I have some problem when testing FFT from MathNet:
The idea is that if I apply FFT to the characteristic function of a gaussian variable I should find the gaussian density function.
When I plot VectorFFT the figure does seems a density function but in zero it does not have value 1, it has value 1.4689690914109.
There must be some problems with the scaling. I tried out all type of FourierOptions in Fourier.Inverse and all type of divisions/multiplications for PI, 2PI, sqrt(2PI) but nothing gives me the value 1 at the center of the density function.
Also, since various definitions of Fourier Transform and its inverse exists, I was wondering which one is implemented by MathNet, I could not find it in the documentation.
Any ideas?
public void DensityGaussian()
{
double eta = 0.1; //step in discrete integral
int pow2 = 256; // N^2
double mu = 0; // centred gaussian
double sigma = 1; // with unitary variance
//FFT
double lambda = 2 * System.Math.PI / (pow2 * eta);
double b = 0.5 * pow2 * lambda;
Complex[] VectorToFFT = new Complex[pow2];
for (int j = 0; j < pow2; j++)
{
double z = eta * j;
if (z == 0) { z = 0.00000000000001; }
VectorToFFT[j] = System.Numerics.Complex.Exp(new Complex(0, b * z));
VectorToFFT[j] *= (System.Numerics.Complex.Exp(new Complex(
-sigma*sigma*z*z, mu * z))); //char function of gaussian
}
Fourier.Inverse(VectorToFFT, FourierOptions.NoScaling);
//scaling
for (int i = 0; i < pow2; i++)
{
VectorToFFT[i] /= (2 * System.Math.PI); //test
}
Console.WriteLine("Is density?");
Assert.IsTrue(1 == 1);
}
Math.NET Numerics supports all common DFT definitions, controllable with the FourierOptions flags enum. They essentially vary on the exponent and on the scaling.
The FourierOptions docs give some hints on how the options affect the effective definition, essentially:
InverseExponent: use a negative sign in the exponent (default uses a positive sign). A prominent implementation with a negative sign is numerical recipes.
AsymmetricScaling/NoScaling: instead of the default symmetric scaling sqrt(1/N) either only scale in the inverse transformation 1/N (like Matlab) or no scaling at all (like numerical recipes). Obviously, without scaling ifft(fft(x)) != x.
Maybe the answer in Calculating a density from the characteristic function using fft in R can help on the specific use case.
Audio noob here and math challenged. I'm working with DirectSound which uses a -10000 to 0 range, converting that to a 0-100 scale.
I found this function here to obtain the millibels based on a percentage:
private int ConvertPercentageToMillibels(double value)
{
double attenuation = 1.0 / 1024.0 + value / 100.0 * 1023.0 / 1024.0;
double db = 10 * Math.Log10(attenuation) / Math.Log10(2);
return (int)(db * 100);
}
I need help getting the inverse of this function, basically to get the percentage based on millibels. Here is what I've got so far, which isn't working:
private double ConvertMillibelsToPercentage(int value)
{
double db = value / 100;
double attenuation = Math.Pow(10, db) / 10 * Math.Pow(10, 2);
double percentage = (1.0 * attenuation) - (1024.0 * 100.0 / 1023.0 * 1024.0);
return percentage;
}
Here you go!
private double ConvertMillibelsToPercentage(int value)
{
double exponent = ((value / 1000.0) + 10);
double numerator = 100.0 * (Math.Pow(2, exponent) - 1);
return numerator / 1023.0;
}
Answer will differ slightly due to obvious issues that arise from going between an int and a double.
