MDS_C: The SMACOF algorithm - scaling by majorizing a convex function

Installing the dependencies:

$ make install-prerequisites

Building the programs

Build the C version:

$ make or $ make c

Build the CUDA C version:

$ make cuda

Switching the compiler can be done by replacing the Makefile variable CC, e.g.:

$ make CC=clang

Build the documentation with:

$ make doc

That target will clean all auto-generated sources

$ make clean

Testing the build

These targets should run without any errors.

$ make run-cubeC-example

$ make run-cubeC-weights-example

This target only works when compiling for cuda of course $ make run-cubeCUDA-example

The results of these example calls can be visualized using the scripts in misc/

$ ./scatterplot <some csv> # allows to plot your results

Your results may have to be transposed first using

$ ./transposecsv.py <input csv> <output csv>

When you would like to melt down your CPU, create a random matrix using the gen-rand.py script that allows you to generate a random matrix of arbitrary size.

Help

Just call the compiled programs or the python helper scripts without parameters in order to obtain a help message, e.g.:

$ ./smacofC
ERROR: wrong number of arguments
####################################################################
# SMACOF - Philipp D. Schubert - [email protected]           #
####################################################################
usage: <prog> <input> <output> <disfunc> <maxiter> <epsilon> <resultdim> <metric_p> <print>
parameter explanation:
        input - a valid path to csv file to analyse: path
        ouput - a filename for the outputfile: path
        disfunc - dissimilarity measure: ...
                0 - euclidean
                1 - cityblock
                2 - minkowski
                3 - correlation
                4 - angularseperatin
                5 - wavehedges
                6 - bahattacharyya
                7 - soergel
                8 - braycurtis
                9 - divergence
                10 - canberra
                11 - none
maxiter - maximum number of iterations to use: a positive integer
epsilon - the maximum error value which is allowed: a positive real_t
resultdim - number of dimensions for result vectors: a positive integer
minkowski_p - a value to adjust the minkowski distance: a positive integer
print - be verbose: 0 or 1
weights - a valid path to csv file containing the weights