private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner)
: base(original, cloner) {
logitModel = new alglib.logitmodel();
logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
allowedInputVariables = (string[])original.allowedInputVariables.Clone();
classValues = (double[])original.classValues.Clone();
}
private MultinomialLogitModel(bool deserializing)
: base(deserializing)
{
if (deserializing)
{
logitModel = new alglib.logitmodel();
}
}
public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable<string> allowedInputVariables, double[] classValues)
: base(targetVariable) {
this.name = ItemName;
this.description = ItemDescription;
this.logitModel = logitModel;
this.allowedInputVariables = allowedInputVariables.ToArray();
this.classValues = (double[])classValues.Clone();
}
private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner)
: base(original, cloner)
{
logitModel = new alglib.logitmodel();
logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
allowedInputVariables = (string[])original.allowedInputVariables.Clone();
classValues = (double[])original.classValues.Clone();
}
private MultinomialLogitModel(MultinomialLogitModel original, Cloner cloner)
: base(original, cloner)
{
logitModel = new alglib.logitmodel();
logitModel.innerobj.w = (double[])original.logitModel.innerobj.w.Clone();
allowedInputVariables = (string[])original.allowedInputVariables.Clone();
classValues = (double[])original.classValues.Clone();
this.factorVariables = original.factorVariables.Select(kvp => new KeyValuePair <string, IEnumerable <string> >(kvp.Key, new List <string>(kvp.Value))).ToList();
}
public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable <string> allowedInputVariables, double[] classValues)
: base(targetVariable)
{
this.name = ItemName;
this.description = ItemDescription;
this.logitModel = logitModel;
this.allowedInputVariables = allowedInputVariables.ToArray();
this.classValues = (double[])classValues.Clone();
}
public double[,] getCoefficients(alglib.logitmodel lm, out int nvars, out int nclasses)
{
if (lm == null)
{
getMnlModel();
}
double[,] coef;
alglib.mnlunpack(lm, out coef, out nvars, out nclasses);
return(coef);
}
public MultinomialLogitModel(alglib.logitmodel logitModel, string targetVariable, IEnumerable <string> doubleInputVariables, IEnumerable <KeyValuePair <string, IEnumerable <string> > > factorVariables, double[] classValues)
: base(targetVariable)
{
this.name = ItemName;
this.description = ItemDescription;
this.logitModel = logitModel;
this.allowedInputVariables = doubleInputVariables.ToArray();
this.factorVariables = factorVariables.Select(kvp => new KeyValuePair <string, IEnumerable <string> >(kvp.Key, new List <string>(kvp.Value))).ToList();
this.classValues = (double[])classValues.Clone();
}
public static IClassificationSolution CreateLogitClassificationSolution(IClassificationProblemData problemData, out double rmsError, out double relClassError)
{
var dataset = problemData.Dataset;
string targetVariable = problemData.TargetVariable;
var doubleVariableNames = problemData.AllowedInputVariables.Where(dataset.VariableHasType <double>);
var factorVariableNames = problemData.AllowedInputVariables.Where(dataset.VariableHasType <string>);
IEnumerable <int> rows = problemData.TrainingIndices;
double[,] inputMatrix = dataset.ToArray(doubleVariableNames.Concat(new string[] { targetVariable }), rows);
var factorVariableValues = dataset.GetFactorVariableValues(factorVariableNames, rows);
var factorMatrix = dataset.ToArray(factorVariableValues, rows);
inputMatrix = factorMatrix.HorzCat(inputMatrix);
if (inputMatrix.Cast <double>().Any(x => double.IsNaN(x) || double.IsInfinity(x)))
{
throw new NotSupportedException("Multinomial logit classification does not support NaN or infinity values in the input dataset.");
}
alglib.logitmodel lm = new alglib.logitmodel();
alglib.mnlreport rep = new alglib.mnlreport();
int nRows = inputMatrix.GetLength(0);
int nFeatures = inputMatrix.GetLength(1) - 1;
double[] classValues = dataset.GetDoubleValues(targetVariable).Distinct().OrderBy(x => x).ToArray();
int nClasses = classValues.Count();
// map original class values to values [0..nClasses-1]
Dictionary <double, double> classIndices = new Dictionary <double, double>();
for (int i = 0; i < nClasses; i++)
{
classIndices[classValues[i]] = i;
}
for (int row = 0; row < nRows; row++)
{
inputMatrix[row, nFeatures] = classIndices[inputMatrix[row, nFeatures]];
}
int info;
alglib.mnltrainh(inputMatrix, nRows, nFeatures, nClasses, out info, out lm, out rep);
if (info != 1)
{
throw new ArgumentException("Error in calculation of logit classification solution");
}
rmsError = alglib.mnlrmserror(lm, inputMatrix, nRows);
relClassError = alglib.mnlrelclserror(lm, inputMatrix, nRows);
MultinomialLogitClassificationSolution solution = new MultinomialLogitClassificationSolution(new MultinomialLogitModel(lm, targetVariable, doubleVariableNames, factorVariableValues, classValues), (IClassificationProblemData)problemData.Clone());
return(solution);
}
private void createRegChart()
{
if (lm == null)
{
lm = getMnlModel();
}
Forms.Stats.frmChart hist = (Forms.Stats.frmChart)ModelHelper.generateProbabilityGraphic(IndependentFieldNames);
System.Windows.Forms.ComboBox cmbPrimary = (System.Windows.Forms.ComboBox)hist.Controls["cmbPrimary"];
cmbPrimary.SelectedValueChanged += new EventHandler(cmbPrimary_SelectedValueChanged);
System.Windows.Forms.TrackBar tb = (System.Windows.Forms.TrackBar)hist.Controls["tbQ"];
tb.Scroll += new EventHandler(tb_RegionChanged);
hist.chrHistogram.Show();
cmbPrimary.SelectedItem = IndependentFieldNames[0];
hist.Show();
}
private MultinomialLogitModel(bool deserializing)
: base(deserializing) {
if (deserializing)
logitModel = new alglib.logitmodel();
}
private void createRegChart()
{
if (lm == null) lm = getMnlModel();
Forms.Stats.frmChart hist = (Forms.Stats.frmChart)ModelHelper.generateProbabilityGraphic(IndependentFieldNames);
System.Windows.Forms.ComboBox cmbPrimary = (System.Windows.Forms.ComboBox)hist.Controls["cmbPrimary"];
cmbPrimary.SelectedValueChanged += new EventHandler(cmbPrimary_SelectedValueChanged);
System.Windows.Forms.TrackBar tb = (System.Windows.Forms.TrackBar)hist.Controls["tbQ"];
tb.Scroll += new EventHandler(tb_RegionChanged);
hist.chrHistogram.Show();
cmbPrimary.SelectedItem = IndependentFieldNames[0];
hist.Show();
}
private MultinomialLogitModel(StorableConstructorFlag _) : base(_)
{
logitModel = new alglib.logitmodel();
}
