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Why are the random numbers not properly scattered?

My task is to implement a random number generator using LCG algorithm.
The task is to generate 1000 axes (x,y) between [-1, 1] and print them on a pane.
If the point is inside the circle of radius 1.0, it will be printed as Red.
Otherwise, Blue.
I used the parameters suggested by Numerical Recipes suggested in this YouTube video. I am following the coding style used in this link.
I am using ZedGraph to show my plots.
Why are the random numbers not properly scattered on the pane?
And, where are the blue points?
Random number generator class:
class MyRandom
{
long m = 4294967296;// modulus
long a = 1664525; // multiplier
long c = 1013904223; // increment
public long nextRandomInt(long seed)
{
return (((a * seed + c) % m));
}
private double nextRandomDouble(long seed)
{
return (2 * (nextRandomInt(seed) / m)) - 1;
}
public double nextRandomDouble(double seed)
{
double new_seed = seed + 1.0;
new_seed = new_seed / 2.0;
new_seed = new_seed * m;
long long_seed = Convert.ToInt64(new_seed);
double new_s = nextRandomInt(long_seed);
new_s = new_s / m;
new_s = new_s * 2;
new_s = new_s - 1;
return new_s;
}
}
Output
Additional Source Code:
Driver Program:
class Program
{
static void Main(string[] args)
{
int N = 1000;
double radius = 1.0;
List<double> rx = new List<double>(); rx.Add(0.0);
List<double> ry = new List<double>(); ry.Add(1.0);
MyRandom r = new MyRandom();
for (int i = 0; i < N; i++)
{
double x = r.nextRandomDouble(rx[rx.Count - 1]);
double y = r.nextRandomDouble(ry[ry.Count - 1]);
rx.Add(x);
ry.Add(y);
}
PlotForm form = new PlotForm();
ZedGraphControl zgControl = form.ZedGrapgControl;
//// get a reference to the GraphPane
GraphPane gPane = zgControl.GraphPane;
gPane.Title.Text = "Random Numbers";
gPane.XAxis.Type = AxisType.Linear;
PointPairList insideCircleList = new PointPairList();
PointPairList outsideCircleList = new PointPairList();
for (int i = 0; i < N; i++)
{
double x = rx[i];
double y = ry[i];
if ((x * x + y * y) < radius)
{
insideCircleList.Add(x, y);
}
else
{
outsideCircleList.Add(x, y);
}
}
LineItem redCurve = gPane.AddCurve("Inside", insideCircleList, Color.Red, SymbolType.Circle);
redCurve.Line.IsVisible = false;
redCurve.Symbol.Fill.Type = FillType.Solid;
LineItem blueCurve = gPane.AddCurve("Outside", outsideCircleList, Color.Blue, SymbolType.Circle);
blueCurve.Line.IsVisible = false;
zgControl.AxisChange();
form.ShowDialog();
Console.ReadLine();
}
}
WinForms Code:
public partial class PlotForm : Form
{
public ZedGraph.ZedGraphControl ZedGrapgControl { get; set; }
public PlotForm()
{
InitializeComponent();
ZedGrapgControl = this.zgc;
}
}

seed should not be a parameter of the random function, but a field within a random class. You set it once, and it changes with every random call.
But to answer your question, you don't save new_seed anywhere. It has to be saved so that it can be used in the next random call. So, your seed is just incremented by one in every new call, and this makes the graph a straight line.

Try using regular C# Random class (https://learn.microsoft.com/en-us/dotnet/api/system.random?view=netframework-4.8), it doesn't seem worth it to "roll your own" in this case.
I think this will be good enough for your purpose.
The current implementation of the Random class is based on a modified
version of Donald E. Knuth's subtractive random number generator
algorithm.
https://learn.microsoft.com/en-us/dotnet/api/system.random?view=netframework-4.8#remarks
If that doesn't meet your requirements you can look into:
https://learn.microsoft.com/en-us/dotnet/api/system.security.cryptography.rngcryptoserviceprovider?view=netframework-4.8

lottery vector with for loop without duplicates [duplicate]

I've searched for a while and been struggling to find this, I'm trying to generate several random, unique numbers is C#. I'm using System.Random, and I'm using a DateTime.Now.Ticks seed:
public Random a = new Random(DateTime.Now.Ticks.GetHashCode());
private void NewNumber()
{
MyNumber = a.Next(0, 10);
}
I'm calling NewNumber() regularly, but the problem is I often get repeated numbers. Some people suggested because I was declaring the random every time I did it, it would not produce a random number, so I put the declaration outside my function. Any suggestions or better ways than using System.Random ? Thank you

I'm calling NewNumber() regularly, but the problem is I often get
repeated numbers.
Random.Next doesn't guarantee the number to be unique. Also your range is from 0 to 10 and chances are you will get duplicate values. May be you can setup a list of int and insert random numbers in the list after checking if it doesn't contain the duplicate. Something like:
public Random a = new Random(); // replace from new Random(DateTime.Now.Ticks.GetHashCode());
// Since similar code is done in default constructor internally
public List<int> randomList = new List<int>();
int MyNumber = 0;
private void NewNumber()
{
MyNumber = a.Next(0, 10);
if (!randomList.Contains(MyNumber))
randomList.Add(MyNumber);
}