EDIT: Per the teach how to fish request, here are the first mathematical steps toward arriving at the solution. I didn't show the whole thing because I didn't want to spoil allll the fun. All log functions should be considered Log base 10 unless otherwise noted:
millibels = db*100; // Beginning to work backward
millibels = 10*Log(attenuation)*(1/Log(2))*1000; // Substituting for db
millibels = 1000*Log(attenuation)/Log(2); // Simplifying
let millibels = m. Then:
m = 1000*Log(attenuation)/Log(2);
from here you can go two routes, you can either use properties of logs to find that:
m = 1000* Log_2(attenuation);// That is, log base 2 here
attenuation = 2^(m/1000);
OR you can ignore that particular property and realize:
attenuation = 10^(m*Log(2)/1000);
Try to work it out from one of the above options by plugging in the value that you know for attenuation:
attenuation = (1/1024)+(percentage/100)*(1023/1024);
And then solving for percentage. Good luck!
PS If you ever get stuck on things like this, I highly recommend going to the math stack exchange - there are some smart people there who love to solve math problems.
OR if you are particularly lazy and just want the answer, you can often simply type this stuff into Wolfram Alpha and it will "magically" give you the answer. Check this out
I'm trying to implement Hanning and Hamming window functions in C#. I can't find any .Net samples anywhere and I'm not sure if my attempts at converting from C++ samples does the job well.
My problem is mainly that looking at the formulas I imagine they need to have the original number somewhere on the right hand side of the equation - I just don't get it from looking at the formulas. (My math isn't that good yet obviously.)
What I have so far:
public Complex[] Hamming(Complex[] iwv)
{
Complex[] owv = new Complex[iwv.Length];
double omega = 2.0 * Math.PI / (iwv.Length);
// owv[i].Re = real number (raw wave data)
// owv[i].Im = imaginary number (0 since it hasn't gone through FFT yet)
for (int i = 1; i < owv.Length; i++)
// Translated from c++ sample I found somewhere
owv[i].Re = (0.54 - 0.46 * Math.Cos(omega * (i))) * iwv[i].Re;
return owv;
}
public Complex[] Hanning(Complex[] iwv)
{
Complex[] owv = new Complex[iwv.Length];
double omega = 2.0 * Math.PI / (iwv.Length);
for (int i = 1; i < owv.Length; i++)
owv[i].Re = (0.5 + (1 - Math.Cos((2d * Math.PI ) / (i -1)))); // Uhm... wrong
return owv;
}
Here's an example of a Hamming window in use in an open source C# application I wrote a while back. It's being used in a pitch detector for an autotune effect.
You can use the Math.NET library.
double[] hannDoubles = MathNet.Numerics.Window.Hamming(dataIn.Length);
for (int i = 0; i < dataIn.Length; i++)
{
dataOut[i] = hannDoubles[i] * dataIn[i];
}
See my answer to a similar question:
https://stackoverflow.com/a/42939606/246758
The operation of "windowing" means multiplying a signal by a window function. This code you found appears to generate the window function and scale the original signal. The equations are for just the window function itself, not the scaling.
I am reading a raw wave stream coming from the microphone. (This part works as I can send it to the speaker and get a nice echo.)
For simplicity lets say I want to detect a DTMF-tone in the wave data. In reality I want to detect any frequency, not just those in DTMF. But I always know which frequency I am looking for.
I have tried running it through FFT, but it doesn't seem very efficient if I want high accuracy in the detection (say it is there for only 20 ms). I can detect it down to an accuracy of around 200 ms.
What are my options with regards to algorithms?
Are there any .Net libs for it?
You may want to look at the Goertzel algorithm if you're trying to detect specific frequencies such as DTMF input. There is a C# DTMF generator/detector library on Sourceforge based on this algorithm.
Very nice implementation of Goertzel is there. C# modification:
private double GoertzelFilter(float[] samples, double freq, int start, int end)
{
double sPrev = 0.0;
double sPrev2 = 0.0;
int i;
double normalizedfreq = freq / SIGNAL_SAMPLE_RATE;
double coeff = 2 * Math.Cos(2 * Math.PI * normalizedfreq);
for (i = start; i < end; i++)
{
double s = samples[i] + coeff * sPrev - sPrev2;
sPrev2 = sPrev;
sPrev = s;
}
double power = sPrev2 * sPrev2 + sPrev * sPrev - coeff * sPrev * sPrev2;
return power;
}
Works great for me.
