Al Asaad alstat

💭

So many ideas, but limited time to code

Statistician · Data Scientist

alstat / Count lines in Git repo

Created October 1, 2025 05:21 — forked from mandiwise/Count lines in Git repo

A command to calculate lines of code in all tracked files in a Git repo

	// Reference: http://stackoverflow.com/questions/4822471/count-number-of-lines-in-a-git-repository

	$ git ls-files \| xargs wc -l

alstat / ts-1.jl

Last active February 10, 2020 15:29

	using Dates
	using Distributions
	using HypothesisTests: ADFTest, LjungBoxTest
	using Knet
	using Measures
	using Plots
	using PlotThemes
	using Random
	using StatsBase: autocor, pacf
	using Turing

alstat / explanation-of-apply.R

Last active March 20, 2019 13:56

	set.seed(1)
	x1 <- rnorm(10)
	x2 <- rnorm(10)
	out <- cbind(x1, x2)
	colMeans(out)

	# colMeans can be computed as follows as well
	apply(out, 2, mean)

	# apply - applies a mean function to column (indicated by 2) of out matrix

alstat / code-sir-aljo-mcmc.R

Created March 20, 2019 13:02

	# Simulating the data
	set.seed(73735911)
	#set.seed(737377911)
	n=100;nu=5;alpha=2;beta=2;sig=1;true=c(alpha,beta,nu)
	x=rnorm(n,1,1)
	y=alpha+betax+sigrt(n,nu)

	par(mfrow=c(1,1))
	plot(x,y, col='blue3', pch=19)

alstat / code-turing.jl

Created February 4, 2019 16:03

Turing.jl Tutorial on Bayesian Linear Regression

	using Turing, Distributions

	# Import RDatasets.
	using RDatasets

	# Import MCMCChain, Plots, and StatPlots for visualizations and diagnostics.
	using MCMCChain, Plots, StatPlots

	# MLDataUtils provides a sample splitting tool that's very handy.
	using MLDataUtils

alstat / sir-aljo.r

Last active April 2, 2018 14:09

alstat / al-ilm-nn-pyr-keras-5.py

Created March 10, 2018 10:12

	f, a = subplots(10, 20)
	for i in arange(10):
	for j in arange(20):
	a[i, j].imshow(x_train[j + 20 * i])
	a[i, j].axis("off")
	a[i, j].set_adjustable('box-forced')

	f.savefig("img1.png", bbox_inches='tight', pad_inches = 0)

alstat / al-ilm-nn-pyr-keras-3.r

Created March 10, 2018 09:18

	items <- list(x_train, y_train, x_test, y_test)

	dim_formatter <- function (x) {
	if (length(dim(x)) > 2)
	paste("(", dim(x)[1], ", ", dim(x)[2], " , ", dim(x)[3], " , ", dim(x)[4], ")", sep = "")
	else
	paste("(", dim(x)[1], ", ", dim(x)[2], ")", sep = "")
	}

	# training set

alstat / al-ilm-nn-pyr-keras-4.py

Last active March 10, 2018 09:18

	items = [x_train, y_train, x_test, y_test]

	# training set
	[type(item) for item in items[:2]] # [<type 'numpy.ndarray'>, <type 'numpy.ndarray'>]
	[item.shape for item in items[:2]] # [(50000, 32, 32, 3), (50000, 1)]

	# testing set
	[type(item) for item in items[2:]] # [<type 'numpy.ndarray'>, <type 'numpy.ndarray'>]
	[item.shape for item in items[2:]] # [(10000, 32, 32, 3), (10000, 1)]

alstat / al-ilm-nn-pyr-keras-2.r

Created March 10, 2018 06:23

	cifar100 <- dataset_cifar100(label_mode = "fine")

	x_train <- cifar100$train$x; y_train <- cifar100$train$y
	x_test <- cifar100$test$x; y_test <- cifar100$test$y