Develop Reference

Store data in a for loop

I have a for loop and what I'd like to do is to store the data of every for cycle in C#.
At the moment it only stores the datas of the last iteration.
Attached my code. Thanks a lot!
for (int i = 0; i < n; i++)
{
if (i <= k - p - 1)
{
alpha[i] = 1;
NewCPVector[i] = CPVector[i];
}
if (k - p <= i && i <= k-1)
{
alpha[i] = (FinalKnotsVector[k] - Initialknots[i]) / (Initialknots[i + p + 1] - Initialknots[i]);
NewCPVector[i] = alpha[i] * CPVector[i] + (1 - alpha[i]) * CPVector[i - 1];
}
if (i >= k)
{
alpha[i] = 0;
NewCPVector[i] = CPVector[i - 1];
}
}

I'm going to assume that your arrays hold double values. (However it could be other types, like float or decimal) you just have to specify that type in the declaration of the list
You could save the data in a List this way:
List<double> data = new List<double>();
for (int i = 0; i < n; i++)
{
if (i <= k - p - 1)
{
alpha[i] = 1;
data.Add(NewCPVector[i] = CPVector[i]);
}
if (k - p <= i && i <= k-1)
{
alpha[i] = (FinalKnotsVector[k] - Initialknots[i]) / (Initialknots[i + p + 1] - Initialknots[i]);
data.Add(alpha[i] * CPVector[i] + (1 - alpha[i]) * CPVector[i - 1]);
}
if (i >= k)
{
alpha[i] = 0;
data.Add(CPVector[i - 1]);
}
}

Loop incrementing operations for sugarcane replication

I'm creating an algorithm to calculate area for sugarcane replication.
I have an initial planted area of 5 hectares. These 5 hectares will be cut off when fully grown and then cloned in the proportion of 1:7 So my second area will have 35 Hectares (5*7)
The next areas will have a decreased propotion, because it gets lower at every cut.
So the third area will be (5*6) + (35*7)
forth area: (5*5) + (35*6) + (245 * 7) and so on. The user will input number for iteration and proportions to multiply.
doing by hand it would something like this:
area[0] = initialArea;
area[1] = area[0] * proportion[0]; // = 35
area[2] = area[0] * proportion[1] + area[1] * proportion[0];
area[3] = (area[0] * proportion[2]) + (area[1] * proportion[1]) + (area[2] * proportion[0]);
area[4] = (area[0] * proportion[3]) + (area[1] * proportion[2]) + (area[2] * proportion[1]) + (area[3] * proportion[0]);
area[5] = (area[0] * proportion[4]) + (area[1] * proportion[3]) + (area[2] * proportion[2]) + (area[3] * proportion[1]) + (area[4] * proportion[0]);
Is there a way I can put this inside a loop?

Yes, you can make that into a for-loop.
The loop for each iteration is:
for (int n = 0; n < iteration; n++)
{
area[iteration] += area[n] * proportion[iteration - n - 1];
}
which you can wrap with the number of iterations:
for (int i = 1; i <= iterations; i++)
{
for (int n = 0; n < i; n++)
{
area[i] += area[n] * proportion[i - n - 1];
}
}
you will have to intialize with area[0] = intialArea. With input:
Console.Write("Enter inital area: ");
double initialArea = double.Parse(Console.ReadLine());
Console.Write("Enter proportions, separate each proportion with spaces: ");
string input = Console.ReadLine();
double[] proportion = input.Split(' ').Select(x => double.Parse(x)).ToArray();
int iterations = proportion.Length;
Console.WriteLine($"Using {iterations} iterations");
double[] area = new double[proportion.Length + 1];
area[0] = initialArea;
for (int i = 1; i <= iterations; i++)
{
Console.Write($"Iteration = {i}: ");
for (int n = 0; n < i; n++)
{
area[i] += area[n] * proportion[i - n - 1];
Console.Write($"area[{n}] = {area[n]} ");
}
Console.WriteLine();
}
Console.ReadLine();

