private static alglib.ndimensional_pgrad CreatePGrad(AutoDiff.IParametricCompiledTerm compiledFunc)
{
return((double[] c, double[] x, ref double func, double[] grad, object o) => {
var tupel = compiledFunc.Differentiate(c, x);
func = tupel.Item2;
Array.Copy(tupel.Item1, grad, grad.Length);
});
}
private static alglib.ndimensional_pfunc CreatePFunc(AutoDiff.IParametricCompiledTerm compiledFunc)
{
return((double[] c, double[] x, ref double func, object o) => {
func = compiledFunc.Evaluate(c, x);
});
}
public static double OptimizeConstants(ISymbolicDataAnalysisExpressionTreeInterpreter interpreter, ISymbolicExpressionTree tree, IRegressionProblemData problemData, IEnumerable <int> rows, bool applyLinearScaling, int maxIterations, bool updateVariableWeights = true, double lowerEstimationLimit = double.MinValue, double upperEstimationLimit = double.MaxValue, bool updateConstantsInTree = true)
{
List <AutoDiff.Variable> variables = new List <AutoDiff.Variable>();
List <AutoDiff.Variable> parameters = new List <AutoDiff.Variable>();
List <string> variableNames = new List <string>();
AutoDiff.Term func;
if (!TryTransformToAutoDiff(tree.Root.GetSubtree(0), variables, parameters, variableNames, updateVariableWeights, out func))
{
throw new NotSupportedException("Could not optimize constants of symbolic expression tree due to not supported symbols used in the tree.");
}
if (variableNames.Count == 0)
{
return(0.0);
}
AutoDiff.IParametricCompiledTerm compiledFunc = func.Compile(variables.ToArray(), parameters.ToArray());
List <SymbolicExpressionTreeTerminalNode> terminalNodes = null;
if (updateVariableWeights)
{
terminalNodes = tree.Root.IterateNodesPrefix().OfType <SymbolicExpressionTreeTerminalNode>().ToList();
}
else
{
terminalNodes = new List <SymbolicExpressionTreeTerminalNode>(tree.Root.IterateNodesPrefix().OfType <ConstantTreeNode>());
}
//extract inital constants
double[] c = new double[variables.Count];
{
c[0] = 0.0;
c[1] = 1.0;
int i = 2;
foreach (var node in terminalNodes)
{
ConstantTreeNode constantTreeNode = node as ConstantTreeNode;
VariableTreeNode variableTreeNode = node as VariableTreeNode;
if (constantTreeNode != null)
{
c[i++] = constantTreeNode.Value;
}
else if (updateVariableWeights && variableTreeNode != null)
{
c[i++] = variableTreeNode.Weight;
}
}
}
double[] originalConstants = (double[])c.Clone();
double originalQuality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
alglib.lsfitstate state;
alglib.lsfitreport rep;
int info;
IDataset ds = problemData.Dataset;
double[,] x = new double[rows.Count(), variableNames.Count];
int row = 0;
foreach (var r in rows)
{
for (int col = 0; col < variableNames.Count; col++)
{
x[row, col] = ds.GetDoubleValue(variableNames[col], r);
}
row++;
}
double[] y = ds.GetDoubleValues(problemData.TargetVariable, rows).ToArray();
int n = x.GetLength(0);
int m = x.GetLength(1);
int k = c.Length;
alglib.ndimensional_pfunc function_cx_1_func = CreatePFunc(compiledFunc);
alglib.ndimensional_pgrad function_cx_1_grad = CreatePGrad(compiledFunc);
try {
alglib.lsfitcreatefg(x, y, c, n, m, k, false, out state);
alglib.lsfitsetcond(state, 0.0, 0.0, maxIterations);
//alglib.lsfitsetgradientcheck(state, 0.001);
alglib.lsfitfit(state, function_cx_1_func, function_cx_1_grad, null, null);
alglib.lsfitresults(state, out info, out c, out rep);
}
catch (ArithmeticException) {
return(originalQuality);
}
catch (alglib.alglibexception) {
return(originalQuality);
}
//info == -7 => constant optimization failed due to wrong gradient
if (info != -7)
{
UpdateConstants(tree, c.Skip(2).ToArray(), updateVariableWeights);
}
var quality = SymbolicRegressionSingleObjectivePearsonRSquaredEvaluator.Calculate(interpreter, tree, lowerEstimationLimit, upperEstimationLimit, problemData, rows, applyLinearScaling);
if (!updateConstantsInTree)
{
UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights);
}
if (originalQuality - quality > 0.001 || double.IsNaN(quality))
{
UpdateConstants(tree, originalConstants.Skip(2).ToArray(), updateVariableWeights);
return(originalQuality);
}
return(quality);
}
