/// <summary>
/// Score the model for regression or classification.
/// For classification, the score is the percentage correct.
/// For regression, the score is mean square error (MSE).
/// http://www.heatonresearch.com/wiki/Mean_Square_Error
/// </summary>
/// <param name="model">The model to score.</param>
/// <param name="testSubject">The test subject.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <returns>The score.</returns>
public static double Score(IGAModel model, Genome testSubject, double[][] input, double[][] ideal)
{
if (ideal[0].Length > 1)
{
return(ScoreClassification(model, testSubject, input, ideal));
}
return(ScoreRegression(model, testSubject, input, ideal));
}
public NURBSElement2D(int id, IGAModel model, IList <Knot> knots, IVector <int> connectivity)
{
this.id = id;
this.knots = knots;
this.connectivity = connectivity;
this.model = model;
this.CreateElementGaussPoints();
}
public ShapeNURBS2D(NURBSElement2D element, IGAModel model, IVector <double> parametricCoordinateKsi, IVector <double> parametricCoordinateHeta, IList <ControlPoint> controlPoints)
{
int numberOfCPHeta = model.KnotValueVectorHeta.Length - model.DegreeHeta - 1;
BSPLines1D bsplinesKsi = new BSPLines1D(model.DegreeKsi, model.KnotValueVectorKsi, parametricCoordinateKsi);
BSPLines1D bsplinesHeta = new BSPLines1D(model.DegreeHeta, model.KnotValueVectorHeta, parametricCoordinateHeta);
nurbsValues = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1), element.gaussPoints.Count);
nurbsDerivativeValuesKsi = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1), element.gaussPoints.Count);
nurbsDerivativeValuesHeta = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1), element.gaussPoints.Count);
int supportKsi = model.DegreeKsi + 1;
int supportHeta = model.DegreeHeta + 1;
int numberOfElementGaussPoints = (model.DegreeKsi + 1) * (model.DegreeHeta + 1);
for (int i = 0; i < supportKsi; i++)
{
for (int j = 0; j < supportHeta; j++)
{
double sumKsiHeta = 0;
double sumdKsiHeta = 0;
double sumKsidHeta = 0;
for (int k = 0; k < numberOfElementGaussPoints; k++)
{
sumKsiHeta += bsplinesKsi.BSPLValues[element.connectivity[k] / numberOfCPHeta, i] *
bsplinesHeta.BSPLValues[element.connectivity[k] % numberOfCPHeta, j] *
controlPoints[k].Weight;
sumdKsiHeta = +bsplinesKsi.BSPLDerivativeValues[element.connectivity[k] / numberOfCPHeta, i] *
bsplinesHeta.BSPLDerivativeValues[element.connectivity[k] % numberOfCPHeta, j] *
controlPoints[k].Weight;
sumKsidHeta = +bsplinesKsi.BSPLValues[element.connectivity[k] / numberOfCPHeta, i] *
bsplinesHeta.BSPLDerivativeValues[element.connectivity[k] % numberOfCPHeta, j] *
controlPoints[k].Weight;
}
for (int k = 0; k < numberOfElementGaussPoints; k++)
{
nurbsValues[k, i *supportHeta + j] = bsplinesKsi.BSPLValues[element.connectivity[k] / numberOfCPHeta, i] *
bsplinesHeta.BSPLValues[element.connectivity[k] % numberOfCPHeta, j] *
controlPoints[k].Weight / sumKsiHeta;
nurbsDerivativeValuesKsi[k, i *supportHeta + j] = bsplinesHeta.BSPLValues[element.connectivity[k] % numberOfCPHeta, j] * controlPoints[k].Weight *
(bsplinesKsi.BSPLDerivativeValues[element.connectivity[k] / numberOfCPHeta, i] * sumKsiHeta -
bsplinesKsi.BSPLValues[element.connectivity[k] / numberOfCPHeta, i] * sumdKsiHeta) / Math.Pow(sumKsiHeta, 2);
nurbsDerivativeValuesHeta[k, i *supportHeta + j] = bsplinesKsi.BSPLValues[element.connectivity[k] / numberOfCPHeta, i] *
controlPoints[k].Weight *
(bsplinesHeta.BSPLDerivativeValues[element.connectivity[k] % numberOfCPHeta, j] * sumKsiHeta -
bsplinesHeta.BSPLValues[element.connectivity[k] % numberOfCPHeta, j] * sumKsidHeta) / Math.Pow(sumKsiHeta, 2);
}
}
}
}
/// <summary>
/// Score the model when the output is regression.
