fuzz1means = [sum(cuts[i1]) / len(cuts[i1]) for cuts in RCs_cuts]
fuzz1_ranks = TOPSIS.getCrispRanks(fuzz1means)

RC_UC_meas1 = [
    [ID] for ID in alt_IDs
]  #capture uncertainty as measured by ratio of 0.1 level support to value at m=1.0
for i in range(len(RC_UC_meas1)):
    RC_UC_meas1[i].append((RCs_cuts[i][alphas.index(0.1)][1] -  - RCs_cuts[i][alphas.index(0.1)][0])/ \
                         RCs_cuts[i][alphas.index(1.0)][1])

#get & diplay dominance (possibility) matrix
DM = [[a] for a in alt_IDs]
for i in range(len(alt_IDs)):
    DM_row = []
    for j in range(alts):
        d = fuzzy.dominance_AlphaCut(alphas, RCs_cuts[i], alphas, RCs_cuts[j])
        #get dominance of alt i over alt j
        DM_row.append(d)
    DM[i].append(DM_row)
print 'Fuzzy Dominance Matrix'
for i in range(len(DM)):
    print DM[i][0], ':', [str(x)[0:5] for x in DM[i][1]], ':', str(
        min(DM[i][1]))[0:5], ':', str(sum(DM[i][1]) / len(DM[i][1]))[0:5]

#rank monte carlo fit means
prob_ranks = TOPSIS.getCrispRanks([nf[0] for nf in norm_fits])

#get monte carlo CIs
CIs = []
for i in range(len(full_RCs)):
    m, cl, cu = confidence_interval(full_RCs[i], confidence=alpha)
#get mean of alpha cut at 1.0 and rank them
i1 = alphas.index(1.0)
fuzz1means = [sum(cuts[i1])/len(cuts[i1]) for cuts in P_cuts]
fuzz1_ranks = AHP.getCrispRanks(fuzz1means)
    
P_UC_meas1 = [[ID] for ID in alt_IDs] #capture uncertainty as measured by ratio of 0.1 level support to value at m=1.0
for i in range(len(P_UC_meas1)):
    P_UC_meas1[i].append((P_cuts[i][alphas.index(0.1)][1] -  - P_cuts[i][alphas.index(0.1)][0])/ \
                         P_cuts[i][alphas.index(1.0)][1])

#get & diplay dominance (possibility) matrix
DM = [[a] for a in alt_IDs];
for i in range(alts):
    DM_row = []
    for j in range(alts):
        d = fuzz.dominance_AlphaCut(alphas, P_cuts[i], alphas, P_cuts[j]) 
            #get dominance of alt i over alt j
        DM_row.append(d)
    DM[i].append(DM_row)

print '\n'
print 'Fuzzy Dominance Matrix'
for i in range(len(DM)): 
    print DM[i][0], ':', [str(x)[0:5] for x in DM[i][1]], ':',  str(min(DM[i][1]))[0:5], ':', str(sum(DM[i][1])/len(DM[i][1]))[0:5]

#rank monte carlo fit means
prob_ranks = AHP.getCrispRanks([nf[0] for nf in norm_fits])
#get monte carlo CIs
CIs = []
for i in range(len(full_Ps)):
    m, cl, cu = confidence_interval(full_Ps[i], confidence=alpha)
#get mean of alpha cut at 1.0 and rank them
i1 = alphas.index(1.0)
fuzz1means = [sum(cuts[i1])/len(cuts[i1]) for cuts in RCs_cuts]
fuzz1_ranks = TOPSIS.getCrispRanks(fuzz1means)

RC_UC_meas1 = [[ID] for ID in alt_IDs] #capture uncertainty as measured by ratio of 0.1 level support to value at m=1.0
for i in range(len(RC_UC_meas1)):
    RC_UC_meas1[i].append((RCs_cuts[i][alphas.index(0.1)][1] -  - RCs_cuts[i][alphas.index(0.1)][0])/ \
                         RCs_cuts[i][alphas.index(1.0)][1])
                         
#get & diplay dominance (possibility) matrix
DM = [[a] for a in alt_IDs];
for i in range(len(alt_IDs)):
    DM_row = []
    for j in range(alts):
        d = fuzzy.dominance_AlphaCut(alphas, RCs_cuts[i], alphas, RCs_cuts[j]) 
            #get dominance of alt i over alt j
        DM_row.append(d)
    DM[i].append(DM_row)
print 'Fuzzy Dominance Matrix'
for i in range(len(DM)): 
    print DM[i][0], ':', [str(x)[0:5] for x in DM[i][1]], ':',  str(min(DM[i][1]))[0:5], ':', str(sum(DM[i][1])/len(DM[i][1]))[0:5]

#rank monte carlo fit means
prob_ranks = TOPSIS.getCrispRanks([nf[0] for nf in norm_fits])

#get monte carlo CIs
CIs = []
for i in range(len(full_RCs)):
    m, cl, cu = confidence_interval(full_RCs[i], confidence=alpha)
    CIs.append([m, cl, cu])
fuzz1means = [sum(cuts[i1]) / len(cuts[i1]) for cuts in P_cuts]
fuzz1_ranks = AHP.getCrispRanks(fuzz1means)

P_UC_meas1 = [
    [ID] for ID in alt_IDs
]  #capture uncertainty as measured by ratio of 0.1 level support to value at m=1.0
for i in range(len(P_UC_meas1)):
    P_UC_meas1[i].append((P_cuts[i][alphas.index(0.1)][1] -  - P_cuts[i][alphas.index(0.1)][0])/ \
                         P_cuts[i][alphas.index(1.0)][1])

#get & diplay dominance (possibility) matrix
DM = [[a] for a in alt_IDs]
for i in range(alts):
    DM_row = []
    for j in range(alts):
        d = fuzz.dominance_AlphaCut(alphas, P_cuts[i], alphas, P_cuts[j])
        #get dominance of alt i over alt j
        DM_row.append(d)
    DM[i].append(DM_row)

print '\n'
print 'Fuzzy Dominance Matrix'
for i in range(len(DM)):
    print DM[i][0], ':', [str(x)[0:5] for x in DM[i][1]], ':', str(
        min(DM[i][1]))[0:5], ':', str(sum(DM[i][1]) / len(DM[i][1]))[0:5]

#rank monte carlo fit means
prob_ranks = AHP.getCrispRanks([nf[0] for nf in norm_fits])
#get monte carlo CIs
CIs = []
for i in range(len(full_Ps)):