reasoning under uncertainty self display( ,'select variable',vartop_order append(varnext_vars |{ch for ch in self children[varif all( in top_order for in self var parents[ch])self display( ,'var_with_no_parents_left',next_varsself display( ,"top_order",top_orderassert set(top_order)==set(self var parents),(top_order,self var parentsself topologicalsort_saved=top_order return top_order the show method uses matplotlib to show the graphical structure of belief network probgraphicalmodels py -(continued def show(self)plt ion(interactive ax plt figure(gca(ax set_axis_off(plt title(self titlebbox dict(boxstyle="round ,pad= ,rounding_size= "for var in reversed(self topological_sort())if self var parents[var]for par in self var parents[var]ax annotate(var namepar positionxytext=var positionarrowprops={'arrowstyle':'<-'},bbox=bboxha='center'elsex, var position plt text( , ,var name,bbox=bbox,ha='center'example belief networks chain of variables the first example belief network is simple chain - - - please do not change thisas it is the example used for testing probgraphicalmodels py -(continued from probvariables import variable from probfactors import problogisticregressionnoisyor boolean [falsetruea variable(" "booleanposition=( , ) variable(" "booleanposition=( , ) variable(" "booleanposition=( , ) variable(" "booleanposition=( , ) f_a prob( ,[],[ , ]version june