Notes-on-using-Lem's-algorithm-interface.md

@@ -46,4 +46,80 @@ digraph G {
     }
 ```
 
+Following the `straight_average_intermediate -- TEST1` in the snippet (XXX) we divide the calculation up into 3 sub-groups: 1,3,4 and 2,5,6 at the bottom level followed by 0,1,2 at the top level.
 
+For sub-group 1,3,4 the opinion and components are defined according to the diagram, noting that Node 1 trusts itself fully (trust_factor = 1) and that `intermediate_results` is empty because we are at the bottom level. 
+
+```python
+opinion1 = OpinionData([0.5,0.5], 1)
+opinion3 = OpinionData([0.6,0.4], 1)
+opinion4 = OpinionData([0.7,0.3], 1)
+intermediate_results = []
+comp1 = ComponentData(opinion1, 1.0, intermediate_results)
+comp3 = ComponentData(opinion3, 0.9, intermediate_results)
+comp4 = ComponentData(opinion4, 0.9, intermediate_results)
+```  
+
+Next we define AlgorithmInput and perform the calculation for the sub-group:
+
+```python
+alginp134 = AlgorithmInput([comp1, comp3, comp4],{})
+output134 = straight_average_intermediate(alginp134)
+``` 
+
+At this point we have intermediate results for the output of the sub-group which can be passed on as input to subsequent calculations. Since comp1 will be in those calculations, we set its `intermediate_results` as follows:
+
+```python
+comp1.intermediate_results = output134.intermediate_results
+```
+
+Next is the 2,5,6 sub-group which is essentially the same except with different probabilities:
+
+```python
+opinion2 = OpinionData([0.5,0.5], 1)
+opinion5 = OpinionData([0.8,0.2], 1)
+opinion6 = OpinionData([0.9,0.1], 1)
+intermediate_results = []
+comp2 = ComponentData(opinion2, 1.0, intermediate_results)
+comp5 = ComponentData(opinion5, 0.9, intermediate_results)
+comp6 = ComponentData(opinion6, 0.9, intermediate_results)
+alginp256 = AlgorithmInput([comp2, comp5, comp6],{})
+output256 = straight_average_intermediate(alginp256)
+print('output256 = ', output256.opinion.pdf_points)
+print('inter_results256 = ', output256.intermediate_results)
+comp2.intermediate_results = output256.intermediate_results
+``` 
+
+Now we are ready to move a level up and do the 0,1,2 sub-group. Since we haven't defined `comp0` yet we do so as follows, noting that it starts with no `intermediate_results`:
+
+```python
+opinion0 = OpinionData([0.5,0.5], 1)
+intermediate_results = []
+comp0 = ComponentData(opinion0, 1.0, intermediate_results)
+```
+
+We also reset the `trust_factor` for `comp1` and `comp2` because now we are representing the trust Node 0 has for them, rather than the trust they have for themselves (as we did previously):
+
+```python
+comp1.trust_factor = 0.9
+comp2.trust_factor = 0.9
+``` 
+
+Next is to define the AlgorithmInput and do the calculation:
+
+```python
+alginp012 = AlgorithmInput([comp0, comp1, comp2],{})
+output012 = straight_average_intermediate(alginp012)
+``` 
+
+Since we're at the top node, this result contains the final average result of the calculation and is seen in the following print statement:
+
+```python
+print('output012 = ', output012.opinion.pdf_points)
+```
+
+We don't need to do this since we're done but if Node 0 were to require transmission of its results to yet a higher level node, we would have to:
+
+```python
+comp0.intermediate_results = output012.intermediate_results
+```