fit_optimize_positive: explicitly specify solver and use matching options
@@ -170,11 +170,11 @@ def fit_optimize_positive(
sigma_k=sigma_k, ridge_weight=ridge_weight)
theta0 = np.random.rand(model.n_param)
theta = opt.minimize(
- _loss_opt,
- theta0,
- options={
- 'xatol': 0.000001,
- 'fatol': 0.000001})
+ fun=_loss_opt,
+ x0=theta0,
+ method='BFGS',
+ tol=0.000001
+ )
theta = theta.x ** 2
norm = np.sum(theta ** 2)
if norm == 0:
