Python GPy_Interface.Bayesian_GPLVM_Training Examples

Example #1

File: run_gplvm_1234.py Project: dechamoungsri/Prosody_modeling

def run_training(db_file, name_out_path, data_type, input_dim):

    db = Utility.load_obj(db_file)

    Y = []

    names = []

    for syl in db:
        feat = syl['TF'][data_type]['data']
        Y.append(feat)
        names.append(syl['id'])
        # sys.exit()

    Y = np.array(Y)

    print Y.shape
    # print Y[0]

    num_inducing = int(len(Y) * 0.01)
    if num_inducing > 100:
        num_inducing = 100
    elif num_inducing < 10:
        num_inducing = 10

    config = {
        'input_dim': input_dim,
        'data': Y,
        'num_inducing': num_inducing,
        'max_iters': 500,
        'missing_data': True,
        'optimize_algo': 'scg'
    }

    print config

    m = GPy_Interface.Bayesian_GPLVM_Training(config)

    print m
    print '---------------------------'
    print m.X
    print '---------------------------'
    print m.input_sensitivity()
    print '---------------------------'

    Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))
    Utility.save_obj(np.array(m.X.mean), '{}/x.pkl'.format(name_out_path))

    Utility.save_obj(m.input_sensitivity(),
                     '{}/input_sensitivity.pkl'.format(name_out_path))

    Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
    Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))

    pass

Example #2

def run_training(db_file, name_out_path):

    db = Utility.load_obj(db_file)

    Y = []

    for syl in db:
        feat = syl['TF']['missing151']['data']
        Y.append(feat)
        # sys.exit()

    Y = np.array(Y)

    print Y.shape
    # print Y[0]

    config = {
        'input_dim': 10,
        'data': Y,
        'num_inducing': int(len(Y) * 0.1),
        'max_iters': 1000,
        'missing_data': True,
        'optimize_algo': 'bfgs'
    }

    print config

    m = GPy_Interface.Bayesian_GPLVM_Training(config)

    print m
    print '---------------------------'
    print m.X
    print '---------------------------'
    print np.array(m.X.mean)
    print '---------------------------'
    print m.input_sensitivity()
    print '---------------------------'

    Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))
    Utility.save_obj(np.array(m.X.mean), '{}/x.pkl'.format(name_out_path))

    Utility.save_obj(m.input_sensitivity(),
                     '{}/input_sensitivity.pkl'.format(name_out_path))

    Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
    Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))

    pass

Example #3

def run_training(db_file, name_out_path):

    db = Utility.load_obj(db_file)

    Y = []

    names = []

    for syl in db:
        feat = syl['TF']['intepolate151']['data']
        Y.append(feat)
        names.append(syl['id'])
        # sys.exit()

    Y = np.array(Y)

    print Y.shape
    # print Y[0]

    config = {
        'input_dim': 10,
        'data': Y,
        'num_inducing': int(len(Y) * 0.1),
        'max_iters': 300,
        'missing_data': True,
        'optimize_algo': 'scg'
    }

    print config

    m = GPy_Interface.Bayesian_GPLVM_Training(config)

    Utility.save_obj(m, '{}/model.pkl'.format(name_out_path))

    Utility.save_obj(names, '{}/names.pkl'.format(name_out_path))
    Utility.save_obj(Y, '{}/training_data.pkl'.format(name_out_path))

    pass