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Write a python class/method that will: 1. Load a npy file into ndarray, assert that the loaded array has shape (*,*, 7) 2. Crop the loaded array along the dimension number 1 (count starts at 0) with a sliding window of length K, and take every 10th sample in dimension number 2, to an array shape of (**, K, 7). Take the 2nd feature (dimension -1) to an array of shape (*,*,K), concatenate along the first dimension to an array of shape (*,K) 3. Normalize each sample (first dimension) of the new array. The normalized array should have, for every i: {mean(arrli;) == 0, std(arrli,;:) == 1} 4. Frow away any sample that has any NaN or inf elements

Write a python class/method that will: 1. Load a npy file into ndarray, assert that the loaded array has shape (*,*, 7) 2. Crop the loaded array along the dimension number 1 (count starts at 0) with a sliding window of length K, and take every 10th sample in dimension number 2, to an array shape of (**, K, 7). Take the 2nd feature (dimension -1) to an array of shape (*,*,K), concatenate along the first dimension to an array of shape (*,K) 3. Normalize each sample (first dimension) of the new array. The normalized array should have, for every i: {mean(arrli;) == 0, std(arrli,;:) == 1} 4. Frow away any sample that has any NaN or inf elements

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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Write a python class/method that will: 1. Load a npy file into ndarray, assert that the loaded array has shape (*,*, 7) 2. Crop the loaded array along the dimension number 1 (count starts at 0) with a sliding window of length K, and take every 10th sample in dimension number 2, to an array shape of (*,*, K, 7). Take the 2nd feature (dimension -1) to an array of shape (*,*,K), concatenate along the first dimension to an array of shape (*,K) 3. Normalize each sample (first dimension) of the new array. The normalized array should have, for every i: {mean(arr[i;) == 0, std(arrli,;:) == 1} 4. Frow away any sample that has any Nan or inf elements
Transcribed Image Text:Write a python class/method that will: 1. Load a npy file into ndarray, assert that the loaded array has shape (*,*, 7) 2. Crop the loaded array along the dimension number 1 (count starts at 0) with a sliding window of length K, and take every 10th sample in dimension number 2, to an array shape of (*,*, K, 7). Take the 2nd feature (dimension -1) to an array of shape (*,*,K), concatenate along the first dimension to an array of shape (*,K) 3. Normalize each sample (first dimension) of the new array. The normalized array should have, for every i: {mean(arr[i;) == 0, std(arrli,;:) == 1} 4. Frow away any sample that has any Nan or inf elements
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