Thanks for your replies. You've been very helpful.
On Oct 24, 1:36 pm, Konrad Hinsen <konrad.hin...@fastmail.net> wrote:
> On 23 Oct 2009, at 22:00, Konrad Hinsen wrote:
>
> > For matrix computations and linear algebra, your best choice is
> > probably
> > the Colt library developed at CERN, or the somewhat parallelized
> > version called Parallel Colt. Colt has arrays up to three dimensions
> > for
> > a couple of data types, enough for matrix stuff but not much more.
>
> > If you need higher-dimensional arrays, e.g. to store complex data
> > sets,
> > your best bet would be the netCDF implementation for Java, which
> > contains a very complete array library that handles any number of
> > dimensions. However, it provides no linear algebra at all.
>
> I should have added that it is possible to convert between netCDF and
> Colt arrays without a costly element-by-element copying procedure. For
> example, the following code inverts a matrix read from a netCDF file
> using Colt:
>
> (ns netcdf-read
> (:import (ucar.nc2 NetcdfFile)
> (cern.colt.matrix.tdouble.impl DenseDoubleMatrix2D)
> (cern.colt.matrix.tdouble.algo DenseDoubleAlgebra)))
>
> (defn unidata-to-colt
> [#^ucar.ma2.ArrayDouble$D2 array]
> (let [java-array-1d (.get1DJavaArray array Double)
> [rows, cols] (.getShape array)]
> (new DenseDoubleMatrix2D rows cols java-array-1d 0 0 cols 1 false)))
>
> (let [matrix (with-open [ncfile (NetcdfFile/open "/Users/hinsen/
> Temp/matrix.nc")]
> (.read (.findVariable ncfile "matrix")))
> colt-matrix (unidata-to-colt matrix)
> la (new DenseDoubleAlgebra)
> inverse (.inverse la colt-matrix)]
> (prn inverse))
>
> Konrad.
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