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/*
Copyright (C) 2011 Klaus Spanderen
This file is part of QuantLib, a free-software/open-source library
for financial quantitative analysts and developers - http://quantlib.org/
QuantLib is free software: you can redistribute it and/or modify it
under the terms of the QuantLib license. You should have received a
copy of the license along with this program; if not, please email
<quantlib-dev@lists.sf.net>. The license is also available online at
<https://www.quantlib.org/license.shtml>.
This program is distributed in the hope that it will be useful, but WITHOUT
ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the license for more details.
*/
package examples;
import org.quantlib.{Array => QArray, _}
object RandomNumbers {
def main(args: Array[String]) : Unit = {
try {
System.loadLibrary("QuantLibJNI")
}
catch {
case ex: UnsatisfiedLinkError => {
println("please check your LD_LIBRARY_PATH variable")
throw ex
}
}
val rng = new GaussianRandomGenerator(
new UniformRandomGenerator(12345))
val fmt = "%8d %10.6f %10.6f %10.6f %10.6f\n"
printf(" #rngs\t mean(expected) error(expected)\tmean\t error\n");
(1 to 10).foreach(i => {
val nRngs = 1024*(1 << i)
val st = new IncrementalStatistics()
(0 until nRngs).foreach(_ => st.add(rng.nextValue()))
printf(fmt, nRngs, 0.0, 1/scala.math.sqrt(nRngs),
st.mean(), st.errorEstimate())
} )
}
}
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