Koushik Khan koushikkhan

Studied Mathematics and Statistics. Interested in data driven research and product development.

koushikkhan / lognormal_simulator_app.R

Created July 22, 2019 14:22

	ln.object <- LogNormalSimulator$new(mu = 5, sigma = 0.5, n_sample = 100000)
	ln.sample <- test$generate_lognormal_sample()
	ln.object$compute_stats()

koushikkhan / lognormal_simulator_class.R

Created July 22, 2019 14:13

	LogNormalSimulator <- R6Class("LogNormalSimulatorClass",
	inherit = GaussianSimulator,
	public = list(
	log_normal_sample = NA,
	initialize = function(mu, sigma, n_sample) {
	super$initialize(mu = mu, sigma = sigma, n_sample = n_sample)
	},

	generate_lognormal_sample = function() {
	normal_sample <- self$generate_sample()

koushikkhan / gaussian_class_usage.r

Created May 28, 2019 19:18

	# Usage of the class

	# 1. create a new object of the class named `sm`
	sm <- GaussianSimulator$new(mu=165, sigma=6.6, n_sample=1000)

	# 2. generate a random sample
	r <- sm$generate_sample()

	# 3. compute basic ststistics of the sample
	basic.stats <- sm$compute_stats()

koushikkhan / gaussian_class.r

Created May 28, 2019 18:54

	# A class to generate sample from Gaussian Distribution
	# mean, standard-deviation and sample size are being
	# expected to come from the user

	library(R6)
	library(ggplot2)

	GaussianSimulator <- R6Class("GaussianSimulatorClass",
	public = list(
	mu = NA,

koushikkhan / irisdata.txt

Created June 29, 2016 11:34 — forked from samueljackson92/irisdata.txt

Code to perform multivariate linear regression using a gradient descent on a data set. Sources: http://cs229.stanford.edu/notes/cs229-notes1.pdf http://stackoverflow.com/questions/17784587/gradient-descent-using-python-and-numpy-machine-learning