def call(self, opt, c):
context = clone_context(c.context)
con = Connection(self.cresource.dp, self.cresource.s, self.cfunction.dp, self.cfunction.s)
context.add_connection(con)
executed = c.executed + [self]
# XXX
forbidden = c.forbidden | set([self])
from mcdp_opt.partial_result import get_lower_bound_ndp
ndp, table = get_lower_bound_ndp(context)
(R, ur), tableres = opt.get_lower_bounds(ndp, table)
lower_bounds = tableres
from mcdp_opt.optimization_state import OptimizationState
s2 = OptimizationState(
opt=opt,
options=c.options,
context=context,
executed=executed,
forbidden=forbidden,
lower_bounds=lower_bounds,
ur=ur,
creation_order=opt.get_next_creation(),
)
return s2
def call(self, opt, c):
context = clone_context(c.context)
ndp = opt.load_ndp(self.id_ndp)
if not self.id_ndp in context.names:
name = self.id_ndp
else:
name = context.new_name(prefix=self.id_ndp)
context.add_ndp(name, ndp)
con = Connection(self.cresource.dp, self.cresource.s, name, self.fname)
context.add_connection(con)
executed = c.executed + [self]
# XXX
forbidden = c.forbidden | set([self])
lower_bounds = dict(**c.lower_bounds)
# get lower bounds for the new one
lower_bounds_a = get_new_lowerbounds(context=context, name=name, lower_bounds=lower_bounds)
for k, u in lower_bounds_a.items():
if len(u.minimals) == 0:
msg = "Unfeasible: %s" % (lower_bounds_a)
raise Exception(msg)
print("new lowerbounds: %s" % lower_bounds_a)
for rname in ndp.get_rnames():
r = CResource(name, rname)
if not r in lower_bounds_a:
msg = "Cannot find resource."
raise_desc(ValueError, msg, r=r, lower_bounds_a=lower_bounds_a)
lower_bounds[r] = lower_bounds_a[r]
del lower_bounds[self.cresource]
from mcdp_opt.partial_result import get_lower_bound_ndp
ndp, table = get_lower_bound_ndp(context)
(_R, ur), _tableres = opt.get_lower_bounds(ndp, table)
from mcdp_opt.optimization_state import OptimizationState
s2 = OptimizationState(
opt=opt,
options=c.options,
context=context,
executed=executed,
forbidden=forbidden,
lower_bounds=lower_bounds,
ur=ur,
creation_order=opt.get_next_creation(),
)
s2.info("Parent: #%s" % c.creation_order)
s2.info("Action: #%s" % self)
return s2
def call(self, opt, c):
context = clone_context(c.context)
ndp = opt.load_ndp(self.id_ndp)
if not self.id_ndp in context.names:
name = self.id_ndp
else:
name = context.new_name(prefix=self.id_ndp)
context.add_ndp(name, ndp)
con = Connection(self.cresource.dp, self.cresource.s, name, self.fname)
context.add_connection(con)
executed = c.executed + [self]
# XXX
forbidden = c.forbidden | set([self])
lower_bounds = dict(**c.lower_bounds)
# get lower bounds for the new one
lower_bounds_a = get_new_lowerbounds(context=context, name=name,
lower_bounds=lower_bounds)
for k, u in lower_bounds_a.items():
if len(u.minimals) == 0:
msg = 'Unfeasible: %s' % (lower_bounds_a)
raise Exception(msg)
print('new lowerbounds: %s' % lower_bounds_a)
for rname in ndp.get_rnames():
r = CResource(name, rname)
if not r in lower_bounds_a:
msg = 'Cannot find resource.'
raise_desc(ValueError, msg, r=r, lower_bounds_a=lower_bounds_a)
lower_bounds[r] = lower_bounds_a[r]
del lower_bounds[self.cresource]
from mcdp_opt.partial_result import get_lower_bound_ndp
ndp, table = get_lower_bound_ndp(context)
(_R, ur), _tableres = opt.get_lower_bounds(ndp, table)
from mcdp_opt.optimization_state import OptimizationState
s2 = OptimizationState(opt=opt, options=c.options,
context=context, executed=executed,
forbidden=forbidden, lower_bounds=lower_bounds, ur=ur,
creation_order=opt.get_next_creation())
s2.info('Parent: #%s' % c.creation_order)
s2.info('Action: #%s' % self)
return s2
def call(self, opt, c):
context = clone_context(c.context)
con = Connection(self.cresource.dp, self.cresource.s,
self.cfunction.dp, self.cfunction.s)
context.add_connection(con)
executed = c.executed + [self]
# XXX
forbidden = c.forbidden | set([self])
from mcdp_opt.partial_result import get_lower_bound_ndp
ndp, table = get_lower_bound_ndp(context)
(R, ur), tableres = opt.get_lower_bounds(ndp, table)
lower_bounds = tableres
from mcdp_opt.optimization_state import OptimizationState
s2 = OptimizationState(opt=opt, options=c.options,
context=context, executed=executed,
forbidden=forbidden, lower_bounds=lower_bounds,
ur=ur, creation_order=opt.get_next_creation())
return s2
def __init__(self, library, options,
flabels, F0s, f0s,
rlabels, R0s, r0s, initial):
f0s = list(f0s)
F0s = list(F0s)
r0s = list(r0s)
R0s = list(R0s)
for i, (fname, F0, f0) in enumerate(zip(flabels, F0s, f0s)):
F0.belongs(f0)
F = initial.get_ftype(fname)
f0s[i] = express_value_in_isomorphic_space(F0, f0, F)
F0s[i] = F
for i, (rname, R0, r0) in enumerate(zip(rlabels, R0s, r0s)):
R0.belongs(r0)
R = initial.get_rtype(rname)
r0s[i] = express_value_in_isomorphic_space(R0, r0, R)
R0s[i] = R
self.library = library
self.options = options
self.flabels = flabels
self.rlabels = rlabels
self.F0s = F0s
self.R0s = R0s
self.r0s = r0s
self.f0s = f0s
context = create_context0(flabels, F0s, f0s, rlabels, R0s, r0s, initial=initial)
from mcdp_opt.optimization_state import OptimizationState
unconnected = []
for o in options:
ndp = library.load_ndp(o)
try:
ndp.check_fully_connected()
except NotConnected as e:
if o.lower() == "raspberrypi2":
#
pass
unconnected.append(o)
for u in unconnected:
options.remove(u)
print('Removing the unusable options %s' % sorted(unconnected))
print('Remaining with %s' % sorted(options))
lower_bounds = {}
for fname, F0, f0 in zip(flabels, F0s, f0s):
r = CResource(get_name_for_fun_node(fname), fname)
lower_bounds[r] = F0.U(f0)
from mcdp_opt.partial_result import get_lower_bound_ndp
ndp, table = get_lower_bound_ndp(context)
(_R, ur), _tableres = self.get_lower_bounds(ndp, table)
self.num_created = 0
s0 = OptimizationState(self, options, context, executed=[], forbidden=set(),
lower_bounds=lower_bounds, ur=ur,
creation_order=self.get_next_creation())
self.root = s0
# open nodes
self.states = [s0]
self.actions = [(s0, ActionExpand())] # tuples of state, action
# connected
self.done = []
# impossible
self.abandoned = []
# expanded
self.expanded = []
# extra ndps not present in library
self.additional = {} # str -> NamedDP
self.iteration = 0
# for visualization
self.G = nx.DiGraph()
self.G_dom = nx.DiGraph() # domination graph
