krishnanraman’s gists

krishnanraman / election.csv

Created February 19, 2025 21:10

election.csv

We can make this file beautiful and searchable if this error is corrected: It looks like row 2 should actually have 3 columns, instead of 2 in line 1.

	Alabama 2,265,090 Republican
	Alaska 338,177 Republican
	Arizona 3,390,161 Republican
	Arkansas 1,182,676 Republican
	California 15,865,475 Democratic
	Colorado 3,192,745 Democratic
	Connecticut 1,759,010 Democratic
	Delaware 512,912 Democratic
	District of Columbia 325,869 Democratic
	Florida 10,893,752 Republican

krishnanraman / qol.csv

Last active June 6, 2024 17:31

oxford qol

Place	Economics	HumanCapital	QOL	Environment	Governance
New York United States	1	4	278	353	184
London United Kingdom	7	1	292	197	72
San Jose United States	3	28	38	53	184
Tokyo Japan	10	2	218	277	26
Paris France	9	6	65	409	161
Seattle United States	4	40	56	40	184
Los Angeles United States	2	19	280	79	184
San Francisco United States	5	34	101	62	184
Melbourne Australia	16	11	185	30	18

krishnanraman / gist:427ad599b68063c80e9a5fa76882d045

Created March 10, 2024 19:10

	# Find x such that x+1, 2x+3,3x+5 and 4x+7 are prime.

	library(DescTools)
	# This works
	subset(expand.grid(x=1:1000),IsPrime(x+1) & IsPrime(2x+3) & IsPrime(3x+5) & IsPrime(4*x+7))
	# Alternate solution
	which(mapply(function(x) IsPrime(x+1) & IsPrime(2x+3) & IsPrime(3x+5) & IsPrime(4*x+7), 1:1000))

krishnanraman / gist:84454eb036853fa73f8a5c4226e64af9

Last active March 10, 2024 19:02

3.R

dim(subset(expand.grid(a=0:80,b=seq(2,80,2),c=c(0,2,5)), a+b+c == 80))

krishnanraman / aime4.R

Created February 3, 2024 00:44

AIME 2024 4

	x<-0:9
	n<-4
	N<-100000
	atleasttwomatch<- 0
	allfourmatch<-0
	for(i in 1:N) {
	a<-sample(x,n)
	b<-sample(x,n)
	s<-length(a[is.element(a, b)])
	if (s > 1) {

krishnanraman / gist:ca3ff546d82b02eabad95458a7b69c3d

Created January 17, 2024 22:42

	x<-seq(1,20)
	n<-100000
	mx<-c()
	for (i in 1:n) {
	mx<-c(mx,max(sample(x,10,replace=F)))
	}
	summary(mx)


	Min. 1st Qu. Median Mean 3rd Qu. Max.

krishnanraman / fitpoly.R

Created May 3, 2022 14:58

poly fit

	> x<-runif(100,0,2)
	> y<-1+x+x^2+x^3
	> summary(lm(y~x))

	Call:
	lm(formula = y ~ x)

	Residuals:
	Min 1Q Median 3Q Max
	-1.334 -1.018 -0.213 0.677 2.742

krishnanraman / test.R

Created December 6, 2020 02:42

hierarchical model Stan

	rm(list=ls())
	library(rstan)
	rstan_options(auto_write = FALSE)
	options(mc.cores = parallel::detectCores())
	N=1000
	data = list(N=N,x1<-rnorm(N,5,2),x2<-rnorm(N,7,2),x3<-rnorm(N,9,2),y<-x1+x2+x3)
	fit <- suppressMessages(stan(file = '~/Desktop/695/test.stan', data = data, iter=11000, warmup=1000, chains=2, seed=483892929, refresh=11000))
	print(fit)
	plot(fit)

krishnanraman / gist:e0aa69ebf67f51668d6691ec68ff6bca

Created November 30, 2020 18:02

chaotic_attractor.R

	rm(list =ls())
	dev.off()
	for(j in 1:4) {

	n=1000
	x=numeric(n)
	y=numeric(n)
	x[1] = rnorm(1,0,0.2)
	y[1] = rnorm(1,0,0.2)
	a=0.51

krishnanraman / gradientdescent.R

Created November 28, 2020 01:48

gradient descent for regression

	# Goal: Find unknown scalar w to minimize L(w)
	# L(w) = sum((y[i] - w*x[i])^2)
	# (x[i], y[i]), i=1..n dataset for linear regression
	#
	# Repeat Iterative Procedure below until convergence:
	# w[i+1] = w[i] - alpha * gradient(L(w), w=w[i])
	#
	set.seed(12345)
	x=seq(-5,5,0.5)
	y = 2*x + rnorm(length(x),0,1)

Krishnan Raman krishnanraman