olpy/classifiers/pa.py
def _get_gamma(self, loss, s_t):
        """Computes the coefficient used to update the weight vector.

        Args:
            loss(:obj:`float`): Loss incurred on the current instance.
            s_t (:obj:`float`): the L2-norm of the vector representing the
                current instance.

        Returns:
            float: the value of gamma to be used.
        """
        return loss / s_t if s_t > 0 else 1
Computes the coefficient used to update the weight vector.

        Args:
            loss(:obj:`float`): Loss incurred on the current instance.
            s_t (:obj:`float`): the L2-norm of the vector representing the
                current instance.

        Returns:
            float: the value of gamma to be used.
        
Computes the coefficient used to update the weight vector.
Computes the coefficient used to update the weight vector .
def _get_gamma(self, loss, s_t):
        return loss / s_t if s_t > 0 else 1
def _get_gamma ( self , loss , s_t ) : return loss / s_t if s_t > 0 else 1
Computes the coefficient used to update the weight vector.
Computes the coefficient used to update the weight vector .
"""Computes the coefficient used to update the weight vector.

        Args:
            loss(:obj:`float`): Loss incurred on the current instance.
            s_t (:obj:`float`): the L2-norm of the vector representing the
                current instance.

        Returns:
            float: the value of gamma to be used.
        """
the value of gamma to be used.
the value of gamma to be used .
float
self
loss
s_t
loss(
`float`): Loss incurred on the current instance.
` float ` ) : Loss incurred on the current instance .
s_t (
`float`): the L2-norm of the vector representing the
current instance.
` float ` ) : the L2 - norm of the vector representing the current instance .