def plot_mean(self, plot_limits=None, fixed_inputs=None,<EOL>resolution=None, plot_raw=False,<EOL>apply_link=False, visible_dims=None,<EOL>which_data_ycols='<STR_LIT:all>',<EOL>levels=<NUM_LIT:20>, projection='<STR_LIT>',<EOL>label='<STR_LIT>',<EOL>predict_kw=None,<EOL>**kwargs):
canvas, kwargs = pl().new_canvas(projection=projection, **kwargs)<EOL>X = get_x_y_var(self)[<NUM_LIT:0>]<EOL>helper_data = helper_for_plot_data(self, X, plot_limits, visible_dims, fixed_inputs, resolution)<EOL>helper_prediction = helper_predict_with_model(self, helper_data[<NUM_LIT:2>], plot_raw,<EOL>apply_link, None,<EOL>get_which_data_ycols(self, which_data_ycols),<EOL>predict_kw)<EOL>plots = _plot_mean(self, canvas, helper_data, helper_prediction,<EOL>levels, projection, label, **kwargs)<EOL>return pl().add_to_canvas(canvas, plots)<EOL>
Plot the mean of the GP.

You can deactivate the legend for this one plot by supplying None to label.

Give the Y_metadata in the predict_kw if you need it.



:param plot_limits: The limits of the plot. If 1D [xmin,xmax], if 2D [[xmin,ymin],[xmax,ymax]]. Defaluts to data limits
:type plot_limits: np.array
:param fixed_inputs: a list of tuple [(i,v), (i,v)...], specifying that input dimension i should be set to value v.
:type fixed_inputs: a list of tuples
:param int resolution: The resolution of the prediction [defaults are 1D:200, 2D:50]
:param bool plot_raw: plot the latent function (usually denoted f) only?
:param bool apply_link: whether to apply the link function of the GP to the raw prediction.
:param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
:param int levels: for 2D plotting, the number of contour levels to use is
:param {'2d','3d'} projection: whether to plot in 2d or 3d. This only applies when plotting two dimensional inputs!
:param str label: the label for the plot.
:param dict predict_kw: the keyword arguments for the prediction. If you want to plot a specific kernel give dict(kern=<specific kernel>) in here