CoCalc Shared FilesPublic Example.sagews
Authors: Tien Chih, Demitri Plessas
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#We first create the raw data matrix
listsize=5 # This determines the number of boxers being compared
rawdata=matrix(QQ,listsize,listsize); #This creates a zero square matrix of the appropriate size, the entries of this matrix are allowed to be rational.

#We then input the win/loss data into the matrix, the += command increments the appropriate entry by the given amount.  If boxer i loses to boxer j, we encode that by rawdata[i,j]+=1.  If Boxer i and j tie, we encode that by rawdata[i,j]+=1/2 and rawdata[j,i]+=1/2
#Recall that in Sage, arrays are enumerated starting at 0, so the first entry is the 0th entry, rather than the 1st.

rawdata[0,1]+=1
rawdata[0,3]+=1 #These encode the losses suffered by A
rawdata[1,2]+=1
rawdata[1,4]+=1/2
rawdata[1,4]+=1 #These encode the losses and tie by boxer B
# Boxers C and D didnot suffer ties or losses and so there are no entries for those rows
rawdata[4,1]+=1/2 #This encodes the tie by E to C

#The following lines encodes an adjacency matrix adj for the underlying unweighted, undirected graph, should there be some interest in it's structure.

for i in range(listsize):
for j in range (listsize):
if rawdata[i,j]!=0:

#The following lines encodes an adjacency matrix adj for the underlying unweighted, directed graph, should there be some interest in it's structure.

dij=matrix(QQ,listsize,listsize);

for i in range(listsize):
for j in range (listsize):
if rawdata[i,j]!=0:
dij[i,j]=1

#The following lines creates the normalized data matrix, where multiple-fights are normalized
normal=matrix(QQ,listsize,listsize)

for i in range(listsize):
for j in range (listsize):
if rawdata[i,j]+rawdata[j,i]!=0:
normal[i,j]=rawdata[i,j]/(rawdata[i,j]+rawdata[j,i])

#If you wish to the normal matrix, comment out the first line under this if listsize<20.  Otherwise, comment out the following 2 lines
#normal #Uncomment this line to see normal if listsize<20
#for i in range (listsize): #Uncomment this and the following line if listsize >=20.
#    print normal.rows()[i]

#We then create the stochastic data matrix:
stoch=matrix(QQ,listsize,listsize)

for i in range(listsize):
rowsum=0
for j in range(listsize):
rowsum=rowsum+normal[i,j]
if rowsum!=0:
for k in range(listsize):
stoch[i,k]=normal[i,k]/rowsum #This stochasticizes rows for non-undefeated fighters.
else:
for l in range(listsize):
stoch[i,l]=1/listsize #This creates the rows for undefeated fighters and allows stoch to be a true markov matrix

#If you wish to the stoch matrix, comment out the first line under this if listsize<20.  Otherwise, comment out the following 2 lines
#stoch #Uncomment this line to see normal if listsize<20
#for i in range (listsize): #Uncomment this and the following line if listsize >=20.
#    print stoch.rows()[i]

#We then create the positive stochastic matrix M

J=matrix(QQ,listsize,listsize);

for i in range(listsize):
for j in range(listsize):
J[i,j]=1 #This creates a square matrix where each value is 1
p=15/100 #The damping factor p
M=(1-p)*stoch+p*(1/listsize)*J #The final matrix M

#If you wish to the M matrix, comment out the first line under this if listsize<20.  Otherwise, comment out the following 2 lines
#M #Uncomment this line to see normal if listsize<20
#for i in range (listsize): #Uncomment this and the following line if listsize >=20.
#    print M.rows()[i]

#The following lines identifies which eigenvalue/vector pair has eigenvalue 1
sizespec=len(M.eigenvectors_left())

stable=0

for i in range(sizespec):
if M.eigenvectors_left()[i][0]=1:
stable=i

#We then list the entries of the appropriate eigenvector in order, mutiplying by 1.0 gives a decimal approximation for easier comparison

for i in range(len(M.eigenvectors_left()[stable][1][0])):
M.eigenvectors_left()[best][1][0][i]*1.0

#We then display the digraph based on the normalized data matrix so as to visualize the win/loss relationship between boxers
G = DiGraph(normal)
H = G.plot(color_by_label=True)
H.show()