def make_shared(self):
"""
Make sure that tensors can be shared across processes
"""
super().make_shared()
self.link_sqsum = [make_shared(m) for m in self.link_sqsum]
self.pred_sqsum = [make_shared(m) for m in self.pred_sqsum]
def make_shared(self):
"""
Make sure that tensors can be shared across processes
"""
super().make_shared()
self.link_sqsum = [make_shared(m) for m in self.link_sqsum]
self.pred_sqsum = [make_shared(m) for m in self.pred_sqsum]
def make_shared(self):
"""
Make sure that tensors can be shared across processes
"""
super().make_shared()
self.link_mean = [make_shared(m) for m in self.link_mean]
self.pred_mean = [make_shared(m) for m in self.pred_mean]
self.link_var = [make_shared(m) for m in self.link_var]
self.pred_var = [make_shared(m) for m in self.pred_var]
def make_shared(self):
"""
Make sure that tensors can be shared across processes
"""
super().make_shared()
self.link_mean = [make_shared(m) for m in self.link_mean]
self.pred_mean = [make_shared(m) for m in self.pred_mean]
self.link_var = [make_shared(m) for m in self.link_var]
self.pred_var = [make_shared(m) for m in self.pred_var]
from math import log
from pydmrs.components import RealPred
from utils import make_shared, is_verb
D = 800
C = 40
half = int(D/2)
with open('/anfs/bigdisc/gete2/wikiwoods/core-5-vocab.pkl', 'rb') as f:
preds = pickle.load(f)
ind = {p:i for i,p in enumerate(preds)}
pred_index = {RealPred.from_string(p):i for p,i in ind.items()}
pred_wei = make_shared(zeros((len(preds), D)))
for filename, offset in [('/anfs/bigdisc/gete2/wikiwoods/word2vec/matrix_nouns400', 0),
('/anfs/bigdisc/gete2/wikiwoods/word2vec/matrix_verbs400', half)]:
with open(filename, 'r') as f:
for line in f:
pred, vecstr = line.strip().split(maxsplit=1)
vec = array(vecstr.split())
pred_wei[ind[pred], offset:offset+half] = vec
# Make vectors longer (av. sum 1.138 over av. 44.9 nonzero entries)
# An average entry is then 0.2, so a predicate is expit(0.2*30 - 3) = 0.95 true
pred_wei *= 8
DATA = '/anfs/bigdisc/gete2/wikiwoods/core-5'
bias = log(D/C - 1)
