KD-Tree for nearest neighbors in SciPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KDTree class from scipy.spatial.

SciPy

from scipy.spatial import [1]

points = [[1, 2], [3, 4], [5, 6]]
tree = KDTree(points)

Drag options to blanks, or click blank then click option'

AKD

Bkdtree

CKD_tree

DKDTree

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase letters like 'kdtree' instead of 'KDTree'.

Misspelling the class name with underscores.

The correct class name to import is KDTree with capital K, D, and T.

2fill in blank

medium

Complete the code to build a KDTree from a list of points.

SciPy

points = [[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]]
tree = [1](points)

Drag options to blanks, or click blank then click option'

AKDTree

Bkdtree

CTreeKD

DKD_tree

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase or misspelled class names.

Trying to call a method instead of the class.

To build the tree, call KDTree(points).

3fill in blank

hard

Fix the error in the code to query the nearest neighbor of a point using the KDTree.

SciPy

query_point = [3, 4]
distance, index = tree.[1](query_point)
print(f"Nearest point index: {index}, distance: {distance}")

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Aquery_point

Bquery

Cnearest

Dsearch

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like 'nearest' or 'search'.

Trying to call 'query_point' which is a variable, not a method.

The correct method to find nearest neighbors is query.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each point to its distance from the query point using the KDTree.

SciPy

distances = {tuple(point): tree.[1](point)[[2]] for point in points}

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Aquery

B0

C1

Dnearest

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong method names.

Accessing the wrong tuple index for distance.

Use query to get (distance, index), and index 0 gives the distance.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each point's index to the point itself if its distance from the query point is less than 5.

SciPy

close_points = {i: points[i] for i in range(len(points)) if tree.query(points[i])[[1]] [2] [3]

Drag options to blanks, or click blank then click option'

A0

B<

C5

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong tuple index for distance.

Using wrong comparison operator or value.

Use index 0 to get distance, then check if distance is less than 5.