decreased fitting tolerance to achieve consistent test results
@@ -165,7 +165,10 @@ def fit_optimize_positive(
pattern_descriptor=pattern_descriptor,
sigma_k=sigma_k, ridge_weight=ridge_weight)
theta0 = np.random.rand(model.n_param)
- theta = opt.minimize(_loss_opt, theta0)
+ theta = opt.minimize(_loss_opt, theta0,
+ options={
+ 'xatol': 0.000001,
+ 'fatol': 0.000001})
return theta.x ** 2 / np.sqrt(np.sum(theta.x ** 4))
