java,scala,apache-commons-math

Your problem comes from the Double type: java.lang.Double is expected while you use scala.Double. Simply try: val mapping = new java.util.ArrayList[Pair[Long, java.lang.Double]]() Then it should work since as you said yourself, ArrayList implements List....

java,apache-commons,apache-commons-math,bernoulli-probability

If you are actually using the distribution methods and performance is important, you should subclass AbstractIntegerDistribution. BinomialDistribution implements the distribution methods using numerical approximations via special functions. These computations carry some overhead and are not necessary in the degenerate (Bernoulli) case, where constants could be returned. As of version 3.4.1,...

java,cluster-analysis,dbscan,apache-commons-math

Try the version in ELKI instead. Apache commons math is unfortunately not very good. I moved away from commons-math because of various small issues. ELKI works much better for me. From a quick look, commons-math is still pretty dead when it comes to cluster analysis... it was last touched for...

java,apache-commons,apache-commons-math

This import: import org.apache.commons.math3.stat.descriptive.SummaryStatistics.*; doesn't import SummaryStatistics itself, just any classes defined within that class. Add: import org.apache.commons.math3.stat.descriptive.SummaryStatistics; as well....

java,mathematical-optimization,apache-commons-math

Polynomials are special functions (in the general sense of "special") and have lots of distinctive, useful properties. My advice is to exploit those properties instead of trying to use a method for more general functions. Specifically, the extreme values of a polynomial are the roots of its derivative (where the...

I don't know the library, but looking at the Javadoc I think this should do the trick: Y = new SparseFieldMatrix(ComplexField.getInstance()); ...

apache,math,apache-commons-math

Note that the smallest value which can be subtracted from 1.0 to yield something less than 1.0 is approximately 1e-16; you can verify this directly. Maybe you should print out chisq.cumulativeProbability(131) itself. I don't know if it's correct but in any case let's not confuse the issue by subtracting it...

java,vector,apache-commons-math

This is the dot product, which you can do with double c = a.dotProduct(b);