cobra/sampling/hr_sampler.py
def _bounds_dist(self, p):
        """Get the lower and upper bound distances. Negative is bad."""

        prob = self.problem
        lb_dist = (p - prob.variable_bounds[0, ]).min()
        ub_dist = (prob.variable_bounds[1, ] - p).min()

        if prob.bounds.shape[0] > 0:
            const = prob.inequalities.dot(p)
            const_lb_dist = (const - prob.bounds[0, ]).min()
            const_ub_dist = (prob.bounds[1, ] - const).min()
            lb_dist = min(lb_dist, const_lb_dist)
            ub_dist = min(ub_dist, const_ub_dist)

        return np.array([lb_dist, ub_dist])
def _bounds_dist(self, p):
        """Get the lower and upper bound distances. Negative is bad."""

        prob = self.problem
        lb_dist = (p - prob.variable_bounds[0, ]).min()
        ub_dist = (prob.variable_bounds[1, ] - p).min()

        if prob.bounds.shape[0] > 0:
            const = prob.inequalities.dot(p)
            const_lb_dist = (const - prob.bounds[0, ]).min()
            const_ub_dist = (prob.bounds[1, ] - const).min()
            lb_dist = min(lb_dist, const_lb_dist)
            ub_dist = min(ub_dist, const_ub_dist)

        return np.array([lb_dist, ub_dist])
def _bounds_dist ( self , p ) : prob = self . problem lb_dist = ( p - prob . variable_bounds [ 0 , ] ) . min ( ) ub_dist = ( prob . variable_bounds [ 1 , ] - p ) . min ( ) if prob . bounds . shape [ 0 ] > 0 : const = prob . inequalities . dot ( p ) const_lb_dist = ( const - prob . bounds [ 0 , ] ) . min ( ) const_ub_dist = ( prob . bounds [ 1 , ] - const ) . min ( ) lb_dist = min ( lb_dist , const_lb_dist ) ub_dist = min ( ub_dist , const_ub_dist ) return np . array ( [ lb_dist , ub_dist ] )
Get the lower and upper bound distances. Negative is bad.
Get the lower and upper bound distances . Negative is bad .