You might try shuffling an array of possible ints if your range is only 0 through 9. This adds the benefit of avoiding any conflicts in the number generation.
var nums = Enumerable.Range(0, 10).ToArray();
var rnd = new Random();
// Shuffle the array
for (int i = 0;i < nums.Length;++i)
{
int randomIndex = rnd.Next(nums.Length);
int temp = nums[randomIndex];
nums[randomIndex] = nums[i];
nums[i] = temp;
}
// Now your array is randomized and you can simply print them in order
for (int i = 0;i < nums.Length;++i)
Console.WriteLine(nums[i]);

NOTE, I dont recommend this :).
Here's a "oneliner" as well:
var result = Enumerable.Range(0,9).OrderBy(g => Guid.NewGuid()).ToArray();

I'm posting a correct implementation of a shuffle algorithm, since the other one posted here doesn't produce a uniform shuffle.
As the other answer states, for small numbers of values to be randomized, you can simply fill an array with those values, shuffle the array, and then use however many of the values that you want.
The following is an implementation of the Fisher-Yates Shuffle (aka the Knuth Shuffle). (Read the "implementation errors" section of that link (search for "always selecting j from the entire range of valid array indices on every iteration") to see some discussion about what is wrong with the other implementation posted here.)
using System;
using System.Collections.Generic;
namespace ConsoleApplication2
{
static class Program
{
static void Main(string[] args)
{
Shuffler shuffler = new Shuffler();
List<int> list = new List<int>{ 1, 2, 3, 4, 5, 6, 7, 8, 9 };
shuffler.Shuffle(list);
foreach (int value in list)
{
Console.WriteLine(value);
}
}
}
/// <summary>Used to shuffle collections.</summary>
public class Shuffler
{
public Shuffler()
{
_rng = new Random();
}
/// <summary>Shuffles the specified array.</summary>
/// <typeparam name="T">The type of the array elements.</typeparam>
/// <param name="array">The array to shuffle.</param>
public void Shuffle<T>(IList<T> array)
{
for (int n = array.Count; n > 1; )
{
int k = _rng.Next(n);
--n;
T temp = array[n];
array[n] = array[k];
array[k] = temp;
}
}
private System.Random _rng;
}
}

This is a unity only answer:
Check this ready-to-use method: Give in a range & count of number you want to get.
public static int[] getUniqueRandomArray(int min, int max, int count) {
int[] result = new int[count];
List<int> numbersInOrder = new List<int>();
for (var x = min; x < max; x++) {
numbersInOrder.Add(x);
}
for (var x = 0; x < count; x++) {
var randomIndex = UnityEngine.Random.Range(0, numbersInOrder.Count);
result[x] = numbersInOrder[randomIndex];
numbersInOrder.RemoveAt(randomIndex);
}
return result;
}

Same as #Habib's answer, but as a function:
List<int> randomList = new List<int>();
int UniqueRandomInt(int min, int max)
{
var rand = new Random();
int myNumber;
do
{
myNumber = rand.Next(min, max);
} while (randomList.Contains(myNumber));
return myNumber;
}
If randomList is a class property, UniqueRandomInt will return unique integers in the context of the same instance of that class. If you want it to be unique globally, you will need to make randomList static.

Depending on what you are really after you can do something like this:
using System;
using System.Collections.Generic;
using System.Linq;
namespace SO14473321
{
class Program
{
static void Main()
{
UniqueRandom u = new UniqueRandom(Enumerable.Range(1,10));
for (int i = 0; i < 10; i++)
{
Console.Write("{0} ",u.Next());
}
}
}
class UniqueRandom
{
private readonly List<int> _currentList;
private readonly Random _random = new Random();
public UniqueRandom(IEnumerable<int> seed)
{
_currentList = new List<int>(seed);
}
public int Next()
{
if (_currentList.Count == 0)
{
throw new ApplicationException("No more numbers");
}
int i = _random.Next(_currentList.Count);
int result = _currentList[i];
_currentList.RemoveAt(i);
return result;
}
}
}

And here my version of finding N random unique numbers using HashSet.
Looks pretty simple, since HashSet can contain only different items.
It's interesting - would it be faster then using List or Shuffler?
using System;
using System.Collections.Generic;
namespace ConsoleApplication1
{
class RnDHash
{
static void Main()
{
HashSet<int> rndIndexes = new HashSet<int>();
Random rng = new Random();
int maxNumber;
Console.Write("Please input Max number: ");
maxNumber = int.Parse(Console.ReadLine());
int iter = 0;
while (rndIndexes.Count != maxNumber)
{
int index = rng.Next(maxNumber);
rndIndexes.Add(index);
iter++;
}
Console.WriteLine("Random numbers were found in {0} iterations: ", iter);
foreach (int num in rndIndexes)
{
Console.WriteLine(num);
}
Console.ReadKey();
}
}
}

