def compair_readout():
esn = ESN(n_inputs = 8,
n_outputs = 1,
n_reservoir = n_reservoir,
spectral_radius = spectral_radius,
sparsity = sparsity,
noise = noise,
input_shift = [0.51293657,0.51293657,0.51293657,0.51293657,0.03489584,0.03489584,0.03489584,0.03489584],
input_scaling = [0.18636639,0.18636639,0.18636639,0.18636639, 0.11791364, 0.11791364, 0.11791364, 0.11791364],
teacher_scaling = 1.45377531,
teacher_shift = -0.7997228,
out_activation = np.tanh,
inverse_out_activation = np.arctanh,
random_state = rng,
silent = False)
esn = ESN(n_inputs = 8,
n_outputs = 1,
n_reservoir = n_reservoir,
spectral_radius = spectral_radius,
sparsity = sparsity,
noise = noise,
input_shift = [0,0,0,0,0,0,0,0],
input_scaling = [0.01,0.01,0.01,0.01,0.01,0.01,3,3],
teacher_scaling = 1.12,
teacher_shift = -0.7,
out_activation = np.tanh,
inverse_out_activation = np.arctanh,
random_state = rng,
silent = False)
esn.penal_tao =0.57201544
esn.penal_c0 = 3.76108161
esn.penal_tao = 0.1
esn.penal_c0 = 3.7
#pred_train = esn.fit(train_ctrl,train_output,inspect=True)
internal_states,transient = esn.train_reservior(train_ctrl,train_output)
# esn_Lasso = copy.deepcopy(esn)
# esn_Ridge = copy.deepcopy(esn)
# esn_ElasticNet = copy.deepcopy(esn)
esn_SCAD = copy.deepcopy(esn)
esn.dump_parameters()
# print "####pin"
# pred_train = esn.train_readout_with_pin(internal_states,train_output,transient)
# test_error("pinv",esn,pred_train)
# exit()
#
# print "####ridge"
# pred_train = esn_Ridge.train_readout_with_ridge(internal_states,train_output,transient)
# test_error("pinv",esn_Ridge,pred_train)
#
# print "####Lasso"
# pred_train = esn_Lasso.train_readout_with_lasso(internal_states,train_output,transient)
# test_error("pinv",esn_Lasso,pred_train)
#
# print "####ElasticNet"
# pred_train = esn_ElasticNet.train_readout_with_elasticnet(internal_states,train_output,transient)
# test_error("pinv",esn_ElasticNet,pred_train)
print "####SCAD"
pred_train = esn_SCAD.train_readout_with_scad(internal_states,train_output,transient)
test_error("pinv",esn_SCAD,pred_train)
