Small adjustments #4
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Feature counts is more readable than fcounts.
Learning rate is more readable than lrate.
Regularization weight is more readable than l2wt.
Data instance is more readable than inst.
Feature vector is more readable than fv.
We use iteritems elsewhere, we should be using itervalues here.
Feature and value are more readable than k and x_k.
1e6 is more readable than 1000000, in which one has to count the zeroes. In the case of a modulo operator, the type change from int to float does not alter correctness. 1e-4 could subjectively be more readable than 0.0001.
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👍 I double-checked (random.seed(0) and diff the output files) and this doesn't change any of the outputs in any way. The changes do make the code easier to follow, so I would recommend merging this. |
ghost
approved these changes
Sep 29, 2019
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Hi. I am a Duolingo user and got intrigued by the paper you published together with Meeder. I applied some Python adjustments to improve the readability of the code which, in my humble opinion, more people should be reading to learn from. To read the edit history more easily, I recommend considering one commit at the time, as I made them as small and self-contained as possible. If merged, I could contribute more. For now I do not know how open you might be to external contributions. Thanks for sharing the code in the first place and greetings from Europe.