I noted that the accepted answer keeps adding int to the list and keeps checking them with if (!randomList.Contains(MyNumber)) and I think this doesn't scale well, especially if you keep asking for new numbers.
I would do the opposite.
Generate the list at startup, linearly
Get a random index from the list
Remove the found int from the list
This would require a slightly bit more time at startup, but will scale much much better.
public class RandomIntGenerator
{
public Random a = new Random();
private List<int> _validNumbers;
private RandomIntGenerator(int desiredAmount, int start = 0)
{
_validNumbers = new List<int>();
for (int i = 0; i < desiredAmount; i++)
_validNumbers.Add(i + start);
}
private int GetRandomInt()
{
if (_validNumbers.Count == 0)
{
//you could throw an exception here
return -1;
}
else
{
var nextIndex = a.Next(0, _validNumbers.Count - 1);
var number = _validNumbers[nextIndex];
_validNumbers.RemoveAt(nextIndex);
return number;
}
}
}

It's may be a little bit late, but here is more suitable code, for example when you need to use loops:
List<int> genered = new List<int>();
Random rnd = new Random();
for(int x = 0; x < files.Length; x++)
{
int value = rnd.Next(0, files.Length - 1);
while (genered.Contains(value))
{
value = rnd.Next(0, files.Length - 1);
}
genered.Add(value);
returnFiles[x] = files[value];
}

with Functional way*
static Func<int> GetNextUniqueIntegerFunc(int min, int max)
{
var list = new List<int>();
var random = new Random();
int getNextValue()
{
while (true)
{
var random_number = random.Next(min, max);
if (!list.Contains(random_number))
{
list.Add(random_number);
return random_number;
}
}
}
return getNextValue;
}

unique random number from 0 to 9
int sum = 0;
int[] hue = new int[10];
for (int i = 0; i < 10; i++)
{
int m;
do
{
m = rand.Next(0, 10);
} while (hue.Contains(m) && sum != 45);
if (!hue.Contains(m))
{
hue[i] = m;
sum = sum + m;
}
}

You could also use a dataTable storing each random value, then simply perform the random method while != values in the dataColumn

randomNumber function return unqiue integer value between 0 to 100000
bool check[] = new bool[100001];
Random r = new Random();
public int randomNumber() {
int num = r.Next(0,100000);
while(check[num] == true) {
num = r.Next(0,100000);
}
check[num] = true;
return num;
}

hi here i posted one video ,and it explains how to generate unique random number
public List<int> random_generator(){
Random random = new Random();
List<int> random_container = new List<int>;
do{
int random_number = random.next(10);
if(!random_container.contains(random_number){
random_container.add(random_number)
}
}
while(random_container.count!=10);
return random_container;
}
here ,,, in random container you will get non repeated 10 numbers starts from 0 to 9(10 numbers) as random.. thank you........

You can use basic Random Functions of C#
Random ran = new Random();
int randomno = ran.Next(0,100);
you can now use the value in the randomno in anything you want but keep in mind that this will generate a random number between 0 and 100 Only and you can extend that to any figure.

Try this:
private void NewNumber()
{
Random a = new Random(Guid.newGuid().GetHashCode());
MyNumber = a.Next(0, 10);
}
Some Explnations:
Guid : base on here : Represents a globally unique identifier (GUID)
Guid.newGuid() produces a unique identifier like "936DA01F-9ABD-4d9d-80C7-02AF85C822A8"
and it will be unique in all over the universe base on here
Hash code here produce a unique integer from our unique identifier
so Guid.newGuid().GetHashCode() gives us a unique number and the random class will produce real random numbers throw this
Sample:
https://rextester.com/ODOXS63244
generated ten random numbers with this approach with result of:
-1541116401
7
-1936409663
3
-804754459
8
1403945863
3
1287118327
1
2112146189
1
1461188435
9
-752742620
4
-175247185
4
1666734552
7
we got two 1s next to each other, but the hash codes do not same.

How to efficiently calculate a moving Standard Deviation

Below you can see my C# method to calculate Bollinger Bands for each point (moving average, up band, down band).
As you can see this method uses 2 for loops to calculate the moving standard deviation using the moving average. It used to contain an additional loop to calculate the moving average over the last n periods. This one I could remove by adding the new point value to total_average at the beginning of the loop and removing the i - n point value at the end of the loop.
My question now is basically: Can I remove the remaining inner loop in a similar way I managed with the moving average?
public static void AddBollingerBands(SortedList<DateTime, Dictionary<string, double>> data, int period, int factor)
{
double total_average = 0;
for (int i = 0; i < data.Count(); i++)
{
total_average += data.Values[i]["close"];
if (i >= period - 1)
{
double total_bollinger = 0;
double average = total_average / period;
for (int x = i; x > (i - period); x--)
{
total_bollinger += Math.Pow(data.Values[x]["close"] - average, 2);
}
double stdev = Math.Sqrt(total_bollinger / period);
data.Values[i]["bollinger_average"] = average;
data.Values[i]["bollinger_top"] = average + factor * stdev;
data.Values[i]["bollinger_bottom"] = average - factor * stdev;
total_average -= data.Values[i - period + 1]["close"];
}
}
}

