static double calcSDev (REngine engine, double[] arr) { // Note: only one quick and slightly dirty way to do it NumericVector rVector = engine.CreateNumericVector(arr); engine.SetSymbol ("x", rVector); return engine.Evaluate ("sd(x)").AsNumeric () [0]; }
/// <summary> /// /// </summary> /// <param name="symbolname"></param> /// <param name="expression"></param> internal void SetSymbol(string symbolname, SymbolicExpression expression) { /* if (!expression.IsProtected) * expression.Protect(); */ engine.SetSymbol(symbolname, expression); }
static void GenetateLatencyVsMessageLengthPlot_RScript(REngine engine, string latencyCsvFilename) { //For formatting purposes, make sure the filename is acceptable for R function read.csv string fileToReadFromCommand = latencyCsvFilename.Replace(@"\", @"/"); //Convert to R character vector CharacterVector cvFilename = engine.CreateCharacterVector(new[] { fileToReadFromCommand }); // and assign it to variable (in R engine) called fileToReadFrom engine.SetSymbol("fileToReadFrom", cvFilename); //And then evaluate the script - this uses the 'fileToReadFrom' in a read.csv call engine.Evaluate(MyRDotNetApplication.Properties.Resources.latencyVsMessageLengthScatterPlot); //R-Script to generate plot }
private static DataFrame UserReported(REngine engine) { // Incomplete data and repro info. // See https://rdotnet.codeplex.com/discussions/569196 NumericVector PreprocessedValue = null; DataFrame PredictedData = null; // Some info was missing. Make up. string StartDate = "2001-01-01"; double[] PreProcessedList = new[] { 1.1, 7.3, 4.5, 7.4, 11.23, 985.44 }; string days = "2"; string interval = "3"; PreprocessedValue = engine.CreateNumericVector(PreProcessedList); // Assign the Utilization value to R variable UtilValue engine.SetSymbol("PreprocessedValue", PreprocessedValue); engine.Evaluate("library(forecast)"); engine.Evaluate("StartDate <- as.Date('" + StartDate + "') + " + days); engine.Evaluate("size = length(seq(from=as.Date('" + StartDate + "'), by='" + "day" + "', to=as.Date(StartDate)))"); engine.Evaluate("startDate <- as.POSIXct('" + StartDate + "')"); engine.Evaluate("endDate <- StartDate + as.difftime(size, units='" + "days" + "')"); engine.Evaluate("PredictDate = seq(from=StartDate, by=" + interval + "*60, to=endDate)"); engine.Evaluate("freq <- ts(PreprocessedValue, frequency = 20)"); engine.Evaluate("forecastnavie <-snaive(freq, Datapoints)"); engine.Evaluate("PredictValue = (forecastnavie$mean)"); engine.Evaluate("PredictedData = cbind(PredictValue, data.frame(PredictDate))"); PredictedData = engine.Evaluate("PredictedData").AsDataFrame(); return PredictedData; }
private static void ReproWorkitem43(REngine engine) { Random r = new Random(0); int N = 500; int n1 = 207; int n2 = 623; var arGroup1Intensities = new double[N][]; var arGroup2Intensities = new double[N][]; for (int i = 0; i < N; i++) { arGroup1Intensities[i] = new double[n1]; arGroup2Intensities[i] = new double[n2]; for (int j = 0; j < n1; j++) arGroup1Intensities[i][j] = r.NextDouble(); for (int j = 0; j < n2; j++) arGroup2Intensities[i][j] = r.NextDouble(); } var res = new GenericVector[N]; NumericVector vGroup1, vGroup2; for (int i = 0; i < N; i++) { vGroup1 = engine.CreateNumericVector(arGroup1Intensities[i]); Console.WriteLine(vGroup1.Length); if (i % 10 == 4) { engine.ForceGarbageCollection(); engine.ForceGarbageCollection(); } vGroup2 = engine.CreateNumericVector(arGroup2Intensities[i]); Console.WriteLine(vGroup2.Length); engine.SetSymbol("group1", vGroup1); engine.SetSymbol("group2", vGroup2); GenericVector testResult = engine.Evaluate("t.test(group1, group2)").AsList(); res[i] = testResult; } }
private static void setValueAndMeasure(REngine engine, Stopwatch s, Func<REngine, Array> fun, string symbolName, string vStatement) { engine.SetSymbol(symbolName, engine.Evaluate(vStatement)); //engine.Evaluate("cat(ls())"); measure(engine, s, fun); }
private static void setValueAndMeasure(REngine engine, Stopwatch s, Func <REngine, Array> fun, string symbolName, string vStatement) { engine.SetSymbol(symbolName, engine.Evaluate(vStatement)); //engine.Evaluate("cat(ls())"); measure(engine, s, fun); }
static void Main(string[] args) { SetupPath(); // current process, soon to be deprecated // There are several options to initialize the engine, but by default the following suffice: REngine engine = REngine.GetInstance(); engine.Initialize(); // required since v1.5 // some random weight samples double[] weight = new double[] { 3.2, 3.6, 3.2, 1.7, 0.8, 2.9, 2, 1.4, 1.2, 2.1, 2.5, 3.9, 3.7, 2.4, 1.5, 0.9, 2.5, 1.7, 2.8, 2.1, 1.2 }; double[] lenght = new double[] { 2, 3, 3.2, 4.7, 5.8, 3.9, 2, 8.4, 5.2, 4.1, 2.5, 3.9, 5, 2.4, 3.5, 0.9, 2.5, 2.7, 2.8, 2.1, 1.2 }; // introduce the samples into R engine.SetSymbol("weight", engine.CreateNumericVector(weight)); engine.SetSymbol("lenght", engine.CreateNumericVector(lenght)); // set the weights and lenghts as a data frame (regular R syntax in string) engine.Evaluate("df <- data.frame(id=c(1:length(weight)), weight = weight,lenght = lenght )"); // evaluate and retrieve mean double avg = engine.Evaluate("mean(df$weight)").AsNumeric().ToArray()[0]; // same for standard deviation double std = engine.Evaluate("sd(df$weight)").AsNumeric().ToArray()[0]; // NumericVector coeff = engine.Evaluate("coefficients(lm(df$weight ~ df$lenght ))").AsNumeric(); // print output in console System.Globalization.CultureInfo ci = new System.Globalization.CultureInfo("en-gb"); //Show in console the weight and lenght data Console.WriteLine(string.Format("Weights: ({0})", string.Join(",", weight.Select(f => f.ToString(ci)) // LINQ expression ))); Console.WriteLine(string.Format("Length: ({0})", string.Join(",", lenght.Select(f => f.ToString(ci)) // LINQ expression ))); Console.WriteLine(string.Format("Sample size: {0}", weight.Length)); Console.WriteLine(string.Format(ci, "Average: {0:0.00}", avg)); Console.WriteLine(string.Format(ci, "Standard deviation: {0:0.00}", std)); var result = engine.Evaluate("lm(df$weight ~ df$lenght)"); engine.SetSymbol("result", result); var coefficients = result.AsList()["coefficients"].AsNumeric().ToList(); double r2 = engine.Evaluate("summary(result)").AsList()["r.squared"].AsNumeric().ToList()[0]; double intercept = coefficients[0]; double slope = coefficients[1]; Console.WriteLine("Intercept:" + intercept.ToString()); Console.WriteLine("slope:" + slope); Console.WriteLine("r2:" + r2); string fileName = "myplot.png"; CharacterVector fileNameVector = engine.CreateCharacterVector(new[] { fileName }); engine.SetSymbol("fileName", fileNameVector); engine.Evaluate("png(filename=fileName, width=6, height=6, units='in', res=100)"); engine.Evaluate("reg <- lm(df$weight ~ df$lenght)"); engine.Evaluate("plot(df$weight ~ df$lenght)"); engine.Evaluate("abline(reg)"); engine.Evaluate("dev.off()"); //The file will save in debug directory Application.Run(new Form1()); // After disposing of the engine, you cannot reinitialize nor reuse it engine.Dispose(); }