Module Import alias identifier ImportFrom alias identifier ImportFrom alias alias alias alias alias ImportFrom alias alias alias FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Num Str Str NameLoad kwarg body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad Call NameLoad attr keyword NameLoad NameLoad Assign NameStore SubscriptLoad Call NameLoad NameLoad Index Num Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad NameLoad Call NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Return Call AttributeLoad Call NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Str NameLoad kwarg body Assign TupleStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameLoad If CompareLtE Call NameLoad NameLoad Num body Assign TupleStore NameStore NameStore NameStore NameLoad If CompareEq Call NameLoad NameLoad Num body Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr Assign NameStore Call NameLoad keyword ListLoad Call AttributeLoad Call NameLoad attr NameLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad NameLoad keyword NameLoad NameLoad orelse If CompareEq NameLoad Str body Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr Assign NameStore Call NameLoad keyword ListLoad Call AttributeLoad Call NameLoad attr NameLoad SubscriptLoad NameLoad ExtSlice Slice Index Num SubscriptLoad NameLoad ExtSlice Index Num Slice AttributeLoad Call AttributeLoad NameLoad attr NameLoad NameLoad attr keyword NameLoad keyword NameLoad NameLoad orelse If CompareEq NameLoad Str body Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr Assign NameStore Call NameLoad keyword ListLoad Call AttributeLoad Call NameLoad attr NameLoad NameLoad NameLoad Call AttributeLoad NameLoad attr NameLoad NameLoad keyword NameLoad NameLoad orelse If CompareEq Call NameLoad NameLoad Num body Pass orelse Raise Call NameLoad Str Return NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults Num Num NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Str NameLoad kwarg body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad Call NameLoad attr NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Assign NameStore SubscriptLoad Call NameLoad NameLoad Index Num Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad TupleLoad NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Return Call AttributeLoad Call NameLoad attr NameLoad NameLoad keyword CompareIsNot NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam defaults kwarg body Assign TupleStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameLoad Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr If CompareLtE Call NameLoad NameLoad Num body If CompareEq Call NameLoad NameLoad Num body Assign NameStore SubscriptLoad NameLoad Index Num Assign NameStore ListLoad For NameStore Call NameLoad SubscriptLoad AttributeLoad SubscriptLoad NameLoad Index Num attr Index Num body Expr Call AttributeLoad NameLoad attr Call AttributeLoad Call NameLoad attr NameLoad SubscriptLoad NameLoad ExtSlice Slice Index SubscriptLoad NameLoad Index Num SubscriptLoad SubscriptLoad NameLoad Index Num ExtSlice Slice Index NameLoad SubscriptLoad SubscriptLoad NameLoad Index Num ExtSlice Slice Index NameLoad keyword NameLoad NameLoad Return Call NameLoad keyword NameLoad orelse Pass orelse Raise Call NameLoad Str decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Num Str Str NameLoad kwarg body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad Call NameLoad attr keyword NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad Assign NameStore SubscriptLoad Call NameLoad NameLoad Index Num Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Return Call AttributeLoad Call NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam defaults kwarg body Assign TupleStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameLoad Assign NameStore SubscriptLoad NameLoad Index Num If CompareLtE Call NameLoad NameLoad Num body If CompareEq Call NameLoad NameLoad Num body Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr Assign NameStore ListComp Call AttributeLoad Call NameLoad attr NameLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad keyword IfExp CompareEq NameLoad Num NameLoad NameLoad NameLoad comprehension NameStore Call NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num orelse If BoolOpAnd CompareEq Call NameLoad NameLoad Num CompareEq NameLoad Str body Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr Assign NameStore ListComp Call AttributeLoad Call NameLoad attr NameLoad NameLoad NameLoad Call AttributeLoad SubscriptLoad NameLoad ExtSlice Slice Index NameLoad attr NameLoad NameLoad NameLoad comprehension NameStore Call NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num orelse Pass Return Call NameLoad keyword NameLoad orelse Raise Call NameLoad Str decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Num Str NameLoad kwarg body Expr Str Assign TupleStore NameStore NameStore Call AttributeLoad Call NameLoad attr NameLoad Assign NameStore SubscriptLoad Call NameLoad NameLoad Index Num Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad Call AttributeLoad NameLoad attr Num Num BinOpMult NameLoad Num Call NameLoad NameLoad NameLoad NameLoad Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Return Call AttributeLoad Call NameLoad attr NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam defaults kwarg body Assign TupleStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameStore NameLoad Assign TupleStore NameStore NameStore NameStore NameLoad Expr Call NameLoad NameLoad AttributeLoad AttributeLoad Call NameLoad attr attr If CompareLtE Call NameLoad