Matrix Multiplication returning wrong value - c#

I am calculating values by using weights and bias from MATLAB trained ANN. trying to code a sigmoid simulation equation, but for some reason C# calculations vary too much than that of MATLAB. i.e. error is too high. I tried to check each step of the equation and found out the specific part that is creating the problem (Emphasized part), but I don't know how to solve this issue, if someone could help, would be a huge favour.
1+(purelin(net.LW{2}×(tansig(net.IW{1}×(1-(abs(2×([inputs]-1)))))+net.b{1}))+net.b{2}))/2
//Normalization of Data
public double Normalization(double x, double xMAx, double xMin)
{
double xNorm = 0.0;
xNorm = (x - xMin) / (xMAx - xMin);
if (xNorm < 0)
xNorm = 0;
if (xNorm > 1)
xNorm = 1;
xNorm = Math.Round(xNorm, 4);
return xNorm;
}
// Equation to calculate ANN based Output Values
public double MetrixCalc(double[] Pn, double[,] W1, double[] W2, double[] b1, double b2, double maxValue, double minValue)
{
double FinalValue = 0;
double[] PnCalc1 = new double[Pn.Length];
double[] PnCalc2 = new double[W1.Length / Pn.Length];
for (int i = 0; i < Pn.Length; i++)
{
PnCalc1[i] = 1 - Math.Abs(2 * (Pn[i] - 1));
}
for (int i = 0; i < (W1.Length / Pn.Length); i++)
{
double PnCalc = 0.0;
for (int j = 0; j < Pn.Length; j++)
{
PnCalc = PnCalc + (W1[i, j] * PnCalc1[j]);
}
PnCalc2[i] = PnCalc;
}
for (int i = 0; i < PnCalc2.Length; i++)
{
//PnCalc2[i] = Math.Tanh(PnCalc2[i] + b1[i]);
PnCalc2[i] = PnCalc2[i] + b1[i];
PnCalc2[i] = 2.0 / (1 + Math.Exp(-2 * (PnCalc2[i]))) - 1;
PnCalc2[i] = Math.Round(PnCalc2[i], 4);
}
double FinalCalc = 0.0;
for (int i = 0; i < PnCalc2.Length; i++)
{
*FinalCalc = FinalCalc + (W2[i] * (PnCalc2[i]));*
//FinalValue = FinalCalc;
}
FinalValue = FinalCalc + b2;
FinalValue = 1 + FinalValue;
FinalValue = (1 + FinalValue) / 2.0;
FinalValue = (FinalValue * (maxValue - minValue)) + minValue;
FinalValue = Math.Round(FinalValue, 4);
FinalValue = Math.Abs(FinalValue);
return FinalValue;
}


Problem is solved.
Problem was with the weights matrix copied from MATLAB. debugging mode saved my life. :)

Related

Get RMS from FFT

I got an array of data voltages and I want to get the RMS value from the FFT that has been applied before to that data. I've seen that RMS in time domain should be equal to RMS(fft) / sqrt(nFFT) from Parseval's Theorem, but gives me different results. I'm using these functions:
1)FFT
public static VectorDPoint FFT(double[] trama, double samplingFreq)
{
double fs = samplingFreq; // Sampling frequency
double t1 = 1 / fs; // Sample time
int l = trama.Length; // Length of signal
// Time vector
//Vector t = Normal(0, l, 1) * t1;
//// Values vector
//Vector y = new Vector(trama);
// We just use half of the data as the other half is simetric. The middle is found in NFFT/2 + 1
int nFFT = (int)Math.Pow(2, NextPow2(l));
if (nFFT > 655600)
{ }
// Create complex array for FFT transformation. Use 0s for imaginary part
Complex[] samples = new Complex[nFFT];
for (int i = 0; i < nFFT; i++)
{
if (i >= trama.Length)
{
samples[i] = new MathNet.Numerics.Complex(0, 0);
}
else
{
samples[i] = new MathNet.Numerics.Complex(trama[i], 0);
}
}
ComplexFourierTransformation fft = new ComplexFourierTransformation(TransformationConvention.Matlab);
fft.TransformForward(samples);
ComplexVector s = new ComplexVector(samples);
s = s / l;
Vector f = (fs / 2.0) * Linspace(0, 1, (nFFT / 2) + 1);
VectorDPoint result = new VectorDPoint();
for (int i = 0; i < (nFFT / 2) + 1; i++)
{
result.Add(new DPoint(f[i], 2 * s[i].Modulus));
}
s = null;
f = null;
samples = null;
return result;
2) RMS
public static double RMSCalculate(double[] channelValues, int samplesNumber, double sampleRate, DateTime currentDate)
{
double[] times = new double[channelValues.Length];
double sampleTime = 0.0;
double period = 0;
times[0] = currentDate.Second + currentDate.Millisecond / 1000.0;
sampleTime = 1 / sampleRate; //s
// Limited samples
for (int i = 1; i < channelValues.Length; i++)
{
times[i] = times[i - 1] + sampleTime;
}
DPoint RMSValues = new DPoint();
RMSValues.Y = 0;
if (channelValues.Length == 1)
{
double x = channelValues[0];
double y = channelValues[0];
RMSValues = new DPoint(x, Math.Abs(y));
}
else
{
for (int i = 0; i < times.Length - 1; i++)
{
period = 0;
if (i + 1 < times.Length)
{
RMSValues.Y += channelValues[i + 1] * channelValues[i + 1] * (times[i + 1] - times[i]);
}
}
period = times[times.Length - 1] - times[0];
RMSValues.Y = RMSValues.Y / period;
RMSValues.Y = Math.Sqrt(RMSValues.Y);
}
return RMSValues.Y;
}

