
p *= Evaluate(dataset, ref row, state);
}
return p;
}
case OpCodes.Div: {
double p = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
p /= Evaluate(dataset, ref row, state);
}
if (currentInstr.nArguments == 1) p = 1.0 / p;
return p;
}
case OpCodes.Average: {
double sum = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
sum += Evaluate(dataset, ref row, state);
}
return sum / currentInstr.nArguments;
}
case OpCodes.Cos: {
return Math.Cos(Evaluate(dataset, ref row, state));
}
case OpCodes.Sin: {
return Math.Sin(Evaluate(dataset, ref row, state));
}
case OpCodes.Tan: {
return Math.Tan(Evaluate(dataset, ref row, state));
}
case OpCodes.Square: {
return Math.Pow(Evaluate(dataset, ref row, state), 2);
}
case OpCodes.Power: {
double x = Evaluate(dataset, ref row, state);
double y = Math.Round(Evaluate(dataset, ref row, state));
return Math.Pow(x, y);
}
case OpCodes.SquareRoot: {
return Math.Sqrt(Evaluate(dataset, ref row, state));
}
case OpCodes.Root: {
double x = Evaluate(dataset, ref row, state);
double y = Math.Round(Evaluate(dataset, ref row, state));
return Math.Pow(x, 1 / y);
}
case OpCodes.Exp: {
return Math.Exp(Evaluate(dataset, ref row, state));
}
case OpCodes.Log: {
return Math.Log(Evaluate(dataset, ref row, state));
}
case OpCodes.Gamma: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else return alglib.gammafunction(x);
}
case OpCodes.Psi: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else if (x <= 0 && (Math.Floor(x) - x).IsAlmost(0)) return double.NaN;
return alglib.psi(x);
}
case OpCodes.Dawson: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
return alglib.dawsonintegral(x);
}
case OpCodes.ExponentialIntegralEi: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
return alglib.exponentialintegralei(x);
}
case OpCodes.SineIntegral: {
double si, ci;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.sinecosineintegrals(x, out si, out ci);
return si;
}
}
case OpCodes.CosineIntegral: {
double si, ci;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.sinecosineintegrals(x, out si, out ci);
return ci;
}
}
case OpCodes.HyperbolicSineIntegral: {
double shi, chi;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.hyperbolicsinecosineintegrals(x, out shi, out chi);
return shi;
}
}
case OpCodes.HyperbolicCosineIntegral: {
double shi, chi;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.hyperbolicsinecosineintegrals(x, out shi, out chi);
return chi;
}
}
case OpCodes.FresnelCosineIntegral: {
double c = 0, s = 0;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.fresnelintegral(x, ref c, ref s);
return c;
}
}
case OpCodes.FresnelSineIntegral: {
double c = 0, s = 0;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.fresnelintegral(x, ref c, ref s);
return s;
}
}
case OpCodes.AiryA: {
double ai, aip, bi, bip;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.airy(x, out ai, out aip, out bi, out bip);
return ai;
}
}
case OpCodes.AiryB: {
double ai, aip, bi, bip;
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else {
alglib.airy(x, out ai, out aip, out bi, out bip);
return bi;
}
}
case OpCodes.Norm: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else return alglib.normaldistribution(x);
}
case OpCodes.Erf: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else return alglib.errorfunction(x);
}
case OpCodes.Bessel: {
var x = Evaluate(dataset, ref row, state);
if (double.IsNaN(x)) return double.NaN;
else return alglib.besseli0(x);
}
case OpCodes.IfThenElse: {
double condition = Evaluate(dataset, ref row, state);
double result;
if (condition > 0.0) {
result = Evaluate(dataset, ref row, state); state.SkipInstructions();
} else {
state.SkipInstructions(); result = Evaluate(dataset, ref row, state);
}
return result;
}
case OpCodes.AND: {
double result = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
if (result > 0.0) result = Evaluate(dataset, ref row, state);
else {
state.SkipInstructions();
}
}
return result > 0.0 ? 1.0 : -1.0;
}
case OpCodes.OR: {
double result = Evaluate(dataset, ref row, state);
for (int i = 1; i < currentInstr.nArguments; i++) {
if (result <= 0.0) result = Evaluate(dataset, ref row, state);
else {
state.SkipInstructions();
}
}
return result > 0.0 ? 1.0 : -1.0;
}
case OpCodes.NOT: {
return Evaluate(dataset, ref row, state) > 0.0 ? -1.0 : 1.0;
}
case OpCodes.XOR: {
//mkommend: XOR on multiple inputs is defined as true if the number of positive signals is odd
// this is equal to a consecutive execution of binary XOR operations.
