Related
This program works with Median filter. I need help with fixing the code. It applies to the line
if ((diffX > -1) && (diffY > -1)...
...in the MedianOfArea method. It would be better if the pixels, which are included in the loop's bounds, would be determined initially. This solution helps to avoid checking every time. Can you help me with fixing it?
namespace Recognizer
{
internal static class MedianFilterTask
{
public static double[,] MedianFilter(double[,] original)
{
var filter = new double[original.GetLength(0), original.GetLength(1)];
var lengthX = original.GetLength(0);
var lengthY = original.GetLength(1);
for (var x = 0; x < lengthX; x++)
for (var y = 0; y < lengthY; y++)
filter[x, y] = MedianOfArea(x, y, original, lengthX, lengthY);
return filter;
}
public static double MedianCount(ref double median, List<double> pixelsFields)
{
pixelsFields.Sort();
var countPixels = pixelsFields.Count;
if (countPixels % 2 == 0)
median = (pixelsFields[countPixels / 2 - 1] + pixelsFields[countPixels / 2]) / 2;
else
median = pixelsFields[countPixels / 2];
return median;
}
public static double MedianOfArea(int x, int y, double[,] original, int lengthX, int lengthY)
{
var pixelsFields = new List<double>();
double median = 0;
for (int areasX = -1; areasX < 2; areasX++)
for (int areasY = -1; areasY < 2; areasY++)
{
var diffX = x + areasX;
var diffY = y + areasY;
if ((diffX > -1) && (diffY > -1) && (diffX < lengthX) && (diffY < lengthY))
pixelsFields.Add(original[diffX, diffY]);
}
MedianCount(ref median, pixelsFields);
return median;
}
}
}
You could iterate directly on diffX (and diffY) and use Min and Max to set the ranges to loop over:
using System;
int startX = Math.Max(0, x-1);
int endX = Math.Min(lengthX, x+2);
int startY = Math.Max(0, y-1);
int endY = Math.Min(lengthY, y+2);
for (int diffX = startX; diffX < endX; diffX++)
for (int diffY = startY; diffY < endY; diffY++)
pixelsFields.Add(original[diffX, diffY]);
UPDATE: The span issues that were mentioned previously were fixed in the .net core 2.1 release (which is currently in preview.) These actually made the Span Vector *faster* than the array Vector...
NB: Testing this on a "Intel Xeon E5-1660 v4" which CPU-Z tells me has Instructions for "MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, EM64T, VT-x, AES, AVX, AVX2, FMA3, RSX" so it should be OK...
Off the back of answering a Vector based question, I thought I would try to implement some BLAS functions. I found that ones that were reading/summing such as dot product were pretty good, but were I was writing back to an array were bad - better than non-SIMD, but barely.
So am I doing something wrong, or is there more work in the JIT required?
The example (assuming x.Length = y.Length, not null etc. blah, blah):
public static void daxpy(double alpha, double[] x, double[] y)
{
for (var i = 0; i < x.Length; ++i)
y[i] = y[i] + x[i] * alpha;
}
In Vector form becomes:
public static void daxpy(double alpha, double[] x, double[] y)
{
var i = 0;
if (Vector.IsHardwareAccelerated)
{
var length = x.Length + 1 - Vector<double>.Count;
for (; i < length; i += Vector<double>.Count)
{
var valpha = new Vector<double>(alpha);
var vx = new Vector<double>(x, i);
var vy = new Vector<double>(y, i);
(vy + vx * valpha).CopyTo(y, i);
}
}
for (; i < x.Length; ++i)
y[i] = y[i] + x[i] * alpha;
}
And, playing around in .NET Core 2.0, I though I would try Span, both naive and Vector form:
public static void daxpy(double alpha, Span<double> x, Span<double> y)
{
for (var i = 0; i < x.Length; ++i)
y[i] += x[i] * alpha;
}
And Vector
public static void daxpy(double alpha, Span<double> x, Span<double> y)
{
if (Vector.IsHardwareAccelerated)
{
var vx = x.NonPortableCast<double, Vector<double>>();
var vy = y.NonPortableCast<double, Vector<double>>();
var valpha = new Vector<double>(alpha);
for (var i = 0; i < vx.Length; ++i)
vy[i] += vx[i] * valpha;
x = x.Slice(Vector<double>.Count * vx.Length);
y = y.Slice(Vector<double>.Count * vy.Length);
}
for (var i = 0; i < x.Length; ++i)
y[i] += x[i] * alpha;
}
So the relative timings on all these are:
Naive 1.0
Vector 0.8
Span Naive 2.5 ==> Update: Span Naive 1.1
Span Vector 0.9 ==> Update: Span Vector 0.6
So am I doing something wrong? I could hardly think of a simpler example, so I don't think so?
