distance.py 文件源码

def parameter_distance(params, dist_metric='canberra', scale='minmax', return_scaled=False):
    """
    Computes distances between subjects' respective parameter estimates

    Parameters
    ----------
    params : ndarray(shape=(nsubjects, nsubjects))
        Array of parameter estimates
    dist_metric : str (default='canberra')
        Distance metric to be used. Can take any value acceptable by ``sklearn.metrics.pairwise_distances``.
    scale : {'minmax', 'standard', 'none'}
        How to scale the parameters for distance computation
    return_scaled : bool
        Whether to return scaled parameters
    """

    if scale != 'none':
        if scale == 'minmax':
            scaler = MinMaxScaler()
        if scale == 'standard':
            scaler = StandardScaler()

        nparams = np.shape(params)[1]
        for j in range(nparams):
            scaledparam = scaler.fit_transform(params[:, j].reshape(-1, 1))
            params[:, j] = scaledparam.flatten()

    if return_scaled is True:
        D = (pairwise_distances(params, metric=dist_metric), params)
    else:
        D = pairwise_distances(params, metric=dist_metric)

    return D