2eef5768774b21335f214e40289d2dd20dfb33c9,GPy/likelihoods/weibull.py,Weibull,d3logpdf_dlink3,#Weibull#Any#Any#Any#,127

Before Change

        :rtype: Nx1 array
        
        // d3lik_dlink3 = (1. - self.beta) / (y - link_f)**3
        d3lik_dlink3 = (y ** self.r) * np.exp(-link_f)
        return d3lik_dlink3

    def exact_inference_gradients(self, dL_dKdiag,Y_metadata=None):
        return np.zeros(self.size)

After Change

        
        // d3lik_dlink3 = (1. - self.beta) / (y - link_f)**3

        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)*(-2/link_f**3+ 6*y**self.r/link_f**4)
        censored = c*6*y**self.r/link_f**4

        d3lik_dlink3 = uncensored + censored
        // d3lik_dlink3 = (y ** self.r) * np.exp(-link_f)
        return d3lik_dlink3

    def exact_inference_gradients(self, dL_dKdiag, Y_metadata=None):

Instances