public static int[] sampleSizeMaxStrata(string strataModelPath, double proportionOfMean = 0.1, double alpha = 0.05)
{
dataPrepStrata strata = new dataPrepStrata();
strata.buildModel(strataModelPath);
int nStrata = strata.Labels.Count;
int[] maxN = new int[nStrata];
for (int i = 0; i < nStrata; i++)
{
Accord.MachineLearning.KMeansCluster k = ((Accord.MachineLearning.KMeans)strata.Model).Clusters[i];
int mx = sampleSizeMaxMean(k.Covariance, k.Mean, proportionOfMean, alpha)[0];
maxN[i] = mx;
}
return(maxN);
}
private static void fillStrataReport(string modelPath, Forms.RunningProcess.frmRunningProcessDialog rp, double proportion, double alpha)
{
dataPrepStrata strata = new dataPrepStrata();
strata.buildModel(modelPath);
List <string> lbl = strata.Labels;
rp.addMessage("Samples by strata (Stratum; number of samples)");
rp.addMessage("-".PadRight(45, '-'));
int[] samples = sampleSizeMaxStrata(modelPath, proportion, alpha);
for (int i = 0; i < samples.Length; i++)
{
rp.addMessage("\t" + lbl[i] + "; " + samples[i].ToString());
}
rp.addMessage("-".PadRight(45, '-'));
rp.addMessage("Total number of samples = " + samples.Sum().ToString());
}
public void buildModel()
{
dataPrepStrata dStrat;
if (InValueRaster == null)
{
dStrat = new dataPrepStrata(InTable, VariableFieldNames, StrataField);
}
else
{
dStrat = new dataPrepStrata(InValueRaster, InStrataRaster);
}
inpath = dStrat.InPath;
n = dStrat.N;
kmeans = dStrat.Model;
lbl = dStrat.Labels;
k = lbl.Count;
prop = 1;
}
private void createStrataVarCovModel(string[] paramArr)
{
Statistics.dataPrepStrata dps = null;
if (paramArr.Length > 4)
{
ITable tbl = getTable(paramArr[1]);
string[] variables = paramArr[2].Split(new char[] { ',' });
string strataField = paramArr[3];
dps = new Statistics.dataPrepStrata(tbl, variables, strataField);
}
else
{
IRaster StrataRaster = rsUtil.createRaster(getRaster(paramArr[1]));
IRaster VariableRaster = rsUtil.createRaster(getRaster(paramArr[2]));
dps = new Statistics.dataPrepStrata(StrataRaster, VariableRaster);
}
dps.writeModel(paramArr[paramArr.Length - 1]);
}
public void buildModel()
{
dataPrepStrata dStrat;
if(InValueRaster==null)
{
dStrat = new dataPrepStrata(InTable, VariableFieldNames, StrataField);
}
else
{
dStrat = new dataPrepStrata(InValueRaster,InStrataRaster);
}
inpath = dStrat.InPath;
n = dStrat.N;
kmeans = dStrat.Model;
lbl = dStrat.Labels;
k = lbl.Count;
prop = 1;
}
private static void fillStrataReport(string modelPath, Forms.RunningProcess.frmRunningProcessDialog rp, double proportion, double alpha)
{
dataPrepStrata strata = new dataPrepStrata();
strata.buildModel(modelPath);
List<string> lbl = strata.Labels;
rp.addMessage("Samples by strata (Stratum; number of samples)");
rp.addMessage("-".PadRight(45, '-'));
int[] samples = sampleSizeMaxStrata(modelPath, proportion, alpha);
for (int i = 0; i < samples.Length; i++)
{
rp.addMessage("\t" + lbl[i] + "; " + samples[i].ToString());
}
rp.addMessage("-".PadRight(45, '-'));
rp.addMessage("Total number of samples = " + samples.Sum().ToString());
}
public static int[] sampleSizeMaxStrata(string strataModelPath, double proportionOfMean = 0.1, double alpha = 0.05)
{
dataPrepStrata strata = new dataPrepStrata();
strata.buildModel(strataModelPath);
int nStrata = strata.Labels.Count;
int[] maxN = new int[nStrata];
for (int i = 0; i < nStrata; i++)
{
Accord.MachineLearning.KMeansCluster k = ((Accord.MachineLearning.KMeans)strata.Model).Clusters[i];
int mx = sampleSizeMaxMean(k.Covariance, k.Mean, proportionOfMean, alpha)[0];
