What is Convex Hull and KDTrees in Scipy explain

This recipe explains what is Convex Hull and KDTrees in Scipy explain

Recipe Objective - What is Convex Hull and KDTrees in Scipy explain?

Convex Hull:

The Convex Hull is a polygon which covers all the given point and Convex Hull is the smallest polygon.

SciPy provides a method "scipy.spatial.ConvexHull()" to create a Convex Hull.

Creating a Convex Hull:

import numpy as np
from scipy.spatial import ConvexHull

points = np.array([
[0, 2],
[1, 3],
[0, 3],
[1, 1],
[3, 0],
[2, 3],
[4, 1],
[3, 5],
[0, 1],
[2, 0]
])

hull = ConvexHull(points)
hull_points = hull.simplices

Visualization:

import matplotlib.pyplot as plt

plt.scatter(points[:,0], points[:,1])
for simplex in hull_points:
plt.plot(points[simplex,0], points[simplex,1], 'k-')

plt.show()

KDTrees:

KDTrees is used for the quick nearest-neighbor lookup at specific points.

SciPy provides a method "scipy.spatial.KDTree()" to look up the nearest neighbors of any point.

query() method returns the distance and the location of the neighbour.

Find the nearest neighbor to point (2,2):

from scipy.spatial import KDTree

points = [(1, 2), (-2, 4), (2, -1), (-2, 3)]

kd_tree = KDTree(points)

rslt = kd_tree.query((2, 2))

print(rslt)

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