alecloudenback

## primes.md

      
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                alecloudenback
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              December 28, 2022 03:33
            
              
                List of primes
              
          
    Primes Benchmark

I got nerd-sniped from this LinkedIn post. On my computer, the R screenshot above ran in 1.564 minutes on my laptop for primes up to 1 million (Macbook Air M1 with arm64 binaries for Julia/R).
I had to give it a shot for comparison's sake. A basic 1:1 translation in Julia was 13.43 seconds (7x faster), and following the same conceptual algorithm (check for remainders up to sqrt(x)) but without as many intermediate allocations was 0.017 seconds (over 5,250x faster).
Prime divisibility check algorithim

R - 93.8 seconds


## aos_benchmark.jl
### A Pluto.jl notebook ###
# v0.19.11

using Markdown
using InteractiveUtils

# ╔═╡ f75704b5-fe24-4a1e-acdb-2e4123ec0906
using MortalityTables

# ╔═╡ dd21605c-6012-401e-a0aa-ed8dc989e00d

## actuarial_prefix_sum.jl
### A Pluto.jl notebook ###
# v0.19.11

using Markdown
using InteractiveUtils

# ╔═╡ 7121f532-2243-11ed-1a91-55b6e85bb022
using MortalityTables

# ╔═╡ 4dc4b2ec-c12e-4dfe-83b2-f8fda9a63838

## annuity_factors.jl
### A Pluto.jl notebook ###
# v0.17.3

using Markdown
using InteractiveUtils

# ╔═╡ c0b5c423-01bc-4321-9531-02f1f014d7f1
using MortalityTables

# ╔═╡ 0a6b50cb-ec54-4e06-a88e-2178f8578c9e

## smith-wilson-grading.jl
using Yields, Plots
yields = [0.02,0.023,0.026,0.029,0.035]
maturities = [1,2,3,7,15]
price = [1,1,1,1,1]
payments = 1
observable_prices = Yields.BulletBondQuotes(yields,maturities,price,payments)
grading_speed = 0.01:0.01:0.25
ultimate_forward = 0.02
curves = [Yields.SmithWilson(observable_prices,ufr=ultimate_forward,α=g) for g in grading_speed ]
times = 0.5:0.5:30

## gwp_anim.py
__author__ = """Peter Davidson FIA"""
__email__ = 'peter.davidson@wefox.com'
__version__ = '0.1'
__license__ = 'If you find this helpful, please say thank you'

import pandas as pd, numpy as np
def shear_array(df):
# This function just shears an array
# E.g. x = np.arange(9).reshape(3,3)
#      shear_array(x)

## apartment-therapy.js
// ==UserScript==
// @name       Apartment Therapy Enhanced Tour Navigation
// @namespace
// @version    0.2
// @description  Allows you to browse Apartment Therapy Tours using the left and right key. For Camille.
// @match      http://*.apartmenttherapy.com/*
// @match      http://www.apartmenttherapy.com/*
// @match      http://apartmenttherapy.com/*
// @copyright  MIT License
// ==/UserScript==
	### A Pluto.jl notebook ###
	# v0.19.11

	using Markdown
	using InteractiveUtils

	# ╔═╡ f75704b5-fe24-4a1e-acdb-2e4123ec0906
	using MortalityTables

	# ╔═╡ dd21605c-6012-401e-a0aa-ed8dc989e00d
	### A Pluto.jl notebook ###
	# v0.17.3

	using Markdown
	using InteractiveUtils

	# ╔═╡ c0b5c423-01bc-4321-9531-02f1f014d7f1
	using MortalityTables

	# ╔═╡ 0a6b50cb-ec54-4e06-a88e-2178f8578c9e
	using Yields, Plots
	yields = [0.02,0.023,0.026,0.029,0.035]
	maturities = [1,2,3,7,15]
	price = [1,1,1,1,1]
	payments = 1
	observable_prices = Yields.BulletBondQuotes(yields,maturities,price,payments)
	grading_speed = 0.01:0.01:0.25
	ultimate_forward = 0.02
	curves = [Yields.SmithWilson(observable_prices,ufr=ultimate_forward,α=g) for g in grading_speed ]
	times = 0.5:0.5:30
	__author__ = """Peter Davidson FIA"""
	__email__ = 'peter.davidson@wefox.com'
	__version__ = '0.1'
	__license__ = 'If you find this helpful, please say thank you'

	import pandas as pd, numpy as np
	def shear_array(df):
	# This function just shears an array
	# E.g. x = np.arange(9).reshape(3,3)
	# shear_array(x)
	// ==UserScript==
	// @name Apartment Therapy Enhanced Tour Navigation
	// @namespace
	// @version 0.2
	// @description Allows you to browse Apartment Therapy Tours using the left and right key. For Camille.
	// @match http://.apartmenttherapy.com/
	// @match http://www.apartmenttherapy.com/*
	// @match http://apartmenttherapy.com/*
	// @copyright MIT License
	// ==/UserScript==