The problem with approaches that calculate the sum of squares is that it and the square of sums can get quite large, and the calculation of their difference may introduce a very large error, so let's think of something better. For why this is needed, see the Wikipedia article on Algorithms for computing variance and John Cook on Theoretical explanation for numerical results)
First, instead of calculating the stddev let's focus on the variance. Once we have the variance, stddev is just the square root of the variance.
Suppose the data are in an array called x; rolling an n-sized window by one can be thought of as removing the value of x[0] and adding the value of x[n]. Let's denote the averages of x[0]..x[n-1] and x[1]..x[n] by µ and µ’ respectively. The difference between the variances of x[0]..x[n-1] and x[1]..x[n] is, after canceling out some terms and applying (a²-b²) = (a+b)(a-b):
Var[x[1],..,x[n]] - Var[x[0],..,x[n-1]]
= (\sum_1^n x[i]² - n µ’²)/(n-1) - (\sum_0^{n-1} x[i]² - n µ²)/(n-1)
= (x[n]² - x[0]² - n(µ’² - µ²))/(n-1)
= (x[n]-µ’ + x[0]-µ)(x[n]-x[0])/(n-1)
Therefore the variance is perturbed by something that doesn't require you to maintain the sum of squares, which is better for numerical accuracy.
You can calculate the mean and variance once in the beginning with a proper algorithm (Welford's method). After that, every time you have to replace a value in the window x[0] by another x[n] you update the average and variance like this:
new_Avg = Avg + (x[n]-x[0])/n
new_Var = Var + (x[n]-new_Avg + x[0]-Avg)(x[n] - x[0])/(n-1)
new_StdDev = sqrt(new_Var)

The answer is yes, you can. In the mid-80's I developed just such an algorithm (probably not original) in FORTRAN for a process monitoring and control application. Unfortunately, that was over 25 years ago and I do not remember the exact formulas, but the technique was an extension of the one for moving averages, with second order calculations instead of just linear ones.
After looking at your code some, I am think that I can suss out how I did it back then. Notice how your inner loop is making a Sum of Squares?:
for (int x = i; x > (i - period); x--)
{
total_bollinger += Math.Pow(data.Values[x]["close"] - average, 2);
}
in much the same way that your average must have originally had a Sum of Values? The only two differences are the order (its power 2 instead of 1) and that you are subtracting the average each value before you square it. Now that might look inseparable, but in fact they can be separated:
SUM(i=1; n){ (v[i] - k)^2 }
is
SUM(i=1..n){v[i]^2 -2*v[i]*k + k^2}
which becomes
SUM(i=1..n){v[i]^2 -2*v[i]*k} + k^2*n
which is
SUM(i=1..n){v[i]^2} + SUM(i=1..n){-2*v[i]*k} + k^2*n
which is also
SUM(i=1..n){v[i]^2} + SUM(i=1..n){-2*v[i]}*k + k^2*n
Now the first term is just a Sum of Squares, you handle that in the same way that you do the sum of Values for the average. The last term (k^2*n) is just the average squared times the period. Since you divide the result by the period anyway, you can just add the new average squared without the extra loop.
Finally, in the second term (SUM(-2*v[i]) * k), since SUM(v[i]) = total = k*n you can then change it into this:
-2 * k * k * n
or just -2*k^2*n, which is -2 times the average squared, once the period (n) is divided out again. So the final combined formula is:
SUM(i=1..n){v[i]^2} - n*k^2
or
SUM(i=1..n){values[i]^2} - period*(average^2)
(be sure to check the validity of this, since I am deriving it off the top of my head)
And incorporating into your code should look something like this:
public static void AddBollingerBands(ref SortedList<DateTime, Dictionary<string, double>> data, int period, int factor)
{
double total_average = 0;
double total_squares = 0;
for (int i = 0; i < data.Count(); i++)
{
total_average += data.Values[i]["close"];
total_squares += Math.Pow(data.Values[i]["close"], 2);
if (i >= period - 1)
{
double total_bollinger = 0;
double average = total_average / period;
double stdev = Math.Sqrt((total_squares - Math.Pow(total_average,2)/period) / period);
data.Values[i]["bollinger_average"] = average;
data.Values[i]["bollinger_top"] = average + factor * stdev;
data.Values[i]["bollinger_bottom"] = average - factor * stdev;
total_average -= data.Values[i - period + 1]["close"];
total_squares -= Math.Pow(data.Values[i - period + 1]["close"], 2);
}
}
}

I've used commons-math (and contributed to that library!) for something very similar to this. It's open-source, porting to C# should be easy as store-bought pie (have you tried making a pie from scratch!?). Check it out: http://commons.apache.org/math/api-3.1.1/index.html. They have a StandardDeviation class. Go to town!

