Research V) Algorithm implementaions of online means

In this page we’ll link and show different algorithm implementations of all the possible online mean/AVG that could be found on interent, by also giving the source of the code.

Arithmetic mean in VB

 
Function mean(arr)
	size = UBound(arr) + 1
	mean = 0
	For i = 0 To UBound(arr)
		mean = mean + arr(i)
	Next
	mean = mean/size
End Function
 
'Example
WScript.Echo mean(Array(3,1,4,1,5,9))

Output:

3.83333333333333

Source: Rosetta code

By clicking the links it’s also possible to see the C# and C++ version.

Geometric mean in VB

 
Function arithmetic_mean(arr)
	sum = 0
	For i = 0 To UBound(arr)
		sum = sum + arr(i)
	Next
	arithmetic_mean = sum / (UBound(arr)+1)
End Function
 
Function geometric_mean(arr)
	product = 1
	For i = 0 To UBound(arr)
		product = product * arr(i)
	Next
	geometric_mean = product ^ (1/(UBound(arr)+1))
End Function
 
Function harmonic_mean(arr)
	sum = 0
	For i = 0 To UBound(arr)
		sum = sum + (1/arr(i))
	Next
	harmonic_mean = (UBound(arr)+1) / sum
End Function
 
WScript.StdOut.WriteLine arithmetic_mean(Array(1,2,3,4,5,6,7,8,9,10))
WScript.StdOut.WriteLine geometric_mean(Array(1,2,3,4,5,6,7,8,9,10))
WScript.StdOut.WriteLine harmonic_mean(Array(1,2,3,4,5,6,7,8,9,10))

Output:

5.5
4.52872868811677
3.41417152147406

Source: Rosetta code

By clicking the links it’s also possible to see the C# and C++ version.

Quadratic mean in VB

 
Function rms(iLow As Integer, iHigh As Integer)
    Dim i As Integer
    If iLow > iHigh Then
        i = iLow
        iLow = iHigh
        iHigh = i
    End If
    For i = iLow To iHigh
        rms = rms + i ^ 2
    Next i
    rms = Sqr(rms / (iHigh - iLow + 1))
End Function
 
Sub foo()
    Debug.Print rms(1, 10)
End Sub

Output:

 6.20483682299543 

Source: Rosetta code

By clicking the links it’s also possible to see the C# and C++ version.

Median in C#

using System;
using System.Linq;
 
namespace Test
{
    class Program
    {
        static void Main()
        {
            double[] myArr = new double[] { 1, 5, 3, 6, 4, 2 };
 
            myArr = myArr.OrderBy(i => i).ToArray();
            // or Array.Sort(myArr) for in-place sort
 
            int mid = myArr.Length / 2;
            double median;
 
            if (myArr.Length % 2 == 0)
            {
                //we know its even
                median = (myArr[mid] + myArr[mid - 1]) / 2.0;
            }
            else
            {
                //we know its odd
                median = myArr[mid];
            }
 
            Console.WriteLine(median);
            Console.ReadLine();
        }
    }
}

Source: Rosetta code

By clicking the links it’s also possible to see the C++ version.

Mode in C#

using System;
using System.Collections;
using System.Collections.Generic;
using System.Linq;
 
namespace Test
{
    class Program
    {
 
        static void Main(string[] args)
        {
            /*
             * We Use Linq To Determine The Mode
             */
            List<int> myList = new List<int>() { 1, 1, 2, 4, 4 }; 
 
            var query =     from numbers in myList //select the numbers
                            group numbers by numbers //group them together so we can get the count
                            into groupedNumbers
                            select new { Number = groupedNumbers.Key, Count = groupedNumbers.Count() }; //so we got a query
            //find the max of the occurence of the mode 
            int max = query.Max(g => g.Count);
            IEnumerable<int> modes = query.Where(x => x.Count == max).Select(x => x.Number);//match the frequence and select the number
            foreach (var item in modes)
            {
                Console.WriteLine(item);
            }
 
            Console.ReadLine();
        }
 
 
 
    }
 
 
}

Source: Rosetta code

By clicking the links it’s also possible to see C++ version.

