public static int sampleSizeKappa(string AccuracyAssessmentModel, double proportionOfKappa = 0.1, double alpha = 0.05)
{
double z = zScore(alpha);
dataGeneralConfusionMatirx gc = new dataGeneralConfusionMatirx();
gc.getXTable(AccuracyAssessmentModel);
double pe = gc.GeneralConfusionMatrix.ChanceAgreement;
double k = gc.Kappa;
double n = (k * (1 - k)) / (1 - pe) * Math.Pow((z / proportionOfKappa), 2);
return(System.Convert.ToInt32(n));
}
public void buildModel()
{
IGeometryBag geoBag = new GeometryBagClass();
IGeometryCollection geoColl = (IGeometryCollection)geoBag;
IFeatureCursor ftCur = projectArea.Search(null, false);
IFeature ftr = ftCur.NextFeature();
while (ftr != null)
{
geoColl.AddGeometry(ftr.Shape);
ftr = ftCur.NextFeature();
}
dgc = new dataGeneralConfusionMatirx();
dgc.getXTable(oModel);
olabels = dgc.Labels.ToList();
xTable = dgc.XTable;
oClmProp = dgc.GeneralConfusionMatrix.ColumnProportions;
nCnts = new double[oClmProp.Length];
adjustXTable((IGeometry)geoBag);
gc = new GeneralConfusionMatrix(xTable);
kappa = gc.Kappa;
ste = gc.StandardError;
writeXTable();
}
public void buildModel()
{
IGeometryBag geoBag = new GeometryBagClass();
IGeometryCollection geoColl = (IGeometryCollection)geoBag;
IFeatureCursor ftCur = projectArea.Search(null, false);
IFeature ftr = ftCur.NextFeature();
while (ftr != null)
{
geoColl.AddGeometry(ftr.Shape);
ftr = ftCur.NextFeature();
}
dgc = new dataGeneralConfusionMatirx();
dgc.getXTable(oModel);
olabels = dgc.Labels.ToList();
xTable = dgc.XTable;
oClmProp = dgc.GeneralConfusionMatrix.ColumnProportions;
nCnts = new double[oClmProp.Length];
adjustXTable((IGeometry)geoBag);
gc = new GeneralConfusionMatrix(xTable);
kappa = gc.Kappa;
ste = gc.StandardError;
writeXTable();
}
private void createAAModel(string[] paramArr)
{
ITable table = getTable(paramArr[1]);
string dependentField = paramArr[2];
string independentField = paramArr[3];
Statistics.dataGeneralConfusionMatirx dpAa = new Statistics.dataGeneralConfusionMatirx(table, dependentField, independentField);
if (paramArr.Length > 5) dpAa.WeightFeild = paramArr[4];
dpAa.writeXTable(paramArr[paramArr.Length-1]);
}
public static int sampleSizeKappa(string AccuracyAssessmentModel, double proportionOfKappa=0.1, double alpha = 0.05)
{
double z = zScore(alpha);
dataGeneralConfusionMatirx gc= new dataGeneralConfusionMatirx();
gc.getXTable(AccuracyAssessmentModel);
double pe = gc.GeneralConfusionMatrix.ChanceAgreement;
double k = gc.Kappa;
double n = (k*(1-k))/(1-pe)*Math.Pow((z/proportionOfKappa),2);
return System.Convert.ToInt32(n);
}
public void selectAccuracyFeaturesToSample(ITable inputTable, string AccuracyAssessmentModelPath, string mapField, double proportionOfMean, double alpha, bool weightsEqual=false)
{
esriUtil.Statistics.dataGeneralConfusionMatirx dGc = new Statistics.dataGeneralConfusionMatirx();
dGc.getXTable(AccuracyAssessmentModelPath);
List<string> labels = dGc.Labels.ToList();
int samplesPerClass = esriUtil.Statistics.dataPrepSampleSize.sampleSizeKappa(AccuracyAssessmentModelPath, proportionOfMean, alpha)/labels.Count + 1;
selectEqualFeaturesToSample(inputTable, mapField, samplesPerClass, weightsEqual);
}