maxN[i] = mx;
}
return maxN;
}
public void selectStrataFeaturesToSample(ITable inputTable, string strataModelPath, string strataFieldName = "Cluster", double proportionOfMean = 0.1, double alpha = 0.05, bool weightsEqual = false)
{
IObjectClassInfo2 objInfo2 = (IObjectClassInfo2)inputTable;
if (!objInfo2.CanBypassEditSession())
{
System.Windows.Forms.MessageBox.Show("Input Table participates in a composite relationship. Please export this table as a new table and try again!");
return;
}
esriUtil.Statistics.dataPrepStrata dpC = new Statistics.dataPrepStrata();
dpC.buildModel(strataModelPath);
List<string> labels = dpC.Labels;
HashSet<string> unqVls = geoUtil.getUniqueValues(inputTable, strataFieldName);
System.Random rd = new Random();
int[] samplesPerCluster = esriUtil.Statistics.dataPrepSampleSize.sampleSizeMaxCluster(strataModelPath, proportionOfMean, alpha);
double[] propPerCluster = esriUtil.Statistics.dataPrepSampleSize.clusterProportions(strataModelPath);
double[] weightsPerCluster = new double[propPerCluster.Length];
double sSamp = System.Convert.ToDouble(samplesPerCluster.Sum());
for (int i = 0; i < weightsPerCluster.Length; i++)
{
weightsPerCluster[i] = propPerCluster[i] / (samplesPerCluster[i] / sSamp);
}
if (weightsEqual)
{
double minProp = weightsPerCluster.Min();
for (int i = 0; i < samplesPerCluster.Length; i++)
{
samplesPerCluster[i] = System.Convert.ToInt32(samplesPerCluster[i] * (weightsPerCluster[i] / minProp));
weightsPerCluster[i] = 1;
}
}
int[] tsPerCluster = new int[propPerCluster.Length];
double[] randomRatioPerClust = new double[propPerCluster.Length];
if (samplesPerCluster.Length != unqVls.Count)
{
System.Windows.Forms.MessageBox.Show("Unique Values in cluster field do not match the number of cluster models!");
return;
}
string sampleFldName = geoUtil.createField(inputTable, "sample", esriFieldType.esriFieldTypeSmallInteger, false);
string weightFldName = geoUtil.createField(inputTable, "weight", esriFieldType.esriFieldTypeDouble, false);
IQueryFilter qf0 = new QueryFilterClass();
qf0.SubFields = strataFieldName;
string h = "";
IField fld = inputTable.Fields.get_Field(inputTable.FindField(strataFieldName));
if (fld.Type == esriFieldType.esriFieldTypeString) h = "'";
for (int i = 0; i < samplesPerCluster.Length; i++)
{
qf0.WhereClause = strataFieldName + " = " + h + labels[i] + h;
int tCnt = inputTable.RowCount(qf0);
tsPerCluster[i] = tCnt;
randomRatioPerClust[i] = System.Convert.ToDouble(samplesPerCluster[i]) / tCnt;
}
IQueryFilter qf = new QueryFilterClass();
qf.SubFields = strataFieldName + "," + sampleFldName + "," + weightFldName;
IWorkspace wks = ((IDataset)inputTable).Workspace;
IWorkspaceEdit wksE = (IWorkspaceEdit)wks;
if (wksE.IsBeingEdited())
{
wksE.StopEditing(true);
}
try
{
ICursor cur = inputTable.Update(qf, false);
int sIndex = cur.FindField(sampleFldName);
int cIndex = cur.FindField(strataFieldName);
int wIndex = cur.FindField(weightFldName);
IRow rw = cur.NextRow();
while (rw != null)
{
string clustStr = rw.get_Value(cIndex).ToString();
int clust = labels.IndexOf(clustStr);
double w = weightsPerCluster[clust];
double rNum = rd.NextDouble();
int ss = 0;
double r = randomRatioPerClust[clust];
if (rNum < r)
{
ss = 1;
}
rw.set_Value(sIndex, ss);
rw.set_Value(wIndex, w);
cur.UpdateRow(rw);
rw = cur.NextRow();
}
System.Runtime.InteropServices.Marshal.ReleaseComObject(cur);
}
catch (Exception e)
{
System.Windows.Forms.MessageBox.Show(e.ToString());
}
}
