I am fitting a Gaussian kernel density estimator to a variable that is the difference of two vectors, called "diff", as follows: gaussian_kde_covfact(diff, smoothing_param) -- where gaussian_kde_covfact is defined as:

class gaussian_kde_covfact(stats.gaussian_kde):
    def __init__(self, dataset, covfact = 'scotts'):
        self.covfact = covfact
        scipy.stats.gaussian_kde.__init__(self, dataset)

    def _compute_covariance_(self):
        '''not used'''
        self.inv_cov = np.linalg.inv(self.covariance)
        self._norm_factor = sqrt(np.linalg.det(2*np.pi*self.covariance)) * self.n

    def covariance_factor(self):
        if self.covfact in ['sc', 'scotts']:
            return self.scotts_factor()
        if self.covfact in ['si', 'silverman']:
            return self.silverman_factor()
        elif self.covfact:
            return float(self.covfact)
        else:
            raise ValueError, \
                'covariance factor has to be scotts, silverman or a number'

    def reset_covfact(self, covfact):
        self.covfact = covfact
        self.covariance_factor()
        self._compute_covariance()

This works, but there is an edge case where the diff is a vector of all 0s. In that case, I get the error:

 File "/srv/pkg/python/python-packages/python26/scipy/scipy-0.7.1/lib/python2.6/site-packages/scipy/stats/kde.py", line 334, in _compute_covariance
    self.inv_cov = linalg.inv(self.covariance)
  File "/srv/pkg/python/python-packages/python26/scipy/scipy-0.7.1/lib/python2.6/site-packages/scipy/linalg/basic.py", line 382, in inv
    if info>0: raise LinAlgError, "singular matrix"
numpy.linalg.linalg.LinAlgError: singular matrix

What's a way to get around this? In this case, I'd like it to return a density that's essentially peaked completely at a difference of 0, with no mass everywhere else.

thanks.