NameLoad Num body If CompareEq Call NameLoad NameLoad Num body Assign NameStore ListLoad For NameStore Call NameLoad SubscriptLoad AttributeLoad NameLoad attr Index Num body Expr Call AttributeLoad NameLoad attr Call AttributeLoad Call NameLoad attr NameLoad SubscriptLoad NameLoad ExtSlice Slice Index SubscriptLoad NameLoad Index Num ListComp SubscriptLoad NameLoad ExtSlice Slice Index NameLoad comprehension NameStore NameLoad keyword NameLoad NameLoad Return Call NameLoad keyword NameLoad orelse Pass orelse Raise Call NameLoad Str decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad NameLoad NameLoad Str Str NameLoad Num Num Num Num Num NameLoad NameLoad NameLoad NameLoad Str NameLoad kwarg body Expr Str Assign NameStore SubscriptLoad Call NameLoad NameLoad Index Num Assign NameStore Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Assign TupleStore NameStore NameStore SubscriptLoad NameLoad Slice Num Num Assign NameStore SubscriptLoad NameLoad Index Num If UnaryOpNot CompareIn Str NameLoad body Assign SubscriptStore NameLoad Index Str TupleLoad SubscriptLoad NameLoad Index Num SubscriptLoad NameLoad Index Num If BoolOpAnd UnaryOpNot CompareIn Str NameLoad CompareEq Call NameLoad NameLoad Num body Assign SubscriptStore NameLoad Index Str TupleLoad SubscriptLoad NameLoad Index Num SubscriptLoad NameLoad Index Num Assign TupleStore NameStore NameStore Call AttributeLoad Call NameLoad attr keyword NameLoad NameLoad Assign NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad IfExp NameLoad Call AttributeLoad NameLoad attr Num Num BinOpMult NameLoad Num TupleLoad NameLoad NameLoad Call NameLoad NameLoad NameLoad NameLoad NameLoad If BoolOpAnd NameLoad UnaryOpNot NameLoad body Assign NameStore NameLoad Assign NameStore Dict If BoolOpAnd Call NameLoad NameLoad Str NameLoad body Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad Str If NameLoad body Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad If BoolOpAnd NameLoad CompareGt NameLoad Num body Assign NameStore Call NameLoad NameLoad SubscriptLoad NameLoad Index Num NameLoad NameLoad NameLoad Call NameLoad NameLoad NameLoad NameLoad NameLoad Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad Str Return Call AttributeLoad Call NameLoad attr NameLoad NameLoad keyword NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad NameLoad NameLoad Str Str NameLoad Num Num Num Num NameLoad NameLoad NameLoad Str NameLoad NameLoad kwarg body Expr Str Return Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Num NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad decorator_list FunctionDef arguments args NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam NameParam defaults NameLoad NameLoad Str body Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str TryExcept body If CompareEq NameLoad Str body If UnaryOpNot NameLoad body Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad Str orelse Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad Str handlers ExceptHandler type NameLoad body Pass If CompareIsNot SubscriptLoad NameLoad Index Num NameLoad body Expr Call AttributeLoad NameLoad attr Call NameLoad NameLoad NameLoad NameLoad NameLoad NameLoad Str Return NameLoad decorator_list
N/A N/A numpy np N/A plotting_library pl plot_util helper_for_plot_data update_not_existing_kwargs helper_predict_with_model get_which_data_ycols get_x_y_var data_plots _plot_data _plot_inducing _plot_data_error plot_mean N/A N/A self plot_limits fixed_inputs resolution plot_raw apply_link visible_dims which_data_ycols levels projection label predict_kw N/A None None None False False None all 20 2d gp mean None kwargs N/A N/A 
    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
     N/A N/A canvas kwargs N/A N/A N/A pl new_canvas projection projection kwargs N/A X N/A N/A get_x_y_var self N/A 0 N/A helper_data N/A helper_for_plot_data self X plot_limits visible_dims fixed_inputs resolution N/A helper_prediction N/A helper_predict_with_model self N/A helper_data N/A 2 plot_raw apply_link None N/A get_which_data_ycols self which_data_ycols predict_kw N/A plots N/A _plot_mean self canvas helper_data helper_prediction levels projection label kwargs N/A N/A N/A N/A pl add_to_canvas canvas plots N/A _plot_mean N/A N/A self canvas helper_data helper_prediction levels projection label N/A 20 2d None kwargs N/A N/A N/A _ free_dims Xgrid x y _ _ resolution helper_data N/A N/A N/A len free_dims 2 N/A N/A N/A mu _ _ helper_prediction N/A N/A N/A len free_dims 1 N/A N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults meanplot_1d N/A plots N/A dict gpmean N/A N/A N/A N/A pl plot canvas N/A Xgrid N/A N/A N/A free_dims mu label label kwargs N/A N/A N/A projection 2d N/A N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults meanplot_2d N/A plots N/A dict gpmean N/A N/A N/A N/A pl contour canvas N/A x N/A N/A N/A 0 N/A y N/A N/A 0 N/A N/A N/A N/A mu reshape resolution resolution T levels levels label label kwargs N/A N/A N/A projection 3d N/A N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults meanplot_3d N/A plots N/A dict gpmean N/A N/A N/A N/A pl surface canvas x y N/A N/A mu reshape resolution resolution label label kwargs N/A N/A N/A N/A len free_dims 0 N/A N/A N/A N/A N/A RuntimeError Cannot plot mean in more then 2 input dimensions N/A plots N/A plot_confidence N/A N/A self lower upper plot_limits fixed_inputs resolution plot_raw apply_link visible_dims which_data_ycols label predict_kw N/A 2.5 97.5 None None None False False None all gp confidence None kwargs N/A N/A 
    Plot the confidence interval between the percentiles lower and upper.
