public void TestDepth()
{
EncogProgramContext context = new EncogProgramContext();
context.DefineVariable("x");
StandardExtensions.CreateAll(context);
PrgGrowGenerator rnd = new PrgGrowGenerator(context, 2);
EncogProgram prg = (EncogProgram)rnd.Generate(new EncogRandom());
RenderCommonExpression render = new RenderCommonExpression();
}
public void TestSimpleClone()
{
EncogProgramContext context = new EncogProgramContext();
context.LoadAllFunctions();
RenderCommonExpression render = new RenderCommonExpression();
EncogProgram prg1 = context.CreateProgram("1*2*3");
EncogProgram prg2 = context.CloneProgram(prg1);
Assert.AreEqual("((1*2)*3)", render.Render(prg1));
Assert.AreEqual("((1*2)*3)", render.Render(prg2));
}
public void TestCloneComplex()
{
EncogProgramContext context = new EncogProgramContext();
context.LoadAllFunctions();
context.DefineVariable("a");
RenderCommonExpression render = new RenderCommonExpression();
EncogProgram prg1 = context.CreateProgram("((a+25)^3/25)-((a*3)^4/250)");
EncogProgram prg2 = context.CloneProgram(prg1);
Assert.AreEqual("((((a+25)^3)/25)-(((a*3)^4)/250))", render.Render(prg1));
Assert.AreEqual("((((a+25)^3)/25)-(((a*3)^4)/250))", render.Render(prg2));
}
private PrgPopulation Create()
{
EncogProgramContext context = new EncogProgramContext();
context.DefineVariable("x");
StandardExtensions.CreateAll(context);
PrgPopulation pop = new PrgPopulation(context, 10);
EncogProgram prg1 = new EncogProgram(context);
EncogProgram prg2 = new EncogProgram(context);
prg1.CompileExpression("x+1");
prg2.CompileExpression("(x+5)/2");
ISpecies defaultSpecies = pop.CreateSpecies();
defaultSpecies.Add(prg1);
defaultSpecies.Add(prg2);
return pop;
}
/// <summary>
/// Create a feed forward network.
/// </summary>
/// <param name="architecture">The architecture string to use.</param>
/// <param name="input">The input count.</param>
/// <param name="output">The output count.</param>
/// <returns>The feedforward network.</returns>
public IMLMethod Create(String architecture, int input,
int output)
{
if (input <= 0)
{
throw new EncogError("Must have at least one input for EPL.");
}
if (output <= 0)
{
throw new EncogError("Must have at least one output for EPL.");
}
IDictionary<String, String> args = ArchitectureParse.ParseParams(architecture);
var holder = new ParamsHolder(args);
int populationSize = holder.GetInt(
MLMethodFactory.PropertyPopulationSize, false, 1000);
String variables = holder.GetString("vars", false, "x");
String funct = holder.GetString("funct", false, null);
var context = new EncogProgramContext();
string[] tok = variables.Split(',');
foreach (string v in tok)
{
context.DefineVariable(v);
}
if (String.Compare("numeric", funct, StringComparison.OrdinalIgnoreCase) == 0)
{
StandardExtensions.CreateNumericOperators(context);
}
var pop = new PrgPopulation(context, populationSize);
if (context.Functions.Count > 0)
{
(new RampedHalfAndHalf(context, 2, 6)).Generate(new EncogRandom(), pop);
}
return pop;
}
public void Eval(String start, String expect)
{
EncogProgramContext context = new EncogProgramContext();
StandardExtensions.CreateNumericOperators(context);
PrgPopulation pop = new PrgPopulation(context, 1);
ICalculateScore score = new ZeroEvalScoreFunction();
TrainEA genetic = new TrainEA(pop, score);
genetic.ValidationMode = true;
genetic.CODEC = new PrgCODEC();
genetic.AddOperation(0.95, new SubtreeCrossover());
genetic.AddOperation(0.05, new SubtreeMutation(context, 4));
genetic.AddScoreAdjuster(new ComplexityAdjustedScore());
genetic.Rules.AddRewriteRule(new RewriteConstants());
genetic.Rules.AddRewriteRule(new RewriteAlgebraic());
EncogProgram expression = new EncogProgram(context);
expression.CompileExpression(start);
RenderCommonExpression render = new RenderCommonExpression();
genetic.Rules.Rewrite(expression);
Assert.AreEqual(expect, render.Render(expression));
}
/// <summary>
/// Construct an Encog program using the specified context and variable
/// holder.
