Create – in both languages C# and VB.NET – a demonstrative program which computes the online arithmetic mean (if it’s a numeric variable) and the distribution for a discrete variable (can use values simulated with RANDOM object).
How i worked
In this application i used a class for store Dogs information, such as weight and breed.
I use Random method for data (i also searched for a database to capture the data, but i didnt’t find it).
I didn’t use a second class to represent the Frequency Distribution but i used a list to store the new dog instances and i used the override method for demostration purpose.
I tried to modulate the code and make it generic, so as to have it more robust and readable. Also for this reason the class has been put in a separate file.
Private Sub Timer_Tick(sender As Object, e As EventArgs) Handles Timer.Tick
Dim Dog As Dog = new_dataset()
ListOfDog.Add(Dog)
print_Database(Dog)
online_mean(Dog)
print_OnArMean()
discrete_distribution(DicOfBreed, Dog.Breed)
discrete_distribution(DicOfAgeDistributio, Dog.Age)
End Sub
Vb.Net
Form1.vb
Public Class Form1
Public R As New Random
'List for store the dog
Public ListOfDog As New List(Of Dog)
'Array of breed
Public PossibleBreed() As String = {"German Shepherd", "Alaskan Malamute", "Beauceron", "Bernese Mountain Dog",
"Black Russian Terrier", "Weimaraner", "Pointer", "Rottweiler", "Siberian Husky",
"Afghan Hound", "Boxer", "Collie", "Weimaraner", "Dalmatian", "Golden Retriver",
"Irish Setter", "Labrador Retriver"}
Public Count As Integer
Public CurrentArMean As Double
'Dictionary for Frequence Distribution of the breed(Breed,count)
Public DicOfBreed As New Dictionary(Of String, Integer)
'Dictionary for Frequence Distribution of the age(Age,count)
Public DicOfAgeDistributio As New Dictionary(Of Integer, Integer)
Private Sub Form1_Load(sender As Object, e As EventArgs) Handles MyBase.Load
inizialized()
End Sub
Public Sub inizialized()
Count = 0
CurrentArMean = 0
DicOfBreed.Clear()
DicOfAgeDistributio.Clear()
ListOfDog.Clear()
Me.RTxTDatabase.Text = ("Breed:".PadRight(25) & "Weight:".PadRight(10) & "Age:" & Environment.NewLine & Environment.NewLine)
Me.RTxtArOnMean.Text = ("Mean: " & Environment.NewLine & Environment.NewLine)
Me.RTxtBreedDistribution.Text = "Distribution: " & Environment.NewLine & Environment.NewLine
End Sub
Public Sub online_mean(CollForMean As Dog)
'Online algo for mean
Count += 1
CurrentArMean = CurrentArMean + (CollForMean.Age - CurrentArMean) / Count
End Sub
Public Overloads Sub discrete_distribution(dictionary As Dictionary(Of String, Integer),
b As String)
If (dictionary.ContainsKey(b)) Then
dictionary(b) += 1
Else
dictionary.Add(b, 1)
End If
End Sub
Public Overloads Sub discrete_distribution(dictionary As Dictionary(Of Integer, Integer), b As Integer)
If (dictionary.ContainsKey(b)) Then
dictionary(b) += 1
Else
dictionary.Add(b, 1)
End If
End Sub
Public Sub print_OnArMean()
Me.RTxtArOnMean.AppendText(CurrentArMean.ToString("N2") & Environment.NewLine)
End Sub
Public Sub print_Database(Dog As Dog)
Me.RTxTDatabase.AppendText(Dog.Breed.PadRight(25) & Dog.Weight.ToString("N2").PadRight(12) & Dog.Age & Environment.NewLine)
End Sub
Public Overloads Sub print_Distribution(dictionary As Dictionary(Of String, Integer))
Me.RTxtBreedDistribution.AppendText("Breed Distribution: " & Environment.NewLine & Environment.NewLine)
Me.RTxtBreedDistribution.AppendText("Count:".PadRight(10).PadLeft(35) & "Relative Freq:".PadRight(18) & "Percentage:" & Environment.NewLine & Environment.NewLine)
For Each k As KeyValuePair(Of String, Integer) In dictionary
Dim RelFreq = k.Value / ListOfDog.Count
