proposal
progressreport
luke's long-distance-bigram initial commit. Doesn't work right now.
initial attempt at making a mongodb database, right now only keeps track of total counts of the words
using mongodb, but too slow with one-by-one inserts. Next step is to try bulk inserts
Tried bulk upserts, didn't speed up process at all. Next will try only inserting documents to the database, never updating, see it that's any faster.
Now, specify the percent of the training data to use, instead of the lines per step (it only does one step now).
now uses percents instead of steps
Updated gitignore for IDE files
Revert "Updated gitignore for IDE files"

This reverts commit c35823f848934d02d6d0f6a992548af4795b7df1.
Updated gitignore
Json export code
copied the averaged perceptron, modified it a bit

Main thing left is to convert training data still to features
updated perceptron
deleted file
this file stores the counts in shelves, but as it is counting, it forgets ldb's that is has only seen once in the last interval of FORGET_lINES lines, in order to free up RAM and make learning go faster
using long-distance-bigrams to guess the location of the missing word seems to be working! File renames: what was bigram_count is now smooth_ldb.py, what was counts.py is now ldb_probs.py. ldb stands for long-distance-bigram.
added method to test on sentences
training on 3 features
fixes to 3 feature training code