int positiveSignals = 0;
for (int i = 0; i < currentInstr.nArguments; i++) {
if (Evaluate(dataset, ref row, state) > 0.0) positiveSignals++;
}
return positiveSignals % 2 != 0 ? 1.0 : -1.0;
}
case OpCodes.GT: {
double x = Evaluate(dataset, ref row, state);
double y = Evaluate(dataset, ref row, state);
if (x > y) return 1.0;
else return -1.0;
}
case OpCodes.LT: {
double x = Evaluate(dataset, ref row, state);
double y = Evaluate(dataset, ref row, state);
if (x < y) return 1.0;
else return -1.0;
}
case OpCodes.TimeLag: {
var timeLagTreeNode = (LaggedTreeNode)currentInstr.dynamicNode;
row += timeLagTreeNode.Lag;
double result = Evaluate(dataset, ref row, state);
row -= timeLagTreeNode.Lag;
return result;
}
case OpCodes.Integral: {
int savedPc = state.ProgramCounter;
var timeLagTreeNode = (LaggedTreeNode)currentInstr.dynamicNode;
double sum = 0.0;
for (int i = 0; i < Math.Abs(timeLagTreeNode.Lag); i++) {
row += Math.Sign(timeLagTreeNode.Lag);
sum += Evaluate(dataset, ref row, state);
state.ProgramCounter = savedPc;
}
row -= timeLagTreeNode.Lag;
sum += Evaluate(dataset, ref row, state);
return sum;
}
//mkommend: derivate calculation taken from:
//http://www.holoborodko.com/pavel/numerical-methods/numerical-derivative/smooth-low-noise-differentiators/
//one sided smooth differentiatior, N = 4
// y' = 1/8h (f_i + 2f_i-1, -2 f_i-3 - f_i-4)
case OpCodes.Derivative: {
int savedPc = state.ProgramCounter;
double f_0 = Evaluate(dataset, ref row, state); row--;
state.ProgramCounter = savedPc;
double f_1 = Evaluate(dataset, ref row, state); row -= 2;
state.ProgramCounter = savedPc;
double f_3 = Evaluate(dataset, ref row, state); row--;
state.ProgramCounter = savedPc;
double f_4 = Evaluate(dataset, ref row, state);
row += 4;
return (f_0 + 2 * f_1 - 2 * f_3 - f_4) / 8; // h = 1
}
case OpCodes.Call: {
// evaluate sub-trees
double[] argValues = new double[currentInstr.nArguments];
for (int i = 0; i < currentInstr.nArguments; i++) {
argValues[i] = Evaluate(dataset, ref row, state);
}
// push on argument values on stack
state.CreateStackFrame(argValues);
// save the pc
int savedPc = state.ProgramCounter;
// set pc to start of function
state.ProgramCounter = (ushort)currentInstr.data;
// evaluate the function
double v = Evaluate(dataset, ref row, state);
// delete the stack frame
state.RemoveStackFrame();
// restore the pc => evaluation will continue at point after my subtrees
state.ProgramCounter = savedPc;
return v;
}
case OpCodes.Arg: {
return state.GetStackFrameValue((ushort)currentInstr.data);
}
case OpCodes.Variable: {
if (row < 0 || row >= dataset.Rows) return double.NaN;
var variableTreeNode = (VariableTreeNode)currentInstr.dynamicNode;
return ((IList)currentInstr.data)[row] * variableTreeNode.Weight;
}
case OpCodes.LagVariable: {
var laggedVariableTreeNode = (LaggedVariableTreeNode)currentInstr.dynamicNode;
int actualRow = row + laggedVariableTreeNode.Lag;
if (actualRow < 0 || actualRow >= dataset.Rows) return double.NaN;
return ((IList)currentInstr.data)[actualRow] * laggedVariableTreeNode.Weight;
}
case OpCodes.Constant: {
var constTreeNode = (ConstantTreeNode)currentInstr.dynamicNode;
return constTreeNode.Value;
}
//mkommend: this symbol uses the logistic function f(x) = 1 / (1 + e^(-alpha * x) )
//to determine the relative amounts of the true and false branch see http://en.wikipedia.org/wiki/Logistic_function
case OpCodes.VariableCondition: {
if (row < 0 || row >= dataset.Rows) return double.NaN;
var variableConditionTreeNode = (VariableConditionTreeNode)currentInstr.dynamicNode;
double variableValue = ((IList)currentInstr.data)[row];
double x = variableValue - variableConditionTreeNode.Threshold;
double p = 1 / (1 + Math.Exp(-variableConditionTreeNode.Slope * x));
double trueBranch = Evaluate(dataset, ref row, state);
double falseBranch = Evaluate(dataset, ref row, state);
return trueBranch * p + falseBranch * (1 - p);
}
default: throw new NotSupportedException();
}
}
}
}