def alt_fuzzy_AHP2(data, weights, score_method, *args):
# takes in inputs:
# data = [[critID, [alt1#, alt2#, alt3#, ...],
# [alt1/alt1, alt1/alt2, alt1/alt3, ...],
# [alt2/alt1, alt2/alt2, alt2/alt3, ...],
# ...]
# [critID, [alt1#, alt2#, alt3#, ...],
# [alt1/alt1, alt1/alt2, alt1/alt3, ...],
# [alt2/alt1, alt2/alt2, alt2/alt3, ...],
# ...]
# ]
# weights = [[critID1, critWeight1], [critID2, critWeight2], ...]
#
# score_method = NOT USED!
#sort the data matrices by criteria ID
data.sort(key = lambda row: row[0])
# modify matrix to create trapezoidal numbers:
#accepts fuzzy numbers up to lenth of 4 (crisp, range, triangular, trapezoidal)
#remove criteria ids and alt IDs and just retain data
crits = [d[0] for d in data] #saving in case needed
alt_IDs = data[0][1]
comp_matrices = []
for i in range(len(data)): #parse each criteria matrix
matrix = []
for j in range(2,len(data[i])): #parse rows of comparison matrix
mat_row = [[1,1,1,1] for j1 in range(2,len(data[i]))]
for k in range(len(data[i][j])): #parse columns of comparison matrix
if len(data[i][j][k]) == 4:
mat_row[k] = data[i][j][k]
elif len(data[i][j][k]) == 3:
mat_row[k] = [data[i][j][k][0], data[i][j][k][1], \
data[i][j][k][1], data[i][j][k][2]]
elif len(data[i][j][k]) == 2:
mat_row[k] = [data[i][j][k][0], data[i][j][k][0], \
data[i][j][k][1], data[i][j][k][1]]
elif len(data[i][j][k]) == 1:
mat_row[k] = [data[i][j][k][0], data[i][j][k][0], \
data[i][j][k][0], data[i][j][k][0]]
matrix.append(mat_row)
comp_matrices.append(matrix)
#for c in comp_matrices:
# print "NEW CRIT"
# for c1 in c: print c1
#get geometric mean of each row in each matrix
p_vectors = []
for i in range(len(comp_matrices)):
priority_vector = [None for x in comp_matrices[i]]
matrix_sum = [0.,0.,0.,0.] #sum row geometric means
for j in range(len(comp_matrices[i])):
row_product = fuzzy.mult_FuzzyTrap(comp_matrices[i][j])
#multiply all the row elements
geom_mean = [x**(1.0/len(comp_matrices[i][j])) for x in row_product]
#get geometric mean
priority_vector[j] = geom_mean #create priority vector of row geometric means
matrix_sum = fuzzy.add_FuzzyTrap([matrix_sum, geom_mean]) #add to matrix sum
priority_vector = [fuzzy.divide_FuzzyTrap(pv, matrix_sum) \
for pv in priority_vector]
p_vectors.append(priority_vector)
#sort and normalize weights
weights.sort(key = lambda row: row[0])
#change weights to fuzzy trapezoidal numbers
for i in range(len(weights)):
if len(weights[i][1]) == 4: pass
elif len(weights[i][1]) == 3:
weights[i][1] = [weights[i][1][0], weights[i][1][1],
weights[i][1][1], weights[i][1][2]]
elif len(weights[i][1]) == 2:
weights[i][1] = [weights[i][1][0], weights[i][1][0],
weights[i][1][1], weights[i][1][1]]
elif len(weights[i][1]) == 1:
weights[i][1] = [weights[i][1], weights[i][1],
weights[i][1], weights[i][1]]
#don't normalize weights
#q = max(max(w[1]) for w in weights)
#for i in range(len(weights)):
# for j in range(len(weights[i][1])):
# weights[i][1][j] = weights[i][1][j] / q
#multiply prority vectors by criteria weights and sum
#return in structure [[altID1, score1], [altID2, score2], ...]