Most important information has already been given above --- but maybe this is still of general interest.
A tiny Java library to calculate moving average and standard deviation is available here:
https://github.com/tools4j/meanvar
The implementation is based on a variant of Welford's method mentioned above. Methods to remove and replace values have been derived that can be used for moving value windows.
Disclaimer: I am the author of the said library.

I just did it with Data From Binance Future API
Hope this helps:
using Newtonsoft.Json;
using System;
using System.Collections.Generic;
using System.Collections.ObjectModel;
using System.ComponentModel;
using System.Data;
using System.Drawing;
using System.IO;
using System.Linq;
using System.Net;
using System.Text;
using System.Threading.Tasks;
using System.Windows.Forms;
using static System.Windows.Forms.VisualStyles.VisualStyleElement;
namespace Trading_Bot_1
{
public class BOLL
{
private BollingerBandData graphdata = new BollingerBandData();
private List<TickerData> data = new List<TickerData>();
public BOLL(string url)
{
string js = getJsonFromUrl(url);
//dynamic data = JObject.Parse(js);
object[][] arrays = JsonConvert.DeserializeObject<object[][]>(js);
data = new List<TickerData>();
for (int i = 1; i < 400; i++)
{
data.Add(new TickerData
{
Date = DateTime.Now,
Open = Convert.ToDouble(arrays[arrays.Length - i][1]),
High = Convert.ToDouble(arrays[arrays.Length - i][2]),
Low = Convert.ToDouble(arrays[arrays.Length - i][3]),
Close = Convert.ToDouble(arrays[arrays.Length - i][4]),
Volume = Math.Round(Convert.ToDouble(arrays[arrays.Length - i][4]), 0),
AdjClose = Convert.ToDouble(arrays[arrays.Length - i][6])
});
}
graphdata.LowerBand.Add(1);
graphdata.LowerBand.Add(2);
graphdata.LowerBand.Add(3);
graphdata.LowerBand.Add(1);
graphdata.UpperBand.Add(1);
graphdata.UpperBand.Add(2);
graphdata.UpperBand.Add(3);
graphdata.UpperBand.Add(4);
graphdata.MovingAverageWindow.Add(10);
graphdata.MovingAverageWindow.Add(20);
graphdata.MovingAverageWindow.Add(40);
graphdata.MovingAverageWindow.Add(50);
graphdata.Length.Add(10);
graphdata.Length.Add(30);
graphdata.Length.Add(50);
graphdata.Length.Add(100);
// DataContext = graphdata;
}
public static string getJsonFromUrl(string url1)
{
var uri = String.Format(url1);
WebClient client = new WebClient();
client.UseDefaultCredentials = true;
var data = client.DownloadString(uri);
return data;
}
List<double> UpperBands = new List<double>();
List<double> LowerBands = new List<double>();
public List<List<double>> GetBOLLDATA(int decPlaces)
{
int datalength = graphdata.SelectedMovingAverage + graphdata.SelectedLength;
string bands = "";
for (int i = graphdata.SelectedLength - 1; i >= 0; i--)
{
List<double> price = new List<double>();
for (int j = 0; j < graphdata.SelectedMovingAverage; j++)
{
price.Add(data[i + j].Close);
}
double sma = CalculateAverage(price.ToArray());
double sigma = CalculateSTDV(price.ToArray());
double lower = sma - (graphdata.SelectedLowerBand * sigma);
double upper = sma + (graphdata.SelectedUpperBand * sigma);
UpperBands.Add(Math.Round( upper,decPlaces));
LowerBands.Add(Math.Round(lower, decPlaces));
bands += (Math.Round(upper, decPlaces) + " / " + Math.Round(lower, decPlaces)) + Environment.NewLine;
// graphdata.ChartData.Add(new ChartData() { SMA = sma, LowerBandData = lower, UpperBandData = upper });
}
//MessageBox.Show(bands);
return new List<List<double>> { UpperBands, LowerBands };
}
public double[] GetBOLLDATA(int decPlaces, string a)
{
List<double> price = new List<double>();
for (int j = 0; j < graphdata.SelectedMovingAverage; j++)
{
price.Add(data[j].Close);
}
double sma = CalculateAverage(price.ToArray());
double sigma = CalculateSTDV(price.ToArray());
double lower = sma - (graphdata.SelectedLowerBand * sigma);
double upper = sma + (graphdata.SelectedUpperBand * sigma);
return new double[] { Math.Round(upper, decPlaces), Math.Round(lower, decPlaces) };
}
private double CalculateAverage(double[] data)
{
int count = data.Length;
double sum = 0;
for (int i = 0; i < count; i++)
{
sum += data[i];
}
return sum / count;
}
private double CalculateVariance(double[] data)
{
int count = data.Length;
double sum = 0;
double avg = CalculateAverage(data);
for (int i = 0; i < count; i++)
{
sum += (data[i] - avg) * (data[i] - avg);
}
return sum / (count - 1);
}
private double CalculateSTDV(double[] data)
{
double var = CalculateVariance(data);
return Math.Sqrt(var);
}
}
public class ChartData
{
public double UpperBandData
{ get; set; }
public double LowerBandData
{ get; set; }
public double SMA
{ get; set; }
}
public class BollingerBandData : INotifyPropertyChanged
{
private ObservableCollection<int> _lowerBand;
private ObservableCollection<int> _upperBand;
private ObservableCollection<int> _movingAvg;
private ObservableCollection<int> _length;
private ObservableCollection<ChartData> _chartData;
private int _selectedLowerBand;
private int _selectedUpperBand;
private int _selectedMovingAvg;
private int _selectedLength;
public BollingerBandData()
{
_lowerBand = new ObservableCollection<int>();
_upperBand = new ObservableCollection<int>();
_movingAvg = new ObservableCollection<int>();
_length = new ObservableCollection<int>();
_chartData = new ObservableCollection<ChartData>();
SelectedLowerBand = 2;
SelectedUpperBand = 2;
SelectedMovingAverage = 20;
SelectedLength = 5;
}
public ObservableCollection<ChartData> ChartData
{
get
{
return _chartData;
}
set
{
_chartData = value;
RaisePropertyChanged("ChartData");
}
}
public ObservableCollection<int> LowerBand
{
get
{
return _lowerBand;
}
set
{
_lowerBand = value;
RaisePropertyChanged("LowerBand");
}
}
public ObservableCollection<int> UpperBand
{
get
{
return _upperBand;
}
set
{
_upperBand = value;
RaisePropertyChanged("UpperBand");
}
}
public ObservableCollection<int> MovingAverageWindow
{
get
{
return _movingAvg;
}
set
{
_movingAvg = value;
RaisePropertyChanged("MovingAverageWindow");
}
}
public ObservableCollection<int> Length
{
get
{
return _length;
}
set
{
_length = value;
RaisePropertyChanged("Length");
}
}
public int SelectedLowerBand
{
get
{
return _selectedLowerBand;
}
set
{
_selectedLowerBand = value;
RaisePropertyChanged("SelectedLowerBand");
}
}
public int SelectedUpperBand
{
get
{
return _selectedUpperBand;
}
set
{
_selectedUpperBand = value;
RaisePropertyChanged("SelectedUpperBand");
}
}
public int SelectedMovingAverage
{
get
{
return _selectedMovingAvg;
}
set
{
_selectedMovingAvg = value;
RaisePropertyChanged("SelectedMovingAverage");
}
}
public int SelectedLength
{
get
{
return _selectedLength;
}
set
{
_selectedLength = value;
RaisePropertyChanged("SelectedLength");
}
}
public event PropertyChangedEventHandler PropertyChanged;
private void RaisePropertyChanged(string propertyName)
{
PropertyChangedEventHandler handler = this.PropertyChanged;
if (handler != null)
{
handler(this, new PropertyChangedEventArgs(propertyName));
}
}
}
public class TickerData
{
public DateTime Date
{ get; set; }
public double Open
{ get; set; }
public double High
{ get; set; }
public double Low
{ get; set; }
public double Close
{ get; set; }
public double Volume
{ get; set; }
public double AdjClose
{ get; set; }
}
}