/// For regression, the score is mean square error (MSE).
/// http://www.heatonresearch.com/wiki/Mean_Square_Error
/// </summary>
/// <param name="model">The model to score.</param>
/// <param name="testSubject">The test subject.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <returns>The score.</returns>
private static double ScoreRegression(IGAModel model, Genome testSubject, double[][] input, double[][] ideal)
{
double error = 0;
double elementCount = 0;
Parallel.For(0, input.Length, row =>
{
double[] output = model.Compute(input[row], testSubject);
for (int col = 0; col < output.Length; col++)
{
double delta = output[col] - ideal[row][col];
error += delta * delta;
elementCount++;
}
});
return(Math.Sqrt(error / elementCount));
}
/// <summary>
/// Score the model when the output is classification.
/// For classification, the score is the percentage correct.
/// </summary>
/// <param name="model">The model to score.</param>
/// <param name="testSubject">The test subject.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <returns>The score.</returns>
private static double ScoreClassification(IGAModel model, Genome testSubject, double[][] input, double[][] ideal)
{
double error = 0;
double elementCount = 0;
int badCount = 0;
Parallel.For(0, input.Length, row =>
{
double[] output = model.Compute(input[row], testSubject);
int outputIndex = FindTopIndex(output);
int idealIndex = FindTopIndex(ideal[row]);
if (outputIndex != idealIndex)
{
badCount++;
}
elementCount++;
});
return(badCount / elementCount);
}
public static void CreateModelFromFile(IGAModel model, string fileName)
{
char[] delimeters = { ' ', '=', '\t' };
Attributes?name = null;
String[] text = System.IO.File.ReadAllLines(fileName);
for (int i = 0; i < text.Length; i++)
{
String[] line = text[i].Split(delimeters, StringSplitOptions.RemoveEmptyEntries);
if (line.Length == 0)
{
continue;
}
try
{
name = (Attributes)Enum.Parse(typeof(Attributes), line[0].ToLower());
}catch (Exception e)
{
throw new KeyNotFoundException("Variable name is not found " + line[0]);
}
switch (name)
{
case Attributes.numberofdimensions:
model.NumberOfDimensions = Int32.Parse(line[1]);
break;
case Attributes.degreeksi:
model.DegreeKsi = Int32.Parse(line[1]);
break;
case Attributes.degreeheta:
model.DegreeHeta = Int32.Parse(line[1]);
break;
case Attributes.degreezeta:
model.DegreeZeta = Int32.Parse(line[1]);
break;
case Attributes.numberofcpksi:
model.NumberOfCPKsi = Int32.Parse(line[1]);
break;
case Attributes.numberofcpheta:
model.NumberOfCPHeta = Int32.Parse(line[1]);
break;
case Attributes.numberofcpzeta:
model.NumberOfCPZeta = Int32.Parse(line[1]);
break;
case Attributes.knotvaluevectorksi:
if (model.DegreeKsi == 0 || model.NumberOfCPKsi == 0)
{
throw new ArgumentOutOfRangeException("Degree Ksi and number of Control Points per axis Ksi must be defined before Knot Value Vector Ksi.");
}
model.KnotValueVectorKsi = new Vector <double>(model.NumberOfCPKsi + model.DegreeKsi + 1);
