Solution:Step 1: Correct COO matrix creationcoo_matrix() requires a tuple (data, (row, col)) and optionally a shape.Step 2: Convert to dense arrayUse .toarray() method to get a dense numpy ndarray.Final Answer:matrix = coo_matrix((data, (row, col)), shape=(4, 4)); dense = matrix.toarray() -> Option

[Solved] Given:data = [11, 22, 33, 44]row = [0, 2, 2, 3]col = [1, 0, 2, 3]Which code snippet correctly creates a COO matrix and converts it to a dense numpy array? | SciPy

SciPy - Sparse Matrices (scipy.sparse)

Given:
data = [11, 22, 33, 44]
row = [0, 2, 2, 3]
col = [1, 0, 2, 3]
Which code snippet correctly creates a COO matrix and converts it to a dense numpy array?

Amatrix = coo_matrix((data, (row, col)), shape=(4, 4)); dense = matrix.toarray()

Bmatrix = coo_matrix(data, row, col); dense = matrix.todense()

Cmatrix = coo_matrix((row, col, data)); dense = matrix.toarray()

Dmatrix = coo_matrix((data, (row, col))); dense = matrix.to_list()

Step-by-Step Solution

Solution:

Step 1: Correct COO matrix creation
coo_matrix() requires a tuple (data, (row, col)) and optionally a shape.
Step 2: Convert to dense array
Use .toarray() method to get a dense numpy ndarray.
Final Answer:
matrix = coo_matrix((data, (row, col)), shape=(4, 4)); dense = matrix.toarray() -> Option A
Quick Check:
Use coo_matrix((data,(row,col))) and .toarray() [OK]

Quick Trick: Use coo_matrix((data,(row,col))) and .toarray() [OK]

Common Mistakes:

MISTAKES

Passing arguments incorrectly to coo_matrix
Using .todense() instead of .toarray()
Calling non-existent methods like to_list()

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Given:

Step 1: Correct COO matrix creation

Step 2: Convert to dense array

Final Answer:

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