def _logDerivsFactorSigma(self, samples, mu, invSigma, factorSigma):
        """ Compute the log-derivatives w.r.t. the factorized covariance matrix components.
        This implementation should be faster than the one in Vanilla. """
        res = zeros((len(samples), self.numDistrParams - self.numParameters))
        invA = inv(factorSigma)
        diagInvA = diag(diag(invA))

        for i, sample in enumerate(samples):
            s = dot(invA.T, (sample - mu))
            R = outer(s, dot(invA, s)) + diagInvA
            res[i] = triu2flat(R)
        return res
dataset/ETHPy150Open pybrain/pybrain/pybrain/optimization/distributionbased/nes.py/OriginalNES._logDerivsFactorSigma/operator `-` -> `+`