Let's say that typical DTMF frequency is 200Hz - 1000Hz. Then you'd have to detect a signal based on between 4 and 20 cycles. FFT will not get you anywhere I guess, since you'll detect only multiples of 50Hz frequencies: this is a built in feature of FFT, increasing the number of samples will not solve your problem. You'll have to do something more clever.
Your best shot is to linear least-square fit your data to
h(t) = A cos (omega t) + B sin (omega t)
for a given omega (one of the DTMF frequencies). See this for details (in particular how to set a statistical significance level) and links to the litterature.
I found this as a simple implementation of Goertzel. Haven't gotten it to work yet (looking for wrong frequency?), but I thought I'd share it anywas. It is copied from this site.
public static double CalculateGoertzel(byte[] sample, double frequency, int samplerate)
{
double Skn, Skn1, Skn2;
Skn = Skn1 = Skn2 = 0;
for (int i = 0; i < sample.Length; i++)
{
Skn2 = Skn1;
Skn1 = Skn;
Skn = 2 * Math.Cos(2 * Math.PI * frequency / samplerate) * Skn1 - Skn2 + sample[i];
}
double WNk = Math.Exp(-2 * Math.PI * frequency / samplerate);
return 20 * Math.Log10(Math.Abs((Skn - WNk * Skn1)));
}
As far as any .NET libraries that do this try TAPIEx ToneDecoder.Net Component. I use it for detecting DTMF, but it can do custom tones as well.
I know this question is old, but maybe it will save someone else several days of searching and trying out code samples and libraries that just don't work.
Spectral Analysis.
All application where you extract frequencies from signals goes under field spectral analysis.
How can I calculate the value of PI using C#?
I was thinking it would be through a recursive function, if so, what would it look like and are there any math equations to back it up?
I'm not too fussy about performance, mainly how to go about it from a learning point of view.
If you want recursion:
PI = 2 * (1 + 1/3 * (1 + 2/5 * (1 + 3/7 * (...))))
This would become, after some rewriting:
PI = 2 * F(1);
with F(i):
double F (int i) {
return 1 + i / (2.0 * i + 1) * F(i + 1);
}
Isaac Newton (you may have heard of him before ;) ) came up with this trick.
Note that I left out the end condition, to keep it simple. In real life, you kind of need one.
How about using:
double pi = Math.PI;
If you want better precision than that, you will need to use an algorithmic system and the Decimal type.
If you take a close look into this really good guide:
Patterns for Parallel Programming: Understanding and Applying Parallel Patterns with the .NET Framework 4
You'll find at Page 70 this cute implementation (with minor changes from my side):
static decimal ParallelPartitionerPi(int steps)
{
decimal sum = 0.0;
decimal step = 1.0 / (decimal)steps;
object obj = new object();
Parallel.ForEach(
Partitioner.Create(0, steps),
() => 0.0,
(range, state, partial) =>
{
for (int i = range.Item1; i < range.Item2; i++)
{
decimal x = (i - 0.5) * step;
partial += 4.0 / (1.0 + x * x);
}
return partial;
},
partial => { lock (obj) sum += partial; });
return step * sum;
}
There are a couple of really, really old tricks I'm surprised to not see here.
atan(1) == PI/4, so an old chestnut when a trustworthy arc-tangent function is
present is 4*atan(1).
A very cute, fixed-ratio estimate that makes the old Western 22/7 look like dirt
is 355/113, which is good to several decimal places (at least three or four, I think).
In some cases, this is even good enough for integer arithmetic: multiply by 355 then divide by 113.
355/113 is also easy to commit to memory (for some people anyway): count one, one, three, three, five, five and remember that you're naming the digits in the denominator and numerator (if you forget which triplet goes on top, a microsecond's thought is usually going to straighten it out).
Note that 22/7 gives you: 3.14285714, which is wrong at the thousandths.
