static void Main(string[] args)
{
if (args.Length != 3)
{
Console.WriteLine("Missing input parameters.");
Console.WriteLine("Command line should be in format: Solver.exe <config file> <file with calculated points> <file for output points>");
//Console.ReadKey();
return;
}
Parser parser = new Parser(args[0], args[1]);
int res = parser.doParse();
if (res != 0)
{
// Console.ReadKey();
return;
}
/*double[][] xf = new double[][]
* {
* new double[] { 0, 0, 0 },
* new double[] { 0, 1, 1 },
* new double[] { 0, 2, 2 },
* new double[] { 1, 0, 1 },
* new double[] { 1, 1, 2 },
* new double[] { 1, 2, 3 },
* new double[] { 2, 0, 2 },
* new double[] { 2, 1, 2 },
* new double[] { 2, 2, 4 }
* };*/
Shepard model = new Shepard(parser.FunctionDimension, parser.Points);
//double[] x = new double[] { 0.5, 0.5, 0 };
//model.Calculate(x);
//Console.WriteLine("f({0}, {1}) = {2}", x[0], x[1], x[2]);
Analyzer analyzer = new Analyzer(model, parser.Points);
double[][] xx = analyzer.Result;
double[][] new_points = new double[parser.PredictionPointAmount][];
for (int i = 0; i < parser.PredictionPointAmount; i++)
{
new_points[i] = new double[xx[i].Length];
Array.Copy(xx[i], new_points[i], xx[i].Length);
model.Calculate(new_points[i]);
// Console.WriteLine(String.Join(", ", xx[i]));
}
Parser.keepSolution(args[2], new_points);
//Console.ReadKey();
}
private int testDefWay(string configFile, string pointFile, Func <double[], double> func)
{
Parser parser = new Parser(configFile, pointFile);
int pointAmount = parser.PointAmount;
double[][] points = new double[parser.PointAmount][];
for (int j = 0; j < parser.PointAmount; j++)
{
points[j] = (double[])parser.Points[j].Clone();
}
int i = 0;
double maxErr = 10;
while (i < 100000000 && maxErr > parser.Approximation)
{
Shepard model = new Shepard(parser.FunctionDimension, points);
Console.WriteLine("Max " + String.Join(", ", model.Max) + " Min " + String.Join(", ", model.Min));
Analyzer analyzer = new Analyzer(model, points);
analyzer.do_default_analyse();
double[][] xx = analyzer.Result;
pointAmount = pointAmount + parser.PredictionPointAmount;
points = getNewPoints(points, analyzer.Result, parser.PredictionPointAmount, parser.FunctionDimension, func);
double[][] new_points = new double[parser.PredictionPointAmount][];
for (int j = 0; j < parser.PredictionPointAmount; j++)
{
new_points[j] = new double[xx[j].Length];
Array.Copy(xx[j], new_points[j], xx[j].Length);
model.Calculate(new_points[j]);
}
double tempErr = 0;
for (int k = 0; k < new_points.Length; k++)
{
double err = Math.Abs(points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension] - new_points[k][parser.FunctionDimension]);
Console.WriteLine(" \n " + (points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension] - new_points[k][parser.FunctionDimension]) + " " + points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension] + " " + new_points[k][parser.FunctionDimension] + " \n ");
if (err > tempErr)
{
tempErr = err;
}
Console.WriteLine("f({0}) real val {1} predict val {2} err {3}", String.Join(", ", xx[k]), points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension], new_points[k][parser.FunctionDimension], err);
}
maxErr = tempErr;
i++;
}
testResult(parser.FunctionDimension, points, func);
return(i);
}
private void testResult(int functionDimension, double[][] points, Func <double[], double> func)
{
Console.WriteLine("\n\nTest result approximation");
Shepard model = new Shepard(functionDimension, points);
for (int i = 1; i < 10; i += 2)
{
double[] a = new double[functionDimension + 1];
double[] b = new double[functionDimension + 1];
for (int j = 0; j < functionDimension; j++)
{
a[j] = model.Min[j] + ((model.Max[j] - model.Min[j]) * i / 10);
b[j] = a[j];
}
model.Calculate(a);
Console.WriteLine("Vector " + String.Join(", ", b) + " Approximation result " + a[functionDimension] + " real " + func(b) + " error " + Math.Abs(a[functionDimension] - func(b)));
}
}
private void analyse_error()
{
Console.WriteLine("Вычисление локальных экстраполянтов");
//вычисляю экстраполянты
Shepard[] sh = new Shepard[xf.Length];
for (int i = 0; i < xf.Length; i++)
{
sh[i] = new Shepard(N, xf, graph[i]);
}
Console.WriteLine("Аппроксимация значений исходной функции на границах доменов");
//пересчитываю значения в узлах решетки только на границах доменов
for (int i = 0; i < grid.Node.Length; i++)
{
if (borderdist[i] > 0)
{
continue;
}
sh[domain[i]].Calculate(grid.Node[i]);
}
