Berkodev

import statistics
import timeit
import csv
import pandas as pd
import numpy as np
import polars as pl


def measure_performance(func, n_runs=50):
    times = timeit.repeat(func, repeat=n_runs, number=1)

Berkodev

import asyncio
import json
import time

from typing import Optional, List

from pydantic import BaseModel, Field

from starlette.responses import StreamingResponse
from fastapi import FastAPI, HTTPException, Request

Berkodev

from numpy import genfromtxt
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

## Data preprocessing
# Load csv dataset into a numpy array
X = genfromtxt('/path/to/dataset', delimiter=',')

# Use MinMaxScaler to scale the data

Berkodev

local cnt = redis.call('INCR', KEYS[1])
if cnt > tonumber(ARGV[1])
then
  return redis.call('PTTL', KEYS[1])
end
if cnt == 1
then
  redis.call('PEXPIRE', KEYS[1], ARGV[2])
end
return 0