def solve(self, *args, **kw):
if len(args) > 1:
self.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
solver = args[0] if len(args) != 0 else kw.get('solver', self.solver)
graph = self.graph # must be networkx instance
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
node2index = dict([(node, i) for i, node in enumerate(nodes)])
index2node = dict([(i, node) for i, node in enumerate(nodes)])
import FuncDesigner as fd, openopt
x = fd.oovars(n, domain=bool)
objective = fd.sum(x)
startPoint = {x: [0] * n}
fixedVars = {}
includedNodes = getattr(kw, 'includedNodes', None)
if includedNodes is None:
includedNodes = getattr(self, 'includedNodes', ())
for node in includedNodes:
fixedVars[x[node2index[node]]] = 1
excludedNodes = getattr(kw, 'excludedNodes', None)
if excludedNodes is None:
excludedNodes = getattr(self, 'excludedNodes', ())
for node in excludedNodes:
fixedVars[x[node2index[node]]] = 0
if openopt.oosolver(solver).__name__ == 'interalg':
constraints = [
fd.NAND(x[node2index[i]], x[node2index[j]]) for i, j in edges
]
P = openopt.GLP
else:
constraints = [
x[node2index[i]] + x[node2index[j]] <= 1 for i, j in edges
]
P = openopt.MILP
p = P(objective,
startPoint,
constraints=constraints,
fixedVars=fixedVars,
goal='max')
for key, val in kw.items():
setattr(p, key, val)
r = p.solve(solver, **kw)
r.solution = [index2node[i] for i in range(n) if r.xf[x[i]] == 1]
r.ff = len(r.solution)
return r
def set_routine(p, *args, **kw):
if len(args) > 1:
p.err('''
incorrect number of arguments for solve(),
must be at least 1 (solver), other must be keyword arguments''')
solver = args[0] if len(args) != 0 else kw.get('solver', p.solver)
import FuncDesigner as fd, openopt
is_interalg = openopt.oosolver(solver).__name__ == 'interalg'
graph = p.graph # must be networkx instance
nodes = graph.nodes()
edges = graph.edges()
n = len(nodes)
node2index = dict((node, i) for i, node in enumerate(nodes))
index2node = dict((i, node) for i, node in enumerate(nodes))
x = fd.oovars(n, domain=bool)
objective = fd.sum(x)
startPoint = {x:[0]*n}
fixedVars = {}
includedNodes = getattr(kw, 'includedNodes', None)
if includedNodes is None:
includedNodes = getattr(p, 'includedNodes', ())
for node in includedNodes:
fixedVars[x[node2index[node]]] = 1
excludedNodes = getattr(kw, 'excludedNodes', None)
if excludedNodes is None:
excludedNodes = getattr(p, 'excludedNodes', ())
for node in excludedNodes:
fixedVars[x[node2index[node]]] = 0
if p.probType == 'DSP':
constraints = []
engine = fd.OR if is_interalg else lambda List: fd.sum(List) >= 1
Engine = lambda d, n: engine([x[node2index[k]] for k in (list(d.keys())+[n])])
for node in nodes:
adjacent_nodes_dict = graph[node]
if len(adjacent_nodes_dict) == 0:
fixedVars[x[node2index[node]]] = 1
continue
constraints.append(Engine(adjacent_nodes_dict, node))
else:
constraints = \
[fd.NAND(x[node2index[i]], x[node2index[j]]) for i, j in edges] \
if is_interalg else \
[x[node2index[i]]+x[node2index[j]] <=1 for i, j in edges]
P = openopt.GLP if is_interalg else openopt.MILP
goal = 'min' if p.probType == 'DSP' else 'max'
p = P(objective, startPoint, constraints = constraints, fixedVars = fixedVars, goal = goal)
for key, val in kw.items():
setattr(p, key, val)
r = p.solve(solver, **kw)
r.solution = [index2node[i] for i in range(n) if r.xf[x[i]] == 1]
r.ff = len(r.solution)
return r