- Compare code quality for histogram implementations
- Refactor code to improve readability, modularity, and performance
- Compare tradeoffs of different histogram implementations
- Lecture and discussion following probability and sampling slides
- Watch a previous recording of this lecture to review and reinforce concepts
After completing this class session and the associated tutorial challenges, students will be able to ...
- Sample words according to their observed frequencies
- Compare tradeoffs with different sampling techniques
- Validate sampling techniques based on relative probabilities
These challenges are the baseline required to complete the project and course. Be sure to complete these before next class session and before starting on the stretch challenges below.
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- Sample words by observed frequency weighting
- Test relative probabilities to validate sampling technique
These challenges are more difficult and help you push your skills and understanding to the next level.
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- Optimize for speed of sampling (read time)
- Optimize for memory (use the least amount of space)
- Solve with only lists and tuples
- Combine other weighting techniques
- Review Make School's probability and sampling slides
- Consult Python's random module documentation, but do not use the
random.choicesfunction (the challenge is to implement this yourself) - If you're really stuck, read the Python Cookbook's Randomly Picking Items with Given Probabilities, but don't read this until you've tried to solve it yourself