def cost(Network, Tract_pop, Tractx, Tracty, Geox, Geoy, cnum):
"""Calculate the overall cost all a new solution: two type: demand-population, supply-transmission(transmission-demand)
"""
x = sf.FeatureScaling2(Network.demandx, np.min(Geox),
np.max(Geox) - np.min(Geox))
y = sf.FeatureScaling2(Network.demandy, np.min(Geoy),
np.max(Geoy) - np.min(Geoy))
Tract_pop1 = sf.FeatureScaling(Tract_pop)
Tractx1 = sf.FeatureScaling(Tractx)
Tracty1 = sf.FeatureScaling(Tracty)
Sum_Cost = 0
Popu_assign2 = np.zeros(len(x))
for i in range(len(Tractx1)):
Dist = np.empty(len(x), dtype=float)
for k in range(len(x)):
Dist[k] = math.sqrt((Tracty1[i] - y[k])**2 +
(Tractx1[i] - x[k])**2)
index = sf.minimumk(Dist, min(cnum, len(x)))
ratio = sf.FeatureScaling3(
np.sum(np.exp(Dist[index])) / np.exp(Dist[index]))
for k in range(len(index)):
Popu_assign2[index[k]] += Tract_pop1[i] * ratio[k]
Sum_Cost += Popu_assign2[index[k]] * Dist[index[k]]
Network.cost = Sum_Cost
def cost(sol, Geox, Geoy, PD, Type, Tractx, Tracty, PD_beforenormalize, cnum):
"""Calculate the overall cost of a new solution: two type: demand-population, supply-transmission(transmission-demand)
Input: sol - new solution: location in the form of the index
Geox -
Geoy -
PD -
Type -
Tractx -
Tracty -
Output: cost of the new solution and the population assignment for the demand nodes of the new solution
"""
Popu_assign, Popu_assign2 = np.zeros(len(sol)), np.zeros(len(sol))
Sum_Cost = 0
for i in range(len(Tractx)):
Dist = np.empty(len(sol), dtype=float)
for k in range(len(sol)):
Dist[k] = math.sqrt((Tracty[i] - Geoy[sol[k][0]])**2 +
(Tractx[i] - Geox[sol[k][1]])**2)
index = sf.minimumk(Dist, min(cnum, len(sol)))
ratio = sf.FeatureScaling3(
np.sum(np.exp(Dist[index])) / np.exp(Dist[index]))
for k in range(len(index)):
Popu_assign[index[k]] += PD_beforenormalize[i] * ratio[k]
Popu_assign2[index[k]] += PD[i] * ratio[k]
if (Type == 'Population'):
Sum_Cost += Popu_assign2[index[k]] * Dist[index[k]]
else:
Sum_Cost += Dist[index[k]]
return Sum_Cost, Popu_assign
def assignprob(self, density):
"""Calculate the assignment probability based on population density
Input:
density - the density of the population distribution
Output: the cumu_prob along with the index transformatino metric
"""
prob = sf.FeatureScaling3(density**self.gamma)
temp1 = len(prob[0])
probflatten = prob.flatten()
cumu_prob = []
cumu_prob_index = []
for i in range(len(probflatten)):
cumu_prob_index.append([i % temp1, int(i / temp1)])
if (i == 0):
cumu_prob.append(probflatten[i])
else:
cumu_prob.append(probflatten[i] + cumu_prob[-1])
return cumu_prob, cumu_prob_index