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

namespace Science.Statistics.BasicStatistics
{
    public class OneSampleTTest
    {
        private double ov;
        private double df;
        private bool tt;
        public OneSampleTTest(StandardErrorAndExpectedValue input, 
            ObservedValue observedValue, int degreesOfFreedom)
        {
            df = (double)degreesOfFreedom;
            if (observedValue.SumQ == true)
            {
                ov = observedValue.Sum;
                tv = (ov - input.ExpectedValueForSum) / input.StandardErrorForSum;
            }
            else if (observedValue.AverageQ == true)
            {
                ov = observedValue.Average;
                tv = (ov - input.ExpectedValueForAverage) / input.StandardErrorForAverage;
            }
            else
            {
                ov = observedValue.Percentage;
                tv = (ov - input.ExpectedValueForPercentage) / input.StandardErrorForPercentage;
            }
            tt = observedValue.TwoTailedQ;
            FindpValue();
        }
        private void FindpValue()
        {
            pv = (1.0 - AreaBetweenMinustAndPlust()) / 2.0;
            if (tt) pv *= 2.0;
            pv *= 100.0;
            if (pv < 0.01) c = "Reject null.";
            else if (pv >= 0.01 && pv < 1.0) c = " Reject null at 1% level.";
            else if (pv >= 1.0 && pv < 5.0) c = " Reject null at 5% level.";
            else c = "Accept null.";
        }
        private double tv;
        public double tValue
        {
            get { return tv; }
            set { tv = value; }
        }
        private double pv;
        public double PValue
        {
            get { return pv; }
            set { pv = value; }
        }
        private string c;
        public string Conclusion
        {
            get { return c; }
        }

        private double func(double x)
        {
            return  Math.Pow(1.0 + (x*x/df),-(df+1.0)/2.0);
        }
        private double AreaBetweenMinustAndPlust()
        {
            Function.ToLastType<double, double> del
                = new Function.ToLastType<double, double>(func);
            IntegrationMidpoint obj = new IntegrationMidpoint(del, -tv, tv);
            obj.Compute();
            return obj.Result / Math.Sqrt(df) / Beta(0.5,df/2.0);
        }
        private double LogGamma(double xx)
        {
            double x, y, tmp, ser;
            double[] cof = new Double[6];
            cof[0] = 76.18009172947146;
            cof[1] = -86.50532032941677;
            cof[2] = 24.01409824083091;
            cof[3] = -1.231739572450155;
            cof[4] = 0.1208650973866179e-2;
            cof[5] = -0.5395239384953e-5;
            int j;
            y = x = xx;
            tmp = x + 5.5;
            tmp -= (x + 0.5) * Math.Log(tmp);
            ser = 1.000000000190015;
            for (j = 0; j <= 5; j++) ser += cof[j] / ++y;
            return -tmp + Math.Log(2.5066282746310005 * ser / x);
        }
        private double Beta(double z, double w)
        {
            return Math.Exp(LogGamma(z) + LogGamma(w) - LogGamma(z + w));
        }
    }
}