Converting a one minute tick data to a five minute OHLC without pandas C#

I am getting once minute data (OHLC) from a data feed (lmax) and I want to re-sample this to a five minute data and ten, fifteen and thirty later on.
I'm using the following logic:
Open=first value of every 5 candles (1st candle open)
High=Highest value of 5 candle
Low=Lowest value of 5 candles
Close=Close of the last candle (5th candle)
Is this the correct way to re-sample data? I feel that this is logical but for some reason there are visible differences between the data feed on their site and my code. I feel that this is the case because I re-sampled it wrong; if i was using Python, I could refer to pandas but not in C# (to my knowledge).
This is the function which resamples the data:
private static List<List<double>> normalize_candles(List<List<double>> indexed_data, int n)
{
double open = 0;
double high = 0;
double low = 5;
double close = 0;
int trunc = 0;
if (indexed_data.Count() % n != 0)
{
trunc = indexed_data.Count() % n;
for (int i = 0; i < trunc; i++)
{
indexed_data.RemoveAt(indexed_data.Count() - 1);
}
}
{
}
List<List<double>> normal_candle_data = new List<List<double>>();
for (int i = 0; i < indexed_data.Count() / n; i++)
{
high = 0;
low = 100;
open = indexed_data[i * n][0];
close = indexed_data[(i * n) + (n - 1)][3];
for (int j = 0; j < n; j++)
{
if (high < indexed_data[(i * n) + j][1])
{
high = indexed_data[(i * n) + j][1];
}
if (low > indexed_data[(i * n) + j][2])
{
low = indexed_data[(i * n) + j][2];
}
}
normal_candle_data.Add(new List<double>());
normal_candle_data[i].Add(open);
normal_candle_data[i].Add(high);
normal_candle_data[i].Add(low);
normal_candle_data[i].Add(close);
}
Console.WriteLine("\n\n");
foreach (var obj in normal_candle_data)
{
Console.WriteLine(obj[0] + "," + obj[1] + "," + obj[2] + "," + obj[3]);
}
Console.WriteLine("size of orignal data is : " + indexed_data.Count() + "\nSize of normalized data is : " + normal_candle_data.Count());
return normal_candle_data;
}

Even number's sum from an integer

How to get the even number' sum from an integer input.
var intInput = 10;
Now i want the even' sum. In this case = 2+4+6+8+10 = 30
var evenCount = 0;
if (i % 2==0)
{
evenCount = evenCount + i;
}
How to achieve this?

var evenCount = (intInput / 2) * (intInput / 2 + 1);
This is just twice the sum of all the integers from zero to half the specified number.
2+4+6+8+10 = 2 (1+2+3+4+5)

How about this?
var sum = Enumerable.Range(1,10).Where(x=> x%2==0).Sum();

int intInput=10;
var evenCount = 0;
for (int i=1;i<=intInput;i++)
{
if (i % 2==0)
{
evenCount = evenCount + i;
}
}

Try
var intInput =10;
var evenValueSum = 0;
for(int i=intInput ;i>0;i--)
{
if(i %2 ==0)
{
evenValueSum += i;
}
}

int end = inputNum / 2;
int sum = 0;
for(int i = 1; i <= end; i++)
sum += i * 2;

int evenCount = 0;
int countFrom = 1;
int countTo = 10;
for (int i = countFrom; i <= countTo; i++) {
if (i % 2 == 0) {
evenCount += i
}
}

Two arrays alignment into one array [duplicate]

Having two arrays of double values, I want to compute correlation coefficient (single double value, just like the CORREL function in MS Excel). Is there some simple one-line solution in C#?
I already discovered math lib called Meta Numerics. According to this SO question, it should do the job. Here is docs for Meta Numerics correlation method, which I don't get.
Could pls somebody provide me with simple code snippet or example how to use the library?
Note: At the end, I was forced to use one of custom implementations.
But if someone reading this question knows good, well documented C#
math library/framework to do this, please don't hesitate and post a link in
answer.