Laplace Transform And Getting The Frequent Value For Gyro

I'm getting x,y,z values from gyro-sensor. Each variable is being sent 10 values per second. In 3 seconds I have;
x=[30values]
y=[30values]
z=[30values]
Some of the values are too different from the others cause of noise. With laplace transform I need to get the most frequent value from my array.
I need to filter the array with Laplace Transform equation. I need to build the equation in C#. How can I implement the array with the equation?

Since this kind of filter (Laplace) is very specialized to certain area of Engineering and needs a person who has good understanding on both the programming language (in this case is C#) and the filter itself, I would recommend you to use such source, rather than code the filter by yourself.
Here is the snippet of the source code:
class Laplace
{
const int DefaultStehfest = 14;
public delegate double FunctionDelegate(double t);
static double[] V; // Stehfest coefficients
static double ln2; // log of 2
public static void InitStehfest(int N)
{
ln2 = Math.Log(2.0);
int N2 = N / 2;
int NV = 2 * N2;
V = new double[NV];
int sign = 1;
if ((N2 % 2) != 0)
sign = -1;
for (int i = 0; i < NV; i++)
{
int kmin = (i + 2) / 2;
int kmax = i + 1;
if (kmax > N2)
kmax = N2;
V[i] = 0;
sign = -sign;
for (int k = kmin; k <= kmax; k++)
{
V[i] = V[i] + (Math.Pow(k, N2) / Factorial(k)) * (Factorial(2 * k)
/ Factorial(2 * k - i - 1)) / Factorial(N2 - k)
/ Factorial(k - 1) / Factorial(i + 1 - k);
}
V[i] = sign * V[i];
}
}
public static double InverseTransform(FunctionDelegate f, double t)
{
double ln2t = ln2 / t;
double x = 0;
double y = 0;
for (int i = 0; i < V.Length; i++)
{
x += ln2t;
y += V[i] * f(x);
}
return ln2t * y;
}
public static double Factorial(int N)
{
double x = 1;
if (N > 1)
{
for (int i = 2; i <= N; i++)
x = i * x;
}
return x;
}
}
coded by Mr. Walt Fair Jr. in CodeProject.

