List of error/loss/cost functions in C# - neural-network

Question

I couldn't find a list in code and this seems like the right place for it.
for a list of activation function look here.
starting with the translation of the functions in this link.
here is my attempt:
(same here not sure if all the math is correct so not posting as answer)
using UnityEngine;
using System.Collections;
using System;
///<summary>
///Evaluation Functions from:
///https://stats.stackexchange.com/questions/154879/a-list-of-cost-functions-used-in-neural-networks-alongside-applications/154880#154880
///D infront means the Deravitive of the function
///error is the result for one perceptron.
///d_error is the derivative needed for backpropagation,
///to calculate the delta weights for the network
///</summary>
[System.Serializable]
public class Evaluation
{
public ErrorFunction errorFunction;
double error;
double d_error;
double td;
double od;
public Evaluation(ErrorFunction _errorFunction)
{
errorFunction = _errorFunction;
}
public double CalculateError(double t, double o)
{
switch(errorFunction)
{
case ErrorFunction.Quadratic:
return Quadratic(t,o);
case ErrorFunction.CrossEntropy:
return CrossEntropy(t,o);
case ErrorFunction.Exponentional:
return Exponentional(t,o);
case ErrorFunction.Hellinger:
return Hellinger(t,o);
case ErrorFunction.KullbackLeibler:
return KullbackLeibler(t,o);
case ErrorFunction.GeneralizedKL:
return GeneralizedKL(t,o);
case ErrorFunction.ItakuraSaito:
return ItakuraSaito(t,o);
}
return 0;
}
public double DerivativeError(double t, double o)
{
switch(errorFunction)
{
case ErrorFunction.Quadratic:
return DQuadratic(t,o);
case ErrorFunction.CrossEntropy:
return DCrossEntropy(t,o);
case ErrorFunction.Exponentional:
return DExponentional(t,o);
case ErrorFunction.Hellinger:
return DHellinger(t,o);
case ErrorFunction.KullbackLeibler:
return DKullbackLeibler(t,o);
case ErrorFunction.GeneralizedKL:
return DGeneralizedKL(t,o);
case ErrorFunction.ItakuraSaito:
return DItakuraSaito(t,o);
}
return 0;
}
public double Quadratic(double t, double o)
{
error = 0.5*Math.Pow((o-t),2);
return error;
}
public double DQuadratic(double t, double o)
{
d_error = t-o;
return d_error;
}
public double CrossEntropy(double t, double o)
{
error = t*Math.Log(o)+(1-t)*Math.Log(1-o);
return -error;
}
public double DCrossEntropy(double t, double o)
{
d_error = (o-t)/((o+1)*o);
return d_error;
}
public double Exponentional(double t, double o)
{
error = Math.Pow((o-t),2);
return Math.PI*Math.Exp(1/Math.PI*error);
}
public double DExponentional(double t, double o)
{
d_error = (2/Math.PI)*(o-t)*Exponentional(t,o);
return d_error;
}
public double Hellinger(double t, double o)
{
error = Math.Pow((Math.Sqrt(o)-Math.Sqrt(t)),2);
return (1/Math.Sqrt(2))*error;
}
public double DHellinger(double t, double o)
{
d_error = (Math.Sqrt(o)-Math.Sqrt(t))/(Math.Sqrt(2)*Math.Sqrt(o));
return d_error;
}
public double KullbackLeibler(double t, double o)
{
error = t*Math.Log(t/o);
return error;
}
public double DKullbackLeibler(double t, double o)
{
d_error = t/o;
return error;
}
public double GeneralizedKL(double t, double o)
{
error = KullbackLeibler(t,o) - t + o;
return error;
}
public double DGeneralizedKL(double t, double o)
{
d_error = (t + o)/o;
return d_error;
}
public double ItakuraSaito(double t, double o)
{
error = t/o - Math.Log(t/o) - 1;
return error;
}
public double DItakuraSaito(double t, double o)
{
d_error = (t+Math.Pow(o,2))/Math.Pow(o,2);
return d_error;
}
}
public enum ErrorFunction
{
none,
Quadratic,
CrossEntropy,
Exponentional,
Hellinger,
KullbackLeibler,
GeneralizedKL,
ItakuraSaito
}