printf('Cost at initial theta (zeros): %f\n', cost); printf('Expected cost (approx) : 0.693\n'); printf('Gradient at initial theta (zeros) - first five values only:\n'); printf (' %f \n', grad (1:5)); printf('Expected gradients (approx) - first five values only: \n'); printf(' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n'); sCompute and display cost and gradient with all-ones theta and lambda est_theta = ones (size (X, 2),1); cost, grad] = costFunctionReg (test_theta, X, y, 10); printf('\nCost at test theta (with lambda = 10): %f\n', cost); printf('Expected cost (approx) : 3.16\n'); printf('Gradient at test theta - first five values only:\n'); printf(' f \n', grad (1:5)); printf('Expected gradients (approx) printf(' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n'); = 10 first five values only:\n');

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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1-Change this code from Matlab to Phython:
* Compute and display initial cost and gradient for regularized logistic regression
[cost, grad] = costFunctionReg (initial_theta, x, y, lambda);
fprintf ('Cost at initial theta (zeros): %f\n', cost);
28 fprintf ('Expected cost (approx) : 0.693\n');
29 fprintf('Gradient at initial theta (zeros) - first five values only:\n');
30 fprintf (' %f \n', grad (1:5));
31 fprintf ('Expected gradients (approx)
fprintf (' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n');
33 % Compute and display cost and gradient with all-ones theta and lambda = 10
test_theta
[cost, grad] =
24
25
26
27
first five values only:\n');
32
ones (size (X, 2),1);
costFunctionReg (test_theta, X, y, 10);
34
35
10): %f\n', cost);
36 fprintf ('\nCost at test theta (with lambda
37 fprintf ('Expected cost (approx) : 3.16\n');
38 fprintf('Gradient at test theta
39 fprintf (' %f \n', grad (1:5));
fprintf ('Expected gradients (approx)
first five values only:\n');
40
first five values only:\n');
41
fprintf (' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
Transcribed Image Text:1-Change this code from Matlab to Phython: * Compute and display initial cost and gradient for regularized logistic regression [cost, grad] = costFunctionReg (initial_theta, x, y, lambda); fprintf ('Cost at initial theta (zeros): %f\n', cost); 28 fprintf ('Expected cost (approx) : 0.693\n'); 29 fprintf('Gradient at initial theta (zeros) - first five values only:\n'); 30 fprintf (' %f \n', grad (1:5)); 31 fprintf ('Expected gradients (approx) fprintf (' 0.0085\n 0.0188\n 0.0001\n 0.0503\n 0.0115\n'); 33 % Compute and display cost and gradient with all-ones theta and lambda = 10 test_theta [cost, grad] = 24 25 26 27 first five values only:\n'); 32 ones (size (X, 2),1); costFunctionReg (test_theta, X, y, 10); 34 35 10): %f\n', cost); 36 fprintf ('\nCost at test theta (with lambda 37 fprintf ('Expected cost (approx) : 3.16\n'); 38 fprintf('Gradient at test theta 39 fprintf (' %f \n', grad (1:5)); fprintf ('Expected gradients (approx) first five values only:\n'); 40 first five values only:\n'); 41 fprintf (' 0.3460\n 0.1614\n 0.1948\n 0.2269\n 0.0922\n');
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