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| import torch | |
| import time | |
| def measure_time(device): | |
| # create random matrix | |
| size=1000 # size of matrix | |
| mat1=torch.randn(size,size, device=device) | |
| mat2=torch.randn(size,size , device=device) | |
| # measure the time taken for matrix multiplication |
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| import mysql.connector | |
| try: | |
| connection = mysql.connector.connect(host='localhost', | |
| database='sakila', | |
| user='root', | |
| password='sql12345678') | |
| sql_select_Query = "select * from customer" | |
| cursor = connection.cursor() |
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| def detect_outliers(data): | |
| outliers=[] | |
| threshold=3 | |
| mean = np.mean(data) | |
| std =np.std(data) | |
| for i in data: | |
| z_score= (i - mean)/std |
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| import numpy as np | |
| import cv2 | |
| def correction( | |
| img, | |
| shadow_amount_percent, shadow_tone_percent, shadow_radius, | |
| highlight_amount_percent, highlight_tone_percent, highlight_radius, | |
| color_percent | |
| ): | |
| """ |