static void Main(string[] args)
{
REngine.SetEnvironmentVariables();
REngine RBlock = REngine.GetInstance();
DirectoryInfo di = new DirectoryInfo("C:\\Users\\" + Environment.UserName + "\\Documents\\File Attachments\\");
string searchPattern = "*";
// Set some bases. These need to be tested for each sample.
int avgRowCount = 100;
int avgColCount = 20;
int fileCount = 0;
// Get our files, Create a library to hold everything
Library.ListofFiles = Library.makeList(di, searchPattern);
// Look Through Each File
foreach (var a in Library.ListofFiles)
{
Console.WriteLine(a.FileName);
string fileLocation = di + a.FileName;
// Open The File, Set the Page
EL.singleExcel thisExcel = new EL.singleExcel().createExcel(fileLocation);
EL.singleExcel.ExcelWorkSheetChange(thisExcel, 1);
// Get Basic Vars
new EDA.excelBasics().basicVars(a, thisExcel);
// Run Series of Operations to Get More Exact Bounds
EDA.lookTriggers LT = new EDA.lookTriggers();
LT.runTriggers(fileCount, a, avgRowCount, avgColCount, out avgRowCount, out avgColCount);
Library.colCounts.Add(a.colCount);
Library.rowCounts.Add(a.rowCount);
// In the event that no rows are found, the file is not counted
if (a.rowCount != 0)
{
fileCount++;
}
EL.singleExcel.CloseSheet(thisExcel);
//Console.ReadLine();
Console.WriteLine("\n");
}
// Run some R analysis
IntegerVector rowR = RBlock.CreateIntegerVector(Library.rowCounts);
IntegerVector colR = RBlock.CreateIntegerVector(Library.colCounts);
RBlock.SetSymbol("rowR", rowR);
RBlock.SetSymbol("colR", colR);
int[] thisTemp;
RBlock.Evaluate("temp <- table(as.vector(rowR))");
thisTemp = RBlock.Evaluate("names(temp)[temp == max(temp)]").AsInteger().ToArray();
Library.groupStats.modeRow = thisTemp[0];
RBlock.Evaluate("temp <- table(as.vector(colR))");
thisTemp = RBlock.Evaluate("names(temp)[temp == max(temp)]").AsInteger().ToArray();
Library.groupStats.modeCol = thisTemp[0];
thisTemp = RBlock.Evaluate("mean(rowR)").AsInteger().ToArray();
Library.groupStats.meanRow = thisTemp[0];
thisTemp = RBlock.Evaluate("mean(colR)").AsInteger().ToArray();
Library.groupStats.meanCol = thisTemp[0];
thisTemp = RBlock.Evaluate("median(rowR)").AsInteger().ToArray();
Library.groupStats.medianRow = thisTemp[0];
thisTemp = RBlock.Evaluate("median(colR)").AsInteger().ToArray();
Library.groupStats.medianCol = thisTemp[0];
thisTemp = RBlock.Evaluate("min(rowR)").AsInteger().ToArray();
Library.groupStats.minRow = thisTemp[0];
thisTemp = RBlock.Evaluate("min(colR)").AsInteger().ToArray();
Library.groupStats.minCol = thisTemp[0];
thisTemp = RBlock.Evaluate("max(rowR)").AsInteger().ToArray();
Library.groupStats.maxRow = thisTemp[0];
thisTemp = RBlock.Evaluate("max(colR)").AsInteger().ToArray();
Library.groupStats.maxCol = thisTemp[0];
thisTemp = RBlock.Evaluate("IQR(rowR)").AsInteger().ToArray();
Library.groupStats.iqrRow = thisTemp[0];
thisTemp = RBlock.Evaluate("IQR(colR)").AsInteger().ToArray();
Library.groupStats.iqrCol = thisTemp[0];
thisTemp = RBlock.Evaluate("quantile(rowR)").AsInteger().ToArray();
Library.groupStats.quantileRow = thisTemp[0];
thisTemp = RBlock.Evaluate("quantile(colR)").AsInteger().ToArray();
Library.groupStats.quantileCol = thisTemp[0];
RBlock.Dispose();
EL.singleExcel.outputObjectToExcel(Library.groupStats);
// Build some training data from previous information, assumptions
object[,] theWords = TSR.trainMethods.getTrainData(di, "\\testdata\\trainlist.xlsx");
List <string> words = new List <string>();
TSR.trainMethods.makeTrainList(theWords, words);
theWords = TSR.trainMethods.getTrainData(di, "\\testdata\\looselist.xlsx");
List <string> looseWords = new List <string>();
TSR.trainMethods.makeTrainList(theWords, looseWords);
Dictionary <string, int> discreteTermsDict = new Dictionary <string, int>();
Dictionary <string, int> betterTermsDict = new Dictionary <string, int>();
Dictionary <string, int> secondaryTermsDict = new Dictionary <string, int>();
// Run training scenarios
TSR.trainMethods.runTrainScan(words, looseWords, avgColCount, Library.ListofFiles, out discreteTermsDict, out betterTermsDict, out secondaryTermsDict);
TSR.trainMethods.buildTrainList(discreteTermsDict, words);
TSR.trainMethods.runTrainScan(words, looseWords, avgColCount, Library.ListofFiles, out discreteTermsDict, out betterTermsDict, out secondaryTermsDict);
TSR.trainMethods.buildTrainList(betterTermsDict, looseWords);
TSR.trainMethods.buildTrainList(secondaryTermsDict, looseWords);
// In tests, three cycles make for extremely high confidence
TSR.trainMethods.runTrainScan(words, looseWords, avgColCount, Library.ListofFiles, out discreteTermsDict, out betterTermsDict, out secondaryTermsDict);
// Terms for classifcation
EL.singleExcel.outputListToExcel(words, "strongTrainList");
EL.singleExcel.outputListToExcel(looseWords, "learnedTrainList");
}
public async Task <IActionResult> Get()
{
//_rEngine.Evaluate("install.packages('forecast', dependencies = TRUE)");
//_rEngine.Evaluate("install.packages('lmtest', dependencies = TRUE)");
_rEngine.Evaluate("library(forecast)");
_rEngine.Evaluate("library(lmtest)");
string fileName = "myplot.png";
CharacterVector fileNameVector = _rEngine.CreateCharacterVector(new[] { fileName });
_rEngine.SetSymbol("fileName", fileNameVector);
_rEngine.Evaluate("png(filename=fileName, width=6, height=6, units='in', res=100)");
_rEngine.Evaluate("vector <- as.numeric(AirPassengers)");
_rEngine.Evaluate("z <- ts(vector, frequency = 12, start = c(1949, 1))");
_rEngine.Evaluate("z_train <- ts(z[1:100], frequency = 12)");
_rEngine.Evaluate("z_validate <- ts(z[101:144], frequency = 12)");
_rEngine.Evaluate("fit <- Arima(y = z_train, order = c(1,1,0), seasonal = c(1,1,0), lambda = TRUE)");
_rEngine.Evaluate("predition <- forecast(fit, h = 44)");
//_rEngine.Evaluate("z_train <- ts(z[1:100], frequency = 12");
_rEngine.Evaluate("plot(predition)"); //plot and save file
//_rEngine.Evaluate("lines(fit$fitted, col = 'blue')");
_rEngine.Evaluate("dev.off()");
Byte[] b = System.IO.File.ReadAllBytes(@"C:\Users\admin\source\repos\DataAnalisysApi\DataAnalisysApi\myplot.png"); // You can use your own method over here.
return(File(b, "image/jpeg"));
}
static void Main(string[] args)
{
// Sample code used for updating the documentation at the codeplex web site.
using (REngine engine = REngine.GetInstance())
{
var e = engine.Evaluate("x <- 3");
// You can now access x defined in the R environment.
NumericVector x = engine.GetSymbol("x").AsNumeric();
engine.Evaluate("y <- 1:10");
NumericVector y = engine.GetSymbol("y").AsNumeric();
// Invoking functions; Previously you may have needed custom function definitions
var myFunc = engine.Evaluate("function(x, y) { expand.grid(x=x, y=y) }").AsFunction();
var v1 = engine.CreateIntegerVector(new[] { 1, 2, 3 });
var v2 = engine.CreateCharacterVector(new[] { "a", "b", "c" });
var df = myFunc.Invoke(new SymbolicExpression[] { v1, v2 }).AsDataFrame();
// As of R.NET 1.6, more function call syntaxes are supported.
var expandGrid = engine.Evaluate("expand.grid").AsFunction();
var d = new Dictionary <string, SymbolicExpression>();
d["x"] = v1;
d["y"] = v2;
df = expandGrid.Invoke(d).AsDataFrame();
// querying data frames
engine.SetSymbol("cases", df);
// As of R.NET 1.6, factor to character expressions work consistently with R
engine.Evaluate("head(cases)");
/*
* x y
* 1 1 a
* 2 2 a
* 3 3 a
* 4 1 b
* 5 2 b
* 6 3 b */
var letterCases = engine.Evaluate("cases[,'y']").AsCharacter().ToArray();
// "a","a","a","b","b","b", etc. Same as as.character(cases[,'y']) in R
// This used to return "1", "1", "1", "2", "2", etc. with R.NET 1.5.5
// Equivalent:
letterCases = df[1].AsCharacter().ToArray();
letterCases = df["y"].AsCharacter().ToArray();
// Accessing items by two dimensional indexing
string s = (string)df[1, 1]; // "a"
s = (string)df[3, 1]; // "a"
s = (string)df[3, "y"]; // "b"
// s = (string)df["4", "y"]; // fails because there are no row names
df[3, "y"] = "a";
s = (string)df[3, "y"]; // "a"
df[3, "y"] = "d";
s = (string)df[3, "y"]; // null, because we have an <NA> string in R
// invoking a whole script
// engine.Evaluate("source('c:/src/path/to/myscript.r')");
// TODO
// Date-time objects
}
}
private void btnRun_Click(object sender, EventArgs e)
{
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Collecting Data:";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Marquee;
pfrmProgress.Show();
//Get number of variables
int intnVar = dgvVariables.Rows.Count - 1;
for (int j = 0; j < intnVar; j++)
{
if (dgvVariables.Rows[j].Cells[0].Value.ToString() == "" || dgvVariables.Rows[j].Cells[1].Value.ToString() == "")
{
MessageBox.Show("Please select both variable and standard errors.", "Errors");
return;
}
}
clsSnippet pSnippet = new clsSnippet();
// Gets the variable names and indices
BhattaVariables[] pBVariable = new BhattaVariables[intnVar];
int nFeature = m_pFClass.FeatureCount(null);
for (int j = 0; j < intnVar; j++)
{
//BhattaVariables tmp = new BhattaVariables();
//tmp.variableNames = (string)dgvVariables.Rows[j].Cells[0].Value;
//tmp.errorNames = (string)dgvVariables.Rows[j].Cells[1].Value;
//tmp.idVar = m_pFClass.Fields.FindField(tmp.variableNames);
//tmp.idError = m_pFClass.Fields.FindField(tmp.errorNames);
//tmp.arrVar = new double[nFeature];
//tmp.arrError = new double[nFeature];
//pBVariable.Add(tmp);
pBVariable[j] = new BhattaVariables();
pBVariable[j].variableNames = (string)dgvVariables.Rows[j].Cells[0].Value;
pBVariable[j].errorNames = (string)dgvVariables.Rows[j].Cells[1].Value;
pBVariable[j].idVar = m_pFClass.Fields.FindField(pBVariable[j].variableNames);
pBVariable[j].idError = m_pFClass.Fields.FindField(pBVariable[j].errorNames);
pBVariable[j].arrVar = new double[nFeature];
pBVariable[j].arrError = new double[nFeature];
}
//Get values of var and error from shp file
IFeatureCursor pFCursor = m_pFClass.Search(null, true);
IFeature pFeature = pFCursor.NextFeature();
//Store independent values at Array
int i = 0;
while (pFeature != null)
{
for (int j = 0; j < intnVar; j++)
{
//arrInDepen[j] = new double[nFeature];
pBVariable[j].arrVar[i] = Convert.ToDouble(pFeature.get_Value(pBVariable[j].idVar));
pBVariable[j].arrError[i] = Convert.ToDouble(pFeature.get_Value(pBVariable[j].idError));
}
i++;
pFeature = pFCursor.NextFeature();
}
pfrmProgress.lblStatus.Text = "Creating Distance Matrix";
if (cboDistance.Text == "Bhattacharyya distance") //Bhatta dist
{
string strStartPath = m_pForm.strPath;
string pathr = strStartPath.Replace(@"\", @"/");
m_pEngine.Evaluate("source('" + pathr + "/clustering.R')");
//Create Matrix for Distance calculation
m_pEngine.Evaluate("Bhatta.diss <- matrix(0, " + nFeature.ToString() + ", " + nFeature.ToString() + ")");
StringBuilder[] plotCommmand = new StringBuilder[4];
//Need to optimize 12132017 HK
for (int k = 0; k < nFeature; k++)
{
for (int l = 0; l < k + 1; l++)
{
for (int j = 0; j < 4; j++)
{
plotCommmand[j] = new StringBuilder();
}
plotCommmand[0].Append("x1 <- cbind(");
plotCommmand[1].Append("x2 <- cbind(");
plotCommmand[2].Append("v1 <- cbind(");
plotCommmand[3].Append("v2 <- cbind(");
for (int j = 0; j < intnVar; j++)
{
plotCommmand[0].Append(pBVariable[j].arrVar[k].ToString() + ", ");
plotCommmand[1].Append(pBVariable[j].arrVar[l].ToString() + ", ");
plotCommmand[2].Append(pBVariable[j].arrError[k].ToString() + ", ");
plotCommmand[3].Append(pBVariable[j].arrError[l].ToString() + ", ");
}
for (int j = 0; j < 4; j++)
{
plotCommmand[j].Remove(plotCommmand[j].Length - 2, 2);
plotCommmand[j].Append(")");
m_pEngine.Evaluate(plotCommmand[j].ToString());
}
m_pEngine.Evaluate("Bhatta.diss[" + (k + 1).ToString() + ", " + (l + 1).ToString() + "] <- Bhatta.mdist(x1, x2, v1, v2)");
m_pEngine.Evaluate("Bhatta.diss[" + (l + 1).ToString() + ", " + (k + 1).ToString() + "] <- Bhatta.mdist(x2, x1, v2, v1)");
}
}
pfrmProgress.lblStatus.Text = "Finding Clusters";
m_pEngine.Evaluate("sample.hclu <- hclust(as.dist(Bhatta.diss))");
}
else if (cboDistance.Text == "Euclidean distance")
{
StringBuilder plotCommmand = new StringBuilder();
plotCommmand.Append("Euc.dis <- dist(cbind(");
for (int j = 0; j < intnVar; j++)
{
NumericVector vecVar = m_pEngine.CreateNumericVector(pBVariable[j].arrVar);
m_pEngine.SetSymbol(pBVariable[j].variableNames, vecVar);
plotCommmand.Append(pBVariable[j].variableNames + ", ");
}
plotCommmand.Remove(plotCommmand.Length - 2, 2);
plotCommmand.Append("))");
m_pEngine.Evaluate(plotCommmand.ToString());
pfrmProgress.lblStatus.Text = "Finding Clusters";
m_pEngine.Evaluate("sample.hclu <- hclust(Euc.dis)");
}
string strTitle = "Dendrogram";
string strCommand = "plot(sample.hclu);";
pSnippet.drawPlottoForm(strTitle, strCommand);
int intNClasses = Convert.ToInt32(nudNClasses.Value);
m_pEngine.Evaluate("sample.cut <- cutree(sample.hclu, " + intNClasses.ToString() + ")");
//Save Outputs in SHP
pfrmProgress.lblStatus.Text = "Save Results";
//The field names are related with string[] DeterminedName in clsSnippet
string strOutputFldName = txtOutputFld.Text;
//Get EVs and residuals
NumericVector nvClasses = m_pEngine.Evaluate("sample.cut").AsNumeric();
// Create field, if there isn't
if (m_pFClass.FindField(strOutputFldName) == -1)
{
//Add fields
IField newField = new FieldClass();
IFieldEdit fieldEdit = (IFieldEdit)newField;
fieldEdit.Name_2 = strOutputFldName;
fieldEdit.Type_2 = esriFieldType.esriFieldTypeDouble;
m_pFClass.AddField(newField);
}
else
{
DialogResult dialogResult = MessageBox.Show("Do you want to overwrite " + strOutputFldName + " field?", "Overwrite", MessageBoxButtons.YesNo);
if (dialogResult == DialogResult.No)
{
return;
}
}
//Update Field
pFCursor.Flush();
pFCursor = m_pFClass.Update(null, false);
pFeature = pFCursor.NextFeature();
int featureIdx = 0;
int intOutputFldIdx = m_pFClass.FindField(strOutputFldName);
while (pFeature != null)
{
//Update Residuals
pFeature.set_Value(intOutputFldIdx, (object)nvClasses[featureIdx]);
pFCursor.UpdateFeature(pFeature);
pFeature = pFCursor.NextFeature();
featureIdx++;
}
//Not working properly
////Creating unique value map
//IGeoFeatureLayer geoFeatureLayer = (IGeoFeatureLayer)m_pFLayer;
//IUniqueValueRenderer uniqueValueRenderer = new UniqueValueRendererClass();
//uniqueValueRenderer.FieldCount = 1;
//uniqueValueRenderer.set_Field(0, strOutputFldName);
//IDataStatistics pDataStat = new DataStatisticsClass();
//pDataStat.Field = strOutputFldName;
//ITable pTable = (ITable)m_pFClass;
//ICursor pCursor = pTable.Search(null, true);
//pDataStat.Cursor = pCursor;
//int intUniqValueCnt = pDataStat.UniqueValueCount;
//System.Collections.IEnumerator enumerator = pDataStat.UniqueValues;
//enumerator.Reset();
//ISimpleFillSymbol simpleFillSymbol = new SimpleFillSymbolClass();
//simpleFillSymbol.Style = esriSimpleFillStyle.esriSFSSolid;
//IEnumColors enumColors = CreateAlgorithmicColorRamp(intUniqValueCnt).Colors;
//for (int j = 0; j < intUniqValueCnt; j++)
//{
// string value = enumerator.Current.ToString();
// enumerator.MoveNext();
// simpleFillSymbol = new SimpleFillSymbolClass();
// simpleFillSymbol.Color = enumColors.Next();
// uniqueValueRenderer.AddValue(value, strOutputFldName, simpleFillSymbol as ISymbol);
//}
////IFeatureCursor featureCursor = geoFeatureLayer.FeatureClass.Search(null, false);
////IFeature feature;
////if (featureCursor != null)
////{
//// IEnumColors enumColors = CreateAlgorithmicColorRamp(8).Colors;
//// //int fieldIndex = geoFeatureLayer.FeatureClass.Fields.FindField("continent");
//// for (int j = 0; j < uniqueValueRenderer.ValueCount; j++)
//// {
//// string value = uniqueValueRenderer.get_Value(i);
//// uniqueValueRenderer.set_Symbol(value, simpleFillSymbol as ISymbol);
//// feature = featureCursor.NextFeature();
//// string nameValue = feature.get_Value(intOutputFldIdx).ToString();
//// simpleFillSymbol = new SimpleFillSymbolClass();
//// simpleFillSymbol.Color = enumColors.Next();
//// uniqueValueRenderer.AddValue(nameValue, strOutputFldName, simpleFillSymbol as ISymbol);
//// }
////}
//geoFeatureLayer.Renderer = uniqueValueRenderer as IFeatureRenderer;
//m_pActiveView.Refresh();
//m_pForm.axTOCControl1.Update();
MessageBox.Show("Clustering results are stored in the source shapefile");
pfrmProgress.Close();
}
public ResourcePackage Import(string path, string agencyId)
{
this.harmonizingCache = new HarmonizingCache(MultilingualString.CurrentCulture);
var resourcePackage = new ResourcePackage();
resourcePackage.AgencyId = agencyId;
logger.Debug("Importing RData");
if (string.IsNullOrEmpty(path))
{
throw new ArgumentNullException("fileName");
}
if (!File.Exists(path))
{
throw new ArgumentException("The specified file must exist");
}
string fileNameWithExtension = Path.GetFileName(path);
string fileNameOnly = Path.GetFileNameWithoutExtension(path);
logger.Debug("RData import file: " + fileNameOnly);
resourcePackage.DublinCoreMetadata.Title.Current = fileNameOnly;
// Create the PhysicalInstance.