You probably want to test with 2.1 more than 2.0;
on my laptop (which has poor SIMD compared to my desktop), I get:
daxpy_naive x10000: 144ms
daxpy_arr_vector x10000: 77ms
daxpy_span x10000: 173ms
daxpy_vector x10000: 67ms
daxpy_vector_no_slice x10000: 67ms
using code:
using System;
using System.Diagnostics;
using System.Numerics;
class Program
{
static void Main(string[] args)
{
double alpha = 0.5;
double[] x = new double[16 * 1024], y = new double[x.Length];
var rand = new Random(12345);
for (int i = 0; i < x.Length; i++)
x[i] = rand.NextDouble();
RunAll(alpha, x, y, 1, false);
RunAll(alpha, x, y, 10000, true);
}
private static void RunAll(double alpha, double[] x, double[] y, int loop, bool log)
{
GC.Collect(GC.MaxGeneration);
GC.WaitForPendingFinalizers();
var watch = Stopwatch.StartNew();
for(int i = 0; i < loop; i++)
{
daxpy_naive(alpha, x, y);
}
watch.Stop();
if (log) Console.WriteLine($"{nameof(daxpy_naive)} x{loop}: {watch.ElapsedMilliseconds}ms");
watch = Stopwatch.StartNew();
for (int i = 0; i < loop; i++)
{
daxpy_arr_vector(alpha, x, y);
}
watch.Stop();
if (log) Console.WriteLine($"{nameof(daxpy_arr_vector)} x{loop}: {watch.ElapsedMilliseconds}ms");
watch = Stopwatch.StartNew();
for (int i = 0; i < loop; i++)
{
daxpy_span(alpha, x, y);
}
watch.Stop();
if (log) Console.WriteLine($"{nameof(daxpy_span)} x{loop}: {watch.ElapsedMilliseconds}ms");
watch = Stopwatch.StartNew();
for (int i = 0; i < loop; i++)
{
daxpy_vector(alpha, x, y);
}
watch.Stop();
if (log) Console.WriteLine($"{nameof(daxpy_vector)} x{loop}: {watch.ElapsedMilliseconds}ms");
watch = Stopwatch.StartNew();
for (int i = 0; i < loop; i++)
{
daxpy_vector_no_slice(alpha, x, y);
}
watch.Stop();
if (log) Console.WriteLine($"{nameof(daxpy_vector_no_slice)} x{loop}: {watch.ElapsedMilliseconds}ms");
}
public static void daxpy_naive(double alpha, double[] x, double[] y)
{
for (var i = 0; i < x.Length; ++i)
y[i] = y[i] + x[i] * alpha;
}
public static void daxpy_arr_vector(double alpha, double[] x, double[] y)
{
var i = 0;
if (Vector.IsHardwareAccelerated)
{
var length = x.Length + 1 - Vector<double>.Count;
for (; i < length; i += Vector<double>.Count)
{
var valpha = new Vector<double>(alpha);
var vx = new Vector<double>(x, i);
var vy = new Vector<double>(y, i);
(vy + vx * valpha).CopyTo(y, i);
}
}
for (; i < x.Length; ++i)
y[i] = y[i] + x[i] * alpha;
}
public static void daxpy_span(double alpha, Span<double> x, Span<double> y)
{
for (var i = 0; i < x.Length; ++i)
y[i] += x[i] * alpha;
}
public static void daxpy_vector(double alpha, Span<double> x, Span<double> y)
{
if (Vector.IsHardwareAccelerated)
{
var vx = x.NonPortableCast<double, Vector<double>>();
var vy = y.NonPortableCast<double, Vector<double>>();
var valpha = new Vector<double>(alpha);
for (var i = 0; i < vx.Length; ++i)
vy[i] += vx[i] * valpha;
x = x.Slice(Vector<double>.Count * vx.Length);
y = y.Slice(Vector<double>.Count * vy.Length);
}
for (var i = 0; i < x.Length; ++i)