#where each score if a fuzzy number (a,b,c,d)
scores = []
for j in range(len(alt_IDs)):
scores.append([alt_IDs[j], fuzzy.add_FuzzyTrap(fuzzy.mult_FuzzyTrap([weights[i][1], p_vectors[i][j]]) \
for i in range(len(weights)))])
return scores
def alt_fuzzy_AHP3(data, weights, score_method, *args):
# takes in inputs:
# data = [[critID, [alt1#, alt2#, alt3#, ...],
# [alt1/alt1, alt1/alt2, alt1/alt3, ...],
# [alt2/alt1, alt2/alt2, alt2/alt3, ...],
# ...]
# [critID, [alt1#, alt2#, alt3#, ...],
# [alt1/alt1, alt1/alt2, alt1/alt3, ...],
# [alt2/alt1, alt2/alt2, alt2/alt3, ...],
# ...]
# ]
# weights = [[critID1, critWeight1], [critID2, critWeight2], ...]
#
# score_method = NOT USED!
#sort the data matrices by criteria ID
data.sort(key = lambda row: row[0])
# modify matrix to create trapezoidal numbers:
#accepts fuzzy numbers up to lenth of 4 (crisp, range, triangular, trapezoidal)
#remove criteria ids and alt IDs and just retain data
crits = [d[0] for d in data] #saving in case needed
alt_IDs = data[0][1]
comp_matrices = []
for i in range(len(data)): #parse each criteria matrix
matrix = []
for j in range(2,len(data[i])): #parse rows of comparison matrix
mat_row = [[1,1,1,1] for j1 in range(2,len(data[i]))]
for k in range(len(data[i][j])): #parse columns of comparison matrix
if len(data[i][j][k]) == 4:
mat_row[k] = data[i][j][k]
elif len(data[i][j][k]) == 3:
mat_row[k] = [data[i][j][k][0], data[i][j][k][1], \
data[i][j][k][1], data[i][j][k][2]]
elif len(data[i][j][k]) == 2:
mat_row[k] = [data[i][j][k][0], data[i][j][k][0], \
data[i][j][k][1], data[i][j][k][1]]
elif len(data[i][j][k]) == 1:
mat_row[k] = [data[i][j][k][0], data[i][j][k][0], \
data[i][j][k][0], data[i][j][k][0]]
matrix.append(mat_row)
comp_matrices.append(matrix)
#for c in comp_matrices:
# print "NEW CRIT"
# for c1 in c: print c1
#get computing the normalized value of row sums (i.e. fuzzy synthetic extent)
#by fuzzy arithmetic operations:
for i in range(len(comp_matrices)):
S_i = [None for r in comp_matrices[i]] #row sums
for j in range(len(comp_matrices[i])):
S_i[j] = fuzzy.add_FuzzyTrap(comp_matrices[i][j])
mat_total = fuzzy.add_FuzzyTrap(S_i) #matrix total (for normalization)
S_i = [fuzzy.divide_FuzzyTrap(s,mat_total) for s in S_i] #normalize row sums
#get degrees of possibility S_i > all other S
"""
#sort and normalize weights
weights.sort(key = lambda row: row[0])
#change weights to fuzzy trapezoidal numbers
for i in range(len(weights)):
if len(weights[i][1]) == 4: pass
elif len(weights[i][1]) == 3:
weights[i][1] = [weights[i][1][0], weights[i][1][1],
weights[i][1][1], weights[i][1][2]]
elif len(weights[i][1]) == 2:
weights[i][1] = [weights[i][1][0], weights[i][1][0],
weights[i][1][1], weights[i][1][1]]
elif len(weights[i][1]) == 1:
weights[i][1] = [weights[i][1], weights[i][1],
weights[i][1], weights[i][1]]
#don't normalize weights
#q = max(max(w[1]) for w in weights)
#for i in range(len(weights)):
# for j in range(len(weights[i][1])):
# weights[i][1][j] = weights[i][1][j] / q
#multiply prority vectors by criteria weights and sum
#return in structure [[altID1, score1], [altID2, score2], ...]
#where each score if a fuzzy number (a,b,c,d)
scores = []
for j in range(len(alt_IDs)):
scores.append([alt_IDs[j], fuzzy.add_FuzzyTrap(fuzzy.mult_FuzzyTrap([weights[i][1], p_vectors[i][j]]) \
for i in range(len(weights)))])
"""
return None
