using System;
using System.Collections;

///<Summary>
///C# Math Class for Computing Standard Deviation, Normal Distribution, Probability Density
///Will Be Used In C# Naive Bayes Data Mining Algorithm for Classifying Numeric or Continuous Data
///</Summary>
///<Remarks>
///C# Probability Density Function or Normal Distrubution from
///http://www.experts-exchange.com/Programming/Programming_Languages/C_Sharp/Q_20936306.html
///C# Mean and C# Standard Deviation Functions from Daniel Olson at
///http://authors.aspalliance.com/olson/
///</Remarks>
public class Maths
{      
        ///<Summary>
        ///Main function used to test or execute class functions from the command prompt
        ///</Summary>
        public static void Main()
        {
                //Test Standard Deviation Computation
                double[] arrayOfDoubles = {2.5,2.6,2.8,3.2,3.8,3.9,4.0,4.3,4.4,4.8};
               
                double stdDev = StandardDeviation(arrayOfDoubles);
               
                Console.WriteLine ("Standard Deviation Of Array Of Doubles Is : {0}", stdDev.ToString());
               
                //Test Probability Density or Normal Distribution Computation
                Console.WriteLine("Normal Distribution or Probability Density Is : {0}", NormalDistribution(42, 40, 1.5));
               
                Console.ReadLine();
        }
       
 ///<Summary>
///Calculates standard deviation of numbers in an ArrayList
///</Summary>      
 public static double StandardDeviation(ArrayList num)
  {
    double SumOfSqrs = 0;
    double avg = Average(num);
    for (int i=0; i<num.Count; i++)
    {
      SumOfSqrs += Math.Pow(((double)num - avg), 2);
    }
    double n = (double)num.Count;
    return Math.Sqrt(SumOfSqrs/(n-1));
  }
   
    ///<Summary>
    ///Calculates standard deviation of numbers of doubles data type in an array
    ///</Summary> 
  public static double StandardDeviation(double[] num)
  {
    double Sum = 0.0, SumOfSqrs = 0.0;
    for (int i=0; i<num.Length; i++)
    {
      Sum += num;
      SumOfSqrs += Math.Pow(num, 2);
    }
    double topSum = (num.Length * SumOfSqrs) - (Math.Pow(Sum, 2));
    double n = (double)num.Length;
    return Math.Sqrt( topSum / (n * (n-1)) );
  }
   
   ///<Summary>
  ///Calculates standard deviation of numbers of doubles data type in a column of an array
  ///</Summary> 
  public static double StandardDeviation(double[,] num, int col)
  {
    double Sum = 0.0, SumOfSqrs = 0.0;
    int len = num.GetLength(0);
    for (int i=0; i<len; i++)
    {
      Sum += num[i,col];
      SumOfSqrs += Math.Pow(num[i,col], 2);
    }
    double topSum = (len * SumOfSqrs) - (Math.Pow(Sum, 2));
    double n = System.Convert.ToDouble(len);
    return Math.Sqrt( topSum / (n * (n-1)) );
  }
 
   ///<Summary>
  ///Calculates average of numbers of doubles data type in an array
  ///</Summary> 
  public static double Average(double[] num)
  {
    double sum = 0.0;
    for (int i=0; i<num.Length; i++)
    {
      sum += num;
    }
    double avg = sum / System.Convert.ToDouble(num.Length);

    return avg;
  }
 
   ///<Summary>
  ///Calculates average of numbers of integer data type in an array
  ///</Summary>   
  public static double Average(int[] num)
  {
    double sum = 0.0;
    for (int i=0; i<num.Length; i++)
    {
      sum += num;
    }
    double avg = sum / System.Convert.ToDouble(num.Length);

    return avg;
  }
 
   ///<Summary>
  ///Calculates average of numbers of integer data type in an ArrayList
  ///</Summary> 
  public static double Average(ArrayList num)
  {
    double sum = 0.0;
    for (int i=0; i<num.Count; i++)
    {
      sum += (double)num;
    }
    double avg = sum / System.Convert.ToDouble(num.Count);

    return avg;
  }

     /// <summary>
    /// Calculates Normal Distribution or Probability Density given the mean, and standard deviation
    /// </summary>
    /// <param name="x">The value for which you want the distribution.</param>
    /// <param name="mean">The arithmetic mean of the distribution.</param>
    /// <param name="deviation">The standard deviation of the distribution.</param>
    /// <returns>Returns the normal distribution for the specified mean and standard deviation.</returns>
    public static double NormalDistribution(double x, double mean, double deviation)
    {
        return NormalDensity(x, mean, deviation);
    }
    private static double NormalDensity(double x, double mean, double deviation)
    {
      return Math.Exp(-(Math.Pow((x - mean)/deviation, 2)/2))/Math.Sqrt(2*Math.PI)/deviation;
    }  
}