signal_models.py 文件源码

def score(self,Y,type="loglikelihood",method="BIC"):

        if type=="loglikelihood":
            score=log_likelihood=self.compute_loglikelihood(Y)

        if type=="penalized_loglikelihood":
            N=np.prod(Y.shape)
            if np.iscomplexobj(Y)==True:
                N=N*2
            log_likelihood=self.compute_loglikelihood(Y)
            nb_free_parameters=self.get_nb_free_parameters()
            penalty_term=penalty_term_IC(nb_free_parameters,N,method=method)
            score=-2*log_likelihood+penalty_term
        return score

    ## method for plotting