769299614ec4d02d1421aa562fc8b86c6386fc06,scipy/spatial/kdtree.py,KDTree,__query,#KDTree#Any#Any#Any#Any#Any#,193

Before Change

                a = np.abs(data-x[np.newaxis,:])
                if p==np.inf:
                    ds = np.amax(a,axis=1)
                elif p==1:
                    ds = np.sum(a,axis=1)
                else:
                    ds = np.sum(a**p,axis=1)
                for i in range(len(ds)):
                    if ds[i]<distance_upper_bound:
                        if len(neighbors)==k:
                            heappop(neighbors)

After Change

    def __query(self, x, k=1, eps=0, p=2, distance_upper_bound=np.inf):
        
        side_distances = np.maximum(0,np.maximum(x-self.maxes,self.mins-x))
        if p!=np.inf:
            side_distances**=p
            min_distance = np.sum(side_distances)
        else:
            min_distance = np.amax(side_distances)

        // priority queue for chasing nodes

Instances