/// </summary>
/// <param name="theContext">The context.</param>
/// <param name="theVariables">The variable holder.</param>
public EncogProgram(EncogProgramContext theContext,
EncogProgramVariables theVariables)
{
_context = theContext;
_variables = theVariables;
// define variables
foreach (VariableMapping v in _context.DefinedVariables)
{
_variables.DefineVariable(v);
}
}
/// <summary>
/// Construct the Encog program with the specified context, but create a new
/// variable holder.
/// </summary>
/// <param name="theContext">The context.</param>
public EncogProgram(EncogProgramContext theContext)
: this(theContext, new EncogProgramVariables())
{
}
/// <summary>
/// Generate a EPL population method.
/// </summary>
/// <param name="inputColumns">The input column count.</param>
/// <param name="outputColumns">The output column count.</param>
private void generateEPL(int inputColumns,
int outputColumns)
{
_script.Properties.SetProperty(
ScriptProperties.MlConfigType,
MLMethodFactory.TypeEPL);
String vars = "";
if (inputColumns > 26)
{
throw new EncogError("More than 26 input variables is not supported for EPL.");
}
else if (inputColumns <= 3)
{
var temp = new StringBuilder();
for (int i = 0; i < inputColumns; i++)
{
if (temp.Length > 0)
{
temp.Append(',');
}
temp.Append((char) ('x' + i));
}
vars = temp.ToString();
}
else
{
var temp = new StringBuilder();
for (int i = 0; i < inputColumns; i++)
{
if (temp.Length > 0)
{
temp.Append(',');
}
temp.Append((char) ('a' + i));
}
vars = temp.ToString();
}
_script.Properties.SetProperty(
ScriptProperties.MlConfigArchitecture,
"cycles=" + NEATPopulation.DefaultCycles + ",vars=\"" + vars + "\"");
_script.Properties.SetProperty(ScriptProperties.MlTrainType,
MLTrainFactory.TypeEPLGA);
_script.Properties.SetProperty(
ScriptProperties.MlTrainTargetError, MaxError);
// add in the opcodes
var context = new EncogProgramContext();
if (Goal == AnalystGoal.Regression)
{
StandardExtensions.CreateNumericOperators(context);
}
else
{
StandardExtensions.CreateNumericOperators(context);
StandardExtensions.CreateBooleanOperators(context);
}
foreach (IProgramExtensionTemplate temp in context.Functions.OpCodes)
{
_script.Opcodes.Add(new ScriptOpcode(temp));
}
}
/// <inheritdoc />
public Object Read(Stream istream)
{
var context = new EncogProgramContext();
var result = new PrgPopulation(context, 0);
var reader = new EncogReadHelper(istream);
EncogFileSection section;
int count = 0;
ISpecies lastSpecies = null;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("PARAMS"))
{
IDictionary <string, string> prms = section.ParseParams();
EngineArray.PutAll(prms, result.Properties);
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("EPL-POPULATION"))
{
foreach (string line in section.Lines)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
Population = result,
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (cols[0].Equals("p"))
{
double score;
double adjustedScore;
if (String.Compare(cols[1], "nan", StringComparison.OrdinalIgnoreCase) == 0 ||
String.Compare(cols[2], "nan", StringComparison.OrdinalIgnoreCase) == 0)
{
score = Double.NaN;
adjustedScore = Double.NaN;
}
else
{
score = CSVFormat.EgFormat.Parse(cols[1]);
adjustedScore = CSVFormat.EgFormat.Parse(cols[2]);
}
String code = cols[3];
var prg = new EncogProgram(context);
prg.CompileEPL(code);
prg.Score = score;
prg.Species = lastSpecies;
prg.AdjustedScore = adjustedScore;
if (lastSpecies == null)
{
throw new EncogError(
"Have not defined a species yet");
}
lastSpecies.Add(prg);
count++;
}
}
