3. Recall that the logistic regression hypothesis is defined as: holx) = g(@"x), where function g is the sigmoid function. The sigmoid function is defined as: g(2) = Implement the sigmiod function so it can be called by the rest of your program. When done, try testing a few values by calling sigmoid(x) in the code section below. For large positive values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. Evaluating sigmoid(0) should give you exactly 0.5. Your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid function on every element. * Provide different input values and check your implementation sigmoid(e) function g- sigmoid(z) B - zeros(size(z)); [m,n]=size(z); * Instructions: Compute the sigmoid of each value of z (z can be a matrix, vector or scalar). for i-1:m for j-1:n g(1,1)-1/(1+exp(-z(1,j))); end end end
3. Recall that the logistic regression hypothesis is defined as: holx) = g(@"x), where function g is the sigmoid function. The sigmoid function is defined as: g(2) = Implement the sigmiod function so it can be called by the rest of your program. When done, try testing a few values by calling sigmoid(x) in the code section below. For large positive values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid should be close to 0. Evaluating sigmoid(0) should give you exactly 0.5. Your code should also work with vectors and matrices. For a matrix, your function should perform the sigmoid function on every element. * Provide different input values and check your implementation sigmoid(e) function g- sigmoid(z) B - zeros(size(z)); [m,n]=size(z); * Instructions: Compute the sigmoid of each value of z (z can be a matrix, vector or scalar). for i-1:m for j-1:n g(1,1)-1/(1+exp(-z(1,j))); end end end
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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write code in Phython
![3. Recall that the logistic regression hypothesis is defined as:
holx) = g (@"x),
where function g is the sigmoid function. The sigmoid function is defined as:
g(2):
1+e
Implement the sigmiod function so it can be called by the rest of your program. When done,
try testing a few values by calling sigmoid(x) in the code section below. For large positive
values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid
should be close to 0. Evaluating sigmoid(0) should give you exactly 0.5. Your code should also
work with vectors and matrices. For a matrix, your function should perform the sigmoid
function on every element.
% Provide different input values and check your implementation
sigmoid(0)
function g = sigmoid(z)
g = zeros (size(z));
[m,n]=size(z);
% Instructions: Compute the sigmoid of each value of z (z can be a matrix,
vector or scalar).
for i-1:m
for j-1:n
g(i,j)=1/(1+exp(-z(i,j)));
end
end
end](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F09dcdbec-2c72-47a4-8892-cad949def1f9%2F88a6dfd0-70a0-4772-98bc-b24185f86b07%2Fzct0ds_processed.png&w=3840&q=75)
Transcribed Image Text:3. Recall that the logistic regression hypothesis is defined as:
holx) = g (@"x),
where function g is the sigmoid function. The sigmoid function is defined as:
g(2):
1+e
Implement the sigmiod function so it can be called by the rest of your program. When done,
try testing a few values by calling sigmoid(x) in the code section below. For large positive
values of x, the sigmoid should be close to 1, while for large negative values, the sigmoid
should be close to 0. Evaluating sigmoid(0) should give you exactly 0.5. Your code should also
work with vectors and matrices. For a matrix, your function should perform the sigmoid
function on every element.
% Provide different input values and check your implementation
sigmoid(0)
function g = sigmoid(z)
g = zeros (size(z));
[m,n]=size(z);
% Instructions: Compute the sigmoid of each value of z (z can be a matrix,
vector or scalar).
for i-1:m
for j-1:n
g(i,j)=1/(1+exp(-z(i,j)));
end
end
end
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