C#: Dice Permutation without Repetition

How can I change my C# code below to list all possible permutations without repetitions? For example: The result of 2 dice rolls would produce 1,1,2 so that means 2,1,1 should not appear.
Below is my code:
string[] Permutate(int input)
{
string[] dice;
int numberOfDice = input;
const int diceFace = 6;
dice = new string[(int)Math.Pow(diceFace, numberOfDice)];
int indexNumber = (int)Math.Pow(diceFace, numberOfDice);
int range = (int)Math.Pow(diceFace, numberOfDice) / 6;
int diceNumber = 1;
int counter = 0;
for (int i = 1; i <= indexNumber; i++)
{
if (range != 0)
{
dice[i - 1] += diceNumber + " ";
counter++;
if (counter == range)
{
counter = 0;
diceNumber++;
}
if (i == indexNumber)
{
range /= 6;
i = 0;
}
if (diceNumber == 7)
{
diceNumber = 1;
}
}
Thread.Sleep(1);
}
return dice;
}

The simplest possible way I could think of:
List<string> dices = new List<string>();
for (int i = 1; i <= 6; i++)
{
for (int j = i; j <= 6; j++)
{
for (int k = j; k <= 6; k++)
{
dices.Add(string.Format("{0} {1} {2}", i, j, k));
}
}
}

I have written a class to handle common functions for working with the binomial coefficient, which is the type of problem that your problem falls under. It performs the following tasks:
Outputs all the K-indexes in a nice format for any N choose K to a file. The K-indexes can be substituted with more descriptive strings or letters. This method makes solving this type of problem quite trivial.
Converts the K-indexes to the proper index of an entry in the sorted binomial coefficient table. This technique is much faster than older published techniques that rely on iteration. It does this by using a mathematical property inherent in Pascal's Triangle. My paper talks about this. I believe I am the first to discover and publish this technique, but I could be wrong.
Converts the index in a sorted binomial coefficient table to the corresponding K-indexes.
Uses Mark Dominus method to calculate the binomial coefficient, which is much less likely to overflow and works with larger numbers.
The class is written in .NET C# and provides a way to manage the objects related to the problem (if any) by using a generic list. The constructor of this class takes a bool value called InitTable that when true will create a generic list to hold the objects to be managed. If this value is false, then it will not create the table. The table does not need to be created in order to perform the 4 above methods. Accessor methods are provided to access the table.
There is an associated test class which shows how to use the class and its methods. It has been extensively tested with 2 cases and there are no known bugs.
To read about this class and download the code, see Tablizing The Binomial Coeffieicent.

