featuremapper/distribution.py
def _vector_selectivity(self, d):
        """
        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.
        """
        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.
        
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 , 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.
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 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):
        return d._safe_divide(d.vector_sum()[0], sum(d.values()))
def _vector_selectivity ( self , d ) : return d . _safe_divide ( d . vector_sum ( ) [ 0 ] , sum ( d . values ( ) ) )
Return the magnitude of the vector_sum() divided by the sum_value().
Return the magnitude of the vector_sum () divided by the sum_value () .
"""
        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.
        """
self
d