Add overview, and copy exercise file
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34
notes-and-examples/2026.08.20/exercise2.py
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34
notes-and-examples/2026.08.20/exercise2.py
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# See the overview from 2026.07.31.
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# Note: I added Anaheim to the sample graph here. The solution should work
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# appropriately with either graph.
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sample_graph = {
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'Santa Ana': {
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'Los Angeles': 5,
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'Anaheim': 3,
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'Palm Springs': 50
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},
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'Anaheim': {
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'Los Angeles': 2,
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'Santa Ana': 3
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},
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'Los Angeles': {
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'Anaheim': 2,
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'San Francisco': 25,
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'Santa Ana': 5
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},
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'Palm Springs': {
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'San Francisco': 30,
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'Santa Ana': 50
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},
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'San Francisco': {
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'Los Angeles': 25,
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'Palm Springs': 30
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}
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}
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def possible_paths(start: str, end: str):
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# TODO
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pass
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print(possible_paths('Santa Ana', 'San Francisco'))
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49
notes-and-examples/2026.08.20/overview.md
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notes-and-examples/2026.08.20/overview.md
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## Outline
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We're working on exercise 2 from [2026.07.31](../2026.07.31/overview.md).
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The exercise and starter file is included again below for convenience.
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In a *weighted* road network, return all possible paths that a car can take to
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reach point A to point B. For each path, also sum up the total weight that
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taking that path requires.
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Your algorithm should take the starting and ending points, and return a list
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of tuples. Each tuple should contain the traversal from point A to point B,
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followed by the total weight.
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In this fictitious graph, calling `possible_paths("Santa Ana", "San Francisco")`
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should yield the return below.
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```python
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graph = {
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'Santa Ana': {
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'Los Angeles': 5,
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'Palm Springs': 50
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},
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'Los Angeles': {
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'San Francisco': 25,
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'Santa Ana': 5
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},
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'Palm Springs': {
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'San Francisco': 30,
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'Santa Ana': 50
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},
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'San Francisco': {
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'Los Angeles': 25,
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'Palm Springs': 30
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}
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}
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```
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```
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[
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(
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["Santa Ana", "Los Angeles", "San Francisco"],
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30
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),
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(
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["Santa Ana", "Palm Springs", "San Francisco"],
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80
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)
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]
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```
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