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("EPL-OPCODES"))
{
foreach (String line in section.Lines)
{
IList <string> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
int args = int.Parse(cols[1]);
result.Context.Functions.AddExtension(name, args);
}
}
else if (section.SectionName.Equals("BASIC") &&
section.SubSectionName.Equals("EPL-SYMBOLIC"))
{
bool first = true;
foreach (string line in section.Lines)
{
if (!first)
{
IList <String> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
String t = cols[1];
var vt = EPLValueType.Unknown;
if (string.Compare(t, "f", true) == 0)
{
vt = EPLValueType.FloatingType;
}
else if (string.Compare(t, "b", true) == 0)
{
vt = EPLValueType.BooleanType;
}
else if (string.Compare(t, "i", true) == 0)
{
vt = EPLValueType.IntType;
}
else if (string.Compare(t, "s", true) == 0)
{
vt = EPLValueType.StringType;
}
else if (string.Compare(t, "e", true) == 0)
{
vt = EPLValueType.EnumType;
}
int enumType = int.Parse(cols[2]);
int enumCount = int.Parse(cols[3]);
var mapping = new VariableMapping(
name, vt, enumType, enumCount);
if (mapping.Name.Length > 0)
{
result.Context.DefineVariable(mapping);
}
else
{
result.Context.Result = mapping;
}
}
else
{
first = false;
}
}
}
}
result.PopulationSize = count;
// set the best genome, should be the first genome in the first species
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
// set the leaders
foreach (ISpecies sp in result.Species)
{
if (sp.Members.Count > 0)
{
sp.Leader = sp.Members[0];
}
}
}
return(result);
}
/// <summary>
/// Program entry point.
/// </summary>
/// <param name="app">Holds arguments and other info.</param>
public void Execute(IExampleInterface app)
{
IMLDataSet trainingData = GenerationUtil.GenerateSingleDataRange(
(x) => (3 * Math.Pow(x, 2) + (12 * x) + 4)
, 0, 100, 1);
EncogProgramContext context = new EncogProgramContext();
context.DefineVariable("x");
StandardExtensions.CreateNumericOperators(context);
PrgPopulation pop = new PrgPopulation(context, 1000);
MultiObjectiveFitness score = new MultiObjectiveFitness();
score.AddObjective(1.0, new TrainingSetScore(trainingData));
TrainEA genetic = new TrainEA(pop, score);
genetic.ValidationMode = true;
genetic.CODEC = new PrgCODEC();
genetic.AddOperation(0.5, new SubtreeCrossover());
genetic.AddOperation(0.25, new ConstMutation(context, 0.5, 1.0));
genetic.AddOperation(0.25, new SubtreeMutation(context, 4));
genetic.AddScoreAdjuster(new ComplexityAdjustedScore(10, 20, 10, 20.0));
genetic.Rules.AddRewriteRule(new RewriteConstants());
genetic.Rules.AddRewriteRule(new RewriteAlgebraic());
genetic.Speciation = new PrgSpeciation();
(new RampedHalfAndHalf(context, 1, 6)).Generate(new EncogRandom(), pop);
genetic.ShouldIgnoreExceptions = false;
EncogProgram best = null;
genetic.ThreadCount = 1;
try
{
for (int i = 0; i < 1000; i++)
{
genetic.Iteration();
best = (EncogProgram)genetic.BestGenome;
Console.Out.WriteLine(genetic.IterationNumber + ", Error: "
+ best.Score + ",Best Genome Size:" + best.Size
+ ",Species Count:" + pop.Species.Count + ",best: " + best.DumpAsCommonExpression());
}
//EncogUtility.evaluate(best, trainingData);
Console.Out.WriteLine("Final score:" + best.Score
+ ", effective score:" + best.AdjustedScore);
Console.Out.WriteLine(best.DumpAsCommonExpression());
//pop.dumpMembers(Integer.MAX_VALUE);
//pop.dumpMembers(10);
}
catch (Exception t)
{
Console.Out.WriteLine(t.ToString());
}
finally
{
genetic.FinishTraining();
EncogFramework.Instance.Shutdown();
}
}
/// <summary>
/// Construct the Encog program with the specified context, but create a new
/// variable holder.