355/113 gives you 3.14159292 which isn't wrong until the ten-millionths.
Acc. to /usr/include/math.h on my box, M_PI is #define'd as:
3.14159265358979323846
which is probably good out as far as it goes.
The lesson you get from estimating PI is that there are lots of ways of doing it,
none will ever be perfect, and you have to sort them out by intended use.
355/113 is an old Chinese estimate, and I believe it pre-dates 22/7 by many years. It was taught me by a physics professor when I was an undergrad.
Good overview of different algorithms:
Computing pi;
Gauss-Legendre-Salamin.
I'm not sure about the complexity claimed for the Gauss-Legendre-Salamin algorithm in the first link (I'd say O(N log^2(N) log(log(N)))).
I do encourage you to try it, though, the convergence is really fast.
Also, I'm not really sure about why trying to convert a quite simple procedural algorithm into a recursive one?
Note that if you are interested in performance, then working at a bounded precision (typically, requiring a 'double', 'float',... output) does not really make sense, as the obvious answer in such a case is just to hardcode the value.
What is PI? The circumference of a circle divided by its diameter.
In computer graphics you can plot/draw a circle with its centre at 0,0 from a initial point x,y, the next point x',y' can be found using a simple formula:
x' = x + y / h : y' = y - x' / h
h is usually a power of 2 so that the divide can be done easily with a shift (or subtracting from the exponent on a double). h also wants to be the radius r of your circle. An easy start point would be x = r, y = 0, and then to count c the number of steps until x <= 0 to plot a quater of a circle. PI is 4 * c / r or PI is 4 * c / h
Recursion to any great depth, is usually impractical for a commercial program, but tail recursion allows an algorithm to be expressed recursively, while implemented as a loop. Recursive search algorithms can sometimes be implemented using a queue rather than the process's stack, the search has to backtrack from a deadend and take another path - these backtrack points can be put in a queue, and multiple processes can un-queue the points and try other paths.
Calculate like this:
x = 1 - 1/3 + 1/5 - 1/7 + 1/9 (... etc as far as possible.)
PI = x * 4
You have got Pi !!!
This is the simplest method I know of.
The value of PI slowly converges to the actual value of Pi (3.141592165......). If you iterate more times, the better.
Here's a nice approach (from the main Wikipedia entry on pi); it converges much faster than the simple formula discussed above, and is quite amenable to a recursive solution if your intent is to pursue recursion as a learning exercise. (Assuming that you're after the learning experience, I'm not giving any actual code.)
The underlying formula is the same as above, but this approach averages the partial sums to accelerate the convergence.
Define a two parameter function, pie(h, w), such that:
pie(0,1) = 4/1
pie(0,2) = 4/1 - 4/3
pie(0,3) = 4/1 - 4/3 + 4/5
pie(0,4) = 4/1 - 4/3 + 4/5 - 4/7
... and so on
So your first opportunity to explore recursion is to code that "horizontal" computation as the "width" parameter increases (for "height" of zero).
Then add the second dimension with this formula:
pie(h, w) = (pie(h-1,w) + pie(h-1,w+1)) / 2
which is used, of course, only for values of h greater than zero.
The nice thing about this algorithm is that you can easily mock it up with a spreadsheet to check your code as you explore the results produced by progressively larger parameters. By the time you compute pie(10,10), you'll have an approximate value for pi that's good enough for most engineering purposes.