y[i] += x[i] * alpha;
}
public static void daxpy_vector_no_slice(double alpha, Span<double> x, Span<double> y)
{
int i = 0;
if (Vector.IsHardwareAccelerated)
{
var vx = x.NonPortableCast<double, Vector<double>>();
var vy = y.NonPortableCast<double, Vector<double>>();
var valpha = new Vector<double>(alpha);
for (i = 0; i < vx.Length; ++i)
vy[i] += vx[i] * valpha;
i = Vector<double>.Count * vx.Length;
}
for (; i < x.Length; ++i)
y[i] += x[i] * alpha;
}
}
which is using dotnet build -c Release and dotnet run -c Release, with dotnet --version reporting "2.2.0-preview1-008000" (a "daily" from a little while ago).
On my desktop, I would expect the difference to be even better.
I am having issues with the output of the the result of a math calculation. I have a basic average of an array of double and I assign the result to a Label object, using the ToString() method. When I emulate the average, the label shows the correct value of 15.96 for example, but the same average of the same array, on my Galaxy S3 shows 159.6.
Is there anyone who know what's up and what can I do to make the S3 show the correct value?
Thank you all!
EDIT: passing the result to a label and adding the label to the grid:
double result = Math.Round(NP122.DoAverage(parameters), 2);
CustomLabel label = new CustomLabel();
label.ColNo = grid.ColumnDefinitions.IndexOf(c);
label.FontSize = 25;
label.TextColor = Color.Green;
if (result.ToString() == "NaN")
label.Text = "0";
else
label.Text = result.ToString();
label.IsVisible = true;
for (int i = 0; i < numberOfRows.Length + 2; i++) {
if(i == numberOfRows.Length +1)
Grid.SetRow(label, i);
}
Grid.SetColumn(label, grid.ColumnDefinitions.IndexOf(c));
listaRez.Add(label);
foreach (CustomLabel a in listaRez)
{
if (a.ColNo == grid.ColumnDefinitions.IndexOf(c))
{
grid.Children.Add(a);
}
}
EDIT 2: Custom function for NP122.DoAverage:
public static class NP122
{
public static double Vx, sx, Xm, kn, Xkinf, Xksup;
public static double sum;
public static double sumaProvizorie;
public static double[] valoriKn = new double[25];
public static double ValoareCaracteristicaSuperioara(double[] l)
{
Vx = 0;
sx = 0;
Xm = 0;
kn = 0;
Xkinf = 0;
Xksup = 0;
sum = 0;
sumaProvizorie = 0;
valoriKn[0] = 0;
//more here
valoriKn[24] = 0.35;
if (l.Length < 2 )
{
Xksup = 0;
Xkinf = 0;
}
else
{
Xm = (l.Sum()) / (l.Length);
for (int j = 0; j < l.Length; j++)
{
sumaProvizorie = Math.Round(Math.Pow((l[j] - Xm), 2), 2);
sum += sumaProvizorie;
}
kn = valoriKn[l.Length - 1];
double elements = (1.00 / (l.Length - 1));
double putere = sum;
sx = Math.Round(Math.Sqrt(elements * putere), 4);
Vx = sx / Xm;
Xksup = Xm * (1 + kn * Vx);
Xkinf = Xm * (1 - kn * Vx);
}
return Xksup;
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
I have a 3d-array double[,,] numbers = new double[x,y,z]; and now if one imagines the 3d-array to look like a cube with numbers I need to find the smallest and biggest value of every slice along all three directions.