Me.RTxtBreedDistribution.AppendText(k.Key.PadRight(30) & k.Value.ToString.PadRight(10) & RelFreq.ToString("N2").PadRight(15) &
(RelFreq * 100).ToString("N2") & " %" & Environment.NewLine)
Next
End Sub
Public Overloads Sub print_Distribution(dictionary As Dictionary(Of Integer, Integer))
Me.RTxtBreedDistribution.AppendText(Environment.NewLine & Environment.NewLine & "Age Distribution: " & Environment.NewLine & Environment.NewLine)
Me.RTxtBreedDistribution.AppendText("Count:".PadRight(10).PadLeft(35) & "Relative Freq:".PadRight(18) & "Percentage:" & Environment.NewLine & Environment.NewLine)
For Each k As KeyValuePair(Of Integer, Integer) In dictionary
Dim RelFreq = k.Value / ListOfDog.Count
Me.RTxtBreedDistribution.AppendText(k.Key.ToString.PadRight(30) & k.Value.ToString.PadRight(10) & RelFreq.ToString("N2").PadRight(15) &
(RelFreq * 100).ToString("N2") & " %" & Environment.NewLine)
Next
End Sub
Public Function new_dataset() As Dog
'Large Dog breed Weight's is between 30 and 50kg
'And can be old max(usually) 12 years (used for discrete mean exercise's)
'We consider dogs only over 1 year old
Dim Dog As New Dog With {
.Breed = PossibleBreed(R.Next(0, PossibleBreed.Length)),
.Weight = R.NextDouble * 20 + 30,
.Age = R.Next(1, 13)
}
Return Dog
End Function
Private Sub BtnGo_Click(sender As Object, e As EventArgs) Handles BtnGo.Click
Me.Timer.Start()
End Sub
Private Sub Timer_Tick(sender As Object, e As EventArgs) Handles Timer.Tick
Dim Dog As Dog = new_dataset()
ListOfDog.Add(Dog)
print_Database(Dog)
online_mean(Dog)
print_OnArMean()
discrete_distribution(DicOfBreed, Dog.Breed)
discrete_distribution(DicOfAgeDistributio, Dog.Age)
End Sub
Private Sub BtnStop_Click(sender As Object, e As EventArgs) Handles BtnStop.Click
Me.Timer.Stop()
Me.RTxtArOnMean.AppendText(Environment.NewLine & ListOfDog.Count & " occurrences" & Environment.NewLine)
End Sub
Private Sub BtnDistribution_Click(sender As Object, e As EventArgs) Handles BtnDistribution.Click
Me.RTxtBreedDistribution.Clear()
print_Distribution(DicOfBreed)
print_Distribution(DicOfAgeDistributio)
End Sub
Private Sub BtnClear_Click(sender As Object, e As EventArgs) Handles BtnClear.Click
inizialized()
End Sub
End Class
Dog.vb
Public Class Dog
Public Breed As String
Public Age As Integer
'Public Height As Double
Public Weight As Double
'Public Footage As Double
Public Overrides Function ToString() As String
Return Breed & " " & Age & " " & Weight
End Function
End Class
C#
form1.cs
using System;
using System.Collections.Generic;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
namespace Application4_C
{
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
inizialized();
}
public Random R = new Random();
// List for store the dog
public List<Dog> ListOfDog = new List<Dog>();
// Array of breed
public string[] PossibleBreed = new[] { "German Shepherd", "Alaskan Malamute", "Beauceron", "Bernese Mountain Dog", "Black Russian Terrier", "Weimaraner", "Pointer", "Rottweiler", "Siberian Husky", "Afghan Hound", "Boxer", "Collie", "Weimaraner", "Dalmatian", "Golden Retriver", "Irish Setter", "Labrador Retriver" };
public int Count;
public double CurrentArMean;
// Dictionary for Frequence Distribution of the breed(Breed,count)
public Dictionary<string, int> DicOfBreed = new Dictionary<string, int>();
// Dictionary for Frequence Distribution of the age(Age,count)
public Dictionary<int, int> DicOfAgeDistributio = new Dictionary<int, int>();
public void inizialized()
{
Count = 0;
CurrentArMean = 0;
DicOfBreed.Clear();
DicOfAgeDistributio.Clear();
ListOfDog.Clear();
this.RTxTDatabase.Text = ("Breed:".PadRight(25) + "Weight:".PadRight(10) + "Age:" + Environment.NewLine + Environment.NewLine);