for (int j = 0; j < model.NumberOfCPKsi + model.DegreeKsi + 1; j++)
{
model.KnotValueVectorKsi[j] = Double.Parse(line[j + 1], CultureInfo.InvariantCulture);
}
break;
case Attributes.knotvaluevectorheta:
if (model.DegreeHeta == 0 || model.NumberOfCPHeta == 0)
{
throw new ArgumentOutOfRangeException("Degree Heta and number of Control Points per axis Heta must be defined before Knot Value Vector Heta.");
}
model.KnotValueVectorHeta = new Vector <double>(model.NumberOfCPHeta + model.DegreeHeta + 1);
for (int j = 0; j < model.NumberOfCPHeta + model.DegreeHeta + 1; j++)
{
model.KnotValueVectorHeta[j] = Double.Parse(line[j + 1], CultureInfo.InvariantCulture);
}
break;
case Attributes.knotvaluevectorzeta:
if (model.DegreeZeta == 0 || model.NumberOfCPZeta == 0)
{
throw new ArgumentOutOfRangeException("Degree Zeta and number of Control Points per axis Zeta must be defined before Knot Value Vector Zeta.");
}
model.KnotValueVectorZeta = new Vector <double>(model.NumberOfCPZeta + model.DegreeZeta + 1);
for (int j = 0; j < model.NumberOfCPZeta + model.DegreeZeta + 1; j++)
{
model.KnotValueVectorZeta[j] = Double.Parse(line[j + 1], CultureInfo.InvariantCulture);
}
break;
case Attributes.cpcoord:
if (model.NumberOfDimensions == 2)
{
Matrix2D <double> cpCoordinates = new Matrix2D <double>(model.NumberOfCPKsi * model.NumberOfCPHeta, 3);
cpCoordinates[0, 0] = Double.Parse(line[1], CultureInfo.InvariantCulture);
cpCoordinates[0, 1] = Double.Parse(line[2], CultureInfo.InvariantCulture);
cpCoordinates[0, 2] = Double.Parse(line[3], CultureInfo.InvariantCulture);
for (int j = 1; j < model.NumberOfCPKsi * model.NumberOfCPHeta; j++)
{
i++;
line = text[i].Split(delimeters);
cpCoordinates[j, 0] = Double.Parse(line[0], CultureInfo.InvariantCulture);
cpCoordinates[j, 1] = Double.Parse(line[1], CultureInfo.InvariantCulture);
cpCoordinates[j, 2] = Double.Parse(line[2], CultureInfo.InvariantCulture);
}
model.CreateModelData(cpCoordinates);
}
else
{
Matrix2D <double> cpCoordinates = new Matrix2D <double>(model.NumberOfCPKsi * model.NumberOfCPHeta * model.NumberOfCPZeta, 4);
cpCoordinates[0, 0] = Double.Parse(line[1], CultureInfo.InvariantCulture);
cpCoordinates[0, 1] = Double.Parse(line[2], CultureInfo.InvariantCulture);
cpCoordinates[0, 2] = Double.Parse(line[3], CultureInfo.InvariantCulture);
cpCoordinates[0, 3] = Double.Parse(line[4], CultureInfo.InvariantCulture);
for (int j = 1; j < model.NumberOfCPKsi * model.NumberOfCPHeta * model.NumberOfCPZeta; j++)
{
i++;
line = text[i].Split(delimeters);
cpCoordinates[j, 0] = Double.Parse(line[0], CultureInfo.InvariantCulture);
cpCoordinates[j, 1] = Double.Parse(line[1], CultureInfo.InvariantCulture);
cpCoordinates[j, 2] = Double.Parse(line[2], CultureInfo.InvariantCulture);
cpCoordinates[j, 3] = Double.Parse(line[3], CultureInfo.InvariantCulture);
}
model.CreateModelData(cpCoordinates);
}
break;
case Attributes.end:
return;
}
}
}
/// <summary>
/// Load specified filename.
