Write a python script that will: 1. Load, normalize and concatenate the first 4 days as training data , sliding window k=360. 2. Load and normalize the last day as test data, sliding window k=360. 3. Perform a knn search on the train data for the test data: a. Once using SKlearn-nearestneighbors module, any base algorithm. b. Once using FAISS-library (use a flat index). c. Once using FAISS-library (use any non flat index). 4. Compare the three approaches on speed as a function of n=number_of_neighboors, from n =1 to n=256. 5. Compare the approaches on results similarity for n=10
Write a python script that will: 1. Load, normalize and concatenate the first 4 days as training data , sliding window k=360. 2. Load and normalize the last day as test data, sliding window k=360. 3. Perform a knn search on the train data for the test data: a. Once using SKlearn-nearestneighbors module, any base algorithm. b. Once using FAISS-library (use a flat index). c. Once using FAISS-library (use any non flat index). 4. Compare the three approaches on speed as a function of n=number_of_neighboors, from n =1 to n=256. 5. Compare the approaches on results similarity for n=10
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 script that will:
1. Load, normalize and concatenate the first 4 days as training data , sliding window k=360.
2. Load and normalize the last day as test data, sliding window k=360.
3. Perform a knn search on the train data for the test data:
a. Once using SKlearn-nearestneighbors module, any base
b. Once using FAISS-library (use a flat index).
c. Once using FAISS-library (use any non flat index).
4. Compare the three approaches on speed as a function of n=number_of_neighboors,
from n =1 to n=256.
5. Compare the approaches on results similarity for n=10
![Write a python script that will:
1. Load, normalize and concatenate the first 4 days as training data , sliding window k=360.
2. Load and normalize the last day as test data, sliding window k=360.
3. Perform a knn search on the train data for the test data:
a. Once using SKlearn-nearestneighbors module, any base algorithm.
b. Once using FAISS-library (use a flat index).
c. Once using FAISS-library (use any non flat index).
4. Compare the three approaches on speed as a function of n=number_of_neighboors,
from n =1 to n=256.
5. Compare the approaches on results similarity for n=100.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F4c0302c3-9463-45ae-ba24-48b4ac042a0f%2F375eb294-9a2b-48e7-8feb-a3dd5a4998b2%2F00fbez4_processed.png&w=3840&q=75)
Transcribed Image Text:Write a python script that will:
1. Load, normalize and concatenate the first 4 days as training data , sliding window k=360.
2. Load and normalize the last day as test data, sliding window k=360.
3. Perform a knn search on the train data for the test data:
a. Once using SKlearn-nearestneighbors module, any base algorithm.
b. Once using FAISS-library (use a flat index).
c. Once using FAISS-library (use any non flat index).
4. Compare the three approaches on speed as a function of n=number_of_neighboors,
from n =1 to n=256.
5. Compare the approaches on results similarity for n=100.
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