def norm_resid(self):
        """
        Residuals, normalized to have unit length and unit variance.

        Returns
        -------
        An array wresid/sqrt(scale)

        Notes
        -----
        This method is untested
        """
        if not hasattr(self, 'resid'):
            raise ValueError('need normalized residuals to estimate standard\
 deviation')
        return self.wresid * recipr(np.sqrt(self.scale))
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    def norm_resid(self):
        """
        Residuals, normalized to have unit length and unit variance.

        Returns
        -------
        An array wresid/sqrt(scale)

        Notes
        -----
        This method is untested
        """
        if not hasattr(self, 'resid'):
            raise ValueError('need normalized residuals to estimate standard\
 deviation')
        return self.wresid * recipr(np.sqrt(self.scale))
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 def test_recipr(self):
     X = np.array([[2,1],[-1,0]])
     Y = tools.recipr(X)
     assert_almost_equal(Y, np.array([[0.5,1],[0,0]]))
Esempio n. 4
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 def test_recipr(self):
     X = np.array([[2, 1], [-1, 0]])
     Y = tools.recipr(X)
     assert_almost_equal(Y, np.array([[0.5, 1], [0, 0]]))