You can have the values in separate lists at the same index and use a simple Zip.
var fitResult = new FitResult();
var values1 = new List<int>();
var values2 = new List<int>();
var correls = values1.Zip(values2, (v1, v2) =>
fitResult.CorrelationCoefficient(v1, v2));
A second way is to write your own custom implementation (mine isn't optimized for speed):
public double ComputeCoeff(double[] values1, double[] values2)
{
if(values1.Length != values2.Length)
throw new ArgumentException("values must be the same length");
var avg1 = values1.Average();
var avg2 = values2.Average();
var sum1 = values1.Zip(values2, (x1, y1) => (x1 - avg1) * (y1 - avg2)).Sum();
var sumSqr1 = values1.Sum(x => Math.Pow((x - avg1), 2.0));
var sumSqr2 = values2.Sum(y => Math.Pow((y - avg2), 2.0));
var result = sum1 / Math.Sqrt(sumSqr1 * sumSqr2);
return result;
}
Usage:
var values1 = new List<double> { 3, 2, 4, 5 ,6 };
var values2 = new List<double> { 9, 7, 12 ,15, 17 };
var result = ComputeCoeff(values1.ToArray(), values2.ToArray());
// 0.997054485501581
Debug.Assert(result.ToString("F6") == "0.997054");
Another way is to use the Excel function directly:
var values1 = new List<double> { 3, 2, 4, 5 ,6 };
var values2 = new List<double> { 9, 7, 12 ,15, 17 };
// Make sure to add a reference to Microsoft.Office.Interop.Excel.dll
// and use the namespace
var application = new Application();
var worksheetFunction = application.WorksheetFunction;
var result = worksheetFunction.Correl(values1.ToArray(), values2.ToArray());
Console.Write(result); // 0.997054485501581

Math.NET Numerics is a well-documented math library that contains a Correlation class. It calculates Pearson and Spearman ranked correlations: http://numerics.mathdotnet.com/api/MathNet.Numerics.Statistics/Correlation.htm
The library is available under the very liberal MIT/X11 license. Using it to calculate a correlation coefficient is as easy as follows:
using MathNet.Numerics.Statistics;
...
correlation = Correlation.Pearson(arrayOfValues1, arrayOfValues2);
Good luck!

In order to calculate Pearson product-moment correlation coefficient
http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient
You can use this simple code:
public static Double Correlation(Double[] Xs, Double[] Ys) {
Double sumX = 0;
Double sumX2 = 0;
Double sumY = 0;
Double sumY2 = 0;
Double sumXY = 0;
int n = Xs.Length < Ys.Length ? Xs.Length : Ys.Length;
for (int i = 0; i < n; ++i) {
Double x = Xs[i];
Double y = Ys[i];
sumX += x;
sumX2 += x * x;
sumY += y;
sumY2 += y * y;
sumXY += x * y;
}
Double stdX = Math.Sqrt(sumX2 / n - sumX * sumX / n / n);
Double stdY = Math.Sqrt(sumY2 / n - sumY * sumY / n / n);
Double covariance = (sumXY / n - sumX * sumY / n / n);
return covariance / stdX / stdY;
}

If you don't want to use a third party library, you can use the method from this post (posting code here for backup).
public double Correlation(double[] array1, double[] array2)
{
double[] array_xy = new double[array1.Length];
double[] array_xp2 = new double[array1.Length];
double[] array_yp2 = new double[array1.Length];
for (int i = 0; i < array1.Length; i++)
array_xy[i] = array1[i] * array2[i];
for (int i = 0; i < array1.Length; i++)
array_xp2[i] = Math.Pow(array1[i], 2.0);
for (int i = 0; i < array1.Length; i++)
array_yp2[i] = Math.Pow(array2[i], 2.0);
double sum_x = 0;
double sum_y = 0;
foreach (double n in array1)
sum_x += n;
foreach (double n in array2)
sum_y += n;
double sum_xy = 0;
foreach (double n in array_xy)
sum_xy += n;
double sum_xpow2 = 0;
foreach (double n in array_xp2)
sum_xpow2 += n;
double sum_ypow2 = 0;
foreach (double n in array_yp2)
sum_ypow2 += n;
double Ex2 = Math.Pow(sum_x, 2.00);
double Ey2 = Math.Pow(sum_y, 2.00);
return (array1.Length * sum_xy - sum_x * sum_y) /
Math.Sqrt((array1.Length * sum_xpow2 - Ex2) * (array1.Length * sum_ypow2 - Ey2));
}

