testing testing

[mathematicalmichael]

#9 gets us started with a baseline.

• add badge to README for codecov

• get coverage above 50%

• make final release with commented out code, then delete it pre-v0.1

• ignore plotting for now, just make sure it runs skipping for coverage

[mathematicalmichael]

• test funs

• test that numerical solutions agree for some test problems

• test plot

• test util

• test norm

• test against numpy basics with identity cov.

• test that diagonals getting larger actually shrinks the evaluation

• test problem that you have analytical solutions for

• will be used in numerical comparisons

[mathematicalmichael]

map_sol matches from sklearn.linear_model import Ridge with w and alpha playing the same roles.

r = Ridge(alpha=1, fit_intercept=False).fit(X, y)
map_sol = mf.map_sol(X, np.zeros(100), y, w=1).ravel()
print(r.coef_ - map_sol)

[mathematicalmichael]

next up: how does sample_weights correspond to covariance / prior evaluation.