from Retriever import Retriever
R = Retriever()
# pagerank updates for random names
res = R.fetch_objects(size=1000)
print(res.keys())
n_iterations = 10000
d = 0.7
n = float(len(res.keys()))
for i in range(n_iterations):
# for each name...
for k, v in res.items():
# calculate pagerank...
updated_prob = (1 - d) * 1 / n
# ...by inspecting its parents
for parent_node in v.parents:
# parents with high pageranks but few outbound are best
updated_prob += d * (parent_node.pagerank) / parent_node.n_children
# update node's pagerank
v.update_pagerank(updated_prob)
if i % (n_iterations / 5) == 0:
print("iteration", i)
R.put_objects(res)