It is of course easy to do by simply looping over it, but does C# have any functions to find the smallest and biggest value in a slice?
To explain it a bit further, maybe this "unreal" code will help:
int i;
double[] xmin = new double[x];
double[] xmax = new double[x];
double[] ymin = new double[y];
double[] ymax = new double[y];
double[] zmin = new double[z];
double[] zmax = new double[z];
for(i = 0; i < x; i++)
{
MinOf(numbers[i, y, z]) = xmin[i];
MaxOf(numbers[i, y, z]) = xmax[i];
}
for(i = 0; i < y; i++)
{
MinOf(numbers[x, i, z]) = ymin[i];
MaxOf(numbers[x, i, z]) = ymax[i];
}
for(i = 0; i < z; i++)
{
MinOf(numbers[x, y, i]) = zmin[i];
MaxOf(numbers[x, y, i]) = zmax[i];
}
Hopefully someone can help me with that.
Cheers, Phil13131
You can make methods for enumerating the slices. This is for one dimension, you would need another two, but I think you can manage that:
public static IEnumerable<T> SliceX<T>(T[,,] data, int x) {
for (int y = 0; y < data.GetLength(1); y++) {
for (int z = 0; z < data.GetLength(2); z++) {
yield return data[x, y, z];
}
}
}
Then you can just use the Min and Max methods, but that will of course loop through the data twice:
double min = SliceX(numbers, x).Min();
double max = SliceX(numbers, x).Max();
You can make an extension method that gets both min and max in one iteration:
public static class IEnumerableExtensions {
public static void GetMinMax<T>(this IEnumerable<T> data, out T min, out T max) where T : IComparable<T> {
bool first = true;
min = max = default(T);
foreach (T value in data) {
if (first) {
min = max = value;
first = false;
} else {
if (value.CompareTo(min) < 0) min = value;
if (value.CompareTo(max) > 0) max = value;
}
}
}
}
Usage:
double min, max;
SliceX(numbers, 0).GetMinMax(out min, out max);
Are you looking for something like this?
double[, ,] numbers = new double[2, 2, 2];
numbers[0, 0, 0] = 0;
numbers[0, 0, 1] = 1;
numbers[0, 1, 0] = 2;
numbers[0, 1, 1] = 3;
numbers[1, 0, 0] = 4;
numbers[1, 0, 1] = 5;
numbers[1, 1, 0] = 6;
numbers[1, 1, 1] = 7;
double[] xmax = new double[numbers.GetLength(0)];
double[] ymax = new double[numbers.GetLength(1)];
double[] zmax = new double[numbers.GetLength(2)];
for (int x = 0; x < xmax.Length; x++) xmax[x] = int.MinValue;
for (int y = 0; y < ymax.Length; y++) ymax[y] = int.MinValue;
for (int z = 0; z < zmax.Length; z++) zmax[z] = int.MinValue;
for (int x = 0; x < xmax.Length; x++)
for (int y = 0; y < ymax.Length; y++)
for (int z = 0; z < zmax.Length; z++)
{
xmax[x] = Math.Max(xmax[x], numbers[x, y, z]);
ymax[y] = Math.Max(ymax[y], numbers[x, y, z]);
zmax[z] = Math.Max(zmax[z], numbers[x, y, z]);
}
// xmax == { 3, 7 }
// ymax == { 5, 7 }
// zmax == { 6, 7 }