Enumerable.Range(0, 100000000).Aggregate(0d, (tot, next) => tot += Math.Pow(-1d, next)/(2*next + 1)*4)
using System;
namespace Strings
{
class Program
{
static void Main(string[] args)
{
/* decimal pie = 1;
decimal e = -1;
*/
var stopwatch = new System.Diagnostics.Stopwatch();
stopwatch.Start(); //added this nice stopwatch start routine
//leibniz formula in C# - code written completely by Todd Mandell 2014
/*
for (decimal f = (e += 2); f < 1000001; f++)
{
e += 2;
pie -= 1 / e;
e += 2;
pie += 1 / e;
Console.WriteLine(pie * 4);
}
decimal finalDisplayString = (pie * 4);
Console.WriteLine("pie = {0}", finalDisplayString);
Console.WriteLine("Accuracy resulting from approximately {0} steps", e/4);
*/
// Nilakantha formula - code written completely by Todd Mandell 2014
// π = 3 + 4/(2*3*4) - 4/(4*5*6) + 4/(6*7*8) - 4/(8*9*10) + 4/(10*11*12) - (4/(12*13*14) etc
decimal pie = 0;
decimal a = 2;
decimal b = 3;
decimal c = 4;
decimal e = 1;
for (decimal f = (e += 1); f < 100000; f++)
// Increase f where "f < 100000" to increase number of steps
{
pie += 4 / (a * b * c);
a += 2;
b += 2;
c += 2;
pie -= 4 / (a * b * c);
a += 2;
b += 2;
c += 2;
e += 1;
}
decimal finalDisplayString = (pie + 3);
Console.WriteLine("pie = {0}", finalDisplayString);
Console.WriteLine("Accuracy resulting from {0} steps", e);
stopwatch.Stop();
TimeSpan ts = stopwatch.Elapsed;
Console.WriteLine("Calc Time {0}", ts);
Console.ReadLine();
}
}
}
public static string PiNumberFinder(int digitNumber)
{
string piNumber = "3,";
int dividedBy = 11080585;
int divisor = 78256779;
int result;
for (int i = 0; i < digitNumber; i++)
{
if (dividedBy < divisor)
dividedBy *= 10;
result = dividedBy / divisor;
string resultString = result.ToString();
piNumber += resultString;
dividedBy = dividedBy - divisor * result;
}
return piNumber;
}
In any production scenario, I would compel you to look up the value, to the desired number of decimal points, and store it as a 'const' somewhere your classes can get to it.
(unless you're writing scientific 'Pi' specific software...)
Regarding...
... how to go about it from a learning point of view.
Are you trying to learning to program scientific methods? or to produce production software? I hope the community sees this as a valid question and not a nitpick.
In either case, I think writing your own Pi is a solved problem. Dmitry showed the 'Math.PI' constant already. Attack another problem in the same space! Go for generic Newton approximations or something slick.
#Thomas Kammeyer:
Note that Atan(1.0) is quite often hardcoded, so 4*Atan(1.0) is not really an 'algorithm' if you're calling a library Atan function (an quite a few already suggested indeed proceed by replacing Atan(x) by a series (or infinite product) for it, then evaluating it at x=1.
Also, there are very few cases where you'd need pi at more precision than a few tens of bits (which can be easily hardcoded!). I've worked on applications in mathematics where, to compute some (quite complicated) mathematical objects (which were polynomial with integer coefficients), I had to do arithmetic on real and complex numbers (including computing pi) with a precision of up to a few million bits... but this is not very frequent 'in real life' :)
You can look up the following example code.
I like this paper, which explains how to calculate π based on a Taylor series expansion for Arctangent.
The paper starts with the simple assumption that
Atan(1) = π/4 radians
Atan(x) can be iteratively estimated with the Taylor series
atan(x) = x - x^3/3 + x^5/5 - x^7/7 + x^9/9...
The paper points out why this is not particularly efficient and goes on to make a number of logical refinements in the technique. They also provide a sample program that computes π to a few thousand digits, complete with source code, including the infinite-precision math routines required.
The following link shows how to calculate the pi constant based on its definition as an integral, that can be written as a limit of a summation, it's very interesting:
https://sites.google.com/site/rcorcs/posts/calculatingthepiconstant
The file "Pi as an integral" explains this method used in this post.
First, note that C# can use the Math.PI field of the .NET framework:
https://msdn.microsoft.com/en-us/library/system.math.pi(v=vs.110).aspx
The nice feature here is that it's a full-precision double that you can either use, or compare with computed results. The tabs at that URL have similar constants for C++, F# and Visual Basic.
To calculate more places, you can write your own extended-precision code. One that is quick to code and reasonably fast and easy to program is:
Pi = 4 * [4 * arctan (1/5) - arctan (1/239)]
This formula and many others, including some that converge at amazingly fast rates, such as 50 digits per term, are at Wolfram:
Wolfram Pi Formulas
PI (π) can be calculated by using infinite series. Here are two examples:
Gregory-Leibniz Series:
π/4 = 1 - 1/3 + 1/5 - 1/7 + 1/9 - ...
C# method :
public static decimal GregoryLeibnizGetPI(int n)
{
decimal sum = 0;
decimal temp = 0;
for (int i = 0; i < n; i++)
{
temp = 4m / (1 + 2 * i);
sum += i % 2 == 0 ? temp : -temp;
}
return sum;
}
Nilakantha Series:
π = 3 + 4 / (2x3x4) - 4 / (4x5x6) + 4 / (6x7x8) - 4 / (8x9x10) + ...
C# method:
public static decimal NilakanthaGetPI(int n)
{
decimal sum = 0;
decimal temp = 0;
decimal a = 2, b = 3, c = 4;
for (int i = 0; i < n; i++)
{
temp = 4 / (a * b * c);
sum += i % 2 == 0 ? temp : -temp;
a += 2; b += 2; c += 2;
}
return 3 + sum;
}
The input parameter n for both functions represents the number of iteration.
The Nilakantha Series in comparison with Gregory-Leibniz Series converges more quickly. The methods can be tested with the following code:
static void Main(string[] args)
{
const decimal pi = 3.1415926535897932384626433832m;
Console.WriteLine($"PI = {pi}");
//Nilakantha Series
int iterationsN = 100;
decimal nilakanthaPI = NilakanthaGetPI(iterationsN);
decimal CalcErrorNilakantha = pi - nilakanthaPI;
Console.WriteLine($"\nNilakantha Series -> PI = {nilakanthaPI}");
Console.WriteLine($"Calculation error = {CalcErrorNilakantha}");
int numDecNilakantha = pi.ToString().Zip(nilakanthaPI.ToString(), (x, y) => x == y).TakeWhile(x => x).Count() - 2;
Console.WriteLine($"Number of correct decimals = {numDecNilakantha}");
Console.WriteLine($"Number of iterations = {iterationsN}");
//Gregory-Leibniz Series
int iterationsGL = 1000000;
decimal GregoryLeibnizPI = GregoryLeibnizGetPI(iterationsGL);
decimal CalcErrorGregoryLeibniz = pi - GregoryLeibnizPI;
Console.WriteLine($"\nGregory-Leibniz Series -> PI = {GregoryLeibnizPI}");
Console.WriteLine($"Calculation error = {CalcErrorGregoryLeibniz}");
int numDecGregoryLeibniz = pi.ToString().Zip(GregoryLeibnizPI.ToString(), (x, y) => x == y).TakeWhile(x => x).Count() - 2;
Console.WriteLine($"Number of correct decimals = {numDecGregoryLeibniz}");
Console.WriteLine($"Number of iterations = {iterationsGL}");
Console.ReadKey();
}
The following output shows that Nilakantha Series returns six correct decimals of PI with one hundred iterations whereas Gregory-Leibniz Series returns five correct decimals of PI with one million iterations:
My code can be tested >> here
Here is a nice way:
Calculate a series of 1/x^2 for x from 1 to what ever you want- the bigger number- the better pie result. Multiply the result by 6 and to sqrt().
Here is the code in c# (main only):
static void Main(string[] args)
{
double counter = 0;
for (double i = 1; i < 1000000; i++)
{
counter = counter + (1 / (Math.Pow(i, 2)));
}
counter = counter * 6;
counter = Math.Sqrt(counter);
Console.WriteLine(counter);
}
public double PI = 22.0 / 7.0;