def alt_fuzzy_TOPSIS(dm_data, weights, weight_method, score_method, *args):
print "Peforming Fuzzy TOPSIS"
"""
#get the decision matrix
# [[alt1, [critID, score], [critID, score], ... ],
# [alt2, [critID, score], [critID, score], ... ], ... ]
# scores in the form [a1, a2, a3] where mu(a1, a2, a3) = (0,1,0)
raw_scores = getAvgFuzzyScores_Alt(input_file, score_method)
#get fuzzy weights
weights = getFuzzyWeights_Alt(input_file, weight_method)
print "WEIGHTS:"
for wr in weights: print wr
"""
raw_scores = dm_data
# sort raw scores by criteria ID
for r in raw_scores:
r1 = [r[0]]
r1.extend(sorted(r[1:], key=lambda x: x[0]))
print "RAW SCORES:"
for r in raw_scores:
print r
#normalize the decision matrix
norm_scores = raw_scores
ss = ['sum_squares']
for i in range(1, len(norm_scores[0])):
ss_fzy = [0 for n in norm_scores[0][i][1]]
for j in range(len(norm_scores)): #sum the squares for each criteria
if score_method == 'FT' or score_method == 'FZ':
for k in range(len(ss_fzy)):
ss_fzy[k] = ss_fzy[k] + float(norm_scores[j][i][1][k])**2
for f in ss_fzy:
f = f**0.5 #take sqrt
ss.append(ss_fzy)
for i in range(1, len(norm_scores[0])):
for j in range(
len(norm_scores)): #divide each score by the sum of squares
if score_method == 'FT' or score_method == 'FZ':
for k in range(len(norm_scores[j][i][1])):
norm_scores[j][i][1][
k] = norm_scores[j][i][1][k] / ss[i][-(k + 1)]
print 'Normalized DM: '
for r in norm_scores:
print r
#weight the normalized decision matrix
for i in range(1, len(norm_scores[0])):
for j in range(len(norm_scores)):
for k in range(len(weights)):
if weights[k][0] == norm_scores[j][i][0]:
if score_method == 'FT' and weight_method == 'FT':
norm_scores[j][i][1] = \
fuzzy.mult_FuzzyTri((norm_scores[j][i][1], weights[k][1]))
elif score_method == 'FZ' and weight_method == 'FT':
None
print 'Weighted and Normalized DM: '
for r in norm_scores:
print r
#get positive and negative ideal
pos_ideals = ['A+']
neg_ideals = ['A-']
for i in range(1, len(norm_scores[0])):
pos_ideals.append(norm_scores[0][i][1]) #start with first element
neg_ideals.append(norm_scores[0][i][1]) #start with first element
for i in range(1, len(norm_scores[0])):
for j in range(len(norm_scores)):
#check positive ideal
d1 = fuzzy.dominance_FuzzyTri(pos_ideals[i], norm_scores[j][i][1])
d2 = fuzzy.dominance_FuzzyTri(norm_scores[j][i][1], pos_ideals[i])
print 'A:', pos_ideals[i], ' B:', norm_scores[j][i][
1], ' d(A,B):', d1, ' d(B,A):', d2
if d2 == 1.0 and d1 < 0.99: pos_ideals[i] = norm_scores[j][i][1]
#check negative ideal
d1 = fuzzy.dominance_FuzzyTri(neg_ideals[i], norm_scores[j][i][1])
d2 = fuzzy.dominance_FuzzyTri(norm_scores[j][i][1], neg_ideals[i])
if d1 == 1.0 and d2 < 0.99: neg_ideals[i] = norm_scores[j][i][1]
#for p in pos_ideals: print p
#for p in neg_ideals: print p
#get fuzzy separation distances
# for ideal (a1_i, a2_i, a3_i) fuzzy distance is
# ( SUM((a1-a1_s)^2)^0.5, SUM((a2-a2_s)^2)^0.5, SUM((a3-a3_s)^2)^0.5 )
pos_dist = [] #[alt1_dist, alt2_dist, ...]