var physicalInstance = new PhysicalInstance()
{
AgencyId = agencyId
};
resourcePackage.PhysicalInstances.Add(physicalInstance);
physicalInstance.DublinCoreMetadata.Title.Current = fileNameOnly;
// File location
if (path != null)
{
DataFileIdentification fileID = new DataFileIdentification();
Uri uri;
if (Uri.TryCreate(path, UriKind.RelativeOrAbsolute, out uri))
{
fileID.Uri = uri;
}
fileID.Path = path;
physicalInstance.FileIdentifications.Add(fileID);
}
// Create the DataRelationship.
var dataRelationship = new DataRelationship();
physicalInstance.DataRelationships.Add(dataRelationship);
dataRelationship.AgencyId = agencyId;
dataRelationship.Label.Current = fileNameOnly;
// Load the file into R.
string pathForR = path.Replace("\\", "/");
engine.Evaluate(string.Format("load('{0}')", pathForR));
// Find all the data frames.
var dataFrames = GetDataFrames();
// For each data frame in the RData file, create a LogicalRecord.
foreach (var pair in dataFrames)
{
string name = pair.Key;
var dataFrame = pair.Value;
// TODO This should be tracked per record, not PhysicalInstance.
physicalInstance.FileStructure.CaseQuantity = dataFrame.RowCount;
var logicalRecord = new LogicalRecord()
{
AgencyId = agencyId
};
dataRelationship.LogicalRecords.Add(logicalRecord);
logicalRecord.Label.Current = name;
List <string> variableLabels = null;
var variableLabelsExpr = dataFrame.GetAttribute("var.labels");
if (variableLabelsExpr != null)
{
var labelVector = variableLabelsExpr.AsVector();
variableLabels = new List <string>(labelVector.Select(x => (string)x));
}
for (int i = 0; i < dataFrame.ColumnCount; i++)
{
string columnName = dataFrame.ColumnNames[i];
var column = dataFrame[i];
var variable = new Variable()
{
AgencyId = agencyId
};
logicalRecord.VariablesInRecord.Add(variable);
// Name
variable.ItemName.Current = columnName;
// Label
if (variableLabels != null)
{
variable.Label.Current = variableLabels[i];
}
// Type
if (column.Type == RDotNet.Internals.SymbolicExpressionType.NumericVector)
{
variable.RepresentationType = RepresentationType.Numeric;
variable.Additivity = AdditivityType.Stock;
}
else if (column.Type == RDotNet.Internals.SymbolicExpressionType.IntegerVector)
{
if (column.IsFactor())
{
variable.RepresentationType = RepresentationType.Code;
string[] factors = column.AsFactor().GetLevels();
variable.CodeRepresentation.Codes = GetCodeList(factors, agencyId, resourcePackage);
}
else
{
variable.RepresentationType = RepresentationType.Numeric;
variable.NumericRepresentation.NumericType = NumericType.Integer;
variable.Additivity = AdditivityType.Stock;
}
}
else if (column.Type == RDotNet.Internals.SymbolicExpressionType.CharacterVector)
{
variable.RepresentationType = RepresentationType.Text;
}
}
}
return(resourcePackage);
}
private void InicializeRNet()
{
string rFilePath = @"C:\\Program Files\\R\\R-3.3.1\\library\\Functions.R";
engine.Evaluate("source(\"" + rFilePath + "\")");
}
public void Test(HomeController controller)
{
//require R 2.15, package forecast on R
//var envPath = Environment.GetEnvironmentVariable("PATH");
//var rBinPath = GetRPath(); //C:\Program Files\R\R-2.15.1\bin\i386
//var BinPath = GetWinRegistryPath();
//var envPath = Environment.GetEnvironmentVariable("PATH");
// this statement not work under iis
//Environment.SetEnvironmentVariable("PATH", envPath + Path.PathSeparator + BinPath);
REngine engine = REngine.GetInstance();
engine.Initialize();
string currentPath = @"C:\Workspace\SAM\SAM\VisualAnalytics\bin"; //Directory.GetCurrentDirectory();
string dataPath = currentPath + @"\data\paper.dat";
string readDataCommand = string.Format("predata <- read.table(\"{0}\", header=FALSE)", dataPath).Replace('\\', '/');
//engine.Evaluate("install.packages(\"forecast\")");
engine.Evaluate("library(jsonlite)");
engine.Evaluate("library(forecast)");
String JsonData = @"http://localhost:25910/Home/getBigTable";//controller.getBigTable().Content;
log.Debug("JSON:" + JsonData);
string evaluate = string.Format("data <-fromJSON(\"{0}\")", JsonData);
log.Debug("evaluateString:" + evaluate);
engine.Evaluate(evaluate);
//log.Debug(engine.Evaluate("data"));
//engine.Evaluate("data <- predata[,1]");
var model = engine.Evaluate("fit <- auto.arima(data$Amount)").AsList();
foreach (var item in model)
{
log.Debug(item.ToString());
}
var coef = model["coef"].AsList();
int lengthData = engine.Evaluate("data$Amount").AsNumeric().Length;
double[] dataSeries = new double[lengthData];
double[] errorSeries = new double[lengthData];
engine.Evaluate("data$Amount").AsNumeric().CopyTo(dataSeries, lengthData);
model["residuals"].AsNumeric().CopyTo(errorSeries, lengthData);
//residuals
int arOrder = model["arma"].AsInteger().ElementAt(0);
int maOrder = model["arma"].AsInteger().ElementAt(1);
int arSeasonOrder = model["arma"].AsInteger().ElementAt(2);
int maSeasonOrder = model["arma"].AsInteger().ElementAt(3);
int seasonOrder = model["arma"].AsInteger().ElementAt(4);
int diffOrder = model["arma"].AsInteger().ElementAt(5);
int diffSeasonOrder = model["arma"].AsInteger().ElementAt(6);
double[] arCoef = new double[arOrder];
double[] maCoef = new double[maOrder];
double[] arSeasonCoef = new double[arSeasonOrder];
double[] maSeasonCoef = new double[maSeasonOrder];
double intercept = 0;
int n = model["coef"].AsNumeric().Length;
int start = 0;
model["coef"].AsNumeric().CopyTo(arCoef, arOrder, start, 0);
start += arOrder;
model["coef"].AsNumeric().CopyTo(maCoef, maOrder, start, 0);
start += maOrder;
model["coef"].AsNumeric().CopyTo(arSeasonCoef, arSeasonOrder, start, 0);
start += arSeasonOrder;
model["coef"].AsNumeric().CopyTo(maSeasonCoef, maSeasonOrder, start, 0);
start += maSeasonOrder;
if (n > start)
{
intercept = model["coef"].AsNumeric().ElementAt(start);
}
ArimaModel arimaModel = new ArimaModel(arCoef, maCoef, arSeasonCoef, maSeasonCoef, intercept, (uint)seasonOrder, (uint)diffOrder, (uint)diffSeasonOrder);
Polynomial arModel = arimaModel.ComputeARModel();
Polynomial maModel = arimaModel.ComputeMAModel();
double interceptModel = arimaModel.ComputeIntercept();
double test = arimaModel.ComputeValue(dataSeries, errorSeries, dataSeries.Length);
log.Info("Forecast");
log.Info(test);
log.Info("Model");
log.Info(interceptModel);
log.Info("Ar");
log.Info(arModel.ToString());
log.Info("Ma");
log.Info(maModel.ToString());
//Console.ReadLine();
}
private void declarePackages()
{
engine.Evaluate(string.Format("source('{0}')", RCodeControl.RequireFilesAddress));
}
public void test_StartItems_P(int numofItems, ModelNames.Models paramModel)
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Polytomous Items
CharacterVector modelName = engineObj.CreateCharacterVector(new string[] { paramModel.EnumToString() });
engineObj.SetSymbol("modelName", modelName);
DataFrame PolyItems = engineObj.Evaluate("PolyItems <- genPolyMatrix(" + numofItems + ", 5, model = modelName, same.nrCat = TRUE)").AsDataFrame();
engineObj.SetSymbol("PolyItems", PolyItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
Console.WriteLine("*******************************************");
Console.WriteLine("Polytomous Items, Model : " + paramModel.EnumToString());
Console.WriteLine("*******************************************");
CATItems itemBank = new CATItems(PolyItems[0].Length, paramModel.EnumToString(), 5);
Tuple <CATItems.ColumnNames, int>[] cols = itemBank.GetKeys();
for (int i = 0; i < cols.Length; i++)
{
itemBank.SetItemParamter(cols[i], PolyItems[i].Select(y => (double)y).ToArray());
}
// Call "StartItems" function from R, "MFI" criterion
GenericVector r_StartItems = engineObj.Evaluate("r_StartItems <- startItems(PolyItems, model = modelName, nrItems = 3, theta = 1, halfRange = 2)").AsList();
// Resulting item numbers
IntegerVector items = r_StartItems[0].AsInteger();
DataFrame tempitems = r_StartItems[1].AsDataFrame();
// Resulting Items
CATItems r_CatItems = new CATItems(tempitems[0].Length, paramModel.EnumToString(), 5);
for (int i = 0; i < cols.Length; i++)
{
r_CatItems.SetItemParamter(cols[i], tempitems[i].Select(y => (double)y).ToArray());
}
// Resulting
NumericVector thStart = r_StartItems[2].AsNumeric();
CharacterVector startSelect = r_StartItems[3].AsCharacter();
// Call "StartItems" function from CS
StartItemsModel cs_StartItems = CatRLib.StartItems(itemBank, paramModel.EnumToString(), nrItems: 3, theta: 1, halfRange: 2);
// Check items
if (items.Length == cs_StartItems.items.Length)
{
for (int ind = 0; ind < cs_StartItems.items.Length; ind++)
{
if (items[ind] != cs_StartItems.items[ind])
{
resultFlag = false;
}
}
}
// Check starting ability values
if (thStart.Length == cs_StartItems.thStart.Length)
{
for (int ind2 = 0; ind2 < cs_StartItems.thStart.Length; ind2++)
{
if (thStart[ind2] != cs_StartItems.thStart[ind2])
{
resultFlag = false;
}
}
}
Assert.IsTrue(resultFlag);
}
private static void printMemLimit(REngine engine)
{
Console.WriteLine("************");
Console.WriteLine("*** NOTE: memory.limit() returns: " + engine.Evaluate("memory.limit()").AsNumeric()[0]);
Console.WriteLine("************");
}
private void listView_Click(object sender, SelectionChangedEventArgs e)
{
// Companies id = (Companies)e.AddedItems[0];
// string name = id.Symbol;
if (lvUsers.SelectedIndex != -1)
{
string date;
string fileName = @"myplot2.png";
Companies c = (Companies)lvUsers.SelectedItem;
try
{
HttpWebRequest request = (HttpWebRequest)WebRequest.Create(String.Format("http://ichart.finance.yahoo.com/table.csv?s={0}&ignore=.csv", c.Symbol));
request.Method = WebRequestMethods.Http.Head;
HttpWebResponse response = (HttpWebResponse)request.GetResponse();
response.Close();
if (response.StatusCode != HttpStatusCode.OK)
{
MessageBox.Show("שגיאה: מידע פננסי עבור החברה שנבחרה לא קיים.");
this.Graph.Source = null;
return;
}
}
catch
{
MessageBox.Show("שגיאה: מידע פננסי עבור החברה שנבחרה לא קיים.");
this.Graph.Source = null;
return;
}
//REngine engine;
REngine.SetEnvironmentVariables();
// engine = REngine.GetInstance();
engine.Initialize();
List <string> dates = new List <string>();
List <float> p_list = new List <float>();
for (int i = 14; i > 0; i--)
{
if (DateTime.Now.Subtract(new TimeSpan(i, 0, 0, 0)).DayOfWeek.ToString() != "Sunday" && DateTime.Now.Subtract(new TimeSpan(i, 0, 0, 0)).DayOfWeek.ToString() != "Saturday")
{
date = "" + DateTime.Now.Subtract(new TimeSpan(i, 0, 0, 0)).Year.ToString() + "-" + DateTime.Now.Subtract(new TimeSpan(i, 0, 0, 0)).Month.ToString() + "-" + DateTime.Now.Subtract(new TimeSpan(i, 0, 0, 0)).Day.ToString();
dates.Add(date);
}
}
string temp = "getYahooStockUrl <- function(symbol, start, end, type = \"d\") {\n start <- as.Date(start)\n end <- as.Date(end)\n url <-\"http://ichart.finance.yahoo.com/table.csv?s=%s&a=%d&b=%s&c=%s&d=%d&e=%s&f=%s&g=%s&ignore=.csv\"\n sprintf(url,\ntoupper(symbol),\nas.integer(format(start, \"%m\")) - 1,\nformat(start, \"%d\"),\nformat(start, \"%Y\"),\nas.integer(format(end, \"%m\")) - 1, \nformat(end, \"%d\"),\nformat(end, \"%Y\"),\ntype)} \ndata<- read.csv(getYahooStockUrl(\"" + c.Symbol + "\", \"" + dates.First().ToString() + "\", \"" + dates.Last().ToString() + "\"),\nstringsAsFactors = FALSE)";
DataFrame obj = engine.Evaluate(temp).AsDataFrame();
//engine.Evaluate("plot(data$Close,type=\"l\",col=\"red\")");
if (File.Exists(fileName))
{
this.Graph.Source = null;
File.Delete(fileName);
}
//CharacterVector uVector = engine.CreateCharacterVector(u_date_list_h);
engine.SetSymbol("date", obj["Date"]);
//CharacterVector pVector = engine.CreateCharacterVector(help);
engine.SetSymbol("value", obj["Close"]);
//engine.SetSymbol("obj", obj);
CharacterVector fileNameVector = engine.CreateCharacterVector(new[] { fileName });
engine.SetSymbol("fileName", fileNameVector);
engine.Evaluate("png(filename=fileName, width=6, height=6, units='in', res=100)");
engine.Evaluate("date <- as.Date(date)");
engine.Evaluate("family <- as.factor(data[,4])");
engine.Evaluate("par(bg = \"transparent\")");
engine.Evaluate("plot(value~date,pch=16, col=family, ann=FALSE)");
obj.Close();
engine.Evaluate("dev.off()");
//engine.Dispose();
var bitmap = new BitmapImage();
var stream = File.OpenRead(fileName);
bitmap.BeginInit();
bitmap.CacheOption = BitmapCacheOption.OnLoad;
bitmap.StreamSource = stream;
bitmap.EndInit();
stream.Close();
stream.Dispose();
this.Graph.Source = bitmap;
//MessageBox.Show(Convert.ToString(listView.SelectedIndices[0]));
}
}
private void btnRun_Click(object sender, EventArgs e)
{
if (cboFldnm1.Text == "" || cboFldnm2.Text == "")
{
MessageBox.Show("Please select target field");
return;
}
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Processing:";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Marquee;
pfrmProgress.Show();
REngine pEngine = m_pForm.pEngine;
int nFeature = m_pFClass.FeatureCount(null);
IFeatureCursor pFCursor = m_pFClass.Search(null, true);
IFeature pFeature = pFCursor.NextFeature();
//Get index for independent and dependent variables
//Get variable index
string strVarNM1 = (string)cboFldnm1.SelectedItem;
string strVarNM2 = (string)cboFldnm2.SelectedItem;
int intVarIdx1 = m_pFClass.FindField(strVarNM1);
int intVarIdx2 = m_pFClass.FindField(strVarNM2);
//Store Variable at Array
double[] arrVar1 = new double[nFeature];
double[] arrVar2 = new double[nFeature];
int i = 0;
while (pFeature != null)
{
arrVar1[i] = Convert.ToDouble(pFeature.get_Value(intVarIdx1));
arrVar2[i] = Convert.ToDouble(pFeature.get_Value(intVarIdx2));
i++;
pFeature = pFCursor.NextFeature();
}
pFCursor.Flush();
//Plot command for R