Standard deviation in C#

Library: Math.Net

using System;
using MathNet.Numerics.Statistics;
 
class Program
{
    static void Run(int sampleSize)
    {
        double[] X = new double[sampleSize];
        var r = new Random();
        for (int i = 0; i < sampleSize; i++)
            X[i] = r.NextDouble();
 
        const int numBuckets = 10;
        var histogram = new Histogram(X, numBuckets);
        Console.WriteLine("Sample size: {0:N0}", sampleSize);
        for (int i = 0; i < numBuckets; i++)
        {
            string bar = new String('#', (int)(histogram[i].Count * 360 / sampleSize));
            Console.WriteLine(" {0:0.00} : {1}", histogram[i].LowerBound, bar);
        }
        var statistics = new DescriptiveStatistics(X);
        Console.WriteLine("  Mean: " + statistics.Mean);
        Console.WriteLine("StdDev: " + statistics.StandardDeviation);
        Console.WriteLine();
    }
    static void Main(string[] args)
    {
        Run(100);
        Run(1000);
        Run(10000);
    }
}

Source: Rosetta code

By clicking the links it’s also possible to see the C++ version.

Implementation of K-means algorithm in C++

#include<iostream.h>

#include<conio.h>

void main()

{

    int i1,i2,i3,t1,t2;

    int k0[10];

    int k1[10];

    int k2[10];

    cout<<"\nEnter 10 numbers:\n";

    for(i1=0;i1<10;i1++)

    {

        cin>>k0[i1];

    }

    //initial means

    int m1;

    int m2;

    cout<<"\n Enter initial mean 1:";

    cin>>m1;

    cout<<"\n Enter initial mean 2:";

    cin>>m2;

    

    int om1,om2;    //old means

    do

    {

    

    //saving old means

    om1=m1;

    om2=m2;

    //creating clusters

    i1=i2=i3=0;

    for(i1=0;i1<10;i1++)

    {

        //calculating distance to means

        t1=k0[i1]-m1;

        if(t1<0){t1=-t1;}

        t2=k0[i1]-m2;

        if(t2<0){t2=-t2;}

        if(t1<t2)

        {

            //near to first mean

            k1[i2]=k0[i1];

            i2++;

        }

        else

        {

            //near to second mean

            k2[i3]=k0[i1];

            i3++;

        }

    }

    t2=0;

    //calculating new mean

    for(t1=0;t1<i2;t1++)

    {

        t2=t2+k1[t1];

    }

    m1=t2/i2;

    t2=0;

    for(t1=0;t1<i3;t1++)

    {

        t2=t2+k2[t1];

    }

    m2=t2/i3;

    //printing clusters

    cout<<"\nCluster 1:";

    for(t1=0;t1<i2;t1++)

    {

        cout<<k1[t1]<<" ";

    }

    cout<<"\nm1="<<m1;

    cout<<"\nCluster 2:";

    for(t1=0;t1<i3;t1++)

    {

        cout<<k2[t1]<<" ";

    }

    cout<<"\nm2="<<m2;

    cout<<"\n ----";

    }while(m1!=om1&&m2!=om2);

    cout<<"\n Clusters created";

    //ending

    getch();

}

/* OUTPUT

Enter 10 numbers:

2 4 10 12 3 20 30 11 25 23

 Enter initial mean 1:2

 Enter initial mean 2:16

Cluster 1:2 4 3

m1=3

Cluster 2:10 12 20 30 11 25 23

m2=18

 ----

Cluster 1:2 4 10 3

m1=4

Cluster 2:12 20 30 11 25 23

m2=20

 ----

Cluster 1:2 4 10 3 11

m1=6

Cluster 2:12 20 30 25 23

m2=22

 ----

Cluster 1:2 4 10 12 3 11

m1=7

Cluster 2:20 30 25 23

m2=24

 ----

Cluster 1:2 4 10 12 3 11

m1=7

Cluster 2:20 30 25 23

m2=24

 ----

 Clusters created

*/

Source: ankurm

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