neg_dist = [] #[alt1_dist, alt2_dist, ...]
for j in range(len(norm_scores)):
S_pos = [0 for n in norm_scores[j][1][1]]
S_neg = [0 for n in norm_scores[j][1][1]]
for i in range(1, len(norm_scores[j])):
if score_method == 'FT':
S_pos = [S_pos[0] + (norm_scores[j][i][1][0] - pos_ideals[i][0])**2, \
S_pos[1] + (norm_scores[j][i][1][1] - pos_ideals[i][1])**2, \
S_pos[2] + (norm_scores[j][i][1][2] - pos_ideals[i][2])**2 ]
S_neg = [S_neg[0] + (norm_scores[j][i][1][0] - neg_ideals[i][0])**2, \
S_neg[1] + (norm_scores[j][i][1][1] - neg_ideals[i][1])**2, \
S_neg[2] + (norm_scores[j][i][1][2] - neg_ideals[i][2])**2 ]
elif score_method == 'FZ':
None
for a in S_pos:
a = a**0.5 #take square root to get distance
for a in S_neg:
a = a**0.5 #take square root to get distance
pos_dist.append(S_pos)
neg_dist.append(S_neg)
#print "Positive Separation:", pos_dist
#print "Negative Separation:", neg_dist
#get relative closeness
rel_closeness = []
for i in range(len(pos_dist)):
rel_closeness.append([neg_dist[i][0]/(neg_dist[i][2]+pos_dist[i][2]), \
neg_dist[i][1]/(neg_dist[i][1]+pos_dist[i][1]), \
neg_dist[i][2]/(neg_dist[i][0]+pos_dist[i][0])])
#print "Relative Closeness:"
#for i in range(len(rel_closeness)): print 'Alt', i, ':', rel_closeness[i]
#get rankings
rankings = getFuzzyRanks(rel_closeness, score_method)
#print 'Rankings:', rankings
for i in range(len(rel_closeness)):
for j in range(len(rel_closeness[i])):
rel_closeness[i][j] = float(str(rel_closeness[i][j])[0:6])
#plot results
#labels = ['A'+str(i+1) for i in range(len(rel_closeness))]
labels = ['Alt ' + x[0] for x in norm_scores]
if score_method == 'FT':
fig = plt.figure(figsize=(7, 6))
a = fig.add_subplot(111)
for n in rel_closeness:
a.plot([n[0], n[1], n[2]], [0., 1., 0.], linewidth=2.5)
if score_method == 'FZ':
None
a.set_title('Fuzzy TOPSIS')
a.set_ylabel('Membership')
a.set_xlabel('Relative Closeness')
plt.legend(labels)
#path = '/temp/' + strftime("FUZZY_TOPSIS1_%Y-%m-%d %H:%M:%S", gmtime()).replace(" ", "") + '.png'
#fig.savefig(mainpath+path)
return None
def alt_fuzzy_AHP1(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 = 'FT': fuzzy triangular
# 'FZ': fuzzy trapezoidal
# normalize alternative matrices by sum of columns
#print 'Begininng Analytical Heirarchy Process...'
#print 'Normalizing each Criterion comparison matrix...'