In my tests, both #Dmitry Bychenko's and #keyboardP's code postings above resulted in generally the same correlations as Microsoft Excel over a handful of manual tests I did, and did not need any external libraries.
e.g. Running this once (data for this run listed at the bottom):
#Dmitry Bychenko: -0.00418479432051121
#keyboardP:______-0.00418479432051131
MS Excel:_________-0.004184794
Here is a test harness:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
namespace TestCorrel {
class Program {
static void Main(string[] args) {
Random rand = new Random(DateTime.Now.Millisecond);
List<double> x = new List<double>();
List<double> y = new List<double>();
for (int i = 0; i < 100; i++) {
x.Add(rand.Next(1000) * rand.NextDouble());
y.Add(rand.Next(1000) * rand.NextDouble());
Console.WriteLine(x[i] + "," + y[i]);
}
Console.WriteLine("Correl1: " + Correl1(x, y));
Console.WriteLine("Correl2: " + Correl2(x, y));
}
public static double Correl1(List<double> x, List<double> y) {
//https://stackoverflow.com/questions/17447817/correlation-of-two-arrays-in-c-sharp
if (x.Count != y.Count)
return (double.NaN); //throw new ArgumentException("values must be the same length");
double sumX = 0;
double sumX2 = 0;
double sumY = 0;
double sumY2 = 0;
double sumXY = 0;
int n = x.Count < y.Count ? x.Count : y.Count;
for (int i = 0; i < n; ++i) {
Double xval = x[i];
Double yval = y[i];
sumX += xval;
sumX2 += xval * xval;
sumY += yval;
sumY2 += yval * yval;
sumXY += xval * yval;
}
Double stdX = Math.Sqrt(sumX2 / n - sumX * sumX / n / n);
Double stdY = Math.Sqrt(sumY2 / n - sumY * sumY / n / n);
Double covariance = (sumXY / n - sumX * sumY / n / n);
return covariance / stdX / stdY;
}
public static double Correl2(List<double> x, List<double> y) {
double[] array_xy = new double[x.Count];
double[] array_xp2 = new double[x.Count];
double[] array_yp2 = new double[x.Count];
for (int i = 0; i < x.Count; i++)
array_xy[i] = x[i] * y[i];
for (int i = 0; i < x.Count; i++)
array_xp2[i] = Math.Pow(x[i], 2.0);
for (int i = 0; i < x.Count; i++)
array_yp2[i] = Math.Pow(y[i], 2.0);
double sum_x = 0;
double sum_y = 0;
foreach (double n in x)
sum_x += n;
foreach (double n in y)
sum_y += n;
double sum_xy = 0;
foreach (double n in array_xy)
sum_xy += n;
double sum_xpow2 = 0;
foreach (double n in array_xp2)
sum_xpow2 += n;
double sum_ypow2 = 0;
foreach (double n in array_yp2)
sum_ypow2 += n;
double Ex2 = Math.Pow(sum_x, 2.00);
double Ey2 = Math.Pow(sum_y, 2.00);
double Correl =
(x.Count * sum_xy - sum_x * sum_y) /
Math.Sqrt((x.Count * sum_xpow2 - Ex2) * (x.Count * sum_ypow2 - Ey2));
return (Correl);
}
}
}
Data for the example numbers above:
287.688269702572,225.610842817282
618.9313498167,177.955550192835
25.7778882802361,27.6549569366756
140.847984766051,714.618547504125
438.618761728806,533.48764902702
481.347431274758,214.381256273194
21.6406916848573,393.559209519792
135.30397563209,158.419851317732
334.314685154853,814.275162949821
764.614904770914,50.1435267264692
42.8179292282173,47.8631582287434
237.216836650491,370.488416981179
388.849658539449,134.961087643151
305.903013161804,441.926902444068
10.6625048679591,369.567569480076
36.9316453891488,24.8947204607049
2.10067253471383,491.941975629861
7.94887068492774,573.037801189831
341.738006353722,653.497146697015
98.8424873439793,475.215988045193
272.248712629196,36.1088809138671
122.336823399801,169.158256422336
9.32281673202422,631.076001565473
201.118425176068,803.724831627554
415.514343714115,64.248651454341
227.791637123,230.512133914284
25.3438658925443,396.854282886188
596.238994411304,72.543763144195
230.239735877253,933.983901697669
796.060099040186,689.952468971234
9.30882684202344,269.22063744125
16.5005430148451,8.96549091859045
536.324005148524,358.829873788557
519.694526420764,17.3212184707267
552.628357889423,12.5541588051962
210.516099897454,388.57537739937
141.341571405689,268.082028986924
503.880356335491,753.447006912645
515.494990213539,444.451280259737
973.8670776076,168.922799013985
85.7111146094795,36.3784999169309
37.2147129193017,108.040356312432
504.590177939548,50.3934166889607
482.821039277511,888.984586256083
5.52549206350255,156.717087003271
405.833169031345,394.099059180868
459.249365587835,11.68776424494
429.421127440604,314.216759666901
126.908422469584,331.907062556551
62.1416232716952,3.19765723645578
4.16058817699579,604.04046284223
484.262182311277,220.177370167886
58.6774453314382,339.09660232677
463.482149892246,199.181594849183
344.128297473829,268.531428258182
0.883430369609702,209.346384477963
77.9462970131758,255.221325168955
583.629439312792,235.557751925922
358.409186083083,376.046612200349
81.2148325150902,10.7696774717279
53.7315618049966,274.171515094196
111.284646992239,130.174321939319
317.280491961763,338.077288461885
177.454564264722,7.53587801919127
69.2239431670047,233.693477620228
823.419546454875,0.111916855029723
23.7174749401014,200.989081544331
44.9598299125022,102.633862571155
74.1602278468945,292.485449988155
130.11182449251,23.4682153367755
243.088760058903,335.807090202722
13.3974915991526,436.983231269281
73.3900805168739,252.352352472186
592.144630201228,92.3395205570103
57.7306153447044,47.1416798900541
522.649018382024,584.427794722108
15.3662010204821,60.1693953262499
16.8335716728277,851.401980430541
33.9869734449251,0.930781653584345
116.66608504982,146.126050951949
92.8896130355492,711.765618208687
317.91980889529,322.186540377413
44.8574470732629,209.275617858058
751.201537871362,37.935519233316
161.817758424588,2.83156183493862
531.64078452142,79.1750782491523
114.803219681048,283.106988439852
123.472725123853,154.125248027558
89.9276725453919,63.4626924192825
105.623296753328,111.234188702067
435.72981759707,23.7058234576629
259.324810619152,69.3535200857341
719.885234421531,381.086239833891
24.2674900099018,198.408173349876
57.7761600361095,146.52277489124
77.4594609157459,710.746080866431
636.671781979814,538.894185951396
56.6035279932448,58.2563265684323
485.16099039333,427.849954283261
91.9552873247095,576.92944263617

