Basic statistics/probability formulas using c#

Data science / Machine learning is all about statistics/probability, graphs and linear algebra.

Here are few basics stats functions in c# :

static decimal GetMean(decimal[] inputs)

{

    int count = inputs.Length;

    decimal avg = 0;

    for (int n = 0; n < count; n++)

    {

        avg += inputs[n];

    }

    return Math.Round(avg / count, 1);

}


static decimal GetMedian(int[] inputs)

{

    Array.Sort(inputs);

    int count = 0;


    if ((inputs.Length % 2) == 1)

    {

        count = (inputs.Length - 1) / 2;

        return inputs[count];

    }

    else

    {

        count = inputs.Length / 2;

        decimal a = inputs[count - 1];

        decimal b = inputs[count];

        return Math.Round((a + b) / 2, 1);

    }

}

static decimal GetMode(decimal[] inputs)

{

    //you can substitute below C# lambda express to hasttable technique

    return inputs.GroupBy(n => n).

        OrderByDescending(g => g.Count()).

        Select(g => g.Key).FirstOrDefault();

}


static decimal GetWeightedAvg(decimal[] avg, decimal[] weight)

{

    int count = avg.Length;

    decimal w = 0;

    decimal aAndW = 0;


    for (int n = 0; n < count; n++)

    {

      aAndW +=  avg[n] * weight[n];

      w += weight[n];

    }

    return  Math.Round( (aAndW / w), 1); 

}



static  double GetStandardDeviation(int[] inputs)

{
    int count = inputs.Length;
    int avg = 0; int sum = 0;

    for (int n = 0; n < count; n++)

    {

        sum += inputs[n];

    }

    avg = sum / count; //getting avg or mean

    int preSd = 0;

    for (int n = 0; n < count; n++)

    {

        int tmp = Math.Abs( ((inputs[n] - avg) )); //replace abs to sub and sub from higher to lower

        preSd += tmp * tmp ; 

    }

   return Math.Sqrt( preSd / count);


}