def embedding2d_feat(self, feat, feature_name, alg="se", save=True):
filename = os.path.join(settings.OUTPUT_FOLDER,
"%s-%s.pickle" % (feature_name, alg))
if os.path.exists(filename):
return np.load(filename)
b, u, h, w = feat.shape
feat.transpose(0, 2, 3, 1)
feat.shape = (b * w * h, u)
if alg == 'se':
feat_nse = SpectralEmbedding(n_components=2).fit_transform(feat)
else:
feat_nse = TSNE(n_components=2, verbose=2).fit_transform(feat)
feat_nse.shape = (b, w, h, 2)
if save:
np.save(filename, feat_nse)
return feat_nse
