class NNEncode():
''' Encode points using NN search and Gaussian kernel '''
def __init__(self, NN, sigma, km_filepath='', cc=-1):
if (check_value(cc, -1)):
self.cc = np.load(km_filepath)
else:
self.cc = cc
self.K = self.cc.shape[0]
self.NN = int(NN)
self.sigma = sigma
self.nbrs = NearestNeighbors(n_neighbors=NN,
algorithm='ball_tree').fit(self.cc)
self.closest_neighbor = NearestNeighbors(n_neighbors=1,
algorithm='ball_tree').fit(
self.cc)
self.alreadyUsed = False
def encode_points_mtx_nd(self,
pts_nd,
axis=1,
return_sparse=False,
sameBlock=True):
pts_flt = flatten_nd_array(pts_nd, axis=axis)
P = pts_flt.shape[0]
if (sameBlock and self.alreadyUsed):
self.pts_enc_flt[...] = 0 # already pre-allocated
else:
self.alreadyUsed = True
self.pts_enc_flt = np.zeros((P, self.K))
self.p_inds = np.arange(0, P, dtype='int')[:, na()]
P = pts_flt.shape[0]
if return_sparse:
(dists, inds) = self.nbrs.closest_neighbor(pts_flt)
else:
(dists, inds) = self.nbrs.kneighbors(pts_flt)
wts = np.exp(-dists**2 / (2 * self.sigma**2))
wts = wts / np.sum(wts, axis=1)[:, na()]
self.pts_enc_flt[self.p_inds, inds] = wts
pts_enc_nd = unflatten_2d_array(self.pts_enc_flt, pts_nd, axis=axis)
return pts_enc_nd
def decode_points_mtx_nd(self, pts_enc_nd, axis=1):
pts_enc_flt = flatten_nd_array(pts_enc_nd, axis=axis)
pts_dec_flt = np.dot(pts_enc_flt, self.cc)
pts_dec_nd = unflatten_2d_array(pts_dec_flt, pts_enc_nd, axis=axis)
return pts_dec_nd
