Large datasets (e.g., agemap H & E stain ~40,000 samples) take an extremely
long time in Pychrm to do train/test split and classify operations. Euclidean
distances can be calculated in a parallellized way, e.g., one processor can to
all the samples from a given class.
This would entail exposing the samples in the FeatureSet.data_matrix to C++. A
C++ implemented, Python-wrapped wndchrm classify option would also speed up
computation.
Original issue reported on code.google.com by christop...@nih.gov on 18 Jan 2013 at 9:33
Original issue reported on code.google.com by
christop...@nih.govon 18 Jan 2013 at 9:33