    E.g. the 95% confidence interval is $2.5, 97.5$.
    Note: Only implemented for one dimension!

    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 float lower: the lower percentile to plot
    :param float upper: the upper percentile to plot
    :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 [default:200]
    :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 visible_dims: which columns of the input X (!) to plot (array-like or list of ints)
    :param array-like which_data_ycols: which columns of the output y (!) to plot (array-like or list of ints)
    :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
     N/A N/A canvas kwargs N/A N/A N/A pl new_canvas kwargs N/A ycols N/A get_which_data_ycols self which_data_ycols N/A X N/A N/A get_x_y_var self N/A 0 N/A helper_data N/A helper_for_plot_data self X plot_limits visible_dims fixed_inputs resolution N/A helper_prediction N/A helper_predict_with_model self N/A helper_data N/A 2 plot_raw apply_link N/A lower upper ycols predict_kw N/A plots N/A _plot_confidence self canvas helper_data helper_prediction label kwargs N/A N/A N/A N/A pl add_to_canvas canvas plots legend N/A label None N/A _plot_confidence N/A N/A self canvas helper_data helper_prediction label N/A kwargs N/A N/A N/A _ free_dims Xgrid _ _ _ _ _ helper_data N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults confidence_interval N/A N/A N/A len free_dims 1 N/A N/A N/A N/A len free_dims 1 N/A N/A percs N/A helper_prediction N/A 1 N/A fills N/A N/A d N/A range N/A N/A N/A helper_prediction N/A 0 shape N/A 1 N/A N/A N/A N/A fills append N/A N/A N/A pl fill_between canvas N/A Xgrid N/A N/A N/A N/A free_dims N/A 0 N/A N/A percs N/A 0 N/A N/A N/A d N/A N/A percs N/A 1 N/A N/A N/A d label label kwargs N/A N/A dict gpconfidence fills N/A N/A N/A N/A N/A RuntimeError Can only plot confidence interval in one input dimension N/A plot_samples N/A N/A self plot_limits fixed_inputs resolution plot_raw apply_link visible_dims which_data_ycols samples projection label predict_kw N/A None None None True False None all 3 2d gp_samples None kwargs N/A N/A 
    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? This is usually what you want!
    :param bool apply_link: whether to apply the link function of the GP to the raw prediction.
    :param array-like visible_dims: which columns of the input X (!) to plot (array-like or list of ints)
    :param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
    :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
    :param int levels: for 2D plotting, the number of contour levels to use is
     N/A N/A canvas kwargs N/A N/A N/A pl new_canvas projection projection kwargs N/A ycols N/A get_which_data_ycols self which_data_ycols N/A X N/A N/A get_x_y_var self N/A 0 N/A helper_data N/A helper_for_plot_data self X plot_limits visible_dims fixed_inputs resolution N/A helper_prediction N/A helper_predict_with_model self N/A helper_data N/A 2 plot_raw apply_link None ycols predict_kw samples N/A plots N/A _plot_samples self canvas helper_data helper_prediction projection label kwargs N/A N/A N/A N/A pl add_to_canvas canvas plots N/A _plot_samples N/A N/A self canvas helper_data helper_prediction projection label N/A kwargs N/A N/A N/A _ free_dims Xgrid x y _ _ resolution helper_data N/A samples N/A helper_prediction N/A 2 N/A N/A N/A len free_dims 2 N/A N/A N/A N/A len free_dims 1 N/A N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults samples_1d N/A plots N/A N/A N/A N/A pl plot canvas N/A Xgrid N/A N/A N/A free_dims N/A samples N/A N/A N/A s label N/A N/A s 0 label None kwargs N/A s N/A range N/A N/A samples shape N/A -1 N/A N/A N/A N/A N/A len free_dims 2 N/A projection 3d N/A N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults samples_3d N/A plots N/A N/A N/A N/A pl surface canvas x y N/A N/A N/A samples N/A N/A N/A s reshape resolution resolution kwargs N/A s N/A range N/A N/A samples shape N/A -1 N/A N/A N/A N/A dict gpmean plots N/A N/A N/A RuntimeError Cannot plot mean in more then 1 input dimensions N/A plot_density N/A N/A self plot_limits fixed_inputs resolution plot_raw apply_link visible_dims which_data_ycols levels label predict_kw N/A None None None False False None all 35 gp density None kwargs N/A N/A 
    Plot the confidence interval between the percentiles lower and upper.
