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from PIL import Image | |
import numpy as np | |
from pathlib import Path | |
def open(fname): | |
img = Image.open(fname).resize((384, 640)) | |
return img | |
def array(img): |
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# This script is intended for downloading all four videos on causality by | |
# Jonas Peters (University of Copenhagen) from YouTube. | |
# | |
# Copyright belongs to their appropriate copyright holders. I am only | |
# providing this script for convenience. | |
# | |
# To run this script, first ensure that you have the Python package | |
# `youtube-dl` installed. Assuming you are able to install it into your | |
# favourite computing environment, it should be a single command: | |
# |
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name: pymc4-dev | |
channels: | |
- defaults | |
- conda-forge | |
- ericmjl | |
dependencies: | |
- python=3.6 | |
- jupyter | |
- jupyterlab | |
- conda |
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============================= test session starts ============================== | |
platform darwin -- Python 3.6.7, pytest-4.0.2, py-1.7.0, pluggy-0.8.0 | |
rootdir: /Users/ericmjl/github/software/autograd-sparse, inifile: | |
collected 3 items / 2 deselected | |
tests/test_sparse.py F [100%] | |
=================================== FAILURES =================================== | |
_____________________________ test_sparse_dot_grad _____________________________ |
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name: dl-workshop | |
channels: | |
- defaults | |
- conda-forge | |
- ericmjl | |
dependencies: | |
- python=3.7 | |
- jupyter | |
- jupyterlab | |
- conda |
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from sklearn.metrics import explained_variance_score | |
def var_explained(preds, actual): | |
""" | |
Implementation taken directly from the formula on this page: | |
http://scikit-learn.org/stable/modules/model_evaluation.html#explained-variance-score | |
""" | |
return 1 - ((preds - actual).var() / actual.var()) | |
y_pred = np.array([3, -0.5, 2, 7]) |