def ADA(self, num_passes=20):
""" Train model using ADAgrad with various learning rates """
for eta in 10**np.linspace(-3, 1, 5):
self.learner_name = "ADAGrad %.4f" % eta
print("\n\n" + self.learner_name)
self.optimizer = ADAGrad(self.f_df_wrapper_flattened,
self.xinit_flat.copy(),
self.model.subfunction_references,
learning_rate=eta)
x = self.optimizer.optimize(num_passes=num_passes)
print(np.mean(self.optimizer.f),
"average value at last evaluation")
