private static void TestPendingFinalizersThreadingIssues(REngine e)
{
e.Evaluate("f <- function(a) {if (length(a)!= 1) stop('What goes on?')}");
var f = e.Evaluate("f").AsFunction();
try
{
e.Evaluate("f(letters[1:3])");
}
catch (EvaluationException)
{
}
f.Invoke(e.CreateCharacterVector(new[] { "blah" }));
try
{
f.Invoke(e.CreateCharacterVector(new[] { "blah", "blah" }));
}
catch (EvaluationException)
{
Console.WriteLine("Caught the expected exception");
}
f = null;
GC.Collect();
GC.WaitForPendingFinalizers();
e.Dispose();
Console.WriteLine("Just waiting for crash...");
GC.Collect();
GC.WaitForPendingFinalizers();
}
public static void CreateCharacterVector(REngine engine, int n, Stopwatch s)
{
string[] d = createStringArray(n);
s.Start();
var nvec = engine.CreateCharacterVector(d);
s.Stop();
}
/// <summary>
///
/// </summary>
/// <param name="text"></param>
/// <returns></returns>
public CharacterVector RVector(string[] text)
{
try
{
CharacterVector vector = engine.CreateCharacterVector(text);
vector.Protect();
return(vector);
}
catch (SEHException e)
{
throw new ApplicationException("R Engine has thrown an Exception - memory possibly corrupted; Vector could not be created in R.", e);
}
finally
{
ClearMemory();
}
}
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
}
internal static SymbolicExpression ToVector(REngine engine, IEnumerable values)
{
if (values == null)
{
throw new ArgumentNullException("values", "values to transform to an R vector must not be null");
}
var ints = values as IEnumerable <int>;
var chars = values as IEnumerable <string>;
var cplxs = values as IEnumerable <Complex>;
var logicals = values as IEnumerable <bool>;
var nums = values as IEnumerable <double>;
var raws = values as IEnumerable <byte>;
var sexpVec = values as SymbolicExpression;
if (sexpVec != null && sexpVec.IsVector())
{
return(sexpVec);
}
if (ints != null)
{
return(engine.CreateIntegerVector(ints));
}
if (chars != null)
{
return(engine.CreateCharacterVector(chars));
}
if (cplxs != null)
{
return(engine.CreateComplexVector(cplxs));
}
if (logicals != null)
{
return(engine.CreateLogicalVector(logicals));
}
if (nums != null)
{
return(engine.CreateNumericVector(nums));
}
if (raws != null)
{
return(engine.CreateRawVector(raws));
}
throw new NotSupportedException(string.Format("Cannot convert type {0} to an R vector", values.GetType()));
}
/// <summary>
/// Create an R data frame from managed arrays and objects.
/// </summary>
/// <param name="engine">R engine</param>
/// <param name="columns">The columns with the values for the data frame. These must be array of supported types (double, string, bool, integer, byte)</param>
/// <param name="columnNames">Column names. default: null.</param>
/// <param name="rowNames">Row names. Default null.</param>
/// <param name="checkRows">Check rows. See data.frame R documentation</param>
/// <param name="checkNames">See data.frame R documentation</param>
/// <param name="stringsAsFactors">Should columns of strings be considered as factors (categories). See data.frame R documentation</param>
/// <returns></returns>
public static DataFrame CreateDataFrame(this REngine engine, IEnumerable[] columns, string[] columnNames = null,
string[] rowNames = null, bool checkRows = false, bool checkNames = true, bool stringsAsFactors = true)
{
var df = engine.GetSymbol("data.frame").AsFunction();
SymbolicExpression[] colVectors = ToVectors(engine, columns);
Tuple <string, SymbolicExpression>[] namedColArgs = CreateNamedArgs(colVectors, columnNames);
var args = new List <Tuple <string, SymbolicExpression> >(namedColArgs);
if (rowNames != null)
{
args.Add(Tuple.Create("row.names", (SymbolicExpression)engine.CreateCharacterVector(rowNames)));
}
args.Add(Tuple.Create("check.rows", (SymbolicExpression)engine.CreateLogical(checkRows)));
args.Add(Tuple.Create("check.names", (SymbolicExpression)engine.CreateLogical(checkNames)));
args.Add(Tuple.Create("stringsAsFactors", (SymbolicExpression)engine.CreateLogical(stringsAsFactors)));
var result = df.InvokeNamed(args.ToArray()).AsDataFrame();
return(result);
}
private static void ReproInMemoryDataFrameCreation(REngine e)
{
e.Evaluate("f <- function(a) {if (length(a)!= 1) stop('What goes on?')}");
var f = e.Evaluate("f").AsFunction();
try
{
e.Evaluate("f(letters[1:3])");
}
catch (EvaluationException)
{
}
f.Invoke(e.CreateCharacterVector(new[] { "blah" }));
f.Invoke(e.CreateCharacterVector(new[] { "blah", "blah" }));
// IEnumerable[] columns, string[] columnNames = null, string[] rowNames = null, bool checkRows = false, bool checkNames = true, bool stringsAsFactors = true);
var columns = new[] {
new[]{1,2,3,4,5},
new[]{1,2,3,4,5},
new[]{1,2,3,4,5}
};
var df = e.CreateDataFrame(columns, new[] { "a", "b", "c" });
columns[1] = new[] { 1, 2, 3 };
object blah;
try
{
df = e.CreateDataFrame(columns, new[] { "a", "b", "c" });
blah = df[1, 1];
}
catch
{
}
df = e.CreateDataFrame(columns, new[] { "a", "b", "c" });
blah = df[1, 1];
}
public static void CreateCharacterVector(REngine engine, int n, Stopwatch s)
{
string[] d = createStringArray(n);
s.Start();
var nvec = engine.CreateCharacterVector(d);
s.Stop();
}
internal static SymbolicExpression ToVector(REngine engine, IEnumerable values)
{
if (values == null) throw new ArgumentNullException("values", "values to transform to an R vector must not be null");
var ints = values as IEnumerable<int>;
var chars = values as IEnumerable<string>;
var cplxs = values as IEnumerable<Complex>;
var logicals = values as IEnumerable<bool>;
var nums = values as IEnumerable<double>;
var raws = values as IEnumerable<byte>;
var sexpVec = values as SymbolicExpression;
if (sexpVec != null && sexpVec.IsVector())
return sexpVec;
if (ints != null)
return engine.CreateIntegerVector(ints);
if (chars != null)
return engine.CreateCharacterVector(chars);
if (cplxs != null)
return engine.CreateComplexVector(cplxs);
if (logicals != null)
return engine.CreateLogicalVector(logicals);
if (nums != null)
return engine.CreateNumericVector(nums);
if (raws != null)
return engine.CreateRawVector(raws);
throw new NotSupportedException(string.Format("Cannot convert type {0} to an R vector", values.GetType()));
}
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();
}
