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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 fa(x): 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. def kde (kernel alnha data):

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 fa(x): 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. def kde (kernel alnha data):

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