alts = data[0][1]
norm_data = []
for i in range(len(data)): #parse each criteria matrix
norm_matrix = [data[i][0], data[i][1]] #init norm data matrix for criteria
for j in range(2,len(data[i])): #parse rows of comparison matrix
norm_row = [] #init norm data
for k in range(len(data[i][j])): #parse columns of comparison matrix
if score_method == 'FT':
row_sum = fuzzy.add_FuzzyTri(data[i][j1][k] for j1 in range(2,len(data[i])) )
norm_row.append( fuzzy.divide_FuzzyTri(data[i][j][k], row_sum ))
#divide by sum of column
if score_method == 'FZ':
pass
norm_matrix.append(norm_row) #append row to matrix
norm_data.append(norm_matrix)
#sum rows of normalized matrix into total score
sums = []
for i in range(len(norm_data)): #for each criteria
col = [norm_data[i][0]] #add criteria ID
for j in range(len(alts)): #for each alternative in same order
total = fuzzy.add_FuzzyTri( norm_data[i][ norm_data[i][1].index(alts[j])+2] )
count = len( norm_data[i][ norm_data[i][1].index(alts[j])+2] )
col.append([t/count for t in total])
sums.append(col)
#sort weights (assume already normalized)
#total = sum(w[1] for w in weights)# removed
sorted_weights = []
for s in sums:
for w in weights:
if w[0] == s[0]:
sorted_weights.append(w[1]) #/total)#removed
#multiply columns by criteria weights and sum
scores = []
for j in range(len(alts)):
weighted_scores = []
for i in range(len(sums)):
weighted_scores.append( fuzzy.mult_FuzzyTri([sorted_weights[i], sums[i][(j+1)]]) )
#multiply weight * score
score = fuzzy.add_FuzzyTri(weighted_scores)
scores.append([alts[j], score])
return scores
def alt_fuzzy_TOPSIS(dm_data, weights, weight_method, score_method, *args):
print "Peforming Fuzzy TOPSIS"
"""
#get the decision matrix
# [[alt1, [critID, score], [critID, score], ... ],
# [alt2, [critID, score], [critID, score], ... ], ... ]
# scores in the form [a1, a2, a3] where mu(a1, a2, a3) = (0,1,0)
raw_scores = getAvgFuzzyScores_Alt(input_file, score_method)
#get fuzzy weights
weights = getFuzzyWeights_Alt(input_file, weight_method)
print "WEIGHTS:"
for wr in weights: print wr
"""
raw_scores = dm_data
# sort raw scores by criteria ID
for r in raw_scores:
r1 = [r[0]]
r1.extend(sorted(r[1:], key=lambda x: x[0]))
print "RAW SCORES:"
for r in raw_scores: print r
#normalize the decision matrix
norm_scores = raw_scores
ss = ['sum_squares']
for i in range(1,len(norm_scores[0])):
ss_fzy = [0 for n in norm_scores[0][i][1]]
for j in range(len(norm_scores)): #sum the squares for each criteria
if score_method == 'FT' or score_method == 'FZ':
for k in range(len(ss_fzy)):
ss_fzy[k] = ss_fzy[k] + float(norm_scores[j][i][1][k])**2
for f in ss_fzy: f = f**0.5 #take sqrt
ss.append(ss_fzy)
for i in range(1,len(norm_scores[0])):
for j in range(len(norm_scores)): #divide each score by the sum of squares
if score_method == 'FT' or score_method == 'FZ':
for k in range(len(norm_scores[j][i][1])):
norm_scores[j][i][1][k] = norm_scores[j][i][1][k] / ss[i][-(k+1)]
print 'Normalized DM: '
for r in norm_scores: print r
#weight the normalized decision matrix
for i in range(1,len(norm_scores[0])):
for j in range(len(norm_scores)):
for k in range(len(weights)):
if weights[k][0] == norm_scores[j][i][0]:
if score_method == 'FT' and weight_method == 'FT':
norm_scores[j][i][1] = \
fuzzy.mult_FuzzyTri((norm_scores[j][i][1], weights[k][1]))
elif score_method == 'FZ' and weight_method == 'FT':
None
print 'Weighted and Normalized DM: '
for r in norm_scores: print r
#get positive and negative ideal
pos_ideals = ['A+']
neg_ideals = ['A-']
for i in range(1,len(norm_scores[0])):
pos_ideals.append(norm_scores[0][i][1]) #start with first element
neg_ideals.append(norm_scores[0][i][1]) #start with first element
for i in range(1,len(norm_scores[0])):
for j in range(len(norm_scores)):
#check positive ideal
d1 = fuzzy.dominance_FuzzyTri(pos_ideals[i], norm_scores[j][i][1])
d2 = fuzzy.dominance_FuzzyTri(norm_scores[j][i][1], pos_ideals[i])
print 'A:', pos_ideals[i], ' B:', norm_scores[j][i][1], ' d(A,B):', d1, ' d(B,A):', d2
if d2 == 1.0 and d1 < 0.99: pos_ideals[i] = norm_scores[j][i][1]
#check negative ideal
d1 = fuzzy.dominance_FuzzyTri(neg_ideals[i], norm_scores[j][i][1])
d2 = fuzzy.dominance_FuzzyTri(norm_scores[j][i][1], neg_ideals[i])
if d1 == 1.0 and d2 < 0.99: neg_ideals[i] = norm_scores[j][i][1]
#for p in pos_ideals: print p
#for p in neg_ideals: print p
#get fuzzy separation distances
# for ideal (a1_i, a2_i, a3_i) fuzzy distance is
# ( SUM((a1-a1_s)^2)^0.5, SUM((a2-a2_s)^2)^0.5, SUM((a3-a3_s)^2)^0.5 )
pos_dist = [] #[alt1_dist, alt2_dist, ...]