/// </summary>
/// <param name="filename">The filename.</param>
public void Load(string filename)
{
XDocument xml = XDocument.Load(filename);
XElement root = xml.Element("GeneticAlgorithmUtil");
// read in attributes from the GA node
XElement ga = root.Element("GA");
_populationSize = int.Parse(ga.Element("PopulationSize").Value);
_maxParents = int.Parse(ga.Element("MaxParents").Value);
_mutationPercent = double.Parse(ga.Element("MutationPercent").Value, CultureInfo.InvariantCulture);
// determine the score goal
_maxGoal = (string.Compare(ga.Element("ScoreGoal").Value, "max", true) == 0);
// use reflection to create the model class
string typeStr = ga.Element("Model").Value;
if (ga.Element("Model").Attribute("config") != null)
{
_modelConfig = ga.Element("Model").Attribute("config").Value;
}
Assembly assembly = Assembly.GetExecutingAssembly();
Type type = assembly.GetTypes()
.First(t => t.Name == typeStr);
_model = (IGAModel)Activator.CreateInstance(type);
// read in information from the files node
XElement files = root.Element("Files");
_populationFile = files.Element("PopulationFile").Value;
_trainingFile = files.Element("TrainingFile").Value;
_evaluationFile = files.Element("EvaluationFile").Value;
_evaluationOutputFile = files.Element("EvaluationOutputFile").Value;
// make sure that the filenames have path information
string scriptPath = Path.GetDirectoryName(filename);
if (scriptPath.Length == 0)
{
scriptPath = Directory.GetCurrentDirectory();
}
if (Path.GetDirectoryName(_populationFile).Length == 0)
{
_populationFile = Path.Combine(scriptPath, _populationFile);
}
if (Path.GetDirectoryName(_trainingFile).Length == 0)
{
_trainingFile = Path.Combine(scriptPath, _trainingFile);
}
if (Path.GetDirectoryName(_evaluationFile).Length == 0)
{
_evaluationFile = Path.Combine(scriptPath, _evaluationFile);
}
if (Path.GetDirectoryName(_evaluationOutputFile).Length == 0)
{
_evaluationOutputFile = Path.Combine(scriptPath, _evaluationOutputFile);
}
// read in normalization field data
XElement fmt = root.Element("DataFileFormat");
_predictField = fmt.Element("PredictField").Value;
_normalizeHigh = double.Parse(fmt.Element("Normalize").Attribute("max").Value, CultureInfo.InvariantCulture);
_normalizeLow = double.Parse(fmt.Element("Normalize").Attribute("min").Value, CultureInfo.InvariantCulture);
// read in the normalized fields
XElement fields = root.Element("DataFileFormat");
foreach (XElement fieldElement in fields.Elements("Fields").Elements("Field"))
{
var dataField = new DataField();
string actionStr = fieldElement.Attribute("action").Value;
string nameStr = fieldElement.Attribute("name").Value;
dataField.Name = nameStr;
if (string.Compare(actionStr, "norm", true) == 0)
{
dataField.Action = FieldAction.Normalize;
}
else if (string.Compare(actionStr, "class", true) == 0)
{
dataField.Action = FieldAction.Class;
}
else if (string.Compare(actionStr, "ignore", true) == 0)
{
dataField.Action = FieldAction.Ignore;
}
else if (string.Compare(actionStr, "direct", true) == 0)
{
dataField.Action = FieldAction.Direct;
}
dataField.NormalizedLow = NormalizeLow;
dataField.NormalizedHigh = NormalizeHigh;
