new public void buildModel(string modelPath)
{
outmodelpath = modelPath;
using (System.IO.StreamReader sr = new System.IO.StreamReader(outmodelpath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.PAIREDTTEST)
{
System.Windows.Forms.MessageBox.Show("Not a PairedTTest Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
inpath = sr.ReadLine();
StrataField = sr.ReadLine();
VariableFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
lbl = sr.ReadLine().Split(new char[] { ',' }).ToList();
int[] sCnt = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToInt32(s)).ToArray();
cateDic.Clear();
for (int i = 0; i < lbl.Count; i++)
{
cateDic.Add(lbl[i], sCnt[i]);
}
sumX.Clear();
sumX2.Clear();
for (int i = 0; i < lbl.Count; i++)
{
sumX.Add((from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray());
sumX2.Add((from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray());
}
sr.Close();
}
}
public void buildModel(string modelPath)
{
outmodelpath = modelPath;
using (System.IO.StreamReader sr = new System.IO.StreamReader(outmodelpath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.CovCorr)
{
cov = new double[1, 1];
System.Windows.Forms.MessageBox.Show("Not a Variance Covariance Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
inpath = sr.ReadLine();
VariableFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
sumClms = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
cov = new double[sumClms.Length, sumClms.Length];
sumCross = new double[sumClms.Length, sumClms.Length];
string[] crossStr = sr.ReadLine().Split(new char[] { ',' });
string[] covStr = sr.ReadLine().Split(new char[] { ',' });
for (int i = 0; i < sumClms.Length; i++)
{
for (int j = 0; j < sumClms.Length; j++)
{
int indexVl = (i * sumClms.Length) + j;
sumCross[i, j] = System.Convert.ToDouble(crossStr[indexVl]);
cov[i, j] = System.Convert.ToDouble(covStr[indexVl]);
}
}
sr.Close();
}
}
public void buildModel(string modelPath)
{
outmodelpath = modelPath;
using (System.IO.StreamReader sr = new System.IO.StreamReader(outmodelpath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.TTEST)
{
System.Windows.Forms.MessageBox.Show("Not a TTest Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
inpath = sr.ReadLine();
stratafld = sr.ReadLine();
VariableFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
prop = System.Convert.ToDouble(sr.ReadLine());
k = System.Convert.ToInt32(sr.ReadLine());
lbl = sr.ReadLine().Split(new char[] { ',' }).ToList();
kmeans = new KMeans(k);
KMeansClusterCollection kmeansColl = kmeans.Clusters;
for (int i = 0; i < k; i++)
{
double[] mns = (from s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
string[] covVlsStr = sr.ReadLine().Split(new char[] { ',' });
double p = System.Convert.ToDouble(sr.ReadLine());
double[,] cov = new double[VariableFieldNames.Length, VariableFieldNames.Length];
for (int j = 0; j < VariableFieldNames.Length; j++)
{
for (int l = 0; l < VariableFieldNames.Length; l++)
{
int indexVl = (j * VariableFieldNames.Length) + l;
cov[l, j] = System.Convert.ToDouble(covVlsStr[indexVl]);
}
}
KMeansCluster kc = new KMeansCluster(kmeansColl, i);
kc.Mean = mns;
kc.Covariance = cov;
kc.Proportion = p;
}
sr.Close();
}
}
public void buildModel(string modelPath)
{
outmodelpath = modelPath;
using (System.IO.StreamReader sr = new System.IO.StreamReader(outmodelpath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.Cluster)
{
System.Windows.Forms.MessageBox.Show("Not a Cluster Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
cType = (clusterType)System.Enum.Parse(typeof(clusterType), sr.ReadLine());
inpath = sr.ReadLine();
VariableFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
prop = System.Convert.ToDouble(sr.ReadLine());
k = System.Convert.ToInt32(sr.ReadLine());
lbl = sr.ReadLine().Split(new char[] { ',' }).ToList();
switch (cType)
{
case clusterType.KMEANS:
setKMeansCluster(sr);
break;
case clusterType.BINARY:
setBinaryCluster(sr);
break;
case clusterType.GAUSSIANMIXTURE:
setGaussianCluster(sr);
break;
default:
break;
}
sr.Close();
}
}
public void buildModel(string modelPath)
{
outmodelpath = modelPath;
using (System.IO.StreamReader sr = new System.IO.StreamReader(outmodelpath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.PCA)
{
egVec = new double[1, 1];
System.Windows.Forms.MessageBox.Show("Not a PCA Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
inpath = sr.ReadLine();
VariableFieldNames = sr.ReadLine().Split(new char[] { ',' });
corr = new double[VariableFieldNames.Length, VariableFieldNames.Length];
egVec = new double[VariableFieldNames.Length, VariableFieldNames.Length];
n = System.Convert.ToInt32(sr.ReadLine());
meanVector = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
stdVector = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
string[] corrLg = sr.ReadLine().Split(new char[] { ',' });
