if __name__=="__main__":
#rel_closeness, rankings = alt_crisp_TOPSIS("alternative_data.txt", 'C', 'C', 0)
#print rel_closeness
#print rankings
weights = [['001', 3378.],['002', 2881.],['003', 1751.],['004', 1472.],['005', 1436.]]
weight_ranges = [ ['001', [0.09, 1.00]], \
['002', [0.08, 0.90]], \
['003', [0.04, 0.60]], \
['004', [0.04, 0.56]], \
['005', [0.04, 0.55]] ]
#TESTING:
import alt_data_reader as data_reader
print "Testing... GO"
qual_data, quant_data = data_reader.read_data('alt_evals_data.csv', 10)
data1 = data_reader.reduce_data(qual_data, 'AVG')
data2 = data_reader.reduce_data(qual_data, 'AVG_RANGE')
results = alt_crisp_TOPSIS(data1, weights, 1)
#TEST MONTECARLO
from itertools import cycle
n = 3000
quants = 50
alts = 10
rank_counter, full_ranks, quantiles, q_counter, norm_fits, full_RCs = prob_TOPSIS_uniform(data2, weight_ranges, n, quants, alts, 'U')
print "RANK COUNTER"
['003', [0.04, 0.26, 0.60]], \
['004', [0.04, 0.22, 0.56]], \
['005', [0.04, 0.22, 0.55]] ]
weights_trap = [ ['001', [0.09, 0.26, 0.77, 1.00]], \
['002', [0.08, 0.21, 0.68, 0.90]], \
['003', [0.04, 0.13, 0.43, 0.60]], \
['004', [0.04, 0.10, 0.38, 0.56]], \
['005', [0.04, 0.10, 0.37, 0.55]] ]
input_ranges = [[1, 9], [100., 550.], [0.0, 1.0], [0., 25.],
[0.0, 1.0]] #display input ranges
########### Get Data ###########
#qual_data, quant_data = data_reader.read_data('alt_evals_data.csv', 10)
#qual_data_old, quant_data_old = data_reader.read_data('alt_evals_data_real.csv', 10)
qual_data2, quant_data2 = data_reader.read_data(
'alt_evals_data_thesis_final_31aug15.csv', 10)
data_avg = data_reader.reduce_data(quant_data2, 'AVG')
data_avgRange = data_reader.reduce_data(quant_data2, 'AVG_RANGE')
data_tri = data_reader.reduce_data(quant_data2, 'FUZZ_TRI_1')
data_trap = data_reader.reduce_data(quant_data2, 'FUZZ_TRAP_UNI')
########### Run Monte Carlo TOPSIS ###########
alts = 10 #number of alternatives
n = 10000 #number of iterations
quants = 20 #quantiles
top_N = 4 #number of top alternatives to plot (CDF, PDF, etc.)
create_plots = True #flag to create plots
rank_counter, full_ranks, quantiles, q_counter, norm_fits, full_RCs = \
TOPSIS.prob_TOPSIS_uniform(data_tri, weights_tri, n, quants, alts, 'T')
if __name__ == "__main__":
weight_labels = ['Empty Wt.', 'Max AS', 'Hover Eff.', 'L/D', 'Prop. Eff.']
weights = [ ['001', 0.51],['002', 0.44],['003', 0.26], \
['004', 0.22],['005', 0.22]]
weights_ranges = [ ['001', [0.09, 1.00]], \
['002', [0.08, 0.90]], \
['003', [0.04, 0.60]], \
['004', [0.04, 0.56]], \
['005', [0.04, 0.55]] ]
weights_tri = [ ['001', [0.09, 0.51, 1.00]], \
['002', [0.08, 0.44, 0.90]], \
['003', [0.04, 0.26, 0.60]], \
['004', [0.04, 0.22, 0.56]], \
['005', [0.04, 0.22, 0.55]] ]
weights_trap = [ ['001', [0.09, 0.26, 0.77, 1.00]], \
['002', [0.08, 0.21, 0.68, 0.90]], \
['003', [0.04, 0.13, 0.43, 0.60]], \
['004', [0.04, 0.10, 0.38, 0.56]], \
['005', [0.04, 0.10, 0.37, 0.55]] ]
#TESTING:
import alt_data_reader as data_reader
print "Testing... GO"
qual_data, quant_data = data_reader.read_data('alt_evals_data.csv', 10)
data1 = data_reader.reduce_data(qual_data, 'AHP_RANGE')
data_tri = data_reader.reduce_data(qual_data, 'AHP_FUZZ_TRI_1')
data_trap = data_reader.reduce_data(qual_data, 'AHP_FUZZ_TRAP_1')
results = alt_fuzzy_AHP3(data_tri, weights_tri, 'FT', 0)
