Hello, all. I am announcing a C extension module which computes univariate and multivariate probability density functions by means of a kernel-based approach. The module includes functions to perform the estimation using the following kernels: * One-dimensional data: Epanechnikov Biweight Triangular * 2 or 3-dimensional data: Epanechnikov Multivariate gaussian For multivariate data, there is the optional feature of scaling each axis by means of a matrix. This approach allows the definition of Fukunaga-type estimators. The functions in the module are used as follows: import KPDF # edata and gdata must be numpy arrays of shapes (N,) and (E,) # while h is a scalar # pdf1 is a numpy array which holds the PDF evaluated at points # gdata with experimental data in edata. pdf1.shape=(E,) pdf1=KPDF.UPDFEpanechnikov(edata,gdata,h) # For multivariate estimation, edata and gdata must be numpy arrays of # shapes (N,2|3) and (E,2|3) while h is a scalar # pdf2 is a numpy array which holds the PDF evaluated at points # gdata with experimental data in edata. pdf2.shape=(E,) pdf2=KPDF.MPDFEpanechnikov(e2data,g2data,h) # For Fukunaga-type estimators, Sm1 must be a numpy array 2x2(3x3) # and holds the covariance matrix. sqrtdetS is the square root of the # determinant pdf2=KPDF.MPDFEpanechnikov(e2data,g2data,h,Sm1,sqrtdetS) There is not a lot of documentation in the module, but I have a serious commitment of preparing it soon. It can be downloaded from: http://starship.python.net/crew/jsaenz/KPDF.tar.gz Feedback of interested users will be greatly appreciated. Regards. Jon Saenz. | Tfno: +34 946012470 Depto. Fisica Aplicada II | Fax: +34 944648500 Facultad de Ciencias. \\ Universidad del Pais Vasco \\ Apdo. 644 \\ 48080 - Bilbao \\ SPAIN