Daily Data Science Puzzle
import numpy as np n = 100 # dimensionality W = np.zeros((n,n)) for i in range(len(W)): W[i][i] = 2 X = np.ones((n,n)) Y = W * X print(int(Y[-1][-1]))
What is the output of this puzzle?
Numpy is a popular Python library for data science focusing on linear algebra. When working with numpy, you must be fluent with matrix operations (e.g. multiplication).
This puzzle performs a simple linear regression calculation. It tests your understanding of three numpy concepts.
First, you can specify the shape of the numpy array as a tuple (n,m) where n is the number of rows and m the number of columns.
Second, you can create new numpy arrays of a specified shape using the functions
zeros(). The initial values of such a numpy array are 1s and 0s, respectively.
Third, you can do matrix multiplication using the intuitive multiplication operator ‘*’. Each cell (i,j) of the new matrix is the product of the row vector i of the first matrix with the column vector j of the second matrix.
As a result, we print the last element of the two-dimensional matrix Y (bottom-right).
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