private void BuildRuleTree(Rule rule, IForecastingDataSets datasets)
{
if (rule.Type == RuleType.Interior)
{
if (rule.LeftChild != null)
{
BuildRuleTree(rule.LeftChild, datasets);
}
if (rule.RightChild != null)
{
BuildRuleTree(rule.RightChild, datasets);
}
}
else
{
IForecastingDataSets extracted = ExtractSubSets(rule.InheritedSplitLocations.ToArray(),
rule.InheritedSplitValues.ToArray(), datasets, rule.Type);
ModelParameter mp = new ModelParameter();
if (extracted.Length > 0)
{
MLRModel mlr = new MLRModel(mp);
mlr.Train(extracted);
rule.Model = mlr;
}
}
}
public static IForecastingModel CreateForecastingModel(string name)
{
IForecastingModel model = null;
switch (name)
{
case "Artificial Neural Network":
AnnModelParameter annPara = new AnnModelParameter();
model = new NeuralNetworkModel(annPara);
break;
case "HIGANN":
AnnModelParameter annPara1 = new AnnModelParameter();
model = new NeuralNetworkModel(annPara1);
break;
case "Support Vector Machine":
Heiflow.AI.SVM.Parameter p = new Heiflow.AI.SVM.Parameter();
model = new SVMModel(p);
break;
case "Multiple Linear Regression":
ModelParameter mp = new ModelParameter();
model = new MLRModel(mp);
break;
case "Genetic Programming":
GPModelParameter para = new GPModelParameter();
model = new GPModel(para);
break;
case "Model Tree":
Rule root = new Rule(5, 0.47035, RuleType.Interior);
Rule right = new Rule(RuleType.RightLeaf);
root.RightChild = right;
Rule left = new Rule(5, 0.30445, RuleType.Interior);
root.LeftChild = left;
Rule left1 = new Rule(RuleType.LeftLeaf);
left.LeftChild = left1;
Rule right1 = new Rule(9, 0.156, RuleType.Interior);
left.RightChild = right1;
Rule right1_left = new Rule(RuleType.LeftLeaf);
right1.LeftChild = right1_left;
Rule right1_right = new Rule(RuleType.RightLeaf);
right1.RightChild = right1_right;
HybridModelParameter hmp = new HybridModelParameter(root);
model = new HybridModel(hmp);
break;
}
return(model);
}
public override double Forecast(double[] inputVector)
{
HybridModelParameter hmp = mParameter as HybridModelParameter;
MLRModel mlr = hmp.CurrentLeafRule.Model as MLRModel;
if (mlr != null)
{
double y = 0;
for (int i = 0; i < inputVector.Length; i++)
{
y += inputVector[i] * mlr.RegressionCoefficients[i];
}
return(y);
}
else
{
return(0);
}
}
