def partial_pca(mat, nev=6, nt=1):
_check_row_float32(mat)
n, d = mat.shape
avg = mat.mean(axis=0)
mat = mat - avg[numpy.newaxis, :]
singvals = numpy.empty(nev, dtype=numpy.float32)
# pdb.set_trace()
pcamat = yael.fmat_new_pca_part(d, n, nev, yael.numpy_to_fvec_ref(mat), yael.numpy_to_fvec_ref(singvals))
assert pcamat != None
# print "SVs", singvals
pcamat = yael.fvec.acquirepointer(pcamat)
pcamat = yael.fvec_to_numpy(pcamat, (nev, d))
return avg, singvals, pcamat
def partial_pca(mat, nev=6, nt=1):
_check_row_float32(mat)
n, d = mat.shape
avg = mat.mean(axis=0)
mat = mat - avg[numpy.newaxis, :]
singvals = numpy.empty(nev, dtype=numpy.float32)
# pdb.set_trace()
pcamat = yael.fmat_new_pca_part(d, n, nev, yael.numpy_to_fvec_ref(mat),
yael.numpy_to_fvec_ref(singvals))
assert pcamat != None
#print "SVs", singvals
pcamat = yael.fvec.acquirepointer(pcamat)
pcamat = yael.fvec_to_numpy(pcamat, (nev, d))
return avg, singvals, pcamat