Python load_mcda_input_data 예제들

예제 #1

파일: test_mip_mrsort_csv.py 프로젝트: minpegdwende/MRSort-jupyter-notebook

                      "--nseeds",
                      action="store",
                      type="string",
                      dest="nseeds",
                      help="number of seeds")
    parser.add_option("-f",
                      "--filename",
                      action="store",
                      type="string",
                      dest="filename",
                      help="filename to save csv output")
    (options, args) = parser.parse_args()

    while not options.csvfile:
        options.csvfile = raw_input("CSV file containing data ? ")
    data = load_mcda_input_data(options.csvfile)
    if data is None:
        exit(1)

    options.pclearning = read_multiple_integer(options.pclearning,
                                               "Percentage of data to " \
                                               "use in the learning set")
    options.nseeds = read_single_integer(options.nseeds, "Number of seeds")

    dt = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
    default_filename = "%s/test_mip_mrsort-%s-%s.csv" \
                       % (DATADIR, data.name, dt)
    options.filename = read_csv_filename(options.filename, default_filename)

    directory = options.filename + "-data"
    if not os.path.exists(directory):

예제 #2

파일: learn-from-dataset.py 프로젝트: minpegdwende/pymcda

        "%s file.csv meta_mrsort|meta_mrsortc|mip_mrsort|lp_utadis|lp_utadis_compat"
        % sys.argv[0])
    sys.exit(1)


if len(sys.argv) != 3:
    usage()

algo = sys.argv[2]

nseg = 4
nmodels = 20
nloop = 7
nmeta = 40

data = load_mcda_input_data(sys.argv[1])

print(data.c)
worst = data.pt.get_worst(data.c)
best = data.pt.get_best(data.c)

t1 = time.time()

if algo == 'meta_mrsort':
    heur_init_profiles = HeurMRSortInitProfiles
    lp_weights = LpMRSortWeights
    heur_profiles = MetaMRSortProfiles4
elif algo == 'meta_mrsortc':
    heur_init_profiles = HeurMRSortInitProfiles
    lp_weights = LpMRSortMobius
    heur_profiles = MetaMRSortProfilesChoquet

예제 #3

파일: test_mip_mrsort_csv.py 프로젝트: oso/pymcda

                      dest = "csvfile",
                      help = "csv file with data")
    parser.add_option("-p", "--pclearning", action = "store", type="string",
                      dest = "pclearning",
                      help = "Percentage of data to use in learning set")
    parser.add_option("-s", "--nseeds", action = "store", type="string",
                      dest = "nseeds",
                      help = "number of seeds")
    parser.add_option("-f", "--filename", action = "store", type="string",
                      dest = "filename",
                      help = "filename to save csv output")
    (options, args) = parser.parse_args()

    while not options.csvfile:
        options.csvfile = raw_input("CSV file containing data ? ")
    data = load_mcda_input_data(options.csvfile)
    if data is None:
        exit(1)

    options.pclearning = read_multiple_integer(options.pclearning,
                                               "Percentage of data to " \
                                               "use in the learning set")
    options.nseeds = read_single_integer(options.nseeds, "Number of seeds")

    dt = datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
    default_filename = "%s/test_mip_mrsort-%s-%s.csv" \
                       % (DATADIR, data.name, dt)
    options.filename = read_csv_filename(options.filename, default_filename)

    directory = options.filename + "-data"
    if not os.path.exists(directory):

예제 #4

파일: learn-from-dataset.py 프로젝트: oso/pymcda

def usage():
    print("%s file.csv meta_mrsort|meta_mrsortc|mip_mrsort|lp_utadis|lp_utadis_compat" % sys.argv[0])
    sys.exit(1)

if len(sys.argv) != 3:
    usage()

algo = sys.argv[2]

nseg = 4
nmodels = 20
nloop = 7
nmeta = 40

data = load_mcda_input_data(sys.argv[1])

print(data.c)
worst = data.pt.get_worst(data.c)
best = data.pt.get_best(data.c)

t1 = time.time()

if algo == 'meta_mrsort':
    heur_init_profiles = HeurMRSortInitProfiles
    lp_weights = LpMRSortWeights
    heur_profiles = MetaMRSortProfiles4
elif algo == 'meta_mrsortc':
    heur_init_profiles = HeurMRSortInitProfiles
    lp_weights = LpMRSortMobius
    heur_profiles = MetaMRSortProfilesChoquet