I'm bad at math as well, this may or may not be helpful...
Program.cs
namespace Permutation
{
using System;
using System.Collections.Generic;
class Program
{
static void Main(string[] args)
{
Console.WriteLine("Generating list.");
var dice = new List<ThreeDice>();
for (int x = 1; x <= 6; x++)
{
for (int y = 1; y <= 6; y++)
{
for (int z = 1; z <= 6; z++)
{
var die = new ThreeDice(x, y, z);
if (dice.Contains(die))
{
Console.WriteLine(die + " already exists.");
}
else
{
dice.Add(die);
}
}
}
}
Console.WriteLine(dice.Count + " permutations generated.");
foreach (var die in dice)
{
Console.WriteLine(die);
}
Console.ReadKey();
}
}
}
ThreeDice.cs
namespace Permutation
{
using System;
using System.Collections.Generic;
public class ThreeDice : IEquatable<ThreeDice>
{
public ThreeDice(int dice1, int dice2, int dice3)
{
this.Dice = new int[3];
this.Dice[0] = dice1;
this.Dice[1] = dice2;
this.Dice[2] = dice3;
}
public int[] Dice { get; private set; }
// IEquatable implements this method. List.Contains() will use this method to see if there's a match.
public bool Equals(ThreeDice other)
{
// Get the current dice values into a list.
var currentDice = new List<int>(this.Dice);
// Check to see if the same values exist by removing them one by one.
foreach (int die in other.Dice)
{
currentDice.Remove(die);
}
// If the list is empty, we have a match.
return currentDice.Count == 0;
}
public override string ToString()
{
return "<" + this.Dice[0] + "," + this.Dice[1] + "," + this.Dice[2] + ">";
}
}
}
Good luck.

The important part of the question is that you want distinct sets (regardless of order). So for example, a dice roll of [1, 2, 1] is equal to a dice roll of [1, 1, 2].
I'm sure there are a number of ways to skin this cat, but the first thought that comes to mind is to create a EqualityComparer which will compare the list of dice in the way you want, and then use LINQ with the Distinct() method.
Here is the EqualityComparer, which takes 2 List<int> and says they are equal if the elements are all equal (regardless of order):
private class ListComparer : EqualityComparer<List<int>>
{
public override bool Equals(List<int> x, List<int> y)
{
if (x.Count != y.Count)
return false;
x.Sort();
y.Sort();
for (int i = 0; i < x.Count; i++)
{
if (x[i] != y[i])
return false;
}
return true;
}
public override int GetHashCode(List<int> list)
{
int hc = 0;
foreach (var i in list)
hc ^= i;
return hc;
}
}
And here is the code that uses it. I'm using LINQ to build up the list of all combinations... you could also do this with nested for loops but I like this better for some reason:
public static void Main()
{
var values = new[] { 1,2,3,4,5,6 };
var allCombos = from x in values
from y in values
from z in values
select new List<int>{ x, y, z };
var distinctCombos = allCombos.Distinct(new ListComparer());
Console.WriteLine("#All combos: {0}", allCombos.Count());
Console.WriteLine("#Distinct combos: {0}", distinctCombos.Count());
foreach (var combo in distinctCombos)
Console.WriteLine("{0},{1},{2}", combo[0], combo[1], combo[2]);
}
Hope that helps!

Here is generic c# version using recursion (basically the recursive method takes number of dices or number of times the dice has been tossed) and returns all the combinations strings ( for ex, for '3' as per the question - there will be 56 such combinations).
public string[] GetDiceCombinations(int noOfDicesOrnoOfTossesOfDice)
{
noOfDicesOrnoOfTossesOfDice.Throw("noOfDicesOrnoOfTossesOfDice",
n => n <= 0);
List<string> values = new List<string>();
this.GetDiceCombinations_Recursive(noOfDicesOrnoOfTossesOfDice, 1, "",
values);
return values.ToArray();
}
private void GetDiceCombinations_Recursive(int size, int index, string currentValue,
List<string> values)
{
if (currentValue.Length == size)
{
values.Add(currentValue);
return;
}
for (int i = index; i <= 6; i++)
{
this.GetDiceCombinations_Recursive(size, i, currentValue + i, values);
}
}
Below are corresponding tests...
[TestMethod]
public void Dice_Tests()
{
int[] cOut = new int[] { 6, 21, 56, 126 };
for(int i = 1; i<=4; i++)
{
var c = this.GetDiceCombinations(i);
Assert.AreEqual(cOut[i - 1], c.Length);
}
}

Looking for a Histogram Binning algorithm for decimal data

I need to generate bins for the purposes of calculating a histogram. Language is C#. Basically I need to take in an array of decimal numbers and generate a histogram plot out of those.
Haven't been able to find a decent library to do this outright so now I'm just looking for either a library or an algorithm to help me do the binning of the data.
So...
Are there any C# libraries out there that will take in an array of decimal data and output a binned histogram?
Is there generic algorithm for building the bins to be used in generated a histogram?

