def deNuutrosophicMatrix(self, matrix):
result = copy.deepcopy(matrix)
x = Ivns(0, 0, 0, 0, 0, 0)
for i in range(len(matrix)):
for j in range(len(matrix[0])):
result[i][j] = round(x.deNeutrosophic(matrix[i][j]), 3)
return result
def lambdamax(self, arr, w):
Rindex = [0, 0, 0, 0.58, 0.9, 1.12, 1.24, 1.32, 1.41, 1.45, 1.49]
sum = 0
x = Ivns(0, 0, 0, 0, 0, 0)
for i in range(len(arr)):
sum = sum + float(arr[i]) / x.deNeutrosophic(w[i])
result = sum / len(arr)
result = result - len(arr)
result = result / (len(arr) - 1)
result = result / Rindex[len(arr)]
result = round(result, 4)
return result
def getDivDirect(self, listj, col):
divResult = Ivns(0, 0, 0, 0, 0, 0)
divResult.tl = round(float(listj.tl) / float(col.tl), 3)
divResult.tu = round(float(listj.tu) / float(col.tu), 3)
divResult.il = round(float(listj.il) / float(col.il), 3)
divResult.iu = round(float(listj.iu) / float(col.iu), 3)
divResult.fl = round(float(listj.fl) / float(col.fl), 3)
divResult.fu = round(float(listj.fu) / float(col.fu), 3)
return divResult
def getSumDirectForRow(self, matrix, row):
sum = Ivns(0, 0, 0, 0, 0, 0)
for j in range(len(matrix[0])):
sum.tl = round(sum.tl + float(matrix[row][j].tl), 3)
sum.tu = round(sum.tu + float(matrix[row][j].tu), 3)
sum.il = round(sum.il + float(matrix[row][j].il), 3)
sum.iu = round(sum.iu + float(matrix[row][j].iu), 3)
sum.fl = round(sum.fl + float(matrix[row][j].fl), 3)
sum.fu = round(sum.fu + float(matrix[row][j].fu), 3)
return sum
def getSumDirect(self, listj, col):
sum = Ivns(0, 0, 0, 0, 0, 0)
for row in listj:
sum.tl = round(sum.tl + float(row[col].tl), 3)
sum.tu = round(sum.tu + float(row[col].tu), 3)
sum.il = round(sum.il + float(row[col].il), 3)
sum.iu = round(sum.iu + float(row[col].iu), 3)
sum.fl = round(sum.fl + float(row[col].fl), 3)
sum.fu = round(sum.fu + float(row[col].fu), 3)
return sum
def deNuutrosophicSum(self, matrix):
sum = 0
rowSum = []
x = Ivns(0, 0, 0, 0, 0, 0)
for i in range(len(matrix)):
for j in range(len(matrix[0])):
sum = sum + matrix[i][j]
sum = round(sum, 3)
rowSum.append(sum)
sum = 0
return rowSum
def matrixGetCriteriaWeight(self, matrix):
listj = IvnM('')
result = []
sumj = None
NumOfCriteria = len(matrix)
divideBy = Ivns(NumOfCriteria, NumOfCriteria, NumOfCriteria,
NumOfCriteria, NumOfCriteria, NumOfCriteria)
for i in range(NumOfCriteria):
sumj = listj.getSumDirectForRow(matrix, i)
sumj = self.getDivDirect(sumj, divideBy)
result.append(sumj)
return result
def linearSum(self, x, xmax, xmin, beneficial):
ivns = Ivns(0, 0, 0, 0, 0, 0)
MaxMinDiff = ivns.Difference(xmax, xmin)
if beneficial:
overPart = ivns.Difference(x, xmin)
else:
overPart = ivns.Difference(xmax, x)
return ivns.Division(overPart, MaxMinDiff)
def Create(self):
matrix = []
text = self.text.split('\n')
for line in text:
items = line.split(' ')
row = []
for item in items:
item = item.translate({ord(i): None for i in '<>[]'})
item = item.translate({ord(i): None for i in ' '})
numbers = item.split(',')
ivns = Ivns(
numbers[0],
numbers[1],
numbers[2],
numbers[3],
numbers[4],
numbers[5],
)
row.append(ivns)
matrix.append(row)
return matrix
def getSquareSum(self, listj, col):
sum = Ivns(0, 0, 1, 1, 1, 1)
maxitem = None
for row in listj:
sum = sum.Addition(sum, row[col].Power(2))
return sum
def getSumOfXDiffMin(self, listj, col, xmin):
sum = Ivns(0, 0, 1, 1, 1, 1)
maxitem = None
for row in listj:
sum = sum.Addition(sum, sum.Difference(row[col], xmin))
return sum
def getSumOfMaxDiffX(self, listj, col, xmax):
sum = Ivns(0, 0, 1, 1, 1, 1)
maxitem = None
for row in listj:
sum = sum.Addition(sum, sum.Difference(xmax, row[col]))
return sum
def getSum(self, listj, col):
sum = Ivns(0, 0, 1, 1, 1, 1)
for row in listj:
sum = sum.Addition(sum, row[col])
return sum