for r in results: print r
['002', [0.08, 0.44, 0.90]], \
['003', [0.04, 0.26, 0.60]], \
['004', [0.04, 0.22, 0.56]], \
['005', [0.04, 0.22, 0.55]] ]
weights_trap = [ ['001', [0.09, 0.26, 0.77, 1.00]], \
['002', [0.08, 0.21, 0.68, 0.90]], \
['003', [0.04, 0.13, 0.43, 0.60]], \
['004', [0.04, 0.10, 0.38, 0.56]], \
['005', [0.04, 0.10, 0.37, 0.55]] ]
input_ranges = [[1,9],[100.,550.],[0.0,1.0],[0.,25.],[0.0, 1.0]] #display input ranges
########### Get Data ###########
#qual_data, quant_data = data_reader.read_data('alt_evals_data.csv', 10)
#qual_data_old, quant_data_old = data_reader.read_data('alt_evals_data_real.csv', 10)
qual_data2, quant_data2 = data_reader.read_data('alt_evals_data_thesis_final_31aug15.csv', 10)
data_avg = data_reader.reduce_data(quant_data2, 'AVG')
data_avgRange = data_reader.reduce_data(quant_data2, 'AVG_RANGE')
data_tri = data_reader.reduce_data(quant_data2, 'FUZZ_TRI_1')
data_trap = data_reader.reduce_data(quant_data2, 'FUZZ_TRAP_UNI')
########### Run Monte Carlo TOPSIS ###########
alts = 10 #number of alternatives
n = 10000 #number of iterations
quants = 20 #quantiles
top_N = 4 #number of top alternatives to plot (CDF, PDF, etc.)
create_plots = True #flag to create plots
rank_counter, full_ranks, quantiles, q_counter, norm_fits, full_RCs = \
TOPSIS.prob_TOPSIS_uniform(data_tri, weights_tri, n, quants, alts, 'T')
import matplotlib.pyplot as plt
from itertools import cycle
weights = [['001', 3378.], ['002', 2881.], ['003', 1751.], ['004', 1472.],
['005', 1436.]]
weight_ranges = [ ['001', [0.09, 1.00]], \
['002', [0.08, 0.90]], \
['003', [0.04, 0.60]], \
['004', [0.04, 0.56]], \
['005', [0.04, 0.55]] ]
#TESTING:
import alt_data_reader as data_reader
print "Testing... GO"
qual_data, quant_data = data_reader.read_data(
'alt_evals_data_thesis_final.csv', 10)
data1 = data_reader.reduce_data(quant_data, 'AHP_CRISP')
data2 = data_reader.reduce_data(quant_data, 'AHP_RANGE')
data22 = data_reader.reduce_data(quant_data, 'AHP_RANGE_LARGE')
data3 = data_reader.reduce_data(quant_data, 'AHP_FUZZ_TRI_1')
results = alt_crisp_AHP1(data1, weights)
results = alt_crisp_AHP2(data1, weights)
for r in results:
print r
n = 2000
alts = 10
priority_quantiles = 70 #high number due to limited range of priority vector
rank_counter, full_ranks, quantiles, q_counter, norm_fits, full_results = \
if __name__ == "__main__":
import matplotlib.pyplot as plt
from itertools import cycle
weights = [['001', 3378.],['002', 2881.],['003', 1751.],['004', 1472.],['005', 1436.]];
weight_ranges = [ ['001', [0.09, 1.00]], \
['002', [0.08, 0.90]], \
['003', [0.04, 0.60]], \
['004', [0.04, 0.56]], \
['005', [0.04, 0.55]] ]
#TESTING:
import alt_data_reader as data_reader
print "Testing... GO"
qual_data, quant_data = data_reader.read_data('alt_evals_data_thesis_final.csv', 10)
data1 = data_reader.reduce_data(quant_data, 'AHP_CRISP')
data2 = data_reader.reduce_data(quant_data, 'AHP_RANGE')
data22= data_reader.reduce_data(quant_data, 'AHP_RANGE_LARGE')
data3 = data_reader.reduce_data(quant_data, 'AHP_FUZZ_TRI_1')
results = alt_crisp_AHP1(data1, weights)
results = alt_crisp_AHP2(data1, weights)
for r in results: print r
n = 2000
alts = 10
priority_quantiles = 70 #high number due to limited range of priority vector
rank_counter, full_ranks, quantiles, q_counter, norm_fits, full_results = \
