private IKrigingModel SelectModel(Model value)
{
IKrigingModel model;
switch (value)
{
case Model.Circular:
model = new ModelCircular();
break;
case Model.Exponential:
model= new ModelExponential();
break;
case Model.Gaussian:
model= new ModelGaussian();
break;
case Model.Linear:
model = new ModelLineal();
break;
case Model.Spherical:
model= new ModelSpherical();
break;
default:
model= new ModelExponential();
break;
}
return model;
}
public FitModel(SpatialContinuity spatial, Model initialModel)
{
this.model = SelectModel(initialModel);
this.model.C0 = 0;
this.model.C1 = spatial.SemivarianceStatistic.Max;
this.model.Range = spatial.NumberOfLags * spatial.LagSize / 3;
}
public IKrigingModel setFunction(KrigingModelType model, double sill, double range, double nugget)
{
_krigingModel = selectModel(model);
_krigingModel.Nugget = nugget;
_krigingModel.Sill = sill;
_krigingModel.Range = range;
onChangedKriginParameter(EventArgs.Empty);
return(_krigingModel);
}
public void DrawCurve(IKrigingModel method)
{
double maxDist = spatial.GetDistancesValues().Max()/ 150;
double[] dist = new double[150];
double[] sem = new double[150];
for (int i = 0; i < 150; i++) {
dist[i] = i * maxDist;
sem[i] = method.GetValue(dist[i]);
}
DrawCurve("Model", dist, sem, Color.Red);
}
public void DrawCurve(IKrigingModel method, string name, Color color)
{
double maxDist = spatial.GetDistanceStatistic().Max / 150;
double[] dist = new double[150];
double[] sem = new double[150];
for (int i = 0; i < 150; i++)
{
dist[i] = i * maxDist;
sem[i] = method.GetValue(dist[i]);
}
DrawCurve(name , dist, sem, color);
}
/// <summary>
/// Initializes a new instance of the Kriging class
/// </summary>
/// <param name="shapeLayer">Shapefile (POINTS)</param>
/// <param name="idField">Position of field that contains the Z value</param>
/// <param name="model">This class provides the model that will be used</param>
public UniversalKriging(IFeatureSet shapeLayer, int idField, IKrigingModel model)
: base(shapeLayer, idField)
{
this.method = "Universal Kriging";
if (model != null)
{
this.mmodel = model;
}
this.CalculateDistances();
// this.CreatePoints(shapeLayer, idField);
//// GenerateMatrix(ref msemiVarTeo, ref inverMatrix, ref mdistances);
}
public void SetKrigingModel(IKrigingModel model)
{
this.KrigingModel = model;
}
public IKrigingModel getFunction(KrigingModelType model, double sill, double range, double nugget)
{
_msill = new Parameter(sill);
_mrange = new Parameter(range);
_mnugget = new Parameter(nugget);
_distance = new Parameter();
// Parameter[] regressionParameters=null;
// Parameter[] observedParameters = null; ;
_observedParameters = new[] { _distance };
// regressionParameters = new Parameter[] { mrange, msill,mnugget };
defineParametersCalculation();
switch (model)
{
case KrigingModelType.circular:
_krigingModel = new ModelCircular();
_regressionFunction = () =>
_distance > 0 && _distance <= Math.Abs(_mrange)
? _mnugget +
(((2 * Math.Abs(_msill)) / Math.PI) *
(((_distance / Math.Abs(_mrange)) *
Math.Sqrt(1 - ((_distance * _distance) / (Math.Abs(_mrange) * Math.Abs(_mrange))))) +
Math.Asin(_distance / Math.Abs(_mrange))))
: _distance > Math.Abs(_mrange) ? _mnugget + Math.Abs(_msill) : 0
;
break;
case KrigingModelType.exponential:
_krigingModel = new ModelExponential();
_regressionFunction = () => _distance > 0 ?
Math.Abs(_mnugget) + (Math.Abs(_msill) * (1 - Math.Exp(-3 * Math.Abs(_distance) / Math.Abs(_mrange))))
: 0;
break;
case KrigingModelType.gaussian:
_krigingModel = new ModelGaussian();
_regressionFunction = () => _distance > 0 ?
Math.Abs(_mnugget) +
(Math.Abs(_msill) *
(1 -
Math.Exp(-3 * (Math.Abs(_distance) / Math.Abs(_mrange)) * (Math.Abs(_distance) / Math.Abs(_mrange)))))
: 0;
break;
case KrigingModelType.spherical:
_krigingModel = new ModelSpherical();
_regressionFunction = () =>
_distance > 0 && _distance <= Math.Abs(_mrange) ?
Math.Abs(_mnugget) + (Math.Abs(_msill) * ((1.5 * (_distance / Math.Abs(_mrange))) -
(0.5 * (_distance / Math.Abs(_mrange)) *
(_distance / Math.Abs(_mrange)) *
(_distance / Math.Abs(_mrange))))) :
_distance > Math.Abs(_mrange) ?
