Programming assignments for Cal Tech's Machine Learning class by Professor Yaser Abu-Mostafa. My notes for this class are available on my site.
The assignments are done in Python with NumPy and other SciPy packages.
I created a Question class in common.question that can help grade the assignment. It takes a label (the question itself), a list of possible choices from the assignment, the actual answer (from the answer key) and an optional scoring function for each choice. Its constructor is:
Question(label, choices, answer, score=None)For example, for question 8 in assignment 2 asks for the in-sample error and can be specified as:
question8 = Question("8. in sample error",
[0, 0.1, 0.3, 0.5, 0.8], 'd')
in_sample_error = experiment()
question8.check(in_sample_error)The output for this is:
8. in sample error
result: 0.506176
nearest: d. 0.5
answer: d. 0.5
+ CORRECT
The Question class scores every choice in the list based on its distance to the result the experiment yielded. The optional score function can be provided to score more sophisticated answers, for example question 9 in assignment 2 asks for the hypothesis that is closest to the one that the experiment returned. This can be expressed as:
question9 = Question("9. hypothesis",
[[-1, -0.5, 0.08, 0.13, 1.50, 1.50],
[-1, -0.5, 0.08, 0.13, 1.50, 15.0],
[-1, -0.5, 0.08, 0.13, 15.0, 1.50],
[-1, -1.5, 0.08, 0.13, 0.05, 0.05],
[-1, -0.5, 0.08, 1.50, 0.15, 0.15]], 'a',
# polynomial scoring lambda
lambda result, choice:
sum([abs(result_coeff - coeff)
for result_coeff, coeff
in zip(result, choice)]))This has the intended result:
9. hypothesis
result: [-0.90466334 -0.00694238 0.01946925 0.01881652 1.45797108 1.43164961]
nearest: a. [-1, -0.5, 0.08, 0.13, 1.5, 1.5]
answer: a. [-1, -0.5, 0.08, 0.13, 1.5, 1.5]
+ CORRECT