Console.WriteLine("Вычисление ошибки аппроксимации функции на границах доменов");
//вычисляю ошибку на границах доменов
error = new double[grid.Node.Length];
for (int i = 0; i < grid.Node.Length; i++)
{
if (borderdist[i] > 0)
{
continue;
}
error[i] = 0;
foreach (var adj in grid.Neighbours(i))
{
double d = distanceF(grid.Node[i], grid.Node[adj]);
if (error[i] < d)
{
error[i] = d;
}
}
}
Console.WriteLine("Интерполяция ошибки аппроксимации функции на области доменов");
//интерполирую ошибку
double max = 0;
for (int i = 0; i < grid.Node.Length; i++)
{
int brd = bordernear[i];
double err = error[brd];
error[i] = err * (1 - borderdist[i]);
if (max < error[i])
{
max = error[i];
}
}
Console.WriteLine("Нормировка функции ошибки");
//нормирую ошибку
if (max > 0)
{
for (int i = 0; i < grid.Node.Length; i++)
{
error[i] = error[i] / max;
}
}
int maxcandidates = Math.Min(candidates.Length, 1000);
if (candidates.Length > maxcandidates)
{
Console.WriteLine("Предварительный отбор {1} из {0} кандидатов", candidates.Length, maxcandidates);
}
Console.WriteLine("Упорядочение {0} потенциальных кандидатов", maxcandidates);
//сортировка потенциальных кандидатов
for (int i = 0; i < maxcandidates - 1; i++)
{
for (int j = i + 1; j < candidates.Length; j++)
{
if (error[candidates[i]] < error[candidates[j]])
{
int temp = candidates[i];
candidates[i] = candidates[j];
candidates[j] = temp;
}
}
}
Console.WriteLine("Формирование списка кандидатов", maxcandidates);
candidates = Tools.Sub(candidates, 0, maxcandidates);
xfcandidates = Tools.Sub(grid.Node, candidates);
for (int i = 0; i < xfcandidates.Length; i++)
{
//func.Calculate(xfcandidates[i]);
for (int j = N; j < N + M; j++)
{
xfcandidates[i][j] = 0;
}
}
Console.WriteLine("Процесс расчета закончен успешно");
}
private int testWithRandomForest(string configFileToLearn, string pointFileToLearn, string configFile, string pointFile, Func <double[], double> funcToLearn, Func <double[], double[]> derivativeFuncToLearn, Func <double[], double> func, Func <double[], double[]> derivativeFunc)
{
Parser parserToLearn = new Parser(configFile, pointFile);
int pointAmountToLearn = parserToLearn.PointAmount;
Shepard model = new Shepard(parserToLearn.FunctionDimension, parserToLearn.Points);
Analyzer analyzer = new Analyzer(model, parserToLearn.Points);
Classifiers.IClassifier cls = analyzer.learn_random_forest_on_grid(funcToLearn, derivativeFuncToLearn, parserToLearn.Approximation);
Parser parser = new Parser(configFile, pointFile);
int pointAmount = parser.PointAmount;
double[][] points = new double[parser.PointAmount][];
for (int j = 0; j < parser.PointAmount; j++)
{
points[j] = (double[])parser.Points[j].Clone();
}
int i = 0;
double maxErr = 10;
while (i < 10 && maxErr > parser.Approximation)
{
model = new Shepard(parser.FunctionDimension, points);
analyzer = new Analyzer(model, points);
analyzer.do_random_forest_analyse(cls, parser.Approximation, func, derivativeFunc);
double[][] xx = analyzer.Result;
pointAmount = pointAmount + parser.PredictionPointAmount;
double[][] newPoints = new double[pointAmount][];
for (int j = 0; j < pointAmount; j++)
{
if (j < pointAmount - parser.PredictionPointAmount)
{
newPoints[j] = (double[])points[j].Clone();
}
else
{
newPoints[j] = (double[])xx[j - pointAmount + parser.PredictionPointAmount].Clone();
newPoints[j][parser.FunctionDimension] = func(newPoints[j]);
}
}
points = newPoints;
double[][] new_points = new double[parser.PredictionPointAmount][];
for (int j = 0; j < parser.PredictionPointAmount; j++)
{
new_points[j] = new double[xx[j].Length];
Array.Copy(xx[j], new_points[j], xx[j].Length);
model.Calculate(new_points[j]);
}
double tempErr = 0;
for (int k = 0; k < new_points.Length; k++)
{
double err = Math.Abs(points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension] - new_points[k][parser.FunctionDimension]);
Console.WriteLine(" \n " + (points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension] - new_points[k][parser.FunctionDimension]) + " " + points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension] + " " + new_points[k][parser.FunctionDimension] + " \n ");
if (err > tempErr)
{
tempErr = err;
}
Console.WriteLine("f({0}) real val {1} predict val {2} err {3}", String.Join(", ", xx[k]), points[pointAmount - parser.PredictionPointAmount + k][parser.FunctionDimension], new_points[k][parser.FunctionDimension], err);
}
maxErr = tempErr;
i++;
}
testResult(parser.FunctionDimension, points, func);
return(i);
}
