- act2vec, trace2vec, log2vec, model2vec https://link.springer.com/chapter/10.1007/978-3-319-98648-7_18
- apk2vec https://arxiv.org/abs/1809.05693
- app2vec http://paul.rutgers.edu/~qma/research/ma_app2vec.pdf
- ast2vec https://arxiv.org/abs/2103.11614
- attribute2vec https://arxiv.org/abs/2004.01375
- author2vec http://dl.acm.org/citation.cfm?id=2889382
- baller2vec https://arxiv.org/abs/2102.03291
- bb2vec https://arxiv.org/abs/1809.09621
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import numpy as np | |
import pdb | |
from sklearn.datasets import make_classification | |
from sklearn.mixture import GaussianMixture as GMM | |
def fisher_vector(xx, gmm): | |
"""Computes the Fisher vector on a set of descriptors. |
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""" | |
Helper module for displaying ROOT canvases in ipython notebooks | |
Usage example: | |
# Save this file as rootnotes.py to your working directory. | |
import rootnotes | |
c1 = rootnotes.default_canvas() | |
fun1 = TF1( 'fun1', 'abs(sin(x)/x)', 0, 10) | |
c1.SetGridx() |