EvaluationTools.py 文件源码

def glassoBonaFidePartial(gl,X,TrueCov):
    #take a 
    ep=EmpiricalCovariance().fit(X)
    emp_cov=ep.covariance_
    _,precs=graph_lasso_path(X, gl.cv_alphas_)
    best_score = -np.inf
    best_ind=0
    for i in xrange(len(gl.cv_alphas_)):
        try:
            this_score = log_likelihood(TrueCov, precs[i])
            if this_score >= .1 / np.finfo(np.float64).eps:
                this_score = np.nan
            if(this_score>best_score):
                best_score=this_score
                best_ind=i
        except:
            print 'exited:',best_score
            continue
    covariance_, precision_, n_iter_ = graph_lasso(
            emp_cov, alpha=gl.cv_alphas_[best_ind], mode=gl.mode, tol=gl.tol*5., max_iter=gl.max_iter, return_n_iter=True)
    return np.abs(toPartialCorr(precision_))