Arjun Prakash arjunprakash027

🎯

Focusing

Your friendly neighbourhood developer Enginnering Intern @ Intercom

arjunprakash027 / playing_around_reccomender_systems.ipynb

Created September 23, 2023 07:29

A small book reccomender system using python

Sorry, something went wrong. Reload?

Sorry, we cannot display this file.

Sorry, this file is invalid so it cannot be displayed.

arjunprakash027 / message_queue_redis.py

Last active September 22, 2023 18:41

A python code to implement producer and consumer in async redis queue

	import redis
	import sys
	import time
	import json
	import random

	redis_conn = redis.Redis(host='localhost', port=6379)

	def producer():
	for i in range(20):

arjunprakash027 / cosine_similarity.rs

Last active September 21, 2023 19:08

A rust code to find cosine similarity between 2 vectors

	#[no_mangle]
	pub extern "C" fn cosine_similarity(array1:const i32,array2:const i32,len: isize) -> f32{
	let mut numerator: i32 = 0;
	for i in 0..len {
	numerator += unsafe { array1.offset(i) } unsafe { *array2.offset(i) };
	}

	let mut x: i32 = 0;
	for i in 0..len{
	println!("{}",unsafe { *array1.offset(i) } as f32);

arjunprakash027 / task_queue.py

Last active September 20, 2023 17:24

A SIMPLE BACKGROUND TASK QUEUE IMPLEMENTED IN PYTHON

	import multiprocessing
	import queue

	class TaskManager:
	def __init__(self):
	self.taskq = multiprocessing.Queue()
	self.resultq = multiprocessing.Queue()

	def worker(self):
	while True:

arjunprakash027 / compare_sentences_cosine.py

Last active September 19, 2023 17:50

Compare similarity between 2 sentences using cosine similarity

	from gensim.parsing.preprocessing import STOPWORDS
	import re
	import numpy as np
	from numpy.linalg import norm



	def find_similarity(x,y):
	#print(STOPWORDS)
	token_rules = r'\s+\|[,\.]'

arjunprakash027 / cosine_similarity.py

Last active September 18, 2023 16:30

A python code to perform cosine similarity

	import numpy as np
	from numpy.linalg import norm
	import math

	vector1 = np.array([1, 2])
	vector2 = np.array([4, 5])

	cosine_similarity = np.dot(vector1,vector2)/(norm(vector1)*norm(vector2))

	print("cosine similarity:",cosine_similarity)