/// <summary>
///Ctor. In the LMA fit N is the number of data points, M is the number of fit parameters.
///Call <code>fit()</code> to start the actual fitting.
/// </summary>
/// <param name="function">The model function to be fitted. Must be able to take M input parameters.</param>
/// <param name="parameters">The initial guess for the fit parameters, length M.</param>
/// <param name="dataPoints">The data points in an array, <code>double[0 = x, 1 = y][point index]</code>. Size must be <code>double[2][N]</code>.</param>
/// <param name="weights">The weights, normally given as: <code>weights[i] = 1 / sigma_i^2</code>.
/// If you have a bad data point, set its weight to zero. If the given array is null,
/// a new array is created with all elements set to 1.</param>
/// <param name="alpha">A Matrix instance. Must be initiated to (M x M) size.
/// In this case we are using the GeneralMatrix type from the open source JAMA library</param>
/// <param name="argDeltaChi2">delta chi square</param>
/// <param name="argMaxIter">maximum number of iterations</param>
public LMA(LMAFunction function, double[] parameters,
double[][] dataPoints, double[] weights,
GeneralMatrix alpha,
double argDeltaChi2, int argMaxIter)
{
if (dataPoints[0].Length != dataPoints[1].Length)
{
throw new ArgumentException("Data must have the same number of x and y points.");
}
if (dataPoints.Length != 2)
{
throw new ArgumentException("Data point array must be 2 x N");
}
this.function = function;
this.parameters = parameters;
this.dataPoints = dataPoints;
this.weights = CheckWeights(dataPoints[0].Length, weights);
this.incrementedParameters = new double[parameters.Length];
this.alpha = alpha;
this.beta = new double[parameters.Length];
this.da = new double[parameters.Length];
minDeltaChi2 = argDeltaChi2;
maxIterations = argMaxIter;
lambda = Constants.lambda;
}
/// <summary>
///Ctor. In the LMA fit N is the number of data points, M is the number of fit parameters.
///Call <code>fit()</code> to start the actual fitting.
/// </summary>
/// <param name="function">The model function to be fitted. Must be able to take M input parameters.</param>
/// <param name="parameters">The initial guess for the fit parameters, length M.</param>
/// <param name="dataPoints">The data points in an array, <code>double[0 = x, 1 = y][point index]</code>. Size must be <code>double[2][N]</code>.</param>
/// <param name="weights">The weights, normally given as: <code>weights[i] = 1 / sigma_i^2</code>.
/// If you have a bad data point, set its weight to zero. If the given array is null,
/// a new array is created with all elements set to 1.</param>
/// <param name="alpha">A Matrix instance. Must be initiated to (M x M) size.
/// In this case we are using the GeneralMatrix type from the open source JAMA library</param>
/// <param name="argDeltaChi2">delta chi square</param>
/// <param name="argMaxIter">maximum number of iterations</param>
public LMA( LMAFunction function, double[] parameters,
double[][] dataPoints, double[] weights,
GeneralMatrix alpha,
double argDeltaChi2, int argMaxIter)
{
if (dataPoints[0].Length != dataPoints[1].Length)
throw new ArgumentException("Data must have the same number of x and y points.");
if (dataPoints.Length != 2)
throw new ArgumentException("Data point array must be 2 x N");
this.function = function;
this.parameters = parameters;
this.dataPoints = dataPoints;
this.weights = CheckWeights(dataPoints[0].Length, weights);
this.incrementedParameters = new double[parameters.Length];
this.alpha = alpha;
this.beta = new double[parameters.Length];
this.da = new double[parameters.Length];
minDeltaChi2 = argDeltaChi2;
maxIterations = argMaxIter;
lambda = Constants.lambda;
}
