def Connect(loc, m, sourcenum):
"""Connect the vertices generated in Function Nodeloc - generate the adjacent matrix
Input: loc - vertex location geographically
m - the most m cloest facilities to be connected
Output: distmatrix - the distance matrix of the vertices
adjmatrix - the adjacent matrix of the vertices
"""
distmatrix = np.zeros([len(loc), len(loc)])
for i in range(len(loc)):
for j in range(len(loc)):
distmatrix[i, j] = sf.dist(loc[i, 0], loc[i, 1], loc[j, 0], loc[j,
1])
#Calculate the adjacent matrix
adjmatrix = np.zeros([len(loc), len(loc)])
for i in range(len(distmatrix) - sourcenum):
index = sorted(range(len(distmatrix[i + sourcenum, :])),
key=lambda k: distmatrix[i + sourcenum, :][k])
adjmatrix[i + sourcenum, index[0:m]] = 1
adjmatrix[index[0:m], i + sourcenum] = 1
return distmatrix, adjmatrix
def Distmatrix(self):
"""Calculate the distance matrix for the vertices in the network
"""
self.Dismatrix = np.zeros((self.nodenum, self.nodenum))
for i in range(len(self.Dismatrix)):
for j in range(len(self.Dismatrix)):
self.Dismatrix[i, j] = sf.dist(self.y[i], self.x[i], self.y[j],
self.x[j])
self.Dismatrix[j, i] = self.Dismatrix[i, j]
def Distmatrix(self):
"""Define the distance matrix of this dependency
Ex: self.distmatrix[i, j] - the distance between node i in network1 and node j in network2
Output: 2D numpy array, the distance matrix of the dependency
"""
self.distmatrix = np.zeros((self.nodenum1, self.nodenum2), dtype=float)
for i in range(self.nodenum1):
for j in range(self.nodenum2):
self.distmatrix[i, j] = sf.dist(self.network1.y[self.network1.demandseries[i]], self.network1.x[self.network1.demandseries[i]], \
self.network2.y[self.network2.supplyseries[j]], self.network2.x[self.network2.supplyseries[j]])
def Distmatrix(self):
"""Define the distance matrix of this dependency
Ex: self.distmatrix[i, j] - the distance between node i in network3 and middle point of link j in internet from network1 to network2
Output: 2D numpy array, the distance matrix of the dependency
"""
self.distmatrix = np.zeros((self.nodenum3, self.linknum), dtype=float)
for i in range(self.nodenum3):
for j in range(self.linknum):
self.distmatrix[i, j] = sf.dist(self.network3.y[self.network3.demandseries[i]], self.network3.x[self.network3.demandseries[i]], \
self.internet1net2.edgelist[j]["middley"], self.internet1net2.edgelist[j]["middlex"])
def distmatrix(self):
"""Set up the distance matrix of the network
"""
import numpy as np
self.nodexnormal, self.nodeynormal = sf.FeatureScaling(
self.nodex), sf.FeatureScaling(self.nodey)
self.dmatrix = np.empty((self.nodenum, self.nodenum), dtype=float)
self.dnormalmatrix = np.empty((self.nodenum, self.nodenum),
dtype=float)
for i in range(self.nodenum):
for j in range(i, self.nodenum):
self.dmatrix[i, j] = sf.dist(self.nodey[i], self.nodex[i],
self.nodey[j], self.nodex[j])
self.dmatrix[j, i] = self.dmatrix[i, j]
self.dnormalmatrix[i, j] = sf.dist(self.nodeynormal[i],
self.nodexnormal[i],
self.nodeynormal[j],
self.nodexnormal[j])
self.dnormalmatrix[j, i] = self.dnormalmatrix[i, j]
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