def __init__(self, roadmap, length_attr='length' ) :
super(PairUniformRoadmap,self).__init__(roadmap, length_attr)
uniform = roadprob.UniformDist( self.roadmap, self.length_attr )
self.distr = uniform
# compute carry and fetch --- using roadmap movers complexity
lendict = { road : data.get( self.length_attr, 1 )
for _,__, road, data in self.roadmap.edges_iter( keys=True, data=True )
}
total = sum( lendict.values() )
W = { road : roadlen / total
for road, roadlen in lendict.iteritems() }
if False :
W2 = { (r1,r2) : w1*w2
for r1,w1 in W.iteritems()
for r2,w2 in W.iteritems() }
self._meancarry = roadEd.roadEd( self.roadmap, W2, length_attr=self.length_attr )
else :
rategraph = nx.DiGraph()
for r1,w1 in W.iteritems() :
for r2,w2 in W.iteritems() :
rategraph.add_edge( r1, r2, rate=w1*w2 )
self._meancarry = roadcplx.carryMileageRate( self.roadmap, rategraph )
self._meanfetch = 0.
def __init__(self) :
roadmap = ACC2014Square()
super(ACC2014Distr,self).__init__(roadmap, 'length' )
rategraph = nx.DiGraph()
rategraph.add_edge( 'N', 'E', rate=1./5 )
rategraph.add_edge( 'W', 'E', rate=1./5 )
rategraph.add_edge( 'W', 'S', rate=3./5 )
self._meancarry = roadcplx.carryMileageRate( roadmap, rategraph )
self._meanfetch = roadcplx.fetchMileageRate( roadmap, rategraph )
# for sampler
weights = { (r1,r2) : data.get( 'rate' )
for r1,r2, data in rategraph.edges_iter( data=True )
}
#weights[ ('N','E') ] = 1./5
#weights[ ('W','E') ] = 1./5
#weights[ ('W','S') ] = 3./5
self.wset = roadprob.WeightedSet(weights)
probe = RoadmapEMD()
probe.horizon = 10000.
complexity_computed = []
complexity_estimated = []
def showresults() :
plt.scatter( complexity_computed, complexity_estimated )
for t in range(1) :
roadnet, rategraph = get_sim_setting( mu=2. )
R = totalrate( rategraph )
n_rategraph = scale_rates( rategraph, 1. / R )
enroute_velocity = carryMileageRate( roadnet, n_rategraph )
balance_velocity = fetchMileageRate( roadnet, n_rategraph )
complexity = enroute_velocity + balance_velocity
#complexity = moverscomplexity( roadnet, n_rategraph )
for k in range(1) :
numveh = np.random.randint(1,5+1)
rate_predicted = convert_complexity_and_servicerate( complexity, numveh )
simrates = scale_rates( n_rategraph, .99 * rate_predicted )
probe.scenario( roadnet, simrates, numveh, 1. )
probe.sim_init()
probe.run_sim()