GammaLn NameSpace In C#

I purchased the book Numerical Methods, Algorithms and Tools in C# by Waldemar Dos Passos.
On page 463, there is the method:
public static double FactorialLn(int n)
{
if(n < 0)
{
throw new Exception("Input value must be > 0");
}
else
{
return GammaLn(n+1.0);
}
}
I found the namespace where the function GammaLn resides: MicrosoftResearch.Infer.Maths
but Visual Studio 2010 does not recognize it and I was unable to find it in the .NET reference.
Please help me get the program to compile.
Thank you in advance.
I finally found the dource code of the GammaLn method itself here: http://seungwon.tistory.com/9
One does need nothing else when one possesses the very real thing, as Andrey correctly pointed out, but until then ...
I reproduce the method here for future reference, in case the site above stops from functioning.
/// <summary>
/// http://seungwon.tistory.com/9
/// GammaLn函数
/// </summary>
/// <param name="x"></param>
/// <returns></returns>
public static double GammaLn(double x)
{
double result = 0.0;
double d1 = -5.772156649015328605195174e-1;
double[,] p1 = {{4.945235359296727046734888e0},{ 2.018112620856775083915565e2},
{2.290838373831346393026739e3},{ 1.131967205903380828685045e4},
{2.855724635671635335736389e4},{ 3.848496228443793359990269e4},
{2.637748787624195437963534e4},{ 7.225813979700288197698961e3}};
double[,] q1 = {{6.748212550303777196073036e1},{ 1.113332393857199323513008e3},
{7.738757056935398733233834e3},{ 2.763987074403340708898585e4},
{5.499310206226157329794414e4},{ 6.161122180066002127833352e4},
{3.635127591501940507276287e4},{ 8.785536302431013170870835e3}};
double d2 = 4.227843350984671393993777e-1;
double[,] p2 = {{4.974607845568932035012064e0},{ 5.424138599891070494101986e2},
{1.550693864978364947665077e4},{ 1.847932904445632425417223e5},
{1.088204769468828767498470e6},{ 3.338152967987029735917223e6},
{5.106661678927352456275255e6},{ 3.074109054850539556250927e6}};
double[,] q2 = {{1.830328399370592604055942e2},{ 7.765049321445005871323047e3},
{1.331903827966074194402448e5},{ 1.136705821321969608938755e6},
{5.267964117437946917577538e6},{ 1.346701454311101692290052e7},
{1.782736530353274213975932e7},{ 9.533095591844353613395747e6}};
double d4 = 1.791759469228055000094023e0;
double[,] p4 = {{1.474502166059939948905062e4},{ 2.426813369486704502836312e6},
{1.214755574045093227939592e8},{ 2.663432449630976949898078e9},
{2.940378956634553899906876e10},{ 1.702665737765398868392998e11},
{4.926125793377430887588120e11},{5.606251856223951465078242e11}};
double[,] q4 = {{2.690530175870899333379843e3},{ 6.393885654300092398984238e5},
{4.135599930241388052042842e7},{ 1.120872109616147941376570e9},
{1.488613728678813811542398e10},{1.016803586272438228077304e11},
{3.417476345507377132798597e11},{ 4.463158187419713286462081e11}};
double[,] c = {{-1.910444077728e-03},{ 8.4171387781295e-04},
{-5.952379913043012e-04},{ 7.93650793500350248e-04},
{-2.777777777777681622553e-03},{ 8.333333333333333331554247e-02},
{ 5.7083835261e-03}};
double eps = 2.2204e-016;
if (x <= 0)
{
//报错！
}
else
{
double xden = 0.0;
double xnum = 0.0;
result = x;
if (x > 0 && x <= eps)
{
result = -Math.Log(x);
}
else if ((x > eps) && (x <= 0.5))
{
double y = x;
xden = 1;
xnum = 0;
for (int i = 0; i < 8; i++)
{
xnum = xnum * y + p1[i, 0];
xden = xden * y + q1[i, 0];
}
result = -Math.Log(y) + (y * (d1 + y * (xnum / xden)));
}
else if ((x > 0.5) && (x <= 0.6796875))
{
double xm1 = (x - 0.5) - 0.5;
xden = 1;
xnum = 0;
for (int i = 0; i < 8; i++)
{
xnum = xnum * xm1 + p2[i, 0];
xden = xden * xm1 + q2[i, 0];
}
result = -Math.Log(x) + xm1 * (d2 + xm1 * (xnum / xden));
}
else if ((x > 0.6796875) && (x <= 1.5))
{
double xm1 = (x - 0.5) - 0.5;
xden = 1;
xnum = 0;
for (int i = 0; i < 8; i++)
{
xnum = xnum * xm1 + p1[i, 0];
xden = xden * xm1 + q1[i, 0];
}
result = xm1 * (d1 + xm1 * (xnum / xden));
}
else if ((x > 1.5) && (x <= 4))
{
double xm2 = x - 2;
xden = 1;
xnum = 0;
for (int i = 0; i < 8; i++)
{
xnum = xnum * xm2 + p2[i, 0];
xden = xden * xm2 + q2[i, 0];
}
result = xm2 * (d2 + xm2 * (xnum / xden));
}
else if ((x > 4) && (x <= 12))
{
double xm4 = x - 4;
xden = -1;
xnum = 0;
for (int i = 0; i < 8; i++)
{
xnum = xnum * xm4 + p4[i, 0];
xden = xden * xm4 + q4[i, 0];
}
result = d4 + xm4 * (xnum / xden);
}
else if (x > 12)
{
double y = x;
double r = c[6, 0];// 等于：double r = repmat(c[6, 0], 1)[0,0];
double ysq = y * y;
for (int i = 0; i < 6; i++)
{
r = r / ysq + c[i, 0];
}
r = r / y;
double corr = Math.Log(y);
double spi = 0.9189385332046727417803297;
result = r + spi - 0.5 * corr + y * (corr - 1);
}
}
return result;
}

