57c4306d9282b8d3fc815336c86c7af64f75a756,GPy/plotting/gpy_plot/gp_plots.py,,_plot_confidence,#,150

Before Change

    update_not_existing_kwargs(kwargs, pl.defaults.confidence_interval)
    _, percs = helper_predict_with_model(self, Xgrid, plot_raw, apply_link, (lower, upper), which_data_ycols, **predict_kw)

    return dict(gpconfidence=pl.fill_between(canvas, Xgrid, percs[0], percs[1], **kwargs))

    
    

After Change

    
    update_not_existing_kwargs(kwargs, pl.defaults.confidence_interval)
    
    if len(free_dims)<=1:
        if len(free_dims)==1:
            _, percs = helper_predict_with_model(self, Xgrid, plot_raw, apply_link, 
                                                 (lower, upper), 
                                                 ycols, predict_kw)
    
            fills = []
            for d in ycols:
                fills.append(pl.fill_between(canvas, Xgrid[:,free_dims[0]], percs[0][:,d], percs[1][:,d], **kwargs))
            return dict(gpconfidence=fills)
        else:
            pass //Nothing to plot!
    else:
        raise RuntimeError("Can only plot confidence interval in one input dimension")


def plot_density(self, plot_limits=None, fixed_inputs=None,
              resolution=None, plot_raw=False,
              apply_link=False, 
              which_data_ycols="all",

Instances