/// </summary>
/// <param name="theContext">The context.</param>
public EncogProgram(EncogProgramContext theContext)
: this(theContext, new EncogProgramVariables())
{
}
/// <inheritdoc />
public Object Read(Stream istream)
{
var context = new EncogProgramContext();
var result = new PrgPopulation(context, 0);
var reader = new EncogReadHelper(istream);
EncogFileSection section;
int count = 0;
ISpecies lastSpecies = null;
while ((section = reader.ReadNextSection()) != null)
{
if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("PARAMS"))
{
IDictionary<string, string> prms = section.ParseParams();
EngineArray.PutAll(prms, result.Properties);
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("EPL-POPULATION"))
{
foreach (string line in section.Lines)
{
IList<String> cols = EncogFileSection.SplitColumns(line);
if (String.Compare(cols[0], "s", StringComparison.OrdinalIgnoreCase) == 0)
{
lastSpecies = new BasicSpecies
{
Age = int.Parse(cols[1]),
BestScore = CSVFormat.EgFormat.Parse(cols[2]),
Population = result,
GensNoImprovement = int.Parse(cols[3])
};
result.Species.Add(lastSpecies);
}
else if (cols[0].Equals("p"))
{
double score;
double adjustedScore;
if (String.Compare(cols[1], "nan", StringComparison.OrdinalIgnoreCase) == 0
|| String.Compare(cols[2], "nan", StringComparison.OrdinalIgnoreCase) == 0)
{
score = Double.NaN;
adjustedScore = Double.NaN;
}
else
{
score = CSVFormat.EgFormat.Parse(cols[1]);
adjustedScore = CSVFormat.EgFormat.Parse(cols[2]);
}
String code = cols[3];
var prg = new EncogProgram(context);
prg.CompileEPL(code);
prg.Score = score;
prg.Species = lastSpecies;
prg.AdjustedScore = adjustedScore;
if (lastSpecies == null)
{
throw new EncogError(
"Have not defined a species yet");
}
lastSpecies.Add(prg);
count++;
}
}
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("EPL-OPCODES"))
{
foreach (String line in section.Lines)
{
IList<string> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
int args = int.Parse(cols[1]);
result.Context.Functions.AddExtension(name, args);
}
}
else if (section.SectionName.Equals("BASIC")
&& section.SubSectionName.Equals("EPL-SYMBOLIC"))
{
bool first = true;
foreach (string line in section.Lines)
{
if (!first)
{
IList<String> cols = EncogFileSection.SplitColumns(line);
String name = cols[0];
String t = cols[1];
var vt = EPLValueType.Unknown;
if (string.Compare(t, "f", true) == 0)
{
vt = EPLValueType.FloatingType;
}
else if (string.Compare(t, "b", true) == 0)
{
vt = EPLValueType.BooleanType;
}
else if (string.Compare(t, "i", true) == 0)
{
vt = EPLValueType.IntType;
}
else if (string.Compare(t, "s", true) == 0)
{
vt = EPLValueType.StringType;
}
else if (string.Compare(t, "e", true) == 0)
{
vt = EPLValueType.EnumType;
}
int enumType = int.Parse(cols[2]);
int enumCount = int.Parse(cols[3]);
var mapping = new VariableMapping(
name, vt, enumType, enumCount);
if (mapping.Name.Length > 0)
{
result.Context.DefineVariable(mapping);
}
else
{
result.Context.Result = mapping;
}
}
else
{
first = false;
}
}
}
}
result.PopulationSize = count;
// set the best genome, should be the first genome in the first species
if (result.Species.Count > 0)
{
ISpecies species = result.Species[0];
if (species.Members.Count > 0)
{
result.BestGenome = species.Members[0];
}
// set the leaders
foreach (ISpecies sp in result.Species)
{
if (sp.Members.Count > 0)
{
sp.Leader = sp.Members[0];
}
}
}
return result;
}
