Exemple #1
0
def compute_distr_ns(states, W, b_v, b_h, steps=10, T=t_sim*1000):
    nT = int(T)
    distr = kl_tools.states2distr(states[:nT],N_v+N_c+N_h)+1
    distr/= distr.sum()
    res_distr_cut = np.zeros(steps)
    nTi = np.logspace(2,np.log10(nT),10,base=10)
    for i in range(steps):
        d  = kl_tools.states2distr(states[ :nTi[i] ], N_v+N_c+N_h)+1
        d /= d.sum()
        res_distr_cut[i] = kl_divergence_pdf(d, W, b_v, b_h)/entropy(d)

    return distr, nTi, res_distr_cut
Exemple #2
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def wrap_run(runID = 0):
    if isinstance(runID, int):
        r, W, b_v, b_h = run_NS()
    elif isinstance(runID, dict):
        r, W, b_v, b_h = run_NS(runID)

    states_ns = []
    for rr in r: 
        states = states_NS(rr['Mv'], rr['Mh'], t_conv = 1.0)
        distr = kl_tools.states2distr(states,N_v+N_c+N_h)+1
        states_ns.append(states)


    return states_ns, W, b_v, b_h