this.RTxtArOnMean.Text = ("Mean: " + Environment.NewLine + Environment.NewLine);
this.RTxtBreedDistribution.Text = "Distribution: " + Environment.NewLine + Environment.NewLine;
}
public void online_mean(Dog CollForMean)
{
// Online algo for mean
Count += 1;
CurrentArMean = CurrentArMean + (CollForMean.Age - CurrentArMean) / Count;
}
public void discrete_distribution(Dictionary<string, int> dictionary, string b)
{
if ((dictionary.ContainsKey(b)))
dictionary[b] += 1;
else
dictionary.Add(b, 1);
}
public void discrete_distribution(Dictionary<int, int> dictionary, int b)
{
if ((dictionary.ContainsKey(b)))
dictionary[b] += 1;
else
dictionary.Add(b, 1);
}
public void print_OnArMean()
{
this.RTxtArOnMean.AppendText(CurrentArMean.ToString("N2") + Environment.NewLine);
}
public void print_Database(Dog Dog)
{
this.RTxTDatabase.AppendText(Dog.Breed.PadRight(25) + Dog.Weight.ToString("N2").PadRight(12) + Dog.Age + Environment.NewLine);
}
public void print_Distribution(Dictionary<string, int> dictionary)
{
this.RTxtBreedDistribution.AppendText("Breed Distribution: " + Environment.NewLine + Environment.NewLine);
this.RTxtBreedDistribution.AppendText("Count:".PadRight(10).PadLeft(35) + "Relative Freq:".PadRight(18) + "Percentage:" + Environment.NewLine + Environment.NewLine);
foreach (KeyValuePair<string, int> k in dictionary)
{
var RelFreq = k.Value / (double)ListOfDog.Count;
this.RTxtBreedDistribution.AppendText(k.Key.PadRight(30) + k.Value.ToString().PadRight(10) + RelFreq.ToString("N2").PadRight(15) + (RelFreq * 100).ToString("N2") + " %" + Environment.NewLine);
}
}
public void print_Distribution(Dictionary<int, int> dictionary)
{
this.RTxtBreedDistribution.AppendText(Environment.NewLine + Environment.NewLine + "Age Distribution: " + Environment.NewLine + Environment.NewLine);
this.RTxtBreedDistribution.AppendText("Count:".PadRight(10).PadLeft(35) + "Relative Freq:".PadRight(18) + "Percentage:" + Environment.NewLine + Environment.NewLine);
foreach (KeyValuePair<int, int> k in dictionary)
{
var RelFreq = k.Value / (double)ListOfDog.Count;
this.RTxtBreedDistribution.AppendText(k.Key.ToString().PadRight(30) + k.Value.ToString().PadRight(10) + RelFreq.ToString("N2").PadRight(15) + (RelFreq * 100).ToString("N2") + " %" + Environment.NewLine);
}
}
public Dog new_dataset()
{
// Large Dog breed Weight's is between 30 and 50kg
// And can be old max(usually) 12 years (used for discrete mean exercise's)
// We consider dogs only over 1 year old
Dog Dog = new Dog()
{
Breed = PossibleBreed[R.Next(0, PossibleBreed.Length)],
Weight = R.NextDouble() * 20 + 30,
Age = R.Next(1, 13)
};
return Dog;
}
private void BtnGo_Click_1(object sender, EventArgs e)
{
Timer.Start();
}
private void BtnStop_Click_1(object sender, EventArgs e)
{
this.Timer.Stop();
this.RTxtArOnMean.AppendText(Environment.NewLine + ListOfDog.Count + " occurrences" + Environment.NewLine);
}
private void BtnDistribution_Click_1(object sender, EventArgs e)
{
this.RTxtBreedDistribution.Clear();
print_Distribution(DicOfBreed);
print_Distribution(DicOfAgeDistributio);
}
private void BtnClear_Click(object sender, EventArgs e)
{
inizialized();
}
private void Timer_Tick(object sender, EventArgs e)
{
Dog Dog = new_dataset();
ListOfDog.Add(Dog);
print_Database(Dog);
online_mean(Dog);
print_OnArMean();
discrete_distribution(DicOfBreed, Dog.Breed);
discrete_distribution(DicOfAgeDistributio, Dog.Age);
}
}
}
Dog.cs
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace Application4_C
{
public class Dog
{
public string Breed;
public int Age;
// Public Height As Double
//public double Weight;
// Public Footage As Double
public override string ToString()
{
return Breed + " " + Age + " " + Weight;
}
}
}
Statistics Course 