Public Function Correlation(ByRef array1() As Double, ByRef array2() As Double) As Double
'siehe https://stackoverflow.com/questions/17447817/correlation-of-two-arrays-in-c-sharp
'der hier errechnete "Pearson correlation coefficient" muss noch quadriert werden, um R-Squared zu erhalten, siehe
'https://en.wikipedia.org/wiki/Coefficient_of_determination
Dim array_xy(array1.Length - 1) As Double
Dim array_xp2(array1.Length - 1) As Double
Dim array_yp2(array1.Length - 1) As Double
Dim i As Integer
For i = 0 To array1.Length - 1
array_xy(i) = array1(i) * array2(i)
Next i
For i = 0 To array1.Length - 1
array_xp2(i) = Math.Pow(array1(i), 2.0)
Next i
For i = 0 To array1.Length - 1
array_yp2(i) = Math.Pow(array2(i), 2.0)
Next i
Dim sum_x As Double = 0
Dim sum_y As Double = 0
Dim EinDouble As Double
For Each EinDouble In array1
sum_x += EinDouble
Next
For Each EinDouble In array2
sum_y += EinDouble
Next
Dim sum_xy As Double = 0
For Each EinDouble In array_xy
sum_xy += EinDouble
Next
Dim sum_xpow2 As Double = 0
For Each EinDouble In array_xp2
sum_xpow2 += EinDouble
Next
Dim sum_ypow2 As Double = 0
For Each EinDouble In array_yp2
sum_ypow2 += EinDouble
Next
Dim Ex2 As Double = Math.Pow(sum_x, 2.0)
Dim Ey2 As Double = Math.Pow(sum_y, 2.0)
Dim ReturnWert As Double
ReturnWert = (array1.Length * sum_xy - sum_x * sum_y) / Math.Sqrt((array1.Length * sum_xpow2 - Ex2) * (array1.Length * sum_ypow2 - Ey2))
Correlation = ReturnWert
End Function