Here is a simple bucket function I use. Sadly, .NET generics doesn't support a numerical type contraint so you will have to implement a different version of the following function for decimal, int, double, etc.
public static List<int> Bucketize(this IEnumerable<decimal> source, int totalBuckets)
{
var min = source.Min();
var max = source.Max();
var buckets = new List<int>();
var bucketSize = (max - min) / totalBuckets;
foreach (var value in source)
{
int bucketIndex = 0;
if (bucketSize > 0.0)
{
bucketIndex = (int)((value - min) / bucketSize);
if (bucketIndex == totalBuckets)
{
bucketIndex--;
}
}
buckets[bucketIndex]++;
}
return buckets;
}

I got odd results using #JakePearson accepted answer. It has to do with an edge case.
Here is the code I used to test his method. I changed the extension method ever so slightly, returning an int[] and accepting double instead of decimal.
public partial class Form1 : Form
{
public Form1()
{
InitializeComponent();
Random rand = new Random(1325165);
int maxValue = 100;
int numberOfBuckets = 100;
List<double> values = new List<double>();
for (int i = 0; i < 10000000; i++)
{
double value = rand.NextDouble() * (maxValue+1);
values.Add(value);
}
int[] bins = values.Bucketize(numberOfBuckets);
PointPairList points = new PointPairList();
for (int i = 0; i < numberOfBuckets; i++)
{
points.Add(i, bins[i]);
}
zedGraphControl1.GraphPane.AddBar("Random Points", points,Color.Black);
zedGraphControl1.GraphPane.YAxis.Title.Text = "Count";
zedGraphControl1.GraphPane.XAxis.Title.Text = "Value";
zedGraphControl1.AxisChange();
zedGraphControl1.Refresh();
}
}
public static class Extension
{
public static int[] Bucketize(this IEnumerable<double> source, int totalBuckets)
{
var min = source.Min();
var max = source.Max();
var buckets = new int[totalBuckets];
var bucketSize = (max - min) / totalBuckets;
foreach (var value in source)
{
int bucketIndex = 0;
if (bucketSize > 0.0)
{
bucketIndex = (int)((value - min) / bucketSize);
if (bucketIndex == totalBuckets)
{
bucketIndex--;
}
}
buckets[bucketIndex]++;
}
return buckets;
}
}
Everything works well when using 10,000,000 random double values between 0 and 100 (exclusive). Each bucket has roughly the same number of values, which makes sense given that Random returns a normal distribution.
But when I changed the value generation line from
double value = rand.NextDouble() * (maxValue+1);
to
double value = rand.Next(0, maxValue + 1);
and you get the following result, which double counts the last bucket.
It appears that when a value is same as one of the boundaries of a bucket, the code as it is written puts the value in the incorrect bucket. This artifact doesn't appear to happen with random double values as the chance of a random number being equal to a boundary of a bucket is rare and wouldn't be obvious.
The way I corrected this is to define what side of the bucket boundary is inclusive vs. exclusive.
Think of
0< x <=1 1< x <=2 ... 99< x <=100
vs.
0<= x <1 1<= x <2 ... 99<= x <100
You cannot have both boundaries inclusive, as the method wouldn't know which bucket to put it in if you have a value that is exactly equal to a boundary.
public enum BucketizeDirectionEnum
{
LowerBoundInclusive,
UpperBoundInclusive
}
public static int[] Bucketize(this IList<double> source, int totalBuckets, BucketizeDirectionEnum inclusivity = BucketizeDirectionEnum.UpperBoundInclusive)
{
var min = source.Min();
var max = source.Max();
var buckets = new int[totalBuckets];
var bucketSize = (max - min) / totalBuckets;
if (inclusivity == BucketizeDirectionEnum.LowerBoundInclusive)
{
foreach (var value in source)
{
int bucketIndex = (int)((value - min) / bucketSize);
if (bucketIndex == totalBuckets)
continue;
buckets[bucketIndex]++;
}
}
else
{
foreach (var value in source)
{
int bucketIndex = (int)Math.Ceiling((value - min) / bucketSize) - 1;
if (bucketIndex < 0)
continue;
buckets[bucketIndex]++;
}
}
return buckets;
}
The only issue now is if the input dataset has a lot of min and max values, the binning method will exclude many of those values and the resulting graph will misrepresent the dataset.