2.3 Question 2c In your answers above, you hard coded a lot of your work. In this problem, you'll build a more general kernel density estimator function. Implement the KDE function which computes: n 15 Ka(x, z1) fa(x) = n i=1 Where z, are the data, a is a parameter to control the smoothness, and K is the kernel density function passed as kernel.

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...
icon
Related questions
Question
2.3 Question 2c
In your answers above, you hard coded a lot of your work. In this problem, you'll build a more general kernel density estimator function.
Implement the KDE function which computes:
fa(x) =
1
E Ka(x, z;)
i=1
Where z; are the data, a is a parameter to control the smoothness, and K, is the kernel density function passed as kernel.
[23]: def kde (kernel, alpha, x, data):
Compute the kernel density estimate for the single query point x.
Args:
kernel: a kernel function with 3 parameters: alpha, x, data
alpha: the smoothing parameter to pass to the kernel
x: a single query point (in one dimension)
data: a numpy array of data points
Returns:
The smoothed estimate at the query point x
II II ||
Transcribed Image Text:2.3 Question 2c In your answers above, you hard coded a lot of your work. In this problem, you'll build a more general kernel density estimator function. Implement the KDE function which computes: fa(x) = 1 E Ka(x, z;) i=1 Where z; are the data, a is a parameter to control the smoothness, and K, is the kernel density function passed as kernel. [23]: def kde (kernel, alpha, x, data): Compute the kernel density estimate for the single query point x. Args: kernel: a kernel function with 3 parameters: alpha, x, data alpha: the smoothing parameter to pass to the kernel x: a single query point (in one dimension) data: a numpy array of data points Returns: The smoothed estimate at the query point x II II ||
Expert Solution
trending now

Trending now

This is a popular solution!

steps

Step by step

Solved in 2 steps

Blurred answer
Recommended textbooks for you
Computer Networking: A Top-Down Approach (7th Edi…
Computer Networking: A Top-Down Approach (7th Edi…
Computer Engineering
ISBN:
9780133594140
Author:
James Kurose, Keith Ross
Publisher:
PEARSON
Computer Organization and Design MIPS Edition, Fi…
Computer Organization and Design MIPS Edition, Fi…
Computer Engineering
ISBN:
9780124077263
Author:
David A. Patterson, John L. Hennessy
Publisher:
Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:
9781337569330
Author:
Jill West, Tamara Dean, Jean Andrews
Publisher:
Cengage Learning
Concepts of Database Management
Concepts of Database Management
Computer Engineering
ISBN:
9781337093422
Author:
Joy L. Starks, Philip J. Pratt, Mary Z. Last
Publisher:
Cengage Learning
Prelude to Programming
Prelude to Programming
Computer Engineering
ISBN:
9780133750423
Author:
VENIT, Stewart
Publisher:
Pearson Education
Sc Business Data Communications and Networking, T…
Sc Business Data Communications and Networking, T…
Computer Engineering
ISBN:
9781119368830
Author:
FITZGERALD
Publisher:
WILEY