StringBuilder plotCommmand = new StringBuilder();
string strStartPath = m_pForm.strPath;
string pathr = strStartPath.Replace(@"\", @"/");
pEngine.Evaluate("source('" + pathr + "/ESDA_LEE/AllFunctions_LARRY.R')");
pEngine.Evaluate("source('" + pathr + "/ESDA_LEE/AllFunctions_neighbor.R')");
pEngine.Evaluate("source('" + pathr + "/ESDA_LEE/AllFunctions_SASbi.R')");
//Get the file path and name to create spatial weight matrix
string strNameR = m_pSnippet.FilePathinRfromLayer(m_pFLayer);
if (strNameR == null)
{
return;
}
//Create spatial weight matrix in R
pEngine.Evaluate("library(spdep); library(maptools)");
pEngine.Evaluate("sample.shp <- readShapePoly('" + strNameR + "')");
//pEngine.Evaluate("sample.nb <- poly2nb(sample.shp, queen=FALSE)");
pEngine.Evaluate("sample.nb <- poly2nb(sample.shp)");
NumericVector vecVar1 = pEngine.CreateNumericVector(arrVar1);
pEngine.SetSymbol("sample.v1", vecVar1);
NumericVector vecVar2 = pEngine.CreateNumericVector(arrVar2);
pEngine.SetSymbol("sample.v2", vecVar2);
//string strRSigLv = nudRsigLv.Value.ToString();
//string strLSigLv = nudLsigLv.Value.ToString();
//string strLSig = cboLocalL.Text;
//string strRsig = cboLocalPearson.Text;
//string strRowStd = cboRowStandardization.Text;
//string strMaxRanges = nudMaxRange.Value.ToString();
//string strHigherOrder = cboHigherOrder.Text;
string strNonZero = null;
if (chkDiagZero.Checked)
{
strNonZero = "FALSE";
}
else
{
strNonZero = "TRUE";
}
double[] dblLoclLisa = null;
if (cboMeasure.Text == "Lee's L")
{
pEngine.Evaluate("sample.result <- LARRY.bivariate.LISA.lee(sample.v1, sample.v2, 1:length(sample.nb), sample.nb, style = 'W', diag.zero = " + strNonZero + ")");
dblLoclLisa = pEngine.Evaluate("as.numeric(sample.result$local.L)").AsNumeric().ToArray();
}
else if (cboMeasure.Text == "Local Pearson")
{
pEngine.Evaluate("sample.result <- LARRY.bivariate.LISA.pearson(sample.v1, sample.v2, 1:length(sample.nb))");
if (cboMapOption.Text == "Local Pearson")
{
dblLoclLisa = pEngine.Evaluate("as.numeric(sample.result$local.pearson)").AsNumeric().ToArray();
}
else if (cboMapOption.Text == "z-score of variable 1")
{
dblLoclLisa = pEngine.Evaluate("as.numeric(sample.result$z.x)").AsNumeric().ToArray();
}
else if (cboMapOption.Text == "z-score of variable 2")
{
dblLoclLisa = pEngine.Evaluate("as.numeric(sample.result$z.y)").AsNumeric().ToArray();
}
}
//Save Output on SHP
//Add Target fields to store results in the shapefile // Keep loop
for (int j = 0; j < 1; j++)
{
string strfldName = lvFields.Items[j].SubItems[1].Text;
if (m_pFClass.FindField(strfldName) == -1)
{
IField newField = new FieldClass();
IFieldEdit fieldEdit = (IFieldEdit)newField;
fieldEdit.Name_2 = strfldName;
fieldEdit.Type_2 = esriFieldType.esriFieldTypeDouble;
m_pFClass.AddField(newField);
}
}
//Update Field
pFCursor = m_pFClass.Update(null, false);
pFeature = pFCursor.NextFeature();
string strLocalLISAFldName = lvFields.Items[0].SubItems[1].Text;
int intSpQuadFldIdx = m_pFClass.FindField(strLocalLISAFldName);
int featureIdx = 0;
while (pFeature != null)
{
pFeature.set_Value(intSpQuadFldIdx, dblLoclLisa[featureIdx]);
pFCursor.UpdateFeature(pFeature);
pFeature = pFCursor.NextFeature();
featureIdx++;
}
pFCursor.Flush();
if (chkMap.Checked)
{
ITable pTable = (ITable)m_pFClass;
pFCursor = m_pFClass.Search(null, false);
IDataStatistics pDataStat = new DataStatisticsClass();
pDataStat.Field = strLocalLISAFldName;
pDataStat.Cursor = (ICursor)pFCursor;
IStatisticsResults pStatResults = pDataStat.Statistics;
double dblMax = pStatResults.Maximum;
double dblMin = pStatResults.Minimum;
int intBreaksCount = m_pBiLISASym.Length + 1;
double[] cb = new double[intBreaksCount];
//Assign Min and Max values for class breaks
cb[0] = dblMin;
cb[intBreaksCount - 1] = dblMax;
for (int k = 0; k < intBreaksCount - 2; k++)
{
cb[k + 1] = m_pBiLISASym[k].UValue;
}
IClassBreaksRenderer pCBRenderer = new ClassBreaksRenderer();
pCBRenderer.Field = strLocalLISAFldName;
pCBRenderer.BreakCount = intBreaksCount - 1;
pCBRenderer.MinimumBreak = cb[0];
//' use this interface to set dialog properties
IClassBreaksUIProperties pUIProperties = (IClassBreaksUIProperties)pCBRenderer;
pUIProperties.ColorRamp = "Custom";
ISimpleFillSymbol pSimpleFillSym;
//int[,] arrColors = CreateColorRamp();
////Add Probability Value Manually
//string[] strsProbLabels = new string[] { "(0.01)", "(0.05)", "(0.1)", "(0.1)", "(0.05)", "(0.01)" };
//' be careful, indices are different for the diff lists
for (int j = 0; j < intBreaksCount - 1; j++)
{
pCBRenderer.Break[j] = cb[j + 1];
pCBRenderer.Label[j] = m_pBiLISASym[j].Label;
pUIProperties.LowBreak[j] = cb[j];
pSimpleFillSym = new SimpleFillSymbolClass();
IRgbColor pRGBColor = m_pSnippet.getRGB(m_pBiLISASym[j].R, m_pBiLISASym[j].G, m_pBiLISASym[j].B);
pSimpleFillSym.Color = (IColor)pRGBColor;
pCBRenderer.Symbol[j] = (ISymbol)pSimpleFillSym;
}
IFeatureLayer pNewFLayer = new FeatureLayerClass();
pNewFLayer.FeatureClass = m_pFClass;
pNewFLayer.Name = lvFields.Items[0].SubItems[0].Text + " of " + m_pFLayer.Name;
IGeoFeatureLayer pGFLayer = (IGeoFeatureLayer)pNewFLayer;
pGFLayer.Renderer = (IFeatureRenderer)pCBRenderer;
m_pActiveView.FocusMap.AddLayer(pGFLayer);
m_pActiveView.Refresh();
m_pForm.axTOCControl1.Update();
pfrmProgress.Close();
}
else
{
MessageBox.Show("Complete. The results are stored in the shape file");
}
}
private async void CalculateScores()
{
List <double> xRange = mWindow.ParseRange(Delays);
List <double> yRange = mWindow.ParseRange(Values);
if (!double.TryParse(MaxValue, out maxValueA) || (xRange.Count != yRange.Count))
{
mInterface.SendMessageToOutput("Error in computing ranges, inputs must be equal in length.");
mInterface.SendMessageToOutput("Counts for Delays and Values were " + xRange.Count + " and " + yRange.Count + " respectively.");
mInterface.SendMessageToOutput("Max value was: " + maxValueA);
MessageBox.Show("Hmm, check your ranges. These don't seem paired up");
return;
}
mInterface.SendMessageToOutput("---------------------------------------------------");
mDiscount.maxValue = maxValueA;
mDiscount.xArray = xRange;
mDiscount.yArray = yRange;
mInterface.SendMessageToOutput("Results of Johnson & Bickel criteria: " + mDiscount.getJohnsonBickelCriteria());
var chartWin = new ChartingWindow();
Chart chart = chartWin.FindName("MyWinformChart") as Chart;
chart.Series.Clear();
// Style chart areas
chart.ChartAreas[0].AxisX.MajorGrid.Enabled = false;
chart.ChartAreas[0].AxisX.Minimum = 0;
chart.ChartAreas[0].AxisX.Title = "Delays";
chart.ChartAreas[0].AxisY.MajorGrid.Enabled = false;
chart.ChartAreas[0].AxisY.Minimum = 0;
chart.ChartAreas[0].AxisY.Title = "Values";
int seriesCount = 0;
if (runAUC)
{
mInterface.SendMessageToOutput("---------------------------------------------------");
mInterface.SendMessageToOutput("Calculating Area Under Curve...");
double AUC = await GetAreaUnderCurve();
mInterface.SendMessageToOutput("Results of AUC were " + AUC.ToString("0.0000"));
var series = new Series
{
Name = "AUC",
Color = System.Drawing.Color.Blue,
IsVisibleInLegend = true,
IsXValueIndexed = false,
ChartType = SeriesChartType.Point
};
chart.Series.Add(series);
for (int i = 0; i < xRange.Count; i++)
{
chart.Series[seriesCount].Points.AddXY(xRange[i], yRange[i]);
}
chart.Legends.Add(series.Name);
seriesCount++;
}
if (runExp)
{
mInterface.SendMessageToOutput("---------------------------------------------------");
mInterface.SendMessageToOutput("Fitting Exponential Model...");
double kVal = 0;
try
{
NumericVector delays = engine.CreateNumericVector(xRange.ToArray());
engine.SetSymbol("delays", delays);
NumericVector values = engine.CreateNumericVector(yRange.ToArray());
engine.SetSymbol("values", values);
GenericVector testResult = engine.Evaluate("nls(values ~ " + maxValueA + " * exp(-a*delays), start = list(a = 0))").AsList();
engine.SetSymbol("exponentialModel", testResult);
kVal = engine.Evaluate("coef(exponentialModel)").AsNumeric().First();
}
catch (EvaluationException evalExc)
{
System.Console.WriteLine(evalExc.ToString());
mInterface.SendMessageToOutput("R methods failed to achieve convergence. Defaulting to SnS backups...");
/*
* Fall-back method if convergence issues found
*/
ExponentialModel exponentialModel = await GetExponentialModel();
kVal = exponentialModel.Constant;
}
double exponentialED50 = mDiscount.getExponentialED50(kVal);
double expR2 = mDiscount.getExponentialR2(kVal);
mInterface.SendMessageToOutput("Results of Exponential fit were k: " + kVal.ToString("0.0000") + ", r2: " + expR2.ToString("0.0000"));
mInterface.SendMessageToOutput("Results of exponential ED50: " + exponentialED50.ToString("0.0000"));
var series = new Series
{
Name = "Exponential Model",
Color = System.Drawing.Color.Purple,
IsVisibleInLegend = true,
IsXValueIndexed = false,
ChartType = SeriesChartType.Line
};
chart.Series.Add(series);
double projected;
for (int i = (int)xRange[0]; i < xRange[xRange.Count - 1]; i++)
{
projected = maxValueA * System.Math.Exp(-kVal * i);
chart.Series[seriesCount].Points.AddXY(i, projected);
}
chart.Legends.Add(series.Name);
seriesCount++;
}
if (runHyp)
{
mInterface.SendMessageToOutput("---------------------------------------------------");
mInterface.SendMessageToOutput("Fitting hyperbolic Model...");
double kVal = 0;
try
{
NumericVector delays = engine.CreateNumericVector(xRange.ToArray());
engine.SetSymbol("delays", delays);
NumericVector values = engine.CreateNumericVector(yRange.ToArray());
engine.SetSymbol("values", values);
GenericVector testResult = engine.Evaluate("nls(values ~ " + maxValueA + " / (1 + a*delays), start = list(a = 0))").AsList();
engine.SetSymbol("hyperbolicModel", testResult);
kVal = engine.Evaluate("coef(hyperbolicModel)").AsNumeric().First();
}
catch (EvaluationException evalExc)
{
/*
* Fall-back method if convergence issues found
*/
System.Console.WriteLine(evalExc.ToString());
mInterface.SendMessageToOutput("R methods failed to achieve convergence. Defaulting to SnS backups...");
HyperbolicModel hyperbolicModel = await GetHyperbolicModel();
kVal = hyperbolicModel.Constant;
}
double hyperbolicED50 = mDiscount.getAinslieED50(kVal);
double hypR2 = mDiscount.getHyperbolicR2(kVal);
mInterface.SendMessageToOutput("Results of hyperbolic fit were k: " + kVal.ToString("0.0000") + ", r2: " + hypR2.ToString("0.0000"));
mInterface.SendMessageToOutput("Results of hyperbolic ED50: " + hyperbolicED50.ToString("0.0000"));
var series = new Series
{
Name = "Hyperbolic Model",
Color = System.Drawing.Color.LimeGreen,
IsVisibleInLegend = true,
IsXValueIndexed = false,
ChartType = SeriesChartType.Line
};
chart.Series.Add(series);
double projected;
for (int i = (int)xRange[0]; i < xRange[xRange.Count - 1]; i++)
{
projected = maxValueA / (1 + kVal * i);
chart.Series[seriesCount].Points.AddXY(i, projected);
}
chart.Legends.Add(series.Name);
seriesCount++;
}
if (runQuasi)
{
mInterface.SendMessageToOutput("---------------------------------------------------");
mInterface.SendMessageToOutput("Fitting quasi-hyperbolic Model...");
double[] kVal = new double[2];
try
{
NumericVector delays = engine.CreateNumericVector(xRange.ToArray());
engine.SetSymbol("delays", delays);
NumericVector values = engine.CreateNumericVector(yRange.ToArray());
engine.SetSymbol("values", values);
GenericVector testResult = engine.Evaluate("nls(values ~ " + maxValueA + " * a * exp(-b*delays), start = list(a = 0.5, b = 0.001))").AsList();
engine.SetSymbol("quasiModel", testResult);
kVal = engine.Evaluate("coef(quasiModel)").AsNumeric().ToArray();
}
catch (EvaluationException evalExc)
{
System.Console.WriteLine(evalExc.ToString());
mInterface.SendMessageToOutput("R methods failed to achieve convergence. Defaulting to SnS backups...");
/*
* Backup methods
*/
QuasiHyperbolicModel quasiHyperbolicModel = await GetQuasiHyperbolicModel();
kVal = new double[] { quasiHyperbolicModel.Beta, quasiHyperbolicModel.Constant };
}
double quasiED50 = mDiscount.getQuasiED50(kVal[0], kVal[1], maxValueA);
double quasiR2 = mDiscount.getQuasiHyperbolicR2(kVal[0], kVal[1]);
mInterface.SendMessageToOutput("Results of quasi-hyperbolic fit were beta: " + kVal[0].ToString("0.0000") + " k: " + kVal[1].ToString("0.0000") + ", r2: " + quasiR2.ToString("0.0000"));
mInterface.SendMessageToOutput("Results of quasi-hyperbolic ED50: " + quasiED50.ToString("0.0000"));
var series = new Series
{
Name = "Quasi-Hyperbolic Model",
Color = System.Drawing.Color.Magenta,
IsVisibleInLegend = true,
IsXValueIndexed = false,
ChartType = SeriesChartType.Line
};
chart.Series.Add(series);
double projected;
for (int i = (int)xRange[0]; i < xRange[xRange.Count - 1]; i++)
{
projected = maxValueA * kVal[0] * System.Math.Exp(-kVal[1] * i);
chart.Series[seriesCount].Points.AddXY(i, projected);
}
chart.Legends.Add(series.Name);
seriesCount++;
}
if (runMyerson)
{
mInterface.SendMessageToOutput("---------------------------------------------------");
mInterface.SendMessageToOutput("Fitting hyperboloid (Myerson's) Model...");
double[] kVal = new double[2];
try
{
NumericVector delays = engine.CreateNumericVector(xRange.ToArray());
engine.SetSymbol("delays", delays);
NumericVector values = engine.CreateNumericVector(yRange.ToArray());
engine.SetSymbol("values", values);
GenericVector testResult = engine.Evaluate("nls(values ~ " + maxValueA + " / ((1 + a*delays)^b), start = list(a = 0.001, b = 0.2))").AsList();
engine.SetSymbol("myersonModel", testResult);
kVal = engine.Evaluate("coef(myersonModel)").AsNumeric().ToArray();
}
catch (EvaluationException evalExc)
{
System.Console.WriteLine(evalExc.ToString());
mInterface.SendMessageToOutput("R methods failed to achieve convergence. Defaulting to SnS backups...");
/*
* Backup methods
*/
HyperboloidMyersonModel hyperboloidModelMyerson = mDiscount.GetHyperboloidMyerson();
kVal = new double[] { hyperboloidModelMyerson.Constant, hyperboloidModelMyerson.Scaling };
}
double hyperboloidMyersonED50 = mDiscount.getMyersonED50(kVal[0], kVal[1]);
double myersonR2 = mDiscount.getHyperboloidMyersonR2(kVal[0], kVal[1]);
mInterface.SendMessageToOutput("Results of hyperboloid fit (Myerson) were k: " + kVal[0].ToString("0.0000") + " s: " + kVal[1].ToString("0.0000") + ", r2: " + myersonR2.ToString("0.0000"));
mInterface.SendMessageToOutput("Results of hyperboloid (Myerson) ED50: " + hyperboloidMyersonED50.ToString("0.0000"));
var series = new Series
{
Name = "Hyperboloid (M) Model",
Color = System.Drawing.Color.DarkCyan,
IsVisibleInLegend = true,
IsXValueIndexed = false,
ChartType = SeriesChartType.Line
};
chart.Series.Add(series);
double projected;
for (int i = (int)xRange[0]; i < xRange[xRange.Count - 1]; i++)
{
projected = mDiscount.LookupWithS(kVal[0], maxValueA, i, kVal[1]);
chart.Series[seriesCount].Points.AddXY(i, projected);
}
chart.Legends.Add(series.Name);
seriesCount++;
}
if (runRachlin)
{
mInterface.SendMessageToOutput("---------------------------------------------------");
mInterface.SendMessageToOutput("Fitting hyperboloid (Rachlin's) Model...");
/*
* Nuke after verifying with R
*/
double[] kVal = new double[2];
try
{
NumericVector delays = engine.CreateNumericVector(xRange.ToArray());
engine.SetSymbol("delays", delays);
NumericVector values = engine.CreateNumericVector(yRange.ToArray());
engine.SetSymbol("values", values);
GenericVector mod = engine.Evaluate("nls.control(maxiter = 1000)").AsList();
GenericVector testResult = engine.Evaluate("nls(values ~ " + maxValueA + " / (1 + (a*delays)^b), start = list(a = 0.001, b = 0.2))").AsList();
engine.SetSymbol("rachlinModel", testResult);
kVal = engine.Evaluate("coef(rachlinModel)").AsNumeric().ToArray();
}
catch (EvaluationException evalExc)
{
System.Console.WriteLine(evalExc.ToString());
mInterface.SendMessageToOutput("R methods failed to achieve convergence. Defaulting to SnS backups...");
/*
* Backup methods
*/
HyperboloidRachlinModel hyperboloidModelRachlin = await GetHyperboloidRachlin();
kVal = new double[] { hyperboloidModelRachlin.Constant, hyperboloidModelRachlin.Scaling };
}
double hyperboloidRachlinED50 = mDiscount.getRachlinED50(kVal[0], kVal[1], maxValueA);
double rachlinR2 = mDiscount.getHyperboloidRachlinR2(kVal[0], kVal[1]);
mInterface.SendMessageToOutput("Results of hyperboloid fit (Rachlin) were k: " + kVal[0].ToString("0.0000") + " s: " + kVal[1].ToString("0.0000") + ", r2: " + rachlinR2.ToString("0.0000"));
mInterface.SendMessageToOutput("Results of hyperboloid (Rachlin) ED50: " + hyperboloidRachlinED50.ToString("0.0000"));
var series = new Series
{
Name = "Hyperboloid (R) Model",
Color = System.Drawing.Color.Crimson,
IsVisibleInLegend = true,
IsXValueIndexed = false,
ChartType = SeriesChartType.Line
};
chart.Series.Add(series);
double projected;
for (int i = (int)xRange[0]; i < xRange[xRange.Count - 1]; i++)
{
projected = mDiscount.lookUpWithSInside(kVal[0], maxValueA, i, kVal[1]);
chart.Series[seriesCount].Points.AddXY(i, projected);
}
chart.Legends.Add(series.Name);
seriesCount++;
}
chart.Legends[0].IsDockedInsideChartArea = true;
chartWin.Owner = windowRef;
chartWin.Show();
}
public DataTable getCorpTable(string Rname, int stocknum)
{
engine.Initialize();
string fullRFilePath = Path.Combine(currentPath, Rname + ".R");
if (!File.Exists(fullRFilePath))
{
return(null);
}
var curPath = engine.CreateCharacter(currentPath);
engine.SetSymbol("stocknum", engine.CreateInteger(stocknum));
engine.SetSymbol("curPath", curPath);
engine.Evaluate("setwd(curPath)");
engine.Evaluate(String.Format("source(\"{0}\")", Rname + ".R"));
DataFrame output = engine.GetSymbol("output").AsDataFrame();
DataTable table = new DataTable();
table.Columns.Add("Included", typeof(bool));
foreach (var name in output.ColumnNames)
{
Type t;
switch (output[name].Type)
{
case SymbolicExpressionType.NumericVector:
t = typeof(double); break;
case SymbolicExpressionType.IntegerVector:
t = typeof(Int32); break;
case SymbolicExpressionType.CharacterVector:
t = typeof(string); break;
case SymbolicExpressionType.LogicalVector:
t = typeof(bool); break;
case SymbolicExpressionType.RawVector:
t = typeof(byte); break;
default: t = null; break;
}
table.Columns.Add(name);
if (t != null)
{
table.Columns[name].DataType = t;
}
}
foreach (DataFrameRow row in output.GetRows())
{
DataRow newRow = table.Rows.Add();
newRow["Included"] = true;
foreach (var name in output.ColumnNames)
{
if ((output[name].Type == SymbolicExpressionType.NumericVector ||
output[name].Type == SymbolicExpressionType.IntegerVector) &&
!(name.Contains("현재가") || name.Contains("배당") || name.Contains("RANK")))
{
newRow[name] = (double.Parse(row[name].ToString())) / 1e8;
}
else
{
newRow[name] = row[name];
}
}
}
return(table);
}
static void Main(string[] args)
{
var scriptFile = @"C:\projects\Hackathon\crmcognitive\R-scripts\Data_Grouping.r";
var jsonResponse = @"C:\projects\Hackathon\crmcognitive\R-scripts\output10json.txt";
if (args.Length > 0)
{
scriptFile = args[0];
jsonResponse = args[1];
}
Console.Write("click to start ...");
Console.ReadKey();
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
engine.Initialize();
string text = File.ReadAllText(scriptFile);
var myFunc = engine.Evaluate(text).AsFunction();
var v1 = engine.CreateCharacter(jsonResponse);
var df = myFunc.Invoke(new SymbolicExpression[] { v1 }).AsDataFrame();
var dt = RDataFrameToDataSet(df);
string[] cols;
string[,] vals;
string test = "";
var valueStrings = new string[df.RowCount];
var arrValueStrings = new string[1, df.RowCount];
var strList = new List <string>();
for (var i = 0; i < df.RowCount; i++)
{
for (var j = 0; j < df.ColumnCount; j++)
{
test = test + Convert.ToString(df[j][i]) + ",";
}
test = test.Remove(test.Length - 1, 1);
valueStrings[i] = test;
strList.Add(test);
test = "";
}
for (var x = 0; x < valueStrings.Length; x++)
{
arrValueStrings[0, x] = valueStrings[x];
}
var arrString = strList.ToArray();
//Values = new string[,] { {Convert.ToString(valueStrings[0])} };
var strArr = new List <string[]> {
valueStrings
};
var splitStrArr = strArr.ToArray();
Console.Write("click to finish ...");
Console.ReadKey();
}
public void test_NextItem_D(int numofItems, ModelNames.CriterionTypes paramCriterion)
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Dichotomous Items
DataFrame DichoItems = engineObj.Evaluate("DichoItems <- genDichoMatrix(items = " + numofItems + ")").AsDataFrame();
engineObj.SetSymbol("DichoItems", DichoItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
CATItems itemBank = new CATItems(DichoItems[0].Length, false);
// New Dictionary
itemBank.SetItemParamter(CATItems.ColumnNames.a, DichoItems[0].Select(y => (double)y).ToArray());
itemBank.SetItemParamter(CATItems.ColumnNames.b, DichoItems[1].Select(y => (double)y).ToArray());
itemBank.SetItemParamter(CATItems.ColumnNames.c, DichoItems[2].Select(y => (double)y).ToArray());
itemBank.SetItemParamter(CATItems.ColumnNames.d, DichoItems[3].Select(y => (double)y).ToArray());
// Call "thetaEst" function
//NumericVector r_th = engineObj.Evaluate("r_th <- thetaEst(rbind(DichoItems[3,], DichoItems[13,]), c(0, 1), method = \"WL\")").AsNumeric();
//engineObj.SetSymbol("r_th", r_th);
//double cs_th = CatRLib.ThetaEst(itemBank.FindItem(new int[] { 3, 13 }), x: new int[] { 0, 1 }, model: "", method: ModelNames.EstimaatorMethods.WL.EnumToString());
// Call "NextItem" function from R with parameter "out"
GenericVector r_NextItem = engineObj.Evaluate("r_NextItem <- nextItem(DichoItems, theta = 0, out = c(3, 13), criterion = \"" + paramCriterion.ToString() + "\")").AsList();
// Call "NextItem" function from R with response pattern "x"
//GenericVector r_NextItem = engineObj.Evaluate("r_NextItem <- nextItem(DichoItems, x = c(0, 1), out = c(3, 13), theta = r_th, criterion = \"" + paramCriterion.ToString() + "\")").AsList();
// Selected item
NumericVector item = r_NextItem[0].AsNumeric();
DataFrame par = r_NextItem[1].AsDataFrame();
// Item parameter
CATItems parItems = new CATItems(par[0].Length, false);
parItems.SetItemParamter(CATItems.ColumnNames.a, par[0].Select(y => (double)y).ToArray());
parItems.SetItemParamter(CATItems.ColumnNames.b, par[1].Select(y => (double)y).ToArray());
parItems.SetItemParamter(CATItems.ColumnNames.c, par[2].Select(y => (double)y).ToArray());
parItems.SetItemParamter(CATItems.ColumnNames.d, par[3].Select(y => (double)y).ToArray());
// Value of "info"
NumericVector info = r_NextItem[2].AsNumeric();
// Criterion
CharacterVector startSelect = r_NextItem[3].AsCharacter();
// Call "NextItem" function from CS with parameter "out"
NextItemModel cs_NextItem = CatRLib.NextItem(itemBank, theta: 0, Out: new int[] { 3, 13 }, criterion: (int)paramCriterion);
// Call "NextItem" function from CS with response pattern "x"
//NextItemModel cs_NextItem = CatRLib.NextItem(itemBank, theta: cs_th, Out: new int[] { 3, 13 }, x: new int[] { 0, 1 }, criterion: (int)paramCriterion);
// Checking item & other values
if (item[0] != cs_NextItem.item)
{
resultFlag = false;
}
if (decimal.Round((decimal)info[0], 3) != decimal.Round((decimal)cs_NextItem.info, 3))
{
resultFlag = false;
}
Assert.IsTrue(resultFlag);
}
public static bool Init(Thor model)
{
try
{
_model = model;
Engine.AutoPrint = false;
Engine.Evaluate(".libPaths('./RPackages')");
Engine.Evaluate("if (!require('ks')) install.packages('ks', lib='./RPackages',repos='https://cran.uni-muenster.de')");
Engine.Evaluate("if (!require('ggplot2')) install.packages('ggplot2', lib='./RPackages',repos='https://cran.uni-muenster.de')");
Engine.Evaluate("if (!require('scales')) install.packages('scales', lib='./RPackages',repos='https://cran.uni-muenster.de')");
Engine.Evaluate("if (!require('gridExtra')) install.packages('gridExtra', lib='./RPackages',repos='https://cran.uni-muenster.de')");
Engine.Evaluate("if (!require('scatterplot3d')) install.packages('scatterplot3d', lib='./RPackages',repos='https://cran.uni-muenster.de')");
Engine.Evaluate("suppressMessages(require('ks', quietly=TRUE))");
Engine.Evaluate("suppressMessages(library(ks,lib.loc='./RPackages'))");
Engine.Evaluate("require('ggplot2', quietly=TRUE)");
Engine.Evaluate("suppressMessages(library(ggplot2,lib.loc='./RPackages'))");
Engine.Evaluate("require('scales', quietly=TRUE)");
Engine.Evaluate("suppressMessages(library(scales,lib.loc='./RPackages'))");
Engine.Evaluate("require(gridExtra)");
Engine.Evaluate("suppressMessages(library(gridExtra,lib.loc='./RPackages'))");
Engine.Evaluate("require(scatterplot3d)");
Engine.Evaluate("suppressMessages(library(scatterplot3d,lib.loc='./RPackages'))");
Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
return(true);
}
catch (EvaluationException)
{
return(false);
}
}
public void test_StartItems_D(int numofItems)
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Dichotomous Items
DataFrame DichoItems = engineObj.Evaluate("DichoItems <- genDichoMatrix(items = " + numofItems + ")").AsDataFrame();
engineObj.SetSymbol("DichoItems", DichoItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
CATItems itemBank = new CATItems(DichoItems[0].Length, false);
// New Dictionary
itemBank.SetItemParamter(CATItems.ColumnNames.a, DichoItems[0].Select(y => (double)y).ToArray());
itemBank.SetItemParamter(CATItems.ColumnNames.b, DichoItems[1].Select(y => (double)y).ToArray());
itemBank.SetItemParamter(CATItems.ColumnNames.c, DichoItems[2].Select(y => (double)y).ToArray());
itemBank.SetItemParamter(CATItems.ColumnNames.d, DichoItems[3].Select(y => (double)y).ToArray());
// Call "StartItems" function from R, "MFI" criterion
GenericVector r_StartItems = engineObj.Evaluate("r_StartItems <- startItems(DichoItems, nrItems = 3, theta = 1, halfRange = 2)").AsList();
// Resulting item numbers
IntegerVector items = r_StartItems[0].AsInteger();
DataFrame tempitems = r_StartItems[1].AsDataFrame();
// Resulting Items
CATItems r_CatItems = new CATItems(tempitems[0].Length, false);
r_CatItems.SetItemParamter(CATItems.ColumnNames.a, tempitems[0].Select(y => (double)y).ToArray());
r_CatItems.SetItemParamter(CATItems.ColumnNames.b, tempitems[1].Select(y => (double)y).ToArray());
r_CatItems.SetItemParamter(CATItems.ColumnNames.c, tempitems[2].Select(y => (double)y).ToArray());
r_CatItems.SetItemParamter(CATItems.ColumnNames.d, tempitems[3].Select(y => (double)y).ToArray());
// Ability value
NumericVector thStart = r_StartItems[2].AsNumeric();
// Criterion
CharacterVector startSelect = r_StartItems[3].AsCharacter();
// Call "StartItems" function from CS
StartItemsModel cs_StartItems = CatRLib.StartItems(itemBank, nrItems: 3, theta: 1, halfRange: 2);
// Check selected items
if (items.Length == cs_StartItems.items.Length)
{
for (int ind = 0; ind < cs_StartItems.items.Length; ind++)
{
if (items[ind] != cs_StartItems.items[ind])
{
resultFlag = false;
}
}
}
// Check starting ability values
if (thStart.Length == cs_StartItems.thStart.Length)
{
for (int ind2 = 0; ind2 < cs_StartItems.thStart.Length; ind2++)
{
if (thStart[ind2] != cs_StartItems.thStart[ind2])
{
resultFlag = false;
}
}
}
Assert.IsTrue(resultFlag);
}
/// <summary>
/// Models the change.
