ml.py 文件源码

def fit_single_subject(subject=4):
    df = pd.read_pickle('data.pkl')
    print('Fitting model for subject {}'.format(subject))
    df_s = df.loc[subject]

    cues = (0, 1, 2)
    for cue in cues:
        ml = ML(df_s[df_s['cue']==cue])
        r = ml.ml_estimation()
        H_inv = r.hess_inv.todense()
        print('\t cue:{:d}'.format(cue))
        print('\t\tr:\n\t\t\t{}\n'.format(r.x))
        print('\tInverse of Hessian:\n{}\n'.format(H_inv))


    globals().update(locals())