glove_solution.py
89c563b7bfa6master

glove_solution.py
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			#!/usr/bin/env python3
			from scipy.sparse import *
			import numpy as np
			import pickle
			import random


			def main():
			print("loading cooccurrence matrix")
			with open('cooc.pkl', 'rb') as f:
			cooc = pickle.load(f)
			print("{} nonzero entries".format(cooc.nnz))

			nmax = 100
			print("using nmax =", nmax, ", cooc.max() =", cooc.max())

			print("initializing embeddings")
			embedding_dim = 20
			xs = np.random.normal(size=(cooc.shape[0], embedding_dim))
			ys = np.random.normal(size=(cooc.shape[1], embedding_dim))

			eta = 0.001
			alpha = 3 / 4

			epochs = 10

			for epoch in range(epochs):
			print("epoch {}".format(epoch))
			for ix, jy, n in zip(cooc.row, cooc.col, cooc.data):
			logn = np.log(n)
			fn = min(1.0, (n / nmax) ** alpha)
			x, y = xs[ix, :], ys[jy, :]
			scale = 2 * eta * fn * (logn - np.dot(x, y))
			xs[ix, :] += scale * y
			ys[jy, :] += scale * x
			np.save('embeddings', xs)


			if __name__ == '__main__':
			main()