/// </summary>
/// <param name="JSONmodelWithWeather">The jso nmodel with weather.</param>
/// <param name="modelName">Name of the model.</param>
/// <param name="wc">The wc.</param>
/// <exception cref="System.Exception">ARIMA error</exception>
public void modelChange(string JSONmodelWithWeather, string modelName, WeatherColumns wc)
{
this.Init();
//
string modelData = "modelData";
string evaluate = string.Format("{0} <-fromJSON(\'{1}\')", modelData, JSONmodelWithWeather);
//log.Debug("evaluateString:" + evaluate);
rEngine.Evaluate(evaluate);
string weatherModel = "modelData";
//evaluate = string.Format("{0} <-fromJSON(\'{1}\')", weatherModel, JSONweather);
//log.Debug("evaluateString:" + evaluate);
//rEngine.Evaluate(evaluate);
CreateWeatherMatrix(modelData, wc);
//log.Debug(engine.Evaluate("data"));
//engine.Evaluate("data <- predata[,1]");
evaluate = string.Format(modelName + "<- auto.arima({0}$Amount,xreg={1})", modelData, WEATHERMATRIX);
SymbolicExpression model;
try
{
model = rEngine.Evaluate(evaluate);
}
catch (Exception ex)
{
throw new Exception("ARIMA error");
}
//foreach (var item in model.AsList())
//{
// log.Debug(item.ToString());
//}
var coef = model.AsList()["coef"].AsList();
int lengthData = rEngine.Evaluate(modelData + "$Amount").AsNumeric().Length;
double[] dataSeries = new double[lengthData];
double[] errorSeries = new double[lengthData];
model.AsList()["x"].AsNumeric().CopyTo(dataSeries, lengthData);
model.AsList()["residuals"].AsNumeric().CopyTo(errorSeries, lengthData);
modelWeatherColumns = wc;
//residuals
}
public void testEapEST_and_EapSEM_P(int NumOfItems, ModelNames.Models paramModel)
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Polytomous Items
CharacterVector modelName = engineObj.CreateCharacterVector(new string[] { paramModel.EnumToString() });
engineObj.SetSymbol("modelName", modelName);
DataFrame PolyItems = engineObj.Evaluate("PolyItems <- genPolyMatrix(" + NumOfItems + ", 5, model = modelName, same.nrCat = TRUE)").AsDataFrame();
engineObj.SetSymbol("PolyItems", PolyItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
Console.WriteLine("*******************************************");
Console.WriteLine("Polytomous Items, Model : " + paramModel.EnumToString());
Console.WriteLine("*******************************************");
CATItems itemBank = new CATItems(PolyItems[0].Length, paramModel.EnumToString(), 5);
Tuple <CATItems.ColumnNames, int>[] cols = itemBank.GetKeys();
for (int i = 0; i < cols.Length; i++)
{
itemBank.SetItemParamter(cols[i], PolyItems[i].Select(y => (double)y).ToArray());
}
#region "Test block for Ability Values (th)"
double[] th = CatRcs.Utils.CommonHelper.Sequence(-6, 6, length: 11);
for (int j = 0; j < th.Length; j++)
{
string temp = th[j].ToString(nfi);
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(" + "th = " + th[j].ToString(nfi) + ", PolyItems, " + "model = modelName)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
// Call "EapEST" function from R
NumericVector r_eapEst = engineObj.Evaluate("result_Eap <- eapEst(PolyItems, x_val, model = modelName)").AsNumeric();
// Call "EapEST" function from CatRCS
double cs_eapEst = CatRLib.EapEST(itemBank, x, paramModel.EnumToString());
/* EapEst function, line 111 needs to be optimized for passing the Test
* Check EapSEM function too !! */
// Call "EapSEM" function from R
NumericVector r_eapSem = engineObj.Evaluate("result_EapSem <- eapSem(result_Eap, PolyItems, x_val, model = modelName)").AsNumeric();
// Call "EapSEM" function from CatRCS
double cs_eapSem = CatRLib.EapSEM(cs_eapEst, itemBank, x, paramModel.EnumToString());
if (r_eapEst[0] - cs_eapEst > testEpsilon ||
r_eapSem[0] - cs_eapSem > testEpsilon)
{
resultFlag = false;
}
}
Assert.IsTrue(resultFlag);
#endregion
}
private void btnRun_Click(object sender, EventArgs e)
{
if (cboFldnm1.Text == "" || cboFldnm2.Text == "")
{
MessageBox.Show("Please select target field");
return;
}
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Processing:";
pfrmProgress.pgbProgress.Style = ProgressBarStyle.Marquee;
pfrmProgress.Show();
REngine pEngine = m_pForm.pEngine;
// Creates the input and output matrices from the shapefile//
string strLayerName = cboTargetLayer.Text;
int intLIndex = m_pSnippet.GetIndexNumberFromLayerName(m_pActiveView, strLayerName);
ILayer pLayer = m_pForm.axMapControl1.get_Layer(intLIndex);
IFeatureLayer pFLayer = pLayer as IFeatureLayer;
IFeatureClass pFClass = pFLayer.FeatureClass;
int nFeature = pFClass.FeatureCount(null);
IFeatureCursor pFCursor = pFLayer.Search(null, true);
IFeature pFeature = pFCursor.NextFeature();
//Get index for independent and dependent variables
//Get variable index
string strVarNM1 = (string)cboFldnm1.SelectedItem;
string strVarNM2 = (string)cboFldnm2.SelectedItem;
int intVarIdx1 = pFClass.FindField(strVarNM1);
int intVarIdx2 = pFClass.FindField(strVarNM2);
//Store Variable at Array
double[] arrVar1 = new double[nFeature];
double[] arrVar2 = new double[nFeature];
int i = 0;
while (pFeature != null)
{
arrVar1[i] = Convert.ToDouble(pFeature.get_Value(intVarIdx1));
arrVar2[i] = Convert.ToDouble(pFeature.get_Value(intVarIdx2));
i++;
pFeature = pFCursor.NextFeature();
}
pFCursor.Flush();
//Plot command for R
StringBuilder plotCommmand = new StringBuilder();
string strStartPath = m_pForm.strPath;
string pathr = strStartPath.Replace(@"\", @"/");
pEngine.Evaluate("source('" + pathr + "/ESDA_LEE/AllFunctions_LARRY.R')");
pEngine.Evaluate("source('" + pathr + "/ESDA_LEE/AllFunctions_neighbor.R')");
pEngine.Evaluate("source('" + pathr + "/ESDA_LEE/AllFunctions_SASbi.R')");
//Get the file path and name to create spatial weight matrix
string strNameR = m_pSnippet.FilePathinRfromLayer(pFLayer);
if (strNameR == null)
{
return;
}
//Create spatial weight matrix in R
pEngine.Evaluate("library(spdep); library(maptools)");
pEngine.Evaluate("sample.shp <- readShapePoly('" + strNameR + "')");
//pEngine.Evaluate("sample.nb <- poly2nb(sample.shp, queen=FALSE)");
pEngine.Evaluate("sample.nb <- poly2nb(sample.shp)");
NumericVector vecVar1 = pEngine.CreateNumericVector(arrVar1);
pEngine.SetSymbol("sample.v1", vecVar1);
NumericVector vecVar2 = pEngine.CreateNumericVector(arrVar2);
pEngine.SetSymbol("sample.v2", vecVar2);
string strRSigLv = nudRsigLv.Value.ToString();
string strLSigLv = nudLsigLv.Value.ToString();
string strLSig = cboLocalL.Text;
string strRsig = cboLocalPearson.Text;
string strRowStd = cboRowStandardization.Text;
string strMaxRanges = nudMaxRange.Value.ToString();
string strHigherOrder = cboHigherOrder.Text;
string strNonZero = null;
if (chkDiagZero.Checked)
{
strNonZero = "FALSE";
}
else
{
strNonZero = "TRUE";
}
pEngine.Evaluate("sample.result <- LARRY.bivariate.spatial.range(sample.v1, sample.v2, 1:length(sample.nb), sample.nb, maxsr = " + strMaxRanges
+ ", pearson.sig = " + strRSigLv + ", lee.sig = " + strLSigLv + ", method.pearson = '" + strRsig + "', method.lee = '" +
strLSig + "', type.higher.nb = '" + strHigherOrder + "', type.row.stand = '" + strRowStd + "', alternative = 'two', diag.zero = "
+ strNonZero + ")");
string[] strSPRanges = pEngine.Evaluate("as.character(sample.result$final.max.sr)").AsCharacter().ToArray();
//Save Output on SHP
//Add Target fields to store results in the shapefile // Keep loop
for (int j = 0; j < 1; j++)
{
string strfldName = lvFields.Items[j].SubItems[1].Text;
if (pFClass.FindField(strfldName) == -1)
{
IField newField = new FieldClass();
IFieldEdit fieldEdit = (IFieldEdit)newField;
fieldEdit.Name_2 = strfldName;
fieldEdit.Type_2 = esriFieldType.esriFieldTypeString;
pFClass.AddField(newField);
}
}
//Update Field
pFCursor = pFClass.Update(null, false);
pFeature = pFCursor.NextFeature();
string strSpRangeFldName = lvFields.Items[0].SubItems[1].Text;
int intSpQuadFldIdx = pFClass.FindField(strSpRangeFldName);
int featureIdx = 0;
while (pFeature != null)
{
pFeature.set_Value(intSpQuadFldIdx, strSPRanges[featureIdx]);
pFCursor.UpdateFeature(pFeature);
pFeature = pFCursor.NextFeature();
featureIdx++;
}
pFCursor.Flush();
if (chkMap.Checked)
{
ITable pTable = (ITable)pFClass;
IUniqueValueRenderer pUniqueValueRenderer = new UniqueValueRendererClass();
pUniqueValueRenderer.FieldCount = 1;
pUniqueValueRenderer.set_Field(0, strSpRangeFldName);
ISimpleFillSymbol pSymbol;
IQueryFilter pQFilter = new QueryFilterClass();
int intTotalCount = 0;
string strHeading = null;
for (int j = 0; j < m_pRangeSymbols.Length; j++)
{
pSymbol = new SimpleFillSymbolClass();
pSymbol.Style = esriSimpleFillStyle.esriSFSSolid;
pSymbol.Color = m_pSnippet.getRGB(m_pRangeSymbols[j].R, m_pRangeSymbols[j].G, m_pRangeSymbols[j].B);
if (j % 5 == 0)
{
intTotalCount = 0;
for (int k = 0; k < 5; k++)
{
pQFilter.WhereClause = strSpRangeFldName + " = '" + m_pRangeSymbols[j + k].Value + "'";
int intCnt = pTable.RowCount(pQFilter);
intTotalCount += intCnt;
}
strHeading = m_pRangeSymbols[j].Heading + " (" + intTotalCount.ToString() + ")";
pUniqueValueRenderer.AddValue(m_pRangeSymbols[j].Value, strHeading, (ISymbol)pSymbol);
pUniqueValueRenderer.set_Label(m_pRangeSymbols[j].Value, m_pRangeSymbols[j].Label);
}
else
{
pUniqueValueRenderer.AddValue(m_pRangeSymbols[j].Value, strHeading, (ISymbol)pSymbol);
pUniqueValueRenderer.set_Label(m_pRangeSymbols[j].Value, m_pRangeSymbols[j].Label);
}
}
//string strNotSig = "not sig.";
//pSymbol = new SimpleMarkerSymbolClass();
//pSymbol.Style = esriSimpleMarkerStyle.esriSMSCircle
//pSymbol.Color = m_pSnippet.getRGB(255, 255, 255);
//pQFilter.WhereClause = strFinalQuadFldName + " = '" + strNotSig + "'";
//intTotalCount = pTable.RowCount(pQFilter);
//pUniqueValueRenderer.AddValue(strNotSig, null, (ISymbol)pSymbol);
//pUniqueValueRenderer.set_Label(strNotSig, "Not significant (" + intTotalCount.ToString() + ")");
pUniqueValueRenderer.UseDefaultSymbol = false;
IFeatureLayer pNewFLayer = new FeatureLayerClass();
pNewFLayer.FeatureClass = pFClass;
pNewFLayer.Name = "Bivariate Spatial Range";
IGeoFeatureLayer pGFLayer = (IGeoFeatureLayer)pNewFLayer;
pGFLayer.Renderer = (IFeatureRenderer)pUniqueValueRenderer;
m_pActiveView.FocusMap.AddLayer(pGFLayer);
m_pActiveView.Refresh();
m_pForm.axTOCControl1.Update();
pfrmProgress.Close();
}
else
{
MessageBox.Show("Complete. The results are stored in the shape file");
}
}
public void testEPV_D()
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Dichotomous Items
DataFrame DichoItems = engineObj.Evaluate("DichoItems <- genDichoMatrix(items = 200)").AsDataFrame();
engineObj.SetSymbol("DichoItems", DichoItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
CATItems itemBank = new CATItems(DichoItems[0].Select(y => (double)y).ToArray(), DichoItems[1].Select(y => (double)y).ToArray(),
DichoItems[2].Select(y => (double)y).ToArray(), DichoItems[3].Select(y => (double)y).ToArray());
// test for different theta values
double[] th = CatRcs.Utils.CommonHelper.Sequence(-6, 6, length: 11);
var stopwatch = new Stopwatch();
stopwatch.Restart();
for (int t = 0; t < th.Length; t++)
{
string temp = th[t].ToString(nfi);
// Dichotomous Items
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- DichoItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(" + th[t].ToString(nfi) + ", it_given_R)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
int[] index = new int[] { 4, 8 };
CATItems it_given = new CATItems(index.Length); // itemBank.FindItem(new int[] { 4, 8 });
for (int i = 0; i < index.Length; i++)
{
it_given.a[i] = itemBank.a[index[i] - 1];
it_given.b[i] = itemBank.b[index[i] - 1];
it_given.c[i] = itemBank.c[index[i] - 1];
it_given.d[i] = itemBank.d[index[i] - 1];
}
// Call "EPV" function from R
NumericVector r_epv = engineObj.Evaluate("r_epv <- EPV(DichoItems, 1, x_val, " + th[t].ToString(nfi) + ", it_given_R)").AsNumeric();
stopwatch.Restart();
// Call "EPV" function from CatRCS
double cs_epv = CatRLib.EPV(itemBank, 1, x, th[t], it_given, "");
Console.WriteLine("Time taken: " + stopwatch.ElapsedMilliseconds);
if (r_epv[0] - cs_epv > testEpsilon)
{
resultFlag = false;
}
Console.WriteLine(r_epv[0] + " " + cs_epv);
}
Assert.IsTrue(resultFlag);
}
static void runR(double[,] square)
{
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
// REngine requires explicit initialization.
// You can set some parameters.
engine.Initialize();
NumericMatrix group1 = engine.CreateNumericMatrix(square);
//NumericMatrix group1 = engine.CreateNumericMatrix(new double[,] {
// { 0, 0, 0, 0, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 1, 1, 1 },
// { 0, 0, 0, 1, 1 },
// { 0, 0, 0, 0, 1 }
//});
engine.SetSymbol("group1", group1);
GenericVector testResult = engine.Evaluate("eigen(group1)").AsList();
Console.WriteLine(testResult);
var hokus = testResult["values"].AsIntegerMatrix();
var eigenValues = testResult["values"].AsNumeric().ToArray();
var vectorOfVectors = testResult["vectors"].AsNumeric();
int indexNextEigenVector = 0;
// double[] eigenVectors
//for (int i = 0; i < eigenVectors.Length; i++)
//{
// if ()
//}
//Array.Sort(eigenValues, eigenVectors);
//_allStatInfo.Sort(new Comparison<StatInfo>((x, y) => DateTime.Compare(x.date, y.date)));
double sum = eigenValues.Sum();
double[] kumulative = new double[eigenValues.Length];
//
bool firstOccurrence = false;
int thresholdIndex = -1;
double thresholdValue = 0.6;
for (int i = 0; i < eigenValues.Length; i++)
{
double normalize = eigenValues[i] / sum;
if (i == 0)
{
kumulative[i] = normalize;
}
else
{
kumulative[i] = normalize + kumulative[i - 1];
}
if (kumulative[i] >= thresholdValue && firstOccurrence == false)
{
firstOccurrence = true;
thresholdIndex = i;
}
}
// engine.Evaluate("plot(group2, type = 'l', col = 'red', lwd = 10,ylab='eigen values')");
var normalizeEigenVaules = engine.CreateNumericVector(kumulative);
engine.SetSymbol("v", normalizeEigenVaules);
// ylim = c(0, 1),type = 'b')
engine.Evaluate("plot(v, type = 'l', col = 'red', lwd = 10,ylab='eigen values',type = 'b')");
var hokz = testResult["values"].AsList().ToArray();
Console.WriteLine("P-value = {0}", hokus[1, 0]);
// return eigenValues;
}
public void testEPV_P(int NumOfItems, ModelNames.Models paramModel)
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Polytomous Items
CharacterVector modelName = engineObj.CreateCharacterVector(new string[] { paramModel.EnumToString() });
engineObj.SetSymbol("modelName", modelName);
DataFrame PolyItems = engineObj.Evaluate("PolyItems <- genPolyMatrix(" + NumOfItems + ", 5, model = modelName, same.nrCat = TRUE)").AsDataFrame();
engineObj.SetSymbol("PolyItems", PolyItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
Console.WriteLine("*******************************************");
Console.WriteLine("Polytomous Items, Model : " + paramModel.EnumToString());
Console.WriteLine("*******************************************");
CATItems itemBank = new CATItems(PolyItems[0].Length, paramModel.EnumToString(), 5);
Tuple <CATItems.ColumnNames, int>[] cols = itemBank.GetKeys();
for (int i = 0; i < cols.Length; i++)
{
itemBank.SetItemParamter(cols[i], PolyItems[i].Select(y => (double)y).ToArray());
}
#region "Test block for Ability Values (th)"
double[] th = CatRcs.Utils.CommonHelper.Sequence(-6, 6, length: 11);
for (int j = 0; j < th.Length; j++)
{
string temp = th[j].ToString(nfi);
// Polytomous Items
DataFrame it_given_R = engineObj.Evaluate("it_given_R <- PolyItems[c(4, 8),]").AsDataFrame();
engineObj.SetSymbol("it_given_R", it_given_R);
//Creation of a response pattern for theta value
engineObj.Evaluate("set.seed(1)");
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(" + th[j].ToString(nfi) + ", it_given_R, model = modelName)").AsNumeric();
engineObj.SetSymbol("x_val", x_val);
int[] x = x_val.Select(y => (int)y).ToArray();
CATItems it_given = itemBank.FindItem(new int[] { 4, 8 });
// Call "EPV" function from R
NumericVector r_epv = engineObj.Evaluate("r_epv <- EPV(PolyItems, 1, x_val, " + th[j].ToString(nfi) + ", it_given_R, model = modelName)").AsNumeric();
// Call "EPV" function from CatRCS
double cs_epv = CatRLib.EPV(itemBank, 1, x, th[j], it_given, paramModel.EnumToString());
if (r_epv[0] - cs_epv > testEpsilon)
{
resultFlag = false;
}
}
Assert.IsTrue(resultFlag);
#endregion
}
private void button4_Click(object sender, EventArgs e)
{
int FeatureNum = app.FeaName.GetLength(0);
int SampleNum = app.SamName.GetLength(0);
List <double> prob = new List <double>();
List <double> stat = new List <double>();
List <double> pvalue = new List <double>();
double[] bonferroni = new double[FeatureNum];
double[] fdr = new double[FeatureNum];
//int length;
int NAnum = 0;
REngine.SetEnvironmentVariables();
REngine MSS = REngine.GetInstance();
MSS.Initialize();
NumericMatrix Freq = MSS.CreateNumericMatrix(app.FreqMatrix);
MSS.SetSymbol("Freq", Freq);
NumericMatrix Count = MSS.CreateNumericMatrix(app.CountMatrix);
MSS.SetSymbol("Count", Count);
CharacterVector SampleName = MSS.CreateCharacterVector(app.SamName);
CharacterVector FeatureName = MSS.CreateCharacterVector(app.FeaName);
MSS.SetSymbol("FeatureName", FeatureName);
MSS.SetSymbol("SampleName", SampleName);
List <string> SampleNameFreq = new List <string>();
for (int i = 0; i < SampleNum; i++)
{
SampleNameFreq.Add(SampleName[i] + "Freq");
}
IntegerVector RFeaNum = MSS.CreateInteger(FeatureNum);
NumericVector Ralpha = MSS.CreateNumeric(double.Parse(this.textBox1.Text.ToString()));
MSS.SetSymbol("n", RFeaNum);
MSS.SetSymbol("alpha", Ralpha);
List <List <double> > Freqtemp = new List <List <double> >();
List <double> FreqSum = new List <double>();
MSS.Evaluate("leastnum = ceiling ( qnorm(alpha/2)^2 )");
double leastnum = MSS.Evaluate("leastnum").AsNumeric().First();
if (this.comboBox1.SelectedIndex == 0)
{
for (int i = 0; i < FeatureNum; i++)
{
pvalue.Add(100);
for (int j = 0; j < SampleNum - 1; j++)
{
for (int k = j + 1; k < SampleNum; k++)
{
double probtemp = (app.CountMatrix[i, j] + app.CountMatrix[i, k]) / (app.SampleTotal[j] + app.SampleTotal[k]);
double stattemp = (app.FreqMatrix[i, j] - app.FreqMatrix[i, k]) / Math.Sqrt(probtemp * (1 - probtemp) * (1 / app.SampleTotal[j] + 1 / app.SampleTotal[k]));
if (double.IsNaN(stattemp))
{
stattemp = 0;
}
NumericVector Rstat = MSS.CreateNumeric(stattemp);
MSS.SetSymbol("stat", Rstat);
MSS.Evaluate("p.value <- 2*(pnorm(-abs(stat)))");
double pvaluetemp;
if ((this.comboBox2.SelectedIndex == 1) && (app.CountMatrix[i, j] < leastnum) && (app.CountMatrix[i, k] < leastnum))
{
pvaluetemp = 100;
}
else
{
pvaluetemp = MSS.GetSymbol("p.value").AsNumeric().First();
}
if (pvaluetemp != 100)
{
pvalue[i] = Math.Min((double)pvalue[i], (double)pvaluetemp);
}
}
}
}
NumericVector Rpvalue = MSS.CreateNumericVector(pvalue);
MSS.SetSymbol("p.value", Rpvalue);
MSS.Evaluate("NAnum = length(p.value[p.value == 100])");
NAnum = Convert.ToInt32(MSS.GetSymbol("NAnum").AsNumeric().First());
MSS.Evaluate("p.value[p.value == 100] = NA");
MSS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
MSS.Evaluate("bonferroni.p[which(bonferroni.p == NA)] = 100");
MSS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
MSS.Evaluate("fdr.p[which(fdr.p == NA)] = 100");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = MSS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = MSS.GetSymbol("fdr.p").AsNumeric()[i];
}
}
else if (this.comboBox1.SelectedIndex == 1)
{
for (int i = 0; i < FeatureNum; i++)
{
pvalue.Add(100);
}
for (int j = 0; j < SampleNum - 1; j++)
{
for (int k = 1; k < SampleNum; k++)
{
double Sum1 = 0;
double Sum2 = 0;
for (int i = 0; i < FeatureNum; i++)
{
Sum1 = Sum1 + app.CountMatrix[i, j];
Sum2 = Sum2 + app.CountMatrix[i, k];
}
for (int i = 0; i < FeatureNum; i++)
{
NumericVector n11 = MSS.CreateNumeric(app.CountMatrix[i, j]);
NumericVector n21 = MSS.CreateNumeric(app.CountMatrix[i, k]);
NumericVector n12 = MSS.CreateNumeric(Sum1 - app.CountMatrix[i, j]);
NumericVector n22 = MSS.CreateNumeric(Sum2 - app.CountMatrix[i, k]);
MSS.SetSymbol("n11", n11);
MSS.SetSymbol("n12", n12);
MSS.SetSymbol("n21", n21);
MSS.SetSymbol("n22", n22);
MSS.Evaluate("compare <- matrix(c(n11,n12,n21,n22),nr=2)");
MSS.Evaluate("p.value <- fisher.test(compare)$p.value");
double pvaluetemp = MSS.GetSymbol("p.value").AsNumeric().First();
pvalue[i] = Math.Min((double)pvalue[i], (double)pvaluetemp);
}
}
}
MSS.Evaluate("bonferroni.p <- p.adjust(p.value,\"bonferroni\")");
MSS.Evaluate("fdr.p <- p.adjust(p.value,\"fdr\")");
for (int i = 0; i < FeatureNum; i++)
{
bonferroni[i] = MSS.GetSymbol("bonferroni.p").AsNumeric()[i];
fdr[i] = MSS.GetSymbol("fdr.p").AsNumeric()[i];
}
}
List <string> Annotation = new List <string>();
if (this.checkBox1.Checked)
{
if (this.radioButton2.Checked)
{
string strConnCOG;
strConnCOG = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/COG.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnCOG = new OleDbConnection(strConnCOG);
OleConnCOG.Open();
String sqlCOG = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelCOG = new OleDbDataAdapter(sqlCOG, OleConnCOG);
app.OleDsExcleCOG = new DataSet();
OleDaExcelCOG.Fill(app.OleDsExcleCOG, "Sheet1");
OleConnCOG.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExcleCOG.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExcleCOG.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExcleCOG.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
else if (this.radioButton1.Checked)
{
string strConnPFAM;
strConnPFAM = "Provider=Microsoft.ACE.OLEDB.12.0;" + "Data Source=" + System.Windows.Forms.Application.StartupPath + "/PFAM.xlsx" + ";" + "Extended Properties=\"Excel 12.0;HDR=YES;IMEX=1\"";
OleDbConnection OleConnPFAM = new OleDbConnection(strConnPFAM);
OleConnPFAM.Open();
String sqlPFAM = "SELECT * FROM [Sheet1$]";
OleDbDataAdapter OleDaExcelPFAM = new OleDbDataAdapter(sqlPFAM, OleConnPFAM);
app.OleDsExclePFAM = new DataSet();
OleDaExcelPFAM.Fill(app.OleDsExclePFAM, "Sheet1");
OleConnPFAM.Close();
for (int i = 0; i < FeatureNum; i++)
{
for (int j = 0; j < app.OleDsExclePFAM.Tables[0].Rows.Count; j++)
{
if (string.Equals(FeatureName[i], app.OleDsExclePFAM.Tables[0].Rows[j][0].ToString()))
{
Annotation.Add(app.OleDsExclePFAM.Tables[0].Rows[j][1].ToString());
}
}
if (Annotation.Count < i + 1)
{
Annotation.Add("No Annotation!");
}
}
}
}
DataTable dt = new DataTable();
dt.Columns.Add("Feature", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(app.SamName[i], typeof(double));
}
dt.Columns.Add("p.value", typeof(double));
dt.Columns.Add("bonferroni.p", typeof(double));
dt.Columns.Add("fdr.p", typeof(double));
dt.Columns.Add("Annotation", typeof(string));
for (int i = 0; i < SampleNum; i++)
{
dt.Columns.Add(SampleNameFreq[i], typeof(double));
}
for (int i = 0; i < FeatureNum; i++)
{
DataRow dr = dt.NewRow();
dr[0] = FeatureName[i];
for (int j = 1; j < SampleNum + 1; j++)
{
dr[j] = app.CountMatrix[i, j - 1];
}
if (pvalue[i] == 100)
{
dr[SampleNum + 1] = DBNull.Value;
dr[SampleNum + 2] = DBNull.Value;
dr[SampleNum + 3] = DBNull.Value;
}
else
{
dr[SampleNum + 1] = pvalue[i];
dr[SampleNum + 2] = bonferroni[i];
dr[SampleNum + 3] = fdr[i];
}
if (this.checkBox1.Checked)
{
dr[SampleNum + 4] = Annotation[i];
}
else
{
dr[SampleNum + 4] = null;
}
for (int j = 0; j < SampleNum; j++)
{
dr[j + SampleNum + 5] = app.FreqMatrix[i, j];
}
dt.Rows.Add(dr);
}
DataTable dtCopy = dt.Copy();
DataTable dttemp = dt.Copy();
dttemp.Clear();
DataView dv = dt.DefaultView;
dv.Sort = "p.value";
dtCopy = dv.ToTable();
for (int i = 0; i < NAnum; i++)
{
DataRow row = dtCopy.Rows[i];
dttemp.Rows.Add(row.ItemArray);
}
for (int i = 0; i < NAnum; i++)
{
dtCopy.Rows.RemoveAt(0);
}
dtCopy.Merge(dttemp);
Microsoft.Office.Interop.Excel.Application xlApp = new Microsoft.Office.Interop.Excel.Application();
System.Globalization.CultureInfo CurrentCI = System.Threading.Thread.CurrentThread.CurrentCulture;
System.Threading.Thread.CurrentThread.CurrentCulture = new System.Globalization.CultureInfo("en-US");
Microsoft.Office.Interop.Excel.Workbooks workbooks = xlApp.Workbooks;
Microsoft.Office.Interop.Excel.Workbook workbook = workbooks.Add(Microsoft.Office.Interop.Excel.XlWBATemplate.xlWBATWorksheet);
Microsoft.Office.Interop.Excel.Worksheet worksheet = (Microsoft.Office.Interop.Excel.Worksheet)workbook.Worksheets[1];
Microsoft.Office.Interop.Excel.Range range;
long totalCount = dtCopy.Rows.Count;
long rowRead = 0;
float percent = 0;
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[1, i + 1] = dtCopy.Columns[i].ColumnName;
range = (Microsoft.Office.Interop.Excel.Range)worksheet.Cells[1, i + 1];
range.Interior.ColorIndex = 15;
range.Font.Bold = true;
}
for (int r = 0; r < dtCopy.Rows.Count; r++)
{
for (int i = 0; i < dtCopy.Columns.Count - SampleNum; i++)
{
worksheet.Cells[r + 2, i + 1] = dtCopy.Rows[r][i].ToString();
}
rowRead++;
percent = ((float)(100 * rowRead)) / totalCount;
}
xlApp.Visible = true;
int pnum = 0;
for (int i = 0; i < FeatureNum; i++)
{
try
{
if (double.Parse(dtCopy.Rows[i][SampleNum].ToString()) < double.Parse(this.textBox1.Text.ToString()))
{
pnum++;
}
}
catch
{ }
}
double[,] df = new double[Math.Min(10, FeatureNum), SampleNum];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
df[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 5 + j].ToString());
}
}
if (this.checkBox2.Checked)
{
string[] rownamesdf = new string[Math.Min(10, FeatureNum)];
for (int i = 0; i < Math.Min(10, FeatureNum); i++)
{
rownamesdf[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownamesdf = MSS.CreateCharacterVector(rownamesdf);
MSS.SetSymbol("Rownamedf", Rrownamesdf);
NumericMatrix Rdf = MSS.CreateNumericMatrix(df);
MSS.SetSymbol("Freqdf", Rdf);
NumericVector RRow = MSS.CreateNumeric(Math.Min(10, FeatureNum));
MSS.SetSymbol("selrow", RRow);
MSS.Evaluate("Freqdf <- as.data.frame(Freqdf)");
MSS.Evaluate("rownames(Freqdf) <- Rownamedf");
MSS.Evaluate("colnames(Freqdf) <- SampleName");
MSS.Evaluate("colournum <- rainbow(dim(Freqdf)[2])");
MSS.Evaluate("plotdata <- t(Freqdf)");
MSS.Evaluate("windows()");
MSS.Evaluate("barplot(plotdata,main=\"features with top varition\",ylab=\"Freq\",beside=TRUE,horiz=FALSE, cex.names=0.6,col=colournum)");
MSS.Evaluate("legend(\"topright\",SampleName,fill=colournum)");
}
if (pnum > 0)
{
double[,] dfall = new double[pnum, SampleNum];
for (int i = 0; i < pnum; i++)
{
for (int j = 0; j < SampleNum; j++)
{
dfall[i, j] = double.Parse(dtCopy.Rows[i][SampleNum + 5 + j].ToString());
}
}
string[] rownamesall = new string[pnum];
for (int i = 0; i < pnum; i++)
{
rownamesall[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownamesall = MSS.CreateCharacterVector(rownamesall);
MSS.SetSymbol("Rownameall", Rrownamesall);
NumericMatrix Rdfall = MSS.CreateNumericMatrix(dfall);
MSS.SetSymbol("Freqdfall", Rdfall);
NumericVector RRowall = MSS.CreateNumeric(pnum);
MSS.SetSymbol("selrowall", RRowall);
MSS.Evaluate("Freqdfall <- as.data.frame(Freqdfall)");
MSS.Evaluate("rownames(Freqdfall) <- Rownameall");
MSS.Evaluate("colnames(Freqdfall) <- SampleName");
MSS.Evaluate("distance <- as.dist(1-abs(cor(Freqdfall)))");
if (this.checkBox3.Checked)
{
MSS.Evaluate("fit <- cmdscale(distance, eig=TRUE, k=2)");
MSS.Evaluate("x <- fit$points[,1]");
MSS.Evaluate("y <- fit$points[,2]");
MSS.Evaluate("minx <- min(x)");
MSS.Evaluate("miny <- min(y)");
MSS.Evaluate("maxx <- max(x)");
MSS.Evaluate("maxy <- max(y)");
MSS.Evaluate("randx <- maxx - minx");
MSS.Evaluate("randy <- maxy - miny");
MSS.Evaluate("llimx <- minx - randx/10");
MSS.Evaluate("hlimx <- maxx + randx/3");
MSS.Evaluate("llimy <- miny - randy/10");
MSS.Evaluate("hlimy <- maxy + randy/3");
MSS.Evaluate("windows()");
MSS.Evaluate("plot(x,y,xlab=\"Coordinate 1\",ylab=\"Coordinate 2\",main=\"MDS\", pch=c(0,1,2,5,6), col=rainbow(7), type=\"p\",xlim = c(llimx,hlimx), ylim = c(llimy,hlimy))");
if (this.comboBox3.SelectedIndex == 0)
{
MSS.Evaluate("legend(\"topright\",colnames(Freqdfall),pch=c(0,1,2,5,6),col=rainbow(7),cex = 0.8)");
}
else if (this.comboBox3.SelectedIndex == 1)
{
MSS.Evaluate("text(x,y,labels=SampleName,pos=4)");
}
}
if (this.checkBox4.Checked)
{
MSS.Evaluate("windows()");
MSS.Evaluate("plot(hclust(distance),main =\"Samples Clust\")");
}
}
else
{
MessageBox.Show("No differntially abundant features!!");
}
if (this.checkBox5.Checked)
{
int Rownum = 0;
for (int i = 0; i < FeatureNum; i++)
{
double tempSum = 0;
double tempMean = 0;
for (int j = 0; j < SampleNum; j++)
{
tempSum = tempSum + app.FreqMatrix[i, j];
}
tempMean = tempSum / (SampleNum);
if (tempSum > 0)
{
FreqSum.Add(tempSum);
List <double> tempRow = new List <double>();
for (int j = 0; j < SampleNum; j++)
{
tempRow.Add(app.FreqMatrix[i, j] / tempMean);
}
Freqtemp.Add(tempRow);
Rownum = Rownum + 1;
}
}
for (int i = 0; i < Rownum; i++)
{
for (int j = 0; j < SampleNum; j++)
{
Freqtemp[i][j] = Math.Log(Freqtemp[i][j], 2);
if (Freqtemp[i][j] > 1)
{
Freqtemp[i][j] = 1;
}
else if (Freqtemp[i][j] < -1)
{
Freqtemp[i][j] = -1;
}
}
}
double[,] dfhm = new double[Math.Min(500, Rownum), SampleNum];
for (int i = 0; i < Math.Min(500, Rownum); i++)
{
for (int j = 0; j < SampleNum; j++)
{
dfhm[i, j] = double.Parse(Freqtemp[i][j].ToString());
}
}
string[] rownameshm = new string[Math.Min(500, Rownum)];
for (int i = 0; i < Math.Min(500, Rownum); i++)
{
rownameshm[i] = dtCopy.Rows[i][0].ToString();
}
CharacterVector Rrownameshm = MSS.CreateCharacterVector(rownameshm);
MSS.SetSymbol("Rownamehm", Rrownameshm);
NumericMatrix Rdfhm = MSS.CreateNumericMatrix(dfhm);
MSS.SetSymbol("Freqdfhm", Rdfhm);
NumericVector RRowhm = MSS.CreateNumeric(Math.Min(500, Rownum));
MSS.SetSymbol("plotnum", RRowhm);
MSS.Evaluate("Freqdfhm <- as.data.frame(Freqdfhm)");
MSS.Evaluate("rownames(Freqdfhm) <- Rownamehm");
MSS.Evaluate("colnames(Freqdfhm) <- SampleName");
MSS.Evaluate("Freqdfhm <- as.matrix(Freqdfhm)");
MSS.Evaluate("library(pheatmap)");
MSS.Evaluate("windows()");
if (this.checkBox6.Checked)
{
if (this.checkBox7.Checked)
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=T,cluster_rows=T)");
}
else
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=F,cluster_rows=T)");
}
}
else
{
if (this.checkBox7.Checked)
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=T,cluster_rows=F)");
}
else
{
MSS.Evaluate("pheatmap(Freqdfhm[1:plotnum,],show_rownames=F,cluster_rows=F)");
}
}
}
this.Close();
}
public void testEapEST_and_EapSEM_D()
{
resultFlag = true;
REngine.SetEnvironmentVariables();
REngine engineObj = REngine.GetInstance();
// Loading a library from R
engineObj.Evaluate("library(catR)");
// Test for item banks with different number of items
for (int i1 = 0; i1 < numberOfItems.Length; i1++)
{
// Dichotomous Items
DataFrame DichoItems = engineObj.Evaluate("DichoItems <- genDichoMatrix(items = " + numberOfItems[i1] + ")").AsDataFrame();
engineObj.SetSymbol("DichoItems", DichoItems);
// Adapting with the existing "CAT-Items" type (Wrapper)
CATItems itemBank = new CATItems(DichoItems[0].Select(y => (double)y).ToArray(), DichoItems[1].Select(y => (double)y).ToArray(),
DichoItems[2].Select(y => (double)y).ToArray(), DichoItems[3].Select(y => (double)y).ToArray());
// test for different theta values
double[] th = CatRcs.Utils.CommonHelper.Sequence(-6, 6, length: 11);
var stopwatch = new Stopwatch();
stopwatch.Restart();
for (int i2 = 0; i2 < th.Length; i2++)
{
string temp = th[i2].ToString(nfi);
//Creation of a response pattern for theta value
NumericVector x_val = engineObj.Evaluate("x_val <- genPattern(" + th[i2].ToString(nfi) + ", DichoItems)").AsNumeric();
int[] x = x_val.Select(y => (int)y).ToArray();
// Call "EapEST" function from R
NumericVector r_eapEst = engineObj.Evaluate("result_Eap <- eapEst(DichoItems, x_val)").AsNumeric();
// Call "EapSEM" function from R
NumericVector r_eapSem = engineObj.Evaluate("result_EapSem <- eapSem(result_Eap, DichoItems, x_val)").AsNumeric();
stopwatch.Restart();
// Call "EapEST" function from CatRCS
double cs_eapEst = CatRLib.EapEST(itemBank, x, "");
// Call "EapSEM" function from CatRCS
double cs_eapSem = CatRLib.EapSEM(cs_eapEst, itemBank, x, "");
Console.WriteLine("Time: " + stopwatch.ElapsedMilliseconds);
if (r_eapEst[0] - cs_eapEst > testEpsilon ||
r_eapSem[0] - cs_eapSem > testEpsilon)
{
resultFlag = false;
}
}
}
Assert.IsTrue(resultFlag, "Test Passed!");
}
/// <summary>
/// Initializes the rengine.
