Starting scikit learning preprocessing
trying to get syntax right
Dimensionality reduction ftw
lazy tutorial copy-paste, would rather do scikit really
Running 'pip freeze > requirements.txt' makes this stuff a bit more portable
Move some stuff into a util module
Merge branch 'sparse' into scikit_preprocess
Whee! 35000 test cases, 35000 training, same accuracy as with 6k tokens.
remove f.txt
Package-ified the tutorial neural network stuff
Merge branch 'scikit_preprocess' into mlp
Input seems to work, output needs changing
Something's still broken. How do you fucking predict with this damn thing...
Logistic regression predicts full distribution

This is extremely interesting. The original code just tries to make sure
the largest number in the distribution is the same as the correct label.
This code tries to match the whole distribution (get close to 0s for
wrong digits, get close to 1s for correct digits).

What's interesting is
that when you report the *max* difference (over any digit) between
expected an actual, and take the mean of that, you get about twice the
'error' as if you're just trying to classify. This means that picking
the best 'hides' an error, where it's 50% sure that it's a 4, but 100%
sure it's a 9, for example.

Alternatively, you can report the mean,
which gives bonus points for getting close to zero for 9/10 labels,
instead of just throwing away the example if you misclassify.

Interesting stuff.
Logistic regression works, has bad accuracy
So I guess logistic regression is kind of working?