the point queried is one of data items used to build the tree) the second nearest neighbour is taken rather than the first. This will return the two nearest neighbours, which you could apply further logic to such that cases where the distance returned is zero (i.e. BFGS, Nelder-Mead simplex, Newton Conjugate Gradient, COBYLA or SLSQP. Unconstrained and constrained minimization of multivariate scalar functions (minimize ()) using a variety of algorithms (e.g. This module contains the following aspects. The function has lots of other options, so if for example you wanted to make sure to get the nearest neighbour that isn't itself try: tree.query(, k=2) The scipy.optimize package provides several commonly used optimization algorithms. This means that is the first row of your original data in array, which is expected given your data is y = x on the range. So in this case when you query for the nearest to you are getting: distance to nearest: 0.0 The locations of the neighbors in self.data. In either case the hits are sorted by distance Then d is an object array of shape tuple, containing lists Neighbors are indicated with infinite distances. Primarily designed (fpfp, channels3) Create a boolean mask as a numpy array from the shapely polygon mask np. K is one, or tuple+(k,) if k is larger than one. If x has shape tuple+(self.m,), then d has shape tuple if In this case I think you have confused the return from your tree.query(.) call.
I have used scipy.spatial before, and it appears to be a nice improvement (especially wrt the interface) as compared to scikits.ann.
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