_fields.Add(dataField);
_fieldMap.Add(dataField.Name, dataField);
}
}
public ShapeNURBS3D(NURBSElement3D element, IGAModel model, IVector <double> parametricCoordinateKsi, IVector <double> parametricCoordinateHeta, IVector <double> parametricCoordinateZeta, IList <ControlPoint> controlPoints)
{
int numberOfCPHeta = model.KnotValueVectorHeta.Length - model.DegreeHeta - 1;
int numberOfCPZeta = model.KnotValueVectorZeta.Length - model.DegreeZeta - 1;
BSPLines1D bsplinesKsi = new BSPLines1D(model.DegreeKsi, model.KnotValueVectorKsi, parametricCoordinateKsi);
BSPLines1D bsplinesHeta = new BSPLines1D(model.DegreeHeta, model.KnotValueVectorHeta, parametricCoordinateHeta);
BSPLines1D bsplinesZeta = new BSPLines1D(model.DegreeZeta, model.KnotValueVectorZeta, parametricCoordinateZeta);
nurbsValues = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1) * (model.DegreeZeta + 1), element.gaussPoints.Count);
nurbsDerivativeValuesKsi = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1) * (model.DegreeZeta + 1), element.gaussPoints.Count);
nurbsDerivativeValuesHeta = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1) * (model.DegreeZeta + 1), element.gaussPoints.Count);
nurbsDerivativeValuesZeta = new Matrix2D <double>((model.DegreeKsi + 1) * (model.DegreeHeta + 1) * (model.DegreeZeta + 1), element.gaussPoints.Count);
int supportKsi = model.DegreeKsi + 1;
int supportHeta = model.DegreeHeta + 1;
int supportZeta = model.DegreeZeta + 1;
int numberOfElementGaussPoints = (model.DegreeKsi + 1) * (model.DegreeHeta + 1) * (model.DegreeZeta + 1);
for (int i = 0; i < supportKsi; i++)
{
for (int j = 0; j < supportHeta; j++)
{
for (int k = 0; k < supportZeta; k++)
{
double sumKsiHetaZeta = 0;
double sumdKsiHetaZeta = 0;
double sumKsidHetaZeta = 0;
double sumKsiHetadZeta = 0;
for (int m = 0; m < numberOfElementGaussPoints; m++)
{
int indexKsi = element.connectivity[m] / (numberOfCPHeta * numberOfCPZeta);
int indexHeta = element.connectivity[m] % (numberOfCPHeta * numberOfCPZeta) / numberOfCPZeta;
int indexZeta = element.connectivity[m] % (numberOfCPHeta * numberOfCPZeta) % numberOfCPZeta;
sumKsiHetaZeta += bsplinesKsi.BSPLValues[indexKsi, i] *
bsplinesHeta.BSPLValues[indexHeta, j] *
bsplinesZeta.BSPLValues[indexZeta, k] *
controlPoints[m].Weight;
sumdKsiHetaZeta += bsplinesKsi.BSPLDerivativeValues[indexKsi, i] *
bsplinesHeta.BSPLValues[indexHeta, j] *
bsplinesZeta.BSPLValues[indexZeta, k] *
controlPoints[m].Weight;
sumKsidHetaZeta += bsplinesKsi.BSPLValues[indexKsi, i] *
bsplinesHeta.BSPLDerivativeValues[indexHeta, j] *
bsplinesZeta.BSPLValues[indexZeta, k] *
controlPoints[m].Weight;
sumKsiHetadZeta += bsplinesKsi.BSPLValues[indexKsi, i] *
bsplinesHeta.BSPLValues[indexHeta, j] *
bsplinesZeta.BSPLDerivativeValues[indexZeta, k] *
controlPoints[m].Weight;
}
for (int m = 0; m < numberOfElementGaussPoints; m++)
{
int indexKsi = element.connectivity[m] / (numberOfCPHeta * numberOfCPZeta);
int indexHeta = element.connectivity[m] % (numberOfCPHeta * numberOfCPZeta) / numberOfCPZeta;
int indexZeta = element.connectivity[m] % (numberOfCPHeta * numberOfCPZeta) % numberOfCPZeta;
nurbsValues[m, i *supportHeta *supportZeta + j * supportZeta + k] =
