using System;
using System.Collections.Generic;
using System.Text;
using System.Linq;


namespace FittingExample
{
    class Program
    {
        public static void function_cx_1_func(double[] parameters, double[] x, ref double func, object obj)
        {
            
            double a = parameters[0];
            double b = parameters[1];
            double c = parameters[2];

           func = x[0] * Math.Tan(a + (b / (1 + (x[0] / c)))*Math.PI/180);
        }
        /*
        public static void function_cx_1_grad(double[] parameters, double[] x, ref double func, double[] grad, object obj)
        {
            double a = parameters[0];
            double b = parameters[1];
            double c = parameters[2];

            func = x[0] * Math.Tan(a + (b / (1 + (x[0] / c))));
            
            grad[0] = x[0] / (Math.Pow(Math.Cos((a * x[0] + (a + b) * c) / (x[0] + c)), 2)); //ovo je "df/da"
            grad[1] = c * x[0] / (x[0] + c) * (Math.Pow(Math.Cos((a * x[0] + (a + b) * c) / (x[0] + c)), 2)); //ovo je "df/db"
            grad[2] = b * x[0] * x[0] / (Math.Pow((x[0] + c), 2)) * (Math.Pow(Math.Cos((a * x[0] + (a + b) * c) / (x[0] + c)), 2)); //ovo je "df/dc"
        }
        */
        public static double[] GetScaling(double[] arr, double min, double max)
        {
            
            double m = (max - min) / (arr.Max() - arr.Min()); 
            double c = min - arr.Min() * m;
            var newarr = new double[arr.Length];
            for (int i = 0; i < newarr.Length; i++)
                newarr[i] = m * arr[i] + c;
            return newarr;
        }

        static void Main(string[] args)
        {
            //double[,] sigmaValues = new double[,]{ {0.016}, {0.032}, {0.048}, {0.064}, {0.08}, {0.096}, {0.112}, {0.128}, {0.144}, {0.16}, {0.176} };
            double[,] sigTestValues = new double[,] { { 0 }, { 50 }, { 100 }, { 150 }, { 200 }, { 250 }, { 300 }, { 350 }, { 400 },
                        { 450 }, { 500 }, { 550 }, { 600 }, { 650 }, { 700 }, { 750 }, { 800 }, { 850 }, { 900 }, { 950 }, { 1000 } };

            double[] tauValues = { 0.000,34.011,59.616,80.539,98.557,114.634,129.346,143.057,156.013,168.384,180.291,191.825,203.053,
                                     214.029,224.792,235.376,245.806,256.105,266.289,276.373,286.369};

            double sr = 3.0;

            double[] tarValues = new double[tauValues.Length];

            for (int i = 0; i < tauValues.Length; i++)
            {
                tarValues[i] = tauValues[i] / sr;
            }
            
            double[] initialTestGuess = { 10.2, 28.81, 250.98 };

            //double[] scaledGuess = GetScaling(initialTestGuess, 0.3, 0.5);

            double[] bndl = new double[] { 0, 0, 0 };//0.0001
            double[] bndu = new double[] { 10.2, 28.81, 250.98 };

            double[] s = new double[] { 1e7, 1e7, 1e8 };//skaliranje vrednosti koje su date u initialGuess parametru 1e7,1e7,1e8            

            double epsf = 0;
            double epsx = 0.000001;
            //double epsx = 0;
            int maxits = 0;
            //napomena: Ako je epsf=epsx=maxits=0, tada se koriste podrazumevane vrednosti za zaustavljanje
            int info;
            alglib.lsfitstate state;            
            alglib.lsfitreport rep;
            double diffstep = 0.00001; //0.00001;

            try
            {
                alglib.lsfitcreatef(sigTestValues, tarValues, initialTestGuess, diffstep, out state);                
                alglib.lsfitsetcond(state, epsf, epsx, maxits);
                alglib.lsfitsetbc(state, bndl, bndu);
                alglib.lsfitsetscale(state, s);
                alglib.lsfitfit(state, function_cx_1_func, null, null);
                alglib.lsfitresults(state, out info, out initialTestGuess, out rep);
                
                Console.WriteLine("Without gradient: ");
                Console.WriteLine("A(Degrees)= " + initialTestGuess[0]);
                Console.WriteLine("B(Degrees)= " + initialTestGuess[1] );
                Console.WriteLine("C(kPA)= " + initialTestGuess[2]);

                Console.WriteLine("RMS: "+ rep.rmserror);
                Console.WriteLine("AVG Error: " + alglib.ap.format(initialTestGuess, 3));

                System.Console.ReadLine();
            }
            catch (Exception ex)
            {
                
                Console.WriteLine(ex.Message);
            }
        }
    }
}