- QSTK.qstklearn.fastknn: This package is an implementation of a novel improvement to KNN
which speeds up query times
- QSTK.qstklearn.gendata
- QSTK.qstklearn.hmm: This package includes code for representing and learning HMM's.
- QSTK.qstklearn.kdtknn: A simple wrapper for scipy.spatial.kdtree.KDTree for doing KNN
- QSTK.qstklearn.mldiagnostics
- QSTK.qstklearn.parallelknn: This is an implementation of the K nearest neighbor learning
algorithm. The implementation is trivial in that the near neighbors
are calculated naively- without any smart tricks. Euclidean
distance is used to calculate the distance between two points. The
implementation also provides some coarse parallelism. If the
par_query function is used then the query points are split up
equally amongst threads and their near neighbors are calculated in
parallel. If the number of threads to use is not specified then no
of threads = no of cores as returned by the cpu_count function.
This may not be ideal.
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