public void CanGenerateGenoType()
{
var randomGenerator = new UniformRandomGenerator();
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters = new EaGeneExpressionParameters(4, possibleFunctions, possibleTerminals);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
var genoType = genoTypeFactory.GetGenoType();
Assert.NotNull(genoType);
}
public void todo(IDataSet dataSet)
{
var randomGenerator = new UniformRandomGenerator();
var possibleFunctions = new List <IGenoTypeNode>
{
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>();
foreach (var mappedColumn in dataSet.MappedColumns)
{
possibleTerminals.Add(new FeatureTerminal(mappedColumn.Key));
}
var eaGeneExpressionParameters = new EaGeneExpressionParameters(10, possibleFunctions, possibleTerminals);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
var genoType = genoTypeFactory.GetGenoType();
var phenoTypeTree = new PhenoTypeTree(genoType.GenoTypeNodes);
var stringExpresssion = phenoTypeTree.ToString();
// TODO make mapped columns more sophisticated
var mappedColumnsUsedInExpression = new Dictionary <string, int>();
foreach (var mappedColumn in dataSet.MappedColumns)
{
if (stringExpresssion.Contains(mappedColumn.Key))
{
mappedColumnsUsedInExpression.Add(mappedColumn.Key, mappedColumn.Value);
}
}
var expression = new Expression(stringExpresssion);
var numberOfRows = dataSet.MappedData.GetLength(0);
var sum = 0.0;
for (var row = 0; row < numberOfRows; row++)
{
foreach (var usedMappedColumn in mappedColumnsUsedInExpression)
{
expression.Parameters[usedMappedColumn.Key.Replace("]", "").Replace("[", "")] = dataSet.MappedData[row, usedMappedColumn.Value];
}
if (!expression.HasErrors())
{
var test = (double)expression.Evaluate();
sum = sum + test;
}
}
}
public void CanTranslateTest3()
{
var randomGenerator = new UniformRandomGenerator();
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Division(),
new Plus(),
new Minus(),
new Minimum(),
new Maximum(),
new Not(),
new Exp(),
new Sinus(),
new Cosinus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters = new EaGeneExpressionParameters(20, possibleFunctions, possibleTerminals);
var parameterTerminalFactory = new ParameterTerminalFactory(eaGeneExpressionParameters, randomGenerator);
var genoTypeFactory = new GenoTypeFactory(eaGeneExpressionParameters, randomGenerator, parameterTerminalFactory);
eaGeneExpressionParameters.ParameterTypeInteger = true;
eaGeneExpressionParameters.ConstantProbability = 0;
var genoType = genoTypeFactory.GetGenoType();
var phenoTypeTree = new GeneExpression.PhenoTypeTree(genoType.GenoTypeNodes);
var expresssion = phenoTypeTree.ToString();
}
public void CanCrossover()
{
var listLeft = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus(),
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var listRight = new List <IGenoTypeNode>
{
new Sinus(),
new Multiplication(),
new Division(),
new Minus(),
new FeatureTerminal("a1"),
new FeatureTerminal("b1"),
new FeatureTerminal("c1"),
new FeatureTerminal("d1")
};
var possibleFunctions = new List <IGenoTypeNode>
{
new SquareRoot(),
new Multiplication(),
new Plus(),
new Minus()
};
var possibleTerminals = new List <IGenoTypeNode>
{
new FeatureTerminal("a"),
new FeatureTerminal("b"),
new FeatureTerminal("c"),
new FeatureTerminal("d")
};
var eaGeneExpressionParameters =
new EaGeneExpressionParameters(4, possibleFunctions, possibleTerminals)
{
CrossoverProbability = 1.0
};
IGenoType left = new GeneExpression.GenoType {
GenoTypeNodes = listLeft
};
IGenoType right = new GeneExpression.GenoType {
GenoTypeNodes = listRight
};
var randomGenerator = new UniformRandomGenerator();
var genotypeCrossoverator1 = new GenoTypeCrossoveratorBasic1(randomGenerator, eaGeneExpressionParameters);
var phenoTypeTreeLeft1 = new GeneExpression.PhenoTypeTree(left.GenoTypeNodes);
var phenoTypeTreeRight1 = new GeneExpression.PhenoTypeTree(right.GenoTypeNodes);
genotypeCrossoverator1.PerformCrossover(ref left, ref right);
var phenoTypeTreeLeft2 = new GeneExpression.PhenoTypeTree(left.GenoTypeNodes);
var phenoTypeTreeRight2 = new GeneExpression.PhenoTypeTree(right.GenoTypeNodes);
Assert.NotEqual(phenoTypeTreeLeft1, phenoTypeTreeLeft2);
Assert.NotEqual(phenoTypeTreeRight1, phenoTypeTreeRight2);
}