bsplinesKsi.BSPLValues[indexKsi, i] *
bsplinesHeta.BSPLValues[indexHeta, j] *
bsplinesZeta.BSPLValues[indexZeta, k] *
controlPoints[m].Weight / sumKsiHetaZeta;
nurbsDerivativeValuesKsi[m, i *supportHeta *supportZeta + j * supportZeta + k] =
(bsplinesKsi.BSPLDerivativeValues[indexKsi, i] * sumKsiHetaZeta -
bsplinesKsi.BSPLValues[indexKsi, i] * sumdKsiHetaZeta) *
bsplinesHeta.BSPLValues[indexHeta, j] *
bsplinesZeta.BSPLValues[indexZeta, k] *
controlPoints[m].Weight / Math.Pow(sumKsiHetaZeta, 2);
nurbsDerivativeValuesHeta[m, i *supportHeta *supportZeta + j * supportZeta + k] =
bsplinesKsi.BSPLValues[indexKsi, i] *
(bsplinesHeta.BSPLDerivativeValues[indexHeta, j] * sumKsiHetaZeta -
bsplinesHeta.BSPLValues[indexHeta, j] * sumKsidHetaZeta) *
bsplinesZeta.BSPLValues[indexZeta, k] *
controlPoints[m].Weight / Math.Pow(sumKsiHetaZeta, 2);
nurbsDerivativeValuesZeta[m, i *supportHeta *supportZeta + j * supportZeta + k] =
bsplinesKsi.BSPLValues[indexKsi, i] *
bsplinesHeta.BSPLValues[indexHeta, j] *
(bsplinesZeta.BSPLDerivativeValues[indexZeta, k] * sumKsiHetaZeta -
bsplinesZeta.BSPLValues[indexZeta, k] * sumKsiHetadZeta) *
controlPoints[m].Weight / Math.Pow(sumKsiHetaZeta, 2);
}
}
}
}
}
private static void SolveIsogeometricModelExample()
{
IGAModel model = new IGAModel();
IGAModelReader.CreateModelFromFile(model, "Cantilever2D biquadratic");
}
/// <summary>
/// Score the model when the output is regression.
/// For regression, the score is mean square error (MSE).
/// http://www.heatonresearch.com/wiki/Mean_Square_Error
/// </summary>
/// <param name="model">The model to score.</param>
/// <param name="testSubject">The test subject.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <returns>The score.</returns>
private static double ScoreRegression(IGAModel model, Genome testSubject, double[][] input, double[][] ideal)
{
double error = 0;
double elementCount = 0;
Parallel.For(0, input.Length, row =>
{
double[] output = model.Compute(input[row], testSubject);
for (int col = 0; col < output.Length; col++)
{
double delta = output[col] - ideal[row][col];
error += delta * delta;
elementCount++;
}
});
return Math.Sqrt(error / elementCount);
}
/// <summary>
/// Score the model when the output is classification.
/// For classification, the score is the percentage correct.
/// </summary>
/// <param name="model">The model to score.</param>
/// <param name="testSubject">The test subject.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <returns>The score.</returns>
private static double ScoreClassification(IGAModel model, Genome testSubject, double[][] input, double[][] ideal)
{
double error = 0;
double elementCount = 0;
int badCount = 0;
Parallel.For(0, input.Length, row =>
{
double[] output = model.Compute(input[row], testSubject);
int outputIndex = FindTopIndex(output);
int idealIndex = FindTopIndex(ideal[row]);
if (outputIndex != idealIndex)
{
badCount++;
}
elementCount++;
});
return badCount / elementCount;
}
/// <summary>
/// Score the model for regression or classification.
/// For classification, the score is the percentage correct.
/// For regression, the score is mean square error (MSE).
