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sparse_move.py
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sparse_move.py
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"""Movement for sparse graphs."""
from graph import Graph, BAGraph, Node, Cluster
from typing import Dict, Tuple
from operator import itemgetter
from functools import partial
import multiprocessing as mp
def _escape(j: Node, uf: Dict[Node, Cluster]) -> None:
"""If no cluster attract node j than we move it into an empty cluster.
Parameters
---------
j: node
actual node
uf: list of clusters
clustering
start: minimal value in uf
Returns
-------
new cluster Id for node j"""
# search for an empty cluster (free cluster-id)
occurs = set(uf.keys())
for k, v in uf.items(): # mark the used clusters
occurs.discard(v)
# c1 = g.conflicts(uf) # 4test
# f1 = g.calc_attract(j, uf, lower, upper) # 4test
k = occurs.pop()
uf[j] = k
def the_all(uf: Dict[Node, Cluster], g: Graph) -> bool:
"""Move all the nodes into the most attractive clusters.
We use updated forces.
Parameters
---------
uf: list of clusters
clustering
g: the graph of the tolerance relation
lower, upper: clusters
limit of computations
Returns
-------
something modified?"""
# c1 = g.conflicts(uf) # 4test
changed = False
for i in range(g.size):
forces = g.calc_attract(i, uf) # recalculate forces
new_cluster, attract = max(forces.items(), key=itemgetter(1))
old_cluster = uf[i]
if (attract > 0 and forces[old_cluster] < attract) or attract < 0:
if attract < 0:
# print(i, forces, uf, g.edges)
_escape(i, uf, g.start)
changed = True
else:
uf[i] = new_cluster
changed = True
# c2 = g.conflicts(uf) # 4test
# if c1 <= c2:
# print("move {} into {}".format(i, cluster))
# print("forces for {}: {}".format(i, forces))
# print("uf:", uf)
# print("before: {}, after: {}".format(c1,c2))
# raise ValueError('seq move')
# c1 = c2
return changed
def _to_move(uf: Dict[Node, Cluster], g: BAGraph) -> Dict[Node, Tuple[Cluster, int]]:
"""Calculate "in parallel" for each node the most attractive clusters.
Parameters
---------
uf: list of clusters
clustering
g: the graph of the tolerance relation
Returns
-------
most attractive clusters for nodes"""
moves = {}
# print(g.neighbours)
for k, c in uf.items():
forces = g.calc_attract(k, uf) # forces contains attract of old_cluster
new_cluster, attract = max(forces.items(), key=itemgetter(1))
old_cluster = c
if (attract > 0 and forces[old_cluster] < attract) or attract < 0:
# print("m({})={}/{} from {} ({})".format(i,cluster,a, forces, uf[i]))
moves[k] = (new_cluster, attract)
# print("m: ", m)
return moves
def independent(uf: Dict[Node, Cluster], g: Graph) -> bool:
"""Move independent nodes to their most attractive clusters.
Parameters
---------
uf: list of clusters
clustering
g: the graph of the tolerance relation
Returns
-------
something modified?"""
moves = _to_move(uf, g)
if moves == {}:
return False
# c1 = g.conflicts(uf) # 4test
forbidden = set()
#print("moves:", moves)
for n, ca in moves.items():
new_cluster, attract = ca
old_cluster = uf[n]
if attract < 0 and old_cluster not in forbidden:
# go somewhere else
#forces = g.calc_attract(i,uf)
#print(i, forces, uf[i], moves)
new_cluster = _escape(n, uf)
forbidden.add(old_cluster)
forbidden.add(new_cluster)
else:
# joined to a more attractive cluster
if old_cluster not in forbidden and new_cluster not in forbidden:
forbidden.add(old_cluster)
forbidden.add(new_cluster)
uf[n] = new_cluster
# c2 = g.conflicts(uf) # 4test
# if c1 <= c2:
# print("before: {}, after: {}, moves:{}".format(c1, c2, moves))
# raise ValueError('sm-independent')
return True
def _one_move(uf, g, nodes):
moves = []
for k in nodes:
old_cluster = uf[k]
forces = g.calc_attract(k, uf) # forces contains attract of old_cluster
new_cluster, attract = max(forces.items(), key=itemgetter(1))
if (attract > 0 and forces[old_cluster] < attract) or attract < 0:
moves.append((k, new_cluster, attract))
return moves
def independent_par(uf: Dict[Node, Cluster], g: Graph) -> bool:
"""Move independent nodes to their most attractive clusters.
Parameters
---------
uf: list of clusters
clustering
g: the graph of the tolerance relation
Returns
-------
something modified?"""
one_m = partial(_one_move, uf, g)
gs = g.size//4
parts = [g.nodes[:gs], g.nodes[gs:2*gs], g.nodes[2*gs:3*gs], g.nodes[3*gs:]]
with mp.Pool(4) as p: # quad core
# results = p.imap_unordered(one_m, g.nodes)
results = p.map(one_m, parts)
moves = [i for sl in results for i in sl] # párhuzamos verzió multiprocessing.map segítségével
if not moves:
return False
c1 = g.conflicts(uf) # 4test
# print("c1", c1, moves)
forbidden = set()
# print("moves:", moves)
for n, new_cluster, attract in moves:
old_cluster = uf[n]
if attract < 0 and old_cluster not in forbidden:
new_cluster = _escape(n, uf)
forbidden.add(old_cluster)
forbidden.add(new_cluster)
else:
# joined to a more attractive cluster
if old_cluster not in forbidden and new_cluster not in forbidden:
forbidden.add(old_cluster)
forbidden.add(new_cluster)
uf[n] = new_cluster
c2 = g.conflicts(uf) # 4test
if c1 <= c2:
print("before: {}, after: {}, moves:{}".format(c1, c2, moves))
raise ValueError('sm-independent')
return True