specify solver for fit_optimize
@@ -132,7 +132,12 @@ def fit_optimize(model, data, method='cosine', pattern_idx=None,
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,
+ method='BFGS',
+ tol=0.000001
+ )
theta = theta.x
norm = np.sum(theta ** 2)
if norm == 0:
