3972b4bd9a11a86d7d5fcb2dbb1b81a81eba5e37,GPy/kern/_src/linear.py,Linear,update_gradients_full,#Linear#Any#Any#Any#,76

Before Change

            if X2 is None:
                self.variances.gradient = np.array([np.sum(dL_dK * tdot(X[:, i:i + 1])) for i in range(self.input_dim)])
            else:
                product = X[:, None, :] * X2[None, :, :]
                self.variances.gradient = (dL_dK[:, :, None] * product).sum().sum(0)
        else:
            self.variances.gradient = np.sum(self._dot_product(X, X2) * dL_dK)

After Change

        if self.ARD:
            if X2 is None:
                //self.variances.gradient = np.array([np.sum(dL_dK * tdot(X[:, i:i + 1])) for i in range(self.input_dim)])
                self.variances.gradient = np.einsum("ij,iq,jq->q", dL_dK, X, X)
            else:
                //product = X[:, None, :] * X2[None, :, :]
                //self.variances.gradient = (dL_dK[:, :, None] * product).sum(0).sum(0)
                self.variances.gradient = np.einsum("ij,iq,jq->q", dL_dK, X, X2)

Instances