def violin_plots_confidence(n_generations, n_agents, bottleneck,
length_inputs):
"""
"""
# FIX: it works for any number of agents and generations
# but doesn't show the difference between generations and agents with colours
data = iteration.iterate(n_generations, n_agents, bottleneck,
length_inputs)
plt.violinplot(data.as_matrix())
plt.title("Bottleneck: {}".format(bottleneck))
plt.xlabel("Generations")
plt.ylabel("Confidence for all inputs")
plt.show()
cpt_idle = 0
i_snap = 0
d12 = 0
duration = 0
coact_pos = np.zeros((p, p))
coact_neg = coact_pos.copy()
r_i_k, r_i_S_A, r_i_S_B, sig_i_k, m_mu, dt_r_i_k_act, dt_r_i_S_A, \
dt_r_i_S_B, theta_i_k, dt_theta_i_k, h_i_k = initialisation.network(
J_i_j_k_l, delta__ksi_i_mu__k, g_A, w, cue_mask)
for iT in tqdm(range(nT)):
iteration.iterate(J_i_j_k_l, delta__ksi_i_mu__k, tS[iT],
analyseTime, analyseDivergence, sig_i_k, r_i_k,
r_i_S_A, r_i_S_B, theta_i_k, h_i_k, m_mu,
dt_r_i_S_A, dt_r_i_S_B, dt_r_i_k_act,
dt_theta_i_k, cue, t_0, g_A, w, cue_mask)
# Saving data for plots
# if tS[iT] > t_0+tau_1:
# print((np.outer(m_mu, m_mu)).shape)
# coact = np.outer(m_mu, m_mu)
# tmp_ind = coact > coact_pos
# coact_pos[tmp_ind] = coact[tmp_ind]
# tmp_ind = coact < coact_neg
# coact_neg[tmp_ind] = coact[tmp_ind]
duration = tS[iT]-t_0
retrieved_pattern = np.argmax(m_mu)