/// </summary>
///
/// <param name="label"> The label. </param>
/// <param name="busyBar"> The busy bar control. </param>
///
/// <returns>
/// True if it succeeds, false if it fails.
/// </returns>
public static bool InitRengine(Label label, ProgressBar busyBar)
{
//! There seems to be a mismatch between the name of the MyDocuments special folder on BV5/6 (Dutch/Englis).
//! See https://stackoverflow.com/questions/606483/what-is-the-meaning-of-these-windows-environment-variables-homedrive-homepath
String mydocuments = Environment.GetFolderPath(Environment.SpecialFolder.MyDocuments);
String mydocfolder = mydocuments.Split(new Char[] { Path.DirectorySeparatorChar }).Last();
Logger.Info($"MyDocuments: '{mydocuments}'.");
String display = GetDisplayName(Environment.SpecialFolder.MyDocuments);
if (mydocfolder != display)
{
Logger.Warn($"MyDocuments Name/DisplayName Miscmatch: '{mydocfolder}' / '{display}'.");
}
String homedrive = Environment.GetEnvironmentVariable("HOMEDRIVE");
String homepath = Environment.GetEnvironmentVariable("HOMEPATH");
String homeshare = Environment.GetEnvironmentVariable("HOMESHARE");
Logger.Info($"HOMEDRIVE: '{homedrive}'.");
Logger.Info($"HOMEPATH: '{homepath}'.");
Logger.Info($"HOMESHARE: '{homeshare}'.");
//! Fix for OUNL BV5/6 (GetFolderPath is Dutch where GetDisplayName returns English).
//
if (!Directory.Exists(mydocuments) || !String.IsNullOrEmpty(homeshare))
// || !mydocuments.EndsWith(display)
{
Logger.Info($"MyDocuments issues, using alternative method");
if (!String.IsNullOrEmpty(homedrive))
{
mydocuments = Path.Combine(
homedrive + homepath, mydocfolder);
}
else if (!String.IsNullOrEmpty(homeshare))
{
mydocuments = Path.Combine(homeshare + homepath, mydocfolder);
}
Logger.Info($"MyDocuments: '{mydocuments}'.");
}
label.InvokeIfRequired(o =>
{
label.Text = "Installing R packages";
});
busyBar.InvokeIfRequired(o =>
{
busyBar.Show();
busyBar.Value = busyBar.Maximum;
});
try
{
//! Check R Engine version.
//
REngine.SetEnvironmentVariables();
//! There are several options to initialize the engine, but by default the following suffice:
engine = REngine.GetInstance();
Logger.Info($"Using R v{engine.DllVersion}.");
//! R uses only major.minor for custom library folders.
//
String ver = engine.DllVersion;
if (ver.Count(p => p == '.') == 2)
{
ver = ver.Substring(0, ver.LastIndexOf('.'));
}
String rpath = $"{mydocuments.Replace("\\", "/")}/R/win-library/{ver}";
Logger.Info($"R Install Path: '{rpath}'.");
//! veg 15-07-2019
//
//! NOTE: We need an R version that allows Metrics 1.2 or higher.
// DllVersion returns v3.6.1 (not 3.6 we need for the private library path)
// Debug.WriteLine($"R.dll v{engine.DllVersion}");
//! veg 15-07-2019
// NOTE: We also need Metrics to be installed.
//
// install.packages('Metrics')
// unloadNamespace('Metrics')
// library('Metrics')
// install.packages('Metrics','R_LIBS=%HOMEDRIVE/%HOMEPATH%/Documents/R/win-library/3.6')
//
// In My Documents .Renviron file with location library like:
// R_LIBS=%HOMEDRIVE%/%HOMEPATH%/Documents/R/win-library/3.6
//---R
try
{
Logger.Info($"Checking presence of R 'Metrics' package.");
engine.Evaluate($"library('Metrics', lib.loc = '{rpath}')");
}
catch (EvaluationException)
{
Logger.Info($"Downloading and Installing R 'Metrics' package.");
Logger.Info($"Using '{CRAN}' mirror.");
engine.Evaluate($"install.packages('Metrics', repos='{CRAN}', lib='{rpath}')");
Logger.Info($"Loading 'Metrics' package into R.");
engine.Evaluate($"library('Metrics', lib.loc = '{rpath}')");
}
//---R
Logger.Info($"Checking R 'Metrics' package version.");
{
CharacterVector e3 = engine.Evaluate("utils::packageVersion('Metrics')").AsCharacter();
String MetricsVersion = e3[0].Trim(new char[] { 'c', '(', ')' }).Replace(',', '.');
Logger.Info($"Detected Metrics v{MetricsVersion}.");
//! Check Metrics Version to be 0.1.4 to check if the R version is recent enough.
//
Double mul = 1;
Double chk = 0;
foreach (String part in MetricsVersion.Split('.'))
{
chk += mul * Int32.Parse(part);
mul /= 10;
}
Logger.Info($"Detected R v{ver}.");
if (chk < 0.14)
{
Logger.Error($"R Version to low to load Metrics v0.1.4 or higher. Please upgrade R.");
return(false);
}
}
Logger.Info($"Finding download source for 1.6.9 version of R psych package and its dependencies.");
/*
* //! Psych dependends on mnormt.
* //
*
* //---R
* try
* {
* Logger.Info($"Checking presence of R 'mnormt' package.");
*
* engine.Evaluate($"library('mnormt', lib.loc = '{rpath}')");
* }
* catch (EvaluationException)
* {
* Logger.Info($"Downloading and Installing R 'mnormt' package.");
*
* engine.Evaluate($"install.packages('mnormt', repos='{CRAN}', lib='{rpath}')");
* }
*
* //---R
*
* //https://cran.r-project.org/package=%{packname}&version=%{version}#/%{packname}_%{version}.tar.gz
* //
*
* try
* {
* Logger.Info($"Checking presence of R 'psych' package.");
*
* engine.Evaluate($"library('psych', lib.loc = '{rpath}')");
* }
* catch (EvaluationException)
* {
* Logger.Info($"Downloading and Installing R 'psych' package.");
*
* //! Fails because downloaded file is a tar.gz (but is saved without extension).
* //
* //engine.Evaluate($"install.packages('{CRAN}/package=psych&version=1.6.9', repos=NULL, type='source', lib='{rpath}')");
* //
* engine.Evaluate($"install.packages('{CRAN}/src/contrib/Archive/psych/psych_1.6.9.tar.gz', repos=NULL, type='source', lib='{rpath}')");
*
* Logger.Info("Loading 'psych' package into R.");
* engine.Evaluate($"library('psych', lib.loc = '{rpath}')");
* }
*
*
*
* //---R
*/
try
{
Logger.Info($"Checking presence of R 'psych' package.");
engine.Evaluate($"library('psych', lib.loc = '{rpath}')");
}
catch (EvaluationException)
{
Logger.Info($"Downloading and Installing R 'psych' package.");
Logger.Info($"Using '{CRAN}' mirror.");
engine.Evaluate($"install.packages('psych', repos='{CRAN}', lib='{rpath}')");
Logger.Info($"Loading 'psych' package into R.");
engine.Evaluate($"library('psych', lib.loc = '{rpath}')");
}
return(true);
}
catch (Exception e)
{
Logger.Error($"{e.GetType().Name} - {e.Message}.");
MessageBox.Show(
$"Error initializing Rengine:\r\n\r\n{e.Message}",
Application.ProductName,
MessageBoxButtons.OK,
MessageBoxIcon.Warning);
return(false);
}
finally
{
label.InvokeIfRequired(o =>
{
label.Text = String.Empty;
});
busyBar.InvokeIfRequired(o =>
{
busyBar.Hide();
busyBar.Value = busyBar.Minimum;
});
}
}
private void analisServer()
{
//////////////// Парсинг папки с логами
for (int i = 0; i < xForPlot.Count; i += 3)
{
if (startAnalisisSec <= Convert.ToInt32(xForPlot[i]))
{
startAnalisisIdx = i; break;
}
}
for (int i = xForPlot.Count - 2; i >= 0; i -= 3)
{
if (endAnalisisSec >= Convert.ToInt32(xForPlot[i]))
{
endAnalisisIdx = i; break;
}
}
int[] dailyWork = new int[howManyDaysBetween];
int[] scaleMass = new int[15840];
double workingTime = 0;
double maxworkingTime = 0;
double minworkingTime = Convert.ToInt32(xForPlot[startAnalisisIdx + 1]) - Convert.ToInt32(xForPlot[startAnalisisIdx]);
for (int i = 0; i < scaleMass.Length; i++)
{
scaleMass[i] = 0;
}
for (int i = 0; i < dailyWork.Length; i++)
{
dailyWork[i] = 0;
}
for (int i = startAnalisisIdx + 1; i <= endAnalisisIdx; i += 3)
{
if ((Convert.ToInt32(xForPlot[i]) - Convert.ToInt32(xForPlot[i - 1])) > maxworkingTime)
{
maxworkingTime = Convert.ToInt32(xForPlot[i]) - Convert.ToInt32(xForPlot[i - 1]);
}
if (((Convert.ToInt32(xForPlot[i]) - Convert.ToInt32(xForPlot[i - 1])) < minworkingTime) &&
(Convert.ToInt32(xForPlot[i]) - Convert.ToInt32(xForPlot[i - 1])) != 0)
{
minworkingTime = Convert.ToInt32(xForPlot[i]) - Convert.ToInt32(xForPlot[i - 1]);
}
workingTime += Convert.ToInt32(xForPlot[i]) - Convert.ToInt32(xForPlot[i - 1]);
for (int j = Convert.ToInt32(xForPlot[i - 1]) / 60; j < Convert.ToInt32(xForPlot[i]) / 60; j++)
{
scaleMass[j] = Convert.ToInt32(xForPlot[i + 1]);
}
}
DateTime tn = new DateTime(2016, 2, 29);
int idx = 0;
if (firstDate != tn)
{
idx = 1440 * firstDate.Day;
}
for (int i = 0; i < dailyWork.Length; i++)
{
for (int j = idx + 1440 * i; j < 1440 * i + 1440 + idx; j++)
{
dailyWork[i] += scaleMass[j];
}
dailyWork[i] /= 1440;
}
//////////////////////Analis
double[] dailyWorkExpSmooth = new double[dailyWork.Length];
double[] dailyWorkExpSmoothLog = new double[dailyWorkExpSmooth.Length];
double coeffReA = 0;
double coeffReB = 0;
double sumXY = 0;
double sumX = 0;
double sumY = 0;
double sumSqX = 0;
dailyWorkExpSmooth[0] = dailyWork[0];
for (int i = 1; i < dailyWorkExpSmooth.Length; i++)
{
dailyWorkExpSmooth[i] = dailyWorkExpSmooth[i - 1] + 0.5 * (dailyWork[i] - dailyWorkExpSmooth[i - 1]);
}
//foreach (double i in dailyWorkExpSmooth)
//{
// richTextBox1.Text += Convert.ToString(i) + "\n";
//}
for (int i = 0; i < dailyWorkExpSmooth.Length - 4; i++)
{
sumXY += Math.Log(i + 1) * dailyWorkExpSmooth[i];
sumX += Math.Log(i + 1);
sumY += dailyWorkExpSmooth[i];
sumSqX += Math.Pow(Math.Log(i + 1), 2);
}
coeffReA = ((dailyWorkExpSmooth.Length - 4) * sumXY - sumX * sumY) / ((dailyWorkExpSmooth.Length - 4) * sumSqX - Math.Pow(sumX, 2));
coeffReB = (sumY - coeffReA * sumX) / (dailyWorkExpSmooth.Length - 4);
for (int i = 0; i < dailyWorkExpSmoothLog.Length; i++)
{
dailyWorkExpSmoothLog[i] = coeffReA * Math.Log(i + 1) + coeffReB;
}
///////////////////////////
//richTextBox1.Text = "";
//richTextBox1.Text += "" + Convert.ToString(howManyDaysBetween) + "\n";
//richTextBox1.Text += Convert.ToString(firstDate) + " " + Convert.ToString(secondDate) + "\n";
//richTextBox1.Text += Convert.ToString(dailyWorkExpSmooth.Length - 4) + "\n";
//richTextBox1.Text += Convert.ToString(coeffReA) + " " + Convert.ToString(coeffReB) + "\n sumXY " + Convert.ToString(sumXY) + " SumX" + Convert.ToString(sumX) + " SumY " + Convert.ToString(sumY) + " SumX^2 " + Convert.ToString(sumSqX) + "\n";
//foreach (double i in dailyWork)
//{
// richTextBox1.Text += string.Format("{0}\n", i);
//}
double downtime = (endAnalisisSec - startAnalisisSec - workingTime);
double coefOfProd = workingTime / (endAnalisisSec - startAnalisisSec) * 100;
// minworkingTime = minworkingTime / 60 / 60;
label16.Visible = true;
label17.Visible = true;
label18.Visible = true;
label19.Visible = true;
label20.Visible = true;
secMinHour(workingTime, label16, "Working time: ");
secMinHour(downtime, label17, "Downtime: ");
label18.Text = string.Format("Сoefficient of productivity: {0}%\n", (int)coefOfProd);
secMinHour(maxworkingTime, label19, "Maximum worktime: ");
secMinHour(minworkingTime, label20, "Minimum worktime: ");
//richTextBox1.Text += string.Format("Working time: {0}\n", workingTime);
//richTextBox1.Text += string.Format("Downtime: {0}\n", downtime);
//richTextBox1.Text += string.Format("Сoefficient of productivity: {0}%\n", coefOfProd);
//richTextBox1.Text += string.Format("Maximum worktime: {0} hours\n", maxworkingTime);
//richTextBox1.Text += string.Format("Minimum worktime: {0} hours\n", minworkingTime);
///////////////////////////using R
string typeOfModel = toolStripComboBox1.Text;
if (dailyWork.Length <= 3)
{
MessageBox.Show("Time serie cannot consist from one element!", "Error!", MessageBoxButtons.OK, MessageBoxIcon.Error);
}
else
{
REngine.SetEnvironmentVariables();
REngine engine = REngine.GetInstance();
// REngine requires explicit initialization.
// You can set some parameters.
engine.Initialize();
double[] forR = new double[dailyWork.Length];
for (int i = 0; i < forR.Length; i++)
{
forR[i] = dailyWork[i];
mSW.WriteLine(Convert.ToString(forR[i]) + " " + Convert.ToString(dailyWork[i] + "\n"));
}
NumericVector dd = engine.CreateNumericVector(forR);
engine.SetSymbol("dd", dd);
engine.Evaluate("library(FBN)");
engine.Evaluate("fit <- medianFilter(dd, windowSize = 3)");
engine.Evaluate("library(forecast)");
//engine.Evaluate("capture.output (fit$fitted[, 1], file = 'tmp.txt')");
//Double[] massForPlotInR = new Double [dailyWork.Length - 1];
//String[] strForMassForPlotInR = null;
//if (File.Exists("tmp.txt"))
//{
// StreamReader rForTmp = new StreamReader("tmp.txt"); int t = 0;
// while (t < 4)
// {
// rForTmp.ReadLine();
// t++;
// }
// int ii = 0;
// string str = null;
// while ((str = rForTmp.ReadLine()) != null)
// {
// // string str = rForTmp.ReadLine();
// str = str.Replace('.', ',');
// strForMassForPlotInR = str.Split(' ');
// for (int j = 1; j < strForMassForPlotInR.Length; j++)
// {
// if ((strForMassForPlotInR[j] != "") && (!(strForMassForPlotInR[j].Contains('['))))
// {
// massForPlotInR[ii] = Convert.ToDouble(strForMassForPlotInR[j]);
// ii++;
// }
// //i = strForMassForPlotInR.Length - 1;
// }
// }
// //for (int i = 0; i < massForPlotInR.Length; i++)
// //{
// // richTextBox2.Text += Convert.ToString(massForPlotInR[i]) + "\n";
// //}
// rForTmp.Close();
//}
//NumericVector fit_fitted = engine.CreateNumericVector(massForPlotInR);
//engine.SetSymbol("fit_fitted", fit_fitted);
if (radioButton2.Checked == true)
{
engine.Evaluate("plot(c(dd, median(dd[(length(dd)-3):length(dd)])), type = 'p', col = 'blue', xlab = 'Time', ylab = 'Processor load')");
engine.Evaluate("lines(c(dd, NA), type = 'o')");
engine.Evaluate("legend('topleft', c('Initial data', 'Forecast'), col = c('black','blue'), pch = c(1,1))");
}
else if (radioButton1.Checked == true)
{
engine.Evaluate("plot(dd, xlab = 'Days', ylab = 'Processor load', type = 'l')");
engine.Evaluate("lines(fit, col = 'red', type = 'l')");
engine.Evaluate("legend('topleft', c('Initial data', 'Model'), col = c('black','red'), pch = c(1,1))");
//engine.Evaluate("legend()");
//engine.Evaluate("lines(fit_fitted, col = 'red')");
}
NumericVector ddRes = engine.Evaluate("median(dd[(length(dd)-3):length(dd)])").AsNumeric();
label21.Text = string.Format("Forecast on day {0}%", string.Join(", ", ddRes));
label21.Visible = true;
}
}
public string run_ruvseq(List <string> group1, List <string> group2)
{
try
{
engine = REngine.GetInstance();
engine.Initialize();
engine.Evaluate(string.Format("Sys.setenv(PATH = paste('{0}', ';', Sys.getenv('PATH'), sep=''))", this.rPath));
engine.Evaluate("Sys.getenv()");
engine.Evaluate("library('RUVSeq')");
engine.Evaluate("library('DESeq2')");
engine.Evaluate("library('RColorBrewer')");
engine.Evaluate("matrix <- read.table('" + inputFile + "', header=TRUE, sep=',', fill=TRUE, row.names=1)");
engine.Evaluate("matrix <- matrix + 1;" +
"matrix <- round(matrix, 0);" +
"filter <- apply(matrix, 1, function(x) length(x[x > 5]) >= 2);" +
"filtered <- matrix[filter,];" +
"genes <- rownames(filtered)[grep('^ENS', rownames(filtered))];" +
"spikes <- rownames(filtered)[grep('^ERCC', rownames(filtered))];");
CharacterVector colnames = engine.Evaluate("colnames(filtered)").AsCharacter();
var x = engine.CreateCharacterVector(this.groupSeparation(colnames, group1, group2));
engine.SetSymbol("x", x);
engine.Evaluate("print(x)");
engine.Evaluate("print(colnames(filtered))");
engine.Evaluate("colors <- brewer.pal(4, 'Set1');" +
"set <- newSeqExpressionSet(as.matrix(filtered), phenoData = data.frame(x, row.names=colnames(filtered)));" +
"set <- betweenLaneNormalization(set, which='upper');" +
"set1 <- RUVg(set, spikes, k=1);" +
"write.csv(normCounts(set1), paste('" + outputFileDirectory + "\\\\" + outputFile + "','_normalizedCounts.csv', sep=''));" +
"write.csv(counts(set1), paste('" + outputFileDirectory + "\\\\" + outputFile + "', '_counts.csv', sep = ''));" +
"write.csv(pData(set1), paste('" + outputFileDirectory + "\\\\" + outputFile + "','_colData.csv', sep = ''));");
engine.Evaluate(@"png(file = paste('" + outputFileDirectory + "\\\\" + outputFile + "','_graphRLE_RUV.png', sep = ''), height = 720, width = 1280);" +
"plotRLE(set1, outline = FALSE, ylim = c(-4, 4), col = colors[x], xaxt = 'n', ann = FALSE);" +
"text(x = seq(1, length(colnames(filtered))) - .2, par('usr')[3] - .4, labels = colnames(filtered), adj = c(1.5, 1.5), srt = 60, pos = 1, xpd = TRUE, cex = 1);" +
"axis(1, at = seq(1, length(colnames(filtered)), by = 1), labels = FALSE);" +
"dev.off();");
engine.Evaluate("png(file = paste('" + outputFileDirectory + "\\\\" + outputFile + "', '_graphPCA_RUV.png', sep = '), height = 720, width = 1280);" +
"plotPCA(set1, col = colors[x], cex = 1.2);" +
"dev.off();");
return("Success");
}
catch (RDotNet.EvaluationException e)
{
return(e.ToString());
}
finally
{
engine.Dispose();
}
}
private void btnGenerate_Click(object sender, EventArgs e)
{
try
{
frmProgress pfrmProgress = new frmProgress();
pfrmProgress.lblStatus.Text = "Processing:";
pfrmProgress.Show();
int intProgress = 0;
REngine pEngine = mForm.pEngine;
//Declare constants
double dblDistError = Convert.ToDouble(txtDistance.Text);
double dblDirection = Convert.ToDouble(txtDirections.Text);
int intNSimulation = Convert.ToInt32(txtNSimulation.Text);
string strX = cboX.Text;
string strY = cboY.Text;
mForm.strValue = cboFieldName.Text;
//Load Source Layer
string strLayerName = cboSourceLayer.Text;
int intLIndex = pSnippet.GetIndexNumberFromLayerName(pActiveView, strLayerName);
ILayer pLayer = mForm.axMapControl1.get_Layer(intLIndex);
IFeatureLayer pFLayer = pLayer as IFeatureLayer;
IFeatureClass pFClass = pFLayer.FeatureClass;
int nFeature = pFClass.FeatureCount(null);
IFeatureCursor pFCursor = pFLayer.Search(null, true);
IFeature pFeature = pFCursor.NextFeature();
//Load Target Layer
mForm.strTargetLayerName = cboTargetLayer.Text;
int intTargetIndex = pSnippet.GetIndexNumberFromLayerName(pActiveView, mForm.strTargetLayerName);
ILayer pTargetLayer = mForm.axMapControl1.get_Layer(intTargetIndex);
IFeatureLayer pFTargetLayer = pTargetLayer as IFeatureLayer;
IFeatureClass pFTargetClass = pFTargetLayer.FeatureClass;
int nTargetFeature = pFTargetClass.FeatureCount(null);
//Store X, Y and Value to array
double[] vecValue = new double[nFeature];
double[,] arrXY = new double[nFeature, 2];
int intXIdx = pFClass.FindField(strX);
int intYIdx = pFClass.FindField(strY);
int intVIdx = pFClass.FindField(mForm.strValue);
int i = 0;
while (pFeature != null)
{
vecValue[i] = Convert.ToDouble(pFeature.get_Value(intVIdx));
arrXY[i, 0] = Convert.ToDouble(pFeature.get_Value(intXIdx));
arrXY[i, 1] = Convert.ToDouble(pFeature.get_Value(intYIdx));
i++;
pFeature = pFCursor.NextFeature();
}
pFCursor.Flush();
////Plot command for R
//StringBuilder plotCommmand = new StringBuilder();
//Get the file path and name to create spatial weight matrix
string strNameR = pSnippet.FilePathinRfromLayer(pFTargetLayer);
if (strNameR == null)
{
return;
}
//Load Library and Data
pEngine.Evaluate("library(maptools);library(foreign);library(sp);library(circular);");
pEngine.Evaluate("ct.shp <- readShapePoly('" + strNameR + "')");
//Create Shapepointdataframe in R
NumericMatrix nmXY = pEngine.CreateNumericMatrix(arrXY);
pEngine.SetSymbol("xy.coord.old", nmXY);
NumericVector nvValue = pEngine.CreateNumericVector(vecValue);
pEngine.SetSymbol("LEAD", nvValue);
pEngine.Evaluate("v.bll <- as.data.frame(LEAD)");
pEngine.Evaluate("old.points <- SpatialPointsDataFrame(xy.coord.old, v.bll)");
string strOverMethod = "";
if (cboStat.Text == "Mean")
{
strOverMethod = ", fn=mean";
}
//Save Original value
pEngine.Evaluate("n.obs <- nrow(v.bll)");
pEngine.Evaluate("ori.value <- as.matrix(over(ct.shp, old.points" + strOverMethod + "))");
NumericVector nvOriValue = pEngine.Evaluate("ori.value").AsNumeric();
mForm.arrOrivalue = new double[nTargetFeature];
nvOriValue.CopyTo(mForm.arrOrivalue, nTargetFeature);
//Create Matrix to store simulation results
pEngine.Evaluate("results.matrix <- matrix(NA, nrow=nrow(ct.shp), ncol=" + intNSimulation.ToString() + ")");
//Simulation
for (int j = 1; j <= intNSimulation; j++)
{
//Progress bar
intProgress = j * 100 / intNSimulation;
pfrmProgress.pgbProgress.Value = intProgress;
//pfrmProgress.pgbProgress.Refresh();
pfrmProgress.lblStatus.Text = "Processing (" + intProgress.ToString() + "%)";
//pfrmProgress.lblStatus.Refresh();
pfrmProgress.Invalidate();
//Create Direction from vonmises distribution
pEngine.Evaluate("dir.new <- as.numeric(rvonmises(n.obs, mu=circular(" + txtDirections.Text + ", units='degrees'), kappa=" + txtConcetration.Text + "))");
//Add distance error
if (rbtRandom.Checked == true)
{
pEngine.Evaluate("dist.error <- runif(n.obs, 0, " + txtDistance.Text + ")");
}
else
{
pEngine.Evaluate("dist.error <- " + txtDistance.Text);
}
//Create New points and save to SpatialPointsDataFrame
pEngine.Evaluate("x.new <- xy.coord.old[,1] + cos(dir.new)*dist.error; y.new <- xy.coord.old[,2]+ sin(dir.new)*dist.error");
pEngine.Evaluate("xy.coord <- cbind(x.new, y.new)");
pEngine.Evaluate("new.points <- SpatialPointsDataFrame(xy.coord, v.bll)");
//Save results <- have to insert options for summary statistics, (only possilbe by Mean) 1/29/15
pEngine.Evaluate("results.matrix[," + j.ToString() + "] <- as.matrix(over(ct.shp, new.points" + strOverMethod + "))");
}
//Save Simulation results to Array
NumericMatrix nmResults = pEngine.Evaluate("results.matrix").AsNumericMatrix();
mForm.arrSimuResults = new double[nTargetFeature, intNSimulation];
nmResults.CopyTo(mForm.arrSimuResults, nTargetFeature, intNSimulation);
//Create matrix to save summary results(mean, var, mean/var)
pfrmProgress.lblStatus.Text = "Creating Summary";
pfrmProgress.Invalidate();
pEngine.Evaluate("sum.mat <- matrix(NA,nrow=nrow(ct.shp), ncol=6)");
//Calculate mean and variance of simulation
for (int j = 1; j <= nTargetFeature; j++)
{
pEngine.Evaluate("sum.mat[" + j.ToString() + ",1] <- mean(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("sum.mat[" + j.ToString() + ",2] <- var(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("sum.mat[" + j.ToString() + ",5] <- min(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("sum.mat[" + j.ToString() + ",6] <- max(results.matrix[" + j.ToString() + ",])");
pEngine.Evaluate("diff.ori <- (results.matrix[" + j.ToString() + ",]- ori.value[" + j.ToString() + "])^2"); //Needs to be changed
pEngine.Evaluate("sum.mat[" + j.ToString() + ",4] <- sum(diff.ori)/" + intNSimulation.ToString());
}
//Calculate mean/variance
pEngine.Evaluate("sum.mat[,3] <- sum.mat[,2]/sum.mat[,1]");
//Save summary results to array
NumericMatrix nmSummary = pEngine.Evaluate("sum.mat").AsNumericMatrix();
arrSummary = new double[nTargetFeature, 6];
nmSummary.CopyTo(arrSummary, nTargetFeature, 6);
//Remove all memory
pEngine.Evaluate("rm(list=ls(all=TRUE))");
//Enable to the histogram identify tool for simulation results
if (mForm.arrSimuResults != null)
{
chkEnable.Enabled = true;
}
//Add Summary data to Target feature Class
//Create filed to store results
if (pFTargetClass.FindField("Ori_Mean") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Ori_Mean";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Mean_val") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Mean_val";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Var_val") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Var_val";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Mean_Var") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Mean_Var";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("Var_Ori") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "Var_Ori";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("MIN") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "MIN";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
if (pFTargetClass.FindField("MAX") == -1)
{
IFieldEdit addField = new FieldClass();
addField.Name_2 = "MAX";
addField.Type_2 = esriFieldType.esriFieldTypeDouble;
pFTargetClass.AddField(addField);
}
//Save summary results to Target shapefile
int intOriMeanIdx = pFTargetClass.FindField("Ori_Mean");
int intMeanIdx = pFTargetClass.FindField("Mean_val");
int intVarIdx = pFTargetClass.FindField("Var_val");
int intMVIdx = pFTargetClass.FindField("Mean_Var");
int intVOIdx = pFTargetClass.FindField("Var_Ori");
int intMaxIdx = pFTargetClass.FindField("MAX");
int intMinIdx = pFTargetClass.FindField("MIN");
IFeatureCursor pTFUCursor = pFTargetClass.Update(null, false);
IFeature pTFeature = pTFUCursor.NextFeature();
i = 0;
while (pTFeature != null)
{
pTFeature.set_Value(intOriMeanIdx, mForm.arrOrivalue[i]);
pTFeature.set_Value(intMeanIdx, arrSummary[i, 0]);
pTFeature.set_Value(intVarIdx, arrSummary[i, 1]);
pTFeature.set_Value(intMVIdx, arrSummary[i, 2]);
pTFeature.set_Value(intVOIdx, arrSummary[i, 3]);
pTFeature.set_Value(intMinIdx, arrSummary[i, 4]);
pTFeature.set_Value(intMaxIdx, arrSummary[i, 5]);
pTFeature.Store();
i++;
pTFeature = pTFUCursor.NextFeature();
}
pfrmProgress.Close();
MessageBox.Show("Done");
}
catch (Exception ex)
{
MessageBox.Show(this.Handle.ToString() + " Error:" + ex.Message);
return;
}
}