protected SymbolicDiscriminantFunctionClassificationModel(SymbolicDiscriminantFunctionClassificationModel original, Cloner cloner)
: base(original, cloner)
{
classValues = (double[])original.classValues.Clone();
thresholds = (double[])original.thresholds.Clone();
thresholdCalculator = cloner.Clone(original.thresholdCalculator);
}
public SymbolicDiscriminantFunctionClassificationModel(ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDiscriminantFunctionThresholdCalculator thresholdCalculator,
double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
: base(tree, interpreter, lowerEstimationLimit, upperEstimationLimit) {
this.thresholds = new double[0];
this.classValues = new double[0];
this.ThresholdCalculator = thresholdCalculator;
}
private void AfterDeserialization()
{
if (ThresholdCalculator == null)
{
ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
}
}
public SymbolicDiscriminantFunctionClassificationModel(string targetVariable, ISymbolicExpressionTree tree, ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, IDiscriminantFunctionThresholdCalculator thresholdCalculator,
double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue)
: base(targetVariable, tree, interpreter, lowerEstimationLimit, upperEstimationLimit)
{
this.thresholds = new double[0];
this.classValues = new double[0];
this.ThresholdCalculator = thresholdCalculator;
}
public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator)
: base() {
this.name = ItemName;
this.description = ItemDescription;
this.model = model;
this.classValues = new double[0];
this.thresholds = new double[0];
this.thresholdCalculator = thresholdCalculator;
}
private void AfterDeserialization()
{
// BackwardsCompatibility3.4
#region Backwards compatible code, remove with 3.5
if (ThresholdCalculator == null)
{
ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
}
#endregion
}
public DiscriminantFunctionClassificationModel(IRegressionModel model, IDiscriminantFunctionThresholdCalculator thresholdCalculator)
: base()
{
this.name = ItemName;
this.description = ItemDescription;
this.model = model;
this.classValues = new double[0];
this.thresholds = new double[0];
this.thresholdCalculator = thresholdCalculator;
}
private void AfterDeserialization() {
// BackwardsCompatibility3.4
#region Backwards compatible code, remove with 3.5
if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
#endregion
}
protected SymbolicDiscriminantFunctionClassificationModel(SymbolicDiscriminantFunctionClassificationModel original, Cloner cloner)
: base(original, cloner) {
classValues = (double[])original.classValues.Clone();
thresholds = (double[])original.thresholds.Clone();
thresholdCalculator = cloner.Clone(original.thresholdCalculator);
}
private void AfterDeserialization() {
if (ThresholdCalculator == null) ThresholdCalculator = new AccuracyMaximizationThresholdCalculator();
}