prop = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
egVal = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
string[] egVecLg = sr.ReadLine().Split(new char[] { ',' });
for (int i = 0; i < VariableFieldNames.Length; i++)
{
for (int j = 0; j < VariableFieldNames.Length; j++)
{
int indexVl = (i * VariableFieldNames.Length) + j;
corr[i, j] = System.Convert.ToDouble(corrLg[indexVl]);
egVec[i, j] = System.Convert.ToDouble(egVecLg[indexVl]);
}
}
sr.Close();
}
pca = PrincipalComponentAnalysis.FromCorrelationMatrix(meanVector, stdVector, corr);
pca.Compute();
}
private void buildModel(string mdlPath)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(mdlPath))
{
dataPrepBase.modelTypes mType = (dataPrepBase.modelTypes)Enum.Parse(typeof(dataPrepBase.modelTypes), sr.ReadLine());
if (mType != dataPrepBase.modelTypes.KS)
{
System.Windows.Forms.MessageBox.Show("Not a KS Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
Variables = sr.ReadLine().Split(new char[] { ',' });
StrataField = sr.ReadLine();
Oridinate = System.Convert.ToBoolean(sr.ReadLine());
string[] lbl = sr.ReadLine().Split(new char[] { ',' });
string[] pArr = sr.ReadLine().Split(new char[] { ',' });
string[] sArr = sr.ReadLine().Split(new char[] { ',' });
string[] cArr1 = sr.ReadLine().Split(new char[] { ',' });
string[] cArr2 = sr.ReadLine().Split(new char[] { ',' });
for (int i = 0; i < lbl.Length; i++)
{
string l = lbl[i];
double[] p = (from string str in pArr[i].Split(new char[] { ';' }) select System.Convert.ToDouble(str)).ToArray();
double[] s = (from string str in sArr[i].Split(new char[] { ';' }) select System.Convert.ToDouble(str)).ToArray();
int c1 = System.Convert.ToInt32(cArr1[i]);
int c2 = System.Convert.ToInt32(cArr2[i]);
pDic.Add(l, p);
sDic.Add(l, s);
cntDic.Add(l, new int[] { c1, c2 });
}
double[][] minmax1, minmax2, bp1, bp2;
for (int i = 0; i < lbl.Length; i++)
{
string l = lbl[i];
double[] min1 = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
double[] max1 = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
double[] min2 = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
double[] max2 = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
minmax1 = new double[2][];
minmax1[0] = min1;
minmax1[1] = max1;
minMaxDic1.Add(l, minmax1);
minmax2 = new double[2][];
minmax2[0] = min2;
minmax2[1] = max2;
minMaxDic2.Add(l, minmax2);
bp1 = new double[Variables.Length][];
bp2 = new double[Variables.Length][];
for (int j = 0; j < Variables.Length; j++)
{
bp1[j] = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
bp2[j] = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToDouble(s)).ToArray();
}
binPropDic1.Add(l, bp1);
binPropDic2.Add(l, bp2);
int[] clusCnt = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToInt32(s)).ToArray();
clusCountDic.Add(l, clusCnt);
int[] clusSampCnt = (from string s in sr.ReadLine().Split(new char[] { ',' }) select System.Convert.ToInt32(s)).ToArray();
clusSampleCountDic.Add(l, clusSampCnt);
}
pca = new dataPrepPrincipleComponents();
string pcPath = System.IO.Path.GetDirectoryName(mdlPath) + "\\" + System.IO.Path.GetFileNameWithoutExtension(mdlPath) + "_pca.mdl";
pca.buildModel(pcPath);
cluster = new dataPrepClusterKmean();
string clusterPath = System.IO.Path.GetDirectoryName(mdlPath) + "\\" + System.IO.Path.GetFileNameWithoutExtension(mdlPath) + "_cluster.mdl";
cluster.buildModel(clusterPath);
numberOfBins = cluster.Classes;
sr.Close();
}
}
public static void getReport(string modelPath, double proportion = 0.1, double alpha = 0.05)
{
esriUtil.Forms.RunningProcess.frmRunningProcessDialog rp = new Forms.RunningProcess.frmRunningProcessDialog(false);
rp.addMessage("Sample size for " + modelPath + " based on variation");
rp.stepPGBar(50);
rp.Show();
dataPrepBase.modelTypes mType = ModelHelper.getModelType(modelPath);
rp.addMessage("Model Type = " + mType.ToString());
try
{
switch (mType)
{
case dataPrepBase.modelTypes.Accuracy:
fillAaReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.LinearRegression:
fillLrReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.MvlRegression:
fillMvrReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.LogisticRegression:
fillLogisticReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.PLR:
fillPlrReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.CovCorr:
fillCovCorr(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.PCA:
fillPcaReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.Cluster:
fillCluserReport(modelPath, rp, proportion, alpha);
break;
case dataPrepBase.modelTypes.StrataCovCorr:
fillStrataReport(modelPath, rp, proportion, alpha);
break;
default:
rp.addMessage("Can't estimate sample size for this type of model!");
break;
}
}
catch (Exception e)
{
rp.addMessage(e.ToString());
}
finally
{
rp.stepPGBar(100);
rp.enableClose();
}
}
