2eef5768774b21335f214e40289d2dd20dfb33c9,GPy/likelihoods/weibull.py,Weibull,d2logpdf_dlink2,#Weibull#Any#Any#Any#,101

Before Change

            (the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
        
        // hess = (self.beta - 1.) / (y - link_f)**2
        hess = -(y ** self.r) * np.exp(-link_f)
        return hess

    def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):
        

After Change

            (the distribution for y_i depends only on link(f_i) not on link(f_(j!=i))
        
        // hess = (self.beta - 1.) / (y - link_f)**2
        c = np.zeros_like(y)
        if Y_metadata is not None and "censored" in Y_metadata.keys():
            c = Y_metadata["censored"]

        // uncensored = (1-c)* (-(y ** self.r) * np.exp(-link_f))
        // censored = -c*np.exp(-link_f)*y**self.r
        uncensored = (1-c)*(1/link_f**2 -2*y**self.r/link_f**3)
        censored = -c*2*y**self.r/link_f**3
        hess = uncensored + censored
        // hess = -(y ** self.r) * np.exp(-link_f)
        return hess

    def d3logpdf_dlink3(self, link_f, y, Y_metadata=None):

Instances