Broadcasting for distance matrices in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 2D array of points using NumPy.

NumPy

import numpy as np
points = np.array([1])

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

A[[1, 2], [3, 4], [5, 6]]

B[(1, 2), (3, 4), (5, 6)]

C[1, 2, 3, 4]

D[1, 2]

Attempts:

3 left

💡 Hint

Common Mistakes

Using a flat list instead of a list of lists.

Using tuples inside the list without nesting.

We use a list of lists to create a 2D NumPy array representing points with x and y coordinates.

2fill in blank

medium

Complete the code to calculate the difference between each point and all points using broadcasting.

NumPy

diff = points[:, np.newaxis, :] - [1]

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Apoints

Bnp.array(points)

Cpoints.T

Dnp.newaxis

Attempts:

3 left

💡 Hint

Common Mistakes

Using transpose which changes shape incorrectly.

Using np.newaxis alone without the array.

Subtracting the array from itself with proper shape expansion allows broadcasting to compute pairwise differences.

3fill in blank

hard

Fix the error in the code to compute the squared distances between points.

NumPy

sq_dist = np.sum(diff[1]2, axis=2)

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A//

B*

C**

D^

Attempts:

3 left

💡 Hint

Common Mistakes

Using '*' instead of '**' for squaring.

Summing along the wrong axis.

Use the exponent operator '**' to square the differences and sum along the last axis (axis=2) to get squared distances.

4fill in blank

hard

Complete the code to compute the Euclidean distance matrix from squared distances.

NumPy

dist_matrix = np.sqrt(sq_dist) + [1]

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Bnp.zeros_like(sq_dist)

Cnp.ones_like(sq_dist)

Dnp.abs(sq_dist)

Attempts:

3 left

💡 Hint

Common Mistakes

Adding ones or absolute values which change the distances.

Adding unnecessary terms that distort results.

We take the square root of squared distances directly. Adding zeros of the same shape does not change the result but can be used to ensure array type consistency.

5fill in blank

hard

Fill all three blanks to create a dictionary of points with their distances greater than 2.

NumPy

dist_dict = {tuple(points[i]): dist_matrix[i, j] for i in range(len(points)) for j in range(len(points)) if dist_matrix[i, j] [1] 2 and i != j and tuple(points[j]) [2] dist_dict and dist_dict[tuple(points[j])] [3] dist_matrix[i, j]}

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

A>

Bin

C<

Dnot in

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for distance comparison.

Checking 'in' instead of 'not in' for dictionary keys.

We check if distance is greater than 2, ensure the point is not already in the dictionary, and update the dictionary with distances greater than the current stored value.