def funcO5(self, val):
Pared_val = np.hstack(
(self.Data_Grid[:, 2].reshape(-1, 1), val.reshape(-1, 1)))
bivariate_distribution_s = cond.ot_copula_fit(Pared_val)
conditioned_dist_t = cond.ot_compute_conditional(
val, self.bivariate_distribution_data, self.Data_Grid)
conditioned_dist_s = cond.ot_compute_conditional(
val, bivariate_distribution_s, self.Data_Grid)
O5 = np.sum((conditioned_dist_s - conditioned_dist_t)**2)
return O5
print("Bivariate Distributions from DATA: ")
print("----------------------------------------------------------------------")
print("Marginal for Ip(data_grid) =", marginal_data[0])
print("Marginal for Phit(Var) =", marginal_data[1])
print("Copula for marginals =", copula_data)
print("----------------------------------------------------------------------")
print("Simulate Bivariate Distributions information: ")
print("----------------------------------------------------------------------")
print("Estimate marginal for Ip(data_grid) =", marginal_s[0])
print("Estimate marginal for Phit(Var) =", marginal_s[1])
print("Estimate copula for marginals =", copula_s)
print("----------------------------------------------------------------------")
#Conditional_PDF#
conditioned_pdf_s = cond.ot_compute_conditional(Phits,
bivariate_distribution_s,
Data_Grid)
conditioned_pdf = cond.ot_compute_conditional(Phits,
bivariate_distribution_data,
Data_Grid)
graf.Scater(Phits, conditioned_pdf_s, conditioned_pdf, limit_x=limit_Phit)
#Conditional_CDF#
conditioned_cdf_s = cond.ot_compute_conditional_cdf(Phits,
bivariate_distribution_s,
Data_Grid)
conditioned_cdf = cond.ot_compute_conditional_cdf(Phits,
bivariate_distribution_data,
Data_Grid)
graf.Scater(Phits,
conditioned_cdf_s,