    E.g. the 95% confidence interval is $2.5, 97.5$.
    Note: Only implemented for one dimension!

    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 [default:200]
    :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 visible_dims: which columns of the input X (!) to plot (array-like or list of ints)
    :param array-like which_data_ycols: which columns of y to plot (array-like or list of ints)
    :param int levels: the number of levels in the density (number bigger then 1, where 35 is smooth and 1 is the same as plot_confidence). You can go higher then 50 if the result is not smooth enough for you.
    :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
     N/A N/A canvas kwargs N/A N/A N/A pl new_canvas kwargs N/A X N/A N/A get_x_y_var self N/A 0 N/A helper_data N/A helper_for_plot_data self X plot_limits visible_dims fixed_inputs resolution N/A helper_prediction N/A helper_predict_with_model self N/A helper_data N/A 2 plot_raw apply_link N/A N/A np linspace 2.5 97.5 N/A levels 2 N/A get_which_data_ycols self which_data_ycols predict_kw N/A plots N/A _plot_density self canvas helper_data helper_prediction label kwargs N/A N/A N/A N/A pl add_to_canvas canvas plots N/A _plot_density N/A N/A self canvas helper_data helper_prediction label N/A kwargs N/A N/A N/A _ free_dims Xgrid _ _ _ _ _ helper_data N/A N/A mu percs _ helper_prediction N/A N/A update_not_existing_kwargs kwargs N/A N/A N/A pl defaults density N/A N/A N/A len free_dims 1 N/A N/A N/A N/A len free_dims 1 N/A N/A fills N/A N/A d N/A range N/A N/A mu shape N/A 1 N/A N/A N/A N/A fills append N/A N/A N/A pl fill_gradient canvas N/A Xgrid N/A N/A N/A N/A free_dims N/A 0 N/A N/A p N/A N/A N/A d N/A p percs label label kwargs N/A N/A dict gpdensity fills N/A N/A N/A N/A N/A RuntimeError Can only plot density in one input dimension N/A plot N/A N/A self plot_limits fixed_inputs resolution plot_raw apply_link which_data_ycols which_data_rows visible_dims levels samples samples_likelihood lower upper plot_data plot_inducing plot_density predict_kw projection legend N/A None None None False False all all None 20 0 0 2.5 97.5 True True False None 2d True kwargs N/A N/A 
    Convenience function for plotting the fit of a 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.


    If you want fine graned control use the specific plotting functions supplied in the model.

    :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 [default:200]
    :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 which_data_ycols: when the data has several columns (independant outputs), only plot these
    :type which_data_ycols: 'all' or a list of integers
    :param which_data_rows: which of the training data to plot (default all)
    :type which_data_rows: 'all' or a slice object to slice self.X, self.Y
    :param array-like visible_dims: which columns of the input X (!) to plot (array-like or list of ints)
    :param int levels: the number of levels in the density (number bigger then 1, where 35 is smooth and 1 is the same as plot_confidence). You can go higher then 50 if the result is not smooth enough for you.
    :param int samples: the number of samples to draw from the GP and plot into the plot. This will allways be samples from the latent function.
    :param int samples_likelihood: the number of samples to draw from the GP and apply the likelihood noise. This is usually not what you want!
    :param float lower: the lower percentile to plot
    :param float upper: the upper percentile to plot
    :param bool plot_data: plot the data into the plot?
    :param bool plot_inducing: plot inducing inputs?
    :param bool plot_density: plot density instead of the confidence interval?
    :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
    :param {2d|3d} projection: plot in 2d or 3d?
    :param bool legend: convenience, whether to put a legend on the plot or not.
