def likelihood_distance(loglik_func, data, params, diff_metric='sq', dist_metric='cosine', verbose=False):
"""
Estimates the likelihood of the data from the i'th subject using the parameter estimates of the j'th subject, for all i and j, then computes the distance between subjects' likelihood difference vectors
Parameters
----------
loglik_func : function
The log-likelihood function to be used
data : dict
Data formatted for input into the log-likelihood function
params : ndarray(shape=(nsubjects, nparams))
Array of parameter estimates
diff_metric : {'sq', 'diff', 'abs'}
Which type of difference measure to compute, 'diff' is simple subtractive difference, whereas 'sq' and 'abs' are the squared and absolute differences, respectively
dist_metric : str (default='cosine')
The pairwise distance metric to use. Any option that can be passed into ``sklearn.metrics.pairwise_distances`` can work.
verbose : bool
Whether to print out progress
Returns
-------
ndarray(shape=(nsubjects, nsubjects))
"""
nsubjects = np.shape(params)[0]
D = np.zeros([nsubjects, nsubjects])
for i in range(nsubjects):
S = data[i]['S']
A = data[i]['A']
R = data[i]['R']
if verbose is True:
print('Likelihood Differences: Subject ' + str(i))
# Compute loglikelihood for subject i with own data
LL0 = loglik_func(params=params[i, :],
states=S,
actions=A,
rewards=R)
for j in range(nsubjects):
if i !=j:
LL1 = loglik_func(params=params[j, :],
states=S,
actions=A,
rewards=R)
if diff_metric == 'diff':
D[i, j] = LL1 - LL0
elif diff_metric == 'sq':
D[i, j] = (LL1 - LL0)**2
elif diff_metric == 'abs':
D[i, j] = np.abs(LL1 - LL0)
return pairwise_distances(D, metric=dist_metric)
评论列表
文章目录
