def _vector_selectivity(self, d):
    '\n        Return the magnitude of the vector_sum() divided by the sum_value().\n\n        This quantity is a vector-based measure of the peakedness of\n        the distribution.  If only a single feature_bin has a non-zero value(),\n        the selectivity will be 1.0, and if all bins have the same\n        value() then the selectivity will be 0.0.  Other distributions\n        will result in intermediate values.\n\n        For a distribution with a sum_value() of zero (i.e. all bins\n        empty), the selectivity is undefined.  Assuming that one will\n        usually be looking for high selectivity, we return zero in such\n        a case so that high selectivity will not mistakenly be claimed.\n        To find out whether such cases occurred, you can compare the\n        value of undefined_values() before and after a series of\n        calls to this function.\n        '
    return d._safe_divide(d.vector_sum()[0], sum(d.values()))
Return the magnitude of the vector_sum() divided by the sum_value().

This quantity is a vector-based measure of the peakedness of
the distribution.  If only a single feature_bin has a non-zero value(),
the selectivity will be 1.0, and if all bins have the same
value() then the selectivity will be 0.0.  Other distributions
will result in intermediate values.

For a distribution with a sum_value() of zero (i.e. all bins
empty), the selectivity is undefined.  Assuming that one will
usually be looking for high selectivity, we return zero in such
a case so that high selectivity will not mistakenly be claimed.
To find out whether such cases occurred, you can compare the
value of undefined_values() before and after a series of
calls to this function.
def _vector_selectivity(self, d):
    '\n        Return the magnitude of the vector_sum() divided by the sum_value().\n\n        This quantity is a vector-based measure of the peakedness of\n        the distribution.  If only a single feature_bin has a non-zero value(),\n        the selectivity will be 1.0, and if all bins have the same\n        value() then the selectivity will be 0.0.  Other distributions\n        will result in intermediate values.\n\n        For a distribution with a sum_value() of zero (i.e. all bins\n        empty), the selectivity is undefined.  Assuming that one will\n        usually be looking for high selectivity, we return zero in such\n        a case so that high selectivity will not mistakenly be claimed.\n        To find out whether such cases occurred, you can compare the\n        value of undefined_values() before and after a series of\n        calls to this function.\n        '
    return d._safe_divide(d.vector_sum()[0], sum(d.values()))