def pso_esn_parameters_for_lasso(x):
ic_s =x[0]
ic_e =x[1]
is_s = x[2]
is_e = x[3]
teacher_scaling = x[4]
teacher_shift = x[5]
alpha =x[6]
esn = ESN(n_inputs = 2,
n_outputs = 1,
n_reservoir = n_reservoir,
spectral_radius = spectral_radius,
sparsity = sparsity,
noise = noise,
input_shift = [is_s,is_e],#[0,0]
input_scaling =[ic_s,ic_e],# [0.01, 3]
teacher_scaling = teacher_scaling,#1.12,
teacher_shift = teacher_shift,#-0.7,
out_activation = np.tanh,
inverse_out_activation = np.arctanh,
random_state = rng,
silent = False)
esn.alpha = alpha
internal_states,transient = esn.train_reservior(train_ctrl,train_output)
pred_train = esn.train_readout_with_lasso(internal_states,train_output,transient)
pred_test = esn.predict(test_ctrl)
test_error_rate= np.sqrt(np.mean((pred_test - test_output)**2))
#get function name as title
title = inspect.stack()[0][3]
print "#### {} ## train_error:{},test_error:{}".format(title,esn.train_error_rate,test_error_rate)
return test_error_rate