iPhone: Converting C# code to Objective-C

Can you guys help me converting this C# code to Objective-C?
I don't have a clue about C#/Visual Studio!
public static class BezierSpline
{
public static void GetCurveControlPoints(Point[] knots,
out Point[] firstControlPoints, out Point[] secondControlPoints)
{
int n = knots.Length - 1;
// Calculate first Bezier control points
// Right hand side vector
double[] rhs = new double[n];
// Set right hand side X values
for (int i = 1; i < n - 1; ++i)
rhs[i] = 4 * knots[i].X + 2 * knots[i + 1].X;
rhs[0] = knots[0].X + 2 * knots[1].X;
rhs[n - 1] = (8 * knots[n - 1].X + knots[n].X) / 2.0;
// Get first control points X-values
double[] x = GetFirstControlPoints(rhs);
// Set right hand side Y values
for (int i = 1; i < n - 1; ++i)
rhs[i] = 4 * knots[i].Y + 2 * knots[i + 1].Y;
rhs[0] = knots[0].Y + 2 * knots[1].Y;
rhs[n - 1] = (8 * knots[n - 1].Y + knots[n].Y) / 2.0;
// Get first control points Y-values
double[] y = GetFirstControlPoints(rhs);
// Fill output arrays.
firstControlPoints = new Point[n];
secondControlPoints = new Point[n];
for (int i = 0; i < n; ++i)
{
// First control point
firstControlPoints[i] = new Point(x[i], y[i]);
// Second control point
if (i < n - 1)
secondControlPoints[i] = new Point(2 * knots
[i + 1].X - x[i + 1], 2 *
knots[i + 1].Y - y[i + 1]);
else
secondControlPoints[i] = new Point((knots
[n].X + x[n - 1]) / 2,
(knots[n].Y + y[n - 1]) / 2);
}
}
private static double[] GetFirstControlPoints(double[] rhs)
{
int n = rhs.Length;
double[] x = new double[n]; // Solution vector.
double[] tmp = new double[n]; // Temp workspace.
double b = 2.0;
x[0] = rhs[0] / b;
for (int i = 1; i < n; i++) // Decomposition and forward substitution.
{
tmp[i] = 1 / b;
b = (i < n - 1 ? 4.0 : 3.5) - tmp[i];
x[i] = (rhs[i] - x[i - 1]) / b;
}
for (int i = 1; i < n; i++)
x[n - i - 1] -= tmp[n - i] * x[n - i]; // Backsubstitution.
return x;
}
}
thanks.

double[] tmp = new double[n];
tmp is an array of length n. Each value is not initialized explicitly, but it is implicitly set to the default value of the double type, which is 0. So tmp is an n length array of zeros. {0,0,0,0,0, ... 0}

How to compute ifft from fft?

I've done a fft to get fundamental frequency in real time and to implement high and low pass filters.
Now I want to be able to record to a .wav file after I apply a filter.
First I'll have to invert the fft and that is my question.
What are the steps to do this?
I use the FFT defined in this project.
Here is the code for it:
using System;
using System.Collections.Generic;
using System.Text;
namespace SoundLog
{
public class FourierTransform
{
static private int n, nu;
static private int BitReverse(int j)
{
int j2;
int j1 = j;
int k = 0;
for (int i = 1; i <= nu; i++)
{
j2 = j1 / 2;
k = 2 * k + j1 - 2 * j2;
j1 = j2;
}
return k;
}
static public double[] FFT(ref double[] x)
{
// Assume n is a power of 2
n = x.Length;
nu = (int)(Math.Log(n) / Math.Log(2));
int n2 = n / 2;
int nu1 = nu - 1;
double[] xre = new double[n];
double[] xim = new double[n];
double[] magnitude = new double[n2];
double[] decibel = new double[n2];
double tr, ti, p, arg, c, s;
for (int i = 0; i < n; i++)
{
xre[i] = x[i];
xim[i] = 0.0f;
}
int k = 0;
for (int l = 1; l <= nu; l++)
{
while (k < n)
{
for (int i = 1; i <= n2; i++)
{
p = BitReverse(k >> nu1);
arg = 2 * (double)Math.PI * p / n;
c = (double)Math.Cos(arg);
s = (double)Math.Sin(arg);
tr = xre[k + n2] * c + xim[k + n2] * s;
ti = xim[k + n2] * c - xre[k + n2] * s;
xre[k + n2] = xre[k] - tr;
xim[k + n2] = xim[k] - ti;
xre[k] += tr;
xim[k] += ti;
k++;
}
k += n2;
}
k = 0;
nu1--;
n2 = n2 / 2;
}
k = 0;
int r;
while (k < n)
{
r = BitReverse(k);
if (r > k)
{
tr = xre[k];
ti = xim[k];
xre[k] = xre[r];
xim[k] = xim[r];
xre[r] = tr;
xim[r] = ti;
}
k++;
}
for (int i = 0; i < n / 2; i++)
//magnitude[i] = (float)(Math.Sqrt((xre[i] * xre[i]) + (xim[i] * xim[i])));
decibel[i] = 10.0 * Math.Log10((float)(Math.Sqrt((xre[i] * xre[i]) + (xim[i] * xim[i]))));
//return magnitude;
return decibel;
}
}
}

There are so many really good fft implementations around such as FFTW that I highly recommend using one. They come with ifft as well. Yours, as implemented, will be excruciatingly slow.

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