def get_self_vectors(self_numbers: list, s_matrix: np.matrix) -> list:
"""
Function to get self vectors
:param self_numbers: self numbers of matrix
:param s_matrix: S matrix from earlier Frobenius function
:return: self vectors
"""
self_vectors = []
y_array = []
for number in self_numbers:
temp_array = []
for i in range(len(self_numbers)):
temp_array.insert(0, pow(number, i))
y_array.append(temp_array)
print(template.substitute(string='Y array'))
print(np.matrix(y_array).round(5))
print(template.substitute(string='S matrix'))
print(s_matrix.round(5))
print(template.substitute(string='Self vectors'))
for element in y_array:
vector = np.dot(s_matrix, element)
self_vectors.append(vector)
print(np.matrix(vector).round(5))
return self_vectors
def main():
np.seterr(divide="ignore", invalid="ignore")
adjacencyMatrix = np.genfromtxt('graph.txt', dtype=np.int64, delimiter=" ")
print("Adjacency Matrix from file")
print(adjacencyMatrix)
#Finds the largest index in both columns
largestIndex = adjacencyMatrix[:,0:2].max()
n = largestIndex+1
# Creates matrix of size of file's matrix
mMatrix = np.zeros((n, n))
for row in adjacencyMatrix:
i, j, k= row
mMatrix[j, i] = k
print("Connection Matrix")
print(mMatrix)
beta = 0.85
# Gets columns sums
sumVector = mMatrix.sum(0)
#Replace all 0s with 1
sumVector[sumVector == 0] = 1
# Generates the initial stochastic matrix
stochasticArray = mMatrix / sumVector
print("Stochastic matrix: ")
print(stochasticArray.round(5))
# Beta * stochastic array
betaTimesStoch = stochasticArray * beta
# Size of a column
n = mMatrix.shape[0]
#Leap probabilty
leapProp = (1-beta)/n
# Repeat creates a list of 1 of size n; .transpose turn matrix to vertical
# Original rank vector
r = Matrix(np.repeat(np.divide(1,n), n)).transpose()
print("Original rank vector:")
print(r.round(5))
prevR = np.zeros(r.shape)
iterations = 0
# Looping until they converge
while not np.allclose(r, prevR, rtol= 1.e-3, atol=1.e-3):
prevR = r
r = (betaTimesStoch * prevR) + leapProp
iterations += 1
#Convereged rank vector
print("Converged Rank vector:")
print(r.round(5))
#print("Previous R:")
# print(prevR)
#Total iterations
print("Total iterations", iterations)