def rbf(self, data): self.gene_index += 2 return (gauss(data, (self.genes[self.gene_index - 2] + 1) / 2, (self.genes[self.gene_index - 1] + 1) / 2) - 0.5) * 2
def rbf(self, data): self.gene_index += 3 return self.genes[self.gene_index-3] * gauss(data, (self.genes[self.gene_index - 2] + 1) / 2, (self.genes[self.gene_index - 1] + 1) / 2)
def rbf(self, data):
self.gene_index += 3
return self.genes[self.gene_index - 3] * gauss(
data, (self.genes[self.gene_index - 2] + 1) / 2,
(self.genes[self.gene_index - 1] + 1) / 2)
#--- histograma
hc, hx_ = np.histogram(ne, bins=50, density=True)
hx = 0.5*(hx_[:-1] + hx_[1:]) # `hx` son los valores centrados de c/bin
#--- parametros de la red real
#N_e = ff.count_essential_nodes(fname_graph, fname_ess) # nodos esenciales
N_ie = ff.calc_ne(g=graph, fname_ess=fname_ess) # interacc esenciales
alpha_mean = (N_ie - fit_mu)/n_nodes
alpha_err = fit_sigma/n_nodes
fig = figure(1, figsize=(6,4))
ax = fig.add_subplot(111)
ax.plot(hx, hc, '-ob', label='realizaciones')
ax.plot(hx, ff.gauss(hx, 1., fit_mu, fit_sigma), '-r', lw=3, alpha=0.6, label='ajuste')
ax.axvline(N_ie, ls='--', c='black', lw=3, label='real')
ax.grid(True)
ax.legend(loc='best')
ax.set_xlabel('numero de interacciones entre proteinas esenciales')
ax.set_ylabel('densidad de probabilidad')
fig.savefig(pa.fname_fig, format='png', dpi=200, bbox_inches='tight')
close(fig)
print('alpha: %3.2f +- %3.2f'%(100.*alpha_mean, 100.*alpha_err))
#EOF