     N/A X N/A N/A get_x_y_var self N/A 0 N/A helper_data N/A helper_for_plot_data self X plot_limits visible_dims fixed_inputs resolution N/A N/A xmin xmax N/A helper_data N/A 5 7 N/A free_dims N/A helper_data N/A 1 N/A N/A N/A xlim kwargs N/A N/A N/A kwargs N/A xlim N/A N/A xmin N/A 0 N/A xmax N/A 0 N/A N/A N/A N/A ylim kwargs N/A N/A len free_dims 2 N/A N/A N/A kwargs N/A ylim N/A N/A xmin N/A 1 N/A xmax N/A 1 N/A N/A canvas _ N/A N/A N/A pl new_canvas projection projection kwargs N/A helper_prediction N/A helper_predict_with_model self N/A helper_data N/A 2 plot_raw apply_link N/A plot_density N/A N/A np linspace 2.5 97.5 N/A levels 2 N/A lower upper N/A get_which_data_ycols self which_data_ycols predict_kw samples N/A N/A plot_raw N/A apply_link N/A N/A plot_data False N/A plots N/A N/A N/A N/A hasattr self Z plot_inducing N/A N/A N/A N/A plots update N/A _plot_inducing self canvas visible_dims projection Inducing N/A plot_data N/A N/A N/A N/A plots update N/A _plot_data self canvas which_data_rows which_data_ycols free_dims projection Data N/A N/A N/A plots update N/A _plot_data_error self canvas which_data_rows which_data_ycols free_dims projection Data Error N/A N/A N/A plots update N/A _plot self canvas plots helper_data helper_prediction levels plot_inducing plot_density projection N/A N/A plot_raw N/A samples_likelihood 0 N/A N/A helper_prediction N/A helper_predict_with_model self N/A helper_data N/A 2 False apply_link None N/A get_which_data_ycols self which_data_ycols predict_kw samples_likelihood N/A N/A N/A plots update N/A _plot_samples canvas helper_data helper_prediction projection Lik Samples N/A N/A N/A N/A pl add_to_canvas canvas plots legend legend N/A plot_f N/A N/A self plot_limits fixed_inputs resolution apply_link which_data_ycols which_data_rows visible_dims levels samples lower upper plot_density plot_data plot_inducing projection legend predict_kw N/A None None None False all all None 20 0 2.5 97.5 False True True 2d True None kwargs N/A N/A 
    Convinience function for plotting the fit of a GP.
    This is the same as plot, except it plots the latent function fit of the GP!

    If you want fine graned control use the specific plotting functions supplied in the model.

    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 [default:200]
    :param bool apply_link: whether to apply the link function of the GP to the raw prediction.
    :param which_data_ycols: when the data has several columns (independant outputs), only plot these
    :type which_data_ycols: 'all' or a list of integers
    :param which_data_rows: which of the training data to plot (default all)
    :type which_data_rows: 'all' or a slice object to slice self.X, self.Y
    :param array-like visible_dims: an array specifying the input dimensions to plot (maximum two)
    :param int levels: the number of levels in the density (number bigger then 1, where 35 is smooth and 1 is the same as plot_confidence). You can go higher then 50 if the result is not smooth enough for you.
    :param int samples: the number of samples to draw from the GP and plot into the plot. This will allways be samples from the latent function.
    :param float lower: the lower percentile to plot
    :param float upper: the upper percentile to plot
    :param bool plot_data: plot the data into the plot?
    :param bool plot_inducing: plot inducing inputs?
    :param bool plot_density: plot density instead of the confidence interval?
    :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
    :param dict error_kwargs: kwargs for the error plot for the plotting library you are using
    :param kwargs plot_kwargs: kwargs for the data plot for the plotting library you are using
     N/A N/A plot self plot_limits fixed_inputs resolution True apply_link which_data_ycols which_data_rows visible_dims levels samples 0 lower upper plot_data plot_inducing plot_density predict_kw projection legend kwargs N/A _plot N/A N/A self canvas plots helper_data helper_prediction levels plot_inducing plot_density projection N/A True False 2d N/A N/A N/A N/A plots update N/A _plot_mean self canvas helper_data helper_prediction levels projection Mean N/A N/A N/A N/A projection 2d N/A N/A N/A plot_density N/A N/A N/A N/A plots update N/A _plot_confidence self canvas helper_data helper_prediction Confidence N/A N/A N/A N/A plots update N/A _plot_density self canvas helper_data helper_prediction Density N/A N/A N/A RuntimeError N/A N/A N/A N/A N/A helper_prediction N/A 2 None N/A N/A N/A N/A plots update N/A _plot_samples self canvas helper_data helper_prediction projection Samples N/A plots N/A