replay.py 文件源码

def trajectory_score_array(posterior, slope=None, intercept=None, w=None, weights=None, normalize=False):
    """Docstring goes here

    This is the score that Davidson et al. maximizes, in order to get a linear trajectory,
    but here we kind of assume that that we have the trajectory already, and then just score it.

    w is the number of bin rows to include in score, in each direction. That is, w=0 is only the modes,
    and w=1 is a band of width=3, namely the modes, and 1 bin above, and 1 bin below the mode.

    The score is NOT averaged!"""

    rows, cols = posterior.shape

    if w is None:
        w = 0
    if not float(w).is_integer:
        raise ValueError("w has to be an integer!")
    if slope is None or intercept is None:
        slope, intercept, _ = linregress_array(posterior=posterior)

    x = np.arange(cols)
    line_y = np.round((slope*x + intercept)) # in position bin #s

    # idea: cycle each column so that the top w rows are the band surrounding the regression line

    if np.isnan(slope): # this will happen if we have 0 or only 1 decoded bins
        return np.nan
    else:
        temp = column_cycle_array(posterior, -line_y+w)

    if normalize:
        num_non_nan_bins = round(np.nansum(posterior))
    else:
        num_non_nan_bins = 1

    return np.nansum(temp[:2*w+1,:])/num_non_nan_bins