You declare the variable v as a pointer to struct node, but you don't initialize this pointer. Uninitialized local (non-static) variables have an indeterminate value. Using an uninitialized local (non-static) variable leads to undefined behavior, which is a very common reason for crashes. Modern compilers are good at detecting the...

java,algorithm,search,nearest-neighbor,kdtree

A correct implementation of a KD-tree always finds the closest point(it doesn't matter if points are stored in leaves only or not). Your search method is not correct, though. Here is how it should look like: bestDistance = INF def getClosest(node, point) if node is null return // I will...

numpy,computational-geometry,sparse-matrix,kdtree,sparse-array

Following up on Yves' suggestion, here's an answer, which uses scipy's KDTree: from scipy.spatial.kdtree import KDTree import numpy as np def locally_extreme_points(coords, data, neighbourhood, lookfor = 'max', p_norm = 2.): ''' Find local maxima of points in a pointcloud. Ties result in both points passing through the filter. Not to...

android,opencv,kdtree,orb,opencv4android

I am currently developing a similar application. I would recommend getting something working with a single reference image first for a couple of reasons: It's easier to do and understand if you're just starting out, and you can change it later. For android applications you have limited processing capabilities so...

This is a bug in the docstring. The argument to KDTree must be "array_like", but in Python 3, the object returned by zip is not "array_like". You can change the example to tree = spatial.KDTree(list(zip(x.ravel(), y.ravel()))) or, instead of using zip to create the input to KDTree, you can use,...

database,algorithm,data,spatial,kdtree

I'm assuming your grids are all exactly the same size, and are arranged in a perfect rectangle, as indicated by your image. How about storing all grids in a simple 2D array? You can then find the index of any grid by doing grid_size = 30; index_x = math.floor(user.x/grid_size); index_y...

If you are looking for all points close within a distance of a single point, use scipy.spatial.KDTree.query_ball_point not query_ball_tree. The latter when you need to compare sets of points against each other. import numpy as np from scipy.spatial import KDTree pts = np.array([(1, 1), (2, 1), (3, 1), (4, 1),...

c++,algorithm,sorting,c++11,kdtree

Do you really need the nth element, or do you need an element "near" the middle? There are faster ways to get an element "near" the middle. One example goes roughly like: function rough_middle(container) divide container into subsequences of length 5 find median of each subsequence of length 5 ~...