Math.Abs(_mnugget) + Math.Abs(_msill) :
0;
break;
case KrigingModelType.linear:
_krigingModel = new ModelLineal();
_regressionFunction = () =>
_distance < _mrange ? _mnugget + ((_msill / _mrange) * Math.Abs(_distance)): _mnugget + _msill;
break;
}
if (_regressionParameters.Length == 0)
{
_krigingModel.Range = range;
_krigingModel.Sill = sill;
if (nugget < 0)
{
_krigingModel.Nugget = 0;
}
else
{
_krigingModel.Nugget = nugget;
}
onChangedKriginParameter(EventArgs.Empty);
return(_krigingModel);
}
if (_regressionParameters != null)
{
try {
var levenbergMarquardt = new LevenbergMarquardt(_regressionFunction, _regressionParameters,
_observedParameters, _z);
for (int i = 0; i < 50; i++)
{
levenbergMarquardt.Iterate();
}
defineParametersCalculationValues(sill, range, nugget);
} catch {
_krigingModel.Range = range;
_krigingModel.Sill = sill;
if (nugget < 0)
{
_krigingModel.Nugget = 0;
}
else
{
_krigingModel.Nugget = nugget;
}
onChangedKriginParameter(EventArgs.Empty);
return(_krigingModel);
}
}
onChangedKriginParameter(EventArgs.Empty);
return(_krigingModel);
}
private void uxModels_SelectedIndexChanged(object sender, EventArgs e)
{
int j = 0;
foreach (Model i in Enum.GetValues(typeof(Model)))
{
if (uxModels.SelectedIndex == j)
{
modTemp = TheoreticalModel.SelectModel(i);
if (uxRangeMajor.Text != "" && uxPartialSill.Text != "" && uxNugget.Text != "")
{
modTemp.Range = Convert.ToDouble(uxRangeMajor.Text);
modTemp.C1 = Convert.ToDouble(uxPartialSill.Text);
modTemp.C0 = Convert.ToDouble(uxNugget.Text);
}
}
j++;
}
}
private void FillModelForm()
{
if(this.theo == null)
return;
this.SetModel(this.theo.KrigingModel.ModelType);
MajorRange = this.theo.KrigingModel.Range.ToString();
Nugget = this.theo.KrigingModel.C0.ToString();
Sill = this.theo.KrigingModel.C1.ToString();
modTemp = this.theo.KrigingModel;
}
public IKrigingModel GetFunction(Model model, double sill, double range, double nugget)
{
msill = new Parameter(sill);
mrange = new Parameter(range);
mnugget = new Parameter(nugget);
distance = new Parameter();
// Parameter[] regressionParameters=null;
// Parameter[] observedParameters = null; ;
observedParameters = new Parameter[] { distance };
// regressionParameters = new Parameter[] { mrange, msill,mnugget };
DefineParametersCalculation();
switch (model)
{
case Model.Circular:
krigingModel = new ModelCircular();
regressionFunction = () =>
distance > 0 && distance <= Math.Abs(this.mrange) ? this.mnugget + (((2 * Math.Abs(this.msill)) / Math.PI) * (((distance / Math.Abs(this.mrange)) * Math.Sqrt(1 - ((distance * distance) / (Math.Abs(this.mrange) * Math.Abs(this.mrange))))) + Math.Asin(distance / Math.Abs(this.mrange)))) : distance >Math.Abs(this.mrange)?this.mnugget +Math.Abs(this.msill):0
;
break;
case Model.Exponential:
krigingModel = new ModelExponential();
regressionFunction = () =>distance>0?
Math.Abs(this.mnugget) + (Math.Abs(this.msill) * (1 - Math.Exp(-3 * Math.Abs(distance) / Math.Abs(this.mrange)))):0;
break;
case Model.Gaussian:
krigingModel = new ModelGaussian();
regressionFunction = () =>distance>0?
Math.Abs(this.mnugget) + (Math.Abs(this.msill) * (1 - Math.Exp(-3 * (Math.Abs(distance) / Math.Abs(this.mrange)) * (Math.Abs(distance) / Math.Abs(this.mrange))))):0;
break;
case Model.Spherical:
krigingModel = new ModelSpherical();
regressionFunction = () =>
distance > 0 && distance <= Math.Abs(this.mrange) ?
Math.Abs(this.mnugget) + (Math.Abs(this.msill) * ((1.5 * (this.distance / Math.Abs(this.mrange))) -
(0.5 * (this.distance / Math.Abs(this.mrange)) * (this.distance / Math.Abs(this.mrange)) * (this.distance / Math.Abs(this.mrange))))) :
distance > Math.Abs(this.mrange) ?
Math.Abs(this.mnugget) + Math.Abs(this.msill) :
0;
break;
}
if (regressionParameters.Length == 0)
{
krigingModel.Range =range;
krigingModel.C1 = sill ;
if (nugget < 0)
{
krigingModel.C0 = 0;
}
else
{
krigingModel.C0 = nugget;
}
OnChangedKriginParameter(EventArgs.Empty);
return krigingModel;
}
LevenbergMarquardt levenbergMarquardt;
if (regressionParameters != null)
{
try
{
levenbergMarquardt = new LevenbergMarquardt(regressionFunction, regressionParameters, observedParameters, z,2);
for (int i = 0; i < 50; i++)
{
levenbergMarquardt.Iterate();
}
DefineParametersCalculationValues(sill, range, nugget);
}
catch
{
DefineParametersCalculationValues(sill, range, nugget);
krigingModel.Range = range;
krigingModel.C1 = sill;
if (nugget < 0)
{
krigingModel.C0 = 0;
}
else
{
krigingModel.C0 = nugget;
}
OnChangedKriginParameter(EventArgs.Empty);
return krigingModel;
}
}
OnChangedKriginParameter(EventArgs.Empty);
return krigingModel;
}
public IKrigingModel SetFunction(Model model, double sill, double range, double nugget)
{
krigingModel = SelectModel(model);
krigingModel.C0=nugget;
krigingModel.C1=sill;
krigingModel.Range=range;
OnChangedKriginParameter(EventArgs.Empty);
return krigingModel;
}
