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 Adjmatrix(self):
"""Define the adjacent matrix of this dependency
Input: \
Output: 2D numpy array, the adjacent matrix of the dependency
"""
self.adjmatrix = np.zeros((self.nodenum1, self.nodenum2), dtype=int)
for i in range(self.nodenum2):
minindex = np.array(
sf.minimumk(self.distmatrix[:, i], self.nearestnum))
self.adjmatrix[minindex, i] = 1
def Connect(self, m):
"""Connect the vertices generated in Function Nodeloc - generate the adjacent matrix
Input: m - the most m cloest facilities to be connected
Output: distmatrix - the distance matrix of the vertices
adjmatrix - the adjacent matrix of the vertices
"""
self.distmatrix = np.zeros([len(self.loc), len(self.loc)])
for i in range(len(self.loc)):
for j in range(len(self.loc)):
self.distmatrix[i, j] = sf.dist(self.Geoloc[i, 0], self.Geoloc[i, 1], self.Geoloc[j, 0], self.Geoloc[j, 1])
#Calculate the adjacent matrix
self.adjmatrix = np.zeros([len(self.loc), len(self.loc)])
for i in range(len(self.demandseries)):
minindex = np.array(sf.minimumk(self.distmatrix[:self.demandseries[i], self.demandseries[i]], m))
self.adjmatrix[minindex, self.demandseries[i]] = 1
def connection(self, sampleseq, num):
"""Calculate the adjacent matrix between supply node and transmission node
Input: the nodal neighborhood degree sequence, d: the number of adjacent nodes
Output: the adjacent matrix between supply and transmission nodes
"""
self.Adjmatrix = np.zeros((self.nodenum, self.nodenum), dtype=int)
for i in range(self.supplynum):
minindex = np.array(
sf.minimumk(
self.Dismatrix[self.supplyseries[i],
self.trandemandseries],
sampleseq[self.supplyseries[i]]))
self.Adjmatrix[self.supplyseries[i],
self.trandemandseries[minindex]] = 1
# self.Adjmatrix[minindex, self.supplyseries[i]] = 1
for i in range(self.trannum):
if (np.sum(self.Adjmatrix[self.supplyseries,
self.transeries[i]]) == 0):
minindex = np.array(
sf.minimumk(
self.Dismatrix[self.supplyseries, self.transeries[i]],
num))
self.Adjmatrix[minindex, self.transeries[i]] = 1
# self.Adjmatrix[self.transeries[i], minindex] = 1
# for i in range(self.supplynum):
# minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries[i], self.supplyseries], num))
# self.Adjmatrix[self.supplyseries[i], minindex] = 1
# self.Adjmatrix[minindex, self.supplyseries[i]] = 1
# for i in range(self.trannum):
# if(np.sum(self.Adjmatrix[self.supplyseries, self.transeries[i]]) != 0):
# continue
# minindex = np.array(sf.minimumk(self.Dismatrix[self.supplyseries, self.transeries[i]], num))
# self.Adjmatrix[minindex, self.transeries[i]] = 1
## self.Adjmatrix[self.transeries[i], minindex] = 1
#
for i in range(self.trannum):
minindex = np.array(
sf.minimumk(
self.Dismatrix[self.transeries[i], self.demandseries],
min(sampleseq[self.transeries[i]],
self.demandnum))) + self.supplynum + self.trannum
self.Adjmatrix[self.transeries[i], minindex] = 1
# self.Adjmatrix[minindex, self.transeries[i]] = 1
# for i in range(self.demandnum):
# if(np.sum(self.Adjmatrix[self.transeries, self.demandseries[i]]) == 0):
# minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries, self.demandseries[i]], 1)) + self.supplynum
# self.Adjmatrix[minindex, self.demandseries[i]] = 1
# for i in range(self.trannum):
# minindex = np.array(sf.minimumk(self.Dismatrix[self.transeries[i], self.transeries], num)) + self.supplynum
# self.Adjmatrix[self.transeries[i], minindex] = 1
for i in range(self.demandnum):
if (np.sum(self.Adjmatrix[self.transeries,
self.demandseries[i]]) == 0):
minindex = np.array(
sf.minimumk(
self.Dismatrix[self.transeries, self.demandseries[i]],
num)) + self.supplynum
self.Adjmatrix[minindex, self.demandseries[i]] = 1
# self.Adjmatrix[self.demandseries[i], minindex] = 1
for i in range(self.demandnum):
minindex = np.array(
sf.minimumk(
self.Dismatrix[self.demandseries[i], self.demandseries],
min(sampleseq[self.demandseries[i]] + 1,
self.demandnum))) + self.supplynum + self.trannum
minindex = minindex[1:-1]
for j in range(len(minindex)):
if (self.Adjmatrix[self.demandseries[i], minindex[j]] == 1
or self.Adjmatrix[minindex[j],
self.demandseries[i]] == 1):
continue
self.Adjmatrix[self.demandseries[i], minindex[j]] = 1