Classifies a dataset (in SVM-Light format). For binary classifiers, the result is saved to a text file containing one floating-point number per line: the value of the classification function applied the corresponding testing vector (the signs of these numbers are the classifications). For multiclass classifiers, each line of the output file contains a comma-separated list of values of the classification functions of the classes, applied to the corresponding testing vector (the index of the largest of these is the classification). This implementation handles sparsity using a greedy clustering approach. The small_clusters parameter indicates the size of the clusters: 16 (small) or 256 (not small). Generally, size-256 clusters will give significantly better performance. The active_clusters parameter is the number of clusters which will be active at every point in the greedy clustering algorithm. We have found that 64 works well, but increasing this number will improve the quality of the clustering (at the cost of more time being required to find it). Allowed options: -h [ --help ] display this help -f [ --file ] arg dataset file (SVM-Light format) -i [ --input ] arg input model file -o [ --output ] arg output text file -s [ --small_clusters ] arg (=0) use size-16 instead of size-256 clusters? -a [ --active_clusters ] arg (=64) number of "active" clusters