neg_dist = [] #[alt1_dist, alt2_dist, ...]
for j in range(len(norm_scores)):
S_pos = [0 for n in norm_scores[j][1][1]]
S_neg = [0 for n in norm_scores[j][1][1]]
for i in range(1,len(norm_scores[j])):
if score_method == 'FT':
S_pos = [S_pos[0] + (norm_scores[j][i][1][0] - pos_ideals[i][0])**2, \
S_pos[1] + (norm_scores[j][i][1][1] - pos_ideals[i][1])**2, \
S_pos[2] + (norm_scores[j][i][1][2] - pos_ideals[i][2])**2 ]
S_neg = [S_neg[0] + (norm_scores[j][i][1][0] - neg_ideals[i][0])**2, \
S_neg[1] + (norm_scores[j][i][1][1] - neg_ideals[i][1])**2, \
S_neg[2] + (norm_scores[j][i][1][2] - neg_ideals[i][2])**2 ]
elif score_method == 'FZ': None
for a in S_pos: a = a**0.5 #take square root to get distance
for a in S_neg: a = a**0.5 #take square root to get distance
pos_dist.append(S_pos)
neg_dist.append(S_neg)
#print "Positive Separation:", pos_dist
#print "Negative Separation:", neg_dist
#get relative closeness
rel_closeness = []
for i in range(len(pos_dist)):
rel_closeness.append([neg_dist[i][0]/(neg_dist[i][2]+pos_dist[i][2]), \
neg_dist[i][1]/(neg_dist[i][1]+pos_dist[i][1]), \
neg_dist[i][2]/(neg_dist[i][0]+pos_dist[i][0])])
#print "Relative Closeness:"
#for i in range(len(rel_closeness)): print 'Alt', i, ':', rel_closeness[i]
#get rankings
rankings = getFuzzyRanks(rel_closeness, score_method)
#print 'Rankings:', rankings
for i in range(len(rel_closeness)):
for j in range(len(rel_closeness[i])):
rel_closeness[i][j] = float(str(rel_closeness[i][j])[0:6])
#plot results
#labels = ['A'+str(i+1) for i in range(len(rel_closeness))]
labels = ['Alt ' + x[0] for x in norm_scores]
if score_method == 'FT':
fig = plt.figure(figsize=(7, 6))
a = fig.add_subplot(111)
for n in rel_closeness:
a.plot([n[0], n[1], n[2]], [0.,1.,0.], linewidth=2.5)
if score_method == 'FZ':
None
a.set_title('Fuzzy TOPSIS')
a.set_ylabel('Membership')
a.set_xlabel('Relative Closeness')
plt.legend(labels)
#path = '/temp/' + strftime("FUZZY_TOPSIS1_%Y-%m-%d %H:%M:%S", gmtime()).replace(" ", "") + '.png'
#fig.savefig(mainpath+path)
return None
