def compute_gradient(self, k, indices=None):
        grad = np.zeros(1 + self.K * self.B)

        if indices is None:
            indices = np.arange(len(self.data_list))

        # d_W_d_log_W = self._d_W_d_logW(k)
        for index in indices:
            d_rate_d_W = self._d_rate_d_W(index, k)
            # d_rate_d_log_W = d_rate_d_W.dot(d_W_d_log_W)
            d_ll_d_rate = self._d_ll_d_rate(index, k)
            # d_ll_d_log_W = d_ll_d_rate.dot(d_rate_d_log_W)
            d_ll_d_W = d_ll_d_rate.dot(d_rate_d_W)

            # grad += d_ll_d_log_W
            grad += d_ll_d_W

        # Add the prior
        # d_log_prior_d_log_W = self._d_log_prior_d_log_W(k)
        # grad += d_log_prior_d_log_W

        d_log_prior_d_W = self._d_log_prior_d_W(k)
        assert np.allclose(d_log_prior_d_W[0], 0.0)
        # grad += d_log_prior_d_W.dot(d_W_d_log_W)
        grad += d_log_prior_d_W

        # Zero out the gradient if
        if not self.allow_self_connections:
            assert np.allclose(self.weights[k,1+k*self.B:1+(k+1)*self.B], 0.0)
            grad[1+k*self.B:1+(k+1)*self.B] = 0

        return grad
Compute the gradient of the log likelihood with respect
dataset/ETHPy150Open slinderman/pyhawkes/pyhawkes/models.py/DiscreteTimeStandardHawkesModel.compute_gradient