/// http://www.heatonresearch.com/wiki/Mean_Square_Error
/// </summary>
/// <param name="model">The model to score.</param>
/// <param name="testSubject">The test subject.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <returns>The score.</returns>
public static double Score(IGAModel model, Genome testSubject, double[][] input, double[][] ideal)
{
if (ideal[0].Length > 1)
{
return ScoreClassification(model, testSubject, input, ideal);
}
return ScoreRegression(model, testSubject, input, ideal);
}
/// <summary>
/// Load specified filename.
/// </summary>
/// <param name="filename">The filename.</param>
public void Load(string filename)
{
XDocument xml = XDocument.Load(filename);
XElement root = xml.Element("GeneticAlgorithmUtil");
// read in attributes from the GA node
XElement ga = root.Element("GA");
_populationSize = int.Parse(ga.Element("PopulationSize").Value);
_maxParents = int.Parse(ga.Element("MaxParents").Value);
_mutationPercent = double.Parse(ga.Element("MutationPercent").Value, CultureInfo.InvariantCulture);
// determine the score goal
_maxGoal = (string.Compare(ga.Element("ScoreGoal").Value, "max", true) == 0);
// use reflection to create the model class
string typeStr = ga.Element("Model").Value;
if (ga.Element("Model").Attribute("config") != null)
{
_modelConfig = ga.Element("Model").Attribute("config").Value;
}
Assembly assembly = Assembly.GetExecutingAssembly();
Type type = assembly.GetTypes()
.First(t => t.Name == typeStr);
_model = (IGAModel) Activator.CreateInstance(type);
// read in information from the files node
XElement files = root.Element("Files");
_populationFile = files.Element("PopulationFile").Value;
_trainingFile = files.Element("TrainingFile").Value;
_evaluationFile = files.Element("EvaluationFile").Value;
_evaluationOutputFile = files.Element("EvaluationOutputFile").Value;
// make sure that the filenames have path information
string scriptPath = Path.GetDirectoryName(filename);
if (scriptPath.Length == 0)
{
scriptPath = Directory.GetCurrentDirectory();
}
if (Path.GetDirectoryName(_populationFile).Length == 0)
{
_populationFile = Path.Combine(scriptPath, _populationFile);
}
if (Path.GetDirectoryName(_trainingFile).Length == 0)
{
_trainingFile = Path.Combine(scriptPath, _trainingFile);
}
if (Path.GetDirectoryName(_evaluationFile).Length == 0)
{
_evaluationFile = Path.Combine(scriptPath, _evaluationFile);
}
if (Path.GetDirectoryName(_evaluationOutputFile).Length == 0)
{
_evaluationOutputFile = Path.Combine(scriptPath, _evaluationOutputFile);
}
// read in normalization field data
XElement fmt = root.Element("DataFileFormat");
_predictField = fmt.Element("PredictField").Value;
_normalizeHigh = double.Parse(fmt.Element("Normalize").Attribute("max").Value, CultureInfo.InvariantCulture);
_normalizeLow = double.Parse(fmt.Element("Normalize").Attribute("min").Value, CultureInfo.InvariantCulture);
// read in the normalized fields
XElement fields = root.Element("DataFileFormat");
foreach (XElement fieldElement in fields.Elements("Fields").Elements("Field"))
{
var dataField = new DataField();
string actionStr = fieldElement.Attribute("action").Value;
string nameStr = fieldElement.Attribute("name").Value;
dataField.Name = nameStr;
if (string.Compare(actionStr, "norm", true) == 0)
{
dataField.Action = FieldAction.Normalize;
}
else if (string.Compare(actionStr, "class", true) == 0)
{
dataField.Action = FieldAction.Class;
}
else if (string.Compare(actionStr, "ignore", true) == 0)
{
dataField.Action = FieldAction.Ignore;
}
else if (string.Compare(actionStr, "direct", true) == 0)
{
dataField.Action = FieldAction.Direct;
}
dataField.NormalizedLow = NormalizeLow;
dataField.NormalizedHigh = NormalizeHigh;
_fields.Add(dataField);
_fieldMap.Add(dataField.Name, dataField);
}
}
