Esempi in Python per OptAlg

Esempio n. 1

from algos import OptAlg

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full_S with TR-ABS and hybrid")

    model = LPMC_Full(data_folder, file='12.csv')

    ioa = OptAlg(alg_type='TR-ABS', direction='hybrid')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(ioa, **{'verbose': False, 'max_epochs': 1000, 'batch': 1000})

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

        with open('results/TR-ABS_hybrid.json', 'w') as outfile:
            json.dump(res, outfile)

        print("{}/20 done!".format(i+1))

Esempio n. 2

File: RR_batch.py Progetto: glederrey/HAMABS

from models import LPMC_RR

data_folder = '../../../data/'

if __name__ == "__main__":

    parameters = {''}

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Testing parameters for LPMC_RR_L")

    model = LPMC_RR(data_folder, file='12_13_14.csv')

    ioa = OptAlg(alg_type='LS-ABS', direction='hybrid-inv')

    base_param = {
        'perc_hybrid': 30,
        'thresh_upd': 1,
        'count_upd': 2,
        'window': 10,
        'factor_upd': 2
    }

    main_params = {'verbose': False, 'max_epochs': 1000, 'batch': 1000}
    main_params.update(base_param)

    draws = 20

    res = {}

Esempio n. 3

File: LS-ABS_bfgs-inv.py Progetto: glederrey/HAMABS

from algos import OptAlg

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full_S with LS-ABS and bfgs-inv")

    model = LPMC_Full(data_folder, file='12.csv')

    ioa = OptAlg(alg_type='LS-ABS', direction='bfgs-inv')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(ioa, **{'verbose': False, 'max_epochs': 1000, 'batch': 1000})

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

        with open('results/LS-ABS_bfgs-inv.json', 'w') as outfile:
            json.dump(res, outfile)

        print("{}/20 done!".format(i+1))

Esempio n. 4

from algos import OptAlg

from models import MTMC

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train MTMC with TR and hess")

    model = MTMC(data_folder)

    ioa = OptAlg(alg_type='TR', direction='hess')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(
            ioa, **{
                'verbose': False,
                'max_epochs': 1000,
                'batch': 1000
            })

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

Esempio n. 5

from algos import OptAlg

from models import MTMC

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train MTMC with LS and bfgs")

    model = MTMC(data_folder)

    ioa = OptAlg(alg_type='LS', direction='bfgs')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(
            ioa, **{
                'verbose': False,
                'max_epochs': 1000,
                'batch': 1000
            })

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

Esempio n. 6

File: LS-ABS_hess.py Progetto: glederrey/HAMABS

from algos import OptAlg

from models import LPMC_RR

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_RR_L with LS-ABS and hess")

    model = LPMC_RR(data_folder, file='12_13_14.csv')

    ioa = OptAlg(alg_type='LS-ABS', direction='hess')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(ioa, **{'verbose': False, 'max_epochs': 1000, 'batch': 1000})

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

        with open('results/LS-ABS_hess.json', 'w') as outfile:
            json.dump(res, outfile)

        print("{}/20 done!".format(i+1))

Esempio n. 7

File: TR-ABS_bfgs.py Progetto: glederrey/HAMABS

from algos import OptAlg

from models import LPMC_Full

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_Full_S with TR-ABS and bfgs")

    model = LPMC_Full(data_folder, file='12.csv')

    ioa = OptAlg(alg_type='TR-ABS', direction='bfgs')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(
            ioa, **{
                'verbose': False,
                'max_epochs': 1000,
                'batch': 1000
            })

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

Esempio n. 8

from algos import OptAlg

from models import LPMC_RR

data_folder = '../../../data/'

if __name__ == "__main__":

    if not os.path.exists('./results'):
        os.makedirs('./results')

    print("Train LPMC_RR_L with LS and grad")

    model = LPMC_RR(data_folder, file='12_13_14.csv')

    ioa = OptAlg(alg_type='LS', direction='grad')

    res = {'time': [], 'LL': [], 'epochs': []}

    for i in range(20):

        tmp = model.optimize(ioa, **{'verbose': False, 'max_epochs': 1000, 'batch': 1000})

        res['time'].append(tmp['opti_time'])
        res['LL'].append(tmp['fun'])
        res['epochs'].append(tmp['nep'])

        with open('results/LS_grad.json', 'w') as outfile:
            json.dump(res, outfile)

        print("{}/20 done!".format(i+1))