Python broadcasting. Rewrite the following code without for-loops using vectorization and python broadcasting. (a) Given a data matrix X and vector beta compute a vector yhat: n = X. shape [0] yhat = np.zeros(n) for i in range(n): yhat [i] = beta[0]*X[i,0] + beta[1]*X[i,1] + beta [2]*X[i,1] *X[i,2] yhat beta[0]*X[: ,0] + beta[1]*X[:,1] + beta[2]*X[:,1]*X[:,2]

Python broadcasting. Rewrite the following code without for-loops using vectorization and python broadcasting. (a) Given a data matrix X and vector beta compute a vector yhat: n = X. shape [0] yhat = np.zeros(n) for i in range(n): yhat [i] = beta[0]X[i,0] + beta[1]X[i,1] + beta [2]X[i,1] X[i,2] yhat beta[0]X[: ,0] + beta[1]X[:,1] + beta[2]X[:,1]X[:,2]

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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Question

Python broadcasting. Rewrite the following code without for-loops using vectorization
and python broadcasting.
(a) Given a data matrix X and vector beta compute a vector yhat:
n = X. shape [0]
yhat = np.zeros(n)
for i in range(n):
yhat [i] = beta [0]*X[i, 0] + beta[1]*X[i,1] + beta[2]*X[i, 1]*X[i,2]
yhat
beta[0]*X[: ,0] + beta[1]*X[:,1] + beta[2]*X[:,1]*X[:,2]

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